- Email: [email protected]
Website-Delivered Physical Activity Interventions
Review and Special Articles
Website-Delivered Physical Activity Interventions A Review of the Literature Corneel Vandelanotte, PhD, Kym M. Spathonis, BA, Elizabeth G. Eakin, PhD, Neville Owen, PhD Background: Evidence-based physical activity interventions that can be delivered to large numbers of adults at an acceptable cost are a public health priority; website-delivered programs have this potential. The purpose of this study was to systematically review the research findings and outcomes of website-delivered physical activity interventions and to identify relationships of intervention attributes with behavioral outcomes. Methods:
A structured search of PubMed, Medline, PsycInfo, and Web of Science was conducted for intervention studies published up to July 2006. Studies included in the review were those that (1) used websites or e-mail, (2) had physical activity behavior as an outcome measure, (3) had randomized controlled or quasi-experimental designs, (4) targeted adults, and (5) were published in English.
Results:
Of the fifteen studies reviewed, improvement in physical activity was reported in eight. Better outcomes were identified when interventions had more than five contacts with participants and when the time to follow-up was short (ⱕ3 months; 60% positive outcomes), compared to medium-term (3– 6 months, 50%) and long-term (⬎6 months, 40%) follow-up. There were no clear associations of outcomes with other intervention attributes.
Conclusions: A little over half of the controlled trials of website-delivered physical activity interventions have reported positive behavioral outcomes. However, intervention effects were short lived, and there was limited evidence of maintenance of physical activity changes. Research is needed to identify elements that can improve behavioral outcomes, the maintenance of change and the engagement and retention of participants; larger and more representative study samples are also needed. (Am J Prev Med 2007;33(1):54 – 64) © 2007 American Journal of Preventive Medicine
Introduction
R
egular physical activity decreases the risk of developing cardiovascular disease, diabetes, some cancers, obesity, osteoporosis, and other chronic conditions.1 Public health guidelines for adults recommend participating in at least 30 minutes of moderate-intensity physical activity on 5 or more days of the week.2 However, the proportion of the adult population meeting these guidelines is less than 50% in many industrialized countries.1,3– 6 Thus, finding effective population-based intervention strategies to promote physical activity is a key challenge. The Internet holds promise for wide-scale promotion of physical activity behavior change. Website-delivered interventions have a potentially broad population reach with availability 24 hours per day and widespread From the Cancer Prevention Research Centre, School of Population Health, The University of Queensland, Australia Address correspondence and reprint requests to: Corneel Vandelanotte, PhD, Cancer Prevention Research Centre, School of Population Health, Level 3, Public Health Building, The University of Queensland, Herston Road, Herston, Queensland 4006. E-mail: [email protected].
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accessibility,7,8 they have the potential to be hosted and promoted through state health agencies and nongovernmental organizations,7 and they are potentially costeffective.8 In many industrialized countries, Internet access is greater than 50% and is increasing.9 Internet users are becoming more representative of the overall population, with users including more women, older adults, and people with low levels of educational attainment.10,11 The Internet is identified as an important source of health information by more than half of its users, and may thus be an appropriate delivery medium for health behavior change interventions.12 Website-delivered physical activity interventions have the potential to overcome many of the barriers associated with traditional face-to-face exercise counseling or group-based physical activity programs. An Internet user can seek advice at any time, any place, and often at a lower cost compared with other delivery modalities.13 Although there may be many advantages of using the Internet for delivering health behavior change programs, to reach their public health potential, websitedelivered physical activity interventions must first dem-
Am J Prev Med 2007;33(1) © 2007 American Journal of Preventive Medicine • Published by Elsevier Inc.
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onstrate feasibility and efficacy through rigorous scientific testing. In 2000, the American Journal of Preventive Medicine published a set of articles that identified the potential of interactive health communications, including Internet and website-delivered interventions, for improving health behaviors.14 –17 At that time, there was little empirical literature to support these optimistic predictions. Since then, a wide range of studies evaluating website-delivered health behavior interventions have been reported. Several reviews have examined Internet or website-delivered health behavior change interventions;13,18 –21 however, none have specifically focused on physical activity. A systematic review of study outcomes for website-delivered physical activity interventions is thus timely. Three questions will be addressed: (1) What is the efficacy of website-delivered physical activity interventions? (2) Are there specific intervention elements that influence efficacy? (3) How well are participants engaged and retained? Answers to these questions inform an evaluation of the state of the evidence and are used to formulate guidelines and recommendations for future research.
Methods The study protocol was adapted from previously published reviews22–24 and based on guidelines from the Cochrane Reviewers’ Handbook.25 The articles were independently reviewed and abstracted. Disagreements were discussed among the co-authors until consensus was reached.
Search Strategy and Data Sources A structured electronic database search of PubMed, Medline, PsycInfo, and Web of Science was conducted for intervention studies published up to July 2006. The following search terms were used: “exercise” or “motor activity” or “leisure activities” or “physical activity” or “walking” or “physical fitness” and “Internet” or “website-delivered” or “web-based.” The search terms were limited to adults and English-language peerreviewed publications. Further, contacts with colleagues working in this area and reference lists of relevant publications were examined in an effort to identify all relevant publications.
Selection of Studies For inclusion, studies had to evaluate a website-delivered intervention to improve physical activity that used the Internet or e-mail. Physical activity needed to be an outcome measure, but did not have to be the only or primary study outcome. Studies that also targeted other behaviors (e.g., dietary habits) or that included additional intervention components (e.g., face-to-face or telephone sessions) were included. Although randomized controlled trials were preferred, quasi-experimental studies with a pre-test post-test comparison group design were also allowed.
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Data Extraction Information was extracted and tabulated for studies meeting the inclusion criteria: (1) number of participants at baseline; (2) country where the study was conducted; (3) participation rate (defined as the percentage participating of those who were reached and eligible); (4) attrition; (5) recruitment method; and (6) study setting. Intervention characteristics were listed as “focus” (e.g., physical activity or weight loss), “groups” (a description of the different comparison and control groups), “additional to Internet” (intervention elements that were not Internet delivered), “other behaviors targeted” (behaviors, such as food choices, that were targeted in addition to physical activity), “duration” (length of intervention), and “follow-up” (the number and timing of follow-up measurements using baseline as a reference). A number of specific intervention characteristics related to the use of the Internet were tabulated: the format in which the Internet was used; the frequency of contacts made through the Internet during the study (e.g., e-mail, chat sessions, or weekly modules); and the theory on which the intervention was based. Interactive components of the intervention were also detailed (e.g., online coach, interactive tailoring of advice, discussion forums), as it has been suggested that the level of interactivity of website-delivered interventions is related to engagement and retention of participants.13,26 For this review, the primary outcome measure was change in physical activity behavior. Secondary outcome measures that related to physical activity were also examined; for example, change in body weight or in “stage of change” for physical activity.27
Study Outcomes To determine whether the interventions had a significant impact on physical activity behavior, we separately evaluated outcomes at each follow-up time point: short-term (ⱕ3 months), medium-term (4 – 6 months), and long-term (⬎6 months).22 At each time point, studies were coded as having a positive outcome if significant differences between groups were reported. However, when there was a significant increase in physical activity in all groups, but no significant differences between groups, this was also coded as a positive outcome, but only when different intervention delivery modes were compared to each other (not for studies in which there was a no-intervention control group). To allow for comparison across studies, effect sizes were calculated according to Cohen’s formula.28 However, they were only reported when significant differences between intervention groups were found and when the data reported allowed their calculation. Effect sizes were interpreted according to Cohen’s guidelines:28 ⬍0.5 for a small effect size, 0.5– 0.8 for a medium effect size, and ⬎0.8 for a large effect size.
Results Study Selection The initial search across the four databases yielded 512 publications. Based on title and abstract, reviewing duplicates and irrelevant publications were eliminated, Am J Prev Med 2007;33(1)
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resulting in 77 studies. Reference lists of these publications yielded another 13 publications, in addition to seven publications that were already in the hands of the authors and two in-press publications that were provided by colleagues. After reviewing these 99 publications, 84 were excluded, as they did not meet one or more of the inclusion criteria. The majority (n⫽68) of excluded studies did not evaluate website-delivered physical activity interventions and were theoretic or methodologic papers that primarily discussed the potential for using the Internet to deliver health behavior change interventions. Other publications were excluded because they did not have physical activity as an outcome (n⫽8), they evaluated interventions with children or adolescents (n⫽7), or they were not in the English language (n⫽1). Fifteen studies were included in the final review.26,29 – 46 Table 1 gives an overview of the intervention characteristics of the studies included. Before addressing the three questions posed for this review, descriptive information on the overall attributes of the studies included is presented.
