A review of computer and Internet-based interventions for smoking behavior

Addictive Behaviors 31 (2006) 264–277

A review of computer and Internet-based interventions for

smoking behavior

Scott T. Waltersa,T, Jo Anne Wrightb, Ross Shegogc

aUniversity of Texas School of Public Health, 5323 Harry Hines Blvd, V8.112, Dallas, TX 75390-9128, United StatesbUniversity of Michigan, United States

cCenter for Health Promotion and Prevention Research, University of Texas School of Public Health,

Texas Medical Center, Houston, United States

Abstract

This article reviews studies of computer and Internet-based interventions for smoking behavior, published

between 1995 and August 2004. Following electronic and manual searches of the literature, 19 studies were

identified that used automated systems for smoking prevention or cessation, and measured outcomes related to

smoking behavior. Studies varied widely in methodology, intervention delivery, participant characteristics, follow-up

period, and measurement of cessation. Of eligible studies, nine (47%) reported statistically significant or improved

outcomes at the longest follow-up, relative to a comparison group. Few patterns emerged in terms of subject, design

or intervention characteristics that led to positive outcomes. The bfirst generationQ format, where participants were

mailed computer-generated feedback reports, was the modal intervention format and the one most consistently

associated with improved outcomes. Future studies will need to identify whether certain patients are more likely to

benefit from such interventions, and which pharmacological and behavioral adjuncts can best promote cessation.

D 2005 Elsevier Ltd. All rights reserved.

Keywords: Smoking; Cessation; Computer; Internet; Intervention

1. Introduction

The recent Institute of Medicine (2001) report paints a picture of an outdated healthcare system in

need of innovative and cost-saving methods for improving health outcomes. In this process, smoking

0306-4603/$ -

doi:10.1016/j.a

T Correspond

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see front matter D 2005 Elsevier Ltd. All rights reserved.

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ing author. Tel.: +1 214 648 1519; fax: +1 214 648 1081.

ress: [email protected] (S.T. Walters).

S.T. Walters et al. / Addictive Behaviors 31 (2006) 264–277 265

cessation programs will undoubtedly play a large role. Cigarette smoking is the leading cause of

premature morbidity and mortality in the United States, and is responsible for nearly half a million

deaths each year (Centers for Disease Control and Prevention, 2002, 2003). Unfortunately, the U.S.

Preventive Services Task Force has described tobacco cessation as one of the highest priority services

with the lowest delivery rate (Coffield et al., 2001). Indeed, taking into account the number of current

smokers and the number of people who start smoking each year, public health efforts are currently

making very little headway in reducing the total number of smokers.

Among current smokers, an estimated 41% reported that they had stopped smoking for at least one

day in the previous year because they were trying to quit (Centers for Disease Control and Prevention,

2004). However, rates of long-term cessation are significantly lower (U.S. Department of Health and

Human Services, 2000b). Smoking cessation interventions, particularly those that combine behavioral

and pharmacological methods, can produce rates well above the rates of smokers choosing to quit on

their own (U.S. Department of Health and Human Services, 2000b), but these intensive programs also

have the lowest rates of participation. For instance, free clinical interventions offered by health

maintenance organizations (HMOs) may only enroll only about 1% of eligible persons (Lichtenstein &

Hollis, 1992). Less intensive interventions, such as physician advice and self-help materials, may reach

more eligible persons, but they typically result in lower cessation rates (Lancaster & Stead, 2004; Silagy

& Stead, 2004).

Healthy People 2010 established a goal of reducing the rates of adult smoking from 23.3% to 12%

by the year 2010 (U.S. Department of Health and Human Services, 2000a). If this is to be

accomplished, there will be an increased need for interventions that can be disseminated to larger

numbers of smokers at a relatively low cost. In moving toward this goal, one trend is toward

interventions that can be delivered via mail, computer and the Internet. These new modes of delivery

may be well suited for tailoring self-help materials to the individual, a strategy that is generally more

effective than no intervention (Lancaster & Stead, 2004). In a typical format, smokers are surveyed via

a computerized or paper assessment, and the results are tailored to some characteristic of the individual,

such as gender, dependence level, perceived barriers to quitting, or stage of change. Based on a

theoretical model of motivation and change (e.g., Transtheoretical Model; Prochaska, Norcross, &

