The 'Switch-off 4 Healthy Minds' Randomised Controlled

Reducing Recreational Screen-time in Adolescents:

The ‘Switch-off 4 Healthy Minds’ Randomised Controlled Trial

Mark James Babic

B Teaching / B Health and Physical Education

This thesis is submitted in fulfilment of the requirements for the award of the degree of:

Doctorate of Philosophy (Education)

Faculty of Education and Arts

University of Newcastle

January 2017

ii

Statement of Originality

This thesis contains no material which has been accepted for the award of any other

degree or diploma in any university or other tertiary institution, and to the best of my

knowledge and belief, contains no material previously published or written by another

person, except where due reference has been made in the text. I give consent to the final

version of my thesis being made available worldwide when deposited in the University’s

Digital Repository, subject to the provisions of the Copyright Act 1968.

Signed:

Name: Mark James Babic

Date:

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Thesis by Publication

I hereby certify that this thesis is in the form of a series of published papers of which I am

a joint author. I have included as part of my thesis a written statement from each co-

author, endorsed by the Faculty Assistant Dean (Research Training), attesting to my

contribution to the joint publications.

Signed:


Date:

iv

Copyright Permission

I warrant that I have obtained, where necessary, permission from the copyright owners to

use any third party copyright material reproduced in the thesis (e.g., questionnaires and

figures) or to use any of my own published work (e.g., journal articles) in which the

copyright is held by another party (e.g., publisher, co-author).

Signed:


Date:

v

Conflict of Interest

My research higher degree was supported and funded by a scholarship from Wests

Leagues Club. The ‘Switch-off 4 Healthy Minds’ study was supported by a Hunter

Children’s Research Foundation grant for $25,000. Sponsors had no involvement in the

research process, including the drafting of this thesis or manuscripts contained within.

This trial has been registered with the Australian and New Zealand Clinical Trials

Registry ACTRN12614000163606.

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Supervisors

Primary supervisor

Professor David R. Lubans Priority Research Centre in Physical Activity and Nutrition School of Education Faculty of Education & Arts University of Newcastle, Australia

Co-supervisors

Professor Philip J. Morgan Priority Research Centre in Physical Activity and Nutrition School of Education Faculty of Education & Arts University of Newcastle, Australia

Professor Ronald C. Plotnikoff

Priority Research Centre in Physical Activity and Nutrition School of Education Faculty of Education & Arts University of Newcastle, Australia

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Acknowledgements

The submission of this thesis is attributed to several influential people of which I am

grateful to.

To David Lubans, Phillip Morgan and Ron Plotnikoff, I thank you for your ongoing

efforts and dedication. Your knowledge was invaluable and I thank you all for your time,

friendships, work ethics and mentorship. I want to especially thank David for his

optimism and enthusiasm throughout the difficult times.

To my colleagues in the Priority Research Centre for Physical Activity and Nutrition,

thank you for making my time in the research group enjoyable. I wish to thank Ryan

Hulteen, Jordan Smith, Lee Ashton, Mitch Duncan, Elroy Aguiar, Kristen Cohen, Nick

Riley, Narelle Eather, Wayne Durand, all the Sarah’s and Lisa Spencer for our

friendships. Such appreciation obviously extends to Emma Pollock and Tara Finn who

assisted in running numerous interventions alongside me.

To the school staff, parents, principals, and students of my intervention, your

participation and commitment was treasured.

To the Hunter Medical Research Institute and Wests Leagues Club, I wish to thank

you for your continued research and support of students and the University of Newcastle.

Finally to my wife, there are no words to describe my gratitude.

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Publications arising from this Thesis

This thesis includes four manuscripts, all of which have or are being published in peer-

reviewed journals. At the time of submission, three were published and one was

submitted to a journal for consideration.

Manuscripts in peer-reviewed journals: Published

Babic, M. J., Morgan, P. J., Plotnikoff, R. C., Lonsdale, C., White, R. L., & Lubans, D.

R. (2014). Physical activity and physical self-concept in youth: Systematic review and

meta-analysis. Sports Medicine, 44(11), 1589-1601.

Babic, M. J., Morgan, P. J., Plotnikoff, R. C., Lonsdale, C., Eather, N., Skinner, G.,

Baker, A. L., Pollock, E., & Lubans, D. R. (2015). Rationale and study protocol for

‘Switch-off 4 Healthy Minds’ (S4HM): A cluster randomised controlled trial to reduce

recreational screen-time in adolescents. Contemporary Clinical Trials, 40(1), 150-158.

Babic, M J., Morgan, P. J., Lonsdale, C., Plotnikoff, R. C., Eather, N., Skinner, G.,

Baker, A. L., Pollock, E., & Lubans, D. R. (2016). Intervention to reduce recreational

screen-time in adolescents: Outcomes and mediators from the ‘Switch-Off 4 Healthy

Minds’ (S4HM) cluster randomised controlled trial. Preventive Medicine, 91(1), 50-57.

Manuscripts in peer-reviewed journals: Under review

Babic, M. J., Smith, J. J., Morgan, P. J., Narelle, Eather., Plotnikoff, R. C., Lonsdale, C.,

& Lubans, D. R. (2016) Longitudinal associations between changes in screen-time and

mental health outcomes in adolescents (Mental Health and Physical Activity).

Presentations arising from this thesis

I have presented results from this thesis at international and local conferences

Presentations:




recreational screen-time in adolescents. EHealth Conference, Newcastle, 13th October.

ORAL

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meta-analysis. Be Active Conference, Canberra, 15-18 October. ORAL

Babic, M.J., Smith, J.J., Morgan, P.J., Lonsdale, C., Plotnikoff, R.C., Eather, N., Skinner,

G., Baker, A.L., Pollock, E. & Lubans, D.R. (2016). Intervention to Reduce Recreational

Screen-Time in Adolescents: Outcomes and Mediators from the ‘Switch-Off 4 Healthy

Minds’ (S4HM) Cluster Randomised Controlled Trial. Sports Medicine Australia

Conference, Melbourne, 12-15 October. ORAL

Babic, M.J., Smith, J.J., Morgan, P.J., Eather, N., Plotnikoff, R.C. & Lubans, D. R.

(2016). Longitudinal associations between changes in screen-time and mental health

outcomes in adolescents. Sports Medicine Australia Conference, Melbourne, 12-15

October. ORAL

Additional publications from my PhD candidature

During my PhD candidature, I have co-authored the following papers that are not

included in my thesis:

Manuscripts in peer-reviewed journals: Published

Smith, J., Morgan, P., Plotnikoff, R., Dally, K., Salmon, J., Okely, A., Finn, T., Babic,

M., Skinner, G., Lubans, D. (2014). Rationale and study protocol for the ‘Active Teen

Leaders Avoiding Screen-time’ (ATLAS) group randomised controlled trial: An obesity

prevention intervention for adolescent boys from schools in low-income communities.

Contemporary Clinical Trials, 37(1), 106-119.

Thorne, H. T., Smith, J. J., Morgan, P. J., Babic, M. J., & Lubans, D. R. (2014). Video

game genre preference, physical activity and screen-time in adolescent boys from low-

income communities. Journal of Adolescence, 37(8), 1345-1352.

Manuscripts in peer-reviewed journals: Under review

White, R. L., Babic, M. J., Lonsdale, C., Lubans, D, R. (2016). Domain specific physical

activity and mental health: Systematic review and meta-analyses. American Journal of

Preventive Medicine (In press).

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Nathan, N., Cohen, K., Sutherland, R., Wolfenden, L., Beauchamp, M., Hulteen, R.,

Babic, M. J., Lubans, D. R. (2016). Feasibility and efficacy of the ‘Great Leaders Active

StudentS’ (GLASS) program on children’s physical activity and fundamental movement

skill competency. Journal of Science and Medicine in Sport (Under review).

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Table of Contents

Statement of Originality ................................................................................................... ii Thesis by Publication ....................................................................................................... iii Copyright Permission ...................................................................................................... iv Conflict of Interest ............................................................................................................. v Supervisors ....................................................................................................................... vi Acknowledgements ......................................................................................................... vii Publications arising from this Thesis ........................................................................... viii Table of Contents ............................................................................................................. xi List of Tables ................................................................................................................. xvii List of Figures ............................................................................................................... xviii List of Abbreviations ..................................................................................................... xix Operational Definitions ................................................................................................. xxi Thesis Abstract .............................................................................................................. xxii Statement of Contribution .......................................................................................... xxvi Introduction ........................................................................................................................ 1 Chapter 1 Literature Review ............................................................................................ 4

Part 1 Rationale for Increasing Physical Activity and Reducing Screen-time in Adolescence ................................................................................................................ 6

1.1 Definitions of physical activity, sedentary behaviour and screen-time ..................... 6 1.1.1 Inter-relationships between physical activity and sedentary behaviour .............. 7 1.1.2 Measurements of physical activity and sedentary behaviour .............................. 7 1.1.3 The key period of adolescence ............................................................................ 8

1.2 Guidelines, prevalence and trends .............................................................................. 8 1.2.1 International and national and guidelines of physical activity and screen-time .. 8 1.2.2 Prevalence and trends of physical activity in adolescents ................................. 11 1.2.3 Screen-time prevalence and trends among adolescents ..................................... 11

1.3 Health consequences of inactivity and excessive sedentary behaviour ................... 12 1.3.1 Physical activity and physical health ................................................................. 12 1.3.2 Physical activity and mental health ................................................................... 12 1.3.3 Excessive screen-time and physical health ........................................................ 13 1.3.4 Mental health outcomes of excessive screen-time ............................................ 13

1.4 Mechanisms responsible for the effects of physical activity and screen-time on mental health ............................................................................................................ 14

1.5 Summary .................................................................................................................. 15 Part 2 Understanding Physical Activity and Sedentary Behaviour ............................... 16 1.6 Correlates and determinants of physical activity ..................................................... 16

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1.6.1 Individual correlates of physical activity ........................................................... 16 1.6.2 Social correlates of physical activity ................................................................. 16 1.6.3 Environmental correlates of physical activity ................................................... 17 1.6.4 Issues examining correlates of physical activity ............................................... 17

1.7 Correlates and determinants of screen-time ............................................................. 18 1.7.1 Individual correlates of screen-time .................................................................. 18 1.7.2 Social correlates of screen-time ......................................................................... 19 1.7.3 Environmental correlates of screen-time ........................................................... 19 1.7.4 Issues examining correlates of screen-time ....................................................... 19

1.8 Mediators of physical activity and screen-time behaviour change .......................... 20 1.8.1 Mediators of physical activity in adolescents .................................................... 21 1.8.2 Mediators of screen-time in adolescents ........................................................... 22

1.9 Theories of health behaviour change ....................................................................... 22 1.9.1 Current evidence ................................................................................................ 22 1.9.2 Self-Determination Theory ................................................................................ 22

1.10 Summary of Part 2 .................................................................................................. 24 Part 3 Review of Interventions to Increase Physical Activity and Reduce Screen-time

in Adolescents .......................................................................................................... 25 1.11 Physical activity interventions for adolescents ...................................................... 25 1.12 Interventions to increase physical activity and reduce screen-time ....................... 26 1.13 Screen-time interventions for adolescents .............................................................. 28 1.14 Implementation and scaling up of interventions .................................................... 29 1.15 Summary of Part 3 .................................................................................................. 30 1.16 Thesis aims and hypothesis .................................................................................... 31

Chapter 2 Physical Activity and Physical Self-concept in Youth: Systematic Review and Meta-analysis ..................................................................................................... 32

2.1 Preface ...................................................................................................................... 32 2.2 Abstract .................................................................................................................... 32 2.3 Background .............................................................................................................. 33 2.4 Methods .................................................................................................................... 35

2.4.1 Eligibility criteria ............................................................................................... 35 2.4.2 Search strategy ................................................................................................... 36 2.4.3 Screening ........................................................................................................... 37 2.4.4 Data extraction ................................................................................................... 37 2.4.5 Analytic strategies ............................................................................................. 37 2.4.6 Synthesis of studies not included in the meta-analysis ...................................... 39 2.4.7 Criteria for risk of bias assessment .................................................................... 39

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2.4.8 Description of the synthesis of studies not included in the meta-analysis ........ 41 2.5 Results ...................................................................................................................... 41

2.5.1 Study/sample characteristics .............................................................................. 42 2.5.2 Overall effect size, heterogeneity and significance of moderators .................... 43

2.5.2.1 General physical self-concept ..................................................................... 43

2.5.2.2 Perceived competence ................................................................................. 43

2.5.2.3 Perceived fitness ......................................................................................... 44

2.5.2.4 Perceived appearance .................................................................................. 44

2.5.2.5 Synthesis of findings not included in the meta-analysis ............................. 45

2.6 Risk of bias assessment ............................................................................................ 59 2.7 Testing for publication bias ...................................................................................... 62 2.8 Discussion ................................................................................................................ 63

2.8.1 Overview of findings ......................................................................................... 63 2.8.2 Summary of risk of bias from included studies ................................................. 64 2.8.3 Major findings and potential contributors ......................................................... 65 2.8.4 Practical implications ........................................................................................ 67 2.8.5 Strengths and limitations of the review ............................................................. 67

2.9 Conclusion ................................................................................................................ 68 Chapter 3 Rationale and Study Protocol for ‘Switch-off 4 Healthy Minds’ (S4HM):

A Cluster Randomised Controlled Trial to Reduce Recreational Screen-time in Adolescents ................................................................................................................ 69

3.1 Preface ...................................................................................................................... 69 3.2 Abstract .................................................................................................................... 69 3.3 Background .............................................................................................................. 70 3.4 Methods .................................................................................................................... 71

3.4.1 Study design ...................................................................................................... 71 3.4.2 Sample size calculation ..................................................................................... 74 3.4.3 Setting and screening of participants ................................................................. 74 3.4.4 Blinding and randomisation ............................................................................... 74 3.4.5 Intervention ........................................................................................................ 75 3.4.6 Control group ..................................................................................................... 76

3.5 Outcomes .................................................................................................................. 78 3.5.1 Primary outcome ................................................................................................ 78 3.5.2 Secondary outcomes .......................................................................................... 78

3.5.2.1 Psychological distress ................................................................................. 78

3.5.2.2 Pathological video game use ....................................................................... 79

3.5.2.3 Aggression .................................................................................................. 79

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3.5.2.4 Psychological difficulties ............................................................................ 79

3.5.2.5 Global physical self-concept ....................................................................... 79

3.5.2.6 Household screen-time rules ....................................................................... 80

3.5.2.7 Motivation to limit recreational screen-time ............................................... 80

3.5.2.8 Physical activity .......................................................................................... 80

3.5.2.9 Body mass index ......................................................................................... 81

3.5.2.10 Process evaluation ..................................................................................... 81

3.5.2.11 Statistical methods .................................................................................... 82

3.5.2.12 Results ....................................................................................................... 83

3.6 Discussion ................................................................................................................ 83 3.7 Limitations ............................................................................................................... 86 3.8 Conclusion ................................................................................................................ 87

Chapter 4 Intervention to Reduce Recreational Screen-Time in Adolescents: Outcomes and Mediators from the ‘Switch-off 4 Healthy Minds’ (S4HM) Cluster Randomised Controlled Trial .................................................................... 88

4.1 Preface ...................................................................................................................... 88 4.2 Abstract .................................................................................................................... 88 4.3 Introduction .............................................................................................................. 89 4.4 Methods .................................................................................................................... 90

4.4.1 Study design and participants ............................................................................ 90 4.4.2 Intervention components ................................................................................... 91 4.4.3 Primary outcome ................................................................................................ 92 4.4.4 Secondary outcomes .......................................................................................... 93 4.4.5 Hypothesised mediators ..................................................................................... 93 4.4.6 Process evaluation ............................................................................................. 93 4.4.7 Statistical analysis .............................................................................................. 94

4.5 Results ...................................................................................................................... 95 4.5.1 Primary outcome ................................................................................................ 95 4.5.2 Secondary outcomes .......................................................................................... 95

4.5.2.1 Mediation analysis ...................................................................................... 95

4.5.2.2 Process evaluation ....................................................................................... 96

4.6 Discussion ................................................................................................................ 96 4.7 Conclusions .............................................................................................................. 99 4.8 Competing interests .................................................................................................. 99 4.9 Author contributions ................................................................................................ 99 4.10 Acknowledgements .............................................................................................. 100

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Chapter 5 Longitudinal Associations between Screen-time and Mental Health in Australian Adolescents ........................................................................................... 105

5.1 Preface .................................................................................................................... 105 5.2 Abstract .................................................................................................................. 105 5.3 Introduction ............................................................................................................ 106 5.4 Methods .................................................................................................................. 107

5.4.1 Participants ...................................................................................................... 107 5.4.2 Measures .......................................................................................................... 108

5.4.2.1 Recreational screen-time ........................................................................... 108

5.4.2.2 Mental health ............................................................................................ 108

5.4.2.3 Adiposity ................................................................................................... 109

5.4.2.4 Physical activity ........................................................................................ 109

5.4.2.5 Statistical analysis ..................................................................................... 110

5.5 Results .................................................................................................................... 110 5.5.1 Recreational screen-time and mental health outcomes .................................... 117 5.5.2 Non-recreational screen-time and mental health outcomes ............................. 117

5.6 Discussion .............................................................................................................. 117 5.7 Conclusion .............................................................................................................. 120 5.8 Competing interests ................................................................................................ 120 5.9 Author contributions .............................................................................................. 120 5.10 Acknowledgements .............................................................................................. 120

Chapter 6 Thesis Discussion and Conclusion .............................................................. 121 6.1 Overview ................................................................................................................ 121 Part 1 Associations between Physical Activity, Screen-time and Mental Health in

Adolescents ............................................................................................................. 122 6.2 Overview of findings .............................................................................................. 122 6.3 Strengths and limitations ........................................................................................ 123 6.4 Recommendations for practice and research .......................................................... 124

6.4.1 For practice ...................................................................................................... 124 6.4.2 For future research ........................................................................................... 125

Part 2 Rationale and Evaluation of the S4HM Screen-time Reduction Intervention ... 127 6.5 Overview of findings .............................................................................................. 127

6.5.1 Strengths and limitations ................................................................................. 128 6.5.2 Recommendations for practice and research ................................................... 129

6.5.2.1 For schools and parents ............................................................................. 129

6.5.2.2 For future research .................................................................................... 130

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Part 3 Longitudinal Associations between Screen-time and Mental Health Outcomes ................................................................................................................ 131

6.6 Overview of findings .............................................................................................. 131 6.6.1 Strengths and limitations ................................................................................. 131 6.6.2 Recommendations ........................................................................................... 132

6.6.2.1 For schools and parents ............................................................................. 132

6.6.2.2 For future research .................................................................................... 132

6.7 Conclusion .............................................................................................................. 133

Appendices ...................................................................................................................... 134 Appendix 1: PRISMA checklist ................................................................................... 135 Appendix 2: Human Research Ethics Approval .......................................................... 140 Appendix 3: Principal Information Sheet..................................................................... 145 Appendix 4: Student and Parent Information Statement .............................................. 152 Appendix 5: Principal Consent Form ........................................................................... 158 Appendix 6: Parent and Student Consent Form ........................................................... 160 Appendix 7: Eligibility Screening Questionnaire ........................................................ 164 Appendix 8: Recording Sheet ...................................................................................... 166 Appendix 9: Accelerometer Information Sheet and Activity Log ............................... 167 Appendix 10: Questionnaires ....................................................................................... 171 Appendix 11: Protocol ................................................................................................. 189 Appendix 12: Student End of Study Evaluation Questionnaire ................................... 204 Appendix 12: Parent End of Program Evaluation Questionnaire ................................ 208 Appendix 13: Newsletters ............................................................................................ 210 Appendix 14: Behavioural Contract ............................................................................. 216 Appendix 15: Interactive Presentation ......................................................................... 217

References ....................................................................................................................... 235

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List of Tables

Table 1: International physical activity guidelines for young people .......................... 10 Table 2: International screen-time guidelines for young people ................................. 10 Table 3: Correlates of physical activity in young people ............................................ 18 Table 4: Correlates of screen-time in young people .................................................... 20 Table 5: Summary of articles included in the systematic review ................................ 46 Table 6: Qualitative summary of studies examining the association between physical

activity and physical self-concept ......................................................................... 59 Table 7: Risk of bias results ......................................................................................... 60 Table 8: Intervention components, behaviour change techniques and targeted constructs

in the S4HM intervention ..................................................................................... 77 Table 9: Baseline characteristics of the S4HM study sample .................................... 102 Table 10: Changes in primary and secondary outcomes in the S4HM intervention . 103 Table 11: Mediation analyses for the single mediator models adjusted for sex and SES104 Table 12: Characteristics of the study sample ........................................................... 111 Table 13: Levels of screen-time and mental health across time points in the total sample

and by sex ........................................................................................................... 112 Table 14: Associations of screen-time (T2) and mental health (T2) for the total sample

over the first year of secondary school ............................................................... 114 Table A3.1: Intervention components and evaluation strategies ............................... 146

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List of Figures

Figure 1: Schematic diagram of literature review ................................................................ 5 Figure 2: Statistical mediation model ................................................................................ 21 Figure 3: Self-determination Theory (SDT) ...................................................................... 23 Figure 4: Organismic Integration Theory (OIT) ................................................................ 23 Figure 5: Results of literature search ................................................................................. 42 Figure 6: Study design and flow ........................................................................................ 73 Figure 7: Study design and flow with follow-up data...................................................... 101 Figure 8: Mean screen-time usage across time points in the total sample and by sex ..... 115 Figure 9: Mean mental health scores across time points in the total sample and by sex . 116

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List of Abbreviations

AOR Adjusted Odds Ratio AS!BC Action Schools! British Columbia intervention ASAQ Adolescent Sedentary Activity Questionnaire BMI Body Mass Index BMI z score Body Mass Index z-score CPCLA Children's Participation in Cultural and Leisure Activities survey CI Confidence Intervals CONSORT Consolidated Standard of Reporting Trials CVD Cardiovascular Disease DOiT Dutch Obesity Intervention in Teenagers HEIA HEalth In Adolescents study HRQoL Health Related Quality of Life ICC Intra-class Correlation Coefficient K10 Kessler Psychological Distress Scale Kg Kilogram MET Metabolic Equivalent MLSQ Motivation to Limit Screen-time Questionnaire MVPA Moderate-to-Vigorous Physical Activity N Number NaSSDA National Secondary Students’ Diet and Activity survey NCD Non-Communicable Diseases NSW New South Wales OIT Organismic Integration Theory P Probability (statistical significance level) PA Physical Activity PC Personal Computer PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses PSDQ Physical Self-Description Questionnaire RCT Randomised Controlled Trial SD Standard Deviation SDQ Strength and Difficulties Questionnaire SDT Self-Determination Theory SEIFA Socio-Economic Indexes for Areas S4HM Switch-off 4 Healthy Minds SES Socio-Economic Status SMD Standardised Mean Differences SMS Short-Message Service

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WHO World Health Organization Note. This list represents abbreviations used in the main text of this thesis. Additional

abbreviations in tables are defined in the bottom row.

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Operational Definitions

Sedentary behaviour Sedentary behaviour was defined as

activities characterised by an energy

expenditure ≤ 1.5 metabolic equivalents

Screen-time Screen-time was defined as the time spent

using screen based devices.

Recreational screen-time Recreational screen-time was defined as

screen use for entertainment purposes e.g.

computer use for games/fun.

Non-recreational screen-time Non-recreational screen-time was defined

as screen use for educational purposes e.g.

computer use for homework.

Physical activity Physical activity was defined as any bodily

movement produced by skeletal muscles

requiring energy expenditure.

Mental health Mental health is a sense of well-being,

confidence and self-esteem whereas mental

ill-being is a health problem that may

negatively affect how a person thinks,

behaves and interacts with other people.

Self-concept The term self-concept is a general term

used to refer to how someone thinks about

or perceives themselves.

Adolescence Adolescence was defined as youth aged

between 13 and 18 (which corresponds

with secondary school).

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Thesis Abstract

Background

Secular decreases in physical activity and increases in recreational screen-time among

young people are cause for concern. Both physical inactivity and excessive recreational

screen-time are independently associated with poor physical, social and psychological

health in adolescence. As adolescence marks a key period for establishing health

behaviours, there is a need to identify effective and scalable interventions to address both

physical inactivity and excessive recreational screen-time. Although an abundance of

interventions have been conducted to increase young people’s physical activity, fewer

studies have examined the impact of interventions designed to reduce recreational screen-

time, especially in adolescent populations. Of those studies that have examined screen-

time reduction in young people, few interventions have been designed to be ‘scalable’ or

adopted a theoretical framework to assist in the identification of behaviour change

mechanisms.

Thesis objectives

Presented as a series of studies, this thesis by publication aims to address current gaps in

the literature. The principal focus of this thesis is the development and evaluation of the

‘Switch-off 4 Healthy Minds’ (S4HM) intervention, which was evaluated using a cluster

randomised controlled trial (RCT) in a sample of Australian adolescents. Further, this

thesis presents a series of related studies investigating secondary aims, which are briefly

described below. Given the chronology of the research included within this thesis, and the

importance of providing context to the primary aim, the Secondary aims will be presented

first and are listed in order below.

Secondary aim 1: Review the evidence of associations between physical activity, screen-

time and mental health outcomes in adolescents

The aim of this chapter was to examine associations between health behaviours (i.e.,

physical activity and recreational screen-time) and indicators of mental health among

adolescents. The original objective was to conduct a novel systematic review of studies

that had examined the association between recreational screen-time and self-concept.

However, as too few studies were identified in the preliminary search, an alternate

systematic review focused on physical activity and physical self-concept (general and

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sub-domains) was conducted. Included studies were identified through a structured search

of six electronic databases with no date restrictions. In total, 111 studies were

qualitatively and 64 were quantitatively synthesised. Potential moderators examined

included; sex, age and study design. Perceived competence was most strongly associated

with physical activity (r = 0.30, 95% CI = 0.24 to 0.35, p < 0.001), followed by perceived

fitness (r = 0.26, 95% CI = 0.20 to 0.32, p < 0.001), general physical self-concept (r =

0.25, 95% CI = 0.16 to 0.34, p < 0.001) and perceived physical appearance (r = 0.12, 95%

CI = 0.08 to 0.16, p < 0.001). Sex was a significant moderator for general physical self-

concept and age for perceived appearance as well as perceived competence. No

significant moderators were found for perceived fitness. Overall, significant associations

of a medium effect size were present between general physical self-concept, perceived

competence, perceived fitness and physical activity in young people.

Secondary aim 2: To provide a rationale and present the study protocol for the ‘Switch-

off 4 Healthy Minds’ (S4HM) intervention: A cluster randomised controlled trial to

reduce recreational screen-time in adolescents

The aim of chapter 3 was to describe the methods used in the S4HM intervention and to

provide justification for the examination of each outcome. The primary outcome of the

S4HM intervention was recreational screen-time. Secondary outcomes consisted of

mental health indicators including; physical self-concept, psychological well-being,

psychological difficulties and psychological distress. Objectively measured physical

activity (accelerometry), body mass index (BMI) and hypothesised mediators of

behaviour change (autonomous motivation, controlled motivation, and amotivation) were

explored. The 6-month multi-component intervention was designed to encourage

adolescents to manage their recreational screen-time using a range of evidence-based

strategies. Grounded in Self-determination Theory (SDT), the S4HM intervention

included the following components: an interactive seminar for students, eHealth

messaging, behavioural contract and parental newsletters. This chapter highlighted the

lack of screen-time interventions among adolescents and projected future research was

needed to determine if reducing screen-time aids the prevention and treatment of physical

inactivity and mental health in youth.

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Secondary aim 3: To examine longitudinal associations between changes in screen-time

and mental health outcomes in adolescents

The aim of this chapter was to explore longitudinal associations between changes in

recreational screen-time (both total and device specific) and mental health outcomes

(mental well-being and ill-being) in a sample of Australian adolescents. A subsequent aim

was to examine the association between non-recreational screen-time (computer use for

homework) and mental health. Recreational screen-time (television, DVD, computer,

tablet and mobile phone use), non-recreational screen-time and mental health indicators

(physical self-concept, psychological well-being and psychological difficulties) were

reported on two occasions (Time 1 and Time 2) over the first year of secondary school.

After adjusting for relevant covariates (Time 1 measurements, group allocation, school

clustering, sex, socio-economic status, Time 1 body mass index (BMI) and Time 1

physical activity), multi-level linear mixed models were conducted. Changes in total

recreational screen-time (β = -.09 p = .048) and tablet/phone use (β = -.18, p < .001) were

negatively associated with physical self-concept. Changes in total recreational screen-

time (β = -.20, p = .001) and computer use (β = -.23, p = .003) were negatively associated

with psychological well-being. A positive association was found with television/DVD use

and psychological difficulties (β = .16, p = .015). No associations were found between

indicators of mental health and screen use for homework purposes. Findings suggest

different devices have distinct associations with mental health outcomes. While this study

did not provide causal evidence for the detrimental effect of screen-time on mental health,

findings suggest reducing screen-time may improve mental health in young people.

Primary aim 1: To evaluate the effects of the S4HM intervention by examining outcomes

and potential mediators in a cluster RCT

The aim of this chapter was to evaluate the impact of the S4HM intervention in

adolescents. The primary outcome was recreational screen-time and secondary outcomes

included mental health indicators, physical activity, and BMI. Eligible participants

reported exceeding recreational screen-time recommendations (i.e., > 2 hours/day). In

total, 322 adolescents (mean age = 14.4 ± 0.6 years) from eight secondary schools in New

South Wales, Australia were recruited. The S4HM intervention was a cluster RCT with

study measures at baseline and 6-months (post-intervention). Outcome analyses were

conducted using linear mixed models. Meditation analyses were conducted to determine

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if changes in motivation mediated the intervention effect using a product-of-coefficient

test. At post intervention, significant reductions in screen-time occurred in both groups,

with a greater reduction observed in the intervention group (-50 min/day versus -29

minutes, p <.05 for both). However, the adjusted difference in change between groups

was not statistically significant (mean = -21.3 min/day, p = 0.255). There were no

significant intervention effects for mental health outcomes, physical activity or BMI. It

was found that the intervention effect was partially mediated by increases in autonomous

motivation to limit screen-time but not controlled motivation.

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Statement of Contribution

I was involved in all stages of the S4HM study including; conceptualisation, ethical

approval, recruitment, intervention development, implementation and evaluation. More

specifically, I completed the following tasks:

Ethics approval

In collaboration with my supervisors and the project manager, I assisted in drafting,

revising and submitting ethics applications through the Human Research Ethics

Committees of the University of Newcastle, Newcastle-Maitland Catholic Schools Office

and the Diocese of Broken Bay.

Recruitment

I met with school principals, teachers and students to discuss the S4HM study and

provided each with an overview of the intervention whilst conducting eligibility screening

questionnaires. I was responsible for distributing and collecting principal, student and

parent information and consent letters.

Designing resources

Cooperating with my primary supervisor, I was responsible for developing the S4HM

intervention resources, including: an interactive seminar for students, eHealth messages, a

behavioural contract and six parental newsletters.

Assessments

Partnering with the project manager, I was involved in the organisation of data collection.

I assumed responsibility for leading the baseline and post-program assessments with the

assistants of two research assistants. Training days were held for the research assistants in

preparation for data collection.

Data management

Entry, cleaning and de-identifying all data and the development of a database for analysis

was my responsibility. Statistical analysis of primary and secondary outcomes was a

collaborative effort with my primary supervisor.

1

Introduction

While this thesis is focused primarily on recreational screen-time, it also addresses three

important and inter-related themes: physical activity, general screen-time and mental

health. This thesis begins with a literature review, followed by a series of interrelated

research papers, three of which have been published. The fourth paper is currently under

review in a peer-reviewed journal. The final chapter discusses theoretical and practical

recommendations as a result of the research. A more detailed overview of each chapter,

with citation details of published and in-press articles, is provided below.

Chapter 1: Literature Review

This chapter provides a rationale for the thesis, presenting an overview of the current

literature regarding patterns of health behaviours and their associations with health

outcomes among adolescents. Chapter 1 is divided into three main sections, of which the

first provides a rationale for increasing physical activity and reducing screen-time in

adolescence. The second section involves an examination of health consequences of

inactivity and sedentary behaviour, through an analysis of correlates, determinants,

mediators and theories of health behaviour change. The final section reviews sedentary

behaviour interventions designed to increase physical activity and reduce screen-time in

young people. By reviewing previous literature, including behavioural theories and

interventions; this chapter highlights the significant public health challenges of physical

inactivity, screen-time and mental health.

Chapter 2: Physical Activity and Physical Self-concept in Youth: Systematic Review

and Meta-analysis

Previous studies have found negative associations between recreational screen-time and

indicators of mental health (e.g., depression, anxiety and self-esteem). An initial review

was proposed to examine the relationship between self-concept and recreational screen-

time in adolescents. However, preliminary searches demonstrated that there were not

sufficient studies focusing on this relationship to justify a systematic review and meta-

analysis on this topic. No previous review examining the relationship between physical

activity and physical self-concept could be found; therefore it was determined that a

review focusing on this topic would provide an important contribution to the field.

2

Citation: Babic, MJ, Morgan, PJ, Plotnikoff, RC, Lonsdale, C, White, RL, Lubans, DR.

Physical activity and physical self-concept in youth: systematic review and meta-analysis.

Sports Medicine, 2014; 44(11): 1589–601.

Chapter 3: Rationale and Study Protocol for ‘Switch-off 4 Healthy Minds’ (S4HM):

A Cluster Randomised Controlled Trial to Reduce Recreational Screen-time in

Adolescents

This chapter provides a rationale for reducing recreational screen-time in adolescents and

describes the methods used in the S4HM intervention. Details of the study design,

recruitment, power calculation, randomisation procedures and outcome measures are

provided.

Citation: Babic, MJ, Morgan, PJ, Plotnikoff, RC, Lonsdale, C, Eather, N, Skinner, G,

Baker, AL, Pollock, E, Lubans, DR. Rationale and study protocol for ‘Switch-off 4

Healthy Minds’ (S4HM): a cluster randomised controlled trial to reduce recreational

screen-time in adolescents. Contemporary Clinical Trials, 2015; 40: 150–158.

Chapter 4: Intervention to Reduce Recreational Screen-time in Adolescents:

Outcomes and Mediators from the ‘Switch-off 4 Healthy Minds’ (S4HM) Cluster

Randomised Controlled Trial

Chapter 4 presents the results of a study examining the outcomes and mediating effects of

a screen-time reduction intervention in adolescents. First, outcomes among adolescents

participating in the S4HM intervention are detailed. Second, hypothesised mediation

models are analysed to determine whether improvements in motivation (autonomous,

controlled, amotivation) mediated effects of the intervention.

Citation: Babic, MJ, Morgan, PJ, Lonsdale, C, Plotnikoff, RC, Eather, N, Skinner, G,

Baker, AL, Pollock, E, Lubans, DR. Intervention to reduce recreational screen-time in

adolescents: outcomes and mediators from the ‘Switch-Off 4 Healthy Minds’ (S4HM)

cluster randomised controlled trial. Preventive Medicine, 2016: 91(1): 50–57.

3

Chapter 5: Longitudinal Associations between Screen-time and Mental Health in

Australian Adolescents

This chapter describes the findings from a longitudinal study examining associations

between changes in screen-time and mental health outcomes among adolescents during

their first year of secondary school. A number of proposed mechanisms are provided.

Citation: Babic, MJ, Smith, JJ, Morgan, PJ, Plotnikoff, RC, Lonsdale, C, Lubans, DR.

(2016). Longitudinal associations between changes in screen-time and mental health

outcomes in a sample of Australian adolescents. Mental Health and Physical Activity

(Under review).

Chapter 6: Thesis Discussion and Conclusion

This chapter provides theoretical and practical recommendations based on experiences in

conducting S4HM and appraising current literature. The conclusion aims to summarise

the findings of the work conducted for this thesis and provides suggestions for future

work.

4

Chapter 1

Literature Review

This chapter is divided into three main parts, as shown in Figure 1. In the first part, a

rationale for increasing physical activity and reducing screen-time in adolescence is

established by examining the prevalence and consequences of low physical activity and

high screen-time. The second part focuses on the correlates, determinants, meditators and

theories of health behaviour change that are relevant to physical activity and screen-time.

The third part includes a review of previous interventions that aimed to increase physical

activity and reduce screen-time in young people. A summary based on the existing

literature is provided after each section.

.

5

Figure 1: Schematic diagram of literature review

1.1 Definitions and measurements of physical activity,

sedentary behaviour and screen-time

1.2 Guidelines, prevalence and trends of physical activity and

screen-time in adolescents

1.3 Health consequences of

inactivity, excessive sedentary behaviour

and screen-time

Part 2: Understanding physical activity and screen-time

1.6 Correlates and determinants of physical activity

1.4 Mechanisms responsible for the effects of physical

activity and screen-time and mental

health

1.7 Correlates and determinants of

screen-time

1.8 Mediators of physical activity and

screen-time

1.9 Theories of health behaviour

change

Part 1: Rationale for increasing physical activity and reducing screen-time in adolescence

Part 3: Review of interventions to increase physical activity and reduce screen-time in adolescents

1.11 Physical activity interventions for

adolescents

1.12 Interventions to increase physical activity and reduce screen-time

1.13 Screen-time interventions for

adolescents

1.14 Implementation and scaling up of interventions

6

Part 1

Rationale for Increasing Physical Activity and

Reducing Screen-time in Adolescence

1.1 Definitions of physical activity, sedentary behaviour and screen-time

Physical activity

Physical activity is defined as “any bodily movement produced by skeletal muscles that

require energy expenditure” 1, and includes four components: volume, intensity,

frequency, and type. Volume refers to the minimum number of minutes for which

individuals should engage in physical activity per day. Intensity denotes a degree of

exertion and is commonly expressed as metabolic equivalent of task (MET) (light = 1.8–

2.9, moderate = 3.0–5.9, vigorous 6.0) 2. Frequency indicates the number of times an

individual should participate in physical activity. The final component, type, relates to the

types of physical activities in which individuals should engage (i.e., endurance, strength

or flexibility). Physical inactivity refers to an individual’s failing to achieve enough

physical activity, whereas sedentary behaviour denotes prolonged periods of sitting or

lying.