Study Attributes Descriptives. The total number of participants across the 15 studies was 4845; the number of participants across the studies ranged from 13 to 2121, with seven studies having fewer than 100 participants. All but one trial43 reported higher rates of female than male participation; overall, 3011 (66%) of participants were women. In all studies, the level of educational attainment of participants was high; in total, 84% of participants had a college or university education. Most interventions targeted an increase in total physical activity as an outcome, although some specifically targeted walking, moderate physical activity, vigorous physical activity, or meeting physical activity guidelines. Seven studies intervened on physical activity only, whereas eight studies also addressed other behaviors, most commonly dietary behaviors. Eight interventions had additional components that were not websitedelivered. Nine studies explicitly based their intervention on one or more health behavior theories; the most common were Social Cognitive Theory47 (n⫽3), the Transtheoretical Model27 (n⫽6), and the Theory of Planned Behavior48 (n⫽2). Methodology. Fourteen studies were randomized controlled trials, and one study was a cluster nonrandomized controlled trial.46 Four studies recruited a true no-intervention control group,32,35,39,41 whereas the majority compared a website-delivered intervention to another intervention. Four studies used a different delivery mode (such as print materials or face-to-face counseling) as the control or comparison condition.31,33,34,37 Seven studies used a standard or lowintensity website-delivered intervention as the compar56
ison.29,38,42– 46 Four studies reported the use of computer sampling to randomize participants.11,37,38,45 One study reported that research staff were blind to participants’ treatment condition.37 All studies reported on attrition, and ten reported on participation rate. The use of validated instruments to measure physical activity was reported in all but one study.46 Six studies used intention-to-treat analysis to account for high attrition,33,34,37,43– 45 and six studies reported power calculations.31,34,37,41,44,46
Efficacy of Website-Delivered Physical Activity Interventions Overall outcomes. Positive changes in physical activity behavior were reported in eight of the fifteen websitedelivered intervention studies.33,34,38,39,41– 44 In five studies with positive outcomes, it was possible to calculate effect sizes (range, 0.13 to 0.67), resulting in a small mean effect size of 0.44. Time of follow-up. Ten studies reported short-term outcomes, six38,39,41,42,44,45 with positive changes in physical activity (60%). Eight studies reported mediumterm outcomes, four33,34,43,44 with positive outcomes (50%). Five studies reported long-term outcomes, two34,42 with positive outcomes (40%). Maintenance. With regard to the impact of websitedelivered interventions on the maintenance of physical activity behavior change, it is important to evaluate the time of physical activity measurement relative to the length of the intervention. When physical activity was measured before the end of the intervention, six of seven studies showed positive results (86%). When it was measured immediately after the end of the intervention, five of twelve studies showed positive outcomes (42%). Five studies measured physical activity following the end of the intervention: one within 3 months (negative outcome), three between 3 and 6 months (two with positive outcome), and one longer than 6 months (positive outcome). Secondary outcomes. Eleven studies reported on secondary outcome measures related to physical activity (not reported in Table 1). Five studies evaluated change in body weight (kg), with a significant reduction in weight reported in four of those studies. It is important to note that in two of the weight-loss studies that reported positive weight reduction outcomes,32,45 there were no positive effects on physical activity; although physical activity did not significantly change, it was part of an overall successful strategy to reduce weight, which included dietary behavior change. Four studies evaluated progress in the stage of motivational readiness for change in physical activity, all of which showed positive outcomes. One study mentioned flexibility as a secondary outcome measure; flexibility sig-
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Table 1. Overview of characteristics and outcomes of included studies (U.S. unless otherwise noted) Study
Design and participants
Glasgow (2003)29 Feil (2000)30
Participants: 320 patients Focus: Diabetes self-management with type 2 diabetes Groups: (a) access to Internet (40 –75 years; mean diabetes information ⫹ selfage⫽59) management training; (b) Participant rate: NR access to Internet diabetes Attrition rate: 18% information ⫹ peer support; Recruitment: Primary (c) access to Internet diabetes care offices info ⫹ self-management Setting: Internet training ⫹ peer support; (d) Design: RCT access to Internet diabetes information only. Additional to Internet: Computer with Internet access placed in participants’ home ⫹ initial face-to-face training Other behaviors targeted: Dietary habits Duration: 10 months Follow-up: 10 months Participants: 31 women Focus: PA (50 – 69 years; mean Groups: (a) Website-delivered age⫽56) computer-tailored newsletter; Participant rate: NR (b) standard newsletter Attrition rate: 10% Additional to Internet: Initial Recruitment: Newspaper face-to-face for measurement ad and explaining procedures. Setting: Internet at home Postcard notifying of Design: RCT availability of online newsletter Other behaviors targeted: NA Duration: 2 months Follow-up: 3 months Participants: 44 obese Focus: Weight loss adults (31– 60 years; Groups: (a) Internet therapistmean age⫽46) led (b) in-person support; (c) Participant rate: 57% no treatment Attrition rate: 7% Additional to Internet: 15-week Recruitment: Newspaper therapist-led weight control ads program prior to Internet Setting: Internet intervention (all groups) Design: RCT Other behaviors targeted: Dietary habits Duration: 22 weeks Follow-up: 22 weeks Participants: 122 healthy Focus: Weight loss overweight adults Groups: (a) Internet support; (26 –77 years; (b) frequent in-person support mean age⫽48) ⫹ telephone calls; (c) minimal Participant rate: NR in-person Attrition rate: Additional to Internet: 24-week 6 months: 18% therapist-led weight control 18 months: 24% program prior to Internet Recruitment: Newspaper intervention (all groups) ads Other behaviors targeted: Setting: Internet Dietary habits Design: RCT Duration: 12 months Follow-up: 6, 12, and 18 months
Hageman (2005)31
Harvey-Berino (2002)32
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Intervention
Internet
Outcome measures (OM)
Intervention effects
Process measures
Format: Automated dietary goal Primary OM: moderate PA, setting (all); twice-weekly walking, meeting PA access to online coach for selfguidelines management training (a ⫹ c); Instrument: PA Scale for the diabetes peer support forum, Elderly chat, and newsletters (b ⫹ c) Validated: Yes Frequency: 80 possible contacts with online coach and/or 5 newsletters Interactivity: goal setting, chat, forum, newsletters, online coach Theory: SET and SST
Long-term: No significant Website log on gradually increase in moderate PA, decreases (more than 50%) meeting PA guidelines or during the intervention for all walking between or within groups groups Mean of 7.2 log ons per participant per month Greater web use for peer support groups
Format: 4-page computerPrimary OM: Total PA (kcal/ tailored Internet newsletter day) Frequency: 3 newsletters Instrument: Modified 7-Day Interactivity: Computer-tailored Activity Recall advice Validated: Yes Theory: NR (benefits and barriers of PA, self-efficacy, PA goals)
Short-term: No increases within and between groups for total PA
Format: Biweekly chat sessions Primary OM: Total PA and e-mail facilitated by group Instrument: Paffenbarger PA therapist; self-monitoring questionnaire webpage Validated: Yes Frequency: 11 e-mails/chat sessions Interactivity: chat sessions, video, e-mail, bulletin board, questionnaires; discussion group Theory: NR
Medium-term: No significant About group-a: difference between groups 24% would prefer to be in gr-b in PA change 49% think being in gr-b would be more successful 41% could not communicate effectively compared to 0% in gr-b
Format: Biweekly chat sessions Primary OM: Total PA and e-mail facilitated by group Instrument: Paffenbarger PA therapist, self-monitoring questionnaire webpage Validated: Yes Frequency: 11 e-mails/chat sessions Interactivity: chat sessions, video, e-mail, bulletin board, questionnaires; discussion group Theory: NR
Medium-term: Significant Attendance at 12 months is increase in PA across all greater in gr-b compared to groups. gr-a (54% vs 39%) Long-term: Significant higher At 12 months 70% of gr-a would PA in gr-b at 18 months prefer to be in gr-b More peer support contacts in gr-a No differences in between groups for submitting selfmonitoring data
Initial face-to-face was helpful for retrieving the newsletters (73%) 50% read the newsletters online; 80% printed a copy; 83% read all of the newsletters
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Participants: 232 overweight and obese adults (20-78; mean age⫽46) Partic. Rate: NR Attrition rate: 9 months: 9% 12 months: 24% 18 months: 31% Recruitment: newspaper ads Setting: Interactive TV studios and Internet Design: RCT bold⬎Participants: 151 Kosma (2005)35 Kosma (2005)36 sedentary with physical disabilities adults (1844 years; mean age⫽39) Partic. Rate: NR Attrition rate: 1 month: 50% 6 month: 70% Recruitment: flyer distributed at rehab centers and hospitals Setting: Internet Design: RCT Participants: 655 Marshall university staff (20-49 (2003)37Australia Leslie (2005)26 Australia years; mean age⫽43) Partic. Rate: 59% Attrition rate: 22% Recruitment: University e-mail list Setting: Internet at worksite Design: RCT Participants: 78 McKay (2001)38 sedentary adults with type 2 diabetes.(ⱖ 40 years; mean age⫽52) Partic. Rate: 38% Attrition rate: 13% Recruitment: E-mail postings to diabetes on-line groups Setting: Internet Design: RCT
Focus: Weight loss Groups: (a) Internet support; (b) frequent in-person support ⫹ telephone calls; (c) minimal in-person Additional to Internet: 6 month weight control program with (using interactive TV) prior to internet intervention (all groups) Other behaviors targeted: Dietary habits Duration: 12 months Follow-up: 6, 12 and 18 months Focus: PA Groups: (a) web-based PA program; (b) control Additional to Internet: NA Other behaviors targeted: NA Duration: 4 weeks Follow-up: 4 weeks and 6 months
Format: Biweekly chat sessions Primary OM: Total PA and e-mail facilitated by group Instrument: Paffenbarger PA therapist; self-monitoring questionnaire. webpage Validated: Yes Frequency: 26 e-mails/chat sessions Interactivity: chat sessions; video; e-mail; bulletin board; questionnaires; discussion group Theory: NR
Format: motivational PA website Primary OM: leisure-time PA Short-/Medium-term: No The web-based program had with weekly lesson plans (MET-hours/day) differences in PA between overall good scores for ease of Frequency: 4 Instrument: 13 item PA Scale for groups at 6 months use, appeal, ease of Interactivity: Weekly targeted Individuals with Physical understanding and materials, e-mail, discussion Disabilities helpfulness board Validated: Yes Theory: TTM (also: self-efficacy, decisional balance, goal setting, social support)
Format: Stage-based website ⫹ e-mails sent at 2 week intervals Frequency: 4 Interactivity: Interactive features, stage-based quizzes, goal setting, activity planning, emails Theory: TTM (also: rewards, selfmonitoring, cues to action, social support) Focus: PA Format: Diabetes selfGroups: (a) Diabetes selfmanagement website (login management website ⫹ PA ⱖ1 per week); personal PA tracking ⫹ online coach; (b) database, online coach and PA access to Internet diabetes info resources ⫹ blood glucose tracking Frequency: 5 personal messages Additional to Internet: NA form online coach Other behaviors targeted: NA Interactivity: messages from Duration: 8 weeks coach; peer support group; Follow-up: 8 weeks chat; PA tracking Theory: SEM for diabetes selfmanagement Focus: PA Groups: (a) stage-targeted website program; (b) stagetargeted print program Additional to Internet: NA Other behaviors targeted: NA Duration: 8 weeks Follow-up: 10 weeks
Medium-/ long-term: All - Sig. more self-monitoring and groups sig. increased PA; peer support in gr-a no sig. difference between - Sig. more attendance in gr-b groups - Attendance and self-monitoring were related to weight loss