DiClemente, 1994), the algorithm library generates instructions for each possible survey response. The

resultant feedback, information or advice is then presented on a computer screen or through printed

materials. Indeed, this format has been widely utilized in health behavior areas such as nutrition

education, weight loss, diabetes management, alcohol consumption, HIV risk reduction, and cancer

support and counseling (e.g., Bessell et al., 2002; Brug, Steenhuis, van Assema, & de Vries, 1996;

Cloud, & Peacock, 2001; Firby, Luker, & Caress, 1991; Green & Fost, 1997; Hester, & Delaney, 1997;

Kumar, Bostow, Schapira, & Kritch, 1993; Paperny, 1997; Tate, Jackvony, & Wing 2003; Tate, Wing,

& Winett, 2001).

2. Rationale for the present review

In an earlier review of ten randomized trials of computer-tailored smoking materials, Strecher (1999)

found a significant impact in a majority of studies. Though few patterns emerged, the computer-tailored

materials seemed to be more effective for those in the precontemplation stage of change. Studies that

combined tailored materials with other behavioral or pharmacological interventions also showed

S.T. Walters et al. / Addictive Behaviors 31 (2006) 264–277266

promise. At the time of the Strecher (1999) review, however, computer interventions for smoking were

nearly all limited to using the computer to generate printed feedback, which was then mailed to the

recipient. However, in recent years, a few intervention trials have begun to test more sophisticated

methods. Studies now describe multiple iterations of feedback, specific advice or a tailored plan for

quitting, computer generated e-mail reminders, or other multimedia experiences. Such aspects are

particularly apparent in computer programs targeted at youth, some of which incorporate Flash

technology, interactive responses, chat rooms, or video streaming. As discussed below, these newer

interventions are noteworthy both because of their sophisticated presentation (i.e., they look and feel

different), as well as their ability to customize the intervention ipsitively based on the user’s responses to

the program (i.e., they ask questions and respond to the user). By soliciting information and allowing the

program to respond with visual or audible responses, a computer can better mimic the transactional

qualities of human communication (Cassell, Jackson, & Cheuvront, 1998). Such interventions are also

thought to be more persuasive than static text. That is, if the receiver feels there is a bgive and takeQ inthe transaction, they will be more likely to attend to the message, comprehend the argument, and

consider the position (O’Keefe, 1990). The possibilities for tailoring smoking interventions using

interactive computer programs are vast, but the development costs can be high (Science Panel on

Interactive Communication and Health, 1999). These costs may be justified if the program impacts

populations as effectively as other programs or reaches populations which have typically been resistant

to other kinds of interventions. For these reasons, the present study was undertaken to update the

Strecher (1999) review. We were interested in smoking prevention or intervention programs that used the

computer or Internet to calculate or deliver the intervention. Because our intent was to examine the range

of applications under which the automated systems are being used, we chose to examine both adult

cessation and adolescent prevention studies. Although conceptually different, we hoped that one area

might inform the efforts of the other.

3. Methods

3.1. Search strategy

Medline, CINAHL, and PsycInfo databases were used to locate English-language studies published

between 1995 and August 2004. The bibliographies of retrieved articles were scanned for additional

references. Key search terms included (computer or Internet or web) and (behavior change or

intervention or treatment or therapy) and (smoking or tobacco). The search terms were intentionally

broad to ensure, as much as possible, that all relevant articles would be captured.

3.2. Inclusion criteria

Review criteria included English-language peer-reviewed journal articles published since 1995 that

described intervention trials for smoking prevention or cessation that:

1. Used computers (web-based, server-based, or stand-alone programs) as a significant part of the

intervention.

2. Included at least one comparison or control condition.

S.T. Walters et al. / Addictive Behaviors 31 (2006) 264–277 267

3. Included at least one intervention condition that was achieved without significant human contact. This

eliminated interventions that involved non-automated e-mail, chat rooms, discussion boards, or direct

personal contact as the primary delivery mode.

4. Reported at least one outcome directly related to smoking behavior.

Abstracts of journal articles were examined independently by two reviewers using the above criteria

to determine relevance. In cases where multiple outcomes were reported from a single intervention trial,

we selected the most recent (or longest) follow up available.