Sedentary behaviour and screen-time

Sedentary behaviour consists of a range of activities that are typically performed while

seated and indicated by energy expenditure ≤ 1.5 METs 3, and may include reading,

listening to music or watching television. Screen-time has been used as a proxy measure

of sedentary behaviour in population-based research and contributes the major portion of

time spent sedentary among adolescents 4,5. Screen-time may include time spent using

any screen device, such as watching television, using computers and playing video games 6. Operationally-defined recreational screen-time refers to the use of screen devices for

the purpose of entertainment, whereas non-recreational screen-time refers to the use of

screen devices for homework.

7

1.1.1 Inter-relationships between physical activity and sedentary behaviour

The displacement theory suggests that sedentary behaviours such as screen-time displace

time allocated for physical activity 6. Despite a small number of cross-sectional studies

that report inverse associations between physical activity and time spent in sedentary

behaviours 7,8, most of the available evidence suggests that they are separate constructs

and not functional opposites 9. Consequently, it is important to consider employing valid

and reliable measures for both behaviours 10.

1.1.2 Measurements of physical activity and sedentary behaviour

The accurate assessment of physical activity and sedentary behaviour in young people is

needed to:

- evaluate the effectiveness of interventions;

- identify positive and negative health outcomes;

- estimate population prevalence and trends 2.

Key considerations for measuring both physical activity and sedentary behaviour are

discussed below.

Measurement of physical activity

Physical activity can be measured using objective measures, including heart rate

monitors; direct observations, accelerometers and pedometers 2. Subjective measures of

physical activity include diaries, log books and survey questionnaires 2. It is advisable to

use both objective and subjective measurements, as objective motion devices alone lack

the ability to provide contextual information (i.e., setting and type of activity) 11. The

simultaneous use of accelerometry (which provides an assessment of intensity) and self-

report measures (i.e., a physical activity log providing type, frequency, duration and

context) provides the most comprehensive assessment of physical activity 2.

Measurement of sedentary behaviour

Sedentary behaviours can be measured objectively via motion devices such as

accelerometers and inclinometers, observation and electronic devices specifically

designed to measure screen-time. Subjective measures of sedentary behaviour rely on

8

self-report or proxy reporting by a third party (usually a parent) 11 and also include real-

time data capturing 12.

As screen-time is the most prevalent form of sedentary behaviour among adolescents 5,

additional work in this area is warranted. Specifically, developments are required to

assess nuances associated with modes of questionnaires (e.g., interviewer-administered

versus self-administration); different formats of responses (e.g., continuous or

categorical); the time frame of assessment (e.g., short-term, such as past day or past 7

days, versus habitual patterns such as typical day, usual week, or past year); and the way

in which such factors affect estimates 13. Studies propose the incorporation of both self-

report (to capture behaviour-specific information) and device-based (to measure both total

time and patterns of accumulation) measures of screen-time 13,14.

1.1.3 The key period of adolescence

Unprecedented social and cultural changes are influencing the health and well-being of

the largest generation of young people in history (there are 1.8 billion people aged 10–25

years) 15. Until recently, adolescents have been largely overlooked in global health and

have experienced fewer health gains in comparison to other age groups 15. Adolescence

marks a stage of dynamic brain development in which individuals acquire cognitive,

emotional and social resources that form a foundation of health into adulthood 15.

Moreover, health behaviours such as physical activity and screen-time established during

adolescence appear to track into adulthood 16. Unfortunately, physical activity levels

decline dramatically during adolescence 17-19. One recent systematic review and pooled

analysis reported that the mean percentage of physical activity change per year, across all

studies, was -7.0% (95% CI = -8.8 to -5.2%), ranging from -18.8% to 7.8% 17.

Consequently, investments in adolescent health and well-being may bring both immediate

and long-term health benefits 15.

1.2 Guidelines, prevalence and trends

1.2.1 International and national and guidelines of physical activity and screen-time

Table 1 and Table 2 present summaries of international physical activity and screen-time

guidelines. The Australian national physical activity and screen-time guidelines for

adolescents are provided after the tables.

9

10

Table 1: International physical activity guidelines for young people

Table 2: International screen-time guidelines for young people

Organisation Title Year Ages Recommendations American Academy of Paediatrics 23

Children, Adolescents, and Television

2001 Children and adolescents

Total screen-time should consist of quality programming, ≤ 1–2 hours per day.

Canadian Paediatric Society 24

Impact of Media use on Children and Youth

2002 Children and youth (ages not specified)

≤ 1–2 hours per day

Canadian Society for Exercise Physiology in partnership with the Healthy Active Living and Obesity Research Group 25

Canadian Sedentary Behaviour Guidelines for Children and Youth

2011 Youth (aged 12–17 years)

Limit recreational screen-time < 2 hours per day

In order to meet Australian national guidelines of physical activity and screen-time,

young people aged 13–17 years should:

Body Title Year Ages Recommendation(s) Health Canada and the Canadian Society for Exercise Physiology 20

Canada’s Physical Activity Guide for Children and Youth

2002 10–14 years Increase current physical activity time (in increments of 5–10 minutes) by at least 30 minutes until a total of 90 minutes a day is reached.

Divisions of nutrition and physical activity and adolescent and school health of the US Centers for Disease Control 21

Evidence-based Physical Activity for School-aged Youth

2005 6–18 years Participate in a variety of developmentally appropriate, enjoyable activities for ≥ 60 minutes a day of Moderate to Vigorous Physical Activity.

US Department of Agriculture 22

Dietary Guidelines for Americans

2005 Youth of all ages including adolescents

Preferably all days of the week, if not on most, accrue ≥ 60 minutes of physical activity.

11

• Accumulate at least 60 minutes of moderate-to-vigorous physical activity (MVPA)

every day 26.

• Participate in activities that strengthen muscle and bone at least three times per

week 26.

• Limit screen-time (5–18 years < 2 hours/day) 23,26.

1.2.2 Prevalence and trends of physical activity in adolescence

According to available evidence, 80.3% (95% CI = 80.1 to 80.5) of 13–15-year-olds are

not meeting the physical activity guidelines 27. Two additional comprehensive

international sources among adolescents are the Global School-based Student Health

Survey 28 and the Health Behaviour in School-aged Children Survey 29. By combining

information from both these sources it is estimated that 20% of adolescents worldwide

meet physical activity recommendations 27. In addition, the Active Healthy Kids Global

Alliance has created report cards on physical activity in young people from 38 countries

in six continents (representing 60% of the world’s population) 30. A standardised grading

framework (from A = excellent to F = failing) was used 30. The average grade for overall

physical activity around the world was D (low/poor) 30.

Similar to global trends, the majority of young Australians are not sufficiently active.

A nationally representative sample of secondary students, from years 8 to 11, was

involved in the 2009–2010 National Secondary Students’ Diet and Activity (NaSSDA)

survey, which found that 15% of Australian students met the physical activity guidelines 31. Comparably, the Australian Health Survey indicated that 19% of children and

adolescents (aged 5–17) met the physical activity guidelines 32, with older adolescents the

least likely to meet the recommended guidelines (only 18% of year 8 and 13% of year 11

students) 31.

1.2.3 Screen-time prevalence and trends among adolescents

The majority of adolescents around the world exceed international guidelines for screen-

time. For example, 79.5% of Brazilian 33, 70.6% of British 34, 75% of American 35 and

80% of Canadian 36 adolescents report more than two hours per day of recreational

screen-time. In Australia, the 2007 National Children Nutrition and Physical Activity

Survey reported that 78% of youth aged 9–16 exceeded the recommended screen-time per

day 37. Despite inconsistent trend data internationally 38-41, screen-use has risen

12

dramatically among Australian adolescents over the past decade 42. In 2010, the

Children's Participation in Cultural and Leisure Activities (CPCLA) survey found that

74.5% of adolescents exceeded the screen-time guidelines 43. In 2012, the same survey

reported that closer to 90.0% of adolescents engaged in excessive (> 2 hours) screen-

based activities 44. Additionally, participation in screen-time activity tends to increase

with age 45. For example, Houghton et al. (2015) reported that the proportion of 8–6 year

olds exceeding two hours of screen-time (all forms) per weekday increased from 45% of

8-year-olds to 80% of 16-year-olds 46, while the 2009-2010 NaSSDA survey found that

71% of Australian adolescents exceeded screen-time guidelines on weekdays, compared

with 83% on weekends 47. Trend data also show an increase in screen availability. In

2014, an estimated 40% of households had tablets (up from 27% in 2012) 48, along with

an estimated 68% of Australian adolescents owning a smartphone, compared with 59% in

2013. With such trends emerging, health implications are inevitable.

1.3 Health consequences of inactivity and excessive sedentary behaviour

1.3.1 Physical activity and physical health

Physical activity has long been regarded as an important component of a healthy

lifestyle 49. Numerous systematic reviews conclude that participation in physical activity

can improve young people’s physical health. Improvements include lowering of

cholesterol, blood lipids and blood pressure, reducing the risk of metabolic syndrome and

improving bone mineral density 50-52. Regular physical activity is also known to reduce

the risk of stroke, hypertension and some cancers 19,53 and serve as a protective factor

against CVD, type II diabetes 54-56 and unhealthy weight gain 57. While a dose–response

relationship between physical activity levels and physical health outcomes is well

established, the exact shape of the dose–response curve is not fully understood 58.

Although physiological changes resulting from physical activity has received significant

attention, the effect of physical activity on mental health among adolescents has received

less attention 59.

1.3.2 Physical activity and mental health

Mental health is a state of well-being and effective functioning in which an individual

realises their abilities, is resilient to stresses and is able to contribute positively to their

community 60. Conversely, mental health problems (ill-being) are conditions that

13

negatively affect an individual’s mood, thinking and behaviour (e.g., depression, anxiety,

psychological difficulties and psychological distress) 61. Evidence from cross-sectional,

longitudinal and experimental studies suggests that physical activity is associated with

young people’s mental well-being and ill-being 62-64. Authors of a recent review of

reviews concluded that participation in physical activity has small but beneficial effects

on depression, anxiety, self-esteem and cognitive performance in children and adolescents 65. For example, meta-analysis conducted by Petruzzello et al. revealed small-to-moderate

effects for reducing anxiety (effect size = 0.47) 66. Similarly, Ekeland and colleagues 67

reported small-to-moderate effects (effect size = 0.49) for the impact of physical activity

on global self-esteem among children and young people (3–20 years-old). Independent of

physical activity, sedentary behaviours such as screen-time are also associated with a

variety of physical and mental health concerns 68,69.

1.3.3 Excessive screen-time and physical health

There is now compelling evidence that excessive screen-time is a risk factor for poor

physical health in children and adolescents 70 71,72. More specifically, high levels of

screen-time have been linked with type II diabetes 70, reduced cardiorespiratory fitness 73,

obesity 74,75, cardiovascular disease (CVD) 76, high blood pressure 77 and musculoskeletal

pain 78. Moreover, a dose–response relationship (i.e., each hour per day spent in screen-

time) has been established for increased diastolic blood pressure 79 and metabolic

syndrome in adolescents 35. In addition to the negative effects on physical health,

emerging evidence suggests that excessive screen-time is also associated with poor

mental health.

1.3.4 Mental health outcomes of excessive screen-time

A number of systematic reviews have reported an association of screen-time with

unfavourable mental health outcomes 71,78,80-84. In one of the first reviews on the topic,

Trembley et al. 71 reported an inverse association between screen-time and self-esteem

among children and adolescents (-0.89, 95% CI = -1.67 to -0.11). A more recent review

by Costigan et al. 78 identified six studies that found screen-based behaviour to be

negatively associated with psychological well-being, and positively associated with

depression, in adolescent girls. Another systematic review reported strong evidence that

high levels of screen-time were associated with greater hyperactivity/inattention problems

and internalising problems, as well as less psychological well-being and perceived quality

14

of life, among adolescents 85. However, the majority of previous studies are cross-

sectional;therefore, causation cannot be determined 71. Consequently, further quality

evidence using robust designs (i.e. longitudinal and experimental studies) is needed to

better understand the relationship between screen-time and mental health in young people 86.

1.4 Mechanisms responsible for the effects of physical activity and screen-

time on mental health

Despite an increasing number of studies reporting the positive effects of physical activity

on mental health in children 87 and adolescents 65, the underlying mechanisms responsible

for such effects have not been clearly established 88. A recent systematic review proposed

three broad categories of hypothesised mechanisms: neurological, behavioural and

psychosocial 88. Potential neurological mechanisms include changes in the structural and

functional composition of the brain 88. The behavioural mechanism hypothesis proposes

that physical activity may improve mental health via relevant and associated behaviours,

such as sleep (i.e. duration and efficiency), self-regulation and coping skills 89. The

authors of the review identified the strongest evidence for potential psychosocial

mechanisms 88. In particular, changes in physical self-perceptions coincided with

improvements in self-esteem in five of the six studies evaluating these constructs

together. However, due to limited relevant studies and comparable outcomes, clear

neurological and behavioural mechanisms have not been established 88.

As it remains unclear how the use of screen-based devices may influence mental

health, further investigations are warranted. Proposed mechanisms have included:

objectified/unattainable images of physical appearance 90, sleep problems 91, and exposure

to cyberbullying 92, all of which have resulted in increased risk of mental health

problems 93. Several examples attempting to explain potential causes are explored below.

Social media use among adolescents is common and often involves comparisons of

bodies, images and photos 94. As such, discrepancies between broadcasted ideals and self-

perceptions may have negative mental health consequences due to inflated social pressure

to conform and feelings of body inadequacy 95. In attempting to adhere to idealised social

expectations (e.g., image-based trends like “fitspiration”), adolescents’ feelings of

inadequacy may be exacerbated. Alternately, numerous studies have reported increased

15

negative feelings (e.g., helplessness) 96, levels of depression and social dissatisfaction and

withdrawal 97, and lower levels of self-esteem 98 in response to cyberbullying, which it is

now possible to experience across multiple screen-time mediums.

1.5 Summary

• Physical inactivity and screen-based sedentary behaviours are highly prevalent

among adolescents and have been shown to track into adulthood.

• Screen-time has been used as a proxy measure of sedentary behaviour in

population based research, and contributes the major portion of time spent

sedentary among adolescents.

• Both physical inactivity and excessive recreational screen-time appear to be

independently associated with poor physical and mental health in cross-sectional

studies.

• It is not known whether reducing recreational screen-time can improve mental

health in young people.

16

Part 2

Understanding Physical Activity and Sedentary Behaviour

1.6 Correlates and determinants of physical activity

Addressing physical activity in a public health framework requires an understanding of

the potential correlates (factors associated with activity) and determinants (those with a

causal relationship) 99,100. Improving the understanding of correlates and determinants of

physical activity may also contribute to more effective interventions 101. Consequently,

the potential benefits of examining correlates of physical activity have informed multiple

reviews among adolescents 101-103. As a result, known correlates and the direction of

associations reported in the reviews will be summarised in three categories: individual,

social and environmental correlates (Table 3).

1.6.1 Individual correlates of physical activity

Individual-level correlates have received the most attention in the current research; these

include studies of genetics, movement skill proficiency, perceived competence, body

image and motivation 101. As a result, being male, having a high self-concept and being

Caucasian are well accepted individual-level correlates that are positively associated with

physical activity 101,104-106. Age is consistently inversely associated with physical activity

among adolescents 105,106. The evolving and complex nature of physical activity is

reflected in the recent expansion of correlate reviews examining factors beyond the

individual 101.

1.6.2 Social correlates of physical activity

Social correlates studied among adolescents include parental education, family income,

positive culture for exercise and physical activity levels of parents and peers 105,107. Of the

social factors identified in reviews, support for physical activity from parents and peers

appears to be the most consistent social correlate of physical activity among

adolescents 105,106,108,109. Supplementary reviews 103 suggest that characteristics of the

interpersonal and societal environment are not as closely related to physical activity levels

as those of the school and community environments (e.g., school and neighbourhood

facilities).

17

1.6.3 Environmental correlates of physical activity

The new area of environmental correlates research has explored aspects of walkability,

traffic speed and volume, residential density and access to facilities 101. The most robust

environmental correlates among adolescents are land-use mix (proximity to destinations)

and residential density 101.

1.6.4 Issues examining correlates of physical activity

Although attention has now been drawn to potential factors that may influence physical

activity patterns among adolescents, such literature is clearly not without limitations. The

majority of studies contributing to the systematic reviews are cross-sectional and

therefore unable to determine whether the variables act as mechanisms of behaviour

change 101,103. Moreover, varying degrees of inconsistency in study populations, correlate

assessments and statistical analyses makes it difficult to draw firm conclusions 105.

Despite the few inconsistences identified in several reviews 101,105,106, Table 3 provides a

summary of the associations of individual, social and environmental correlates of

physical activity among adolescents.

18

Table 3: Correlates of physical activity in young people

Type of correlate Correlates Direction Individual Age -

Sex (male) + Ethnicity (Caucasian) + Self-efficacy + Perceived behavioural control + Previous physical activity + Adiposity +/- Perceived barriers -

Social Parental activity NR Family and peer support + Parental education +/- Perceived parental role modelling NR Peer modelling NR

Environmental Land-use mix + Residential density + Local crime - Availability to facilities +/-

Notes: + = positive association; - = inverse association; +/- = both associated and not associated;

NR = Not related.

1.7 Correlates and determinants of screen-time

Of equal importance is a need to understand the correlates and potential determinants of

sedentary behaviour. Reviews examining the correlates of sedentary behaviour among

adolescents have typically focused on screen-time 105,110-116; these include a review of

reviews 117. To improve comparability with physical activity correlates (Table 3),

correlates will also be categorised accordingly as individual, social and environmental,

with a summary presented in Table 4. An examination of the limitations of reviews

follows.

1.7.1 Individual correlates of screen-time

Considerable research has accumulated, focusing on individual-level correlates and

encompassing aspects of adiposity, self-esteem, academic achievement, socio-economic

status and demographic factors 111,118,119. Consistent positive associations of screen-time

19

at the individual level include age, being male, and ethnicity (non-Caucasian) 105,118,120.

Negative associations include self-esteem and academic achievement 71,117,118.

1.7.2 Social correlates of screen-time

The examination of social correlates incorporates aspects of peer support, parental

viewing behaviours, single vs dual parent households, parent education and parental

rules/limitations for screen engagement 118. The strongest positive social correlates of

adolescent screen-time include parental screen engagement and single-parent

households 118,119. It is suggested that these correlates may be confounded by socio-

economic status, which has a significant inverse relationship with screen-time 118,119,121.

Parental rules and limitations regarding screen-viewing is associated with a negative

effect on screen-time, and appears to be one of the most important correlates in young

people 118,121,122. Notably, friends also appear to be a key correlate to influencing screen-

time patterns in females, which highlights the importance of social relationships and

support for females 123.

1.7.3 Environmental correlates of screen-time

The few studies of environmental correlates investigate urban versus rural settings,

week/weekend days, neighbourhood satisfaction and access to screens 118,124. Despite

difficulty determining consistent correlates due to limited studies 107, the majority of a

small number of reviews suggest the inclusion of televisions in the bedroom is positively

related with increased screen-time in adolescents 119,125. In one review 125, the total

number of televisions in a house was positively associated with increased screen-time for

girls, however an earlier review, which included a range of screens (TV, computers,

tablets and smartphones) and is more pertinent to this screen-based, multi-tasking

population 71, established a positive correlation for all adolescents 118. Adolescents living

in urban areas are reported to engage in more screen-time than their rural-living

counterparts, although further research is needed to consolidate correlations 118,119. Such

inconsistencies and gaps in literature require further research to elucidate clearer

associations.

1.7.4 Issues examining correlates of screen-time

Current examinations of potential correlates of screen-time are not without limitations.

These include small sample sizes, restricted geographic areas and little socio-cultural

20

variability 126. The varying definitions of sedentary behaviour and screen-time also

restrict the comparability and consistency of study findings. Further research should aim

to use separate measurements of sedentary behaviours (e.g. TV time versus. computer

time versus. smartphone time) and assess them consistently using reliable methods.

Interventions should aim to alter sedentary behaviours through a global approach that

includes individual, social and environmental factors.

Table 4: Correlates of screen-time in young people

Type of correlate Correlates Direction Individual Age +

Sex (male) + Ethnicity (Caucasian) + Self-esteem - Academic achievement - Adiposity +/-

Social Parental screen-time + Single parent + Parental education - Socio-economic status - Parental rules/limitations on screen-

time -

Peer screen-time +

Environmental Television in the bedroom + Access to screens + Urban residential area +

Note: + = positive association; - = inverse association; +/- = both associated and not associated;

NR = Not related.

1.8 Mediators of physical activity and screen-time behaviour change

The mechanisms responsible for behaviour change among adolescents are poorly

understood 127. Mediation analysis can be used to evaluate whether an intervention’s

success was achieved via changes in the hypothesised mechanisms 128. A visual

representation of mediation is provided in Figure 2.

M α b

21

Figure 2: Statistical mediation model

In the context of an intervention, pathway c’ is known as the direct effect (i.e., the effect

of the intervention on the outcome with adjustment for the mediator). Pathway a

represents the effect of the intervention on the potential mediator (M). Pathway b

represents the association between changes in the mediator and changes in the outcome,

independent of the intervention effect. The product-of-coefficients (AB) represents the

indirect or mediated effect.

Seminal work by Baron and Kenny (1986) 129 and Judd and Kenny (1981) 130

discussed four steps in establishing mediation:

• Step 1: Demonstrate the causal variable is correlated with the outcome. Using Y as

the criterion variable in a regression equation and X as a predictor (estimate and

test path c’ in Figure 2), this step establishes there is an effect that may be

mediated.

• Step 2: Demonstrate the causal variable is correlated with the mediator. Using M

as the criterion variable in the regression equation and X as a predictor (estimate

and test path a in Figure 2), this step involves treating the mediator as an outcome.

• Step 3: Demonstrate the mediator affects the outcome variable using Y as the

criterion variable in a regression equation and X and M as predictors (estimate and

test path b in Figure 2). Notably, the causal variable must be controlled in

establishing the effect of the mediator on the outcome.

• Step 4: Establish that M completely mediates the X-Y relationship; that is, the

effect of X on Y controlling for M (path c') should be zero.

1.8.1 Mediators of physical activity in adolescents

Two reviews have acknowledged the lack of studies examining hypothesised mediators of

physical activity behaviour change interventions involving young people 131,132. However,

both reviews found the strongest support for self-efficacy as a mediator of physical

activity behaviour change among adolescents 131,132. Additionally, attitudes 131,133,134,

X Y c’

22

perceived benefits 134,135, perceived barriers 134,135, self-regulation skills, social support 134,135 intrinsic motivation, autonomy support and self-regulation have also emerged as

potential mediators of physical activity behaviour change in young people 132.

1.8.2 Mediators of screen-time in adolescents

Even fewer studies have examined potential mediators of behaviour change in screen-

time reduction interventions 132. A systematic review of behaviour change mechanisms in

school-based energy balance interventions found that attitudes, social norms and habit

behaviour were not significant mediators132. A more recent study found that parental

regulation did not mediate effects on screen behaviours among adolescents 136. Due to the

dearth of relevant studies 132, further research is needed to explore the potential

mechanisms of screen-time behaviour change in adolescents. The application of relevant

behavioural theories should be at the forefront of such considerations.

1.9 Theories of health behaviour change

1.9.1 Current evidence

An important role of behavioural theories is to provide conceptual models to improve

understanding of the reasons for human actions 137. Behavioural theories can help

researchers identify causal pathways in the achievement of study outcomes 138 and inform

future intervention development and delivery. One key theory of health behaviour that

has been successfully applied among adolescents is Self-determination Theory (SDT) 139.

1.9.2 Self-Determination Theory

Fundamental to SDT is the notion that motivation for a behaviour consists along a

continuum, ranging from non-self-determined to self-determined behavioural

regulation 140. Self-determination Theory posits that satisfying basic psychological needs

of autonomy, competence and relatedness will lead to enhancements to autonomous

motivation 141,142. Autonomy refers to a sense of choice in the behaviour; competence

denotes a perception of mastery to a task; relatedness represents a sense of belonging or

social connectedness 142. Figure 3 shows a depiction of SDT. Studies have shown that

when these basic needs are satisfied, there is a corresponding increase in autonomous

motivation; when these needs are thwarted, there is diminished motivation and well-being 143.

23

Figure 3: Self-determination Theory (SDT). Further details can be seen at: Deci EL, Ryan RM. Intrinsic motivation and self-determination in human behavior. New

York, NY: Springer; 1985.

Organismic Integration Theory (OIT) is a sub-theory of SDT that makes further

distinctions between different types of motivation 144. An illustration of OIT can be seen

in Figure 4.

Figure 4: Organismic Integration Theory (OIT) - can be viewed at: Deci EL, Ryan RM. Handbook of Self-determination Research. Rochester, NY: University of

Rochester Press; 2002.

Specifically, OIT is hypothesised to consist of six different types of regulations: non-

regulation, external regulation, introjected regulation, identified regulation, integrated

regulation and intrinsic regulation 140,144.

Such regulations vary in the amount of autonomy and internalisation an individual is

experiencing. For example, if a person is fully autonomous and the motivation is

completely internalised, they would be considered to be high in intrinsic regulation.

Whereas, if an individual was not experiencing autonomy and was non-regulated, this

would indicate greater amotivation. Amotivation denotes a lack of motivation and

intention to act and perform an action, as an individual may not value the activity 145.

External regulation consists of performing a behaviour to satisfy an external demand or

Competence

Relatedness

Motivation

Autonomy

Behaviour

Image removed due to copyright.

24

obtain external rewards 145. Introjected regulation refers to an action being performed to

avoid negative feelings such as guilt or anxiety 145. Forms of autonomous motivation

include more intrinsic, integrated and identified regulation 145. Identified regulation

encompasses feelings of personal importance and values of a goal, or may identify value

in learning from a task 145. Integrated motivation refers to identification by an individual

with the importance of a behaviour 145. Finally, at the far right (see Figure 4) is intrinsic

regulation, which involves performing of behaviours for enjoyment, fun and personal

satisfaction 145.

Self-determination Theory has been used extensively in cross-sectional, longitudinal

and experimental research to explain physical activity behaviour in young people. It is

hypothesised that sustained health-promoting behaviour, such as physical activity, is a

result of motivation becoming internalised, as controlled forms of motivation would not

promote these behaviours for the long-term 34. A review examining the associations

between self-determined motivation and physical activity levels reported in studies

among adolescents supported this hypothesis, although, effects were only weak to

moderate in size 34. The review concluded that internalised forms of motivation are more

strongly and positively associated with physical activity levels in physical education

classes and leisure-time than is controlled motivation 34.

1.10 Summary of Part 2

• Self-efficacy, sex (male), ethnicity (Caucasian), land-use mix, family and peer

support are factors positively associated with physical activity. Negative

associations include age, perceived barriers and local crime 101,105,106.

• Parental rules are the most consistent correlate of screen-time in young people 122,146. Age, sex, ethnicity (Caucasian), parental screen-time, single-parent

households and television in the bedroom were further associated correlates.

• Mechanisms for change in intervention studies are rarely explored, resulting in

limited empirical evidence to guide intervention design and delivery.

• Multi-component interventions programs require statistical mediation analysis to

help identify mechanisms of behaviour change.

• SDT has emerged as a powerful framework for explaining and changing human

behaviour 141,142,147,148, yet little is known of its utility to guide screen-time

reduction interventions.

25

Part 3

Review of Interventions to Increase Physical Activity and Reduce Screen-time

in Adolescents

This section evaluates interventions in young people with emphasis on interventions

delivered in adolescent populations targeting: i) physical activity, ii) both physical

activity and screen-time, and iii) screen-time.

1.11 Physical activity interventions for adolescents

A number of systematic reviews have been conducted to evaluate the effectiveness of

physical activity interventions in young people 149,150. A key finding from past reviews is

that adolescents are largely underrepresented in comparison to children. Waters and

colleagues posit that, given the large number of studies targeting primary school-aged

youth, there is little need for further efficacy testing of school-based trials among this

population 151. However, given the current research gap regarding the effectiveness of

physical activity interventions among adolescents, recommendations are made for

continued research among this cohort 151.

One encouraging cluster randomised controlled trial was conducted in 15 schools and

involved 2,434 adolescents in seventh and eighth grades 152. Participants were assigned to

one of three arms: i) intervention with parental support, ii) intervention alone, or iii)

control group 152. The intervention combined environmental strategies with education via

computer-tailored feedback 152. Schools were provided with sporting equipment and

created additional opportunities to be physically active during break times and after

school. Half of the intervention schools were invited to an interactive seminar on physical

activity in an attempt to create a supportive social and home environment. Significant

increases in physical activity were found in the “intervention group with parental

support” (+6.4 min/day) and the “intervention alone” group (+4.5 min/day) compared to

the control group 152.

Similar findings were established in a more recent cluster randomised trial called

“Physical Activity 4 Everyone” (PA4E1) 153. Ten disadvantaged secondary schools

benefited from strategies addressing the school curriculum, school environment and

community 153. Formal curriculum adjustments involved: training Physical Education

teachers, personalised physical activity plans for students, additional equipment and

26

curriculum resources. School ethos and environment adjustments consisted of modified

school policies and daily activity programs during break times. Partnerships and services

comprised after-school physical activity programs and parent engagement strategies. At a

12-month follow-up (mid-point), students attending intervention schools participated in

more MVPA (4 min/day) than the control group 153. It was hypothesised that findings

were attributable to the multi-component nature of the intervention and comprehensive

implementation strategies 153.

A review of reviews and systematic update of school-based interventions on physical

activity in adolescents suggest multi-component interventions that combine

environmental, curricular and educational elements are more effective compared with

single-component interventions 149.

1.12 Interventions to increase physical activity and reduce screen-time

A multi-component study by Simon and colleagues 154 randomised eight of 77 schools to

receive a 4-year intervention focused on students’ attitudes, knowledge, and the school

environment. After-school activity programmes were offered at each school in addition to

two curriculum lessons focusing on physical activity and sedentary behaviour (measured

in time engaged in television viewing and computer/video games). Physical activity

significantly increased among the intervention students, in girls (odds ratio [OR] 3.38; p <

0.01) and in boys (OR 1.73; p = 0.01). After adjusting for age, overweight at baseline and

socio-economic factors, those within the treatment school were half as likely to report

continued high screen-time (adjusted odds ratio = 0.54 [girls] and 0.52 [boys]; p < 0.001).

Another school-based intervention reporting positive adjustments to physical activity

and screen-time in participants is “Choice, Control and Change”. This intervention aimed

to improve autonomous motivation for a variety of health-related behaviours in

adolescents. The intervention consisted of a one-off professional development session to

help teachers deliver a total of 33 lessons based around a systematic science-inquiry

process. Teaching provided participants with rationales for behaviour change and was

guided by SDT. Participants in the intervention group reported a significant increase in

purposeful walking for transport and taking the stairs, in addition to significant decreases

in frequency of recreational screen-time 155.

27

The “Active Teen Leaders Avoiding Screen-time” (ATLAS) program also used SDT

and was focused on obesity prevention among adolescent boys from low-income

secondary schools 147,148. The intervention included lunch-time physical activity

mentoring sessions, researcher-led seminars, professional development for teachers and

suitable equipment to enhance school-sport sessions. Participants were provided with

pedometers and access to a smartphone application and a website for self-monitoring.

Additionally, parental newsletters encouraging the restriction of recreational screen-time

were supplied 147. At the conclusion of the 20-week intervention, no significant

intervention effect for physical activity was found, although boys of the intervention

reported significantly less screen-time (mean: -30 ± 10.08 minutes/day; p = 0.3) than their

control group counterparts 148.

The “Dutch Obesity Intervention in Teenagers” (DOiT) health promotion intervention

was conducted in secondary schools and attempted to influence screen-time and physical

activity 156. The intervention consisted of curricular and environmental change strategies,

and reported no significant intervention effects for physical activity and screen-time at 8-

and 12-month assessment periods. However, after 20 months, a significant effect for

reduced screen-viewing in favour of the intervention group was found, albeit only for

boys (-25 minutes/day; 95% CI = -50 to =0.3 minutes/day) 156.

In contrast to DOiT, the HEalth In Adolescents (HEIA) intervention altered physical

activity and screen-based behaviour in girls only 157. The HEIA intervention was a 20

month, multi-component school-based intervention in 37 schools and involved 1465

students (11-year-olds) 157. The intervention consisted of lessons with student booklets,

posters, activity breaks in classrooms, sport equipment, active commuting, fact sheets for

parents and a course for physical education teachers 157. At the midway period (8

months), girls in the intervention group reported a significant difference in

television/DVD viewing and computer/game-use compared to the control group 157. At

post-intervention, the subgroup analyses indicated a significant effect in girls’ physical

activity levels (p < 0.03) but not in boys’ (p = 0.35) 158.

Such findings are in contrast to the school-based intervention “Planet Health” 159,

which consisted of randomly assigned sixth and seventh graders who received a two-year

curriculum. Thirty-two core lessons focusing on diet, television reduction and physical

activity were integrated into regular subjects by teachers. Although there were no

28

statistically significant changes in physical activity, greater decreases in screen-time were

evident in the intervention schools compared to control schools for both boys (adjusted

difference -0.40, p = 0.001) and girls (adjusted difference -0.58, p = 0.001) 159.

Null findings were reported in the web-based computer-tailored intervention

“FATaintPHAT” 160, which provided adolescents with access to eight modules online.

Each module consisted of information about behaviour–health links, an assessment of

behaviour and determinants, individually-tailored feedback on behaviour and

determinants, and goal setting. The lack of an intervention effect was attributed to a short

intervention duration (8 sessions of 15 minutes each within 10 weeks) and the inability to

engage the participants’ family, community or environmental factors 160.

Diverse challenges were faced during the intervention known as ACTIVITAL 161,

which reported varied effects after two stages of implementation. The first stage included

an individual and environmental component tailored to the local context, and resources

that focused on diet, physical activity and screen-time behaviours, while the second stage

focused only on diet and physical activity. Significant intervention effects for screen-time

were achieved after 18 months (β = -25.9 min; p = 0.03). However, these effects were not

maintained once the strategies targeting screen-time were discontinued. Moreover, the

initial reduction in screen-time was followed by a stronger increase, suggesting that

screen behaviours have a strong habitual nature that is difficult to change 161.

1.13 Screen-time interventions for adolescents

Systematic reviews 162,163 assessing the efficacy of screen-time interventions in young

people have concluded that electronic television control devices can decrease screen-time 164-167, particularly among pre-schoolers 168 and young children 164-166. Issues regarding

this reduction strategy in adolescents arise as there is little evidence suggesting that

screen-time reduction persists once the control device is removed 169. Moreover, screen-

time is rapidly becoming more accessible to adolescents through multiple devices such as

smartphones, iPads and handheld video games 162. Although effective strategies to reduce

screen-time among adolescents remain relatively unknown; positive findings from former

interventions warrant further research.

A translational school-based study across 15 schools, called Switch-2-Activity, aimed

to change screen-time behaviour through explicitly-taught curriculum 170. Teachers were

29

provided with lessons consisting of: awareness-raising, self-monitoring, behavioural

contracting and active alternatives when switching off. No significant intervention effects

were found among girls, although intervention boys showed reduced screen-time on

weekends (coefficient = -0.62, 95% CI = -1.15 to -0.10, p = 0.020) 170. As school-based

interventions have access to the majority of adolescents and possess an ethos for

engagement 171, there is support for further implementation of screen-time reduction

interventions in schools.

1.14 Implementation and scaling up of interventions

Schools are an effective setting for the implementation of physical activity and screen-

time interventions, as young people spend considerable time in school 172. This time

presents numerous opportunities for the promotion of, and participation in, physical

activities including sessions before, during and after school, as well as structured play

during recess and lunch breaks 173. The connections schools have with governments and

community groups facilitate further physical activity opportunities through establishing

safe active transport routes and inviting specialist instructors such as karate teachers or

dancers 173. In addition, schools provide a natural setting where interventions can target

multiple levels; that is, students and the environment 172. However, to improve population

health, there is a need to comprehend how effective health interventions can be when they

are scaled up, implemented and sustained in real world settings 174,175. Consideration

should therefore be made for various factors including networks, cost, training, ongoing

support and an examination of implementation after adoption 176.

The literature is relatively meagre when considering the dissemination of school-based

health promotion programmes. One notable exception is Action Schools! British

Columbia (AS!BC) 177,178, which was scaled-up and disseminated throughout primary

schools in British Columbia 179. Action Schools! BC aimed to support a school’s capacity

to create individualised plans to improve physical activity. Participants in focus groups

from AS!BC reported successful implementation in the first year of dissemination 179.

Specifically, four themes emerged at the school level regarding benefits of

implementation including: enhanced partnerships within the community, links to

resources, creation of a positive culture within the school and links to environmental

initiatives 179. Implementation was not without challenges, with time being the most

commonly reported barrier 179. Additional noted challenges of implementation and

30

dissemination included: lack of resources and leadership (staff turnover) 179. These

findings suggest teacher training and support are important contributions to successful

implementation 179. Further resources must be provided as well as school-level leadership

nurtured, to sustain implementation after scale-up 179. In principle, by seeking and

understanding effective strategies for scaling up interventions, researchers should aim to

find ways to strengthen active living into the wider public and policy to shift societal

trends to more active living styles 180. There is a clear need for research to shift from

tightly controlled intensive interventions targeting individuals to “scalable” interventions

that have greater external validity.

1.15 Summary of Part 3

• Schools are ideal settings to target both behaviours, as children and adolescents

spend most of their time in this setting 149. Schools also present an avenue for

accessing parents, and interventions that target both the child and the family have

been highlighted as being particularly effective for facilitating changes in screen-

time behaviours 181-183.

