Computer-Delivered Interventions for Health Promotion and Behavioral Risk Reduction: A Meta-Analysis of 75 Randomized Controlled Trials, 1988 – 2007
aCenter for Health, Intervention, and Prevention, University of Connecticut
bCenter for Health and Behavior, Syracuse University
Use of computers to promote healthy behavior is increasing. To evaluate the efficacy of these computer-delivered interventions, we conducted a meta-analysis of the published literature.
Studies examining health domains related to the leading health indicators outlined in Healthy People 2010 were selected. Data from 75 randomized controlled trials, published between 1988 and 2007, with 35,685 participants and 82 separate interventions were included. All studies were coded independently by two raters for study and participant characteristics, design and methodology, and intervention content. We calculated weighted mean effect sizes for theoretically-meaningful psychosocial and behavioral outcomes; moderator analyses determined the relation between study characteristics and the magnitude of effect sizes for heterogeneous outcomes.
Compared with controls, participants who received a computer-delivered intervention improved several hypothesized antecedents of health behavior (knowledge, attitudes, intentions); intervention recipients also improved health behaviors (nutrition, tobacco use, substance use, safer sexual behavior, binge/purge behaviors) and general health maintenance. Several sample, study and intervention characteristics moderated the psychosocial and behavioral outcomes.
Computer-delivered interventions can lead to improved behavioral health outcomes at first post-intervention assessment. Interventions evaluating outcomes at extended assessment periods are needed to evaluate the longer-term efficacy of computer-delivered interventions.
Keywords: health, behavior, computer, intervention, meta-analysis
Increasing recognition of the importance of behavior for health and the rapidly escalating cost of healthcare conspire to create a strong need for widespread dissemination of interventions to promote health and prevent disease. Traditional public health and clinical interventions cannot fully address this need because of resource constraints, limited access to hard-to-reach populations, and other factors. These conditions have created the impetus for innovative approaches to health education and promotion.
Benefits of computer-delivered interventions (CDIs) include uniformity of intervention delivery, 24-hour access, and the ability to tailor an intervention to an individual (Budman et al. 2003
). Although the first two benefits address the delivery of CDIs, the last feature promises to enhance intervention efficacy. Health behavior change models, such as the information, motivation, and behavioral skills model (IMB) and the transtheoretical model, suggest that tailoring intervention content enhances behavioral change (Fisher and Fisher 1992
; Prochaska and DiClemente 1982
). Tailoring can increase both the efficacy of an intervention as well as user satisfaction and completion of the program by allowing for a more engaging personalized experience (Ryan and Lauver 2002
). Computerized tailoring allows for an individualized experience based on the user’s responses to material in the program. The combined elements of tailoring and open access to CDIs serve important functions. They allow for intervention delivery that is engaging, accessible, and faithfully implemented (Kaufmann 2007
). In addition, CDIs can be delivered to individuals living in remote locations (Schopp et al. 2006
) or those with physical limitations. CDIs may address antecedents of a health behavior (e.g., knowledge or attitudes) as well as directly addressing the behavior itself. If efficacious, CDIs may also be cost-saving.
Because CDIs vary on many dimensions (e.g., use of tailoring, dose, interactivity, theoretical basis, target behavior and population), a systematic review to identify the components of effective CDIs and their relative impact on behaviors is needed. Previous reviews of CDIs have either been descriptive (Evers et al. 2003
) or limited to a single health behavior (e.g., smoking; Walters et al. 2006
). A meta-analytic review comparing web-based versus print applications of health interventions showed positive, though widely varying, benefits (Wantland et al. 2004
). These reviews suggest that web-based CDIs can be efficacious (Wofford et al. 2005
This meta-analytic review goes beyond the previous reviews by comparing CDIs across health domains and providing a more comprehensive picture of their benefits. We examine the effects of CDIs on a variety of health constructs and behaviors. Psychosocial, behavioral, and educational CDIs were located through a systematic review of the literature. We examine the extent to which these interventions affect (a) knowledge, (b) hypothesized determinants or mediators of health-behavior change (e.g., attitudes, intentions, norms, self-efficacy), (c) behavioral outcomes (physical activity, nutrition, tobacco use, substance use, safer sexual behavior, and overall health-maintenance), and (d) objective behavioral outcomes (weight loss, diabetes control, binging/purging); we also examine whether sample and intervention characteristics moderate intervention efficacy.
Search strategy and study selection
Published (or in press) manuscripts were retrieved through: (a) electronic reference databases (PsycINFO, PubMED, ERIC, CRISP, and the Cochrane Library); (b) reference sections of relevant review or published studies; (c) examining contents of relevant journals (e.g., Journal of the American Medical Informatics Association, American Journal of Public Health), and (d) sending manuscript requests via e-mail listservs (e.g., Games for Health) and to relevant authors.
Studies were included if they (a) examined health domains related to one of the leading health indicators delineated in Healthy People 2010
(U.S. Department of Health and Human Services 2007
); (b) implemented a CDI; (c) used a randomized controlled trial (RCT) design with a control condition; (d) assessed hypothesized antecedents of health-behavior change (e.g., knowledge, self-efficacy), the health behavior itself (e.g., physical activity), or an objective health outcomes (e.g., weight loss); and (e) provided sufficient information to calculate between-group effect size estimates. Studies were retained if the only face-to-face contact consisted of an initial basic computer orientation session with no intervention content; and were excluded if the intervention occurred in a group format; or computers were used only to tailor information presented in a non-computerized format (e.g., computer-tailored mailed personalized feedback; Dijkstra et al. 1998
). Of the 27 study authors contacted for additional statistical information, 60% responded with the necessary information; the remaining cases were estimated from information in the article when possible.