3.3. Data synthesis

A qualitative analysis was undertaken due to the significant heterogeneity between studies in terms of

subject characteristics, intervention delivery, and outcome measure. Comparisons between studies were

made on the basis of continuous (i.e., no smoking from baseline to follow-up) or point abstinence (e.g., no

smoking for a period of time prior to follow-up) rates, which wewere able to derive frommost studies. This

provided the most useful basis for comparison as the studies differed markedly on most other attributes.

4. Results

Literature searches yielded 199 unique references that met our search criteria. After reviewing articles

for relevance, 19 were retained for this review. Four studies were focused on adolescent smoking

prevention, while 15 targeted adult smokers. Tables 1 and 2 summarize the resulting studies. The number

of study participants ranged from 65 to 8352. Participants ranged from 11 to 65 years old, and the

percent of female participants varied from 40.5% to 100%. Follow-up periods ranged 1 to 24 months.

Studies included both treatment seeking, as well as non-treatment seeking participants.

4.1. Adolescent studies

Interventions targeting adolescents have the goals of delaying onset of smoking among those who

have never tried cigarettes or have only experimented with use, or encouraging cessation among regular

users. Of the four studies that targeted adolescents, two reported a significant reduction in smoking

initiation and prevalence as the result of computer-tailored material sent to the home of the student.

Ausems, Mesters, van Breukelen, and de Vries (2002) randomized elementary school students to

receive: (1) a seven-session in-school program focused on the social factors that influence people to

smoke, education about the effects of smoking on the body, and training in refusal skills; (2) three mailed

letters, tailored to students based on beliefs, efficacy, and intent to smoke; (3) both in-school and mailed

conditions, or; (4) control. At a 6-month follow-up, the mailed condition reduced smoking initiation

(10.4% vs. 18.1%) and continuation (13.1% vs. 23.5%) relative to control. The effects of the in-school

program did not significantly differ from control, nor was the combined approach superior to the mailed-

only condition. Using a similar intervention design with vocational school students, Ausems, Mesters,

van Breukelen, & De Vries (2004) found that those in the in-school condition who had ever tried a

cigarette were less likely to continue smoking at 12 months relative to control (29.4% vs. 42.2%,

respectively). The mailed condition prevented smoking initiation at 18 months, relative to control

Table 1

Effects of computer interventions on smoking cessation among adolescents

Study Participants Follow-up

(Months)

Intervention conditions Smoking Summary of findings

Initiat Contin

Ausems

et al.

(2002)

3734 students

ages 11–12

6 1. Seven-session teacher-led

program

14.9ab 21.6ab Mailed intervention reduced

smoking initiation and

prevalence at 5 months.

No effect of the in-school

program over control, or

combined program over

mailed intervention.

2. Three tailored letters

mailed to students’ homes

10.4a 13.1a

3. Seven session teacher-led

program+3 tailored letters

mailed to students’ homes

15.2ab 14.2ab

4. No treatment control 18.1b 23.5b

Ausems

et al.

(2004)

36 vocational

schools (students

ages 12–16 years)

6/12 1. Three-session teacher-led

program

28.0a 29.4a In-school intervention reduced

smoking at 12 months, as

compared to control. Mailed

intervention reduced smoking

initiation at 18 months, as

compared to control (in-school

condition not assessed at 18

months). No additional effect

of the combined intervention.

2. Three tailored letters

mailed to students’ homes

25.0a 37.0ab

3. Three-session teacher-led

program+3 tailored letters

mailed to students homes

29.4a 45.0ab

4. No treatment control 40.9a 42.2b

Aveyard

et al.

(2001)

8352 students

ages 13–14

12/24 1. Three class sessions+3

interactive computer sessions

17.7a 83.2ay No benefit of intervention in

terms of initiation or cessation

at 1 or 2 years.2. No treatment control 16.5a 85.0ay

Pallonen

et al.

(1998)

135 adolescent

smokers

6 1. Tailored computer program

focused on smoking cessation

59.4az 30% of participants made at

least one 24-h quit attempt at

6 months, with no differences

between groups.

2. Standard baction orientedQcomputer program

59.4az

Initiat=Percent of baseline never-smokers who initiated smoking during longest follow-up period; Contin=Percent of baseline

ever-smokers who reported smoking at longest follow-up period.