Primary OM: Short-term: About gr-a: - Total PA: - No sig. PA increase between - 28% read half of info on - Sitting time or within groups website and 62% read half of - Meeting PA guidelines - 10% of gr-a and 11% of gre-mails Instrument: IPAQ Validated: Yes b became sufficiently active - 46% visited website and use at follow-up declined rapidly - Average time spend on website was 9 min, viewing 18 pages
Primary OM: Total PA and Short-term: - More logon, web-pages viewed, walking (min/week) - Sig. PA and walking messages posted, satisfaction Instrument: 11 items from the increase in both groups; and longer session duration in BRFSS no sig. differences between gr-a Validated: Yes groups - Mean of 4.4 logons per month - Sig. larger PA increase in - Decline of program use in both gr-a compared to gr-b for groups participants that used the program 3 or more times (ES⫽0.54)
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Table 1. Overview of characteristics and outcomes of included studies (U.S. unless otherwise noted)(continued) Napolitano(2003)39 Sciamanna (2002)40
Plotnikoff (2005)41 Canada
Rovinak (2005)42
Spittaels (2007)43 Belgium
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Participants: 65 sedentary hospital employees (18-65 years; mean age⫽43) Partic. Rate: 67% Attrition rate: 20% Recruitment: via e-mail, print and intranet of hospitals Setting: Internet at home or worksite Design: RCT Participants: 2121 worksite employees (mean age⫽45) Partic. Rate: 16% Attrition rate: 18% Recruitment: E-mail at 5 worksites Setting: Internet at worksite Design: RCT Participants: 61 sedentary women (20-54 years; mean age⫽40) Partic. Rate: 90% Attrition rate: 21% Recruitment: radio, TV, newspaper and flyers Setting: Internet Design: RCT
Participants: 526 adults (25-55 years; mean age⫽40) Partic. Rate: 92% Attrition rate: 29% Recruitment: e-mails, posters and newsletters in 6 worksites Setting: home or worksite Internet Design: RCT Participants: 91 healthy, overweight hospital employees. (18-60 years; mean age⫽41) Partic. Rate: 80% Attrition rate: 22% Recruitment: via e-mail and intranet of hospital Setting: Internet Design: RCT
Focus: PA Groups: (a) Website ⫹ e-mail; (b) waiting list control group Additional to Internet: NA Other behaviors targeted: NA Duration: 3 months Follow-up: 1 and 3 months
Format: Access to Internet Web site plus weekly e-mail tip sheets Frequency: 12 e-mails Interactivity: targeted info, activity quiz, e-mail, web links Theory: SCT and TTM
Primary OM: moderate PA and walking (min/week) Instrument: BRFSS Validated: Yes
Focus: PA and nutrition Groups: (a) e-mail PA messages (b) waiting list control group Additional to Internet: NA Other behaviors targeted: Dietary habits Duration: 12 weeks Follow-up: at week 13
Format: Weekly e-mail messages; access to website with all the messages and resource file Frequency: 12 e-mails Interactivity: E-mails Theory: SCT, TTM, TPB
Primary OM: Short-term: NR - Total MET/min PA - Sig. higher increase in total Workplace PA level MET/min PA in gr-a Instrument: Godin Leisure Time compared to gr-b Exercise questionnaire (ES⫽0.13) Validated: Yes - Sig. increase in workplace PA levels in both groups; no difference between groups
Focus: Walking Groups: (a) e-mail walking program with general SCT feedback; (b) e-mail walking program with tailored SCT feedback Additional to Internet: 30 min introductory session with walking instructions (both groups) Other behaviors targeted: NA Duration: 12 weeks Follow-up: 3 weeks and 1 year Focus: PA Groups: (a) online tailored PA advice ⫹ e-mails; (b) online tailored PA advice; (c) online non-tailored standard PA advice Additional to Internet: NA Other behaviors targeted: NA Duration: 8 weeks Follow-up: 6 months
Format: Walking logs submitted via e-mail; tailored e-mail feedback response; reminders and walking info e-mailed before walking logs were due Frequency: 24 e-mails Interactivity: Tailored e-mails Theory: SCT
Primary OM: - 1 mile walk time - days and minutes walked in past 2 weeks Instrument: - 1-Mile Walk Test - 2 items from National Health Interview Survey Validated: Yes
Short-term: - Sig. improvement in 1-mile walk test time in gr-b (ES⫽0.67) - Improvement in estimated VO2max in gr-b (trend p⫽0.08) Long-term: Gr. b walked twice as often in past 2 weeks as gr-a (trend p⫽0.08)
- Greater program satisfaction in gr-b - No differences in reading program manual, weekly tips or walking feedback between groups - No difference in using walking logs
Format: Website with tailored feedback Stage-based e-mails (gr-a only) Frequency: 1 tailored advice ⫹ 5 e-mails Interactivity: immediate onscreen feedback, e-mails and interactive action plan Theory: TTM and TPB
Primary OM: -Total PA (mins/week) - Moderate ⫹ high-intensity PA Instrument: 31 item IPAQ Validated: Yes
Medium-term: Sig. increase in total PA and moderate and high-intensity PA in all groups; no sig. differences between groups
- Tailored advice more read, printed and discussed with others compared to standard advice - E-mail is helpful for 50% of gr-a - Better recall and self-reported effectiveness in tailored groups
Focus: Weight loss Groups: (a) Internet education; (b) Internet education ⫹ Internet behavior therapy Additional to Internet: Introductory weight-control session (all groups) Other behaviors targeted: Dietary habits Duration: 6 months. Follow-up: 3 and 6 months
Format: weekly behavioral lessons via e-mail (gr-b); selfmonitoring webpage Frequency: 24 e-mails Interactivity: Self-monitoring (online PA diaries); e-mail; bulletin board Theory: NR (social support, stimulus control, stress management)
Primary OM: Energy expended in PA Instrument: Paffenbarger PA questionnaire Validated: Yes
Short-/medium-term: Sig. - Greater login in gr-b at all increase in PA in both times groups at 3 and 6 months; - Login frequency is sig. no sig. differences correlated with weight loss in between groups both groups - Mean of 4.3 logins per participant per month
Short-term: NR - Moderate PA was sig. higher in gr-a at 1 month (ES⫽0.32) compared to gr-b - Walking was sig. higher in gr-a at 1 (ES⫽0.29) and 3 (ES⫽0.58) months compared to gr-b
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Primary OM: - Light/moderate PA - vigorous PA Instrument: 2 items Validated: NR Format: Website with computertailored PA info, resource library, and printout options Frequency: at least 1 visit to website Interactivity: Computer-tailored advice Theory: TTM Woolf (2006)46
BRFSS, Behavioral Risk Factor Surveillance System; CT, controlled trial; ES, effect size; IPAQ, International Physical Activity Questionnaire; gr-a, Group (a); gr-b, Group (b); MET, metabolic equivalent; NA, not applicable; NR, not reported; PA, physical activity; RCT, randomized controlled trial; SCT, social cognitive theory; SEM, socioecologic model; SET, self-efficacy theory; SST, social support theory: TPB, Theory of Planned Behavior; TTM, Trans-Theoretical Model.
- Lower website use in gr-a - No differences in user satisfaction between groups Short-/Medium-term: Greater improvement in lightmoderate PA in gr-a at 1 month only (trend: P ⫽ 0.1)
Process measures
Sort-/Medium-/long-term: Sig. NR increase in energy expenditure at 3 months only in gr-b (ES⫽0.56)
Focus: Weight loss Groups: (a) Internet weight loss program; (b) internet weight loss program ⫹ e-mail counseling Additional to Internet: Introductory weight-control session (all groups) Other behaviors targeted: Dietary habits Duration: 12 months Follow-up: 3, 6 and 12 months Participants: 256 patients Focus: Multiple behaviors (18-60 years) Groups: (a) computer-tailored Partic. Rate: 27% website; (b) static information Attrition rate: 69% website Recruitment: Promotion Additional to Internet: NA of website in 6 family Other behaviors targeted: practices via posters, Dietary habits, smoking and telephone hold-line alcohol use messages Setting: Duration: Access to website until Internet follow-up Design: CT Follow-up: 1 and 4 months
Intervention effects
Format: 5 therapist e-mails per Primary OM: Energy expended week in first month; once per in PA week from month 2 (gr. b); Instrument: Paffenbarger PA self-monitoring webpage questionnaire Frequency: 68 e-mails Validated: Yes Interactivity: Weekly tip and link; message board; self-monitoring (online PA diaries); e-mails Theory: NR Participants: 92 overweight adults at risk for diabetes (ⱖ 45 years; mean age⫽49) Partic. Rate: 63% Attrition rate: 16% Recruitment: Newspaper advertisements Setting: Internet Design: RCT Tate (2003)45
Outcome measures (OM) Internet Design and participants Intervention Study
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nificantly increased in both intervention and control groups, however, there were no significant differences between the groups.31
Specific Intervention Elements That Influence Efficacy Intervention duration. In eight studies, the duration of the intervention was equal to or shorter than 3 months, with five (63%) showing positive outcomes. Three interventions lasted between 3 and 6 months, one with positive results (33%). In the four interventions that lasted longer than 6 months, two reported positive results (50%). Number of contacts. Six interventions had one to five “contacts” with participants over the Internet such as via e-mail, weekly website modules, chat sessions, or guidance from an online coach. Only one of these studies (17%) showed positive outcomes. Interventions with more than five contacts were more successful, with seven of nine reporting positive outcomes (78%). Theory-based interventions. Five of nine studies that explicitly stated that the intervention was based on a health behavior theory reported positive outcomes (56%) compared with three of six that did not (50%). Initial face-to-face contact. All of the interventions with additional components to the website delivery of the intervention (eight of the fifteen reviewed) also had an initial face-to-face consultation with participants. This initial contact was used to explain the use of the website-delivered intervention before starting the intervention. Four of those eight studies had positive physical activity outcomes (50%), compared with four of the seven studies that had no additional components (57%). Other behaviors targeted. Seven studies intervened on physical activity only, with four reporting positive outcomes (57%). Studies also targeting dietary behaviors (n⫽8) were as effective, with four of eight (50%) showing positive changes in physical activity. Interaction method. An attempt was made to evaluate whether there was a difference in outcomes for interventions in which participants passively interacted with the intervention materials (e.g., following a link to the website in an e-mail message) versus those in which participants were stimulated and prompted to take action themselves (e.g., engage in an online chat session; enter self-monitored data onto the website). Due to the wide diversity of intervention methods and the use of multiple intervention methods simultaneously, it was not possible to classify the studies into such distinct categories. Behavioral modification. An attempt was made to evaluate whether there was a difference in outcomes for
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interventions that didactically provided physical activity information, versus those that used behavior modification techniques (e.g., goal setting, reinforcement, social support, assessment, and feedback). Again, it was not possible to classify the studies into such categories.
Engagement and Retention of Participants Attrition ranged from 7% to 69% across studies and showed a high overall average of 27% (nine studies had attrition of 20% or higher; five them used intention-totreat to analyze their data). Most studies had some process measures; however, only five studies reported objective data on website usage.26,29,38,44,46 Each of those studies reported low exposure to intervention materials, due to a decline in website use as the intervention progressed. Some studies found that higher levels of website use were associated with stronger intervention effects.44 Outcomes associated with interactive intervention elements were examined. The majority of studies (13 of 15) used several interactive intervention elements such as e-mail, chat sessions, activity planning, discussion groups, an online coach and videos; seven of these studies (54%) had positive outcomes. Eleven studies used e-mail in the intervention, and seven of these (63%) had positive outcomes. Of the four studies using computer tailoring, two (50%) showed positive outcomes. Twelve studies had regular periodic contact with participants to intervene on physical activity and accomplished this by using online coaching, chat sessions, e-mails, or weekly website modules; seven (58%) had positive outcomes.