• Based on the current evidence, it is not clear whether interventions are more

successful if they target single or multiple health behaviours (i.e., physical activity

and screen-time). Nevertheless, based on the small number of studies that have

been conducted and the current prevalence estimates, there is an urgent need to

design scalable screen-time reduction interventions for adolescents.

31

1.16 Thesis aims and hypothesis

Primary aim

The primary aim of this thesis is to evaluate the effects of the S4HM intervention by

examining outcomes and potential mediators in a cluster RCT.

Primary hypothesis

Adolescents randomised to the intervention group will demonstrate favourable alterations

in i) screen-time, ii) mental health outcomes, iii) physical activity and iv) BMI.

Secondary aims

Secondary aims of this thesis are to:

1. Review the evidence of associations between physical activity, screen-time and

mental health outcomes in adolescents.

2. Provide a rationale and present the study protocol for the S4HM intervention.

3. Examine longitudinal associations of changes between screen-time and mental

health outcomes in adolescents.

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Chapter 2

Physical Activity and Physical Self-concept in Youth:

Systematic Review and Meta-analysis

2.1 Preface

This chapter presents the results of a systematic review and meta-analysis of studies

examining the associations of physical activity and various indicators of mental health for

children and adolescents. This study was conducted to investigate Secondary aim 1 of this

thesis. Therefore, to provide the context for the main analysis of this thesis, a systematic

review was first conducted examining the associations between the health related

behaviour of physical activity and physical self-concept (general and sub-domains).

The contents of this chapter were published in Sports Medicine in November, 2014.



meta-analysis. Sports Medicine, 44(11), 1589-1601.

2.2 Abstract

Objective: The primary aim of this systematic review and meta-analysis was to

determine the strength of associations between physical activity and physical self-concept

(general and sub-domains) in children and adolescents. The secondary aim was to

examine potential moderators of the association between physical activity and physical

self-concept.

Methods: A systematic search of six electronic databases (MEDLINE, CINAHL,

SPORTDiscus, ERIC, Web of Science and Scopus) with no date restrictions was

conducted. Random effects meta-analyses with correction for measurement were

employed. The associations between physical activity and general physical self-concept

and sub-domains were explored. A risk of bias assessment was conducted by two

reviewers.

Results: The search identified 64 studies to be included in the meta-analysis. 33 studies

addressed multiple outcomes of general physical self-concept: 28 studies examined

general physical self-concept, 59 examined perceived competence, 25 examined

33

perceived fitness, and 55 examined perceived appearance. Perceived competence was

most strongly associated with physical activity (r = 0.30, 95% CI = 0.24 to 0.35, p <

0.001), followed by perceived fitness (r = 0.26, 95% CI = 0.20 to 0.32, p < 0.001),

general physical self-concept (r = 0.25, 95% CI = 0.16 to 0.34, p < 0.001) and perceived

physical appearance (r = 0.12, 95% CI = 0.08 to 0.16, p < 0.001). Sex was a significant

moderator for general physical self-concept (p < 0.05) and age was a significant

moderator for perceived appearance (p ≤ 0.01) and perceived competence (p < 0.05). No

significant moderators were found for perceived fitness.

Conclusion: Overall, a significant association has been consistently demonstrated

between physical activity and physical self-concept and its various sub-domains in

children and adolescents. Age and sex are key moderators of the association between

physical activity and physical self-concept.

2.3 Background

The physical health benefits of physical activity are extensive and include reduced risk of

coronary heart disease, type II diabetes, some cancers and osteoporosis as well as

improved physical fitness and bone strength 21,50. In addition, participation in physical

activity may improve psychological health and help prevent and treat the development of

mental health disorders such as depression and anxiety 65,184,185. Mental health disorders

represent a significant public health burden 186, yet mental health is not only the absence

of a mental disorder, but a state of psychological well-being in which individuals realise

their own ability and potential 187. The self-concept construct is vital to psychological

well-being 188 and is the term used to describe an individual’s awareness of their qualities

and limitations 189. Individuals who feel good about themselves and their abilities are

resilient to the challenges of life, and self-concept facilitates other aspects of well-being

including happiness, motivation, and anxiety 188. A hierarchical organisation of general

self-concept has been posited by Shavelson and colleagues 189, with general self-concept

at the apex that includes academic and non-academic sub-domains. Academic self-

concept consists of subject specific facets of self (e.g., English, history and

mathematics)190, while the non-academic sub-domain is further divided into social,

emotional and physical self-concepts. Physical self-concept (sometimes referred to as

physical self-perceptions) is then separated into perceived physical ability and perceived

physical appearance 189.

34

Although known by different names, perceived physical ability (or competence) is

considered to be a central determinant of behaviour and is included in prominent social

cognitive theories including, competence motivation theory (perceived competence) 191,

self-determination theory (competence) 192, social cognitive theory (self-efficacy) 193 and

theory of planned behaviour (perceived behavioural control) 194. In the physical activity

domain, perceived competence is generally operationalised as confidence to perform

sport and outdoor games 195, while perceived behavioural control and self-efficacy are

defined as confidence to overcome barriers to participation. Self-efficacy, perceived

competence and perceived behavioural control are three of the most commonly measured

psychological correlates of physical activity and there is evidence for their utility as

determinants of behaviour 101,105,106,108. Indeed, in a recent review of reviews, Bauman and

colleagues 101 described health status and self-efficacy as the “clearest correlates” of

physical activity in adults. The same authors concluded that perceived behavioural control

and self-efficacy were the strongest psychological determinants of physical activity in

adolescents, but did not find sufficient evidence that perceived competence was a

determinant of behaviour.

In contrast to social cognitive models, the exercise and self-esteem model

(EXSEM)196, was developed to explore the pathways by which self-esteem is influenced

by physical training. Based on Shavelson’s hierarchical organisation of general self-

concept 189, the model proposes that confidence in one’s abilities to perform specific

exercises and sports-related activities generalise to a broader perceived physical

competence 197. Therefore, in this model, self-efficacy to complete specific exercise-

related tasks is considered an outcome rather than a determinant of activity. More

recently, Stodden and colleagues’ proposed a conceptual model that positioned perceived

competence as a mediator of the association between motor skill competence and physical

activity 198. In their model, motor skill competence was considered to be the “primary

underlying mechanism that promotes engagement in physical activity”, with perceived

competence playing an increasingly important role as children develop the cognitive

skills to accurately differentiate between actual and perceived motor competence 199,200.

In summary, it is not clear if general physical self-concept and sub-domains are

outcomes, mediators or moderators of physical activity in young people 201. Numerous

studies have modelled physical self-concept and sub-domains as determinants of physical

35

activity 202-207, while others have explored the impact of exercise and physical activity

programs on physical self-perceptions 208. However, no previous review has

systematically evaluated the evidence for the association between physical activity and

physical self-concept in children and adolescents. Providing a summary of existing

studies may assist in the design of physical activity interventions and/or provide evidence

for the positive effects of physical activity on well-being. Therefore, the primary aim of

this systematic review and meta-analysis was to determine the association between

physical activity and physical self-concept in young people by reviewing cross-sectional,

experimental and longitudinal studies. The secondary aim of this review was to examine

potential moderators of the association between physical activity and physical self-

concept.

2.4 Methods

2.4.1 Eligibility criteria

A study was considered eligible for this review if it met the following inclusion criteria:

(a) study included quantitative assessment of leisure-time physical activity. Physical

activity was defined as ““body movement produced by the skeletal muscles which results

in a substantial increase over the resting energy expenditure”209. (b) study included the

quantitative assessment of physical self-concept or sub-domains (c) study included a

quantitative assessment of the association between physical activity and physical self-

concept or sub-domains (d) study participants were school-aged children or adolescents

(i.e., aged 4 to 20 years), (e) published full text and peer reviewed. For a study to be

included in the meta-analysis it was required to report a correlation coefficient or

standardised regression coefficient for the association between physical activity and

physical self-concept or sub-domains (studies that did not provide this information but

examined the association between physical activity and physical self-concept are included

in Table 6).

Excluded studies were those which: (a) were published in languages other than

English, (b) reported only qualitative data, (c) included participants that were targeted

groups from special populations (e.g., people with mental illness, psychiatric disorders,

developmental delays and developmental co-ordination or eating disorders) and (d)

conference abstracts, dissertations, thesis or non-peer reviewed studies. Finally, studies

36

examining the impact of physical activity programs on physical self-concept or sub-

domains were not included if they did not examine the association between changes in

physical activity and changes in self-perceptions.

To allow for the aggregation of findings, scales/questionnaires assessing similar

constructs of different names were combined in the meta-analyses. For example,

‘perceived appearance’ was presented in different studies as body image, body

attractiveness, body esteem. All of these constructs were considered to represent an

individual’s assessment of their body size and/or shape, with a higher score representing a

more positive self-evaluation. ‘Perceived competence’ was operationally defined as an

individual’s assessment of their ability to perform sports and recreational activities.

Although related to perceived confidence, ‘perceived fitness’ was operationalised as an

individual’s evaluation of their health-related physical fitness. Validation studies of

commonly used scales, including the Physical Self-Perception Profile and the Physical

Self-Description Questionnaire have demonstrated that perceptions of fitness are unique

constructs 210,211. Scales assessing the different components of physical fitness (i.e.,

strength, endurance, flexibility) were combined for the meta-analyses.

2.4.2 Search strategy

The literature search was conducted on the 3rd August 2013. Studies were identified

through a structured electronic database search of the following databases: MEDLINE,

CINAHL, SPORTDiscus, ERIC, Web of Science and Scopus. Search terms included a

combination of key words including: (“Physical activit*” OR exercise OR active OR

motor*) AND (adolescence OR teenage OR children OR student OR youth OR boy OR

girl) AND (Adoles* OR teen* OR child* OR student OR youth OR boy OR girl OR

school OR primary OR elementary OR high OR secondary OR grade) AND (“physical

self-concept” OR “physical self-worth” OR perceived competence OR “physical self-

perception” OR “physical appearance” OR body image). The strings were further limited

to those aged 5-20 years and English language. Only articles published in peer-reviewed

journals were considered. The search was executed by MB with the assistance of a

professional librarian; reference lists of included studies were manually cross-referenced

for possible additional studies. The literature search was conducted in accordance to the

standards applicable in the ‘Preferred Reporting Items for Systematic Reviews and Meta-

Analysis’ (PRISMA) statement 212 (Appendix1).

37

2.4.3 Screening

Two authors (MB and RW) independently assessed each identified study for relevance to

the review based on the title, abstract, and full text. In the event of a disagreement,

consensus was reached by discussion with a third member (DRL). In the first stage,

studies were screened based on title and abstract. Relevant full text articles were searched

and evaluated for inclusion. Reference lists of included studies were reviewed for

potential papers.

2.4.4 Data extraction

The extracted data included authors, country in which the study was conducted, sample

(number, age, and sex), study design, location, measure of physical activity, measure of

physical self-concept, reliability of tools, outcomes, the intervention (dose and length),

year of publication, sample size, number/percentages of males/females (where provided).

When details of mean age were not available, an average was calculated from the age

range provided. If a study used more than one physical activity variable, the variable that

was most closely aligned with the following definition: “meeting physical activity

guidelines during leisure-time” was used 213. As studies often included multiple statistical

analyses (e.g., correlation, multiple regression) the results from the highest level of

analysis were used (i.e., multivariate or analyses that accounted for potential confounders

were favoured over bivariate analyses). For example, if a study reported both bivariate

correlations and multiple regression models, results from the regression models were

included in the meta-analysis. If a study reported both longitudinal and cross-sectional

results, the longitudinal findings were included in the meta-analysis. This was performed

to avoid the double counting of studies and because longitudinal study designs are

considered to provide a more robust test of theory 214.

2.4.5 Analytic strategies

Meta-analyses were conducted using Comprehensive Meta-Analysis (CMA) Version 2

software program (Englewood, New Jersey, USA)215. Effect sizes for each study were

calculated before and after correcting for measurement error. Measurement error

procedures were based on the reliabilities of the measures as presented in the study or

from prior published literature with the same instrument. In cases with single items or

where reliabilities were not reported, we used rxy = 0.70 based on a conservative, yet

38

acceptable judgment of reliability 216. In cases where coefficients had already been

corrected (e.g., structural equation models), no additional correction procedures were

used.

The general aim of a meta-analysis is to provide a more powerful estimate of the effect

size (or associations between variables), than what can be achieved in a single study

under a specific set of assumptions and conditions. Two types of statistical models are

used to create weighted averages when conducting meta-analyses. The fixed effects

model assumes that sampling error accounts for differences in the observed effects, while

random effects models produce within study (sampling) and between studies

(variance)217. Random effects models are considered more appropriate when data are

heterogeneous 217,218, however both models are reported in the current review for

comparative purposes. Along with the weighted average effect sizes, we computed the

95% confidence intervals. If the confidence interval does not include zero, then the effect

size is statistically significant at the p < 0.05 level. Correlations between variables were

interpreted as follows: 0.1-0.29 (weak), 0.3-0.49 (moderate) and 0.5-1.0 (strong)21.

Rosenthal’s classic fail safe N 219 and Duval and Tweedie’s ‘Trim and fill’ procedure 220,221 were used to assess the extent of publication bias. Rosenthal’s classic fail safe

provides an indication of the number of studies needed with a mean effect of zero before

the overall effect would no longer be statistically significant. Alternatively, the ‘Trim and

fill’ procedure selectively removes extreme effect sizes from small studies and replaces

them with imputed values to produce a more symmetrical funnel plot, which generates a

less biased overall effect size 220,221.

Separate meta-analyses were carried out for: i) general physical self-concept; ii)

perceived competence; iii) perceived fitness, and; iv) perceived appearance. Studies that

were separated by sex and or cohort years were treated as separate studies in the meta-

analysis. We report the weighted average effect sizes and the 95% confidence intervals.

The Q and I2 statistics were calculated to determine the heterogeneity of the average

effect sizes. Q tests are used to determine if the observed variance in effect sizes is no

greater than what is expected by sampling error alone, while the I2 statistic (I2 = 100% ×

(Q - df)/Q) is used to quantify the degree of heterogeneity 222. The I2-value provides the

percentage of total variation across studies due to heterogeneity 222 rather than chance. A

value of 0% indicates no observed heterogeneity, while larger values indicate increasing

39

heterogeneity. Based on existing recommendations, I2 values of 25%, 50% and 75% were

considered low, moderate and high, respectively 222.

Subgroup moderator analyses are conducted in meta-analyses to offer an

understanding of the strength and/or direction of association between independent and

dependent variables 219. Moderator analyses were also conducted using corrected r’s and

random effects models. The following demographic and methodological variables were

tested as potential moderators: i) sex (i.e., girls only, boys only and mixed), ii) age (i.e.,

childhood, early adolescence and late adolescence), iii) study design (i.e., cross-sectional,

experimental, and longitudinal). Age was categorised according to definitions from the

World Health Organisation, childhood (pre 10 years), early adolescence (10-14) and late

adolescence (15-19) 223. For the moderator analysis, we used QB to explore the impact of

the categorical variables on the effect size. QB is used for testing the differences between

effect sizes.

2.4.6 Synthesis of studies not included in the meta-analysis

A synthesis of studies not included in the meta-analysis was conducted. Results were

coded using the method first employed by Sallis, Prochaska and Taylor (2000). If 0-

33.3% of studies reported a significant association, results were classified as having no

association (0). If 34-59% of studies reported a significant association or if fewer than

four studies were included, the results were classified as being inconsistent/uncertain (?).

If >60% of studies find a significant association, the results were classified as positive (+)

or negative (-), depending on the direction of the association. If studies employed

multiple analyses, only finding from the highest level of analysis (i.e., multivariate) were

considered.

2.4.7 Criteria for risk of bias assessment

The PRISMA statement recommends that systematic reviews include an evaluation of the

methodological risks of bias that may have a bearing on the individual study findings 224.

Potential risk of bias will depend upon the study design and objectives. For example, the

Cochrane risk of bias tool 225 consists of five items that are known to influence the

estimates of an intervention’s effectiveness in randomised controlled trials and includes

items relating to sequence generation, allocation concealment blinding, treatment of

outcome data and selective outcome reporting. Two authors (MB and RW) independently

40

assessed the ‘risk of bias’ of the included studies using methodological items and coding.

Studies were assessed for ‘risk of bias’ using criteria adapted from research reviewing the

associations between sedentary behaviour and health indicators 226. A ‘risk of bias’ score

for each study was completed on a 5 point scale by assigning a ‘’ if the study met the

criteria or a ‘’ if the study failed to meet the criteria (Electronic Supplementary Material

Table S3).

The studies were examined based on criteria including: (a) Study schools and/or

participants were randomly selected from the target population (for experimental studies,

the process of randomisation was clearly described and adequately carried out). A ‘’

was awarded if the sample was randomly selected from the target population or

participants were randomly allocated to conditions for experimental studies. A ‘’ was

given if convenience sampling was used or if the process of randomisation was not

adequately described. (b) Adequate description of baseline study sample (individuals

entering the study) for key demographic characteristics (number of participants and their

mean age (or age range) and sex). A ‘’ was awarded if the study reported the proportion

of males and females and age range and/or mean for participants. A ‘’ was given if the

study provided only one or no characteristic(s). (c) Adequate assessment of physical self-

concept and sub-domains (if used). A ‘’ was awarded if authors reported at least one

‘acceptable’ reliability statistic for all physical self-concept measures (e.g., Cronbach

alphas of ≥ 0.70 or test-retest reliability intraclass correlation coefficient; which describes

how strongly units in the same group resemble each other, of ≥ 0.70), or uses an

established method. A ‘’ was given if a single item measure was used or the study did

not report reliability statistics. (d) Adequate assessment of physical activity. A ‘’ was

awarded if objective measures were used (i.e., heart rate monitors, accelerometers,

pedometers, direct observations) or if authors cited adequate validity data for self-report

measures in the study population. A ‘’ was given for self-report measures when authors

did not report validity data. A ‘’ was also given if the validity being measured was

related to fitness and not physical activity. (e) Appropriate adjustment for covariates (i.e.,

age and sex) in the statistical analysis (exploring the association between physical activity

and physical self-concept). A ‘’ was awarded if authors adjusted for age or pubertal

status or if authors reported separate findings for boys and girls and different age groups

(if students were from the same grade at school this was considered acceptable). A ‘’

was given if authors did not adjust for age and sex.

41

2.4.8 Description of the synthesis of studies not included in the meta-analysis

A synthesis of studies not included in the meta-analysis was conducted. Of the 47 studies,

29 were cross-sectional, 8 were experimental and 10 were longitudinal. Results were

coded using the method employed by Sallis et al.106 . If 0-33.3% of studies reported a

significant association, results were classified as having no association (0). If 34-59% of

studies reported a significant association or if fewer than four studies were included, the

results were classified as being inconsistent/uncertain (?). If >60% of studies found a

significant association, the results were classified as positive (+) or negative (-),

depending on the direction of the association. If studies employed multiple analyses, only

findings from the highest level of analysis (i.e., multivariate) were considered.

2.5 Results

The literature search yielded a total of 4666 potentially relevant citations (Figure 5).

Following screening procedures, 332 full text articles were retrieved and reviewed. A

total of 111 were considered eligible for the review. A total of 64 studies were included in

the meta-analysis consisting of 47 cross-sectional, 12 longitudinal and five experimental

studies.

42

Figure 5: Results of literature search

2.5.1 Study/sample characteristics

In terms of country the study was conducted, the USA provided 17 studies, England 12,

Australia 7, Canada 5, UK 4, Spain, Finland and Sweden provided 2 each Taiwan, Hong

Kong, Mexico, Norway, Germany, Scotland, Cyprus, Poland, Jamaica, Greece, Estonia,

Italy and China had a single study included (Electronic Supplementary Material Table

Records identified through database searching (n =

4,666) Sc

reen

ing

Incl

uded

E

ligib

ility

Id

entif

icat

ion Additional records

identified through other sources (n = 10)

Records after duplicates removed (n = 3,711)

Records screened (n = 3,711)

Records excluded (n = 3,379)

Full-text articles assessed for eligibility

(n = 332)

Full-text articles excluded (n = 221), with reasons

Age of participants (n = 37) Review or abstract only (n = 8) Language other than English (n

= 1) Specialised population or

developmental delays (n = 17) Assessed fitness not physical

activity (n = 10) Full text unavailable (n = 1) Did not measure association

between physical activity and physical self-concept (n = 143)

Unclear (n = 4)

Studies included in qualitative synthesis

(n = 111)

Studies included in quantitative synthesis

(meta-analysis) (n = 64)

43

S1). A total of 167 independent samples were used in the meta-analysis, which included

data from 24,546 girls, 15,215 boys (The sex of 7130 participants was not specified).

2.5.2 Overall effect size, heterogeneity and significance of moderators

2.5.2.1 General physical self-concept

After correcting for measurement error, the random effects model yielded a weak to

moderate effect size of r = 0.25 (95% CI = 0.16 to 0.34, p < 0.001), suggesting that

increased higher physical activity levels were associated with higher levels of general

physical self-concept (Electronic Supplementary Material Figure S1). Sex emerged as a

statistically significant moderator of effects (p < 0.05). Results by sex category were r =

0.40 (95% CI = 0.32 to 0.48, p < 0.001) for boys (4 studies), r = 0.26 (95% CI = 0.16 to

0.36, p < 0.001) for girls (15 studies) and r = 0.20 (95% CI = -0.01 to 0.39, p > 0.05) for

the mixed sample (9 studies).

Study design and age were not significant moderators of effects (p > 0.5). This is

because the association between general physical self-concept and physical activity was

not significantly different between sub-groups (e.g., the effect size estimates were similar

for cross-sectional, experimental and longitudinal study designs). Results by study design

category were r = 0.25 (95% CI = 0.13 to 0.36, p < 0.001) for cross-sectional designs, r =

0.27 (95% CI = 0.11 to 0.42, p < 0.001) for longitudinal designs and r = 0.30 (95% CI =

0.12 to 0.47, p < 0.005) for experimental designs. Results by age category were r = 0.26

(95% CI = 0.15 to 0.37, p < 0.001) for early adolescence (23 studies) and r = 0.22 (95%

CI = 0.04 to 0.40, p < 0.05) for late adolescence (5 studies).

2.5.2.2 Perceived competence

The random effects model correcting for measurement error revealed a moderate effect

size of r = 0.33 (95% CI = 0.27 to 0.39, p < 0.001). Age and emerged as a statistically

significant moderator of effects (p < 0.05) and a total of 59 samples were extracted. Of

these, 1 involved children, 45 were included early adolescents, and 13 studies included

late adolescents. Results by age category were r = 0.08 (95% CI = -0.12 to 0.28, p < 0.5)

for children, r = 0.35 (95% CI = 0.28 to 0.42, p < 0.001) for early adolescents and r =

0.31 (95% CI = 0.19 to 0.41, p < 0.001) for late adolescents.

44

Sex and study design were not significant moderators of effects (p > 0.5). A total of 59

samples were extracted. Results by sex category were r = 0.32 (95% CI = 0.19 to 0.45, p

< 0.001) for boys, r = 0.33 (95% CI = 0.23 to 0.42, p < 0.001) for girls and r = 0.35 (95%

CI = 0.25 to 0.43, p < 0.001) for the mixed sample. Results by study design category were

r = 0.32 (95% CI = 0.24 to 0.39, p < 0.001) for cross-sectional designs, r = 0.34 (95% CI

= 0.24 to 0.43, p < 0.001) for longitudinal designs and r = 0.66 (95% CI = 0.31 to 0.85, p

< 0.001) for experimental designs

2.5.2.3 Perceived fitness

Higher levels of perceived fitness were moderately associated with increased physical

activity in the random effects model r = 0.30 (95% CI = 0.23-0.36, p < 0.001) (Electronic

Supplementary Material Figure S3). Sex, age and study design were not moderators of the

association (p > 0.05). Results by sex category were r = 0.40 (95% CI = 0.32 to 0.48, p <

0.001) for boys, r = 0.30 (95% CI = 0.23 to 0.37, p < 0.001) for girls and r = 0.25 (95%

CI = 0.02 to 0.45, p < 0.05) for the mixed sample. Results by age category were r = 0.31

(95% CI = 0.24 to 0.37, p < 0.001) for early adolescents and r = 0.28 (95% CI = 0.13 to

0.42, p < 0.001) for late adolescents. Results by study design category were r = 0.32 (95%

CI = 0.25 to 0.39, p < 0.001) for cross-sectional designs and r = 0.21 (95% CI = 0.07 to

0.34, p < 0.01) for longitudinal designs.

2.5.2.4 Perceived appearance

After correcting for measurement error, the random effects model revealed a weak

association between perceived appearance and physical activity, r = 0.14 (95% CI = 0.09

to 0.18 p < 0.001). Age emerged a statistically significant moderator of effects (p < 0.01).

A total of 55 samples were extracted and of these, 33 and 22 involved early adolescents

and adolescents, respectively. The effect size for early adolescents was r = 0.19 (95% CI

= 0.13 to 0.24, p < 0.001) and for late adolescents was r = 0.07 (95% CI = 0.01 to 0.13, p

< 0.05). Sex and study design were not significant moderators of effects (p > 0.5). Results

by sex category were r = 0.13 (95% CI = 0.03 to 0.24, (p < 0.05) for boys, r = 0.13 (95%

CI = 0.07 to 0.19, p < 0.001) for girls and r = 0.16 (95% CI = 0.06 to 0.25, p < 0.001) for

the mixed sample. Results by study design category were r = 0.14 (95% CI = 0.09 to

0.18, p < 0.001) for cross-sectional designs, r = 0.16 (95% CI = 0.11 to 0.21, p < 0.001)

for longitudinal designs and r = 0.13 (95% CI = -0.09 to 0.33, p > 0.05) for experimental

designs.

45

2.5.2.5 Synthesis of findings not included in the meta-analysis

Overall, there were consistent positive associations between physical activity and

physical self-concept and its sub-domains. A summary of all papers examined are

reported in Table 5. A summary of findings is reported only reporting qualitative data are

reported in Table 6.

46

Table 5: Summary of articles included in the systematic review

Study Sample Design Physical self-concept measure

Physical activity measure

Analysis Findings Physical self-concept and physical activity measure

Altintas and Asçi (2008)

N = 803 383 girls and 420 boys Age range 11-14 Turkey

Cross-sectional

CY-PSPP Weekly activity checklist

MANOVA Females in the high PA group had higher GPSE scores than those in the low PA group including SC, PC, STR, GPSW but not BA. Males in the high PA group had higher GPSE scores than those in the low PA group including SC, PC, STR, GPSW and BA.

NR

Annesi (2006)

2003 N = 41 2005 N = 84 Control N = 40 Age range 9-12 USA

Experimental PSCS PA recall questionnaire

Bivariate correlations

Increases in PA were significantly associated with increases GPSC over the 12 weeks.

2003 Group PA and GPSC r = .39, (p < .05) PA and GPSC Wk12 r = .09, (p > .05) 2005 Group PA and GPSC r = .26, (p < .05) PA and GPSC Wk12 r = .21, (p > .05)

Annesi et al. (2009)

N = 43 22 girls and 21 boys Age range 7-12 Canada

Experimental SDQ PA recall questionnaire Muscular STR push-up test

Linear multiple regression

Association between PA and GPSC (β = .11) (p = .47). Models included changes in self-efficacy and general self.

NR

Barnett et al. (2008)

2000 N = 1045 2006/7 N =

Longitudinal (7 years)

PSPP Adolescent Physical Activity Recall

Bivariate correlations SEM

Positive SC is a predictor of PA SEM (SC and PA) r = .34,

PA and SC girls r = .37, (p = .01) PA and SC boys

47




276 Age range 7.9-11.9 Australia

Questionnaire (p = < .01). SEM included associations of PA, SC, childhood locomotor skill and childhood object control skills.

r = .25, (p = .01)

Biddle and Wang (2003)

N = 516 girls Age range 11–16 England

Cross-sectional

PSPP-C PA recall questionnaire


PA was significantly correlated to GPSC and PC. Models included associations of BA and GPSW.

PA and SC r = .17, (p < .001) PA and BA r = -.02, (p > .05) PA and PC r = .10, (p < .005) PA and STR r = .07, (p > .05) PA and GPSW r = .08, (p > .05)

Carroll and Loumidis (2001)

N = 922 454 girls and 468 boys Age range 10–11 Britain

Cross-sectional

Self-perceived competence in PE scale

PA recall questionnaire

MANOVA Individuals who perceived themselves as more competent in PE participate in more PA and higher levels of intensity than those who perceived themselves to be less competent.

NR

Chen et al. (2010)

N = 883 431 girls and 452 boys Age range 12-16 Taiwan

Cross-sectional

Multidimensional Body-Self Relations Questionnaire – Appearance Evaluation

Taiwan National Physical Activity Survey


PA was positively related with body dissatisfaction for girls, but not for boys.

Girls PA and BD r = .19, (p < .01) PA and APP r = -.12, (p > .05) Boys PA and BD r = -.02, (p > .05) PA and APP r = .02, (p > .05)

Crocker et al. (2006)

N = 501 girls Age range 14-15 (1st year) Age range 16–17 (3rd year)

Longitudinal (24 months) T1 = baseline T2 = approximately

PSPP SPAS

PAQ-A Bivariate correlations

Correlations at 3 intervals indicated that all physical self-perceptions and global self-esteem scores were significantly correlated

Cross-sectional T1 PA and SPA r = -.08, (p > .05) PA and GPSW r = .30, (p < .05) PA and BA r = .12, (p < .05)

48




Canada one year after baseline T3 = final set

with PA; with PC being the dominant correlate. PC (β = .48), SC (β = .19), and BA (β = −.14) were significant individual predictors of PA.

PA and PC r = .53, (p < .05) PA and SC r = .47, (p < .05) PA and STR r = .33, (p < .05) T2 PA and SPA r = -.09, (p < .05) PA and GPSW r = .39, (p < .05) PA and BA r = .14, (p < .05) PA and PC r = .57, (p < .05) PA and SC r = .52, (p < .05) PA and STR r = .36, (p < .05) T3 PA and SPA r = -.16, (p < .05) PA and GPSW r = .37, (p < .05) PA and BA r = .18, (p < .05) PA and PC r = .55, (p < .05) PA and SC r = .51, (p < .05) PA and STR r = .38, (p < .05) Longitudinal PA and SPA r = -.08, (p > .05) PA and PSW r = .18, (p < .05) PA and BA r = .10, (p < .05) PA and PC r = .34, (p < .05) PA and SC r = .26, (p < .05) PA and STR r = .22, (p < .05)

Crocker et al. (2000)


Cross-sectional

PSPP PAQC Bivariate correlations SEM

All physical self-perceptions were statistically significantly (p < .05) correlated with PA among both girls (r = 0.26-0.47) and boys (r = 0.28-0.47). All PSPP subdomains were

Girls PA and PC r = .47, (p < .05) PA and SC r = .46, (p < .05) PA and BA r = .27, (p < .05) PA and STR r = .36, (p < .05) PA and GPSW r = .38, (p < .05 Boys

49




moderately correlated in boys (r = 0.48-0.68) and girls (r = 0.42-0.67). SEM Girls PA and SC r = .28, (p > .05) PA and STR r = .05, (p > .05) PA and BI r = .10, (p > .05) SEM Boys PA and SC r = .37, (p > .05) PA and STR r = .07, (p > .05) PA and BI r = .24, (p > .05)

PA and PC r = .47, (p < .05) PA and SC r = .46, (p < .05) PA and BA r = .28, (p < .05) PA and STR r = .35, (p < .05) PA and GPSW r = .39, (p < .05)

Daley (2002)

N = 1230 601 girls and 629 boys Age range 14-15 England

Cross-sectional

CY-PSPP PA questionnaire from the ‘Young People and Sport’ survey

ANOVA Univariate analyses revealed that children who indicated that they participated in extra-curricular PA reported significantly higher scores for BA, (F = 11.26, p < .01) and GPSW (F = 13.55, p = .01).

NR

Duncan et al. (2004)

N = 277 111 girls and 166 boys Age range 11-14 United Kingdom

Cross-sectional

Body Esteem Scale for children



Results indicated no significant relationships between body image and PA (p > .05).

Girls PA and Body Esteem r = -.16, (p > .05) Boys PA and Body Esteem r = .05, (p > .05)

50




Duncan et al. (2006)

N = 276 110 girls and 166 boys Age range 11-14 Britain

Cross-sectional

Figure rating scale



Relationships were evident between average daily energy expenditure and children’s perceived current body shape for all children (r= .09) (p > .05). Boys reported a significant association with PA and BA.

Girls PA and current body shape r= .12, (p > .05) Boys PA and current body shape r= .47, (p > .05)

Eccles and Harold (1991)

T1 N = 2700 T2 N = 875 USA

Longitudinal T1 (2 years) T2 (4 years)

Self-Concept of Ability questionnaire



Physical self-concept of ability was positively associated with free time involvement in sport in both girls and boys.

PA and self-concept of ability in girls r = .44, (p < .001) PA and self-concept of ability in boys r = .47, (p < .001)

Goldfield et al. (2011)


Experimental

Body esteem scale for adolescents and adults

Godin leisure-time exercise questionnaire

Regression model

Vigorous PA was significantly correlated with the external attribution subscale of the body esteem scale for adolescents in males (r = .20) (p < .001) but not in females (r = .00) (p > .05). Vigorous PA was the strongest correlate of positive body image findings in the overall sample.

Girls Mild PA r = -.03, (p > .05) Moderate PA r = .00, (p > .05) Vigorous PA r = .00, (p > .05) Boys Mild PA r = -.01, (p > .05) Moderate PA r = .06, (p > .05) Vigorous PA r = .20, (p < .001) PA and External Attribution

Goldfield et al. (2007)

N = 30 (overweight) 17 girls and 13 boys Age range 8-12

Experimental PSPP-C PSWS

Accelerometers ANOVA Bivariate correlations

Correlational analyses indicated that increases in PA were associated with increases in BA (r = .43, p = .017), PC (r = .38, p = .04), and GPSW (r = .44, p

Increases in PA were associated with increases in perceived PC (r = .54, (p < 01) BA (r = .55, (p < .01) GPSW (r = .44, (p < .05)

51




Canada = .01). Changes in PA were not associated with changes in STR, or GSE.

Guinn et al. (1997)

N = 254 girls Age range 13-15 Mexico

Cross-sectional

Dusek’s Abbreviated form of Second- Jourard Body Cathexis Scale



BI was statistically significantly associated with PA involvement r = .27, (p < .001)

PA and BI r = .27, (p < .001

Haugen et al. (2013)

T1 = 2005 N = 1207 T2 = 2008 N = 632 Total N = 1839 889 girls and 950 boys Age 15 years Norway

Cross-sectional

SPAA PA recall questionnaire


Results indicated PA predicted SC in both genders, but not APP. PA was strongly associated with SC, lesser with BI in both girls and boys. An inverse relationship was present among girls and boys when examined though the direct fitness outcomes effects on PA.

Girls PA and SC r = .24, (p =< .01) PA and APP r = .07, (p => .05) Boys PA and SC r = .25, (p =< .01) PA and APP r = .01, (p => .05) Direct girls PA and SC r =.007, (p = > .05) PA and APP r =-.002, (p = > .05) Direct boys PA and SC r =.073, (p = < .01) PA and APP r =-.015, (p = < .01)

Kololo et al. (2012)

N = 2277 1191 girls and 1086 boys Age 15 years Poland

Cross-sectional

Body Image Sub-scale from the Body Investment Scale

MVPA indicator Logistic regression

A negative self-assessment of body image was associated with an increased risk of insufficient PA (OR =

NR

52




1.29; CI (OR): 1.02-1.63). Positive body image reduced the risk of having insufficient PA (OR = 0.64; CI (OR): 0.52-0.79).

Lau et al. (2005)


Cross-Sectional

PSPP PA recall questionnaire


Perceived competence was positively associated with sport participation.

Sport participation and PC r = .28, (p < .05)

Lubans et al. (2011)

N = 1518 girls Mean age = 13.4, SD = 0.4 years Australia

Cross-sectional

P-CPP Accelerometers Bivariate correlations SEM

All the physical self-concept subscales (i.e. SC, BA, PC and STR) were all statistically significant and associated with PA. PSW was significantly associated with PA in the SEM. The model also included PA and SE, enjoyment of PA, school PA, social support and the use of PA behavioural strategies.

PA and PSW r = .17, (p < .01) PA and SC r = .20, (p < .01) PA and PC r = .21, (p < .01) PA and BA r = .12, (p < .01) PA and STR r = .15, (p < .01)

Malete (2004)

N = 903 492 girls and 411 boys Age Range 13-18 Africa

Cross-sectional

SPAA PA recall questionnaire

MANOVA PC was not associated with PA.

NR

Malete et al. (2008)

N = 1052 614 girls and 426 boys

Cross-sectional



PSP subscales were not associated with patterns of involvement in sport. PSW

PA and PSW r = -.13, (p < .01) PA and BA r = -.09, (p < .05) PA and PC r = -.00, (p > .05)

53




Age Range 12-19 Jamaica

was negatively associated with participating in sport. PC was not associated with participating in sport.

Marsh (1996)

N = 192 79 girls and 113 boys Age range 13-15 Australia

Cross-sectional

PSDQ PA self-report (hours/typical week)

Bivariate correlations Linear regression

PSDQ subscales were significantly associated with PA in girls and boys.

Times/typical week PA and STR r = .41, (p < .05) PA and SC r = .38, (p < .05) PA and BA r = .12, (p < .05) PA and GPSW r = .24, (p < .05)

Marsh et al. (2006)

N = 2786 1393 girls and 1393 boys Greece

Longitudinal (6 months)


Multilevel modelling

Reciprocal effects model results show there were was statistically significant effects of T1 physical self-concept on T2 exercise behaviour and of T1 exercise behaviour on T2 physical self-concept.

NR

Monteiro et al. (2011)

N = 234 113 girls and 121 boys Age range 10-17 Portugal

Cross-sectional

Collins’ Child Figure Drawings scale

Baecke questionnaire Habitual PA index

Logistic regression

High levels of PA were associated with a protective effect on negative BA.