Letters indicate significant ( p b .05) contrasts between intervention conditions: a=a, a=ab, b=ab, apbpc.y Continuation rates: bRegular weekly smokingQ at baseline, bregular weekly smokingQ at follow-up.z No 24 h quit attempts during the last 2 months.

S.T. Walters et al. / Addictive Behaviors 31 (2006) 264–277268

(27.2% vs. 47.9%, respectively), but the in-school condition was not reassessed at 18 months. There was

no additional effect for the combined condition at any follow-up point.

Conflicting results were reported by Aveyard et al. (2001) who failed to find differences between

groups that received a classroom lecture or computer intervention. At 1- and 2-year follow-ups, the

groups showed similar rates of smoking and stage-of-change movement. Pallonen et al. (1998) compared

a computer intervention with material consistent with the student’s stage of change, to a computer

intervention that emphasized only bactionQ strategies (e.g., suggestions for changing behavior). At a 6-

month follow-up, the authors found no difference between the two groups in terms of cessation.

4.2. Adult studies

All adult interventions targeted cessation in regular smokers. Of the 15 studies targeting adult

smokers, seven reported significantly improved cessation among treatment subjects as compared to

control subjects at the longest follow-up.

Table 2

Effect of computer interventions on smoking cessation among adults

Study Participants Follow-up

(Months)

Intervention conditions Cessation Summary of findings

24 h 30 day

Borland

et al.

(2003)

1578 smokers

calling a

quitline

3/6/12 1. Mailed tailored advice +

telephone counseling

25.6a Telephone counseling increased

cessation at 3 months, but not at

9 or 12 months, with no other

differences between groups.

2. Mailed tailored advice 22.6a

3. Mailed non-tailored

materials

22.1a

Curry et al.

(1995)

1137 smokers

enrolled in an

HMO

3/12/21 1. Mailed self-help booklet 11.0ay Telephone counseling increased

cessation at 3 months, with no

other differences between groups.

2. Self-help booklet+

tailored feedback

10.0ay

3. Self-help booklet+

tailored feedback+

telephone counseling

15.0ay

4. No treatment control 13.0ay

Dijkstra,

De Vries,

and

Roijackers

(1998)

1546 smokers 14 1. Tailored letter on outcomes

of smoking cessation

2.4ab* All types of feedback produced

more 24-h quit attempts, but not

7 day quit attempts, over control.

Combined letter produced higher

continuous abstinence as

compared to control at 14

months.

2. Tailored letter with

self-efficacy enhancing

information

3.3ab*

3. Tailored letter with

outcomes and self-efficacy

information

4.8a*

4. No treatment control 1.6b*

Dijkstra,

De Vries,

Roijackers,

and van

Breukelen

(1998)

752 smokers

with low

readiness

to change

4 1. Three tailored letters+

self-help guide

1.8az Three tailored letters increased

forward stage of change

movement and intent to quit at

6 months. No difference

between the groups in 7 day

quit attempts.

2. Three tailored letters 7.4az

3. One tailored letter+

self-help guide

0.8az

4. One tailored letter 0.9az

5. Non-tailored letter 3.6az

Dijkstra

et al.

(1999)

843 smokers

with low

readiness

to change

6 1. Three tailored letters 3.2az Three tailored letters increased

forward stage of change

movement and self-efficacy at

6 months. No difference

between the groups in 7 day

quit attempts.

2. One tailored letter 4.4az

3. Non-tailored materials 3.5az

4. No treatment control 5.5az

Etter &

Perneger

(2001)

2934 daily

smokers

7 1. Tailored letters+

stage-matched booklets

5.8a Intervention increased abstinence

at 7 months. Intervention was

ineffective among less educated

smokers and precontemplators.

2. No treatment control 2.2b

Lawrence

et al.

(2003)

918 pregnant

smokers in

general

practice

clinics

30 weeks

gestation,

10 days

postnatal

1. Standard smoking advice 1.7a 1.4af Adding stage-matched self-help

materials and an interactive

computer program did not

improve cessation rates at either

30-weeks gestation or 10-days

postnatal, relative to standard

advice from a midwife.