Discussion This systematic review found modest evidence for the efficacy of website-delivered physical activity interventions, with a little over half of the studies reporting significant positive behavioral changes. However, the effect sizes for studies that found positive outcomes were small, and the intervention effects were mostly short lived. There was a clear decrease in efficacy when time to follow-up increased, with studies that had longer follow-ups being associated with less positive outcomes. However, among the handful of studies measuring maintenance of physical activity following the end of the intervention, there was some suggestion of maintenance of outcomes, with three of five studies showing positive outcomes. When evaluating specific intervention elements, the number of contacts with participants was the only factor associated with efficacious outcomes. Interventions with greater than five communications, such as via e-mail, discussion boards, chat sessions, or online coaches demonstrated more positive changes in physical activity than those with five or fewer contacts. This is July 2007
not surprising, as it is reported in the health behavior change literature that efficacy increases when intervention intensity increases.49,50 However, this has also been reported with regard to the duration of the intervention51—an observation that could not be confirmed in this review. There seemed to be no clear relationship between the duration of the intervention and outcomes. Interventions shorter than 3 months did relatively well; five of eight had positive outcomes. The lower efficacy rates for interventions longer than 3 months might be explained by the strong decline in website usage over the course of the intervention, as discussed in the information to follow. Only five of the fifteen studies reported objective data on website usage. These studies used electronic databases linked to the website-delivered program to monitor “hits” and the time that participants spent interacting with the intervention materials. They all reported low website usage and a decline in use as the intervention progressed. From this, and in combination with the overall high levels of attrition and the modest medium- and long-term outcomes of the studies evaluated, it can be concluded, as has been argued previously,13,26 that engagement and retention of participants in website-delivered physical activity interventions is challenging. Within this context, it is encouraging that the shorter-term interventions showed good results; short and effective interventions are needed where engagement and retention of participants in longerlasting interventions appears to be difficult. However, to increase the long-term efficacy of website-delivered interventions and to produce true maintenance of physical activity behavior change, methods to increase website use and exposure to intervention materials and subsequently increase engagement and retention of participants need to be developed. This is emphasized by the fact that stronger intervention effects were found within subgroups of participants that used the website more and thus had more exposure to intervention materials.44 However, it is possible that only those participants that were very motivated to increase their physical activity were willing to keep using the websitedelivered intervention and therefore had better outcomes. Nevertheless, gathering objective data on website usage is technically possible and is an important indicator of intervention effectiveness that should be used in all future trials. Increasing the interactivity of these interventions, to enhance engagement and retention of participants, has been the most consistent recommendation made in the literature.13,26,39,52,53 However, the results of this review do not indicate that this alone will be sufficient to increase intervention effectiveness. The majority of studies evaluated (n⫽13) already used several interactive elements in their interventions. Most studies also used e-mail (n⫽11) and had some form of periodic contact with participants (n⫽12). Nevertheless, engageAm J Prev Med 2007;33(1)
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ment and retention of participants in these studies was low. It has also been suggested that using a form of computer tailoring that is embedded in the websitedelivered intervention might increase interactivity and in turn enhance participant retention and outcomes.8,54 In using computer tailoring, an online questionnaire is linked to scoring algorithms that allow participants to be provided with immediate and personally relevant feedback about their health behavior, plus individually adapted tips and suggestions on how to change.22,55 Four studies evaluated this technique and two found positive results. However, given the small numbers of studies, it is difficult to draw any conclusions about the use of computer tailoring, other than that it is a potential strategy to increase efficacy of website-delivered interventions that need to be further explored. If it appears that incorporating interactive elements in website-delivered interventions is not enough to engage and retain participants, what then may be the main source of difference between those studies that found positive outcomes and those that did not? To examine this question, an attempt to evaluate the method of interaction (active or passive) used by the interventions and to determine whether they used behavioral modification techniques was made. It was hypothesized that interventions that stimulate action and use behavior modification techniques might be more effective in engaging and retaining participants. However, as previously mentioned, it was impossible to classify the studies into such distinct categories. Direct comparisons of specific intervention elements are thus needed to determine which are effective and to help build the evidence base necessary to inform future website-delivered interventions. The findings of this review indicate that programs without a face-to-face contact with participants at the beginning of the intervention are as effective as those that have an initial face-to-face contact. This might be explained by the absence of actual behavioral counseling in those initial contacts. This observation has important implications for the implementation of websitedelivered interventions, as their reach would be greatly extended if face-to-face contact was not necessary. Intervening on multiple health behaviors simultaneously also did not seem to influence physical activity outcomes negatively, when compared with studies that intervened on physical activity only. Website programs provide opportunities to test the hypothesis that change in one behavioral risk factor might serve as a stimulus or “gateway” for change in other health behaviors and might create additive or synergistic intervention effects.56,57 Although the Internet is increasingly used by people with lower educational attainment,11 this is clearly not yet reflected in the samples recruited in the studies 62
reviewed (84% of all participants were college or university educated); this limits the generalizability of findings for those who are less well educated. Similarly, not using intention-to-treat analysis might introduce bias into the results; only six studies used intention-totreat analysis, and attrition was high in most studies (20% or higher in nine studies). Seven studies had less than 100 participants, and some of those might not have been adequately powered to find the differences in physical activity levels between the groups that they were examining. Further, it has been reported that men make more use of the Internet compared with women;58 however, this is in contrast with the overrepresentation of women in the intervention trials reviewed. Finally, some website-delivered interventions that were not effective for increasing physical activity might not have been published, which introduces potential bias into the results of this review, as it depends entirely on published studies. Based on this review, it is recommended that future website-delivered physical activity studies/interventions: 1. Study more representative populations, particularly by including men and participants with lower levels of educational attainment. 2. Use larger sample sizes that provide greater statistical power to detect intervention effects on physical activity and enable long-term follow-up. 3. Present data with and without intention-to-treat analysis and report on differential attrition, as attrition tends to be high in website-delivered trials. 4. Measure physical activity at least 6 months after the end of the intervention in order to capture true maintenance of behavior change. 5. Use objective measures of website usage that capture true implementation and exposure to intervention materials. 6. Make direct comparisons of particular intervention elements to allow for distinguishing effective from ineffective intervention components. 7. Explore the optimal duration and intensity of interventions; results from this review suggest that at least five contacts with participants are needed, however, it is unclear what optimal duration should be, given the decrease in engagement and retention as the intervention progresses.
Conclusion Research on website-delivered physical activity interventions is still at an early stage of development, and although progress has been made, and several studies show promising results, much remains to be learned about optimizing and enhancing these interventions to increase their efficacy. Intervening to influence physical activity over the Internet does not appear to be as
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straightforward as earlier optimistic predictions had suggested. However, this review suggests that the Internet still holds potential for delivering effective physical activity interventions to large populations. Research on specific intervention elements is needed to understand how best to use websites and other Internet-related communication technology as delivery methods for physical activity interventions. No financial conflict of interest was reported by the authors of this paper.
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19. Kirsch SED, Lewis FM. Using the World Wide Web in health-related intervention research: a review of controlled trials. Comput Inform Nurs 2004;22:8 –18. 20. Wofford JL, Smith ED, Miller DP. The multimedia computer for officebased patient education: a systematic review. Patient Educ Couns 2005;59:148 –157. 21. Wantland DJ, Portillo CJ, Holzemer WL, Slaughter R, McGhee EM. The effectiveness of web-based vs. non-web-based interventions: a meta-analysis of behavioral change outcomes. J Med Int Res 2005; 6:e40. 22. Kroeze W, Werkman A, Brug J. A systematic review of randomized trials on the effectiveness of computer-tailored education on physical activity and dietary behaviors. Ann Behav Med 2006;31:205–23. 23. Marcus BH, Owen N, Forsyth LH, Cavill NA, Fridinger F. Physical activity interventions using mass media, print media, and information technology. Am J Prev Med 1998;15:362–78. 24. Ryan P, Lauver DR. The efficacy of tailored interventions. J Nurs Scholarsh 2002;34:331–7. 25. Green S, Higgins J, eds. The Cochrane handbook for systematic reviews of interventions. Version 4.2.5. Chinchester: Wiley; 2006. 26. Leslie E, Marshall AL, Owen N, Bauman A. Engagement and retention of participants in a physical activity website. Prev Med 2005;40:54 –9. 27. Prochaska JO, Velicer WF. The transtheoretical model of health behavior change. Am J Health Promot 1997;12:38 – 48. 28. Cohen J. Statistical power analysis for the behavioral sciences. New York: Academic Press; 1977. 29. Glasgow RE, Boles SM, McKay HG, Feil EG, Barrere M. The D-Net diabetes self-management program: long-term implementation, outcomes, and generalization results. Prev Med 2003;36:410 –9. 30. Feil EG, Glasgow RE, Boles S, Mckay HG. Who participates in Internetbased self-management programs? A study among novice computer users in a primary care setting. Diab Educ 2000;26:806 –11. 31. Hageman PA, Walker SN, Pullen CH. Tailored versus standard internetdelivered interventions to promote physical activity in older women. J Geriatr Phys Ther 2005;28:28 –33. 32. Harvey-Berino J, Pintauro SJ, Gold EC. The feasibility of using Internet support for the maintenance of weight loss. Behav Mod 2002a;26:103–16. 33. Harvey-Berino J, Pintauro S, Buzzell P, Gold EC. Effect of Internet support on the long-term maintenance of weight loss. Obes Res 2004;12:320 –9. 34. Harvey-Berino J, Pintauro S, Buzzell P, DiGiulio M, Gold BC, Moldovan C, et al. Does using the Internet facilitate the maintenance of weight loss? Int J Obes 2002b;26:1254 – 60. 35. Kosma M, Cardinal BJ, McCubbin JA. A pilot study of a web-based physical activity motivational program for adults with physical disabilities. Disabil Rehabil 2005;27:1435– 42. 36. Kosma M, Cardinal BJ, McCubbin JA. Longitudinal effects of a web-based physical activity motivational program among adults with physical disabilities. Res Q Exerc Sport 2005;76:A116. 37. Marshall AL, Leslie ER, Bauman AE, Marcus BH, Owen N. Print versus website physical activity programs: a randomized trial. Am J Prev Med 2003;25:88 –94. 38. McKay HG, King D, Eakin EG, Seeley JR, Glasgow RE. The diabetes network Internet-based physical activity intervention: a randomized pilot study. Diabetes Care 2001;24:1328 –34. 39. Napolitano MA, Fotheringham M, Tate D, Sciamanna C, Leslie E, Owen N, et al. Evaluation of an Internet-based physical activity intervention: a preliminary investigation. Ann Behav Med 2003;25:92–9. 40. Sciamanna CN, Lewis B, Tate D, Napolitano MA, Fotheringham M, Marcus BH. User attitudes toward a physical activity promotion website. Prev Med 2002;35:612–5. 41. Plotnikoff RC, McCargar LJ, Wilson PM, Loucaides CA. Efficacy of an e-mail intervention for the promotion of physical activity and nutrition behavior in the workplace context. Am J Health Promot 2005;19:422–9. 42. Rovniak LS, Hovell MF, Wojcik JR, Winett RA, Martinez-Donate AP. Enhancing theoretical fidelity: an e-mail-based walking program demonstration. Am J Health Promot 2005;20:85–95. 43. Spittaels H, De Bourdeaudhij I, Brug J, Vandelanotte C. Effectiveness of an online computer-tailored physical activity intervention in a real life setting. Health Educ Res. 2007;22:385–96. 44. Tate DF, Wing RR, Winett RA. Using Internet technology to deliver a behavioral weight loss program. JAMA 2001;285:1172–7. 45. Tate DF, Jackvony EH, Wing RR. Effects of Internet behavioral counseling on weight loss in adults at risk for type 2 diabetes: a randomized trial. JAMA 2003;289:1833– 6.