NR

Moreno and Cervello (2005)

N = 2330 1130 girls and 1200 boys Mean age = 14.8, SD = .91 Spain

Cross-sectional


MANOVA Individuals whom participated in PA once a week or less had lower scores in SC, PC and STR than those that participated in PA more than 3 times a week.

NR

Murcia and Antonio (2005)

N = 565 306 girls and 259 boys

Cross-sectional

PSPP PA self-report ANOVA Exercisers had higher BA, SC and PC than non-exercisers. Students who

NR

54




Age range 12-16 Spain

participated in PA outside PE had significantly higher PC. For all subscales (p < .001).

Moreno-Murcia et al. (2011)

N = 472 213 Girls and 259 boys Age range 16-20 Spain

Cross-sectional

PSPP Habitual PA questionnaire

Bivariate correlations SEM

In boys, SC (β = .77) and BA (β = .15) were significantly associated with PA. In girls, SC (β = .70) was positively and BA (β = -.13) negatively associated with PA. The SEM also included PA intention and tobacco and alcohol consumption.

PA and SC r = .63, (p < .01) PA and BA r = .21, (p < .01)

Morgan et al. (2008)

257 at baseline N = 104 -follow up USA


CY-PSPP Pedometers Bivariate correlations Multiple regression

Physical self-perception measures were significantly related to changes in steps/day over a 27-month period. SC emerged the most important predictor and inversely related to PA change over a 27-month period.

T1 PA and T1 cognitions SC r = .42, (p < .01) PC r = .36, (p < .01) BA r = .22, (p < .05) STR r = .25, (p < .05) PSW r = .38, (p < .01) T1 PA and T2 cognitions SC r = .07, (p > .05) PC r = .24, (p < .05) BA r = .18, (p > .05) STR r = .09, (p > .05) PSW r = .28, (p < .01)

Neumark-Sztainer et al. (2004)

N = 4746 Age range 11-18 USA

Cross-sectional

Body shape satisfaction scale

Modified leisure time exercise questionnaire Youth risk behaviour survey

Multiple and logistic regression

Boys with lower BA reported significantly less PA. Girl’s trends were similar, but associations were not statistically

NR

55




significant.

Paxton et al. (2004)

N = 63 Age range 9-14 USA

Cross-sectional

Perceived physical competence scale

PAQ-C Bivariate correlations

Bivariate correlations were significant between PA and SC, PA and BA.

PA and SC r = .34, (p < .01) PA and BA r = .45, (p < .01)

Planinšec and Fošnarič (2005)

N = 364 185 girls and 179 boys Mean age = 6.4, SD = .3 Slovenia

Cross-sectional

Children’s Physical Self Concept Scale

PA self-report ANOVA The high active PA group scored significantly higher on the GPSC, BA, SP) than the low active group.

NR

Raudseep et al. (2002)

N = 253 119 girls and 134 boys Age range 11-14 Estonia

Cross-sectional

CY-PSPP PA self-report Bivariate correlations Multiple regression

All subdomains of the CY-PSPP (SC, STR, PC and PSW) were significantly (p < .05) associated with PA in both sexes.

Girls PA and BA r = .30, (p < .01) PA and SC r = .21, (p < .05) PA and STR r = .17, (p < .05) PA and PC r = .20, (p < .05) PA and GPSW r = .23, (p < .01) Boys PA and BA r = .17, (p < .05) PA and SC r = .33, (p < .01) PA and STR r = .37, (p < .01) PA and PC r = .31, (p < .01) PA and GPSW r = .30, (p < .01)

Raustorp et al. (2006)

Year 2000 Cohort N =501 Age range 7-14 Year 2003 Cohort N = 375 Age range 15-

Longitudinal (3 years)

CY-PSPP Pedometers Logistic regression

In girls, BA (r = .86, p < .001) showed the strongest correlation to PSW. In boys, PC (r = .89, (p < .001) showed the strongest correlation to PSW.

NR

56




18 Sweden

Raustorp et al. (2005)

2 Tests 2 Groups Group 1 N = 48 27 girls and 21 boys Age range 11-12 Group 2 N = 501 253 girls and 248 boys Age range 10-14 Sweden

Cross-sectional

CY-PSPP Pedometers Bivariate correlations Multiple regression

In boys a correlation between the sub-domains of the CY – PSPP and PA was reported. In girls, there was a poor correlation between PA and GPSW.

Girls PA and SC r = .19, (p < .05) PA and BA r = .18, (p < .05) PA and STR r = .17, (p < .05) PA and GPSW r = .13, (p < .05) Boys PA and SC r = .35, (p < .05) PA and BA r = .30, (p < .05) PA and STR r = .36, (p < .05) PA and GPSW r = .27, (p < .05)

Scarpa and Nart (2012)

N = 394 221 girls and 173 boys Age range 12-13 Italy

Cross-sectional

PSDQ PA self-report Bivariate correlations

Positive associations between SC scales and PA.

PA and END r = .53, (p < .001) PA and FLX r = .20, (p < .001) PA and STR r = .37, (p < .001) PA and CRD r = .43, (p < .001) PA and SS r = .55, (p < .001)

Sollerhead et al. (2008)

N =206 92 girls and 114 boys Age range 8-12 Sweden

Cross-sectional

PCQ PA recall questionnaire

Logistic regression

Physically active children had more positive self-perceptions and SC.

NR

Sullivan (2002)

N = 1602 810 girls and 792 boys Age range 11-12

Cross-sectional

PCSC PA recall questionnaire

Multiple regression

A positive association was evident between PA and SC for both girls and boys.

NR

57




Ireland Trautwein et al. (2008)

Year 2001 Cohort N = 1185 675 girls and 510 boys Mean age = 9.67 2002 N = 1095


PSPP-C PA recall questionnaire

Multiple regression

Positive self-concepts at T1 and T2 were statistically significant. Decrease between T1 and T2. A reciprocal relationship between physical self-concept and physical ability. T1 PSC positivity predicted T2 PSC.

T1 PA and PSC r = .06, (p < .05) T2 PA and PSC r = .45, (p < .001)

Wang and Biddle (2001)


Cross-sectional

Task and ego orientation in sport questionnaire Conceptions of the nature of athletic ability questionnaire PSPP-C



Positive correlation between PA and PC as well as PA and GPSW.

PA and PC r = .56, (p < .01) PA and GPSW r = .43, (p < .01)

Zan (2008) N =307 158 girls and 149 boys Age range 12-15 USA

Cross-sectional

The self-perceived competence in physical education scale

Pedometers Multiple regression

PA was positively associated with PC (β = .23) (p < .05)

NR

Zhang et al. (2011)

N = 286 143 girls and 143 boys Mean age = 13.4, SD = 1.0 USA

Experimental PE modified health care climate questionnaire Perceived needs satisfaction

PA questionnaire for older children


Positive association between PA and PC

PA and PC r = .44, (p < .01)

Note: APP = Appearance. BA = Body Attractiveness. BD = Body Dissatisfaction. BI = Body Image. CRD = Co-ordination. CY-PSPP = Children and Youth Physical Self-Perception Profile. END = Endurance. FLX = Flexibility. GPSC = Global Physical Self Concept. GPSE = Global Physical Self Esteem. GSE = Global Self Esteem. GPSW = Global Physical Self Worth. N = Number of

58

participants. NR = Not recorded. PA = Physical Activity. PAQ-A = Physical Activity Questionnaire for Adolescents. PAQ-C = Physical Activity Questionnaire for Children. PC = Physical Conditioning. PCQ = Perception and Confidence Questionnaire. PCSC = Perceived Competence Scale for Children. PE = Physical Education. PSCS = Physical Self Concept Subscale. PSPP = Physical Self Perception Profile. PSPPC-C = Physical Self-Perception Profile for Children. PSWS = Physical Self Worth Scale. SC = Sports Competence. SDQ = Self-Description Questionnaire. SEM = Structural Equation Model. SP = Self-Perception. SPA = Social Physique Anxiety Scale. SPAS = Social Physique Anxiety Scale. STR = Physical Strength. SS = Sport Skill. β = Standardised beta co-efficient.

59

Table 6: Qualitative summary of studies examining the association between physical

activity and physical self-concept

Note: + + = Strong evidence of a positive association

2.6 Risk of bias assessment

Inter-rater reliability metrics for the risk of bias assessments indicated adequate

percentage of agreement (94%) for the 320 items (Table 7). Thirteen studies (20%)

provided an adequate description of the random sampling process, 59 studies (92%)

provided an adequate description of the study sample, 63 studies (98%) provided a valid

measure of physical activity, 47 studies (73%) provided a valid measure of physical self-

concept and 17 studies (27%) adjusted for covariates.

Risk of bias assessment criteria included the following and results of the risk of bias

are presented in table 7.

1. Study schools and/or participants were randomly selected from the target

population (for experimental studies, the process of randomisation was clearly

described and adequately carried out).

1: Study sample was randomly selected from the target population or

participants were randomly allocated to conditions for experimental studies.

0: If convenience sample was used or if the process of randomisation was not

adequately described).

Measure Significantly associated with physical activity

Not significantly associated with physical activity

Summary coding n/Na for benefit

%

Association

General physical self-concept

201,227-247 235,248-250 22/26 ++

Perceived competence

228-

232,235,236,240,242,243,2

46-249,251-260

235,238,247,249,261 24/29 ++

Perceived fitness

227,230,231,235,242,251,2

54,255,257,261,262 235,249 11/13 ++

Perceived appearance

228-

233,235,237,240,243,251,254,

255,257,262-266

235,237,241,242,249,251,2

65,267,268 19/28 ++

60

2. Adequate description of baseline study sample (individuals entering the study) for

key demographic characteristics (number of participants and their mean age (or

age range) and sex).

1: If they report proportion of males and females and age range and/or mean for

participants.

0: One or less provided.

3. Adequate assessment of physical self-concept and sub-domains (if used).

1: If authors report at least one ‘acceptable’ reliability statistic for all physical

self-concept measures (e.g., Cronbach alphas of > .70 or test-retest reliability

ICC of > .70).

0: For single item measures or studies that don’t report reliability statistics.

4. Adequate assessment of physical activity.

1: If objective measures were used (i.e., heart rate monitors, accelerometers,

pedometers, direct observations) or if authors cited adequate validity data for

self-report measures in the study population.

0: For self-report measures when authors did not report validity data.

5. Appropriate adjustment for covariates (i.e., age and sex) in the statistical analyses.

1: If authors adjusted for age and sex OR if authors reported separate findings

for boys and girls and different age groups (if students were from the same

grade at school this was considered acceptable).

0: If authors did not adjust for age and sex.

Table 7: Risk of bias results

Study Risk of bias 1

Risk of bias 2

Risk of bias 3

Risk of bias 4

Risk of bias 5

Abarca-SOS et al. (2013)

1 1 1 1 0

Annesi (2006) 0 1 1 0 0 Annesi et al. (2008) 1 1 1 1 0 Baker & Davison (2011)

0 1 1 1 1

Barnett et al. (2011) 1 1 1 1 0

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Risk of bias 2

Risk of bias 3

Risk of bias 4

Risk of bias 5

Barnett et al. (2008) 1 1 1 1 0 Bevans et al. (2010) 0 1 1 1 0 Biddle and Goudas (1996)

0 0 1 0 0

Biddle & Wang (2003) 1 1 1 0 0 Chen et al. (2010) 1 1 1 1 0 Cheng et al. (2003) 1 1 1 1 0 Craft et al. (2003) 0 1 1 0 1 Crocker et al. (2003) 0 1 1 1 1 Crocker et al. (2006) 0 1 1 1 1 Crocker et al. (2000) 0 1 1 1 0 Cumming et al. (2011) 0 1 1 1 0 Dishman et al. (2006) 0 1 1 1 1 Douthitt (1994) 0 0 1 1 0 Duncan et al. (2004) 1 1 1 1 1 Duncan et al. (2006) 0 1 1 1 0 Dunton et al. (2003) 0 1 1 1 0 Dunton et al. (2006) 0 1 1 1 0 Eccles & Harold (1991) 0 0 1 0 1 Gillison et al. (2011) 0 1 1 1 0 Goldfield et al. (2011) 0 1 1 1 0 Guinn et al. (1997) 1 1 1 0 0 Haugen et al. (2013) 0 1 1 0 1 Ingledew & Sullivan (2002)

0 1 0 0 1

Jaakkola et al. (2013) 0 1 1 1 1 Jackson et al. (2013) 0 1 1 1 0 Kalaja et al. (2010) 0 1 1 1 0 Knowles et al. (2009) 0 1 1 1 0 Lau et al. (2004) 0 1 1 0 0 Lau et al. (2006) 0 1 1 0 0 Loucaides et al. (2004) 0 0 1 0 0 Lubans et al. (2011) 1 1 1 1 1 Luszcynsk & Abraham (2012)

0 1 1 1 0

Malete et al. (2008) 0 1 1 0 0 Markland and Ingledew (2007)

0 1 1 1 0

Marsh (1996) 0 1 1 0 0 Martin et al. (2006) 0 1 1 1 0 Moreno-Murcia et al. (2011)

0 1 1 1 0

Morgan et al. (2008) 0 0 1 1 0 Morgan et al. (2008) 1 1 1 1 1 Niven et al. (2009) 0 1 1 1 1 Niven et al. (2007) 0 1 1 1 1

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Risk of bias 2

Risk of bias 3

Risk of bias 4

Risk of bias 5

Papaioannou et al. (2006)

1 1 1 1 0

Paxton et al. (2004) 0 1 1 1 0 Plotnikoff et al. (2007) 0 1 1 1 1 Raudseep et al. (2002) 1 1 1 1 0 Raustorp et al. (2009) 0 1 1 1 1 Raustorp et al. (2005) 0 1 1 1 0 Rodriguez & McGovern (2005)

0 1 1 1 1

Scarpa & Nart (2012) 0 1 1 0 0 Slater et al. (2006) 0 1 1 0 0 Smart et al. (2012) 0 1 1 1 0 Standage et al. (2012) 0 1 1 1 0 Stein et al. (2007) 0 1 1 0 1 Vierling et al. (2007) 0 1 1 1 0 Wang & Biddle (2001) 1 1 1 0 0 Wang et al. (2002) 0 1 1 1 0 Wang et al. (2010) 0 1 1 1 0 Welk and Schaben (2004)

0 1 1 1 0

Welk et al. (2003) 0 1 1 1 0 Zhang et al. (2011) 0 1 1 1 0

2.7 Testing for publication bias

The classic fail-safe N was high for general physical self-concept (N = 3909), perceived

competence (N = 89188), perceived fitness (N = 3450) and perceived appearance (N =

2932). Therefore, a large number of studies with a mean effect of zero would be

necessary before the overall effects found in the present study would become not

statistically significant. Thus, the significant associations observed in these meta-analyses

are likely not the result of publication bias towards significant findings.

In addition, Duval and Tweedie’s ‘Trim and fill’ procedure [43] was used to compute a

random-effects estimate of the unbiased effect size. No studies were trimmed for either

perceived fitness or perceived appearance; however, two studies were trimmed for

general physical self-concept and 18 were trimmed for perceived competence. The

general physical self-concept meta-analysis trimmed for extreme values (2 studies) had

little impact on the overall estimate, while the trimmed perceived competence meta-

analysis (18 studies) resulted in a weaker effect size of r = 0.22 (95% CI = 0.15 to 0.29).

This finding suggests there is evidence of publication bias that contributed to the

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observed overall effect size for the association between perceived competence and

physical activity.

2.8 Discussion

2.8.1 Overview of findings

The findings from this systematic review and meta-analysis suggest that young people

with stronger beliefs about their physical characteristics are more likely to engage in

physical activity than those who report lower levels of physical self-concept 269,270.

However, it is not clear if participation in physical activity leads to improvements in

physical self-concept or those with high levels of physical self-concept are attracted to

physical activity. Notably, the strength of association between physical activity and

physical self-concept (and sub-domains) did not upon depend upon how the data were

treated (i.e., whether physical self-concept was the dependent or independent variable)

and there is conflicting evidence in the literature regarding associations of this nature. For

example, according to the model proposed by Stodden and colleagues128, perceived

competence is a mediator of the relationship between motor skill competence and

physical activity. The model describes two different spirals; one for those who are active

with high levels of perceived and actual motor skill competence and another for those

who live sedentary lifestyles and possess low levels of competency. As children grow, the

divide increases with a positive spiral of engagement leading to higher physical activity

levels and a negative spiral of disengagement contributing to physical inactivity.

Alternatively, the Exercise and Self-Esteem Model (EXSEM) considers self-efficacy

or perceived competence in exercise and sport-related tasks as outcomes of participation.

Although there is sufficient evidence from our review and previous studies to conclude

that there is a bi-directional association between physical activity and physical self-

concept, researchers working in this area are encouraged to conduct mediation analyses to

assist in unravelling the nature of the association between physical self-concept and

physical activity. Furthermore, separate analyses that model the bidirectional nature of

general physical self-concept and its subdomains as both mediators and moderators of

physical activity are needed.

The meta-analysis effect sizes from the current review are similar, but slightly smaller,

than those found in previous reviews examining the effects of exercise on self-esteem in

64

young people 67 and adults 271. While it is plausible to suggest that larger associations

would be observed between physical activity and physical self-concept, as compared to

global self-esteem which is both more stable and distal from the impact of physical

activity 196, both previous reviews were focused on the effects of participation in

structured exercise programs. Exercise is planned and repetitive bodily movement done to

improve or maintain health-related fitness 272 and according to the EXSEM, individuals

who experience improvements in fitness, should also experience changes in global self-

esteem (via changes in physical self-perceptions which are more proximal to exercise

participation). In contrast, the current review was designed to examine the association

between leisure-time physical activity and physical self-concept. Physical activity

measures capture a range of organised and non-organised activities and, in the case of

objective measures such as accelerometers and pedometers, also collect incidental and

lifestyle physical activity (e.g., walking and riding for transportation). Overall, the

findings of this systematic review suggest that physical self-concept is important for

physical activity in young people and the sub-domains of physical self-concept may play

a unique role.

2.8.2 Summary of risk of bias from included studies

The findings of this review should be interpreted with some caution as 54 (84%) of the

included studies were found to have a high risk of bias. It is a concern that the majority of

studies assessed physical activity using a self-report measure. Self-report of physical

activity can suffer from reporting bias [70], attributable to a combination of social

desirability bias and the cognitive challenges associated with estimating frequency and

duration of physical activity, especially in children [71]. Furthermore, common method

artefact may result in stronger correlation coefficients, when two outcomes are measured

using the same method of assessment (i.e., self-report) 273. In addition, few of the studies

included participants who were randomly selected from nationally representative

populations, which may limit the generalizability of our findings. Only a small percentage

of studies adjusted for relevant covariates, which may confound the association between

physical self-concept and physical activity. Finally, most of the studies included in this

review were cross-sectional and while a number of longitudinal studies were included,

such studies do not provide the same level of evidence generated from experimental

studies.

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2.8.3 Major findings and potential contributors

This is the first systematic review and meta-analysis of studies examining the association

between physical activity and physical self-concept in children and adolescents. The

findings suggest that general physical self-concept and its sub-domains (i.e., perceived

competence, perceived fitness and perceived appearance) are significantly associated with

physical activity in young people. Sex was a significant moderator of the association

between physical activity and general physical self-concept, with stronger associations

found for boys. Age was also a significant moderator of the association between physical

activity and perceived competence and perceived appearance. Notably, study design did

not emerge as a significant moderator of the association between physical activity and

physical self-concept or any of its sub-domains. Due to the small number of experimental

studies, it is not possible to determine if the findings from experimental studies were

significantly different to cross-sectional and longitudinal studies.

Perceived competence was found to have the strongest association with physical

activity and age emerged as a significant moderator, with the strongest association found

in early adolescents. Evidence suggests that young children do not possess the cognitive

skills to accurately assess their motor skill competence. As a result, young children often

report inflated levels of perceived competence 199,200, which may explain the weak

associations found among children in our review. Stodden and colleagues 198 suggest that

perceived motor skill competence will not be strongly correlated to actual levels of motor

skill competence nor physical activity during the early childhood years, but by middle

childhood they will develop a “sophisticated cognitive capacity to more accurately

compare themselves to their peers”. Alternative explanations for the moderating effects of

age should be considered as the association between perceived competence and physical

activity was slightly weaker in late adolescents. As children progress into adolescence,

traditional team sports become less important as young people are exposed to, and

participate in more lifelong physical activities (e.g., resistance training, walking, aerobics

etc.) 21. Many lifelong activities are attractive to young people, especially those with low

levels of perceived competence, because they do not require competence in fundamental

and sports-specific movement skills 274. As many perceived competence scales include

items focused on proficiency in traditional team sports, they may not capture adolescents’

66

perceptions of their abilities in non-traditional physical activities. Such activities make an

increasingly important contribution to adolescents’ leisure-time physical activity 198,260,269.

Perceived fitness was found to have the second strongest association with physical

activity in children and adolescents. Perceived fitness may be amenable to change and

experimental studies have demonstrated that well-designed physical activity or exercise

programs can increase perceived fitness in adolescents 205,207,232. However, these studies

were not included in the meta-analysis because they did not examine the association

between changes in physical activity and changes in physical self-perceptions. Studies

often report the association between changes in physical self-concept and actual fitness 232; however, physical activity and fitness are only weakly related in young people 233,234.

Research examining the association between changes in physical self-concept and

changes in both fitness and behaviour is warranted. Increasing perceived fitness may have

utility as a strategy for increasing physical activity levels in young people, but further

testing of this hypothesis in experimental studies is required. Notably, none of the

hypothesised moderators were statistically significant.

Perceived appearance was found to have the weakest association with physical activity

in the current review. Age was a significant moderator of this association, with the

strongest associations found in young adolescents. A recent longitudinal study found that

the association between physical activity and perceived body attractiveness weakened

over the 12-month study period in a sample of adolescent girls 235. This finding suggests

an increasing divergence between girls’ perceptions of their appearance and their

involvement in physical activity as they progress through adolescence 236. Such results

may be attributable to bodily changes and increases in body fat that occur with maturation

(i.e., through puberty) 236. Although it is possible that perceived appearance becomes less

important to adolescent girls over time, it is likely that this finding reflects an increasing

dissatisfaction with their bodies and a disconnect between their actual body shape and

their perceived body shape 237-239,275. For example, a recent nationally representative

sample of French adolescents found that one third of adolescents misperceived their body

weight and that girls were more likely to overestimate their body weight than boys. This

possibility is alarming and provides further support for the importance of enhancing

adolescent girls’ acceptance of their bodies in attempts to promote physical activity 240,241.

67

2.8.4 Practical implications

Evidence from this systematic review and meta-analysis suggests that physical self-

perceptions (both general and subdomains), are related to physical activity participation in

young people. Although it remains unclear if physical self-perceptions are mediators or

outcomes, there is sufficient evidence to suggest that physical activity interventions may

benefit from strategies designed specifically to enhance physical self-concept. While it

may not be possible to specifically target general physical self-concept, learning

experiences and teaching styles that promote a mastery climate may assist in developing

both perceived and actual motor skill competence 276-278. Furthermore, exercise programs

that include fitness education, where students learn about the effects of physical activity

on fitness and help children link health-related fitness to present and future health status,

can improve perceived and actual fitness levels in young people 279,280. Fitness testing has

an important role to play in this process, but it is important that those administering tests,

use appropriate methods that minimise adverse reactions to fitness testing and maximise

effort, enjoyment, and motivation in young people 281.

2.8.5 Strengths and limitations of the review

The strengths of this review include the adherence to the PRISMA statement, the large

number of studies identified and the inclusion of meta-analyses. Despite these strengths,

some limitations should be noted. First, although this review was comprehensive, we did

not include studies that were published in languages other than English and we did not

include unpublished studies. Second, we did not include studies that examined the

association between physical fitness and physical self-concept, as this was considered

beyond the scope of the already extensive review. Third, the definition and assessment of

physical self-concept and subdomains was not consistent across studies. For example, the

global physical self-concept subscale from the Physical Self-Description Questionnaire

(PSDQ)282 includes items that require respondents to evaluate how they feel about

themselves in the physical domain (e.g., I feel good about who I am and what I can do

physically). For the purpose of our review, we did not exclude studies that described their

measure as a physical self-concept scale, but included items that measured physical self-

esteem. Additionally, most of the studies published to date on this topic are cross-

sectional or longitudinal; and such studies do not provide the same level of evidence

generated from experimental studies.

68

2.9 Conclusion

The results of this systematic review and meta-analysis have demonstrated a significant,

association between physical activity and physical self-concept in youth. However, due to

study heterogeneity and the high risk of bias observed in the included studies, these

findings should be interpreted with caution. Although we were unable to establish

causality, strategies to increase physical self-concept and sub-domains, particularly

perceived physical fitness and competence, may have a role to play in promoting physical

activity in young people. In addition, these results highlight the importance of

understanding the physical-self and its links to health-related behaviours in youth. Further

studies are needed to determine the mechanisms responsible for the effects of physical

activity on physical self-concept.

69

Chapter 3

Rationale and Study Protocol for ‘Switch-off 4 Healthy Minds’ (S4HM):

A Cluster Randomised Controlled Trial to Reduce Recreational Screen-

time in Adolescents

3.1 Preface

This chapter presents the protocols and a rationale for the S4HM cluster RCT, including

details on the study design, intervention components, methodology of assessments and

analytical procedures. This study was conducted to explore Secondary aim 2 of this

thesis.

The contents of this chapter were published in Contemporary Clinical Trials in May,

2015.




recreational screen-time in adolescents. Contemporary Clinical Trials, 40, 150-158.

3.2 Abstract

Background: Excessive recreational screen-time (i.e., screen use for entertainment) is a

global public health issue associated with adverse mental and physical health outcomes.

Considering the growing popularity of screen-based recreation in adolescents, there is a

need to identify effective strategies for reducing screen-time among adolescents. The aim

of this paper is to report the rationale and study protocol for the ‘Switch-off 4 Healthy

Minds’ (S4HM) study, an intervention designed to reduce recreational screen-time among

adolescents.

Methods: The S4HM intervention will be evaluated using a cluster randomised

controlled trial in eight secondary schools (N =322 students) in New South Wales,

Australia. The 6-month multi-component intervention will encourage adolescents to

manage their recreational screen-time using a range of evidence-based strategies. The

intervention is grounded in Self-Determination Theory (SDT) and includes the following

components: an interactive seminar for students, eHealth messaging, behavioural contract

and parental newsletters. All outcomes will be assessed at baseline and at 6-months (i.e.,

70

immediate post-test). The primary outcome is recreational screen-time measured by the

Adolescent Sedentary Activity Questionnaire (ASAQ). Secondary outcomes include: self-

reported psychological well-being, psychological distress, global physical self-concept,

resilience, pathological video gaming and aggression, and objectively measured physical

activity (accelerometry) and body mass index (BMI). Hypothesised mediators of

behaviour change will also be explored.

Discussion: The S4HM study will involve the evaluation of an innovative, theory-driven,

multi-component intervention that targets students and their parents, and is designed to

reduce recreational screen-time in adolescents. The intervention has been designed for

scalability and dissemination across Australian secondary schools.

Keywords: Screen, Behaviour, School, Physical activity.

3.3 Background

Over the past 20 years, young people’s recreational screen-time (screen-based

entertainment) has increased rapidly 42,168,283. Recreational screen-time refers to the time

spent using electronic devices such as televisions, computers, video games, and

multimedia devices (e.g., tablets, iPads, iPods, iPhones/ smartphones) for entertainment

purposes. The majority of young Australians 284, Europeans 285, and North Americans 285,286 exceed the screen-based recreational guidelines of less than 2-hours per day 287.

Specifically relating Australian secondary students; 42% of girls and 45% of boys spend

2-4 hours per day engaged in screen recreation 284. Comparably, 69% of girls and 71% of

boys from the Netherlands, 68% of girls and 74% of boys from England, 60% of girls and

65% of boys from Canada exceed screen-time recommendations 288. The existing high

levels of screen-time represent an immediate public health concern, as evidence suggests

that excessive recreational screen-time ( > two hours) is positively associated with a range

of adverse physical and mental health outcomes including; obesity 289,290, hypertension 291, increased aggressive behaviour, decreased empathy, reduced pro social behaviour 292

and depression 16,71,293.

Given that sedentary behaviours established during adolescence have shown to track

into adulthood 16, it is important to intervene at an early age. Schools provide convenient

access to the majority of young people and possess the necessary facilities, personnel and

ethos to engage youth 171. Although there is strong evidence suggesting that interventions

71

delivered in the school setting can improve health behaviours in young people, school-

based interventions that include a parental component appear to be more successful 150,182.

Parents influence their children’s lifestyle behaviours in a number of ways including,

parental regulation, rule setting 121,136,146,294, reinforcing positive behaviours, parental

concerns 295 and role modelling 296. Indeed, parents may provide the key to reducing

screen-time in young people as aspects of the family and home environments appear to be

related to the amount of screen use 297. For example, in the ‘Health In Adolescents’ study,

parental regulation of screen behaviours was associated with changes in screen-time 136.

Consequently, empowering parents with specific strategies to manage their children’s

screen-time may assist in promoting healthier lifetime behaviours 298.

Adolescence represents a period of increasing autonomy, as young people are provided

with more freedom over their discretionary time 299, and increased opportunities to make

choices and pursue goals 300. Although it has been suggested adolescents find it

challenging to manage their recreational screen-time, a recent study demonstrated

amotivation was positively associated with self-reported screen-time, while both

controlled and autonomous motivation were inversely associated with screen-time,.

Adolescents, who understand and value the importance of limiting their screen-time,

engage in less screen-time than those who are not concerned with the consequences of

excessive screen-time which is consistent with the principles of SDT 142. Based on the

high levels of recreational screen-time observed among adolescents across the globe and

the adverse health outcomes associated with such behaviours, there is an urgent need to

develop and evaluate interventions to reduce screen-time in adolescents. Therefore, the

aim of this paper is to provide the rationale and study description for the Switch-off 4

Healthy Minds (S4HM) school-based intervention.

3.4 Methods

3.4.1 Study design

The S4HM intervention will be evaluated using a cluster randomised controlled trial

(RCT). The 6-month intervention will target male and female adolescents in Grade 7 (first

year of secondary school) in Catholic schools in New South Wales, Australia (2014). One

school consists of only female students, the remaining seven are co-educational.

Assessments were conducted at baseline [April-June (Term 2) 2014], and will be repeated

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post-program [August-December (Term 4) 2014]. The design, conduct and reporting for

this RCT will adhere to the Consolidated Standards of Reporting Trials (CONSORT)

guidelines for clustered trials 301. Ethics approval for the study was obtained from the

Human Research Ethics Committees of the University of Newcastle, Newcastle-Maitland

Catholic Schools Office and the Diocese of Broken Bay. School principals, parents and

students provided written informed consent. School principals, parents and students

(hereafter referred to as participants) provided written informed consent.

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Figure 6: Study design and flow

Participants eligible (n = 935)

Participants ineligible (n = 219)

Participants completed baseline assessments

(n = 322)

Schools consented (n = 8)

Students completed eligibility screening

questionnaire (n = 1154)

Randomised by school (n = 8)

Allocation

Intervention group 4 secondary

schools (n = 167)

6-Month

Control group 4 secondary

schools (n = 155)

To be analysed for primary and

secondary outcomes (n = 155)

Schools invited to participate

(n = 12)

Schools declined to participate (n = 2)

Reasons: ♦ Not willing (n = 1)

♦ Schools over committed to other programs (n = 1)

Enrolment

To be analysed for primary and secondary outcomes (n = 167)

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3.4.2 Sample size calculation

A power calculation was conducted to determine the sample size required to detect

changes in the primary outcome [i.e., recreational screen-time from the ASAQ] 302.

Calculations were based on 80% power with alpha levels set at p < 0.05, using an

intraclass correlation coefficient (ICC) of 0.03 and a standard deviation of 91 minutes,

based on a previous school-based study 147. Using the ‘design effect’, an estimate was

made on the extent to which the sample size should be inflated to accommodate for the

homogeneity in the clustering of effects at the school level. It was calculated that a study

sample of N = 320 students (i.e., 40 students from 8 schools) would provide adequate

power to detect a between group difference of 42 minutes per day of recreational screen-

time.

3.4.3 Setting and screening of participants

Eligible schools were low fee paying Catholic secondary schools located in the

Newcastle, Hunter, and Central Coast areas of NSW. The Catholic education system has

grown to be the second biggest sector after government schools in Australia and 735,403

students attended Australian Catholic schools in 2012 303. The regions involved in the

current study have lower indices of socio-economic status than other parts of New South

Wales. All eligible schools (N = 20) were sent an information letter inviting them to

participate in the study. The first eight schools to provide written consent were recruited

into the study (see Figure 6). All students in grade 7 at the study schools were invited in

Term 1, 2014, to complete a screen-time eligibility questionnaire. The questionnaire

asked students to report their time spent using screen-based recreation on a typical school

day. Students were considered eligible to participate in the study if they reported ≥ 2

hours/day of recreational screen-time. During this visit, students who satisfied the

eligibility criteria were provided with an overview of the study and invited to participate.

Information and consent letters were sent home with students and the first 40 students

from each school to return signed consent letters were recruited into the study.

3.4.4 Blinding and randomisation

Once baseline assessments were conducted, schools were randomised to one of two study

arms (i) S4HM intervention group or (ii) control group. Schools were matched according

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to their size, location and socio-economic status using the Socio-Economic Indexes for

Areas index of relative socio-economic disadvantage 304 and then pairs of schools were

randomly allocated to one of the two study groups using a computer-based random

number-producing algorithm. This method ensured an equal chance of allocation to each

group. The randomisation was conducted by a researcher not involved in the current

project. Assessors will be blinded to group allocation at post-test.

3.4.5 Intervention

S4HM is grounded in Self-determination Theory (SDT)142 tenets and is designed to

increase adolescents’ autonomous motivation 305 to limit their recreational screen-time by

satisfying their basic psychological needs for autonomy, competence, and relatedness.

Autonomy refers to an individual’s need to feel in control of their behaviour and goals 142,

while competence refers to the need to gain mastery of tasks and learn different skills 142.

Finally, relatedness refers to an individual’s desire to experience a sense of belonging and

attachment to other people 142. The S4HM intervention components, behaviour change

strategies and hypothesised mediators are described in Table 8.

Students’ autonomy to limit their recreational screen-time will be targeted using a

variety of strategies. For example, participants will be provided with an opportunity to

specify their preferred technological social media to receive their eHealth messaging from

the following: Twitter, Facebook, Kik or text messages. Through newsletters, parents will

be encouraged to include their children in designing household screen-time rules and to

give their children choice relating consequences of exceeding screen-time limits.

Perceptions of competence will be supported using positive reinforcement throughout the

study, self-monitoring, goal setting to reduce recreational screen-time and behavioural

contracts. Such strategies will allow for a positive reflection on abilities and may assist in

building competence. Feelings of relatedness will be targeted by encouraging participants

to make connections through face-to-face meetings and competing with others in screen-

time reduction challenges, including family and friends throughout the study. In addition,

participants will be encouraged to share the social media messaging information with

their friends (face-to-face), whilst challenging participants to include friends or relatives

in proposed physical challenges. Encouraging a network of support provides safety and

supports growth; such strategies are aimed to provide a sense of belonging. A key feature

of the support provided will be the encouragement of rules, as providing instructions have

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been found to be significantly inversely associated with screen-time 146. Additionally,

advice on role modelling to parents will also be an important factor, as parent and child

levels of screen viewing are strongly related 296.

Parents will receive hard copies of the monthly newsletters in the mail. Six newsletters

have been developed for parents to help support them whilst managing their children’s

screen-time. The newsletters provide parents with information and various tips, strategies

and research regarding limiting recreational screen-time and the consequences of

excessive screen-time. Parents will also be provided with conflict resolution strategies to

help with any arguments that may arise as a result of reducing screen-time. In addition,

the fifth newsletter will include a behaviour contract and a list of potential screen-time

rules.

3.4.6 Control group

To prevent possible compensatory rivalry and resentful demoralisation, the control

schools will be provided with the program after the follow-up assessments.

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Table 8: Intervention components, behaviour change techniques and targeted constructs in the S4HM intervention

Intervention component

Dose Description Behaviour change strategies Hypothesised mediators

1) Interactive seminar Once at the start of the intervention (60 minutes)

The interactive seminar will be delivered by a member of research team to students during school hours. The session will focus on the consequences of excessive screen-time and the benefits of reducing screen-time. Students will be given the opportunity to ask any questions and interact throughout the session using Turning point™ interactive polling.

Information on consequences

Prompt intention formation Provide instruction General encouragement

Motivation to limit screen-time

Perceived autonomy Perceived

competence Perceived relatedness

2) eHealth 50 prompts over 6 months. Bi-weekly

Participants will select their preferred method for receiving eHealth messages from the following: Twitter, Facebook, Kik or text messages. Messages will address the consequences of excessive screen-time and the importance of self-management (self-monitoring screen-time and goal setting for increasing/decreasing behaviours).

Provide information about behaviour health link

Prompt self-monitoring of behaviours

Prompt barrier identification Prompt specific goal setting


3) Behavioural contract

Once Students will be asked to sign a screen-time behavioural contract in the second month of the intervention. The contract provided describes appropriate replacement behaviour and encourages the creation of a list of; potential screen-time rules, benefits of limiting screen-time, possible barriers of limiting screen-time, possible solutions to such barriers and consequences of exceeding screen-time limits.

Prompt specific goal setting Prompt identification as a

role model


Perceived autonomy Perceived

competence

4) Parental newsletters

6 over 6 months (1 per month)

The newsletters will be sent to parents and focus on: household screen-time rules, consequences of excessive screen-time, strategies to manage parent/child conflict arising from screen-time rules and home challenges to reduce recreational screen-time. For example, setting clear rules, placing limits on screen-time, and not having screen-based media in bedrooms will aim to encourage fewer hours of screen-time in adolescents.