2. Smoking advice+stage-

matched self-help manual

4.3a 2.6af

3. Smoking advice+stage-

matched self-help manual+

interactive computer

program

5.7a 3.1af

S.T. Walters et al. / Addictive Behaviors 31 (2006) 264–277 269

Table 2 (continued)

Study Participants Follow-up

(Months)

Intervention conditions Cessation Summary of findings

24 h 30 day

Lenert

et al.

(2004)

485 web users 1 1. Educational smoking

webpage

7.5ay Automated email reminders

increased selection of a quit

date, 24-h quit attempts, and

30-day quit rates, at 12 months.

2. Enhanced webpage+

timed email messages

focused on quit date.

13.6by

Lennox

et al.

(2001)

2553 adult

smokers

6 1. Tailored letter on smoking

cessation

3.5ay Non-tailored letter produced

greater validated cessation

rates than control at 6 months.

Tailored letters did not increase

cessation, but did increase heavy

smokers’ readiness to quit.

2. Non-tailored letter 4.4aby

3. No treatment control 2.6by

O’Neill

et al.

(2000)

65 college

undergraduate

daily smokers

1/3/7 1. Computer intervention

focused on smoking cessation

19.0a* Intervention produced increased

motivation at 6 weeks. Trend

towards greater cessation in

intervention group at 1 and 3

months. 30% of participants

reported cessation at 7 months,

with no differences between

groups.

2. Non-smoking-oriented

control

14.0a*

Prochaska,

Velicer,

Fava,

Rossi et al.

(2001)

4144 smokers

recruited over

the phone

6/12/18/24 1. Mailed tailored feedback

at 0, 3 and 6 months

25.6a 23.4a Intervention increased abstinence

rates over control at 24 months,

with increasing intervention

effects over time.

2. No treatment control 19.7b 16.7b

Prochaska,

Velicer,

Fava,

Ruggiero

et al.

(2001)

1447 smokers

enrolled in

an HMO

6/12/18 1. Mailed tailored feedback 23.2a 21.4a Computer condition increased

abstinence over control at 18

months, with no additive effect

of counselor contact.

2. Tailored feedback+

3 counselor phone calls

23.2a 21.9a

3. Tailored feedback+

stimulus control computer

14.6b 11.9b

4. No treatment control 17.5c 14.7c

Shiffman

et al.

(2000)

3627 smokers

who purchased

nicotine gum

1.5/3 1. Mailed tailored support

materials

36.2a Computer-tailored materials

increased abstinence rates at

6 and 12 weeks, with no

additional effect of phone call.

2. Tailored support materials+

brief phone contact

35.5a

3. Standard user’s guide and

audiotape

24.7b

Shiffman

et al.

(2001)

3683 smokers

who purchased

nicotine patch

1.5 1. Mailed tailored support

materials

36.0a No overall difference between

groups, but tailored materials

produced higher quit rates at 6

and 12 weeks if participants

used the materials.

2. Standard user’s guide and

audiotape

33.1a

Velicer

et al.

(1999)

2882 smoking

employees of

a managed

care company

6/12/18 8 groups, crossed by number of

contact points (1, 2, 3, 6) and

interactive vs. non-interactive

mailed materials:

Interactive feedback produced

greater abstinence rates at 12 and

18 months. No dose-response

effect for number of contacts.

! (Non-interactive material)�(1, 2, 3, 6 contacts)

16.5a§ 13.1a§

! (Interactive material)�(1, 2, 3, 6 contacts)

21.6b§ 18.4b§

S.T. Walters et al. / Addictive Behaviors 31 (2006) 264–277270

S.T. Walters et al. / Addictive Behaviors 31 (2006) 264–277 271

The modal intervention format involved computer-generated feedback and advice sent through the

mail. Prochaska and colleagues tested the efficacy of mailed material in two trials. In the first trial

(Prochaska, Velicer, Fava, Rossi, & Tsoh, 2001), smokers were randomized to a mailed intervention or

control. The mailed intervention included information about the pros and cons of quitting, a normative

comparison, and advice commensurate with the smoker’s stage of change. At 24 months, the mailed

information produced 26% point abstinence, as compared to 20% in the control condition. In the second

study (Prochaska, Velicer, Fava, Ruggiero et al., 2001), a series of brief provider phone contacts were

tested as an adjunct to the mailed report. At 24 months, those who received the report again showed

greater point abstinence (21%) as compared to control (15%), with no additional benefit of phone contact.