Studies that fulfilled these criteria available as of May 31, 2007 were included. When several publications provided information about a single study, effect sizes were calculated separately for each measurement occasion. However, due to the small number of studies with multiple follow-ups per health domain, we focused on the first measurement occasion only. When more than one control or comparison condition was used (e.g., standard of care and wait-list), the control condition with the least contact (e.g., wait-list) was used as the comparison condition for ease of interpretation. Using these criteria, 75 RCTs sampling 35,685 participants with a retention rate of 86% (SD = 28%) at first follow-up qualified for the meta-analysis (). More details on the search strategies are available upon request from the authors.
Selection process for the 75 RCTs included in the final sample from all studies retrieved.
For each study, effect size estimates were calculated from the information provided in the report(s). Effect sizes were calculated for the psychological outcomes of health prevention (a) attitudes, (b) intentions, (c) social norms, and (d) self-efficacy; the behavioral outcomes of (a) physical activity, (b) nutrition, (c) tobacco use, (d) substance use (i.e., alcohol and/or drugs), (e) safer sexual behavior (e.g., increased condom use,), (f) reducing binging/purging, and (g) general health maintenance (e.g., adherence to medical advice); and the objective behavioral outcomes of (a) weight loss, (b) diabetes-related outcomes (e.g., blood pressure, glucose, lipids), and (c) weight gain/maintenance (for studies examining eating disorders). Psychosocial outcomes were assessed via self-report, behavioral outcomes were assessed via self-report and/or objective measures.
Effect Size Derivation
Effect sizes (d
) were calculated as the mean differences between the intervention and control group divided by the pooled standard deviation (SD
; Cohen 1988). If the pooled SD
was unavailable or could not be derived from the reported statistics, the denominator was replaced with another form of SD
(e.g., pretest SD
; Lipsey and Wilson 2001
). When means and SDs
were unavailable, other statistical information (e.g., t
- or F
-values) was used to calculate d
(Johnson and Eagly 2000
; Lipsey and Wilson 2001
). If a study reported dichotomous outcomes (e.g., frequencies), we calculated an odds ratio and transformed it to d
using the Cox transformation (Sánchez-Meca et al. 2003
). If statistical information was not available, and could not be obtained from the authors, and
the study reported no significant between-group differences, we estimated that effect size to be zero1
. In calculating d
, we controlled for baseline differences between the intervention and control condition(s) when pre-intervention measure were available (Becker 1988
). A positive effect size indicated greater health benefit (e.g., decreased tobacco use) for participants in the intervention vs. control condition; effect sizes were corrected for sample size bias (Hedges 1981
). DSTAT 2.0 (Johnson and Wood 2006
) was used to calculate effect sizes.
Multiple effect sizes from individual studies resulted when they reported more than one outcome, multiple intervention conditions, or when outcomes were separated by sample characteristics (e.g., gender). When a study contained multiple measures of the same outcome (e.g., daily alcohol use and peak blood alcohol concentration), the effect sizes were averaged. Effect sizes calculated for each intervention and by sample (e.g., when studies separated outcomes by gender) were treated as separate studies to retain independence. Of the 75 RCTs included in the meta-analysis, 82 separate interventions (k) were evaluated.
Coding and Reliability
Two researchers independently coded study information (e.g., theory used), sample characteristics (e.g., ethnicity, gender), risk characteristics (e.g., current alcohol use), design and measurement specifics (e.g., number of follow-ups), and content of intervention and control condition(s) (e.g., number of sessions). Twenty studies were randomly selected to examine reliability. For the categorical dimensions, raters agreed on 43% to 100% of the judgments (mean Cohen’s κ = .61). Reliability for the continuous variables was calculated using the intraclass correlation coefficient (
ranged from 0.18 to 1.00, with an average
= 0.90 across categories. Coding disagreements were resolved through discussion.
Weighted mean effect sizes, d+
s, were calculated using random-effects procedures (Lipsey and Wilson 2001
), such that individual studies’ effect sizes were weighted by the inverse of their random-effects variance. The homogeneity statistic,
, was computed to determine whether each set of d+
s shared a common effect size. To further assess heterogeneity, the I2
index and its 95% confidence intervals were calculated to assess the inconsistencies in a set of effect sizes (Higgins and Thompson 2002
; Huedo-Medina et al. 2006
). If the 95% confidence interval around the I2
index includes a zero, the set of effect sizes are considered homogeneous. If
was significant and
index indicated medium to high variability, the relation between study characteristics and the magnitude of the effects were examined using a weighted least squares regression analyses with weights equivalent to the inverse of the fixed-effects variance. Analyses were conducted in Stata 10.0 (StataCorp 2007
) using macros provided by Lipsey and Wilson (2001)
Study and Sample Characteristics
A detailed summary table of the 75 RCTs included in the meta-analysis is available from the authors (http://digitalcommons.uconn.edu/chip_docs/23
). Most studies (73%) used theory to guide their intervention design with 20 studies (36%) using more than one perspective. Studies focused on: overweight and obesity concerns (nutrition, weight, and/or diabetes; 37%), substance use (alcohol and/or other drugs; 23%), tobacco use (11%), sexual behavior (HIV/STD or pregnancy prevention; 8%), physical activity (5%), eating disorders (4%), or other health concerns (e.g., sun exposure; 5%). Several studies (7%) focused on multiple health behaviors (e.g., alcohol, drugs, and tobacco use). All studies randomly assigned participants or groups to conditions, and evaluated participants at both pre- and post-test. The median number of follow-ups was one (M
= 1.63; SD
= 0.87; range = 1 to 5). The initial post-intervention assessment (the focus of this review) occurred an average of 7.46 weeks (SD
= 19.85; range = 0 to 104, Mdn
= 0.14) after the intervention.