Provide information about behaviour health link

Prompt self-monitoring of behaviours

Prompt specific goal setting Information on

consequences General encouragement

Perceived competence


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3.5 Outcomes

All research assistants participated in an assessment workshop before baseline data

collection. A protocol manual with detailed instructions for conducting assessments was

used by research assistants during baseline data collection and will be used during follow-

up assessments. Based on our previous studies 148,306, we anticipate a retention rate of at

least 80%. All assessments will be conducted by trained research assistants during a

single seating of approximately 30 minutes duration. The times will be chosen by the

school to minimise disruptions for the staff and students during classes.

3.5.1 Primary outcome

Recreational screen-time

Recreational screen-time was measured using the Adolescent Sedentary Activity

Questionnaire (ASAQ) 302. The ASAQ requires participants to report the time they spend

doing the following activities during a normal school week: i) watching televisions, ii)

watching DVD's/videos, iii) using the computer for fun, iv) using tablets/

iPads/iPods/iPhones etc. (the final category was added to the original measure).Total

screen-time is then determined as the sum of time spent in each screen behaviour. The

ASAQ has acceptable reliability (Cronbach's α = .78 and .90 for girls and boys in grade 8

respectively)302, and is considered a comprehensive measure of sedentary behaviours

among young people 302. The classification of ‘acceptable’ is referring to previous

literature indicating Cronbach alphas of 0.7 to be an acceptable reliability coefficient 307.

3.5.2 Secondary outcomes

3.5.2.1 Psychological distress

The 10-item Kessler Psychological Distress Scale 308 was used to provide a global

measure of distress. The K10 is based on questions about anxiety and depressive

symptoms experienced in the past four weeks 309. Scores range from 10 to 50. Scores

under 20 indicates likelihood to be well, 20-24 an individual is likely to have a mild

mental disorder, 25-29 indicates a possibility of having moderate mental disorder and

individuals with scores of 30 and over are suspected to have a severe mental disorder. The

K10 has shown acceptable reliability (Cronbach's α = .93)308 in Australians aged >18.

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3.5.2.2 Pathological video game use

Gentile's pathological video gaming scale 310 was employed to gather information

regarding video-gaming habits and parental involvement in gaming and to determine who

met clinical-style criteria for pathological gaming. The scale contains 11 questions

pertaining to cognitions and behaviours indicative of pathological gaming (e.g., ‘Have

you ever lied to family or friends about how much you play video games?’). Students

responded either ‘Yes’ (=1), ‘No’ (=0), or ‘Sometimes’ (=0.5) to each question. A sum

total of ≥6 qualifies a subject as a pathological gamer. Gentile's pathological video

gaming scale has reported acceptable reliability for U.S. adolescents aged 8-18

(Cronbach's α= .78)310.

3.5.2.3 Aggression

Aggressive behaviour was assessed using an aggression scale designed for young

adolescents 311. Students were asked to report how many times in the last week they

engaged in 11 specific aggressive behaviours (e.g., ‘I teased students to make them

angry’). Responses range from 0 to 6 or more times per week for each aggressive

behaviour. Items were summed to produce a total aggression score (possible range 0 to

66). This scale has demonstrated acceptable content and construct validity in both

adolescent females and males (Cronbach's α = .87)311.

3.5.2.4 Psychological difficulties

The Strength and Difficulties Questionnaire (SDQ) 312 is a brief behavioural screening

questionnaire for 3-16 year olds. The 25 items are divided between five scales; emotional

symptoms, conduct problems, hyperactivity/inattention, peer relationship problems and

prosocial behaviour, all of which identify problems with; conduct, emotions, peer

relations and hyperactivity 313. A self-report version of the SDQ has also been validated in

children of 11 years or over 314. The SDQ reported acceptable reliability in European

sixth, seventh and eighth graders. (Cronbach’s α = .88)314.

3.5.2.5 Global physical self-concept

The global physical self-concept subscale from the Physical Self-Description

Questionnaire (PSDQ)211 was used to provide a measure of self-concept in the physical

domain. Students were asked to respond on a 6-point scale (1 = ‘False’, to 6 = ‘True’)

how true each statement was for them (e.g., ‘I am a physically strong person’). The PSDQ

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provides an acceptable method for measuring physical self-concept in adolescents

(Cronbach’s α = .88) 211.

3.5.2.6 Household screen-time rules

Household screen-time rules were measured using items developed by Ramirez et al. 146.

Students were required to respond ‘No’, ‘Sometimes’, or ‘Yes’ for each of the five items

relating to screen-time rules within their family home (e.g., ‘In your home do your

parents/caregivers have the following rules about screen use? i.e., No recreational

screen-time before homework’). The items were originally designed to apply specifically

to television /DVD or computer use and were adapted to apply to all screen-time devices.

The kappa statistic was used to assess reliability of the dichotomous responses for the

rules items and agreements on rules between parent and adolescent. Parent and adolescent

reliability and agreement for rules regarding sedentary behaviours vary. Parents’ test-

retest reliability coefficients are reported to be consistently higher for each item (κ range:

.44–.70) as compared with adolescents’ (κ range: .43–.61) 146.

3.5.2.7 Motivation to limit recreational screen-time

The Motivation to Limit Screen-time Questionnaire (MLSQ) 305 was used to assess

participants' motivation for limiting their recreational screen-time. The MLSQ contains 9

questions relating to the three broad motivational regulations outlined in SDT (i.e.,

autonomous motivation, controlled motivation, and amotivation) 142. A positive score

represents autonomous motivation to limit screen-time. The MLSQ has demonstrated

satisfactory construct validity and test–retest reliability in adolescent boys (Cronbach’s α

= .82).

3.5.2.8 Physical activity

Physical activity was assessed using GENEActiv (Model GAT04, Activinsights Ltd,

Cambridgeshire England) wrist worn accelerometers. The devices were worn by

participants during waking and sleeping hours and water activities for seven consecutive

days. Data were collected and stored in five second epochs. GENEActiv wrist worn

accelerometers have displayed acceptable intra-and inter-instrumental reliability and

provide a valid and reliable estimate of physical activity in young people 315,316 .

Thresholds for the classification of activity intensity were taken from recent research

undertaken using the GENEActiv accelerometers 315,316. Wrist worn devices have the

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potential for higher subject compliance and emerging evidence suggests that have they

acceptable criterion (r = 0.91) and concurrent validity in adolescents (r = 0.85), when

compared to oxygen consumption and Actigraph GT1M activity counts, respectively 315.

3.5.2.9 Body mass index

Height and weight. Weight was measured to the nearest 0.1 kg without shoes, in light

clothing using a portable digital scale (Model no. UC-321PC, A&D Company Ltd, Tokyo

Japan) and height was recorded to the nearest 0.1 cm using a portable stadiometer (Model

no. PE087, Mentone Educational Centre, Australia). BMI was calculated using the

standard equation (weight [kg] / height [m]2) and BMI z-scores were calculated using the

‘LMS’ method 317. All assessments will be conducted by trained research assistants at the

study schools. Prior to baseline data collection, research assistants participated in an

assessment training workshop. A protocol manual with detailed instructions for

conducting assessments was used by research assistants during baseline data collection

and will be used during follow-up assessments.

3.5.2.10 Process evaluation

Process data will be collected to complement the outcome data. Process measures

including; i) student retention, ii) adherence iii) feasibility and iv) satisfaction data will be

collected from parents regarding what they believed was successful about the program.

Parents will be given an opportunity to provide information regarding the usefulness of

each of the components they were involved in including; newsletters, screen-time rules

settings, behavioural contract. Parents will be asked to rank each of the intervention

components based on their utility for supporting behaviour change. Similarly, using a

process evaluation questionnaire, students will be given an opportunity to provide details

on what they believe to be effective in reducing their recreational screen-time including;

reading newsletters with their parents, using suggested strategies to reduce screen-time,

receiving prompts or the presentation delivered at their school. The aims of the process

evaluations are: i) to examine participants’ views of the various intervention components,

ii) to determine how the intervention was implemented, and iii) identify the effectiveness

of various intervention components. Responses will be collected and examined to

determine which of the S4HM components are necessary for behaviour change. This

information may be used in future interventions.

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3.5.2.11 Statistical methods

Differences between groups at baseline will be examined using chi squares and

independent sample t tests in SPSS Statistics for Windows, Version 20.0 (2010 SPSS

Inc., IBM Company Armonk, NY) and alpha levels will be set at p < 0.05. The mixed

models will be analysed using the PROC MIXED statement in SAS version 9.1 (SAS

Institute Inc.). The models will be used to assess the impact of treatment (S4HM or

control), time (treated as categorical with levels baseline and 6-months) and the group-by-

time interaction, these three terms forming the base model. The models will be specified

to adjust for the clustered nature of the data and will include all randomised participants

in the analysis. Mixed models are robust to the biases of missing data and provide an

appropriate balance of type 1 and type 2 errors 318. Mixed model analyses are consistent

with the intention-to-treat principle, assuming the data are missing at random 319.

Differences between completers and those who drop out of the study will be examined

using Chi-square and independent samples t-tests. Multiple imputations will be

considered as a sensitivity analysis if the dropout rate is substantial (>30%). Multiple

imputation uses other variables in the data set to predict the missing values and will be

conducted using the expectation maximisation technique in SPSS. Hypothesised

mediators of physical activity and screen-time rules will be examined using multilevel

linear analysis and a product-of-coefficients test 320. Moderators of intervention effects

will be explored using linear mixed models with interaction terms for the following: i) sex

(boys and girls), ii) SES (based on participants’ household postcode SES), iii) weight

status (healthy weight, overweight/obese), and iv) baseline recreational screen-time (2

hours/ day of screen-time or > 2 hours/day). Subgroups analyses will be conducted if

significant (p < 0.1) interaction effects are identified. A per-protocol analysis will be

conducted to determine the intervention effect among participants who received the

intervention as intended. Participants will be included in the per-protocol analysis if: i)

they received the eHealth messages, ii) they attended the interactive seminar, iii) both

parent and child signed the behavioural contract, and iv) their parents read the study

newsletters.

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3.5.2.12 Results

The study design and flow is presented in Figure 6. Of the schools that were contacted,

eight consented to participate and two declined. Eligibility screening was completed by

1107 students, of whom 918 (83%) were considered eligible. The recruitment target of 40

students per school was achieved in seven of the eight schools and a total of 323 students

completed baseline assessments.

3.6 Discussion

Recreational screen-time use among adolescents has increased at an exponential rate and

the majority of young people in developed nations exceed the screen-time

recommendations 42 . A number of well-designed 289,321 studies have found individuals

spending >2 hours a day in front of televisions, are more likely to have higher blood

pressure 321 and cholesterol levels 289. Additionally studies have shown a significant dose-

response relationship between screen-time and various adverse health outcomes

including: risks of Type 2 diabetes, CVD and all-cause mortality 71. Excessive screen-

time not only affects an individual’s physical health, it is inversely associated with

indicators of mental health 81, such as self-esteem 83 . Adverse effects are further

demonstrated in a recently published article which described ‘Facebook Depression’ as

preteens and teens are experiencing classic symptoms of depression from spending

excessive time on social media sites 322. Therefore, reducing screen-time is a potential

strategy to prevent and treat health concerns 323.

Reducing screen-time has been identified as a key strategy for improving the physical

and psychosocial health of young people 71,324. The current evidence base of effective

interventions is limited. Although screen-time is often targeted in lifestyle interventions

focused on increasing physical activity and improving dietary behaviours, no previous

intervention has focused solely on reducing recreational screen-time in adolescents.

Recent systematic reviews have demonstrated that multi-component interventions

targeting screen-time can achieve small, but statistically significant decreases in young

people’s screen-time 162,163. This is a notable finding as the determinants of physical

activity and screen-time are indeed different, and unique strategies may be required to

modify specific lifestyle behaviours as one intervention strategy may not cover the

diverse needs of various subgroups 325. Interventions designed for specific groups have

been suggested and trialled with differing results 325. Notably, it is of additional concern

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that previous lifestyle interventions studies have focused on reducing television viewing 326 and have largely ignored the other forms of recreational screen-time, such as video

game playing and using the internet for social media, which are particularly popular

among young people.

Therefore, identifying strategies to reduce the time that young people spend engaged in

recreational screen-time is a challenging endeavour. Although previous studies have

achieved some success in reducing television viewing in child populations 283,323, few

studies have successfully reduced screen-time in adolescents. The S4HM intervention

will target students in the first year of secondary school and eligible students will be those

who are currently exceeding screen-time recommendations. Of the few systematic

reviews examining intervention strategies to limit screen-time in adolescents 162,283, none

have examined strategies to discourage parents from placing televisions in their children's

bedrooms or remove televisions 323. Recommendations have been made to specifically

address the removal of televisions from children’s bedrooms in order to reduce screen-

time in young people 323. In response to such findings, S4HM will provide advice to

parents and adolescents regarding the positioning and time allowances of television using

both newsletters and social media prompts. Studies have also found parental rules and

limits on screen-time may reduce screen-time 146,327. Demonstrated in a recent systematic

review and meta-analysis; multi-component interventions may be the most effective way

to reduce recreational screen-time among adolescents, thus its presence in S4HM 283.

Increasing parental awareness of the consequences of excessive screen-time may assist

in achieving screen-time behaviour change in adolescent populations 323. S4HM aims to

support parents through monthly newsletters containing information on; household rules,

dangers of social media, video game addiction, consequences of excessive screen-time

and the importance of role-modelling. Each of the concepts are designed to engage and

educate parents and their children, as previous studies have identified closer family

communication and improved school performance as a result of reducing screen use in

adolescents 328. S4HM aims to provide information regarding various skills parents can

adopt, or continue to use, in order to reduce recreational screen-time. S4HM aims to

provide such guidance through suggestions of developing constructive practical

alternatives to screen-time.

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To the authors’ knowledge, this is the first intervention to incorporate a social media

component into a screen-time reduction intervention; however the student has to choose

that option. The components used in the S4HM program were originally developed for

previous interventions targeting adolescents 147,306, but were refined according to the

tenets of Self-Determination Theory (SDT), which recommends a socialisation approach

that focuses on autonomy support as an alternative to rewards (which can lead to

controlled rather autonomous regulation). The ‘Nutrition and Enjoyable Activity for Teen

Girls’ (NEAT Girls)306 and the ‘Active Teen Leaders Avoiding Screen-time’ (ATLAS) 148 programs were successful in reducing screen-time in adolescents attending schools in

low-income communities. NEAT Girls was a school-based obesity prevention

intervention targeting low-active girls focused on increasing physical activity, improving

dietary behaviours and reducing recreational screen-time 306. ATLAS was a multi-

component school-based program informed by self-determination theory that included

interactive seminars, self-monitoring (using a smartphone application) and parental

newsletters focused on screen-time reduction 147. After 12-months, there was a significant

between group difference for screen-time, in favour of participants in the NEAT Girls

intervention (adjusted mean difference −30.67 minutes/day; 95% CI, −62.43 to −1.06)306.

A similar intervention effect for screen-time was observed among boys who participated

in the ATLAS intervention over the 8-month study period (adjusted mean difference –30

minutes/day, p = 0.03)148. In addition, after completing the ATLAS program, almost half

of the group agreed or strongly agreed that the push prompt messages reminded them to

be more active and/or reduce their screen-time 148,329, indicating the potential of prompts

as a viable strategy for reducing screen-time in adolescents.

The expansion and adoption of new methods of communication provide exciting

opportunities to deliver health behaviour change interventions. Delivering interventions

via text messages, emails, social media applications and short-message service (SMS)

presents prospects to: i) reach a wide population, ii) individually tailor messages using a

variety of mediums (Kik, Facebook etc.) and iii) provide instant delivery of health

behaviour information. A recent review suggested that SMS-delivered interventions have

positive short-term behavioural outcomes, but further research is required to evaluate

their long-term efficacy and determine the features that are appropriate 330.

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Parental newsletters have been used extensively to deliver health behaviour

information to parents in school-based physical activity and nutrition interventions 331-333 .

Resources typically focus on increasing knowledge and parental confidence, and

promoting healthy parenting practices 334. Of note, NEAT Girls utilised four parental

newsletters over the 12-month intervention. Newsletters addressed the following: i)

information about the objectives of the NEAT Girls program, ii) national guidelines for

physical activity, nutrition and screen-time, iii) individual feedback regarding their child’s

health behaviours as reported by participants in the baseline data collection, and iv)

household strategies to promote healthy lifestyles. The information was designed to raise

awareness and encourage parents to support their daughters’ health behaviours. Similarly,

S4HM parents will receive six newsletters focused on the following: i) potential

consequences of excessive screen-use among youth, ii) strategies for reducing screen-

based recreation in home environment (such as recommended household, rules,

behavioural contract and the importance of role modelling) and iii) strategies for avoiding

conflict when implementing household screen-time rules.

The S4HM intervention will include one 60 minute interactive seminar. The face-to-

face seminar is scalable and is designed be delivered by people with training in health

education (e.g., the research team or personal development, health and physical education

teachers).

3.7 Limitations

There are some limitations that should be noted. The findings from this study may not be

fully generalisable to the broader Australian community, as students were recruited from

Catholic secondary schools. It is possible that parents in the study sample may be more

receptive to reading and using newsletters and more mindful of their students' screen

behaviours. While screen device ownership does not appear to be associated with socio-

economic status 335, adolescents from low-income backgrounds engage in higher levels of

recreational screen-time, in comparison to those from middle and high income families 336. Finally, due to the inclusion of an ‘all girls’ school and because more girls returned

their consent letters, the study sample includes a larger proportion of female students.

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3.8 Conclusion

This paper has outlined the rationale and study protocol for the S4HM recreational

screen-time reduction intervention for adolescents. The intervention has a strong

theoretical foundation and incorporates novel strategies to decrease recreational screen-

time. The S4HM intervention will also improve our understanding of psychological and

cognitive mechanisms of behaviour change through the assessment of a number of

potential mediators. Improved understanding of these relationships could help in

developing interventions to promote general well-being among adolescents.

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Chapter 4

Intervention to Reduce Recreational Screen-Time in Adolescents:

Outcomes and Mediators from the ‘Switch-off 4 Healthy Minds’

(S4HM) Cluster Randomised Controlled Trial

4.1 Preface

This chapter presents the outcomes and mediators from the S4HM study, including details

on the intervention effects on the primary and secondary outcomes. This study was

conducted to investigate Primary aim 1 of this thesis.

The contents of this chapter were published in Preventive Medicine in August, 2016.

Babic, M. J., Smith, J. J., Morgan, P. J., Lonsdale, C., Plotnikoff, R. C., Eather, N.,

Skinner, G., Baker, A. L., Pollock, E., & Lubans, D. R. (2016). Intervention to Reduce

Recreational Screen-Time in Adolescents: Outcomes and Mediators from the ‘Switch-Off

4 Healthy Minds’ (S4HM) Cluster Randomised Controlled Trial. Preventive Medicine (In

Press).

4.2 Abstract

Introduction: The primary objective was to evaluate the impact of the ‘Switch-off 4

Healthy Minds’ (S4HM) intervention on recreational screen-time in adolescents.

Methods: Cluster randomised controlled trial with study measures at baseline and 6-

months (post-intervention). Eligible participants reported exceeding recreational screen-

time recommendations (i.e., > 2 hours/day). In total, 322 adolescents (mean age = 14.4 ±

0.6 years) from eight secondary schools in New South Wales, Australia were recruited.

The S4HM intervention was guided by Self-Determination Theory and included: an

interactive seminar, eHealth messaging, a behavioural contract and parental newsletters.

The primary outcome was recreational screen-time. Secondary outcomes included mental

health (i.e., well-being, psychological distress, self-perceptions), objectively measured

physical activity, and body mass index (BMI). Outcome analyses were conducted using

linear mixed models and mediation was examined using a product-of-coefficients test.

Results: At post-intervention, significant reductions in screen-time were observed in both

groups, with a greater reduction observed in the intervention group (-50 min/day versus -

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29 minutes, p <.05 for both). However, the adjusted difference in change between groups

was not statistically significant (mean = -21.3 min/day, p = 0.255). There were no

significant intervention effects for mental health outcomes, physical activity or BMI.

Significant mediation effects for autonomous motivation were found.

Conclusions: Participants in both the S4HM intervention and control groups significantly

reduced their screen-time, with no group-by-time effects. Enhancing autonomous

motivation might be a useful intervention target for trials aimed at reducing adolescents’

recreational screen-time.

Trial Registration: ACTRN12614000163606

Keywords: Screen, sedentary behaviour, school, physical activity.

4.3 Introduction

Excessive recreational screen-time is associated with numerous adverse physical 71,298 and

mental health 337,338 outcomes in youth. Despite international guidelines recommending

young people limit their recreational screen-time to less than two hours per day 35,

between 70-80% of Western youth exceed these recommendations 34,36,339. As excessive

screen-time is a major public health issue in many Western countries, there is a need for

scalable interventions that can reach a large proportion of the youth population.

According to a recent meta-analysis of screen-time interventions, home-based

interventions have been more successful than those conducted in schools 181. However,

few of the included studies targeted adolescents, and it is therefore unclear which

intervention approaches are most effective for this priority population. While parental

involvement is considered an important determinant of success in youth screen-time

interventions 181, engaging parents in such interventions remains challenging 340. Schools

have the facilities and personnel to support the implementation of interventions 173, but

may also have value as an avenue for accessing and engaging parents. Indeed, embedding

health promotion interventions within schools may give health promotion programs the

exposure and credibility needed to convince parents to participate. Moreover, there is a

rationale for evaluating interventions that meaningfully incorporate parental engagement

within school-based programs.

90

Evidence suggests theory-based screen-time interventions have been more effective

than those that do not report a theoretical framework 162. Therefore, an additional priority

for interventions should be the application of behavioural theories, and the evaluation of

theoretical mediators of behaviour change. Self-determination theory (SDT) is a

motivational theory which posits that human motivation and behaviour are influenced by

the satisfaction (or thwarting) of individuals’ basic psychological needs for autonomy

(sense of choice or volition), competence (sense of capability or mastery) and relatedness

(sense of connectedness with others) 140. According to SDT, satisfaction of these

psychological needs will promote autonomous (or self-determined) forms of motivation.

Autonomous motivation reflects more ‘internalised’ reasons for engaging in (or avoiding)

a behaviour. For example, an individual may decide to maintain an active lifestyle or limit

their alcohol consumption due to the perceived health or social benefits. Autonomous

motives are considered to be more strongly related to behavioural enactment than

controlled motives, which involve engaging in or changing behaviour on the basis of

external demands or social pressures 140. Accordingly, behaviour change strategies that

enable individuals to feel their decisions are self-endorsed (rather than imposed) should

result in a greater likelihood of initial behaviour change and ongoing behaviour

maintenance 143.

The aim of the present study is to evaluate the efficacy of the ‘Switch-off 4 Healthy

Minds’ (S4HM) intervention, a novel and theoretically based screen-time intervention for

adolescents. We hypothesise that adolescents in the S4HM intervention will report

significantly lower levels of recreational screen-time at 6-month post-intervention,

compared to those in a wait-list control group. In addition, we hypothesise that changes in

screen-time over the study period will be mediated by changes in adolescents’

autonomous motivation to limit their screen-time.

4.4 Methods

4.4.1 Study design and participants

The study was conducted and reported in accordance with the Consolidated Standards of

Reporting Trials (CONSORT) Statement 341,342, and the methods have previously been

described in detail 343. Ethics approval for the study was obtained from the University of

Newcastle, Newcastle-Maitland Catholic Schools Office and the Diocese of Broken Bay.

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All Catholic secondary schools (N = 20) located in the Hunter region of New South

Wales, Australia were invited to participate, and the first eight schools to provide written

consent were accepted (Figure 7). Students in Grade 7 at the study schools completed an

eligibility questionnaire, which asked them to report their total time spent using screen

devices for the purposes of recreation on a typical school day. Students failing to meet

national screen-time guidelines (i.e., > 2hours/day) were considered eligible and invited

to participate, and the first 40 students from each school to return signed consent letters

were included. The intervention was evaluated using a parallel group cluster randomised

controlled trial (RCT) design. Prior to baseline assessments, schools were matched on key

demographic variables (e.g., size, location and socio-economic status) and randomly

allocated to the S4HM intervention group or a wait-list control group. The S4HM group

received the intervention over a 6- month period, whereas the control group were asked to

continue with their usual behaviours and school curriculum. At the end of the study

period the control group was offered the S4HM program. Baseline assessments were

conducted at the study schools by trained research assistants between April and June,

2014 and follow-up assessments were conducted between October and December, 2014.

Basic demographic information (i.e., sex, country of birth, language spoken at home) and

self-report measures were collected in exam-like conditions using an online survey and

Apple iPads, and physical measures were conducted discretely by a same-sex assessor.

4.4.2 Intervention components

The S4HM intervention components were guided by SDT, targeted both students and

their parents, and were designed to be scalable. A detailed description of each

intervention component can be seen in Table 8. At the beginning of the study period,

students participated in an interactive seminar delivered at the school by a member of the

research team. The purpose of the interactive seminar was to provide students with a

rationale for behaviour change, by outlining the potential consequences of excessive

screen viewing, as well as the health and social benefits that could be gained by limiting

recreational screen viewing to healthy levels. During this interactive seminar, students

were also taught how to self-monitor their screen-time and were given instructions on

appropriate screen-time goal setting.

The primary intervention component in the present trial was eHealth messaging.

Intervention participants received informational and motivational messages twice per

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week from their preferred social media and messaging systems (i.e., Twitter, Facebook,

Kik, email or text messages). The messages were framed to satisfy students’ basic

psychological needs for autonomy (e.g., “Many Australian adolescents spend more time

on screens on the weekend. Why not plan your weekends in advance?”), relatedness (e.g.,

“Have a competition with ur m8. Who can go the longest without checking their social

media account (Facebook/twitter etc.)”), or competence (e.g., “If you’re watching TV or

using the computer, don’t forget to walk around and stretch. It’s easy and good 4 u, u can

do it!”).

In addition to the student-level strategies, S4HM also targeted the home environment

by sending information to parents. Over the study period, parents were mailed a total of

six newsletters (i.e., one per month) that included information on the consequences of

excessive screen-time and practical strategies for setting limits on screen viewing in the

family home. The third newsletter included a behavioural contract, and parents were

encouraged to involve their child in the creation of a customised contract, that included

clear screen-time goals, as well as rewards/consequences for satisfying or not satisfying

the terms of the contract. Newsletters for parents encouraged the planning of individual

consequences if screen-time remained excessive, for example “loss of privileges to TV,

iPad, phone etc. for a period of time”. Notably, the strategies provided to parents in the

newsletters encouraged parents to interact with their teen in a ‘needs supportive’ manner

and to manage conflict arising from attempts to reduce recreational screen-time, e.g.

“Explain to your teen why it is important to limit their screen-time”. Parents are ‘needs

supportive’ when they support their children’s sense of autonomy, interact with their

children in a warm and responsive manner, and support and encourage self-expression 142.


A detailed description of the study measures is available elsewhere 343. Recreational

screen-time was assessed using the Adolescent Sedentary Activity Questionnaire (ASAQ) 302. The ASAQ required respondents to self-report time spent using different screen

devices on each day of the week, including weekends. Specifically, participants were

asked to report time spent using television, video/DVD, computer, and tablet/smartphone

for entertainment purposes on a usual school week. The final item (i.e.,

tablet/smartphone) was not part of the original ASAQ instrument but was added to reflect

current trends in adolescents’ screen media use. Mean daily screen-time was calculated

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by adding the time spent using each screen device on each day of the week and dividing

by the number of reported days (i.e., 7). The ASAQ has shown acceptable test-retest

reliability among girls (ICC = 0.70, 95% CI = 0.40 to 0.85), and boys (ICC = 0.84, 95%

CI = 0.69 to 0.91) 302.


Weight was measured without shoes, in light clothing using a portable digital scale

(Model no. UC-321PC, A&D Company Ltd, Tokyo Japan) and height was recorded using

a portable stadiometer (Model no. PE087, Mentone Educational Centre, Australia). BMI

was calculated using the standard equation (weight [kg] / height [m]2) and BMI z-scores

were calculated using the ‘LMS’ method 317. Physical activity was assessed over 7 days

using GENEActiv (Model GAT04, Activinsights Ltd, Cambridgeshire England) wrist

worn accelerometers, and activity intensity was determined using existing cut-points 315.

Valid wear time was defined as a minimum of 10 hours per day on at least three days.

Emotional and behavioural problems were assessed using the Strength and Difficulties

Questionnaire (SDQ) 313 and the Kessler Psychological Distress Scale 308 was used to

provide a global measure of distress. Physical self-concept was assessed using a subscale

from Marsh’s Physical Self-Description Questionnaire (PSDQ) 211 and the ‘Flourishing

Scale’ was used to measure participants’ psychological well-being in areas such as

engagement, relationships, self-esteem, meaning, purpose and optimism 344.

4.4.5 Hypothesised mediators

The Motivation to Limit Screen-time Questionnaire (MLSQ) 305 was used to assess

participants' motivation for limiting their recreational screen-time. The MLSQ contains

nine questions relating to the three broad motivational regulations outlined in SDT (i.e.,

autonomous motivation, controlled motivation, and amotivation) (e.g., I try to limit my

screen-time because my parents pressure me to do so) 142.

4.4.6 Process evaluation

To determine satisfaction and engagement with the S4HM intervention, participants and

parents completed a post-program evaluation questionnaire. Using a 5-point scale,

students reported: i) how helpful they found the S4HM intervention for reducing screen-

time, ii) satisfaction with the school-based interactive seminar, and iii) intentions to

decrease screen-time and increase physical activity in the future. Students were also asked

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to indicate if their parents involved them in setting screen-time rules and creating a

screen-time behavioural contract. In addition, students reported on whether their parents

read the newsletters, and were asked to identify the most helpful intervention component

for reducing screen-time. Parents were asked to evaluate if the S4HM study provided

valuable information and useful ideas to limit screen-time. Specifically, parents were

asked to comment on and rank the effectiveness of each of the parental support strategies

(i.e., setting screen-time rules, screen-time contract, and newsletters).

4.4.7 Statistical analysis

Analyses for the primary and secondary outcomes were performed using IBM SPSS

Statistics for Windows version 22 (2010 SPSS Inc., IBM Corp., Armonk, NY), and

statistical significance was set at p < 0.05. Differences between groups at baseline for

those who did not complete follow-up assessments were examined using independent-

sample t-tests and chi-square (χ2) tests. Linear mixed models (adjusted for baseline

values, sex and participant SES) were used to assess the impact of treatment (S4HM or

control), time (treated as categorical with levels baseline and 6-months) and the group-by-

time interaction, these three terms forming the base model. Separate models were

conducted for the primary and secondary outcomes, which were adjusted for the clustered

nature of the data (using a random intercept for school) and included all randomised

participants (i.e., intention-to-treat [ITT]). A sensitivity analysis was conducted with

completed cases only. However, owing to the high retention rate (96%) the results were

consistent with the ITT analyses, and are therefore not reported. Multi-level mediation

analyses (adjusted for school-level clustering) were conducted using Mplus, version 7.11

for Windows (Muthén & Muthén, Los Angeles, CA). Single and multiple mediator

models were tested to assess the potential mediating effects of motivational regulations

(i.e., autonomous, controlled and amotivation) on changes in screen-time. Multi-level

linear regression analysis provided: (i) the regression coefficients for the treatment effect

on the hypothesised mediator at post-test, (Pathway A), (ii) the regression coefficient for

the association between the mediator and screen-time at post-test, independent of

treatment group (Pathway B), and (iii) estimates of the total intervention effect (treatment

predicting screen-time) (Pathway C), and direct effect (total effect adjusted for the

mediator) (Pathway C’). In the final stage, the product of the A and B coefficients (i.e.,

the indirect effect) was computed using Tofghi and Mackinnon’s R-mediation package

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345. Significant mediation was established if the confidence intervals for the estimate of

the indirect effect (Pathway AB) did not include zero.

4.5 Results

Eligibility screening was completed by 1154 students, of whom 935 (81%) were

considered eligible. In total, 322 students were recruited and assessed at baseline, with the

recruitment target achieved in seven of the eight schools. At post-intervention, 308

students completed follow-up assessments, representing a retention rate of 96%. Baseline

characteristics of the study sample are reported in Table 9. There were no significant

differences between completers and study drop-outs for any of the demographic variables

or study outcomes at baseline (p > .05 for all).


Significant reductions in screen-time were observed in both groups from baseline to post-

test (S4HM = -50.5 minutes/day, p < 0.001; Control = -29.2 minutes/day, p = 0.030)

(Table 10). However, the adjusted between-group difference was not statistically

significant (mean = -21.3 minutes/day; p = 0.255).


There were no statistically significant group-by-time effects for any of the mental health

outcomes, BMI or physical activity.

4.5.2.1 Mediation analysis

There were significant intervention effects for autonomous and controlled motivation,

whereas the effects for amotivation were non-significant (Table 11). Significant

associations were observed between changes autonomous motivation and changes in

screen-time (B = -17.83, p < 0.001). Based on the product-of-coefficients tests,

autonomous motivation (AB = -5.40, 95% CI = -12.04 to -0.15) satisfied the criteria for

mediation. In the multiple mediator model, only autonomous motivation was found to

mediate the effect of the intervention on screen-time (AB = -5.61, 95% CI = -12.59 to -

0.10).

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4.5.2.2 Process evaluation

Students reported an overall mean score of 3.5 for the general helpfulness of the S4HM

study (possible range = 1 to 5). In general, students identified the messages (39.5%) and

the interactive seminar (35.5%) as the most important intervention components. S4HM

students reported higher intentions to increase their physical activity (mean = 4.1),

compared with intentions to limit screen-time (mean = 3.7). Less than half (43.1%) of

participants stated that their parents were involved in the setting of screen-time rules,

whereas 44.4% reported that their parents set screen-time rules independently. Only 39

parents (23%) completed the evaluation questionnaire, of which approximately one third

strongly believed the S4HM intervention provided them with valuable information and

useful ideas to limit their child’s screen-time. The majority of responding parents (74.4%)

believed setting household rules was the most effective strategy to manage screen-time,

followed by the behavioural contract (20.5%) and role modelling desired behaviour

(5.1%).

4.6 Discussion

Excessive recreational screen-time is a growing problem in many Western nations, and

high levels of screen-time during the developmental years may have lasting adverse

effects 346. Consequently, there is a need for intervention approaches that demonstrate

both efficacy and reach. The primary objective of this study was to evaluate the impact of

the S4HM intervention on recreational screen-time in a sample of adolescents. Although

screen-time declined to a greater extent for the intervention group, the group-by-time

effect was not statistically significant. Therefore, our primary hypothesis was not

supported. In addition, there were no significant intervention effects for mental health

outcomes, physical activity or BMI.

Although the S4HM intervention was underpinned by theory and utilised novel and

scalable intervention strategies, the null findings for screen-time highlight the challenges

of influencing adolescents’ sedentary behaviours. Indeed, according to a recent review of

reviews, the most successful screen-time interventions have been those conducted with

young children (i.e., < 6 years) 169. Relatively few studies have evaluated the effects of

screen-time interventions conducted with adolescents; and of those that have, findings

have been mixed. The ‘Dutch Obesity Intervention in Teenagers’ (DOiT) 156 was a multi-

component school-based intervention targeting multiple health behaviours among

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adolescents. The DOiT study was theoretically driven and included both curricular and

environmental change strategies. Similar to the present study, no significant intervention

effects were reported for screen-time at the 8- and 12-month assessment periods.

However, after 20-months a significant effect in favour of the intervention group was

found, albeit only for boys (−25 minutes/day; 95% CI =−50 to −0.3 minutes/day). In

another recent school-based trial 161, significant intervention effects for adolescents’

television viewing and total screen-time were achieved after 18-months of intervention

delivery. However, the effects were not maintained once the strategies targeting screen-

time were discontinued 161. Overall, there is a limited understanding of the most effective

strategies for reducing screen-time among youth. Consequently, there have been calls for

mediation analyses to further elucidate the effects of specific intervention strategies 347.

While the between-group difference for screen-time was not statistically significant,

changes did favour the S4HM group. Additionally, the S4HM intervention had a

significant impact on autonomous motivation to limit screen-time, which was found to

mediate changes in screen-time. It has previously been proposed that changes in

motivation are required to influence children’s recreational screen-time 348, and evidence

supporting the importance of motivation for physical activity behaviour 139 lends credence

to this suggestion. Given the positive effects on students’ motivation in the present trial

and the between-group differences favouring intervention students, it is plausible to

suggest that the difference between groups for screen-time may increase over time.

However, longer term follow-up would be required to determine if this is indeed the case.

Although the intervention had a significant impact on both controlled and autonomous

motivation to limit screen-time, only autonomous motivation acted as a significant

mediator of changes in screen-time. This further highlights the importance of supporting

autonomous rather than controlled motives when targeting health behaviour change in

this population. Consistent with the tenets of SDT, it appears adolescents are responsive

to an approach that acknowledges their desire for autonomy. Future programs could target

autonomous motivation to reduce screen-time by: (i) providing opportunities for self-

evaluation and self-regulation; (ii) clearly describing expected behaviours and providing a

rationale for behaviour change that is valued by participants 349; and (iii) supporting

individuals in making independent decisions about their behaviours. It is likely that active

participation of both youth and their parents in the choice and development of

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intervention strategies may lead to more acceptable and attractive strategies and thereby

more effective interventions 350.

Parents have a significant influence on their children’s screen viewing patterns,

through the provision of screens in the home, modelling of behaviour, co-viewing and

enforcement of screen-time rules 146,297. Educating parents about screen-time guidelines

and prompting them to set screen-time limits have been identified as potential strategies

for reducing screen-time among youth 327. Although parents were targeted in the present

trial, lack of engagement may explain the weak study findings. For example, few parents

completed the process evaluation questionnaire, and of those that did only one third

reported reading the S4HM newsletters. It is unclear to what extent parents implemented

the strategies provided within the newsletters. However, given the seemingly low

engagement, it is likely that few parents implemented meaningful changes to their screen-

time parenting practices. Engaging parents in heath behaviour interventions remains

challenging and the most feasible and scalable strategies (e.g., sending educational

material to the home) also appear to be the least effective 169. Further research examining

how to effectively engage parents is therefore needed.