Using a similar design, Curry, McBride, Grothaus, Loutie, and Wagner (1995) recruited smokers from

a large health maintenance organization (HMO) who were not requesting help with cessation. Using an

incremental design, the authors randomized participants to receive: (1) a self-help manual; (2) a self-help

manual and computer-generated feedback; (3) a self-help manual, computer-generated feedback, and

three telephone calls to reinforce use of the self-help materials; or (4) control. The feedback included

information on stage of change, self-identified reasons for wanting to quit, and cessation advice. The

counseling condition focused on encouraging smokers to use the self-help materials. Telephone

counseling (but not mailed feedback) significantly increased cessation at 3 months (11%, 4%, 5%, and

6% point prevalence for counseling, feedback, booklet, and control conditions, respectively). However,

differences were nonsignificant at 12 and 21 months.

Etter and Perneger (2001) randomized participants to a mailed intervention or control. An average of

1.5 times over a 6-month period, intervention participants received a letter tailored to stage-of-change,

level of dependence, self-efficacy, and other characteristics. Seven months after beginning the program,

intervention participants were more likely to be abstinent than control participants (5.8% vs. 2.2% point

prevalence). In a similar study, Dijkstra, De Vries, and Roijackers (1998) found that tailored information

that emphasized both the benefits of smoking cessation, as well as advice on cessation skills, was more

effective than either presented in isolation.

Three studies compared computer-tailored information to similar, but untailored, information.

Borland, Balmford, Segan, Livingston, and Owen (2003) randomized smokers calling a quitline service

to: (1) mailed untailored self-help materials, (2) computer-generated tailored advice, or (3) computer-

generated tailored advice and brief telephone counseling. At a 3 month follow-up, more individuals in

the tailored advice and counseling condition were abstinent (21% point prevalence) than in the other

conditions (12%), but by 12 months, the effect was nonsignificant when adjusted for multiple

comparisons. Lennox et al. (2001) compared computer-tailored information, non-tailored information, or

control. Interestingly, the authors found that among heavier smokers, the non-tailored letter (but not the

tailored letter) produced greater cessation rates as compared to control. At 6 months, the non-tailored

Notes to Table 2:

Cessation=Rates of cessation at longest follow-up for 24 h point abstinence and 30 day point abstinence. Letters indicate

significant ( p b .05) contrasts between intervention conditions within outcome category: a=a, a=ab, b=ab, apbpc.* Continuous abstinence.y 7 day point abstinence.z 7 day quit attempt.§ Values averaged across number of contacts.f Sustained abstinence of 10 weeks.

S.T. Walters et al. / Addictive Behaviors 31 (2006) 264–277272

group reported cessation rates of 4.4%, as compared to 3.5% and 2.6% in the tailored and control groups,

respectively.

Three studies tested multiple iterations of feedback. Dijkstra, De Vries, Roijackers, and van

Breukelen (1998) found that multiple tailored letters were more effective than either non-tailored

letters or a single tailored letter. In another study, Dijkstra, De Vries, and Roijackers (1999) found that

multiple tailored letters produced greater stage transition and intent to quit, but not actual quit

attempts. Finally, Velicer, Prochaska, Fava, LaForge, and Rossi (1999) compared interactive vs. non-

interactive computer reports, crossed with number of mailed contacts (i.e., one, two, three, or six). At

an 18-month follow-up, there was an effect of the stage-matched materials (average 30-day cessation

rates of 18.4% and 13.1% for tailored and non-tailored, respectively), but no dose-response effect in

terms of number of mailings.

Two studies tested the efficacy of computer-generated feedback as an adjunct to nicotine replacement

therapy. One study (Shiffman, Paty, Rohay, Di Marino, & Gitchell, 2000) evaluated the effectiveness of

feedback materials as an adjunct to nicotine polycrilex gum. In this study, participants who had

purchased nicotine gum were randomized to receive computer-tailored materials with or without a phone

call, or to receive only the standard information packaged with the gum. At 12 weeks, participants who

had received the tailored information reported significantly higher 30-day abstinence rates (27%) as

compared to control (18%), with no additional effect of the phone call (27%). The intervention in this

study appeared to be effective across gender, level of dependence, and self-efficacy. In contrast,

Shiffman, Paty, Rohay, Di Marino, and Gitchell (2001) did not find an effect when similar tailored

information was compared to standard materials among smokers receiving the nicotine patch. At 12

weeks, abstinence rates did not differ between the two groups. However, among participants who

reported using the materials, cessation rates were generally higher.