Participants were predominately female (66%; k = 73), White (69%; k = 49), and adults (M age = 31.05, SD = 14.56; range = 8.28 to 63.00; k = 64). Most of the samples were located within the U. S. (73%) and Europe (15%) and were drawn primarily from a community/clinical (59%) or school/university (38%) environment. Of the 24 studies reporting participant computer skills, 71% of participants had some prior computer experience. Potential risk characteristics of the sample included: alcohol use (k = 16), marijuana use (k = 4,), drug use other than alcohol/marijuana (k = 8), current drug/alcohol treatment (k = 9), and tobacco use (k= 14). For the studies reporting participants’ sexual activity (k= 8) and pregnancy (k= 9), 76% were sexually active and 13% were currently pregnant. Only one study reported sampling participants diagnosed with a severe mental disorder.
Computer-Delivered Intervention Recruitment, Delivery, and Content
Participants were typically recruited via the community (e.g., flyers, community centers, or internet advertisements; 39%), school/university (32%), or clinical contact (26%); two studies recruited participants from drug treatment or prison. Interventions were usually conducted in school/university (21%) or clinic/hospital (21%) settings; some interventions were used at home (9%), community centers (5%), retail settings (4%), worksites (4%), or prison (1%). Most of the studies delivered the intervention using a single computer-based approach (e.g., internet, CD-ROM; 92%) only; some studies (7%) used a computer program and electronic peer support, chat rooms, or individual counseling.
The 75 studies examined 82 separate interventions (i.e., some studies evaluated more than one condition or were divided by group). The single-approach interventions consisted of a median of three (range = 1 to 36) computer-delivered sessions of 21 minutes (range = 4 to 120) each; the multiple approach interventions consisted of a median of 11 sessions (range = 8 to 58) of individual computer-delivered sessions of 45 minutes (range = 15 to 60; k = 6), a median of 42 sessions (range = 3 to 80) of electronic peer support of 6 minutes each (range = 1 to 10; k = 2), and a median of 8 sessions (range = 8 to 24) of electronic chat rooms of 60 minutes each (range = 15 to 60; k = 3). Many interventions (65%) were tailored to the individual (e.g., by readiness-to-change); 18% of the interventions were tailored to the group (e.g., gender, ethnicity). All interventions included health information (e.g., diet/weight, HIV, alcohol), 88% included a motivational component (52% personalized risk assessment, 45% cost/benefits, 37% individual concerns, 36% social norms, 18% sample-specific concerns, and 14% perceived vulnerability), and 89% included a skills training component (71% passive skills training—demonstration or information, 62% planning, 30% self-efficacy, 15% active skills training such as role-playing preventative behaviors). Supplemental materials (e.g., coupons, computer-related equipment) were provided in 15% of the interventions.
Description of Control Conditions
The most typical comparison condition (51%), was an assessment-only control. Active comparison conditions (N = 40) (e.g., education-only, brief or altered form of intervention) had a median of 2 sessions of 19 minutes each. Active comparisons consisted of standard education only (26%), irrelevant content that was time matched (10%), brief versions (mailed, printed, or face-to-face) of the intervention (10%), or time-matched relevant content (4%). Supplemental materials (e.g., manuals, brochures) were provided in 15% of the active comparisons conditions.
Intervention Impact on Knowledge and Psychological Outcomes
Because there were few studies (k
≤ 4) that examined knowledge, attitudes, intentions, social norms, and self-efficacy for each health domain, effect sizes are represented as a combination of the various health domains. CDIs improved health-related knowledge, attitudes, and behavioral intentions but did not improve social norms or self-efficacy relative to controls (See ). All of the effects lacked homogeneity,
were significant, except for behavioral intentions. Moderator tests investigated the variability in the effect sizes for knowledge, health-prevention attitudes, social norms, and self-efficacy.
Efficacy of the 82 computer-delivered interventions to promote health at the first measurement occasion.
Moderators of Intervention Impact on Knowledge, Attitudes, Social Norms, and Self-Efficacy
Univariate regression analyses were conducted to examine potential moderators of knowledge, attitudes, social norms, and self-efficacy. Sample (e.g., proportion women, age), study (e.g., type of control or comparison condition), and intervention (e.g., delivery, dose, content) characteristics were individually examined (see ); the small numbers of cases precluded multiple moderator tests.
Moderators of effect sizes for knowledge, attitudes, social norms, and self-efficacy in computer-delivered interventions to promote health.
CDIs were more successful at improving knowledge when interventions sampled more women, younger participants, and people with prior computer experience, the studies did not use an active control condition, and the interventions targeted diet and/or weight, focused on a condition other than diabetes, and were delivered via the internet.
CDIs improved attitudes more when they sampled younger participants, did not use an active comparison condition, and the interventions did not focus on diet and/or weight, were delivered via the internet and not via CD-ROM or on-site, did not include a motivational component, and were presented in greater doses.
CDIs were more successful at improving social norms if they sampled fewer women, did not use an active comparison condition, and the interventions did not focus on diet and/or weight, did not include a motivational or behavioral skills component, presented the content in greater doses, and conducted assessments at later weeks after the intervention.
CDIs were more successful at increasing participants’ self-efficacy if they sampled more women or younger participants and did not deliver the intervention via CD-ROM.
Intervention Impact on Behavioral and Objective Behavioral Outcomes
CDIs were successful at improving nutrition (d+
= 0.15, 95% CI 0.07, 0.22; ), reducing tobacco use (d+
= 0.33, 95% CI 0.08, 0.59; ), reducing substance use (d+
= 0.24, 95% CI 0.04, 0.43; ), increasing safer sexual behavior (d+
= 0.35, 95% CI 0.10, 0.60; ), reducing binge/purging behaviors (d+
= 0.19, 95% CI 0.04, 0.33; ), and promoting general health maintenance (d+
= 0.18, 95% CI 0.05, 0.30; ). No improvements were observed for physical activity, weight loss, diabetes control, or weight gain/maintenance. Effect sizes were homogeneous within each domain except for tobacco use,
(10) = 372.63, p
< .001, and substance use,
(10) = 52.90, p
< .001; therefore, moderator analyses were conducted only for tobacco and substance use.