Although the causal sequencing has not been clearly established, there is emerging

evidence suggesting that excessive screen-time during youth may lead to poor mental

health outcomes 351,352. As there was no significant between-group difference for screen-

time in the present trial, the lack of intervention effects for mental health indicators is not

surprising. However, there were also no significant within-group effects, despite

significant reductions in screen-time for both groups over the study period. In a recent

obesity prevention trial with low-income adolescents’, changes in screen-time were found

to mediate the effect of the intervention on well-being 353, suggesting that reducing

screen-time may be a viable strategy for improving adolescents’ psychological health.

However, participants in the present trial were from more affluent backgrounds, and the

majority reported good mental health at baseline. Consequently, there may have been

little scope to improve psychological health among this sample.

Strengths of the present study included the robust study design, objectively measured

physical activity and the high retention rate at post-intervention. However, it is important

to acknowledge some limitations. First, while all eligible students were invited to

participate, the study sample consisted predominantly of girls who identified their cultural

99

background as Australian or European. Therefore, caution should be taken in generalising

the findings to other groups. Second, few parents completed the evaluation questionnaire,

making it difficult to determine the extent to which the parent-based strategies were

implemented. Finally, the primary outcome measure (i.e., the ASAQ) was subjective,

introducing the possibility of recall and social desirability biases. The ASAQ has

previously demonstrated satisfactory test-retest reliability, but there is limited evidence

regarding the utility of this measure for detecting changes in screen-time in intervention

studies. Previous studies have used objective measures such as television monitors to

capture screen-time 164. However, logistical barriers precluded the use of these measures

in the current trial. Further, the changing nature of adolescents’ screen-use suggests that

such measures may miss much of the screen-time that adolescents now engage in (i.e.,

tablet, smartphone, handheld video games etc.).

4.7 Conclusions

Screen use for recreation is ubiquitous and the majority of adolescents exceed current

screen-time recommendations 336. In light of this, there is a clear need for effective and

scalable intervention strategies. Despite being theoretically driven, the present trial was

ineffective in its primary aim of reducing recreational screen-time. Significant

intervention effects were observed for participants’ autonomous motivation to limit

screen-time, which mediated changes in screen-time. This finding provides support for

intervention strategies that enhance autonomous motives for behaviour change. However,

given the accepted importance of parents in their children’s health behaviours, continued

research on the most effective methods for engaging parents is warranted.

4.8 Competing interests

The authors have no competing interests to declare.

4.9 Author contributions

DRL, PJM, RCP, NE, GS, CL and ALB obtained funding for the research. All authors

contributed to developing, editing, and approving the final version of the paper. DRL,

PJM, RCP, CL, GS and MB developed the intervention materials. MB and EP were

responsible for data collection and cleaning. DRL is the guarantor and accepts full

responsibility regarding the conduct of the study and the integrity of the data. All authors

have read and approved the final manuscript.

100

4.10 Acknowledgements

This project is funded by a Hunter Medical Research Institute (HMRI) grant. DRL is

funded by an Australian Research Council Future Fellowship. RCP and ALB are funded

by Fellowships from the National Health and Medical Research Council of Australia.

101

Figure 7: Study design and flow with follow-up data

Analysed for primary and secondary

outcomes at follow-up (n = 158)


outcomes at follow-up (n = 150)

Follow-up

Assessments

Schools consented (n = 8)

Students completed eligibility screening

questionnaire (n = 1154)

Participants eligible (n = 935)

Enrolment

Participants ineligible (n = 219)

Participants completed baseline assessments

(n = 322)

Randomised by school (n = 8)

Allocation Intervention group 4 secondary schools

(n = 167)

Baseline

Assessments

Control group 4 secondary schools

(n = 155)


outcomes at baseline (n = 155)

Analysed for primary and secondary outcomes at

baseline (n = 167)

Reasons for withdrawal Absent (n = 2)

Left school (n = 2) Did not complete

assessment (n = 1)

Reasons for withdrawal Absent (n = 7)

Left school (n = 1) Suspended (n = 1)

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Table 9: Baseline characteristics of the S4HM study sample

Notes: a Abbreviations: y = years, SD = standard deviation b Socioeconomic position determined by population decile using Socio-Economic Indexes For Areas of relative socioeconomic disadvantage based on residential postcode (1 = lowest, 10 = highest). c Abbreviations: SD = standard deviation d Abbreviations: BMI = body mass index, SD = standard deviation

Characteristics Control (n = 155)

Intervention (n = 167)

Total (N = 322)

Age, y, mean, SD a 14.33 ± 0.5 14.47 ± 0.6 14.40 ± 0.6 Born in Australia, n Sex, n Female Male

155 (100%)

104 (67%) 51 (33%)

167 (100%)

107 (64%) 60 (36%)

322 (100%)

211 (66%) 111 (34%)

English language spoken at home, n 150 (97%) 166 (99%) 316 (98%) Cultural background, n

Australian 150 (97%) 166 (99%) 316 (98%) European 3 (2%) 1 (1%) 4 (2%) African 0 (0%) 0 (0%) 0 (0%) Asian 2 (1%) 0 (0%) 2 (0%) Middle Eastern 0 (0%) 0 (0%) 0 (0%) Other 0 (0%) 0 (0%) 0 (0%)

Socioeconomic position, n b 1-2 1 (1%) 12 (7%) 13 (4%) 3-4 34 (22%) 50 (30%) 84 (26%) 5-6 108 (70%) 80 (48%) 188 (58%) 7-8 7 (4%) 18 (11%) 25 (8%) 9-10 5 (3%) 7 (4%) 12 (4%)

Weight, kg, mean, SD c 51.20 ± 13.5 51.74 ± 12.5 51.49 ± 12.9 Height, cm, mean, SD c 156.08 ± 7.1 157.81 ± 7.4 156.98 ± 7.3 BMI, kg.m-2 d 20.85 ± 4.5 20.69 ± 3.9 20.73 ± 4.2 Weight status, n

Underweight 15 (10%) 22 (13%) 37 (12%) Healthy weight 83 (53%) 84 (50%) 167 (52%) Overweight 39 (25%) 36 (22%) 75 (23%) Obese 18 (12%) 25 (15%) 43 (13%)

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Notes: BMI = body mass index, CI = 95% confidence intervals, MVPA = moderate-to-vigorous physical activity 114 and 127 students from the control arm recorded valid accelerometer wear time at baseline on weekend days and weekdays, respectively 131 and 137 students from the intervention arm recorded valid accelerometer wear time at baseline on weekend days and weekdays, respectively 96 and 90 students from the control arm recorded valid accelerometer wear time at follow-up on weekend days and week days, respectively 84 and 85 students from the intervention arm recorded valid accelerometer wear time at follow-up on weekend days and week days, respectively Between group differences reported were adjusted for baseline values, sex and socio-economic status.

Table 10: Changes in primary and secondary outcomes in the S4HM intervention

Outcomes Baseline, Mean (CI) 6-month, Mean (CI) Change, Mean (CI) p Adjusted difference in change, Mean (CI)

p

Screen-time (minutes/day) Control Intervention

288.88 (197.18, 380.58) 319.54 (227.91, 411.18)

259.67 (167.95, 351.39) 269.06 (177.40, 360.72)

-29.21 (-55.53.72, -2.89) -50.48 (-76.07, -24.89)

0.030

< 0.001

-21.27 (-57.98, 15.44)

0.255

Psychological difficulties Control Intervention

15.39 (14.35, 16.42) 15.40 (14.38, 16.43)

14.98 (13.94, 16.01) 15.07 (14.04, 16.10)

-0.41 (-1.01, 0.19) -0.33 (-0.91, 0.25)

0.177 0.266

0.83 (-0.75, 0.92)

0.845

Psychological distress Control Intervention

18.53 (16.80, 20.27) 18.22 (16.50, 19.93)

17.75 (16.01, 19.48) 17.63 (15.91, 19.35)

-0.79 (-1.66, 0.08) -0.59 (-1.43, 0.26)

0.076 0.173

0.20 (-1.01, 1.41)

0.745

Physical self-concept

Control Intervention

27.87 (25.11, 30.63) 27.79 (25.04, 30.54)

27.39 (24.63, 30.15) 27.89 (25.13, 30.64)

-0.48 (-1.47, 0.50) 0.09 (-0.86, 1.05)

0.335 0.849

0.57 (-0.80, 1.94)

0.410

Well-being Control Intervention

45.72 (43.26, 48.18) 46.82 (44.37, 49.26)

45.98 (43.52, 48.44) 46.48 (44.03, 48.93)

0.26 (-0.99, -1.51) -0.34 (-1.55, 0.87)

0.685 0.584

-0.60 (-2.34, 1.15)

0.501

MVPA (minutes/day) Control Intervention

36.55 (32.58, 40.53) 36.84 (32.93, 40.76)

34.54 (30.28, 38.80) 30.62 (26.29, 34.94)

-2.01 (-5.82, 1.80) -6.23 (-9.95, -2.50)

0.299 0.001

-4.22 (-9.55, 1.11)

0.120

BMI, (kg.m-2)

Control Intervention

20.61 (19.07, 22.15) 20.50 (18.96, 22.04)

20.90 (19.36, 22.44) 20.76 (19.23, 22.30)

0.29 (0.13, 0.45) 0.27 (0.11, 0.43)

0.001 0.001

-0.02 (-0.25, 0.20)

0.840

BMIz Control Intervention

0.51 (0.16, 0.87) 0.50 (0.15, 0.86)

0.42 (0.07, 0.78) 0.50 (0.15, 0.85)

-0.09 (-0.34, 0.16) -0.00 (-0.25, 0.24)

0.473 0.972

0.09 (-0.26, 0.44)

0.623

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Table 11: Mediation analyses for the single mediator models adjusted for sex and SES

Hypothesised mediators

Treatment on mediators

Mediator on screen-time

Direct effect of treatment on screen-time

Mediated effect

A (SE)a p B (SE)b p C’ (SE)c p AB [95% CI]d

Autonomous motivation

0.30 (0.15) 0.040 -17.83 (4.26) < 0.001 -6.68 (23.03) 0.772 -5.40 [-12.04, -0.15]

Controlled motivation

0.32 (0.15) 0.029 -6.04 (6.28) 0.337 -9.64 (23.68) 0.684 -1.93 [-7.52, 2.10]

Amotivation -0.17 (0.15) 0.058 8.41 (4.44) 0.058 -10.51 (24.46) 0.667 -1.43 [-5.27, 1.11] Notes: SE = standard error, p = significance, CI = confidence intervals, RAI = relative autonomy index. Models are adjusted for clustering, baseline values, sex and SES. a A = estimate of standardised regression coefficient of treatment condition predicting change in hypothesised mediators at 6-months b B = estimate of standardised regression coefficient of the relationship between changes in hypothesised mediators at 6-months and changes in screen-time c C’ = estimate of standardised regression coefficient of treatment condition predicting screen-time with adjustment for mediator d AB = indirect or ‘mediated’ effect (product-of-coefficients estimate)

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Chapter 5

Longitudinal Associations between Screen-time and Mental Health in

Australian Adolescents

5.1 Preface

This chapter presents the protocols and a rationale for the S4HM cluster RCT, including

details on the study design, intervention components, methodology of assessments and

analytical procedures. This study was conducted to examine Secondary aim 3 of this

thesis.

The contents of this chapter will be published in Mental Health and Physical Activity.

Babic, M. J., Smith, J. J., Morgan, P. J., Lonsdale, C., Plotnikoff, R. C., & Lubans, D. R.

(2016). Longitudinal Associations Between Screen-time and Mental Health in Australian

Adolescents. Mental Health and Physical Activity (Under Review).

5.2 Abstract

Introduction

The primary aim was to examine longitudinal associations between changes in screen-

time and mental health outcomes among adolescents.

Methods

Adolescents (N = 322, 65.5% females, mean age = 14.4 ± 0.6 years) reported screen-time

and mental health at two time points over a school year. Multi-level linear regression

analyses were conducted after adjusting for covariates.

Results

Changes in total recreational screen-time (β = -.09 p = .048) and tablet/mobile phone use

(β = -.18, p < .001) were negatively associated with physical self-concept. Changes in

total recreational screen-time (β = -.20, p = .001) and computer use (β = -.23, p = .003)

were negatively associated with psychological well-being. A positive association was

found with television/DVD use and psychological difficulties (β = .16, p = .015). No

associations were found for non-recreational screen-time.

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Conclusion

Changes in recreational screen-time were associated with changes in a range of mental

health outcomes.

Key words: Screen-time, Mental Health, Adolescents, Longitudinal

5.3 Introduction

The World Health Organization define mental health as a state of well-being and effective

functioning in which an individual realises their abilities, is resilient to stresses of life and

is able to make a positive contribution to their community 60. Mental health problems (ill-

being) are conditions that negatively affect an individual’s mood, thinking and behaviour

(e.g., depression, anxiety, psychological difficulties and psychological distress) 61. These

disorders account for 45% of the global burden of disease among adolescents 354,

affecting one in five young people 355. Despite their prevalence and burden to society, the

underlying factors contributing to mental health problems among adolescents are poorly

understood 356. Given half of all cases of mental health problems develop by age 14 and

remain untreated until adulthood 356, there is an urgent need to identify modifiable

determinants of mental health during adolescence.

Excessive screen-time has emerged as a behaviour that may contribute to mental health

(both well-being and ill-being) during adolescence 357. The use of screens is often

necessary for educational purposes, and some recreational screen-time (i.e., using

television, DVD, computer, and tablet/mobile phone) may support young people’s well-

being 46. However, time spent using screens for leisure has dramatically increased in

recent decades 46, and now typically exceeds what can be considered ‘healthy’ use.

Indeed, the vast majority of adolescents (70-80%) exceed the recreational screen-time

guidelines of less than two hours per day 34,339,358.

Systematic reviews have concluded that excessive screen-time is negatively associated

with well-being and positively associated with ill-being in young people 71,78. More

specifically, studies have demonstrated that exposure to high levels of screen-time is

negatively associated with physical self-concept 359,360 and psychological well-being 361.

While other studies have found screen use is positively associated with depression,

anxiety 362,363, psychological difficulties 364,365, and psychological distress 364,366,367 among

adolescents.

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The evidence for the influence of screen-time on mental health in young people is

building, but has been limited by a number of methodological shortcomings. For

example, the majority of studies have been cross-sectional 352, involved the examination

of only one screen medium (usually television) 366, measured a narrow selection of mental

health indicators (typically depression) 363, and failed to statistically control for potential

confounding variables (e.g., adiposity and physical activity) 368,369. Developing a more

comprehensive understanding of the associations between screen-time and mental health

outcomes in adolescence is a critical step toward addressing the high prevalence of

mental health problems in this population.

The primary aim of the present study was to examine longitudinal associations

between changes in screen-time (total and device specific) and multiple indicators of

mental health (well-being and ill-being) among a sample of adolescents. We hypothesised

that changes in recreational screen-time will be: 1) negatively associated with changes in

physical self-concept and psychological well-being; and 2) positively associated with

changes in psychological difficulties, after controlling for potential confounders. A

secondary aim was to examine the association between non-recreational screen-time (i.e.,

for homework) and these mental health outcomes. We hypothesised that non-recreational

screen-time would not be associated with mental health outcomes.

5.4 Methods

5.4.1 Participants

Data for the present investigation were drawn from the Switch-off 4 Healthy Minds study.

A detailed description of the original study protocol and outcomes have been published

previously 343,370. Ethics approval for the study was obtained from the Human Research

Ethics Committees of the University of Newcastle, Newcastle-Maitland Catholic Schools

Office and the Diocese of Broken Bay. Schools, parents and participants provided

informed consent. Catholic secondary schools (N = 20) located in the Hunter region of

New South Wales, Australia were invited to participate and the first eight schools to

provide written consent were accepted. Students in Grade 7 at each of the study schools

completed an eligibility questionnaire, asking them to report their total time spent using

screen devices on a typical school day. Students failing to meet national screen-time

guidelines (i.e., > 2hours/day) were considered eligible and invited to participate. The

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first 40 students from each school to return signed consent letters were included. Time 1

(T1) data were collected at each school between April and June, 2014 and Time 2 (T2)

data (96% of the original sample) were collected between October and December, 2014.

5.4.2 Measures

All assessments were conducted at schools by trained research assistants. Basic

demographic information including: sex, country of birth, socio-economic status (SES)

based on household postcode, and the number of children who speak English at home

were collected (Table 12). Self-report measures were collected in exam-like conditions

using an online survey with Apple iPads and physical measures were conducted discretely

by a same-sex assessor.

5.4.2.1 Recreational screen-time

Screen-time was measured using the Adolescent Sedentary Activity Questionnaire

(ASAQ) 302. The ASAQ required participants to self-report the time spent using a variety

of screen devices on each day of the week, including weekends. Specifically, participants

were asked to report time spent using various screen devices, which included: television,

DVD, computer, and tablet/mobile phone for entertainment purposes on a usual school

week. The final item (i.e., tablet/mobile phone) was not included in the original ASAQ

instrument but was added to reflect current trends in adolescents’ use of screen media.

Non-recreational screen-time consisted of computer use for homework. Mean daily

screen-time was calculated by adding the time spent using each screen device on each day

of the week and dividing by the number of reported days (i.e., 7). The ASAQ has

previously reported acceptable test–retest reliability in girls (ICC = 0.70, 95% CI = 0.40

to 0.85), and boys (ICC = 0.84, 95% CI = 0.69 to 0.91) 302.

5.4.2.2 Mental health

The physical self-concept subscale from Marsh’s Physical Self-Description Questionnaire 211 was used to provide a measure of self-concept in the physical domain. Students

responded to six items on a 6-point scale (1 = ‘False’, to 6 = ‘True’) to how true each

statement was for them (e.g., ‘I am a physically strong person’). Higher scores on this

measure indicate better physical self-concept. The internal consistency of the physical

self-concept subscale among the present sample was high (Cronbach’s α = 0.95).

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Deiner and colleagues’ Flourishing Scale 344 was used to measure participants’

psychological well-being. The Flourishing Scale is a brief 8-item summary measure of a

person's self-perceived success in key areas such as engagement, relationships, self-

esteem, meaning, purpose and optimism 344. Participants were asked to respond using a 7-

point scale (1 = strongly disagree, to 7 = strongly agree) to each item (e.g., ‘I lead a

purposeful and meaningful life’). A summary score is calculated as the sum of each item

with a possible range of 8 to 56. A high score represents a person with many

psychological resources and strengths 344. The Flourishing Scale has shown acceptable

validity and reliability among adolescents 371.

To measure ill-being, participants completed the Strength and Difficulties

Questionnaire 312, which is a behavioural screening questionnaire divided into five

subscales: emotional symptoms, conduct problems, hyperactivity/inattention, peer

relationship problems and prosocial behaviour 313. Four of these are potential problems,

and one is strength-related (prosocial). Participants were asked to respond on a 3-point

scale (“not true” to “certainly true”) to each item (e.g., ‘I worry a lot’). Each subscale is

comprised of five items and the subscale score can range from zero to 10. The

‘difficulties’ summary score is calculated by summing the scores of the four ‘difficulties’

subscales (possible range = 0 - 40) (i.e. all subscales excluding pro-social). Each one-

point increase in the total difficulty score corresponds to an increase in the risk of mental

health disorders 312. The Strength and Difficulties Questionnaire has been validated in

youth 11 years or over 314.

5.4.2.3 Adiposity

Weight was measured without shoes, in light clothing using a portable digital scale

(Model no. UC-321PC, A&D Company Ltd, Tokyo Japan) and height was recorded using

a portable stadiometer (Model no. PE087, Mentone Educational Centre, Australia). Body

mass index (BMI) was calculated using the standard equation (weight [kg] / height [m]2)

and BMI z-scores were calculated using the ‘LMS’ method 317. All assessments were

conducted by trained, same sex research assistants.

5.4.2.4 Physical activity

Physical activity was assessed over seven days using GENEActiv (Model GAT04,

Activinsights Ltd, Cambridgeshire England) wrist worn accelerometers, and activity

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intensity was determined using existing cut-points 315. Valid wear time was defined as a

minimum of ten hours per day on at least three days. GENEActiv wrist worn

accelerometers have displayed acceptable intra-and inter-instrumental reliability and

provide a valid and reliable estimate of physical activity in young people 315. Non-wear

time was defined as 30 minutes of consecutive zeros. Students providing valid

accelerometer wear time are reported in Table 14.

5.4.2.5 Statistical analysis

All analyses were performed using Mplus, version 7.11 for Windows (Muthén & Muthén,

Los Angeles, CA) and statistical significance was set at p < 0.05. Multi-level linear

regression analyses were used to assess associations between screen-time (total and

device specific) at T2 and the presence of mental health at T2. Analyses were adjusted

for: T1 measures of the exposure and outcome variables, group allocation, school

clustering, sex, SES, T1 BMI and T1 physical activity. Previous studies have

demonstrated that sex 372, SES 373, BMI and physical activity 290 are associated with

screen-time in youth. Participants with missing data were not included in the sensitivity

analyses, and results were not affected due to high retention at T2.

5.5 Results

Eligibility screening was completed by 1154 students, of whom 935 (81%) were

considered eligible. Ninety-six percent of individuals were retained across the study

(308/322). Characteristics of the study sample are presented in Table 12.

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Table 12: Characteristics of the study sample

Characteristics Total (N = 322) Age, y, mean, SD a 14.40 ± 0.6 Born in Australia, n Sex, n

322 (100%)

Female 211 (66%) Male 111 (34%) English language spoken at home, n 316 (98%) Cultural background, n

Australian 316 (98%) European 4 (2%) African 0 (0%) Asian 2 (0%) Middle Eastern 0 (0%) Other 0 (0%)

Socioeconomic position, n b 1-2 13 (4%) 3-4 84 (26%) 5-6 188 (58%) 7-8 25 (8%) 9-10 12 (4%)

Weight, kg, mean, SD c 51.49 ± 12.9 Height, cm, mean, SD c 156.98 ± 7.3 BMI, kg.m-2 d 20.73 ± 4.2 Weight status, n

Underweight 37 (12%) Healthy weight 167 (52%) Overweight 75 (23%) Obese 43 (13%)

Notes: a Abbreviations: y = years, SD = standard deviation b Socioeconomic position determined by population decile using Socio-Economic Indexes For Areas of relative socioeconomic disadvantage based on residential postcode (1 = lowest, 10 = highest). c Abbreviations: SD = standard deviation d Abbreviations: BMI = body mass index Descriptive statistics of screen-time and mental health (at both time points) by sex,

including means and standard deviations are reported in Table 13.

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Table 13: Levels of screen-time and mental health across time points in the total sample and by sex

Note: SD = Standard deviation; T1 = Time 1, T2 = Time 2; all screen-time measured in minutes/day

Outcome T1 T2 Recreational screen-time

All mean (SD) Females mean (SD)

Males mean (SD)

All mean (SD) Females mean (SD)

Males mean (SD)

Total screen-time 302.60 (194.80) 290.09 (194.84) 326.37 (193.38) 266.55 (195.10) 258.57 (209.35) 281.54 (164.95) Television/DVD 141.83 (95.20) 139.84 (94.92) 145.60 (96.06) 124.52 (102.97) 121.86 (107.49) 129.53 (94.17) Personal computer use 38.69 (70.10) 26.32 (46.72) 62.20 (96.55) 33.56 (66.94) 23.56 (57.11) 52.35 (79.24) Tablet/mobile phone use 122.08 (101.30) 123.92 (107.18) 118.57 (89.40) 108.46 (92.08) 113.16 (97.86) 99.65 (79.80) Non-recreational screen-time Homework

42.43 (36.09)

43.34 (36.42)

40.69 (35.56)

37.75 (37.35)

37.12 (32.65)

38.93 (45.00)

Mental health outcomes Physical self-concept 27.63 (7.41) 27.44 (7.66) 27.97 (6.94) 27.34 (8.23) 26.72 (8.68) 28.50 (7.20) Psychological well-being 46.61 (7.78) 47.33 (7.55) 45.24 (8.07) 46.57 (8.15) 47.20 (7.97) 45.39 (8.40) Psychological difficulties 15.46 (4.05) 15.64 (3.88) 15.11 (4.34) 14.98 (4.64) 15.01 (4.36) 14.92 (5.15)

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Table 14 reports the associations between screen-time and mental health indicators. Mean

screen-time and mental health scores at both time points by sex are presented in Figures 8

and 9 respectively.

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Table 14: Associations of screen-time (T2) and mental health (T2) for the total sample over the first year of secondary school

Screen-time at (T2) Mental health (T2) Model 1 B (SE) Model 1 β (SE) p value R2

Total recreational screen-time Physical self-concept -0.003 (0.002) -0.09 (0.046) .048 0.563 Psychological well-being -0.008 (0.002) -0.20 (0.059) .001 0.357 Psychological difficulties 0.004 (0.002) 0.16 (0.090) .087 0.418 Television/DVD Physical self-concept 0.002 (0.003) 0.03 (0.039) .471 0.545 Psychological well-being -0.007 (0.007) -0.10 (0.088) .257 0.340 Psychological difficulties 0.007 (0.003) 0.16 (0.064) .015 0.417 Personal computer use Physical self-concept -0.004 (0.007) -0.04 (0.062) .565 0.549 Psychological well-being -0.025 (0.008) -0.23 (0.081) .003 0.361 Psychological difficulties 0.009 (0.005) 0.14 (0.072) .054 0.417 Tablet/mobile phone use Physical self-concept -0.015 (0.003) -0.18 (0.040) < .001 0.585 Psychological well-being -0.008 (0.005) -0.11 (0.056) .078 0.347 Psychological difficulties 0.001 (0.004) 0.02 (0.089) .799 0.405 Homework Physical self-concept 0.019 (0.012) 0.09 (0.057) .124 0.551 Psychological well-being -0.002 (0.015) -0.01 (0.074) .874 0.335 Psychological difficulties -0.001 (0.023) -0.01 (0.182) .968 0.402

Note: T2 = Time 2, B = unstandardised regression coefficient, β = standardised regression coefficient, R2 = coefficient of determination, SE = standard error. Results are adjusted for: baseline values, group allocation, clustering, sex, SES, T1 measurements, BMI and physical activity. 245 and 264 students recorded valid accelerometer wear time at Time 1 on weekend days and weekdays, respectively. 180 and 175 students recorded valid accelerometer wear time at Time 2 on weekend days and weekdays, respectively.

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Figure 8: Mean screen-time usage across time points in the total sample and by sex

0

50

100

150

200

250

300

350M

inut

es/d

ay

Screens

Screen-time among all participants

T1 T2

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Figure 9: Mean mental health scores across time points in the total sample and by sex

0

5

10

15

20

25

30

35

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50

Scal

e sc

ores

Mental health outcomes

Mental health among all participants

T1 T2

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5.5.1 Recreational screen-time and mental health outcomes

Changes in total recreational screen-time (β = -.09, p = .048) and tablet/mobile phone use

(β = -.18, p < .001) were negatively associated with physical self-concept. Changes in

total recreational screen-time (β = -.20, p = .001) and computer use (β = -.23, p = .003)

were negatively associated with psychological well-being. A positive association was

found between television/DVD use and psychological difficulties (β = .16, p = .015).

5.5.2 Non-recreational screen-time and mental health outcomes

No associations were found between any of the indicators of mental health and changes in

screen use for homework.

5.6 Discussion

The primary aim of this study was to examine associations between changes in

recreational screen-time and changes in mental health outcomes among a sample of

adolescents in the first year of secondary school. Significant associations were found

between changes in total and device-specific recreational screen-time and a range of

mental health outcomes. No clear device-specific trends emerged. There was no

association between non-recreational screen-time and mental health outcomes.

Changes in both total recreational screen-time and tablet/mobile phone use were

negatively associated with changes in physical self-concept. Previous cross-sectional

studies among adolescents have reported negative associations between screen-time

(television/DVD and video games use) and physical self-concept 359 as well as physical

attractiveness 360. However, no significant associations were found in a cross-sectional

study examining the relationship between screen-time (across multiple devices) and

physical self-concept in a sample of adolescent girls from schools located in low income

communities 374. It is not clear how the use of screen-based devices might influence

physical self-concept, but it is likely to be a complex process. It is possible that the

emerging influence of social media technology commonly used by adolescents on

tablets/mobile phones (such as Facebook, Instagram, Snapchat and DailyBooth) may

explain the adverse associations in physical-self-concept observed in the current study.

Social media typically involves the sharing of images and photos, which may encourage

adolescents to compare themselves with their peers 94. As a consequence of engaging with

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these social media platforms, discrepancies between broadcasted ideals and self-

perceptions of adolescents may have negative mental health consequences due to inflated

social pressure to conform, feelings of body inadequacy 95, and unhealthy changes in

behaviour.

Changes in total recreational screen-time and computer use were negatively associated

with psychological well-being. Our findings are consistent with the recent ATLAS

school-based obesity prevention program for adolescent boys, which found that

reductions in recreational screen-time partially mediated the effect of the intervention on

well-being assessed using the same measure 353. Notably, computer use may negatively

impact adolescents’ psychological well-being through a number of mechanisms. One

such potential mechanism relates to cyberbullying (i.e., harassment through technology

via chat forums or online gaming). Previous studies have reported increased negative

feelings (e.g., helplessness) 96, levels of depression, social dissatisfaction, withdrawal 97,

and lower levels of self-esteem 98 in response to cyberbullying. Alternatively, as most

adolescents use computers and are connected to the internet 375, compulsive internet use

may be another mechanism responsible for the present findings. An increasing number of

adolescents experience difficulties in regulating internet use 361,376, and compulsive

internet users are more depressed, stressed, lonely, often have lower self-esteem 361,376

and demonstrate lower psychological well-being 361,376.

Associations between changes in screen-time and psychological difficulties were

inconclusive and only television/DVD use was found to be significantly associated with

this outcome. Prior cross-sectional 377 and longitudinal 367 studies have demonstrated

exposure to screen-time may be associated with analogous psychiatric difficulties.

Comparably, numerous studies have produced inconsistent findings 364,367, or report no

association 378 in young people. It is possible the varying findings may be due to

differences in the measurement of screen-time (in addition to the combining of time

engaged in television and DVD viewing) and/or the duration of follow-up periods.

Establishing causal mechanisms responsible for impairments presents a challenge, as

television/DVD use may influence mental health in a variety of ways.

Previous studies suggest elevated levels of psychological distress can lead to changes

in behaviour in adolescents. For example, studies have shown that television use

(especially if the content is violent) may contribute to conduct problems 364,365, and may

119

predict aggression and attention problems 338,379. In addition, the nature of screen viewing

(how adolescents watch, what they watch, and with whom) may have important

implications. Television/DVD use may impact on excitement, concentration and attention

levels 364; contribute to feelings of loneliness, anxiety and unhappiness as they are often

viewed in solitude 380-382; and reduce prosocial behaviour (associated with reduced levels

of empathy through exposure to violent content) 383. Alternatively, the negative effect of

screen-time on mental health may be due to the displacement of opportunities to

participate in activities that promote mental health 82,293. Such activities may include sleep 384, physical activity 385 or social activities 293.

The current study builds on previous research by examining associations between

changes in multiple screen devices and indicators of well-being and ill-being during the

first year of secondary school. Strengths of this study include the high participant

retention, robust multi-level modelling, use of objectively measured physical activity, and

adjustment for relevant covariates However, there are some limitations that should be

noted. Although the associations between screen-time and mental health were statistically

significant, the magnitude of effects were small (Range -.23 to .16). It has previously

been suggested that a minimum effect size of .2 is required for an association to be

considered meaningful 386. Considering the associations observed in the present study

were typically below this threshold, it is possible our findings are trivial. However, the

magnitude of effects may reflect the fact that participants had relatively good mental

health, and/or other psychological resources to buffer them from mental health problems

(e.g. supportive family environment, social capital etc.). Alternatively, the adverse effects

of increasing screen use may accumulate over time and stronger associations might be

seen with longer duration follow-up.

In addition, participants were predominantly female and ethnically homogeneous,

limiting generalizability of findings. Recreational screen-time was measured by self-

report which remain a significant challenge in accurately assessing sedentary behaviour

due to the possibility of recall and social desirability biases 387. However, there are also

few objective measures of screen-time that can feasibly be used for research. The current

study focused solely on the volume of daily screen-time and did not measure the content

being viewed on the various devices examined. It remains unknown whether it is the

content or volume of screen-based recreation that explains associations between screen

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use and mental health, and our findings do not provide evidence of causality. It is also not

possible to conclusively attribute all of the negative mental health effects reported in this

study to screen-time since sleep patterns and social influences were not assessed.

5.7 Conclusion

This study makes a unique contribution by examining how changes in total and device-

specific screen-time relate to changes in a variety of mental health indicators in

adolescents during the first year of secondary school. Significant associations were found

between changes in total and device-specific recreational screen-time and mental health

outcomes, no clear device-specific trends emerged. Our findings, although important,

identify the need for further research examining how different devices impact on mental

health, relative to their multiple purposes (i.e., gaming, communication, education).

Further longitudinal and experimental studies are needed to improve our understanding of

the casual mechanisms that explain how screen-time impacts upon mental health

outcomes.

5.8 Competing interests

The authors have no competing interests to declare.

5.9 Author contributions

All authors contributed to developing, editing, and approving the final version of the

paper.

5.10 Acknowledgements

This project is funded by a Hunter Medical Research Institute (HMRI) grant. DRL is

funded by an Australian Research Council Future Fellowship. RCP is funded by

Fellowships from the National Health and Medical Research Council of Australia.

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Chapter 6

Thesis Discussion and Conclusion

6.1 Overview

The primary aim of this thesis was to:

1. Evaluate the effects of the S4HM intervention by examining outcomes and

potential mediators in a cluster RCT (Chapter 4).

The secondary aims were to:

1. Review the evidence of associations between physical activity, screen-time and

mental health outcomes in adolescents (Chapters 1 and 2).

2. Provide a rationale and present the study protocol for the S4HM intervention

(Chapter 3).

3. Examine longitudinal associations of changes between screen-time and mental

health outcomes in adolescents (Chapter 4).

As this thesis is presented as a series of publications, key findings for each aim have been

discussed and compared to the current literature in previous chapters. Thus, the purpose

of this final chapter is to synthesise key findings, explore strengths and limitations of the

conducted work and identify implications for future research. This chapter corresponds

with Chapters 1–5 and encompasses the primary and secondary aims. The discussion is

organised into three parts:

• Part 1: Associations between Physical Activity, Screen-time and Mental Health in

Adolescents;

• Part 2: Rationale and Evaluation of the S4HM Screen-time Reduction Intervention;

and,

• Part 3: Longitudinal Associations between Screen-time and Mental Health

Outcomes.

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Part 1

Associations between Physical Activity, Screen-time and Mental Health in

Adolescents

A key objective of the literature and systemic review was to improve understanding of the

associations between physical activity, screen-time and mental health outcomes among

adolescents. Chapter 1 provided a summary of the inter-relationships between physical

activity, screen-time and mental health. Although studies among adolescents have found

participation in physical activity to be associated with decreased anxiety and

depression 388,389, the interaction or possible associations with screen-time was seldom

examined. Therefore, it remains unclear if the association of screen-time exposure on

psychological function is caused by a lack of physical activity, or exposure to recreational

screen-time, or an interaction between the two. Moreover, previous research has focused

on indicators of mental ill-being, such as depression and anxiety 390 However, the term

“mental health” embodies a broad concept which includes a range of constructs relating

to well-being (e.g., self-esteem, resilience) and ill-being (e.g., depression, anxiety).

Therefore, to better inform efforts to promote mental health among young people, further

examination of associations between physical activity, screen-time and positive indicators

of mental health is required. A preliminary search of multiple databases (MEDLINE,

CINAHL, SPORTDiscus, ERIC, Web of Science and Scopus) identified too few studies

examining associations between screen-time and physical self-concept to warrant a

systematic review and meta-analysis. Alternatively, a systematic review focused on the

associations between physical activity and physical self-concept (including sub domains)

in young people was conducted.

6.2 Overview of findings

Review the evidence of associations between physical activity, screen-time and mental

health outcomes in adolescents (Secondary aim 1).

The findings in Chapter 1 suggest levels of participation in physical activity and screen-

time operate independently and synergistically to increase risk of mental health problems

in adolescents 362,391. Specifically, evidence suggests there is a positive effect of physical

activity on mental health and an inverse association between screen-time and mental

health in young people 392. Additional research examining the influence of screen-time on

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well-being in adolescence is warranted, as the majority of previous research has focused

on indicators of ill-being (e.g., depression).

Results from studies and a meta-analysis in young people, described in Chapter 2,

revealed a significant association between general physical self-concept, perceived

competence, perceived fitness and physical activity. The effect sizes observed in the

review demonstrated that perceived competence had the strongest association with

physical activity, followed by perceived fitness, general physical self-concept and

perceived physical appearance 393. Effect sizes were slightly smaller but are consistent

with previous reviews examining the effects of exercise on self-esteem in young people 67

and adults 271. Sex was a significant moderator for general physical self-concept, with

results strongest in boys, followed by girls and mixed. Additionally, age was a significant

moderator for perceived appearance and perceived competence. No significant

moderators were found for perceived fitness. Notably, due to the small number of

experimental studies, it was not possible to determine if findings from experimental

studies were significantly different from the cross-sectional and longitudinal studies.