Three adult studies tested direct contact with interactive computer programs. Lawrence, Aveyard,

Evans, and Cheng (2003) evaluated a cessation program for expectant mothers. Pregnant smokers

presenting at a general practice clinic were randomized to receive: (1) brief cessation advice delivered by

a midwife, (2) bEnhancedQ cessation advice from a midwife trained in the Transtheoretical Model, plus a

series of self-help manuals, or (3) bEnhancedQ cessation advice from a midwife trained in the

Transtheoretical Model, a series of self-help manuals, and a 20-min computer program that provided

feedback on stage-of-change and other aspects of smoking. At 30-weeks gestation and 10 days postnatal,

there were no significant differences between groups in terms of cessation rates. Lenert, Munoz, Perez,

and Bansod (2004) tested an automated email system that sent timed educational messages to smokers.

The authors used two consecutive waves of participants visiting a website. The first wave received a

single-point-in-time educational message via a website, and the second wave received an enhanced

website intervention that also sent follow-up emails. Although the two groups were not randomly

assigned, the authors suggest that they did not differ on factors associated with 30-day quit rates. At a

30-day follow-up, the enhanced intervention appeared to increase the rate at which participants set a quit

date (97%) as compared to the standard website (91%). Participants in the enhanced group also reported

greater rates of 24-h quit attempts (83% vs. 54%) and 30-day quit rates (13.6% vs. 7.5%). Finally,

O’Neill, Gillispie, and Slobin (2000) compared a computer intervention targeted to college students to a

non-smoking focused control intervention. The smoking intervention consisted of four computer

sessions that progressed along the lines of the stages of change. Early sessions emphasized

consciousness raising and self-evaluation, whereas later sessions targeted commitment and planning.

At the end of the 6-week study period, 48% of the intervention participants showed forward movement

S.T. Walters et al. / Addictive Behaviors 31 (2006) 264–277 273

in their stage of change as compared to 21% in the control group. Although there was an initial trend

towards greater quitting in the intervention group, by seven months quit rates did not differ between the

groups.

5. Discussion

While computer-based smoking prevention and cessation programs show promise in influencing

tobacco-related behaviors, published studies show mixed results in terms of translating the educational

experience to real-world practice. Of the 19 automated, computer-based interventions that we reviewed,

nine (47%) showed evidence of effectiveness at the longest follow-up.

This review should not be considered an exhaustive analysis. The scope of our investigations was

limited to English-language publications catalogued in three specialist databases. Published studies of

this type are also subject to publication bias-the tendency for studies that show beneficial effects to be

published. As we have noted, study components, subject characteristics (demographics and intention to

treat), length of follow-up, and outcome measures were highly variable. This heterogeneity of methods

makes it difficult to determine consistent predictors of efficacy. Insufficient reporting of the

interventions, subject characteristics, and outcome measures further compounded this difficulty. For

instance, in some studies the outcome measure was simply described as babstinenceQ or bpointabstinenceQ with no further information on how these outcomes were operationalized.

Despite these limitations, three observations are consistent with a previous review (Strecher, 1999).

Studies with smokers who were treatment seeking (e.g., who wanted to quit smoking) typically resulted

in higher abstinence rates than studies that used general population samples of smokers. Second, most

studies that used multiple follow-up points found that the effects of the intervention were lessened with

time. Finally, in all cases, the intervention conditions produced rates of abstinence that were at least

equivalent, if not higher, than non-treatment controls. That is, no intervention condition seemed to make

smokers more likely to smoke relative to control.