Figure 2 Forest plot of the effect sizes and their 95% confidence intervals for nutrition interventions (k = 19). The size of the square representing each effect size is proportional to its weight in the analysis; larger squares indicate a study that was weighted (more ...)
Figure 3 Forest plot of the effect sizes and their 95% confidence intervals for tobacco use interventions (k = 11). The size of the square representing each effect size is proportional to its weight in the analysis; larger squares indicate a study that was weighted (more ...)
Figure 4 Forest plot of the effect sizes and their 95% confidence intervals for substance use interventions (k = 11). The size of the square representing each effect size is proportional to its weight in the analysis; larger squares indicate a study that was weighted (more ...)
Figure 5 Forest plot of the effect sizes and their 95% confidence intervals for safer sexual behavior interventions (k = 4). The size of the square representing each effect size is proportional to its weight in the analysis; larger squares indicate a study that (more ...)
Figure 6 Forest plot of the effect sizes and their 95% confidence intervals for binging/purging behavior interventions (k = 6). The size of the square representing each effect size is proportional to its weight in the analysis; larger squares indicate a study (more ...)
Figure 7 Forest plot of the effect sizes and their 95% confidence intervals for general health maintenance interventions (k = 7). The size of the square representing each effect size is proportional to its weight in the analysis; larger squares indicate a study (more ...) Moderators of intervention impact on tobacco and substance use
Both tobacco and substance use interventions were more successful when investigators sampled more users and were delivered in greater doses (). Although tobacco interventions showed greater improvements when assessment occurred at a longer interval after the intervention, substance use showed improvement with a shorter interval. Tobacco interventions also were more successful when investigators sampled younger participants, did not use an active comparison condition, when presented via CD-ROM and on-site but not via the internet, and did not include a motivational component. No other sample, study, or intervention characteristics moderated the impact of CDIs on tobacco or substance use.
Moderators of effect sizes for computer-delivered interventions addressing tobacco and substance use.
The current meta-analysis integrates the results of 75 randomized clinical trials that have included more than 35,000 participants and evaluated 82 separate computer-delivered, health promotion interventions. Meta-analysis of these data supports the following conclusions.
First, CDIs can help individuals to make improvements in a variety of health behaviors, including nutrition; tobacco and substance use; sexual behavior; binging/purging episodes; and general health maintenance. The evidence for CDI-stimulated behavior change across six, widely-varying health behaviors is somewhat surprising as one might hypothesize that some behaviors (e.g., substance use) might be more difficult to change. The range of health behaviors targeted by CDIs has been broad, and the sampling suggests the full range of prevention applications (i.e., universal to selected to indicated; Gordon, 1983
). It is unlikely that CDIs would be sufficient for a long-standing clinical disorder, as suggested by the lack of improvement for weight loss and diabetes management. Notwithstanding this caveat, the CDIs included in this meta-analysis did help to improve substance use and binging/purging behaviors that are often resistant to change even with therapist-facilitated interventions. There is precedent for self-initiated change for such health habits as smoking, when a modest but influential experience provides the proverbial “straw that broke the camel’s back,” (e.g., Carey et al. 1993
). We hypothesize that CDIs may help some participants to organize previously latent motivation and behavioral skills at a critical moment, leading to improved health behaviors. That the CDIs were relatively brief in duration seems consistent with this hypothesis.
Second, as expected the CDIs led to short-term changes in several theoretically-relevant antecedent conditions, which are hypothesized to precede behavior change (see Bandura 1997
; Fishbein and Ajzen 1975
; Fisher and Fisher 1992
). Interventions may prompt change on these elements, which in turn influence behavior. Changes in these psychological antecedents are consistent with current thinking about the determinants of health-behavior change.
Third, the magnitude of the antecedent condition and actual behavior change tends to be small to medium by meta-analytic conventions; nonetheless, these changes compare favorably to other commonly-implemented public health and medical interventions. From a public health perspective, even small changes are meaningful at the population level (Rose 1992
Fourth, several sample, study, and intervention features explained variability in knowledge, psychological, and behavioral outcomes:
- CDIs implemented with older participants tended to be less efficacious for increasing knowledge, attitudes, self-efficacy, and decreasing tobacco use. Older samples may: (a) be more resistant to change (e.g., Krosnick and Alwin 1989), reflecting greater habit strength, (b) lack computer experience and/or self-efficacy (Forester Research Inc. 2003; Marquié et al. 2002), and (c) have slower internet connections (Kwak et al. 2004) making some CDIs more difficult to use thus undermining compliance. As computer access and skills improve the latter two explanations should diminish; if the effect continues, it would support the habit strength explanation.
- As expected, use of an active comparison condition weakened the impact of CDIs on knowledge, attitudes, social norms, and tobacco use. Providing individuals with any active intervention content is likely to lead to some change in the psychological antecedents and/or behavior. This pattern corroborates results from prior meta-analyses showing that any active comparison condition, including a placebo, decreases the difference between the treatment and control groups (Grissom 1996; Lipsey and Wilson 1993).
- Delivery mechanisms moderated the impact of the intervention on knowledge, psychological, and behavioral outcomes: (a) when delivered via CD-ROM, the impact of the CDI was weakened for attitudes and self-efficacy but enhanced smoking cessation; (b) when delivered via the internet, CDIs were efficacious at improving knowledge and attitudes but did not reduce tobacco use; and (c) on-site administration weakened the impact of CDIs on attitudes but strengthened their impact on tobacco use reduction. CD-ROMs were used primarily on-site whereas internet–based CDIs occurred primarily off-site. Compared to off-site use, on-site CDIs may have required stronger initial motivation, and/or resulted in greater compliance, hypotheses that warrant further study.