6.3 Strengths and limitations

The systematic review included a number of important strengths. One such strength was

the comprehensive search strategy utilised, which covered six databases with no date

restrictions. In addition, the conduct and reporting of the systematic review was

performed and adhered to the PRISMA statement 212,224. Such procedures were followed

to ensure that findings and recommendations were produced through a rigorous and

transparent process that could be replicated. Broad inclusion criteria ensured a strong

focus was placed on the scope of the included studies. In addition, a risk-of-bias

assessment was conducted to determine the quality of evidence. After a comprehensive

examination of the methodological quality, a quantitative synthesis was conducted using

Comprehensive meta-analysis software version 2 (Biostat, Englewood). As no previous

review had systematically evaluated the evidence for the association between physical

activity and physical self-concept in children and adolescents, the overall novelty of the

work conducted is a further strength. Despite such strengths, a number of limitations

should be noted.

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A main limitation of the systematic review relates to the assessment of physical self-

concept and subdomains. To allow for the aggregation of findings, scales/questionnaires

assessing similar constructs of different names were combined in the meta-analyses.

Specifically, these were not consistent across studies and as a result were categorised by

the authors of the review. Another limitation noted was the exclusion of unpublished

studies. Furthermore, studies were required to be published in English. Notably, most

studies in this field are cross-sectional; as such, they do not provide the same level of

evidence generated from experimental studies. Due to the small number of experimental

studies, it is not possible to determine whether their findings are significantly different

from cross-sectional and longitudinal studies. Finally, as the majority of work was

conducted in the United States, findings provided little cross-cultural variability.

6.4 Recommendations for practice and research

6.4.1 For practice

The association between physical activity and physical self-concept is likely to be bi-

directional. Therefore, increasing young people’s physical perceptions may increase their

activity levels, and participation in activity may further enhance self-perceptions.

Recommendations for practice include the following:

• Teachers and parents are encouraged to implement strategies to enhance physical

self-perceptions (both general and subdomains) and in particular, perceived

competence. Strategies should facilitate outcomes such as moral and social

development, motor/skill competence and positive self-perceptions 394. As such, a

supportive environment at school and home where students have opportunities for

success in optimally challenging physical activity experiences may nurture feelings

of competence and increase physical activity.

• Psychological effects of perceiving oneself as competent, independent of actual

competence, may also have a tangible impact on adolescents’ desire to engage in

physical activity.

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• To support adherence for adolescents with low levels of perceived competence, the

promotion of non-competitive and lifelong physical activities may be a more

attractive alternative than traditional competitive team sports. Such suggestions are

made in light of evidence that demonstrates that team sport participation declines

during adolescence 21. Lifelong physical activities which include swimming,

cycling and walking may benefit young people as they do not require competence

in fundamental and sports-specific movement skills 274, and may easily be carried

into adulthood.

• As both athletic competence and physical appearance are viewed as key social

determinants of physical activity, findings suggest that peer relationships are key

elements to target in young people 395. Recently, physical activity has been

positively associated with peer relationships 392; thus it is likely that social effects

could serve as motivators. Additional intervention efforts should focus on

developing individual competence through adjustments of peer interactions; that is,

attempt to capitalise on the protective factors associated with support from peers.

6.4.2 For future research

This review has highlighted important gaps in the current literature. The following

recommendations for research are provided to progress the field:

• Additional evidence is required from methodologically-rigorous experimental

studies, to enable a better understanding of whether general physical self-concept

and subdomains are outcomes, mediators or moderators of participation in physical

activity.

• Future research in this area should also explore the interactions between physical

activity and physical self-concept, to determine whether changes in self-concept

are only commensurate with physical activity changes in the context of an

intervention.

• It may be more challenging to improve the physical self-concept of girls and

younger children. Results suggest that future school-based interventions should

target these sub-groups of the population to provide the appropriate support for

being physically active.

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• There is a need to capture adolescents’ perceptions of their abilities in non-

traditional physical activities. As current scales only explore traditional team

sports, new perceived competence scales are required to explore lifelong physical

activities.

• Further research is needed to explore the direction and strength of the relationship

between physical self-perceptions and time spent engaged in total and device-

specific recreational screen-time.

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Part 2

Rationale and Evaluation of the S4HM Screen-time Reduction Intervention

A review of studies in Chapter 1 highlighted important gaps to be addressed to progress

the field. First, an examination of the association between screen-time and positive

indicators of mental health warrants investigation. Second, physical self-concept and, in

particular, perceived competence may be essential components to target in future

interventions. Third, there is a clear lack of experimental evidence for interventions to

reduce screen-time and improve mental health in adolescents. These evidence gaps

provided the rationale for the development, implementation and evaluation of the S4HM

intervention. Specifically, the intervention was designed to reduce screen-time and

improve mental health (i.e., physical self-concept) through strategies/theories

underpinned by competence. A description of the study design, the S4HM intervention

components and outcomes were described in detail in Chapters 3 and 4. In addition to the

study protocol (Chapter 3), this component included investigations into the Secondary

aim 2, Primary aim 1 and the Primary hypothesis. The main objective of Chapter 3 was to

evaluate the efficacy of S4HM.


Evaluate the effects of the S4HM intervention by examining outcomes and potential

mediators in a cluster RCT (Primary aim 1).

Chapter 4 presented the primary and secondary outcomes of the S4HM intervention. As

reported, there were no intervention effects for recreational screen-time, mental health,

physical activity or BMI. Based on these findings, Primary hypothesis 1 was not

supported. Although the between-group differences for recreational screen-time was not

statistically significant, at post-intervention there was a greater reduction observed in the

intervention group (-50 minutes/day versus -29 minutes; p <.05 for within group change

for intervention and control). Despite substantial reductions in screen-time in both groups

over the six-month study period, no significant group-by-time effects were observed in

psychological difficulties, psychological distress, physical self-concept or psychological

well-being.

A product-of-coefficients test was used to explore the potential mediating effects of

motivation (i.e., autonomous, controlled and amotivation) on changes in screen-time. It is

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to be noted that product-of-coefficients tests can be used to test mediation effects in the

absence of a significant intervention effect. Significant intervention effects for

autonomous (B = 0.30, p = 0.040) and controlled (B = 0.32, p = 0.029) motivation were

found, whereas the effects for amotivation (B = -0.17, p = 0.058) were non-significant.

Most importantly, significant associations were observed between changes in autonomous

motivation and changes in screen-time (B = -17.83, p < 0.001). In the multiple-mediator

model, changes in autonomous motivation was found to mediate the effect of the

intervention on screen-time (AB = -5.61, 95% CI = -12.59 to -0.10). Consequently, one of

the three hypothesised mediation pathways was supported.

6.5.1 Strengths and limitations

Only one previous study has used SDT in an intervention to reduce recreational screen-

time in adolescents (ATLAS 147,148), thus the findings presented in this thesis add to the

limited evidence base. The S4HM intervention was adequately powered and recruited 322

adolescents and retention was high, as 96% of participants completed the follow-up

assessments. Finally, the mediator analysis provided a better understanding of the causal

pathways for motivation on screen-time, which had not been previously explored within

the literature.

Despite these strengths, there are some limitations that should be noted. First, the use

of a questionnaire to assess screen-time is a limitation due to recall bias and social

desirability. It is possible that the examination of screen-time did not account for multiple

screen use simultaneously, as the ASAQ uses the sum of time reported using individual

screen devices to calculate total screen-time 302. Second, compliance to the accelerometer

monitoring protocol was poor, which is consistent with previous research with

adolescents 306,396. Third, insufficient contact and engagement with the participants’

parents may have influenced outcomes, as only 39 parents (approximately 25%)

completed the evaluation questionnaire, suggesting a lack of parental involvement.

Fourth, the elapsed time between measurements may have been too short to detect

significant changes in the S4HM participants. Finally, the study population was relatively

affluent and included a limited number of adolescent males (34.5%), thus potentially

limiting the generalisability of the findings to other populations.

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6.5.2 Recommendations for practice and research

Based on the available evidence, there is a clear need for effective and scalable

interventions designed to reduce recreational screen-time during adolescence.

Recommendations for schools, parents and future research are provided below.

6.5.2.1 For schools and parents

The following recommendations are provided for schools and parents when focusing on

screen-time reduction in adolescents:

• School-based educational programs may benefit from targeting autonomous

motivation to limit screen-time. For example, students should be provided with

choice when propositions are made to engage in healthier alternatives at school

during breaks (walking, sports, reading or socialising).

• As parental engagement appears a continual problem for interventions; novel ways

to engage, support and maintain parental involvement are also required to assist

change at the family level. Therefore, parental engagement must be planned for and

embedded within interventions. Maintaining frequent contact with parents through,

for example, social media “group” pages may promote engagement and additional

access to support through discussion in the group network.

• Parents are encouraged to promote their children’s volitional functioning to reduce

screen-time by taking their child’s frame of reference, providing a personally

relevant rationale when introducing rules, and by allowing choice whenever

possible 397. Additional household strategies include setting rules (i.e., balance),

prioritising responsibilities to use media mindfully (i.e., educational use, not

endless internet searching) and encouraging socialisation in the real-world (i.e.,

help connect off screen for development).

• Considerations should be made for parental support that increases effectiveness of

screen-time interventions 181. Proposed key features of effective parental support

strategies include the establishment of mutual priorities, on-going monitoring and

evaluation of the intervention’s impact, collaboration and engagement (i.e.,

involvement in strategies to adjust behaviours with sustained support, resourcing

and training).

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6.5.2.2 For future research

Consistent with global trends 33,34,36,398, the majority of students (83%) exceeded national

screen-time guidelines. As such, there is a clear need for “scalable” interventions to

reduce screen-time in adolescent populations. Recommendations for future research are

provided below:

• The modification of the ASAQ to include mobile phone and tablet use was

important, as such devices constituted the second largest duration of screen-time.

With many existing questionnaires failing to assess the multiple forms of

recreational screen-time popular among young people, amendments and validation

studies are required. Screen-time questionnaires also need to evolve to assess and

distinguish simultaneous screen-use (using more than one device at once).

• As little is known regarding the mechanisms of screen-time behaviour change,

future studies should conduct mediation analyses.

• As researchers are faced with challenges engaging parents in interventions, it is

also recommended that future interventions conduct qualitative research to identify

potential barrier solutions.

• Although there is emerging evidence suggesting that excessive screen-time during

youth may lead to poor mental health outcomes 351,352, this understanding has not

been clearly established. To better understand such associations, additional

longitudinal and experimental studies are required 399 to elucidate potential

directions of causality 85.

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Part 3

Longitudinal Associations between Screen-time and Mental Health Outcomes

The final component of this thesis examined the longitudinal associations between

changes in screen-time (total and device-specific) and changes in mental health

(positive/negative well-being) during the first year of secondary school (Chapter 5 and the

Secondary aim 3).


Examine longitudinal associations of changes between screen-time and mental health

outcomes in adolescents (Secondary aim 3).

Findings from this study suggest that reductions in screen-time may improve mental

health outcomes in adolescents. Total recreational screen-time and tablet/mobile phone

use was negatively associated with changes in physical self-concept. In addition, changes

in total recreational screen-time and computer use were negatively associated with

psychological well-being. Changes in television/DVD use were positively associated with

changes in psychological difficulties. No significant associations were found for changes

in non-recreational screen-time.

6.6.1 Strengths and limitations

The strengths of this study include the longitudinal design, high rate of participant

retention, robust multi-level modelling, objectively measured physical activity, and the

use of validated measures of screen-time and mental health. However, several limitations

should also be acknowledged. First, participants were predominantly female and the study

sample was ethnically homogeneous, which may limit the generalisability of the findings

to other populations. Second, the assessment of total and device-specific screen-time

using a self-report measure is a study limitation. Notably, the ASAQ, like most screen-

time questionnaires, does not assess purpose or content of screen-based recreation.

Therefore, it is not possible to attribute all negative mental health effects to screen-time

conclusively since negative media, disturbed sleeping patterns and social influences could

be influential. Finally, due to measurement bias, it is not possible to determine if screen-

time actually reduced over the study period.

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6.6.2 Recommendations

The findings provide some support that strategies to reduce screen-time may improve

mental health in adolescents. The following recommendations for schools, parents and

future research are thus provided:

6.6.2.1 For schools and parents

Schools, teachers, and educational authorities are encouraged to consider the following:

• Limiting of adolescents’ recreational screen-time during the school day and in the

after-school period.

• Parents should attempt to monitor what content their children are exposed

to/engage in, as this may potentially exacerbate negative inferences. Specifically, it

may be important to limit exposing teens or preteens too early to the effects of

social media/online gaming, as this may negatively influence their mental health

via self-image and cyberbullying.

• As a part of health lessons, schools are encouraged to teach behavioural skills (e.g.,

goal setting and self-monitoring) regarding screen-time minimisation.

6.6.2.2 For future research

Overall, the study findings support the design and delivery of screen-time reduction

interventions targeting adolescents. The following suggestions are provided for future

research:

• Future studies are encouraged to explore the potential mechanisms that explain the

negative effect of recreational screen-time on mental health in adolescents. For

example, does lack of sleep mediate the relationship between screen-time and

mental health in young people?

• Understanding what type of screen may have a more influential effect on mental

health would allow for more focused intervention strategies and would be of great

interest to parents, policy makers and guideline developers.

• Further examination and use of more precise metrics of screen viewing are

required. Specifically, further research is needed, with more precise estimates, on

how screens are being used (i.e., in which context and what is the content).

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6.7 Conclusion

The S4HM intervention was designed to reduce screen-time among adolescents who

reported exceeding screen-time recommendations. Despite being theoretically-driven, the

S4HM intervention was unsuccessful in reducing recreational screen-time in comparison

to a control group. Significant intervention effects were observed for participants’

autonomous motivation to limit screen-time, which mediated changes in screen-time.

This finding provides support for intervention strategies that enhance autonomous

motives for behaviour change.

Both physical inactivity and recreational screen-time are risk factors for poor mental

health during adolescence 362,391. Mental health problems often emerge during

adolescence, and account for 45% of the global burden of disease among adolescents 354,

affecting one in five young people 355. Despite reductions in screen-time, the S4HM

intervention did not have a significant effect on mental health. While previous studies

have demonstrated the positive effect of physical activity interventions on mental health

outcomes in adolescents 65, it remains unknown whether reducing screen-time can

enhance well-being and alleviate ill-being in this population. Additional longitudinal and

experimental research is needed to understand the effects of total and device specific

recreational screen-time on mental health in young people. Improvements in measuring

screen-time and additional mediation analyses may expand the limited evidence-base and

provide insight into effective strategies.

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Appendices

Appendix 1: PRISMA statement Appendix 2: Ethics approval Appendix 3: Principal information statement Appendix 4: Parent/student information statement Appendix 5: Principal consent forms Appendix 6: Parent consent forms Appendix 7: Student eligibility screening questionnaire Appendix 8: S4HM Study protocol Appendix 9: Recording sheet Appendix 10: S4HM study questionnaire Appendix 11: Student evaluation questionnaire Appendix 12: Parent evaluation questionnaire

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Appendix 1: PRISMA checklist

Section/topic # Checklist item Reported

on page #

TITLE

Title 1 Identify the report as a systematic review, meta-analysis, or both.

ABSTRACT

Structured summary 2 Provide a structured summary including, as applicable: background; objectives; data sources; study

eligibility criteria, participants, and interventions; study appraisal and synthesis methods; results;

limitations; conclusions and implications of key findings; systematic review registration number.

INTRODUCTION

Rationale 3 Describe the rationale for the review in the context of what is already known.

Objectives 4 Provide an explicit statement of questions being addressed with reference to participants,

interventions, comparisons, outcomes, and study design (PICOS).

METHODS

Protocol and 5 Indicate if a review protocol exists, if and where it can be accessed (e.g., web address), and, if

136

registration available, provide registration information including registration number.

Eligibility criteria 6 Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g.,

years considered, language, publication status) used as criteria for eligibility, giving rationale.

Information sources 7 Describe all information sources (e.g., databases with dates of coverage, contact with study authors

to identify additional studies) in the search and date last searched.

Search 8 Present full electronic search strategy for at least one database, including any limits used, such that

it could be repeated.

Study selection 9 State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and,

if applicable, included in the meta-analysis).

Data collection

process

10 Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate)

and any processes for obtaining and confirming data from investigators.

Data items 11 List and define all variables for which data were sought (e.g., PICOS, funding sources) and any

assumptions and simplifications made.

Risk of bias in

individual studies

12 Describe methods used for assessing risk of bias of individual studies (including specification of

whether this was done at the study or outcome level), and how this information is to be used in any

data synthesis.

Summary measures 13 State the principal summary measures (e.g., risk ratio, difference in means).

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Synthesis of results 14 Describe the methods of handling data and combining results of studies, if done, including measures

of consistency (e.g., I2) for each meta-analysis.

Page 1 of 2

Section/topic # Checklist item

Reported

on page

#

Risk of bias across

studies

15 Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication

bias, selective reporting within studies).

Additional analyses 16 Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if

done, indicating which were pre-specified.

RESULTS

Study selection 17 Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons

for exclusions at each stage, ideally with a flow diagram.

Study characteristics 18 For each study, present characteristics for which data were extracted (e.g., study size, PICOS,

follow-up period) and provide the citations.

Risk of bias within

studies

19 Present data on risk of bias of each study and, if available, any outcome level assessment (see item

12).

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Results of individual

studies

20 For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data

for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot.

Synthesis of results 21 Present results of each meta-analysis done, including confidence intervals and measures of

consistency.

Risk of bias across

studies

22 Present results of any assessment of risk of bias across studies (see Item 15).

Additional analysis 23 Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression

[see Item 16]).

DISCUSSION

Summary of evidence 24 Summarise the main findings including the strength of evidence for each main outcome; consider

their relevance to key groups (e.g., healthcare providers, users, and policy makers).

Limitations 25 Discuss limitations at study and outcome level (e.g., risk of bias), and at review-level (e.g.,

incomplete retrieval of identified research, reporting bias).

Conclusions 26 Provide a general interpretation of the results in the context of other evidence, and implications for

future research.

FUNDING

139

Funding 27 Describe sources of funding for the systematic review and other support (e.g., supply of data); role

of funders for the systematic review.

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Appendix 2: Human Research Ethics Approval

HUMAN RESEARCH ETHICS COMMITTEE

Notification of Expedited Approval

To Chief Investigator or

Project Supervisor:

Associate Professor David Lubans

Cc Co-investigators /

Research Students:

Professor Ronald Plotnikoff

Dr Chris Lonsdale

Professor Philip Morgan

Professor Amanda Baker

Doctor Geoffrey Skinner

Ms Narelle Eather

Mr Mark Babic

Miss Sarah Kennedy

Miss Emma Pollock

Mrs Tara Finn

Re Protocol: Evaluation of a multi-component intervention

to reduce screen-time in adolescents: The

Stand Up for Healthy Minds study

Date: 07-Feb-2014

Reference No: H-2013-0428

Date of Initial Approval: 07-Feb-2014

Thank you for your Response to Conditional Approval submission to the Human

Research Ethics Committee (HREC) seeking approval in relation to the above protocol.

Your submission was considered under Expedited review by the Chair/Deputy Chair.

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I am pleased to advise that the decision on your submission is Approved effective 07-

Feb-2014.

In approving this protocol, the Human Research Ethics Committee (HREC) is of the

opinion that the project complies with the provisions contained in the National

Statement on Ethical Conduct in Human Research, 2007, and the requirements within

this University relating to human research.

Approval will remain valid subject to the submission, and satisfactory assessment, of

annual progress reports. If the approval of an External HREC has been "noted" the

approval period is as determined by that HREC.

The full Committee will be asked to ratify this decision at its next scheduled meeting.

A formal Certificate of Approval will be available upon request. Your approval

number is H-2013-0428.

If the research requires the use of an Information Statement, ensure this number

is inserted at the relevant point in the Complaints paragraph prior to distribution

to potential participants You may then proceed with the research.

Conditions of Approval

This approval has been granted subject to you complying with the requirements for

Monitoring of Progress, Reporting of Adverse Events, and Variations to the Approved

Protocol as detailed below.

PLEASE NOTE:

In the case where the HREC has "noted" the approval of an External HREC, progress

reports and reports of adverse events are to be submitted to the External HREC only. In

the case of Variations to the approved protocol, or a Renewal of approval, you will

apply to the External HREC for approval in the first instance and then Register that

approval with the University's HREC.

• Monitoring of Progress

Other than above, the University is obliged to monitor the progress of research

projects involving human participants to ensure that they are conducted according to

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the protocol as approved by the HREC. A progress report is required on an annual

basis. Continuation of your HREC approval for this project is conditional upon receipt,

and satisfactory assessment, of annual progress reports. You will be advised when a

report is due.

• Reporting of Adverse Events

1. It is the responsibility of the person first named on this Approval Advice to

report adverse events.

2. Adverse events, however minor, must be recorded by the investigator as

observed by the investigator or as volunteered by a participant in the research.

Full details are to be documented, whether or not the investigator, or his/her

deputies, consider the event to be related to the research substance or

procedure.

3. Serious or unforeseen adverse events that occur during the research or within

six (6) months of completion of the research, must be reported by the person

first named on the Approval Advice to the (HREC) by way of the Adverse

Event Report form (via RIMS at https://rims.newcastle.edu.au/login.asp) within

72 hours of the occurrence of the event or the investigator receiving advice of

the event.

4. Serious adverse events are defined as:

o Causing death, life threatening or serious disability.

o Causing or prolonging hospitalisation.

o Overdoses, cancers, congenital abnormalities, tissue damage, whether

or not they are judged to be caused by the investigational agent or

procedure.

o Causing psycho-social and/or financial harm. This covers everything

from perceived invasion of privacy, breach of confidentiality, or the

diminution of social reputation, to the creation of psychological fears

and trauma.

o Any other event which might affect the continued ethical acceptability

https://rims.newcastle.edu.au/login.asp

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of the project.

5. Reports of adverse events must include:

o Participant's study identification number;

o date of birth;

o date of entry into the study;

o treatment arm (if applicable);

o date of event;

o details of event;

o the investigator's opinion as to whether the event is related to the

research procedures; and

o action taken in response to the event.

6. Adverse events which do not fall within the definition of serious or

unexpected, including those reported from other sites involved in the research,

are to be reported in detail at the time of the annual progress report to the

HREC.

• Variations to approved protocol

If you wish to change, or deviate from, the approved protocol, you will need to submit

an Application for Variation to Approved Human Research (via RIMS at

https://rims.newcastle.edu.au/login.asp). Variations may include, but are not limited to,

changes or additions to investigators, study design, study population, number of

participants, methods of recruitment, or participant information/consent

documentation. Variations must be approved by the (HREC) before they are

implemented except when Registering an approval of a variation from an external

HREC which has been designated the lead HREC, in which case you may proceed as

soon as you receive an acknowledgement of your Registration.

Linkage of ethics approval to a new Grant

HREC approvals cannot be assigned to a new grant or award (i.e. those that were not

identified on the application for ethics approval) without confirmation of the approval


144

from the Human Research Ethics Officer on behalf of the HREC.

Best wishes for a successful project.

Professor Allyson Holbrook

Chair, Human Research Ethics Committee

For communications and enquiries:

Human Research Ethics Administration

Research Services

Research Integrity Unit

The Chancellery

The University of Newcastle

Callaghan NSW 2308

T +61 2 492 17894

F +61 2 492 17164

[email protected]

RIMS website - https://RIMS.newcastle.edu.au/login.asp

Linked University of Newcastle administered funding:

Funding body

Funding project title

First named investigator

Grant Ref

mailto:[email protected]


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Appendix 3: Principal Information Sheet

Research Project: Evaluation of a multi-component intervention to reduce screen-

time in adolescents: The ‘Switch-Off for Healthy Minds” study

PRINCIPAL INFORMATION STATEMENT

Dear Principal,

Your school is invited to participate in the research project identified above which is

being conducted by A/Prof David Lubans, Prof Ron Plotnikoff, Prof Philip Morgan, Dr

Chris Lonsdale, Prof Amanda Baker, Dr Geoff Skinner, Ms Narelle Eather and Mr Mark

Babic from the University of Newcastle. This research is funded by the Hunter Medical

Research Institute (HMRI). This project is part of the postgraduate study of Mr Mark

Babic and will contribute to his PhD. Mark will be supervised by A/Prof David Lubans,

Prof Philip Morgan and Prof Ron Plotnikoff.

Why is this research being done?

The time that young people spend sedentary, especially the time they spend alone

watching television and using computers, is a major public health issue. Current estimates

suggest that young people spend 5–10 hours per day sedentary, of which 2–4 hours is

spent engaged in screen-based recreation (i.e., television, computer and electronic

gaming). The primary aim of this project is to evaluate the impact of an innovative multi-

component intervention to reduce sedentary behaviour (i.e. time spent sitting) on health

and psychological well-being in adolescents.

Who can participate in this research?

Students in grade 7 (1st year of secondary school) at your school who are identified as

eligible by a short screening questionnaire will be invited to participate. We aim to recruit

43 students from each of the schools. Parents of eligible students (Students who record ≥

2hrs/day of recreational screen-time from the screening questionnaire) will also

A/Prof David Lubans School of Education Faculty of Education and Arts University of Newcastle Callaghan NSW 2308 Phone: + 61 (0)2 4921 2049 Fax: +61 (0)2 4921 7407 Email: [email protected]


146

participate by receiving newsletters and supporting the behavioural messages at home.

Once 8 schools have been recruited, we will cease contacting any additional schools.

What choice do you have?

Participation in this research is entirely your choice and only schools where principals

have given their explicit consent will be included in the study. If you do agree to your

school’s participation, you may withdraw from the study at any time without giving a

reason. A decision not to participate or discontinuation of involvement in the study will

not jeopardise your relationship with the University of Newcastle. Similarly, students in

your school will be included in the study only after a consent form has been signed by

their parents/guardians. If they initially agree to participate, they can choose to withdraw

from the study at any time without giving a reason.

What is involved in this study?

Schools who agree to participate will be randomly allocated to either a study program

recipient group or a wait list control group. Schools allocated to the wait list control will

not receive the study program during the study period. However, these schools will

receive a condensed version of the program following the 6 month assessments.

The program will run for two full school terms (terms 1 and 2, 2014) and will aim to

promote physical activity and improve psychological wellbeing among students. Students

in BOTH groups will complete evaluation measures on two occasions during the study

period (baseline and 6-months). The program components and evaluation measures are

listed below in Table 1.

Table A3.1: Intervention components and evaluation strategies

Intervention Components Evaluation of intervention

STUDY INTERVENTION SCHOOLS

Student participants will receive the recreational screen-time intervention and additional

parental support for 6-months.

(i) EHealth messages: Push notifications and text messages will be used to deliver intervention messages. The bi-weekly push-prompt messages (i.e., text messages and emails)

The following measures will be taken two times (baseline and 6-months): • Screen-time will be measured using the

Adolescent Sedentary Behaviour Questionnaire.

-

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will be designed to address the consequences of excessive screen-time and the importance of self-management. In addition, ehealth will include basic features to encourage self-monitoring and goal setting to reduce screen-time.

(ii) ‘Switch-Off for Healthy Minds’ information session: The session will be delivered by a member of research team during school hours. The session will outline the intervention, requirements of the students. Students will be given the opportunity to ask any questions during this session.

(iii) Behavioural Contract: Students will be asked to sign a screen-time behavioural contract prior to commencement of the intervention.

(iv) Newsletters: Parents will be provided with a range 6 newsletters over the 6-month intervention focusing on household screen-time rules, consequences of excessive screen-time, strategies to manage parent/child conflict arising from screen-time rules and home challenges to reduce screen-time.

• Psychological well-being will be measured using the Strengths and Difficulties Questionnaire (SDQ).

- • Physical self-concept measured using a

modified version of the Physical Self-Description Questionnaire

- • Psychological distress will be

measured using the Kessler 10 Questionnaire (K-10).

- • Weight and height will be measured in

a private location using a portable medical scale and stadiometer. Body mass index and age/gender adjusted z-scores will be calculated.

• Physical activity will be measured using accelerometers (student’s normal physical activity at home).

• Social cognitive and environmental mediators of sedentary behaviour change will be assessed using validated scales

CONTROL WAIT-LIST SCHOOLS Student participants will receive the recreational screen-time intervention and additional parental support at the end of the study period. Wait-list control schools will receive all intervention components on completion of the intervention and 6-month follow-up assessments

The following measures will be taken two times (baseline and 6-months): • Screen-time will be measured

using the Adolescent Sedentary Behaviour Questionnaire.

- • Psychological well-being will

be measured using the Strengths and Difficulties Questionnaire (SDQ).

- • Physical self-concept

measured using a modified

148

version of the Physical Self-Description Questionnaire



- • Weight and height will be

measured in a private location using a portable medical scale and stadiometer. Body mass index and age/gender adjusted z-scores will be calculated.



A member of the research team will deliver a presentation to students focusing on the

consequences of excessive screen-time and strategies to reduce screen-time. The other

invention strategies will be delivered directly to parent’s i.e. behavioural contracts,

newsletters and blogs. Assessments (i.e. height, weight and questionnaires) will be

conducted by members of the research team which have completed working with children

background checks; however a member of the schools staff will need to be present to

supervise students.

Study timetable

Date Event Contact schools and offer study invitation Term 1, 2014 Conduct participant eligibility screening Term 1, 2014 Collect consent forms and conduct baseline assessments End of Term 1, 2014 Randomise schools Terms 2-3 2014 Intervention strategies implemented Terms 2-3 2014 Conduct 6-month post-program assessments Term 4, 2014

What are the risks and benefits of participating?

149

Questionnaires will be administered to participants by trained research assistants from the

University of Newcastle. In addition to this, height and weight will be measured behind a

screen board for participant privacy. Completing the questionnaires is entirely the choice

of the participants. Some of the questions are of a personal nature, if you feel

uncomfortable with any question, please ask the University of Newcastle Research Staff

for support and move onto the next question. Participation in this research does not

require students to undertake any physical activity testing, but that they will simply record

any exercise that they take in their spare time. Students will have no greater chance of

injury by participating in this intervention. The program will provide students with an

opportunity to increase their knowledge and skills and improve attitudes toward reducing

screen-time.

How will the information collected be used?

The data collected from this study will contribute to Mark Babic’s PhD (student

researcher) and will be used for journal publications and conference presentations and to

inform future practice for the design of valuable, evidence-based applications that reduce

screen-time in schools.

How long will it take?

The duration of the study will be 6 months (two terms). Once the research team have

received signed consent from the school principal, information sessions lasting

approximately 10 minutes (presented by the research team) will be organised at the school

for interested students and parents. These sessions will be made available to all schools

both during and after school hours. Once all schools and participants have been recruited

for the study, students will receive one information session (delivered by the research

team) during school hours lasting approximately 30 minutes. Assessments including

questionnaires, height and weight will then be conducted during school hours at the

beginning of the study and at 6-month follow up (approximately 30 minutes per child). A

teacher will be asked to supervise during this time. During these assessment time-points,

each participant will also be asked to wear a University of Newcastle owned physical

activity accelerometer to monitor their normal physical activity for 7 days. On completion

of the 7 days, the monitors will be returned to a teacher for a research team member to

collect.

How will privacy be protected?

150

Any personal information provided by students and parents will be stored in a locked

filing cabinet in the Chief Investigator’s office or kept on a password protected computer

which will be confidential to the researchers. The results of the study will be published in

general terms and will not allow the identification of individual students or schools. Once

the data has been collected, de-identified using participant codes and entered into an

electronic data file, questionnaires and other data collection sheets will be destroyed. The

electronic data files will be retained for at least 5 years but no individual will be

identifiable in the data files or published reports.

What do you need to do to participate?

If you are willing for your school to participate in this study, could you please complete

the accompanying Consent Form and return it to the researchers via fax or email. Upon

receipt of your consent, a member of the research team will contact you to organise a

time to visit the school and provide students with information about the study. If you

would like to organise a different route for the dissemination of the Information Sheet

and Consent Form to students, please let A/Prof Lubans know. All students wanting to

participate in this study will be required to return a Consent Form, which his/her

parents/guardians have signed before the study starts.

Further information

Following the completion of the study, the school will be sent a report describing the

findings of the study. Results will also be sent via post to study participants and their

parents. Individual results will not be given to students.

If you would like further information please do not hesitate to contact A/Prof David

Lubans. Thank you for considering this invitation.

A/Prof David Lubans Mr Mark Babic

(Chief Investigator) (Student researcher)

151

Faculty of Education & Arts

School of Education


Phone: (02) 4921 2049

[email protected]

Faculty of Education & Arts

School of Education


Phone: (02) 4921 6299

Mark. [email protected]

This project has been approved by the University’s Ethics Committee, Newcastle-

Maitland Catholic Schools Office and the Diocese of Broken Bay. Should you have

concerns about your rights as a participant in this research, or you have a complaint

about the manner in which the research is conducted, it may be given to the researcher,

or, if an independent person is preferred, to the Human Research Ethics Officer, Research

Office, The Chancellery, The University of Newcastle, University Drive, Callaghan NSW

2308, Australia, telephone (02) 49216333, email [email protected].


mailto:Mark.%[email protected]


152

Appendix 4: Student and Parent Information Statement

Evaluation of a multi-component intervention to reduce screen-time in adolescents:

The ‘Switch-Off for Healthy Minds’ study

STUDENT & PARENT INFORMATION STATEMENT

Dear Student and Parent,

Your child has been invited to participate in the research project identified above which is

being conducted by A/Prof David Lubans, Prof Ron Plotnikoff, Prof Philip Morgan, Dr

Chris Lonsdale, Prof Amanda Baker, Dr Geoff Skinner, Ms Narelle Eather and Mr Mark

Babic from the University of Newcastle. This research is funded by the Hunter Medical

Research Institute (HMRI). This project is part of the postgraduate study of Mr Mark

Babic and will contribute to his PhD. Mark will be supervised by A/Prof David Lubans,

Prof Philip Morgan and Prof Ron Plotnikoff.

Why is this research being done?

The time that young people spend sedentary (sitting or lying down), especially the time

they spend alone watching television and using computers, is a major public health issue.

Current estimates suggest that young people spend 5–10 hours per day sedentary, of

which 2–4 hours is spent engaged in screen-based recreation (i.e., television, computer

and electronic gaming). The primary aim of this project is to evaluate the impact of an

innovative multi-component intervention to reduce sedentary behaviour (i.e. time spent

sitting) on health and psychological well-being in adolescents. This project is part of the

research studies of Mr Mark Babic’s PhD.

Who can participate in this research?

Students in grade 7 (1st year of secondary school) at your school who are identified as

eligible by a short screening questionnaire will be invited to participate. We aim to recruit

43 students from each of the schools. Parents of eligible students (Students who record ≥



153

2hrs/day of recreational screen-time from the screening questionnaire) will also

participate by receiving newsletters and supporting the behavioural messages at home.

What choice do you have?

Participation in this research is entirely your choice and only schools where principals

have given their explicit consent will be included in the study. If you do agree to

participate, you may withdraw from the study at any time without giving a reason. A

decision not to participate or discontinuation of involvement in the study will not

jeopardise any relationships with the University of Newcastle. Students will be included

in the study only after a consent form has been signed by their parents/guardians. If they

initially agree to participate, they can choose to withdraw from the study at any time

without giving a reason.

What is involved in this study?

Schools who agree to participate will be randomly allocated to either a study program

recipient group or a wait list control group. Schools allocated to the wait list control will

not receive the study program during the study period. However, these schools will

receive the program following the 6 month assessments.

The program will run for two full school terms (Terms 1 and 2, 2014) and will aim to

promote physical activity and improve psychological wellbeing among students. Students

in BOTH groups will complete evaluation measures on two occasions during the study

period (baseline and 6-months). The program components and evaluation measures are

listed below in Table 1.

Table 1: Intervention components and evaluation strategies

Intervention Components Evaluation of intervention

STUDY INTERVENTION SCHOOLS

Student participants will receive the recreational screen-time intervention and

additional parental support for 6-months.

(i) EHealth messages: Push notifications and text messages will be used to deliver intervention messages. The bi-weekly push-prompt messages (i.e., text messages and emails) will be



-

154

designed to address the consequences of excessive screen-time and the importance of self-management. In addition, ehealth will include basic features to encourage self-monitoring and goal setting to reduce screen-time.

(ii) ‘Switch-Off for Healthy Minds’ information session: The session will be delivered by a member of research team during school hours. The session will outline the intervention and the requirements of the students. Students will be given the opportunity to ask any questions during this session.

(iii) Behavioural Contract: Students will be asked to sign a screen-time behavioural contract prior to commencement of the intervention.

(iv) Newsletters: Parents will be provided with 6 newsletters over the 6-month intervention focusing on household screen-time rules, consequences of excessive screen-time, strategies to manage parent/child conflict arising from screen-time rules and home challenges to reduce screen-time.

-

• Psychological well-being will be measured using the Strengths and Difficulties Questionnaire (SDQ).

- • Physical self-concept measured

using a modified version of the Physical Self-Description Questionnaire



- • Weight and height will be

measured in a private location using a portable medical scale and stadiometer. Body mass index and age/gender adjusted z-scores will be calculated.



CONTROL WAIT-LIST SCHOOLS Student participants will receive the recreational screen-time intervention and additional parental support at the end of the study period. Wait-list control schools will receive all intervention components on completion of the intervention and 6-month follow-up assessments



- • Psychological well-being will be

measured using the Strengths and Difficulties Questionnaire (SDQ).

- • Physical self-concept measured

using a modified version of the Physical Self-Description Questionnaire


measured using the Kessler 10

155

Questionnaire (K-10). -

• Weight and height will be measured in a private location using a portable medical scale and stadiometer. Body mass index and age/gender adjusted z-scores will be calculated.



A member of the research team will deliver a presentation to students focusing on the

consequences of excessive screen-time and strategies to reduce screen-time. The other

invention strategies will be delivered directly to parent’s i.e. behavioural contracts and

newsletters. Assessments (i.e. height, weight and questionnaires) will be conducted by

members of the research team which have completed working with children background

checks; however a member of the schools staff will need to be present to supervise

students.

What are the risks and benefits of participating?