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46. Woolf SH, Krist AH, Johnson RE, Wilson DB, Rothemich SF, Norman GJ, et al. A practice-sponsored Web site to help patients pursue healthy behaviors: an ACORN study. Ann Fam Med 2006;4:148 –52. 47. Bandura A. Social foundations of thought and action: a social cognitive theory. Englewood Cliffs, NJ: Prentice Hall; 1986. 48. Ajzen I. The theory of planned behavior. Org Behav Human Decision Processes 1991;50:179 –211. 49. Hillsdon M, Foster C, Thorogood M. Interventions for promoting physical activity. Cochrane Database Syst Rev 2005(1). Art No.: CD003180. DOI: 10.1002/14651858.CD003180.pub2. 50. Wylie-Rosett J, Swencionis C, Ginsberg M, Cimino C, Wassertheil-Smoller S, Caban A, et al. Computerized weight loss intervention optimizes staff time: the clinical and cost results of a controlled clinical trial conducted in a managed care setting. J Am Diet Assoc 2001;101:1155– 62. 51. Bautista-Castano I, Doreste J, Serra-Majem L. Effectiveness of interventions in the prevention of childhood obesity. Eur J Epidemiol 2004;19:617–22. 52. Marshall A, Owen N, Bauman AE. Mediated approaches for influencing physical activity: update of the evidence on mass media, print, telephone and website delivery of interventions. J Sci Med Sport 2004;7:74 – 80.
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53. Stout PA, Villegas J, Kim H. Enhancing learning through use of interactive tools on health-related websites. Health Educ Res 2001;16:721–33. 54. Evers KE, Cummins CO, Prochaska JO, Prochaska JM. Online health behavior and disease management programs: are we ready for them? Are they ready for us? J Med Internet Res 2005;7:e27. 55. Kreuter MW, Farrell D, Olevitch L, Brennan LK. Tailoring health messages: customizing communication with computer technology. Mahwah, NJ: Laurence Erlbaum Associates, Inc.; 2000. 56. Campbell MK, Tessaro I, DeVellis B, Benedict S, Kelsey K, Belton L, et al. Effects of a tailored health promotion, program for female blue-collar workers: health works for women. Prev Med 2002;34:313–23. 57. Vandelanotte C, De Bourdeaudhuij I, Sallis JF, Spittaels H, Brug J. Efficacy of sequential or simultaneous interactive computer-tailored interventions for increasing physical activity and decreasing fat intake. Ann Behav Med 2005;29:138 – 46. 58. Fallows D. How women and men use the Internet. The Pew Internet and American Life Project. Washington, DC: Pew Internet Institute; 2005.
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Website-Delivered Physical Activity Interventions A Review of the Literature Corneel Vandelanotte, PhD, Kym M. Spathonis, BA, Elizabeth G. Eakin, PhD, Neville Owen, PhD Background: Evidence-based physical activity interventions that can be delivered to large numbers of adults at an acceptable cost are a public health priority; website-delivered programs have this potential. The purpose of this study was to systematically review the research findings and outcomes of website-delivered physical activity interventions and to identify relationships of intervention attributes with behavioral outcomes. Methods:
A structured search of PubMed, Medline, PsycInfo, and Web of Science was conducted for intervention studies published up to July 2006. Studies included in the review were those that (1) used websites or e-mail, (2) had physical activity behavior as an outcome measure, (3) had randomized controlled or quasi-experimental designs, (4) targeted adults, and (5) were published in English.
Results:
Of the fifteen studies reviewed, improvement in physical activity was reported in eight. Better outcomes were identified when interventions had more than five contacts with participants and when the time to follow-up was short (ⱕ3 months; 60% positive outcomes), compared to medium-term (3– 6 months, 50%) and long-term (⬎6 months, 40%) follow-up. There were no clear associations of outcomes with other intervention attributes.
Conclusions: A little over half of the controlled trials of website-delivered physical activity interventions have reported positive behavioral outcomes. However, intervention effects were short lived, and there was limited evidence of maintenance of physical activity changes. Research is needed to identify elements that can improve behavioral outcomes, the maintenance of change and the engagement and retention of participants; larger and more representative study samples are also needed. (Am J Prev Med 2007;33(1):54 – 64) © 2007 American Journal of Preventive Medicine
Introduction
R
egular physical activity decreases the risk of developing cardiovascular disease, diabetes, some cancers, obesity, osteoporosis, and other chronic conditions.1 Public health guidelines for adults recommend participating in at least 30 minutes of moderate-intensity physical activity on 5 or more days of the week.2 However, the proportion of the adult population meeting these guidelines is less than 50% in many industrialized countries.1,3– 6 Thus, finding effective population-based intervention strategies to promote physical activity is a key challenge. The Internet holds promise for wide-scale promotion of physical activity behavior change. Website-delivered interventions have a potentially broad population reach with availability 24 hours per day and widespread From the Cancer Prevention Research Centre, School of Population Health, The University of Queensland, Australia Address correspondence and reprint requests to: Corneel Vandelanotte, PhD, Cancer Prevention Research Centre, School of Population Health, Level 3, Public Health Building, The University of Queensland, Herston Road, Herston, Queensland 4006. E-mail: [email protected].
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accessibility,7,8 they have the potential to be hosted and promoted through state health agencies and nongovernmental organizations,7 and they are potentially costeffective.8 In many industrialized countries, Internet access is greater than 50% and is increasing.9 Internet users are becoming more representative of the overall population, with users including more women, older adults, and people with low levels of educational attainment.10,11 The Internet is identified as an important source of health information by more than half of its users, and may thus be an appropriate delivery medium for health behavior change interventions.12 Website-delivered physical activity interventions have the potential to overcome many of the barriers associated with traditional face-to-face exercise counseling or group-based physical activity programs. An Internet user can seek advice at any time, any place, and often at a lower cost compared with other delivery modalities.13 Although there may be many advantages of using the Internet for delivering health behavior change programs, to reach their public health potential, websitedelivered physical activity interventions must first dem-
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onstrate feasibility and efficacy through rigorous scientific testing. In 2000, the American Journal of Preventive Medicine published a set of articles that identified the potential of interactive health communications, including Internet and website-delivered interventions, for improving health behaviors.14 –17 At that time, there was little empirical literature to support these optimistic predictions. Since then, a wide range of studies evaluating website-delivered health behavior interventions have been reported. Several reviews have examined Internet or website-delivered health behavior change interventions;13,18 –21 however, none have specifically focused on physical activity. A systematic review of study outcomes for website-delivered physical activity interventions is thus timely. Three questions will be addressed: (1) What is the efficacy of website-delivered physical activity interventions? (2) Are there specific intervention elements that influence efficacy? (3) How well are participants engaged and retained? Answers to these questions inform an evaluation of the state of the evidence and are used to formulate guidelines and recommendations for future research.
Methods The study protocol was adapted from previously published reviews22–24 and based on guidelines from the Cochrane Reviewers’ Handbook.25 The articles were independently reviewed and abstracted. Disagreements were discussed among the co-authors until consensus was reached.
Search Strategy and Data Sources A structured electronic database search of PubMed, Medline, PsycInfo, and Web of Science was conducted for intervention studies published up to July 2006. The following search terms were used: “exercise” or “motor activity” or “leisure activities” or “physical activity” or “walking” or “physical fitness” and “Internet” or “website-delivered” or “web-based.” The search terms were limited to adults and English-language peerreviewed publications. Further, contacts with colleagues working in this area and reference lists of relevant publications were examined in an effort to identify all relevant publications.
Selection of Studies For inclusion, studies had to evaluate a website-delivered intervention to improve physical activity that used the Internet or e-mail. Physical activity needed to be an outcome measure, but did not have to be the only or primary study outcome. Studies that also targeted other behaviors (e.g., dietary habits) or that included additional intervention components (e.g., face-to-face or telephone sessions) were included. Although randomized controlled trials were preferred, quasi-experimental studies with a pre-test post-test comparison group design were also allowed.
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Data Extraction Information was extracted and tabulated for studies meeting the inclusion criteria: (1) number of participants at baseline; (2) country where the study was conducted; (3) participation rate (defined as the percentage participating of those who were reached and eligible); (4) attrition; (5) recruitment method; and (6) study setting. Intervention characteristics were listed as “focus” (e.g., physical activity or weight loss), “groups” (a description of the different comparison and control groups), “additional to Internet” (intervention elements that were not Internet delivered), “other behaviors targeted” (behaviors, such as food choices, that were targeted in addition to physical activity), “duration” (length of intervention), and “follow-up” (the number and timing of follow-up measurements using baseline as a reference). A number of specific intervention characteristics related to the use of the Internet were tabulated: the format in which the Internet was used; the frequency of contacts made through the Internet during the study (e.g., e-mail, chat sessions, or weekly modules); and the theory on which the intervention was based. Interactive components of the intervention were also detailed (e.g., online coach, interactive tailoring of advice, discussion forums), as it has been suggested that the level of interactivity of website-delivered interventions is related to engagement and retention of participants.13,26 For this review, the primary outcome measure was change in physical activity behavior. Secondary outcome measures that related to physical activity were also examined; for example, change in body weight or in “stage of change” for physical activity.27
Study Outcomes To determine whether the interventions had a significant impact on physical activity behavior, we separately evaluated outcomes at each follow-up time point: short-term (ⱕ3 months), medium-term (4 – 6 months), and long-term (⬎6 months).22 At each time point, studies were coded as having a positive outcome if significant differences between groups were reported. However, when there was a significant increase in physical activity in all groups, but no significant differences between groups, this was also coded as a positive outcome, but only when different intervention delivery modes were compared to each other (not for studies in which there was a no-intervention control group). To allow for comparison across studies, effect sizes were calculated according to Cohen’s formula.28 However, they were only reported when significant differences between intervention groups were found and when the data reported allowed their calculation. Effect sizes were interpreted according to Cohen’s guidelines:28 ⬍0.5 for a small effect size, 0.5– 0.8 for a medium effect size, and ⬎0.8 for a large effect size.