- The inclusion of motivational components (e.g., cost/benefits analysis) weakened the impact of the CDIs on attitudes, social norms and tobacco use reduction. It is possible that addressing motivational components increased participants’ ambivalence toward that behavior resulting in less changes (e.g., Conner et al. 1998, 2002;Povey et al. 2001; Sparks et al. 2001). Similar counter-intuitive effects have been observed with face-to-face motivational interventions for smoking (Hettema et al. 2005) and alcohol use (Carey et al. 2006). Delivery mode also may impact efficacy of motivational components in that participants may perceive CDIs to be less persuasive than face-to-face interventions (see Steele et al. 2007).
- Greater intervention dose strengthened the impact of CDIs on attitudes, tobacco and substance use reduction.
Although intriguing, these findings are based on a relatively small set of studies so continued research is needed to clarify the effects of factors such as age, race/ethnicity, delivery mode, use of motivational components, and dose.
Fifth, a virtue of CDIs is the ability to tailors intervention to individuals. Theories of health behavior change suggest tailoring facilitates behavioral change (Fisher and Fisher 1992
; Prochaska and DiClemente 1982
) and in non-CDIs appears to have value (e.g., Noar et al. 2007
; Skinner et al. 1999
). Nonetheless, our meta-analytic review found no support for tailoring activities, whether examined at the individual or the group level. However, we could not fully evaluate the efficacy of tailoring because there was no variability for tailoring in these studies. Future research should more thoroughly evaluate the role of tailoring CDIs on health-behavior change; with appropriate research designs, we predict that tailored interventions will prove more efficacious than non-tailored, “one size fits all” interventions.
Finally, extant data do not address the durability of behavior change: Health behaviors are likely to recur (some would say “relapse”) when environmental conditions change (e.g., during increased stress), and the influence of the intervention wanes. CDIs often exhibited uniform impact on outcomes despite the variability in follow-up duration, suggesting that CDI impact can be durable. Research should more thoroughly investigate the durability of the changes.
As with any meta-analytic investigation, these results should be interpreted mindful of the limitations of both the research literature and our methods. First, there have been relatively few RCTs on this topic; for example, although there have been hundreds of face-to-face intervention trials for sexual risk reduction (see Smoak et al. 2006
), we located only four CDI studies that examining it that met our inclusion criteria. The limited number of studies constrained analyses that might identify moderators of intervention success (or failure), a necessary step for the refinement of CDIs. Second, identification of relevant studies may have been hindered by authors’ use of keywords, publication source, and researchers’ non-responses to listservs or our inquiries (see Matt and Cook 1994
). Third, we could not parse the literature based on the difficulty of the behavior being changed. As alluded earlier, it is reasonable to expect that some participants (e.g., an addicted smoker) or health problems (e.g., weight loss) might respond less well to CDIs; however, the literature is not sufficiently mature to address this issue. Fourth, we focused on immediate post-intervention efficacy because many studies did not provide data from multiple follow-up assessments. Future research will be most helpful if the follow-up periods extended to one year or longer, to address the stability of health promotion gains. Finally, our analyses also focused exclusively on “stand alone” CDIs; research might explore whether the combination of CDIs with a face-to-face interaction, or other human engagement, might optimize the health promoting benefits.
Computer-delivered interventions can lead to immediate post-intervention improvements in health-related knowledge, attitudes, and intentions as well as modifying health behaviors such as dietary intake, tobacco use, substance use, safer sexual behavior, binge/purging behaviors, and general health maintenance. CDIs do not provide benefits in all contexts; the evidence does not support the use of CDIs to improve physical activity, weight loss, or diabetes self-management. Nonetheless, there is sufficient evidence to continue to investigate the benefits and limits of CDIs, to explore patient- and intervention-characteristics that facilitate health behavior change, and to determine the long-term effects of CDIs.
This work was facilitated by National Institute of Mental Health Grant R01-MH58563 to Blair T. Johnson (PI). We thank Nicole Clark, Jessica M. LaCroix, Jennifer Ortiz, and Anna Switaj for their assistance with this project and the study authors who provided additional intervention or statistical information.
- Alterman AI, Baughman TG. Videotape versus computer interactive education in alcoholic and nonalcoholic controls. Alcohol Clin Exp Res. 1991;15:39–44. [PubMed]
- American Lung Association. Trends in tobacco use. 2007. [Online]. Available from: http://www.lungusa.org/site/pp.asp?c=dvLUK9O0E&b=33347 [cited 12 September 2007]
- Anderson ES, Winett RA, Bickley PG, Walberg-Rankin J, Moore JF, Leahy M, Harris CE, Gerkin RE. The effects of a multimedia system in supermarkets to alter shoppers' food purchases. J Health Psych. 1997;2:209–223. [PubMed]
- Anderson ES, Winett RA, Wojcik JR, Winett SG, Bowden T. A computerized social cognitive intervention for nutrition behavior: direct and mediated effects on fat, fiber, fruits, and vegetables, self-efficacy, and outcome expectations among food shoppers. Ann Behav Med. 2001;23:88–100. [PubMed]
- Andrewes DG, O'Connor P, Mulder C, McLennan J, Derham H, Weigall S, Say S. Computerised psychoeducation for patients with eating disorders. Aust NZ J Psychiat. 1996;30:492–497. [PubMed]
- Bandura A. Self-efficacy: The exercise of control. New York: Freeman; 1997.