Questionnaires will be administered to participants by trained research assistants from the

University of Newcastle. In addition to this, height and weight will be measured behind a

screen board for participant privacy. Completing the questionnaires is entirely the choice

of the participants. Some of the questions are of a personal nature, if you feel

uncomfortable with any question, please ask the University of Newcastle Research Staff

for support and move onto the next question. Participation in this research does not

require students to undertake any physical activity testing, but that they will simply record

any exercise that they take in their spare time. Students will have no greater chance of

injury by participating in this intervention. The program will provide students with an

opportunity to increase their knowledge and skills and improve attitudes toward reducing

screen-time. Research staff will be available to answer questions and provide guidance for

students and teachers when required.

How will the information collected be used?

156

The data collected from this study will contribute to Mark Babic’s PhD (student

researcher) and will be used for journal publications and conference presentations and to

inform future practice for the design of valuable, evidence-based applications that reduce

screen-time in schools.

How will privacy be protected?

Any personal information provided by students and parents will be stored in a locked

filing cabinet in the Chief Investigator’s office or kept on a password protected computer

which will be confidential to the researchers. The results of the study will be published in

general terms and will not allow the identification of individual students or schools. Once

the data has been collected, de-identified using participant codes and entered into an

electronic data file, questionnaires and other data collection sheets will be destroyed. The

electronic data files will be retained for at least 5 years but no individual will be

identifiable in the data files or published reports.

What do you need to do to participate?

All students wanting to participate in this study will be required to return a Consent

Form to the teacher, which his/her parents/guardians have signed before the study starts.

Further information

Following the completion of the study, the school will be sent a report describing the

findings of the study. Results will also be sent via post to study participants and their

parents. Individual results will not be given to students. If you would like further

information please do not hesitate to contact A/Prof David Lubans. Thank you for

considering this invitation.

157

_ __________________

A/Prof David Lubans Mr Mark Babic

(Chief Investigator) (Student Researcher)

Faculty of Education & Arts School of Education University of Newcastle Phone: (02) 4921 2049 [email protected]

Faculty of Education & Arts School of Education University of Newcastle Phone: (02) 4921 6299 [email protected]

This project has been approved by the University’s Ethics Committee and Newcastle-

Maitland Catholic Schools Office. Should you have concerns about your rights as a

participant in this research, or you have a complaint about the manner in which the

research is conducted, it may be given to the researcher, or, if an independent person is

preferred, to the Human Research Ethics Officer, Research Office, The Chancellery, The

University of Newcastle, University Drive, Callaghan NSW 2308, Australia, telephone

(02) 49216333, email [email protected].




158

Appendix 5: Principal Consent Form

Research Project: Evaluation of a multi-component intervention to reduce screen-

time in adolescents: The ‘Switch-Off for Healthy Minds’ study

PRINCIPAL CONSENT FORM

Chief Investigators: A/Prof David Lubans, Prof Ron Plotnikoff, Prof Philip Morgan

Dr Chris Lonsdale, Prof Amanda Baker, Dr Geoff Skinner, Ms Narelle Eather and Mr Mark

Babic

I have been given information about the project identified above. I understand that if I consent

to my school’s involvement in this project, consenting students will participate in the study

entitled: Evaluation of a multi-component intervention to reduce screen-time in adolescents:

The ‘Switch-Off for Healthy Minds’ study and my school will be randomly allocated to one of

two interventions:

• The study intervention group: where student participants will receive the

recreational screen-time intervention and additional parental support for 6-months.

OR

• The wait-list control group: where student participants will receive the

recreational screen-time intervention and additional parental support at the end of

the study period.

I understand that consenting students will also complete the following program evaluation

measures on two occasions: psychological well-being, subjective well-being and self-esteem,

weight, height, physical activity, screen-time and social cognitive and environmental mediators

of sedentary behaviour change.

I have had an opportunity to ask A/Prof Lubans questions about the research. I understand that

my school’s participation in this research is voluntary and that my school and my students are



159

free to withdraw from the research project at any time. My refusal to participate or withdrawal

of consent will not affect my relationship with the University of Newcastle.

By signing below I am indicating my consent for my school to participate in this research

project conducted by A/Prof David Lubans. I am also consenting for the provision of time and

space for (1) members of the research team to deliver information to potential student

participants at a recruitment presentation; (2) delivery of the ‘Switch-Off for Healthy Minds’

intervention to consented participants (3) members of the research team to collect evaluation

measures from student participants twice during the study period.

Name of school: ______________________________

Principal’s name: ___________________________________________________

Signature: ____________________________________ Date: ________________

PLEASE FAX OR EMAIL COMPLETED SHEET BACK ASAP TO DAVID

LUBANS- FAX. No. 49212084 OR [email protected]

This project has been approved by University’s Ethics Committee, Newcastle-Maitland

Catholic Schools Office and the Diocese of Broken Bay. Should you have concerns about

your rights as a participant in this research, or you have a complaint about the manner in

which the research is conducted, it may be given to the researcher, or, if an independent

person is preferred, to the Human Research Ethics Officer, Research Office, The

Chancellery, The University of Newcastle, University Drive, Callaghan NSW 2308,

Australia, telephone (02) 49216333, email [email protected].



160

Appendix 6: Parent and Student Consent Form



PARENT CONSENT FORM


Dr Chris Lonsdale, Prof Amanda Baker, Dr Geoff Skinner, Ms Narelle Eather and Mr Mark

Babic

I have been given information about the project identified above. I understand that if I

consent to my school’s involvement in this project, consenting students will participate in

the study entitled: Evaluation of a multi-component intervention to reduce screen-time in

adolescents: The ‘Switch-Off for Healthy Minds’ study and my school will be randomly

allocated to one of two interventions:

• The study intervention recipient group: where student participants will receive the

recreational screen-time intervention and additional parental support for 6-months.

OR

• The wait-list control group: where student participants will receive the

recreational screen-time intervention and additional parental support at the end of

the study period.

I understand that my child will complete the following program evaluation measures:

psychological well-being, subjective well-being and self-esteem, weight, height, physical

activity, screen-time and social cognitive and environmental mediators of sedentary

behaviour change.

I have had an opportunity to ask A/Prof Lubans questions about the research. I have

discussed the project with my child and we understand that their participation in this

research is voluntary and that he/she is free to withdraw from the research project at any



161

time. My refusal to participate or withdrawal of consent will not affect my relationship

with the University of Newcastle.

By signing below I am indicating my consent for my son/daughter to participate in this research

project conducted by A/Prof David Lubans. I am also consenting for the provision of time and

space for (1) members of the research team to deliver information to potential student

participants at a recruitment presentation; (2) delivery of the ‘Switch-Off for Healthy Minds’

intervention to consented participants (3) members of the research team to collect evaluation

measures from student participants twice during the study period.

162

To be returned to school’s office or your child’s PDHPE teacher



PARENT CONSENT FORM


Dr Chris Lonsdale, Prof Amanda Baker, Dr Geoff Skinner, Ms Narelle Eather and Mr

Mark Babic

Student name: ___________________________________________

Parent/guardian name: ________________________________________

Parent Signature: _____________________________

Date: ______________________

Child’s signature: _____________________________

Date: ______________________

Child’s mobile number (if applicable): _______________________________

(This will be used to send a reminder to wear the physical activity monitoring device).

For the receipt of monthly newsletters providing updated information about the program

your child has been participating in, please provide your contact details:

Postal address:

_____________________________________________________________

Parent Email address:

_____________________________________________________________

Parent Mobile Phone:

_____________________________________________________________



163

Please sign the completed consent letter and return to the school’s office or your child’s

PDHPE teacher

This project has been approved by the University’s Ethics Committee and Newcastle-

Maitland Catholic Schools Office. Should you have concerns about your rights as a

participant in this research, or you have a complaint about the manner in which the

research is conducted, it may be given to the researcher, or, if an independent person is

preferred, to the Human Research Ethics Officer, Research Office, The Chancellery, The

University of Newcastle, University Drive, Callaghan NSW 2308, Australia, telephone

(02) 49216333, email [email protected].


164

Appendix 7: Eligibility Screening Questionnaire

Project Title: The ‘Switch Off for Healthy Minds’ study

Chief Investigators: A/Prof David Lubans, Prof Ron Plotnikoff, Prof Philip Morgan,


Mark Babic

Eligibility Screening Questionnaire

Student Name:

School:

ID:

To protect your privacy this cover sheet will be removed and destroyed once you

have been allocated a study number



165

PARTICIPANT SCREENING QUESTIONNAIRE:

Please answer the following question as honestly as possible by circling ONE response.

1. During a typical school day how much time (hours) do you spend watching

television or DVDs, playing video games or using the computer for

entertainment?

- (Please tick one option)

None

Less than 1 hour per day

1 hour per day

2 hours per day

3 hours per day

4 hours per day

5 hours per day

6 hours per day

7 hours per day

More than 7 hours per day

166

Appendix 8: Recording Sheet

Assessment (circle): Baseline 6 months

Date: ______________ School: ____________________________ ID: _________

Student Name:

_____________________________________________________________

Are you Left or Right handed? (Please circle one) L / R

Student Mobile Number: _____________________________

DOB: Month: __ __ Year __ __ __ __

Checklist (COMPLETE IN THE FOLLOWING ORDER):

1) Survey Monkey Survey Completed Yes / No

Assessment 1st Recording 2nd Recording

Height (cm x 0.01=m)

Weight (kg)

167

Appendix 9: Accelerometer Information Sheet and Activity Log

Activity Monitor Information Sheet

Please do not hesitate to call Mark Babic on (02) 4985 4255, please leave a message

at this number if you have any questions or concerns about your monitor.

What does the monitor do?

The monitor records all movement, so that when you watch television, play outside, or

eat dinner, it records how much and how often you move your body.

Does the monitor hurt?

No. The monitor is attached to a soft elastic belt and worn under your clothes. You may

be aware of the monitor when you first start to wear it, but it will not hurt.

When do you put your monitor ON?

- The monitor is to be put on as soon as you wake up each morning.

- You are to wear the monitor under your clothes over the right hip (not in the

middle near their belly-button), making sure that it is the correct way up (The

sticker on the top of the monitor should be facing upwards i.e. pointing towards

the sky). The monitor should fit firmly so that the elastic belt cannot bounce, but

should not be uncomfortably tight.

- Write the time when the monitor is put on (see activity monitor log).

- The monitors are not water-proof, so please remember that the monitor is not to

be worn in the shower, bath or when swimming or playing in aquatic areas.

When do you take OFF the monitor?

- The monitor should be taken off when you go to bed, or if there is a chance that

the monitor could get wet (e.g. playing near water). Please note on the monitor

diary any specific time periods that the monitor is taken off and why (e.g. 3.30-

4.30pm on Wednesday – Swimming at the beach).

- The monitor is to be worn for all waking hours for all 8 days. At the end of each

day please write the time the monitor is taken OFF (see activity monitor log).

What do I do at the end of the 8 days?

168

Please keep wearing your monitor until people collect it from you at school the following

week.

What if I damage or lose the monitor?

You will NOT have to pay for the monitor if you damage or lose it.

The monitors are expensive, so please take care of them. It is quite a sturdy piece of

equipment, but will be damaged if thrown or forcefully dropped. You should not lose the

monitor because it is securely fitted to a belt, and should not be removed except for

during aquatic activities and sleeping.

ACTIVITY MONITOR LOG SHEET

Name ……………………………………….

Monitor ID Number …………........…….…................

School ……………………………………..

Date to be returned to school ……………………..

INSTRUCTIONS:

1. Please shade in the hours during which the activity monitor was WORN

2. When the monitor was NOT WORN please indicate what you were doing and how

long the monitor was not worn for (e.g. – 30 min).

3. Please indicate any time spent swimming, riding a bike or scooter, or playing on a

trampoline.

See the example on the left hand side of the page for how to complete the log.

169

EXAMPLE:

Day of the week Time Monday Time

Mor

ning

Hou

rs

12-1

Sleep

Mor

ning

Hou

rs

12-1

1-2 1-2

2-3 2-3

3-4 3-4

4-5 4-5

5-6 5-6

6-7 6-7

7-8 GET UP 7-8

8-9

BIKE

RIDING

(30 min)

8-9

9-10 9-10

10-11 10-11

11-12 11-12

Afte

rnoo

n / N

ight

hou

rs

12-1

Afte

rnoo

n / N

ight

hou

rs

12-1

1-2 1-2

2-3 2-3

3-4

TAKEN

OFF

FOOTBA

LL 60

min)

3-4

4-5 4-5

5-6 5-6

170

6-7 6-7

7-8 7-8

8-9 BED 8-9

9-10

Sleep

9-10

10-11 10-11

11-12 11-12

171

Appendix 10: Questionnaires




Mark Babic

Questionnaire – Baseline

Student Name: _____________________________

School: ____________________________________

ID: ______________________

- Your answers are confidential and will be looked at by the survey team and no-one

else.

- Take your time to read each question carefully.

HOW TO COMPLETE THIS FORM

• Questions can be answered by placing a tick in a box, circling your answer or

writing your answer in a box.

• Write your answers clearly in the box

• Ask one of the University of Newcastle staff members if you need assistance

• Once completed please return the questionnaire to one of the University of

Newcastle staff members

Completing the questionnaires is entirely the choice of the participants. Some of the questions

are of a personal nature, if you feel uncomfortable with any question, please ask the

University of Newcastle Research Staff for support and move onto the next question.

To protect your privacy this cover sheet will be removed and destroyed once you have been

allocated a study number.

University of Newcastle, Priority Research Centre in Physical Activity and Nutrition

Emma Pollock – Project Manager, [email protected], 4921 6884



172




Mark Babic

Adolescent Sedentary Activity Questionnaire

Student Name: _____________________________

School: ____________________________________

ID: ______________________

• Your answers are confidential and will be looked at by the survey team and no-

one else.

• Take your time to read each question carefully.

HOW TO COMPLETE THIS FORM

• Questions can be answered by placing a tick in a box or writing your answer in a

box.

• Write your answers clearly in the box

• Ask one of the staff if you need help

-

Completing the questionnaires is entirely the choice of the participants. Some of the

questions are of a personal nature, if you feel uncomfortable with any question, please

ask the University of Newcastle Research Staff for support and move onto the next

question.



173

To protect your privacy this cover sheet will be removed and destroyed once you have

been allocated a study number.

1. Think about a normal school week, and write down how long you spend doing the

following activities before and after school each day. You can write fractions like ½

hour or 30 mins

Monday Tuesday Wednesday Thursday Friday Watching television

Watching DVD’s/videos

Using the computer for fun

Using the computer for homework

Doing homework not on the computer

2. Think about a normal weekend; write down how long you spend doing the

following activities on the weekend. You can write fractions like ½ hour or 30 mins

Saturday Sunday

Watching television

Watching DVD’s/videos

Using the computer for fun

Using the computer for homework

Doing homework not on the computer

Thank you for completing this questionnaire.

Hardy, L. L., Booth, M. L., & Okely, A. D. (2007). The reliability of the adolescent

sedentary activity questionnaire (ASAQ). Preventive medicine, 45(1), 71-74.



174




Mark Babic

Physical Self-Concept Questionnaire

Student Name: _____________________________

School: ____________________________________

ID: ______________________

This is a chance to look at yourself. IT IS NOT A TEST. There are no right or wrong

answers. Be sure that your answers show how you feel about yourself.

INSTRUCTIONS: Read each statement carefully and circle ONE option for each

question to indicate how true or false you feel each statement is about you


questions are of a personal nature, if you feel uncomfortable with any question, please ask

the University of Newcastle Research Staff for support and move onto the next question.

175



Please circle the number which is the most correct statement about you.

False Mostly False

More false than true

More true than false

Mostly true

True

1. I am satisfied with the kind of person I am physically

1 2 3 4 5 6

2. Physically, I am happy with myself

1 2 3 4 5 6

3. I feel good about the way I look and what I can do physically

1 2 3 4 5 6

4. Physically I feel good about myself

1 2 3 4 5 6

5. I feel good about who I am and what I can do physically

1 2 3 4 5 6

6. I feel good about who I am physically

1 2 3 4 5 6

Marsh, H. W. (1996). Physical Self-Description Questionnaire: stability and discriminant

validity. Research Quarterly for Exercise and Sport, 67(3), 249-264.

Marsh, H. W., Richards, G. E., Johnson, S., & Roche, L. (1994). Physical Self-

Description Questionnaire: Psychometric properties and a multitrait-multimethod analysis

of relations to existing instruments. Journal of Sport & Exercise Psychology.

176




Mark Babic

Strengths and Difficulties Questionnaire

Student Name: _____________________________

School: ____________________________________

ID: ______________________




question.





177

For each item, please mark the box for Not True, Somewhat True or Certainly True.

Please give your answers on the basis of how things have been for you over the last

six months.

Goodman, R. (1997). The Strengths and Difficulties Questionnaire: a research note.

Journal of child psychology and psychiatry, 38(5), 581-586.

Mellor, D. (2005). Normative data for the Strengths and Difficulties Questionnaire in

Australia. Australian Psychologist, 40(3), 215-222.

178




Mark Babic

Kessler 10 Questionnaire

Student Name: _____________________________

School: ____________________________________

ID: ______________________




question.





179

Please circle the number which is the most correct statement about you.

None of the time

A little of the time

Some of the time

Most of the time

All of the time

1. During the last 30 days, about how often did you feel tired out for no good reason?

1 2 3 4 5

2. During the last 30 days, about how often did you feel nervous?

1 2 3 4 5

3. During the last 30 days, about how often did you feel so nervous that nothing could calm you down?

1 2 3 4 5

4. During the last 30 days, about how often did you feel hopeless?

1 2 3 4 5

5. During the last 30 days, about how often did you feel restless or fidgety?

1 2 3 4 5

6. During the last 30 days, about how often did you feel so restless you could not sit still?

1 2 3 4 5

7. During the last 30 days, about how often did you feel depressed?

1 2 3 4 5

8. During the last 30 days, about how often did you feel that everything was an effort?

1 2 3 4 5

9. During the last 30 days, about how often did you feel so sad that nothing could cheer you up?

1 2 3 4 5

10 During the last 30 days, about how often did you feel worthless?

1 2 3 4 5

Kessler, R.C., Andrews, G., Colpe, .et al. (2002). Short screening scales to monitor

population prevalence’s and trends in non-specific psychological distress. Psychological

Medicine, 32, 959-956.

180




Mark Babic

Self-Report Measures

Student Name: _____________________________

School: ____________________________________

ID: ______________________




question.





181

Screen-Time rules

Recreational screen-time refers to the time you spend sitting while watching television or

DVD’s, playing electronic games (e.g. Xbox, PlayStation), using your iPhone/iPad or

computer for anything other than homework (e.g., Facebook, Twitter, games etc.). Please

answer all questions below:

1) Think about a normal school week, and write down how long you spend in

recreational screen-time before and after school each day:

Activity Monday Tuesday Wednesday Thursday Friday

Hours Mins Hours Mins Hours Mins Hours Mins Hours Mins

How long do

you spend in

Recreational

screen-time?

2) Think about a normal weekend and write down how long you spend in recreational

screen-time for that weekend:

Activity Saturday Sunday

Hours Mins Hours Mins

How long do you spend in Recreational screen-

time?

Hardy, L. L., Booth, M. L., & Okely, A. D. (2007). The reliability of the adolescent

sedentary activity questionnaire (ASAQ). Preventive medicine, 45(1), 71-74.

3) The following questions relate to rules around recreational screen-time in your home.

Recreational screen-time refers to the time you spend sitting while watching television

or DVD’s, playing electronic games (e.g. Xbox, PlayStation), using your iPhone/iPad

or computer for anything other than homework (e.g., Facebook, Twitter, games etc.).

Please answer all questions below by circling ONE RESPONSE per question

182

In your home do your parents/caregivers have

the following rules about screen use?

Yes No Sometimes

1. No recreational screen-time before homework Y N S

2. No recreational screen-time while doing

homework

Y N S

3. Less than 2 hours of recreational screen-time per

day

Y N S

4. No recreational screen-time during daylight hours Y N S

5. No electronic screen devices (i.e. iPhones, iPads,

laptops, televisions etc.) in the bedroom after

bedtime

Y N S

6. No internet without permission Y N S

Ramirez, E.R., Norman, G. J., Rosenberg, D.E., Kerr, J., Saelens, B.E., Durant, N., &

Sallis, J.F. (2011). Adolescent screen-time and rules to limit screen-time in the home.

Journal of Adolescent Health, 48, 379–385.

4) What are your 3 favourite computer/video games?

a)

b)

c)

183


Please indicate how true each statement is for you by selecting/circling ONE RESPONSE

per statement.

Questions

Not at all true

Somewhat true

Very true

1. I try to limit my screen-time because I believe that too much screen-time is bad for my health

0 1 2 3 4 5 6

2. I try to limit my screen-time because my parent(s) will get angry with me if I don’t

0 1 2 3 4 5 6

3. I don’t see why I should try to limit my screen-time

0 1 2 3 4 5 6

4. I try to limit my screen-time because it allows me to do other things that I enjoy

0 1 2 3 4 5 6

5. I try to limit my screen-time because I feel guilty if I spend too much time in front of a screen

0 1 2 3 4 5 6

6. I try to limit my screen-time because I believe it is important

0 1 2 3 4 5 6

7. I can’t see why 0 1 2 3 4 5 6

184

I should bother limiting my screen-time

8. I try to limit my screen-time because my parent(s) pressure me to do so

0 1 2 3 4 5 6

9. I try to limit my screen-time because it gives me time to do other things that are important to me

0 1 2 3 4 5 6

10. I don’t see the point of limiting my screen-time

0 1 2 3 4 5 6

11. I try to limit my screen-time because I don’t want other people to think that I am lazy

0 1 2 3 4 5 6

12. I try to limit my screen-time because I feel it is important

0 1 2 3 4 5 6

13. I don’t see any reason why I should limit my screen-time

0 1 2 3 4 5 6

185

Feelings Scale

Indicate your agreement with each item by circling that response for each statement.

Diener, E., Wirtz, D., Tov, W., Kim-Pietro, C., Choi, D., Oishi, D., & Biswas-Diener, R.

(2009). New measures of well- being: Flourishing and positive and negative feelings.

Social Indicators Research, 39, 247-266.

Strongly

disagree Disagree Slightly

disagree Neither agree nor disagree

Slightly agree

Agree Strongly Agree

1. I lead a purposeful and meaningful life

1 2 3 4 5 6 7

2. My social relationships are supportive and rewarding

1

2 3 4 5 6 7

3. I am engaged and interested in my daily activities

1 2 3 4 5 6 7

4. I actively contribute to the happiness and well-being of others

1 2 3 4 5 6 7

5. I am competent and capable in the activities that are important to me

1 2 3 4 5 6 7

6. I am a good person and live a good life

1 2 3 4 5 6 7

7. I am optimistic about my future

1 2 3 4 5 6 7

8. People respect me

1 2 3 4 5 6 7

186

Video Gaming

The following questions refer to your experiences with video gaming on either a

computer or game console (Xbox 360, PS3 etc.). Please answer by circling ONE

REPONSE PER QUESTION

Questions Yes No Sometimes

1) Over time, have you been spending much more time

thinking about playing video games, learning about video-

game playing, or planning the next opportunity to play?

Y N S

2) Do you need to spend more and more time and/or money

on video games in order to feel the same amount of

excitement?

Y N

S

3) Have you tried to play video games less often or for shorter

periods of time, but are unsuccessful?

Y N S

4) Do you become restless or irritable when attempting to cut

down or stop playing video games?

Y N S

5) Have you played video games as a way of escaping from

problems or bad feelings?

Y N S

6) Have you ever lied to family or friends about how much

you play video games?

Y N S

7) Have you ever stolen a video game from a store or a friend,

or have you ever stolen money in order to buy a video game?

Y N S

8) Do you sometimes skip household chores in order to spend

more time playing video games?

Y N S

9) Do you sometimes skip doing homework in order to spend

more time playing video games?

Y N S

10) Have you ever done poorly on a school assignment or test

because you spent too much time playing video games?

Y N S

11) Have you ever needed friends or family to give you extra

money because you spent too much money on video-game

equipment, software, or game/internet fees?

Y N S

187

Gentile, D.A. (2009). Pathological video game use among youth ages 8 to 18: a national

study. Psychological Science, 20(5), 594–602.

188

Background Information

1 What is your month and year of birth?

Month Year

2 Are you a boy or a girl?

Boy

Girl

3 What language do you speak most at home?

English

Another language (please write it here) ________________

4 Are you an Aboriginal or Torres Strait Islander?

Yes

No

5 What suburb do you live in? __________________________

6 What is the postcode where you live? ___________________

189

Appendix 11: Protocol

Assessment Protocols

Booklet

Switch-Off 4

Healthy Minds

190

Description Page No

Equipment checklist 3

Assessment Recording Sheet 4

Accelerometer Protocol 5

Height 10

Weight 11

Questionnaires 12

Make sure you have the following equipment organised and packed before visiting the

school:

Stadiometer

Weight scales (ensure 4 new AA batteries + spares)

Paper copies of questionnaires

Pencils (1 for each student) & 4 erasers

I Pads x 10

191


Date: ______________ School: ____________________________ ID: _________

Student Name: _____________________________________________________________

Are you Left or Right handed? (Please circle one) L / R

Student Mobile Number: _____________________________

DOB: Month: __ __ Year __ __ __ __

Checklist (COMPLETE IN THE FOLLOWING ORDER):

1) Survey Monkey Survey Completed Yes / No

Assessment 1st Recording 2nd Recording

Height (cm x 0.01=m)

Weight (kg)

192

VISIT ONE

Checklist:

Accelerometers (Check you have packed the required number of monitors)

Accelerometer Master spread sheet

Participant information sheet for accelerometers (1 for each student)

Introduction Script

Thank you for wearing one of our special “physical activity monitors”. A physical

activity monitor tells us how much physical activity you do over the next week and

how hard or easy you are working when being physically active. Today we’re going

to tell you how your monitor works, when you SHOULD and SHOULD NOT wear it,

and show you how to put one on. You get to keep one of these special monitors for the

week so it’s very important that you take good care of it. Next week we will collect

your monitor back from you.

Instructions:

1. Distribute the Activity Monitor Information Sheet (see below) and go through

with the students. DO NOT GIVE OUT MONITORS AT THIS STAGE.

2. Have students turn to the monitor log (see below). Have students fill out the

information at the top of the log sheet and the days of the week across the top of

193

the log table – starting from tomorrow. For example, if the monitors are

distributed to students on a Monday, they are to start filling out the log on

Tuesday. Briefly show students how to fill out the log but instruct that mum or

dad will need to help them complete it at the end of each day.

3. Give each student a monitor.

4. Ask each student to put on their own monitor.

5. Tell the students that they should BEHAVE NORMALLY and not do extra

physical activities just because they are wearing the monitor, but also not to stop

doing things because of wearing the monitor. Reinforce that they are not to swap

monitors with another student.

194

VISIT TWO (one week later):

Checklist:

Master spreadsheet of student’s details

Pencils

Accelerometers (for any students away on first visit)

Accelerometer information sheets and logs (for any students away on first

visit)

Instructions:

1. Distribute monitors to students who were absent during initial distribution.

Go through steps as above.

2. Ensure these new students are added to the MASTER SHEET (including

monitor ID, phone numbers and date of distribution).

3. Collect returned monitors from the liaison teacher. Check off returned

monitors against the MASTER SHEET. Emphasise the importance of

school staff chasing up monitors that have not been returned.

4. Leave a record with the liaison teacher showing students who are yet to

return their activity monitor or who have just been distributed a monitor.

Organise a day with the liaison teacher for when you will return to collect

monitors.

Have you:

• Collected returned accelerometers and logs

• Checked each returned accelerometer no. matches number on

master spreadsheet

• Distributed accelerometers to absent students (see VISIT ONE)

195

INFORMATION SHEET

PHYSICAL ACTIVITY MONITOR

Thank you for your involvement in the Stand Up For Healthy Minds project

conducted by the University of Newcastle. You have been asked to wear a physical

activity monitor for one week to assist with this research. Please find below an

explanation of your monitor. Please do not hesitate to call Tara Finn on (02)

49216299 if you have any questions.

What does the physical activity monitor do?

When worn, the monitor records all movement by duration and intensity. The monitor can

detect how much time is spent participating in activities of varying intensities (e.g. sitting,

walking, running).

How long should the monitor be worn for?

We would like you to wear the monitor for 7 days (including week and weekend days) for

all hours including sleeping. It is important that you behave normally and do not try to do

more activity than usual simply because you are wearing the device.

How is the monitor to be positioned when being worn?

- The monitor is to be positioned on the non-dominant hand.

- Make sure the monitor is NOT upside down – you should be able to read the numbers

printed on the front of the watch.

- The band should fit firmly but not feel uncomfortable (not loose).

When to take the monitor OFF.

196

The monitor should not be taken off when you go to bed at night, the monitor is water

proof so may be worn while showering or swimming. THE MONITORS ARE WATER

PROOF.

Why do I need to complete the daily activity log?

The activity log (see over page) tells us important information about when you wore the

monitor and why it was taken off. To complete the log:

- Each day shade in the hours when you were wearing the monitor. Hours which are

NOT shaded in indicate the monitor was not worn.

- Note any specific time periods that the monitor was taken off and why (e.g. 3.30-

4.30pm on Wednesday – Football).

- Indicate on the log if you participated in any of the following activities and for how

long (these activities are not easily detected by the monitor so it’s important that they

are recorded on the log):

* Riding a bike

* Jumping on a trampoline

* Riding a scooter

What do I do at the end of the 7 days?

You will need to return the monitor to school after 7 days. The due date for returning this

monitor has been recorded at the top of the log sheet (next page). It is VERY

IMPORTANT that the monitor is returned to school on its due date.

The monitors are expensive, so please take care of them. It is quite a sturdy piece of

equipment, but will be damaged if thrown or forcefully dropped.

197

ACTIVITY MONITOR LOG SHEET

Name ………………………………………. Monitor ID Number

…………........…….…................

School …………………………………….. Date to be returned to school

……………………..

INSTRUCTIONS:

4. Please shade in the hours during which the activity monitor was WORN

5. When the monitor was NOT WORN please indicate what you were doing and

how long the monitor was not worn for (e.g. – 30 min).

6. Please indicate any time spent swimming, riding a bike or scooter, or playing on a

trampoline.

See the example on the left hand side of the page for how to complete the log.

198

EXAMPLE:

Day of the week Time Monday Time

Mor

ning

Hou

rs

12-1

Sleep

Mor

ning

Hou

rs

12-1

1-2 1-2

2-3 2-3

3-4 3-4

4-5 4-5

5-6 5-6

6-7 6-7

7-8 GET UP 7-8

8-9

BIKE

RIDING

(30 min)

8-9

9-10 9-10

10-11 10-11

11-12 11-12

Afte

rnoo

n / N

ight

hou

rs

12-1

Afte

rnoo

n / N

ight

hou

rs

12-1

1-2 1-2

2-3 2-3

3-4

TAKEN

OFF

FOOTBA

LL 60

min)

3-4

4-5 4-5

5-6 5-6

199

6-7 6-7

7-8 7-8

8-9 BED 8-9

9-10

Sleep

9-10

10-11 10-11

11-12 11-12

200

Master spreadsheet - Accelerometer tracking

School: _________________________________ Date: ____________


Study ID Name Accel. No. Mobile no. Date Distributed

Date Returned

201

Equipment required: Stadiometer.

Ensure: The floor is hard and level and that the stadiometer is calibrated.

Instructions:

• Shoes and socks off

• Step onto stand with back to column

• Feet together (heels together)

• Ideally heels, buttocks and upper back touch the vertical post

• Stand up straight (tall) hands down by sides

• Look straight ahead

• Breathe in and hold breath

• Bring head board down and crush hair to firmly contacting the

persons head and level (horizontal to ground). Girls may need to take

hair out if up.

• Make sure heels do not lift off floor

• Record height to nearest 0.1 of cm

• Get person to step off stand

202

Equipment Required: Electronic digital scales

Ensure: Scales have been calibrated, and the floor is hard and level.

Instructions:

• Turn scale on and ensure zeroed 0.00 (if required)

• Shoes off, minimal clothing, all objects out of pockets, belt off, heavy

jewellery off (watches, necklaces)

• Record clothing worn on data sheet (may account for fluctuations)

• Instruct student to step onto middle of scale with feet slightly apart and

stand very still with weight evenly balanced on both feet

• Record weight to 0.1 kg

• Step off

• Repeat

• If values differ by more than 0.1 kg repeat again

203

Questionnaire:

• Survey Monkey web based questionnaire to be completed

Instructions:

• Provide students with Ipad to complete Web based Questionnaire.

• Students are to complete on their own. Arrange desks to ensure privacy.

• Go through instructions with students.

• Assist students where necessary (e.g. unsure what an item means).

• Reinforce:

- Students should answer honestly

- Raise hand if unsure about an item

- This is not a test. There are no wrong answers

204

Appendix 12: Student End of Study Evaluation Questionnaire

Name: ________________________

School: ________________________

S4HM: End of program evaluation

Thank you for participating in the S4HM program. We would like to know your

thoughts about the program and we would be grateful if you could complete the

following questionnaire.

1) Overall: Stro

ngly

Dis

agre

e

Dis

agre

e

Neu

tral

Agr

ee

Stro

ngly

Agr

ee

a. The S4HM program was helpful.

SD D N A SA

b. The S4HM program provided me with

important information on why I should limit

i

SD D N A SA

c. The S4HM program provided me with useful

ideas on how to reduce my screen-time. SD D N A SA

d. The S4HM program provided me with useful

ideas on how to increase other behaviours

h l h i l ti it

SD D N A SA

2) S4HM presentation: Stro

ngly

Dis

agre

e

Dis

agre

e

Neu

tral

Agr

ee

Stro

ngly

Agr

ee

a. I enjoyed the S4HM presentation delivered at my

SD D N A SA

205

b. As a result of the S4HM presentation, I am now

aware of why I should limit my screen-time. SD D N A SA

3) S4HM screen-time rules: Yes

No

-

a. My parents/caregivers and I set limits

together on the use of screens.

Y N

b. My parents/caregivers set rules

regarding screen-time for me. Y N

c. Setting limits/goals has helped me to

reduce my screen-time. Y N

4) S4HM screen-time contract:

a. My parents/caregivers and I made a screen-

time contract.

Y N

b. My screen-time contract helped me to

reduce my screen-time. Y N S

5) S4HM messages (email/SMS/Kik/Facebook): Stro

ngly

Dis

agre

e

Dis

agre

e

Neu

tral

Agr

ee

Stro

ngly

Agr

ee

a. The S4HM messages helped me understand

the importance of limiting my screen-time. SD D N A SA

b. The S4HM messages gave me useful ideas on

how to limit my screen-time. SD D N A SA

206

6) S4HM newsletters:

Yes

No

Som

etim

es

a. My parents/caregivers read the S4HM newsletters.

Y N S

b. I read the newsletter my parents were given. Y N S

7) In the future I plan to: St

rong

ly

Dis

agre

e

Dis

agre

e

Neu

tral

Agr

ee

Stro

ngly

Agr

ee

a. Limit my recreational screen-time.

SD D N A SA

b. Increase my physical activity SD D N A SA

c. Keep records of my screen-time SD D N A SA

8) Which part of the S4HM program was the most helpful for reducing your screen-

time?

� Messages (email/SMS/Kik/Facebook)

� PowerPoint presentation delivered at school

� Receiving and using my accelerometer

� Behavioural contract designed by you and your parents/caregivers

207

� Other (please specify):________________________________________________

9) Were there any parts of the S4HM program you DID NOT enjoy? (please list):

_________________________________________________________________________ _________________________________________________________________________ _________________________________________________________________________ _________________________________________________________________________ 10) Do you have any suggestions to improve the S4HM program? _________________________________________________________________________ _________________________________________________________________________ _________________________________________________________________________ _________________________________________________________________________

Thank you for completing this survey

208

Appendix 12: Parent End of Program Evaluation Questionnaire

Name:

EVALUATION SURVEY

Thank you for taking part in the University of Newcastle’s S4HM program. We would like to know what you thought of the program. Please complete the following survey and return in the reply

paid envelope provided. All responses will be treated in confidence.

1) Overall: St

rong

ly

Dis

agre

e

Dis

agre

e

Neu

tral

Agre

e

Stro

ngly

Ag

ree

e. The S4HM program provided my teen and I with valuable information on why we should limit screen-time.

SD D N A SA

f. The S4HM program provided my teen and I with useful ideas on how to reduce screen-time.

SD D N A SA

2) Setting screen-time rules:

a. Helped my teen limit their screen-time by their

own accord. SD D N A SA

b. Created a positive environment to encourage other behaviours. SD D N A SA

c. Helped me enforce appropriate screen-time usage. SD D N A SA

3) The screen-time contract:

a. Helped my teen limit their screen-time.

b. May be used by our family in the future.

4) Newsletters:

a. Provided me with new information on the consequences of excessive screen-time.

b. Provided me with relevant content regarding strategies to manage screen-time.

c. Delivered by mail were an effective means of providing information.

209

5) Please rank in order from 1 (being most valuable) to 3 (being least valuable) the sections of the S4HM newsletters:

□ Did you know? (Scientific facts).

□ Conflict resolution (ideas to manage conflict).

□ Strategies (strategies to manage your teen’s screen-time).

6) Can you please select which ONE of the following strategies

were most successful in managing your teen’s screen-time:

□ Household rules.

□ Behavioural contract.

□ Role modelling.

7) Do you have any suggestions for improving or comments regarding the S4HM program?

Thank you for completing this survey

210

Appendix 13: Newsletters

211

212

213

214

215

216

Appendix 14: Behavioural Contract

217

Appendix 15: Interactive Presentation

Interactive presentation slides have been removed due to copyright. For more information, please contact the author.

218


219


220


221


222


223


224


225


226


227


228


229


230


231


232


233


234

Statement of Contribution:

I attest that Research Higher Degree Candidate Mark James Babic contributed

substantially in terms of the study concept, design, data collection, analysis and

preparation of the following manuscript:

Reference:

Prof. David Lubans Date:

235

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The 'Switch-off 4 Healthy Minds' Randomised Controlled

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