Results Study Selection The initial search across the four databases yielded 512 publications. Based on title and abstract, reviewing duplicates and irrelevant publications were eliminated, Am J Prev Med 2007;33(1)
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resulting in 77 studies. Reference lists of these publications yielded another 13 publications, in addition to seven publications that were already in the hands of the authors and two in-press publications that were provided by colleagues. After reviewing these 99 publications, 84 were excluded, as they did not meet one or more of the inclusion criteria. The majority (n⫽68) of excluded studies did not evaluate website-delivered physical activity interventions and were theoretic or methodologic papers that primarily discussed the potential for using the Internet to deliver health behavior change interventions. Other publications were excluded because they did not have physical activity as an outcome (n⫽8), they evaluated interventions with children or adolescents (n⫽7), or they were not in the English language (n⫽1). Fifteen studies were included in the final review.26,29 – 46 Table 1 gives an overview of the intervention characteristics of the studies included. Before addressing the three questions posed for this review, descriptive information on the overall attributes of the studies included is presented.
Study Attributes Descriptives. The total number of participants across the 15 studies was 4845; the number of participants across the studies ranged from 13 to 2121, with seven studies having fewer than 100 participants. All but one trial43 reported higher rates of female than male participation; overall, 3011 (66%) of participants were women. In all studies, the level of educational attainment of participants was high; in total, 84% of participants had a college or university education. Most interventions targeted an increase in total physical activity as an outcome, although some specifically targeted walking, moderate physical activity, vigorous physical activity, or meeting physical activity guidelines. Seven studies intervened on physical activity only, whereas eight studies also addressed other behaviors, most commonly dietary behaviors. Eight interventions had additional components that were not websitedelivered. Nine studies explicitly based their intervention on one or more health behavior theories; the most common were Social Cognitive Theory47 (n⫽3), the Transtheoretical Model27 (n⫽6), and the Theory of Planned Behavior48 (n⫽2). Methodology. Fourteen studies were randomized controlled trials, and one study was a cluster nonrandomized controlled trial.46 Four studies recruited a true no-intervention control group,32,35,39,41 whereas the majority compared a website-delivered intervention to another intervention. Four studies used a different delivery mode (such as print materials or face-to-face counseling) as the control or comparison condition.31,33,34,37 Seven studies used a standard or lowintensity website-delivered intervention as the compar56
ison.29,38,42– 46 Four studies reported the use of computer sampling to randomize participants.11,37,38,45 One study reported that research staff were blind to participants’ treatment condition.37 All studies reported on attrition, and ten reported on participation rate. The use of validated instruments to measure physical activity was reported in all but one study.46 Six studies used intention-to-treat analysis to account for high attrition,33,34,37,43– 45 and six studies reported power calculations.31,34,37,41,44,46
Efficacy of Website-Delivered Physical Activity Interventions Overall outcomes. Positive changes in physical activity behavior were reported in eight of the fifteen websitedelivered intervention studies.33,34,38,39,41– 44 In five studies with positive outcomes, it was possible to calculate effect sizes (range, 0.13 to 0.67), resulting in a small mean effect size of 0.44. Time of follow-up. Ten studies reported short-term outcomes, six38,39,41,42,44,45 with positive changes in physical activity (60%). Eight studies reported mediumterm outcomes, four33,34,43,44 with positive outcomes (50%). Five studies reported long-term outcomes, two34,42 with positive outcomes (40%). Maintenance. With regard to the impact of websitedelivered interventions on the maintenance of physical activity behavior change, it is important to evaluate the time of physical activity measurement relative to the length of the intervention. When physical activity was measured before the end of the intervention, six of seven studies showed positive results (86%). When it was measured immediately after the end of the intervention, five of twelve studies showed positive outcomes (42%). Five studies measured physical activity following the end of the intervention: one within 3 months (negative outcome), three between 3 and 6 months (two with positive outcome), and one longer than 6 months (positive outcome). Secondary outcomes. Eleven studies reported on secondary outcome measures related to physical activity (not reported in Table 1). Five studies evaluated change in body weight (kg), with a significant reduction in weight reported in four of those studies. It is important to note that in two of the weight-loss studies that reported positive weight reduction outcomes,32,45 there were no positive effects on physical activity; although physical activity did not significantly change, it was part of an overall successful strategy to reduce weight, which included dietary behavior change. Four studies evaluated progress in the stage of motivational readiness for change in physical activity, all of which showed positive outcomes. One study mentioned flexibility as a secondary outcome measure; flexibility sig-
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Table 1. Overview of characteristics and outcomes of included studies (U.S. unless otherwise noted) Study
Design and participants
Glasgow (2003)29 Feil (2000)30
Participants: 320 patients Focus: Diabetes self-management with type 2 diabetes Groups: (a) access to Internet (40 –75 years; mean diabetes information ⫹ selfage⫽59) management training; (b) Participant rate: NR access to Internet diabetes Attrition rate: 18% information ⫹ peer support; Recruitment: Primary (c) access to Internet diabetes care offices info ⫹ self-management Setting: Internet training ⫹ peer support; (d) Design: RCT access to Internet diabetes information only. Additional to Internet: Computer with Internet access placed in participants’ home ⫹ initial face-to-face training Other behaviors targeted: Dietary habits Duration: 10 months Follow-up: 10 months Participants: 31 women Focus: PA (50 – 69 years; mean Groups: (a) Website-delivered age⫽56) computer-tailored newsletter; Participant rate: NR (b) standard newsletter Attrition rate: 10% Additional to Internet: Initial Recruitment: Newspaper face-to-face for measurement ad and explaining procedures. Setting: Internet at home Postcard notifying of Design: RCT availability of online newsletter Other behaviors targeted: NA Duration: 2 months Follow-up: 3 months Participants: 44 obese Focus: Weight loss adults (31– 60 years; Groups: (a) Internet therapistmean age⫽46) led (b) in-person support; (c) Participant rate: 57% no treatment Attrition rate: 7% Additional to Internet: 15-week Recruitment: Newspaper therapist-led weight control ads program prior to Internet Setting: Internet intervention (all groups) Design: RCT Other behaviors targeted: Dietary habits Duration: 22 weeks Follow-up: 22 weeks Participants: 122 healthy Focus: Weight loss overweight adults Groups: (a) Internet support; (26 –77 years; (b) frequent in-person support mean age⫽48) ⫹ telephone calls; (c) minimal Participant rate: NR in-person Attrition rate: Additional to Internet: 24-week 6 months: 18% therapist-led weight control 18 months: 24% program prior to Internet Recruitment: Newspaper intervention (all groups) ads Other behaviors targeted: Setting: Internet Dietary habits Design: RCT Duration: 12 months Follow-up: 6, 12, and 18 months
Hageman (2005)31
Harvey-Berino (2002)32
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Intervention
Internet
Outcome measures (OM)
Intervention effects
Process measures
Format: Automated dietary goal Primary OM: moderate PA, setting (all); twice-weekly walking, meeting PA access to online coach for selfguidelines management training (a ⫹ c); Instrument: PA Scale for the diabetes peer support forum, Elderly chat, and newsletters (b ⫹ c) Validated: Yes Frequency: 80 possible contacts with online coach and/or 5 newsletters Interactivity: goal setting, chat, forum, newsletters, online coach Theory: SET and SST
Long-term: No significant Website log on gradually increase in moderate PA, decreases (more than 50%) meeting PA guidelines or during the intervention for all walking between or within groups groups Mean of 7.2 log ons per participant per month Greater web use for peer support groups
Format: 4-page computerPrimary OM: Total PA (kcal/ tailored Internet newsletter day) Frequency: 3 newsletters Instrument: Modified 7-Day Interactivity: Computer-tailored Activity Recall advice Validated: Yes Theory: NR (benefits and barriers of PA, self-efficacy, PA goals)
Short-term: No increases within and between groups for total PA
Format: Biweekly chat sessions Primary OM: Total PA and e-mail facilitated by group Instrument: Paffenbarger PA therapist; self-monitoring questionnaire webpage Validated: Yes Frequency: 11 e-mails/chat sessions Interactivity: chat sessions, video, e-mail, bulletin board, questionnaires; discussion group Theory: NR
Medium-term: No significant About group-a: difference between groups 24% would prefer to be in gr-b in PA change 49% think being in gr-b would be more successful 41% could not communicate effectively compared to 0% in gr-b
Format: Biweekly chat sessions Primary OM: Total PA and e-mail facilitated by group Instrument: Paffenbarger PA therapist, self-monitoring questionnaire webpage Validated: Yes Frequency: 11 e-mails/chat sessions Interactivity: chat sessions, video, e-mail, bulletin board, questionnaires; discussion group Theory: NR
Medium-term: Significant Attendance at 12 months is increase in PA across all greater in gr-b compared to groups. gr-a (54% vs 39%) Long-term: Significant higher At 12 months 70% of gr-a would PA in gr-b at 18 months prefer to be in gr-b More peer support contacts in gr-a No differences in between groups for submitting selfmonitoring data
Initial face-to-face was helpful for retrieving the newsletters (73%) 50% read the newsletters online; 80% printed a copy; 83% read all of the newsletters