Aside from these broad observations, there remains a lack of clarity about what types of computer-

based applications are most effective. The paradox is that while the number of smoking cessation

programs is growing (high dissemination), we have little understanding of how, why, and under what

conditions, such interventions might work (low evaluation). For instance, although Edwards, Elliott,

Conway, and Woodruff (2003) identified 87 Internet programs devoted to teen smoking cessation, our

review found no outcome studies of any of these programs in the peer reviewed literature. To estimate

and improve the effectiveness of computer- and Internet-based interventions, it will be important for

future research efforts to emphasize the importance of (1) theoretical foundations to design and

develop computer-based programs, and (2) rigorous evaluation methods to determine their

effectiveness.

The use of computers for generating tailored interventions has evolved with the capabilities of

computer technology. As with Strecher’s (1999) review, the modal intervention appeared to follow

what Brug, Campbell, and van Assema (1999) call bfirst generationQ formats. In this format,

participants have no direct contact with the computer. Typically, participants complete a paper

screening instrument, which is then entered into the computer and used to derive mailed feedback. Of

the 14 first generation interventions in this review, eight (57%) reduced smoking over control. In a

bsecond generationQ format, participants interact directly with the system. This format provides

S.T. Walters et al. / Addictive Behaviors 31 (2006) 264–277274

immediate communication between participant and program, but the participant must have access to,

and know how to operate, a computer. Research using second generation tailoring has demonstrated

improved disease management and behavioral determinants of awareness and intentions in other areas

(Bartholomew et al., 2000; Onema, Brug, & Lechner, 2001; Shegog et al., 2005). However, the

effectiveness of second generation tailoring has not been widely tested in the field of smoking

cessation. Only one intervention, a smoking website that generated personalized email messages,

appeared to use this format (Lenert et al., 2004). This intervention increased cessation over control at

12 months. The recently emerging bthird generationQ of computer-based applications employs both

iterative as well as ipsitive feedback, adjusting at multiple time points to the characteristics of the user.

These programs are structured modularly (rather than as a single linear program), so that they can

better adjust to the user’s needs. For instance, whereas a second generation program might tailor a

series of messages, a third generation intervention might add, delete, or rearrange components in

response to the user. None of the third generation interventions in this review (2 for adolescents, 2 for

adults) had a significant impact on smoking over control.

Because the number of computer-based health education programs has increased significantly over

the past ten years, there is a need for smoking prevention and cessation programs that are

theoretically-and empirically-based (Revere & Dunbar, 2001; Rhodes, Fishbein, & Reis, 1997; Skinner

& Kreuter, 1997). Explanatory models of behavior change propose various factors that are thought to

underlie adoption or rejection of a given behavior. Such a theory should be the basis for specifying

program objectives, health behaviors, cognitive determinants of behavior (e.g., knowledge, attitudes,

social perceptions, self-efficacy), change methods, and evaluation and measurement protocols

(Lieberman, 1997; Revere & Dunbar, 2001; Rhodes et al., 1997; Skinner & Kreuter, 1997). Program

design must further impact the array of behavioral determinants by offering an engaging experience

and thereby optimize the chance for translation of computer messages to real-world application

(Shegog et al., 2001). To tailor interventions, programs use a variety of variables, such as gender, level

of problem severity, and motivational readiness (e.g., Stage of Change) for change. However, because

many of these studies did not find a differential effect of the intervention across gender, ethnicity, or

problem severity, future studies will need to determine which types of tailoring are most effective, and

for whom.

Increased rigor in the design of evaluation studies is also necessary to determine which computer-

based smoking programs best affect behavioral outcomes. Adler and Johnson (2000) have noted some

of the shortfalls of existing computer-based research, including demonstration articles over comparison

studies, inexperience of investigators studying computer applications, and studies that compare

interventions that vary in both content and media formats. Future research directions include

investigations of user-media-message interactions to understand effective educational strategies rather

than comparisons of different media approaches, economic analyses regarding the cost and time

benefits of computer-based applications, and diffusion studies that examine how technology might be

best integrated into educational and healthcare settings (Adler & Johnson, 2000; Street & Rimal,

1997).

In the scope of public health interventions, computer-based applications have been available for a

relatively brief time. During this time, however, generations of applications have evolved that have

demonstrated some effectiveness in changing smoking behavior. Increased rigor in design, development,

and evaluation of future programs will provide better insight into how to affect this persistent public

health problem.

S.T. Walters et al. / Addictive Behaviors 31 (2006) 264–277 275

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