Mobile HCI 2014 Workshop: Reimagining Commonly User Interfaces for the Eldlerly

The Value of theLife Course Perspectivefor the Design of Mobile Technology Foong Pin Sym, PhD CandidateNUS Graduate School of Integrated Sciences and EngineeringNUS HCI [email protected]: @interfaceaddict

Mobile HCI 2014 Workshop: Re-imagining commonly used mobile interfaces for older adults

FOONG (NUS)

Introduction and Goals

•Interests: Ageing, health, technology, persuasion, end-of-life decision making

•Goals:▫Feedback and discussion on Life Course

perspective as a research frame

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FOONG (NUS)

The Life Course Perspective

•Experience of Aging is shaped by ...unique personal biographies + location in the social system + historical period

• ‘transitions’ • ‘choice points’

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Image credit: luma photography @ flickr CC

Stoller and Gibson (2000)

FOONG (NUS)

Uses of the life course perspective

In Gerontology In Sociology

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Image credit: osteoporosis_female @ flickr CC

Image credit: Bev Norton @ flickr CC

• In HCI: As predictive factors of elderly technology acceptance – education, work-usage, cognitive ability, socio-economic status

Image credit: Barbara Krawcowlcz @ flickr CC

Older

Younger

65 years old and above

GENERAL POPULATION HCI ACCESS/DISABILITY HCI

ACTIVE AGEING TECHNOLOGIES

Well Unwell

Goal: MAINTENANCE-Work-Physical Health-Screening

Goal: COPING I- normal age-related impairment- reminder systems- accessibility barrier reduction- social/health maintenance

Goal: COPING II- pathological age-related conditions- cognitive orthotics- accessibility barrier reduction- Occupational Therapy- Physical Therapy-Caregiver aids-Telecare

Loss o

f in

dep

en

den

ce

Goal? - Quality of life?- pleasures?- End-of-life care?- CAREGIVER HELP

HCI & Ageing Clock

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Older

Younger

65 years old and above

GENERAL POPULATION HCI ACCESS/DISABILITY HCI

Well Unwell

Loss o

f in

dep

en

den

ce

Transitions #1-Technology ageing with us?-Changes in social networking, relationships, activity, retirement

Transitions #2-Characterized by loss

- Of independence- of technology

habits?

caregivers

caregivers

COPING I COPING II

MAINTENANCE CARING

Transitions #3- How can access technologies age with us?

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HCI & Ageing Clock

Year of birth

1910’s: Colonial

1920’s: Colonial

1930’s: Colonial

1940’s: WWII

1950’s: WWII

1960’s: Ec Boom

1970’s: Ec Boom

1980’s: ICT

1990’s: ICT

2000’s: Millenials

Older

Younger

Well Unwell

COPING I COPING II

MAINTENANCE CARING

Singapore Population Demographics & Implications for Design Goals and Technological Acceptance

2050: Projected population 35% > 60 Parental support ratio from 10: 1 (current) to 2: 1

Q: What sort of tool can accompany us through the life span?

Q: Can this tool help navigate the vagaries of the ageing process, including illness, disability and death?

FOONG (NUS)

Thank You & Questions

•Is it useful to study the transitions?▫Dis-continuity: the challenge of age-related

limitations and ongoing technology renewal▫Whose work should I be looking at?

•Nursing home studies▫Finding the balance between caregiver and

elderly users’ needs?▫‘Toys’ for elderly with Mild Cognitive

Impairment /Dementia?

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FOONG (NUS)

Extra Slides

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FOONG (NUS)

Maintenance

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FOONG (NUS)

Coping I

•Acessibility

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http://app.mot.gov.sg/page_land.aspx?p=/Land_Transport/Meeting_Diverse_Needs/Enhancing_Accessibility.aspx

FOONG (NUS)

Coping II

•http://www.myhappystroke.com/2011/03/modified-constraint-induced-movement.html

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FOONG (NUS)

Coping II• Making Family Care Work: Dependence, Privacy and Remote

Home Monitoring Telecare Systems John Vines, Stephen Lindsay, Gary W. Pritchard, Mabel Lie, David Greathead, Patrick Olivier and Katie Brittain, Culture 2 Fit Lab, Newcastle University Lab, Swansea University

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FOONG (NUS)

Multi-Level Linear Modelling to study mobile phone usage

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Variance of

intercepts

ss1

ss2

ss3

ss4

Variance of slopes Tech ability

Freq of mob usage

Time-varying variables:1. Dependent Variable: mob0-mob3

(frequency of mobile phone usage, measured at time 0,1,2, and 3)

2. Independent Variables: tech0-tech3 (technological ability score, measured at time 0,1,2 and 3)

Time-invariant variables:3. Gender: male or female4. Tech support (received from

family/friends)

Research Question:What are the growth patterns of mobile

phone usage over time? (intra individual differences)

What predicts the growth patterns of mobile phone usage? (inter-individual differences)

Issue: Change over time

FOONG (NUS)

Level-1 model:mobij = β0j + β 1jTimeij + rij

Level-2 model:Intercept: β0j = γ00 + γ 01Genderj + γ 02Supportj + u0j

Slope:

β1j = γ10 + γ 11Genderj + γ 12Supportj + u1j

Variance of

intercepts

ss1

ss2

ss3

ss4

Variance of slopes Tech ability

Freq of mob usage

Multi-Level Linear Modelling to study mobile phone usage

Issue: Change over time9/18/2014

FOONG (NUS)

Speed of population ageing

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