- Baranowski T, Baranowski J, Cullen KW, Marsh T, Islam N, Zakeri I, Honess-Morreale L, deMoor C. Squire's Quest! Dietary Outcome Evaluation of a Multimedia Game. Am J Prev Med. 2003;24:52–61. [PubMed]
- Barber JG. An application of microcomputer technology to the drug education of prisoners. J Alcohol Drug Educ. 1993;38:14–22.
- Becker BJ. Synthesizing standardized mean-change measures. Br J Math Stat Psychol. 1988;41:257–278.
- Bernhardt JM. Tailoring messages and design in a web-based skin cancer prevention intervention. Int Elect J Health Educ. 2001;4:290–297.
- Bersamin M, Paschall MJ, Fearnow-Kenney M, Wyrick D. Effectiveness of a Web-based alcohol-misuse and harm-prevention course among high- and low-risk students. J Am Coll Health. 2007;55:247–254. [PubMed]
- Block G, Wakimoto P, Metz D, Fujii ML, Feldman N, Mandel R, Sutherland B. A Randomized Trial of the Little by Little CD-ROM: Demonstrated Effectiveness in Increasing Fruit and Vegetable Intake in a Low-income Population. Prev Chronic Dis. 2004;1:A08. [PMC free article] [PubMed]
- Bosworth K, Gustafson DH, Hawkins RP. The BARN system: Use and impact of adolescent health promotion via computer. Comput Hum Behav. 1994;10:467–482.
- Bowen AM, Horvath K, Williams ML. A randomized control trial of Internet-delivered HIV prevention targeting rural MSM. Health Educ Res. 2007;22:120–127. [PMC free article] [PubMed]
- Brown JB, Winzelberg AJ, Abascal LB, Taylor CB. An evaluation of an Internet-delivered eating disorder prevention program for adolescents and their parents. J Adolescent Health. 2004;35:290–296. [PubMed]
- Brown SJ, Lieberman DA, Gemeny BA, Fan YC, Wilson DM, Pasta DJ. Educational video game for juvenile diabetes: results of a controlled trial. Med. Inform. 1997;22:77–89. [PubMed]
- Budman SH, Portnoy DB, Villapiano AJ. How to get technological innovation used in behavioral health care: Build it and they still might not come. Psychother: Theor, Res, Practice, Training. 2003;40:45–54.
- Campbell M, Honess-Morreale L, Farrell D, Carbone E, Brasure M. A tailored multimedia nutrition education pilot program for low-income women receiving food assistance. Health Educ Res. 1999;14:157–167. [PubMed]
- Campbell MK, Carbone E, Honess-Morreale L, Heisler-Mackinnon J, Demissie S, Farrell D. Randomized trial of a tailored nutrition education CD-ROM program for women receiving food assistance. J Nutr Educ Behav. 2004;36:58–66. [PubMed]
- Carey KB, Carey MP, Maisto SA, Henson JM. Brief motivational interventions for heavy college drinkers: A randomized controlled trial. J. Cons. Clin. Psychol. 2006;74:943–954. [PMC free article] [PubMed]
- Carey MP, Kalra DL, Carey KB, Halperin S, Richards CS. Stress and unaided smoking cessation: A prospective investigation. J Cons Clin Psych. 1993;61:831–838. [PubMed]
- Chiauzzi E, Green TC, Lord S, Thum C, Goldstein M. My student body: a high-risk drinking prevention web site for college students. J Am Coll Health. 2005;53:263–274. [PMC free article] [PubMed]
- Cohen J. Statistical Power Analysis of the Behavioral Sciences. 2nd ed. New York: Erlbaum; 1998.
- Conner M, Sherlock K, Orbell S. Psychosocial determinants of ecstasy use in young people in the UK. Brit J Health Psych. 1998;3:295–317.
- Conner M, Sparks P, Povey R, James R, Shepherd R, Armitage CJ. Moderator effects of attitudinal ambivalence on attitude-behaviour relationships. Eur J Soc Psychol. 2002;32:705–718.
- Dijkstra A, De Vries H, Roijackers J. Computerized tailored feedback to change cognitive determinants of smoking: A Dutch field experiment. Health Educ Res. 1998;13:197–206. [PubMed]
- Downs JS, Murray PJ, Bruine de Bruin W, Penrose J, Palmgren C, Fischhoff B. Interactive video behavioral intervention to reduce adolescent females' STD risk: A randomized controlled trial. Soc Sci Med. 2004;59:1561–1572. [PubMed]
- Etter J-F. Comparing the Efficacy of Two Internet-Based, Computer-Tailored Smoking Cessation Programs: A Randomized Trial. J Med Internet Res. 2005;7 [PMC free article] [PubMed]
- Evers KE, Prochaska JM, Prochaska JO, Driskell M, Cummins CO, Velicher WF. Strengths and weaknesses of health behavior change programs on the internet. J Health Psychol. 2003;8:63–70. [PubMed]
- Fishbein M, Ajzen I. Belief attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley; 1975.