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Table 1. Overview of characteristics and outcomes of included studies (U.S. unless otherwise noted)(continued) Harvey-Berino (2004)34
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Participants: 232 overweight and obese adults (20-78; mean age⫽46) Partic. Rate: NR Attrition rate: 9 months: 9% 12 months: 24% 18 months: 31% Recruitment: newspaper ads Setting: Interactive TV studios and Internet Design: RCT bold⬎Participants: 151 Kosma (2005)35 Kosma (2005)36 sedentary with physical disabilities adults (1844 years; mean age⫽39) Partic. Rate: NR Attrition rate: 1 month: 50% 6 month: 70% Recruitment: flyer distributed at rehab centers and hospitals Setting: Internet Design: RCT Participants: 655 Marshall university staff (20-49 (2003)37Australia Leslie (2005)26 Australia years; mean age⫽43) Partic. Rate: 59% Attrition rate: 22% Recruitment: University e-mail list Setting: Internet at worksite Design: RCT Participants: 78 McKay (2001)38 sedentary adults with type 2 diabetes.(ⱖ 40 years; mean age⫽52) Partic. Rate: 38% Attrition rate: 13% Recruitment: E-mail postings to diabetes on-line groups Setting: Internet Design: RCT
Focus: Weight loss Groups: (a) Internet support; (b) frequent in-person support ⫹ telephone calls; (c) minimal in-person Additional to Internet: 6 month weight control program with (using interactive TV) prior to internet intervention (all groups) Other behaviors targeted: Dietary habits Duration: 12 months Follow-up: 6, 12 and 18 months Focus: PA Groups: (a) web-based PA program; (b) control Additional to Internet: NA Other behaviors targeted: NA Duration: 4 weeks Follow-up: 4 weeks and 6 months
Format: Biweekly chat sessions Primary OM: Total PA and e-mail facilitated by group Instrument: Paffenbarger PA therapist; self-monitoring questionnaire. webpage Validated: Yes Frequency: 26 e-mails/chat sessions Interactivity: chat sessions; video; e-mail; bulletin board; questionnaires; discussion group Theory: NR
Format: motivational PA website Primary OM: leisure-time PA Short-/Medium-term: No The web-based program had with weekly lesson plans (MET-hours/day) differences in PA between overall good scores for ease of Frequency: 4 Instrument: 13 item PA Scale for groups at 6 months use, appeal, ease of Interactivity: Weekly targeted Individuals with Physical understanding and materials, e-mail, discussion Disabilities helpfulness board Validated: Yes Theory: TTM (also: self-efficacy, decisional balance, goal setting, social support)
Format: Stage-based website ⫹ e-mails sent at 2 week intervals Frequency: 4 Interactivity: Interactive features, stage-based quizzes, goal setting, activity planning, emails Theory: TTM (also: rewards, selfmonitoring, cues to action, social support) Focus: PA Format: Diabetes selfGroups: (a) Diabetes selfmanagement website (login management website ⫹ PA ⱖ1 per week); personal PA tracking ⫹ online coach; (b) database, online coach and PA access to Internet diabetes info resources ⫹ blood glucose tracking Frequency: 5 personal messages Additional to Internet: NA form online coach Other behaviors targeted: NA Interactivity: messages from Duration: 8 weeks coach; peer support group; Follow-up: 8 weeks chat; PA tracking Theory: SEM for diabetes selfmanagement Focus: PA Groups: (a) stage-targeted website program; (b) stagetargeted print program Additional to Internet: NA Other behaviors targeted: NA Duration: 8 weeks Follow-up: 10 weeks
Medium-/ long-term: All - Sig. more self-monitoring and groups sig. increased PA; peer support in gr-a no sig. difference between - Sig. more attendance in gr-b groups - Attendance and self-monitoring were related to weight loss
Primary OM: Short-term: About gr-a: - Total PA: - No sig. PA increase between - 28% read half of info on - Sitting time or within groups website and 62% read half of - Meeting PA guidelines - 10% of gr-a and 11% of gre-mails Instrument: IPAQ Validated: Yes b became sufficiently active - 46% visited website and use at follow-up declined rapidly - Average time spend on website was 9 min, viewing 18 pages
Primary OM: Total PA and Short-term: - More logon, web-pages viewed, walking (min/week) - Sig. PA and walking messages posted, satisfaction Instrument: 11 items from the increase in both groups; and longer session duration in BRFSS no sig. differences between gr-a Validated: Yes groups - Mean of 4.4 logons per month - Sig. larger PA increase in - Decline of program use in both gr-a compared to gr-b for groups participants that used the program 3 or more times (ES⫽0.54)
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Table 1. Overview of characteristics and outcomes of included studies (U.S. unless otherwise noted)(continued) Napolitano(2003)39 Sciamanna (2002)40
Plotnikoff (2005)41 Canada
Rovinak (2005)42
Spittaels (2007)43 Belgium
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Tate (2001)44
Participants: 65 sedentary hospital employees (18-65 years; mean age⫽43) Partic. Rate: 67% Attrition rate: 20% Recruitment: via e-mail, print and intranet of hospitals Setting: Internet at home or worksite Design: RCT Participants: 2121 worksite employees (mean age⫽45) Partic. Rate: 16% Attrition rate: 18% Recruitment: E-mail at 5 worksites Setting: Internet at worksite Design: RCT Participants: 61 sedentary women (20-54 years; mean age⫽40) Partic. Rate: 90% Attrition rate: 21% Recruitment: radio, TV, newspaper and flyers Setting: Internet Design: RCT
Participants: 526 adults (25-55 years; mean age⫽40) Partic. Rate: 92% Attrition rate: 29% Recruitment: e-mails, posters and newsletters in 6 worksites Setting: home or worksite Internet Design: RCT Participants: 91 healthy, overweight hospital employees. (18-60 years; mean age⫽41) Partic. Rate: 80% Attrition rate: 22% Recruitment: via e-mail and intranet of hospital Setting: Internet Design: RCT
Focus: PA Groups: (a) Website ⫹ e-mail; (b) waiting list control group Additional to Internet: NA Other behaviors targeted: NA Duration: 3 months Follow-up: 1 and 3 months
Format: Access to Internet Web site plus weekly e-mail tip sheets Frequency: 12 e-mails Interactivity: targeted info, activity quiz, e-mail, web links Theory: SCT and TTM
Primary OM: moderate PA and walking (min/week) Instrument: BRFSS Validated: Yes
Focus: PA and nutrition Groups: (a) e-mail PA messages (b) waiting list control group Additional to Internet: NA Other behaviors targeted: Dietary habits Duration: 12 weeks Follow-up: at week 13
Format: Weekly e-mail messages; access to website with all the messages and resource file Frequency: 12 e-mails Interactivity: E-mails Theory: SCT, TTM, TPB
Primary OM: Short-term: NR - Total MET/min PA - Sig. higher increase in total Workplace PA level MET/min PA in gr-a Instrument: Godin Leisure Time compared to gr-b Exercise questionnaire (ES⫽0.13) Validated: Yes - Sig. increase in workplace PA levels in both groups; no difference between groups
Focus: Walking Groups: (a) e-mail walking program with general SCT feedback; (b) e-mail walking program with tailored SCT feedback Additional to Internet: 30 min introductory session with walking instructions (both groups) Other behaviors targeted: NA Duration: 12 weeks Follow-up: 3 weeks and 1 year Focus: PA Groups: (a) online tailored PA advice ⫹ e-mails; (b) online tailored PA advice; (c) online non-tailored standard PA advice Additional to Internet: NA Other behaviors targeted: NA Duration: 8 weeks Follow-up: 6 months
Format: Walking logs submitted via e-mail; tailored e-mail feedback response; reminders and walking info e-mailed before walking logs were due Frequency: 24 e-mails Interactivity: Tailored e-mails Theory: SCT
Primary OM: - 1 mile walk time - days and minutes walked in past 2 weeks Instrument: - 1-Mile Walk Test - 2 items from National Health Interview Survey Validated: Yes
Short-term: - Sig. improvement in 1-mile walk test time in gr-b (ES⫽0.67) - Improvement in estimated VO2max in gr-b (trend p⫽0.08) Long-term: Gr. b walked twice as often in past 2 weeks as gr-a (trend p⫽0.08)
- Greater program satisfaction in gr-b - No differences in reading program manual, weekly tips or walking feedback between groups - No difference in using walking logs
Format: Website with tailored feedback Stage-based e-mails (gr-a only) Frequency: 1 tailored advice ⫹ 5 e-mails Interactivity: immediate onscreen feedback, e-mails and interactive action plan Theory: TTM and TPB
Primary OM: -Total PA (mins/week) - Moderate ⫹ high-intensity PA Instrument: 31 item IPAQ Validated: Yes
Medium-term: Sig. increase in total PA and moderate and high-intensity PA in all groups; no sig. differences between groups
- Tailored advice more read, printed and discussed with others compared to standard advice - E-mail is helpful for 50% of gr-a - Better recall and self-reported effectiveness in tailored groups
Focus: Weight loss Groups: (a) Internet education; (b) Internet education ⫹ Internet behavior therapy Additional to Internet: Introductory weight-control session (all groups) Other behaviors targeted: Dietary habits Duration: 6 months. Follow-up: 3 and 6 months
Format: weekly behavioral lessons via e-mail (gr-b); selfmonitoring webpage Frequency: 24 e-mails Interactivity: Self-monitoring (online PA diaries); e-mail; bulletin board Theory: NR (social support, stimulus control, stress management)
Primary OM: Energy expended in PA Instrument: Paffenbarger PA questionnaire Validated: Yes
Short-/medium-term: Sig. - Greater login in gr-b at all increase in PA in both times groups at 3 and 6 months; - Login frequency is sig. no sig. differences correlated with weight loss in between groups both groups - Mean of 4.3 logins per participant per month
Short-term: NR - Moderate PA was sig. higher in gr-a at 1 month (ES⫽0.32) compared to gr-b - Walking was sig. higher in gr-a at 1 (ES⫽0.29) and 3 (ES⫽0.58) months compared to gr-b
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Primary OM: - Light/moderate PA - vigorous PA Instrument: 2 items Validated: NR Format: Website with computertailored PA info, resource library, and printout options Frequency: at least 1 visit to website Interactivity: Computer-tailored advice Theory: TTM Woolf (2006)46
BRFSS, Behavioral Risk Factor Surveillance System; CT, controlled trial; ES, effect size; IPAQ, International Physical Activity Questionnaire; gr-a, Group (a); gr-b, Group (b); MET, metabolic equivalent; NA, not applicable; NR, not reported; PA, physical activity; RCT, randomized controlled trial; SCT, social cognitive theory; SEM, socioecologic model; SET, self-efficacy theory; SST, social support theory: TPB, Theory of Planned Behavior; TTM, Trans-Theoretical Model.