- Fisher JD, Fisher WA. Changing AIDS risk behavior. Psychol Bull. 1992;111:455–474. [PubMed]
- Forester Research Inc. The wide range of abilities and its impact on computer technology. Study Commissioned by Microsoft Corporation. 2003. [Online]. Available from: http://download.microsoft.com/download/0/1/f/01f506eb-2d1e-42a6-bc7b-1f33d25fd40f/ResearchReport.doc [cited 30 August 2007]
- Franko DL, Mintz LB, Villapiano M, Green TC, Mainelli D, Folensbee L, Butler SF, Davidson MM, Hamilton E, Little D, Kearns M, Budman SH. Food, Mood, and Attitude: Reducing Risk for Eating Disorders in College Women. Health Psychol. 2005;24:567–578. [PubMed]
- Gerber BS, Brodsky IG, Lawless KA, Smolin LI, Arozullah AM, Smith EV, Berbaum ML, Heckerling PS, Eiser AR. Implementation and evaluation of a low-literacy diabetes education computer multimedia application. Diabetes Care. 2005;28:1574–1580. [PubMed]
- Glasgow RE, Boles SM, McKay HG, Feil EG, Barrera MJ. The D-Net diabetes self-management program: Long-term implementation, outcomes, and generalization results. Prev Med. 2003;36:410–419. [PubMed]
- Glazebrook C, Garrud P, Avery A, Coupland C, Williams H. Impact of a multimedia intervention "Skinsafe" on patients' knowledge and protective behaviors. Prev Med. 2006;42:449–454. [PubMed]
- Gordon RS. An operational classification of disease prevention. Public Health Rep. 1983;98:107–109. [PMC free article] [PubMed]
- Grissom RJ. The magical number .7 +/− .2: meta-meta-analysis of the probability of superior outcome in comparisons involving therapy, placebo, and control. J Consult Clin Psychol. 1996;64:973–982. [PubMed]
- Harvey-Berino J, Pintauro SJ, Buzzell P, DiGiulio M, Gold BC, Moldovan C, Ramirez E. Does using the internet facilitate the maintenance of weight loss? Int J Obesity. 2002;26:1254–1260. [PubMed]
- Hedges LV. Distribution theory for Glass’s estimator of effect size and related estimators. J Educ Stat. 1981;6:107–128.
- Hedges LV, Olkin L. Statistical methods for meta-analysis. Orlando FL: Academic Press; 1985.
- Hester RK, Delaney HD. Behavioral self-control program for windows: Results of a controlled clinical trial. J Consult Clin Psych. 1997;65:686–693. [PubMed]
- Hester RK, Squires DD, Delaney HD. The Drinker's Check-up: 12-month outcomes of a controlled clinical trial of a stand-alone software program for problem drinkers. J Subst Abuse Treat. 2005;28:159–169. [PubMed]
- Hettema J, Steele J, Miller WR. Motivational interviewing. Annu Rev Clin Psychol. 2005;1:91–111. [PubMed]
- Hewitt M, Denman S, Hayes L, Pearson J, Wallbanks C. Evaluation of 'Sun-safe': a health education resource for primary schools. Health Educ Res. 2001;16:623–633. [PubMed]
- Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21:1539–1558. [PubMed]
- Horan PP, Yarborough MC, Besigel G, Carlson DR. Computer-assisted self-control of diabetes by adolescents. Diabetes Educator. 1990;16:205–211. [PubMed]
- Huedo-Medina TB, Sanchez-Meca J, Marin-Martinez F, Botella J. Assessing heterogeneity in meta-analysis: Q statistic or I2 index? Psychol Methods. 2006;11:193–206. [PubMed]
- Irvine AB, Ary DV, Grove DA, Gilfillan-Morton L. The effectiveness of an interactive multimedia program to influence eating habits. Health Educ Res. 2004;19:290–305. [PubMed]
- Jacobi C, Morris L, Beckers C, Bronisch-Holtze J, Winter J, Winzelberg A, Taylor C. Maintenance of internet-based prevention: a randomized controlled trial. Int J Eat Disorder. 2007;40:114–119. [PubMed]
- Johnson BT, Eagly AH. Quantitative synthesis of social psychological research. In: Reis HT, Judd CM, editors. Handbook of research methods in social and personality psychology. New York: Cambridge University Press; 2000. pp. 496–528.
- Johnson BT, Wood T. DSTAT 2.00: Software for Meta-Analysis. Storrs, CT: Author; 2006.
- Kaufmann PG. Debate: What type of evidence will advance behavioral medicine?. From basic science to population health; Proceedings of the 2007 Annual Meeting of the Society of Behavioral Medicine; Washington, DC. 2007. Mar 21–24, pp. 21–24. [Online]. Available from: http://sbm.org/meeting/2007/slides/Kaufmann_and_glasgow.pdf [cited 5 July 2007]
- Kiene SM, Barta WD. A brief individualized computer-delivered sexual risk reduction intervention increases HIV/AIDS preventive behavior. J Adolesc Health. 2006;39:404–410. [PubMed]
- 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;27:1435–1442. [PubMed]
- Krosnick JA, Alwin DF. Aging and susceptibility to attitude change. J Pers Social Psych. 1989;57:416–425. [PubMed]
- Kumar NB, Bostow DE, Schapira DV, Kritch KM. Efficacy of interactive, automated programmed instruction in nutrition education for cancer prevention. J Cancer Educ. 1993;8:203–211. [PubMed]
- Kwak N, Skoric MM, Williams AE, Poor ND. To broadband or not to broadband: The relationship between high-speed internet and knowledge and participation. J Broadcast Electron. 2004;48:421–445.
- Kypri K, McAnally HM. Randomized controlled trial of a web-based primary care intervention for multiple health risk behaviors. Prev Med. 2005;41:761–766. [PubMed]
- Kypri K, Saunders JB, Williams SM, McGee RO, Langley JD, Cashell-Smith ML, Gallagher SJ. Web-based screening and brief intervention for hazardous drinking: A double-blind randomized controlled trial. Addiction. 2004;99:1410–1417. [PubMed]
- Lawrence T, Aveyard P, Evans O, Cheng KK. A cluster randomised controlled trial of smoking cessation in pregnant women comparing interventions based on the transtheoretical (stages of change) model to standard care. Tob Control. 2003;12:168–177. [PMC free article] [PubMed]
- Lipsey MW, Wilson DB. The efficacy of psychological, educational, and behavioral treatment. Confirmation from meta-analysis. The American psychologist. 1993;48:1181–1209. [PubMed]
- Lipsey MW, Wilson DB. Practical meta-analysis. Thousand Oaks, CA: Sage; 2001.