- Lower website use in gr-a - No differences in user satisfaction between groups Short-/Medium-term: Greater improvement in lightmoderate PA in gr-a at 1 month only (trend: P ⫽ 0.1)
Process measures
Sort-/Medium-/long-term: Sig. NR increase in energy expenditure at 3 months only in gr-b (ES⫽0.56)
Focus: Weight loss Groups: (a) Internet weight loss program; (b) internet weight loss program ⫹ e-mail counseling Additional to Internet: Introductory weight-control session (all groups) Other behaviors targeted: Dietary habits Duration: 12 months Follow-up: 3, 6 and 12 months Participants: 256 patients Focus: Multiple behaviors (18-60 years) Groups: (a) computer-tailored Partic. Rate: 27% website; (b) static information Attrition rate: 69% website Recruitment: Promotion Additional to Internet: NA of website in 6 family Other behaviors targeted: practices via posters, Dietary habits, smoking and telephone hold-line alcohol use messages Setting: Duration: Access to website until Internet follow-up Design: CT Follow-up: 1 and 4 months
Intervention effects
Format: 5 therapist e-mails per Primary OM: Energy expended week in first month; once per in PA week from month 2 (gr. b); Instrument: Paffenbarger PA self-monitoring webpage questionnaire Frequency: 68 e-mails Validated: Yes Interactivity: Weekly tip and link; message board; self-monitoring (online PA diaries); e-mails Theory: NR Participants: 92 overweight adults at risk for diabetes (ⱖ 45 years; mean age⫽49) Partic. Rate: 63% Attrition rate: 16% Recruitment: Newspaper advertisements Setting: Internet Design: RCT Tate (2003)45
Outcome measures (OM) Internet Design and participants Intervention Study
Table 1. Overview of characteristics and outcomes of included studies (U.S. unless otherwise noted)(continued) 60
nificantly increased in both intervention and control groups, however, there were no significant differences between the groups.31
Specific Intervention Elements That Influence Efficacy Intervention duration. In eight studies, the duration of the intervention was equal to or shorter than 3 months, with five (63%) showing positive outcomes. Three interventions lasted between 3 and 6 months, one with positive results (33%). In the four interventions that lasted longer than 6 months, two reported positive results (50%). Number of contacts. Six interventions had one to five “contacts” with participants over the Internet such as via e-mail, weekly website modules, chat sessions, or guidance from an online coach. Only one of these studies (17%) showed positive outcomes. Interventions with more than five contacts were more successful, with seven of nine reporting positive outcomes (78%). Theory-based interventions. Five of nine studies that explicitly stated that the intervention was based on a health behavior theory reported positive outcomes (56%) compared with three of six that did not (50%). Initial face-to-face contact. All of the interventions with additional components to the website delivery of the intervention (eight of the fifteen reviewed) also had an initial face-to-face consultation with participants. This initial contact was used to explain the use of the website-delivered intervention before starting the intervention. Four of those eight studies had positive physical activity outcomes (50%), compared with four of the seven studies that had no additional components (57%). Other behaviors targeted. Seven studies intervened on physical activity only, with four reporting positive outcomes (57%). Studies also targeting dietary behaviors (n⫽8) were as effective, with four of eight (50%) showing positive changes in physical activity. Interaction method. An attempt was made to evaluate whether there was a difference in outcomes for interventions in which participants passively interacted with the intervention materials (e.g., following a link to the website in an e-mail message) versus those in which participants were stimulated and prompted to take action themselves (e.g., engage in an online chat session; enter self-monitored data onto the website). Due to the wide diversity of intervention methods and the use of multiple intervention methods simultaneously, it was not possible to classify the studies into such distinct categories. Behavioral modification. An attempt was made to evaluate whether there was a difference in outcomes for
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interventions that didactically provided physical activity information, versus those that used behavior modification techniques (e.g., goal setting, reinforcement, social support, assessment, and feedback). Again, it was not possible to classify the studies into such categories.
Engagement and Retention of Participants Attrition ranged from 7% to 69% across studies and showed a high overall average of 27% (nine studies had attrition of 20% or higher; five them used intention-totreat to analyze their data). Most studies had some process measures; however, only five studies reported objective data on website usage.26,29,38,44,46 Each of those studies reported low exposure to intervention materials, due to a decline in website use as the intervention progressed. Some studies found that higher levels of website use were associated with stronger intervention effects.44 Outcomes associated with interactive intervention elements were examined. The majority of studies (13 of 15) used several interactive intervention elements such as e-mail, chat sessions, activity planning, discussion groups, an online coach and videos; seven of these studies (54%) had positive outcomes. Eleven studies used e-mail in the intervention, and seven of these (63%) had positive outcomes. Of the four studies using computer tailoring, two (50%) showed positive outcomes. Twelve studies had regular periodic contact with participants to intervene on physical activity and accomplished this by using online coaching, chat sessions, e-mails, or weekly website modules; seven (58%) had positive outcomes.
Discussion This systematic review found modest evidence for the efficacy of website-delivered physical activity interventions, with a little over half of the studies reporting significant positive behavioral changes. However, the effect sizes for studies that found positive outcomes were small, and the intervention effects were mostly short lived. There was a clear decrease in efficacy when time to follow-up increased, with studies that had longer follow-ups being associated with less positive outcomes. However, among the handful of studies measuring maintenance of physical activity following the end of the intervention, there was some suggestion of maintenance of outcomes, with three of five studies showing positive outcomes. When evaluating specific intervention elements, the number of contacts with participants was the only factor associated with efficacious outcomes. Interventions with greater than five communications, such as via e-mail, discussion boards, chat sessions, or online coaches demonstrated more positive changes in physical activity than those with five or fewer contacts. This is July 2007
not surprising, as it is reported in the health behavior change literature that efficacy increases when intervention intensity increases.49,50 However, this has also been reported with regard to the duration of the intervention51—an observation that could not be confirmed in this review. There seemed to be no clear relationship between the duration of the intervention and outcomes. Interventions shorter than 3 months did relatively well; five of eight had positive outcomes. The lower efficacy rates for interventions longer than 3 months might be explained by the strong decline in website usage over the course of the intervention, as discussed in the information to follow. Only five of the fifteen studies reported objective data on website usage. These studies used electronic databases linked to the website-delivered program to monitor “hits” and the time that participants spent interacting with the intervention materials. They all reported low website usage and a decline in use as the intervention progressed. From this, and in combination with the overall high levels of attrition and the modest medium- and long-term outcomes of the studies evaluated, it can be concluded, as has been argued previously,13,26 that engagement and retention of participants in website-delivered physical activity interventions is challenging. Within this context, it is encouraging that the shorter-term interventions showed good results; short and effective interventions are needed where engagement and retention of participants in longerlasting interventions appears to be difficult. However, to increase the long-term efficacy of website-delivered interventions and to produce true maintenance of physical activity behavior change, methods to increase website use and exposure to intervention materials and subsequently increase engagement and retention of participants need to be developed. This is emphasized by the fact that stronger intervention effects were found within subgroups of participants that used the website more and thus had more exposure to intervention materials.44 However, it is possible that only those participants that were very motivated to increase their physical activity were willing to keep using the websitedelivered intervention and therefore had better outcomes. Nevertheless, gathering objective data on website usage is technically possible and is an important indicator of intervention effectiveness that should be used in all future trials. Increasing the interactivity of these interventions, to enhance engagement and retention of participants, has been the most consistent recommendation made in the literature.13,26,39,52,53 However, the results of this review do not indicate that this alone will be sufficient to increase intervention effectiveness. The majority of studies evaluated (n⫽13) already used several interactive elements in their interventions. Most studies also used e-mail (n⫽11) and had some form of periodic contact with participants (n⫽12). Nevertheless, engageAm J Prev Med 2007;33(1)
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ment and retention of participants in these studies was low. It has also been suggested that using a form of computer tailoring that is embedded in the websitedelivered intervention might increase interactivity and in turn enhance participant retention and outcomes.8,54 In using computer tailoring, an online questionnaire is linked to scoring algorithms that allow participants to be provided with immediate and personally relevant feedback about their health behavior, plus individually adapted tips and suggestions on how to change.22,55 Four studies evaluated this technique and two found positive results. However, given the small numbers of studies, it is difficult to draw any conclusions about the use of computer tailoring, other than that it is a potential strategy to increase efficacy of website-delivered interventions that need to be further explored. If it appears that incorporating interactive elements in website-delivered interventions is not enough to engage and retain participants, what then may be the main source of difference between those studies that found positive outcomes and those that did not? To examine this question, an attempt to evaluate the method of interaction (active or passive) used by the interventions and to determine whether they used behavioral modification techniques was made. It was hypothesized that interventions that stimulate action and use behavior modification techniques might be more effective in engaging and retaining participants. However, as previously mentioned, it was impossible to classify the studies into such distinct categories. Direct comparisons of specific intervention elements are thus needed to determine which are effective and to help build the evidence base necessary to inform future website-delivered interventions. The findings of this review indicate that programs without a face-to-face contact with participants at the beginning of the intervention are as effective as those that have an initial face-to-face contact. This might be explained by the absence of actual behavioral counseling in those initial contacts. This observation has important implications for the implementation of websitedelivered interventions, as their reach would be greatly extended if face-to-face contact was not necessary. Intervening on multiple health behaviors simultaneously also did not seem to influence physical activity outcomes negatively, when compared with studies that intervened on physical activity only. Website programs provide opportunities to test the hypothesis that change in one behavioral risk factor might serve as a stimulus or “gateway” for change in other health behaviors and might create additive or synergistic intervention effects.56,57 Although the Internet is increasingly used by people with lower educational attainment,11 this is clearly not yet reflected in the samples recruited in the studies 62
reviewed (84% of all participants were college or university educated); this limits the generalizability of findings for those who are less well educated. Similarly, not using intention-to-treat analysis might introduce bias into the results; only six studies used intention-totreat analysis, and attrition was high in most studies (20% or higher in nine studies). Seven studies had less than 100 participants, and some of those might not have been adequately powered to find the differences in physical activity levels between the groups that they were examining. Further, it has been reported that men make more use of the Internet compared with women;58 however, this is in contrast with the overrepresentation of women in the intervention trials reviewed. Finally, some website-delivered interventions that were not effective for increasing physical activity might not have been published, which introduces potential bias into the results of this review, as it depends entirely on published studies. Based on this review, it is recommended that future website-delivered physical activity studies/interventions: 1. Study more representative populations, particularly by including men and participants with lower levels of educational attainment. 2. Use larger sample sizes that provide greater statistical power to detect intervention effects on physical activity and enable long-term follow-up. 3. Present data with and without intention-to-treat analysis and report on differential attrition, as attrition tends to be high in website-delivered trials. 4. Measure physical activity at least 6 months after the end of the intervention in order to capture true maintenance of behavior change. 5. Use objective measures of website usage that capture true implementation and exposure to intervention materials. 6. Make direct comparisons of particular intervention elements to allow for distinguishing effective from ineffective intervention components. 7. Explore the optimal duration and intensity of interventions; results from this review suggest that at least five contacts with participants are needed, however, it is unclear what optimal duration should be, given the decrease in engagement and retention as the intervention progresses.
Conclusion Research on website-delivered physical activity interventions is still at an early stage of development, and although progress has been made, and several studies show promising results, much remains to be learned about optimizing and enhancing these interventions to increase their efficacy. Intervening to influence physical activity over the Internet does not appear to be as
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straightforward as earlier optimistic predictions had suggested. However, this review suggests that the Internet still holds potential for delivering effective physical activity interventions to large populations. Research on specific intervention elements is needed to understand how best to use websites and other Internet-related communication technology as delivery methods for physical activity interventions. No financial conflict of interest was reported by the authors of this paper.
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