- Lightfoot M, Comulada WS, Stover G. Computerized HIV Preventive Intervention for Adolescents: Indications of Efficacy. Am J Public Health. 2006;96:10–13. [PubMed]
- Maio RF, Shope JT, Blow FC, Gregor MA, Zakrajsek JS, Weber JE, Nypaver MM. A randomized controlled trial of an emergency department-based interactive computer program to prevent alcohol misuse among injured adolescents. Ann Emerg Med. 2005;45:420–429. [PubMed]
- Marquie JC, Jourdan-Boddaert L, Huet N. Do older adults underestimate their actual computer knowledge? Behaviour & Information Technology. 2002;21:273–280.
- Marsch LA, Bickel WK. Efficacy of computer-based HIV/AIDS education for injection drug users. Am J Health Behav. 2004;28:316–327. [PubMed]
- Marshall AL, Leslie E, Bauman A, Marcus B, Owen N. Print versus website physical activity programs: A randomized trial. Am J Prev Med. 2003;25:88–94. [PubMed]
- Matt GE, Cook TD. Threats to the validity of research synthesis. In: Cooper H, Hedges LV, editors. The Handbook of Research Synthesis. New York: Russell Sage Foundation; 1994. pp. 503–520.
- McKay H, King D, Eakin E, Seeley J, Glasgow R. The diabetes network internet-based physical activity intervention: a randomized pilot study. Diabetes Care. 2001;24:1328–1334. [PubMed]
- Meier ST. An exploratory study of a computer-assisted alcohol education program. Comput Hum Serv. 1988;3:111–121.
- Moore MJ, Soderquist J, Werch C. Feasibility and efficacy of a binge drinking prevention intervention for college students delivered via the Internet versus postal mail. J Am Coll Health. 2005;54:38–44. [PubMed]
- Muñoz RF, Lenert LL, Delucchi K, Stoddard J, Perez JE, Penilla C, Pérez-Stable Toward evidence-based Internet interventions: A Spanish/English Web site for international smoking cessation trials. Nicotine Tob Res. 2006;8:77–87. [PubMed]
- Napolitano MA, Fotheringham M, Tate D, Sciamanna C, Leslie E, Owen N, Bauman A, Marcus B. Evaluation of an internet-based physical activity intervention: a preliminary investigation. Ann Behav Med. 2003;25:92–99. [PubMed]
- Neighbors C, Larimer ME, Lewis MA. Targeting misperceptions of descriptive drinking norms: efficacy of a computer-delivered personalized normative feedback intervention. J Consult Clin Psychol. 2004;72:434–447. [PubMed]
- Neumann T, Neuner B, Weiss-Gerlach E, Tonnesen H, Gentilello LM, Wernecke KD, Schmidt K, Schroder T, Wauer H, Heinz A, Mann K, Muller JM, Haas N, Kox WJ, Spies CD. The effect of computerized tailored brief advice on at-risk drinking in subcritically injured trauma patients. J Traum. 2006;61:805–814. [PubMed]
- Noar SM, Benac CN, Harris MS. Does tailoring matter? Meta-analytic review of tailored print health behavior change interventions. Psychological bulletin. 2007;133:673–693. [PubMed]
- Oenema A, Brug J. Feedback strategies to raise awareness of personal dietary intake: Results of a randomized controlled trial. Prev Med. 2003;36:429–439. [PubMed]
- Oenema A, Brug J, Lechner L. Web-based tailored nutrition education: results of a randomized controlled trial. Health Educ Res. 2001;16:647–660. [PubMed]
- Oenema A, Tan F, Brug J. Short-term efficacy of a web-based computer-tailored nutrition intervention: main effects and mediators. Ann Behav Med. 2005;29:54–63. [PubMed]
- Ondersma SJ, Chase SK, Svikis DS, Schuster CR. Computer-based brief motivational intervention for perinatal drug use. J Subst Abuse Treat. 2005;28:305–312. [PMC free article] [PubMed]
- O'Neill HK, Gillispie MA, Slobin K. Stages of change and smoking cessation: A computer-administered intervention program for young adults. Am J Health Promot. 2000;15:93–96. [PubMed]
- Organization for Economic Co-Operation and Development [OECD] 2007 OECD communications outlook 2007. 2007. [Online]. Available from: http://22.214.171.124/oecd/pdfs/browseit/9307021E.pdf[cited 12 July 2007]
- Patten CA, Croghan IT, Meisq TM, Decker PA, Pingree S, Colligan RC, Dornelas EA, Offord KP, Boberg EW, Baumberger RK, Hurt RD, Gustafson DH. Randomized clinical trial of an Internet-based versus brief office intervention for adolescent smoking cessation. Patient Educ Couns. 2006;64:249–258. [PubMed]
- Pew Internet and American Life Project. Health information online. 2005. [Online]. Available from: http://www.pewinternet.org/pdfs/PIP_Healthtopics_May05.pdf [cited 6 July 2007]
- Pew Internet and American Life Project. Internet penetration and impact. 2006. [Online]. Available from: http://www.pewinternet.org/pdfs/PIP_Internet_Impact.pdf [cited 27 June 2007]
- Pigott TD. Methods for handling missing data in research synthesis. In: Cooper H, Hedges LV, editors. The handbook of research synthesis. New York: Russell Sage Foundation; 1994.
- Povey R, Wellens B, Conner M. Attitudes towards following meat, vegetarian and vegan diets: An examination of the role of ambivalence. Appetite. 2001;37:15–26. [PubMed]
- Prochaska JQ, DiClemente CC. Transtheoretical therapy: Toward a more integrative model of change. Psychother Theor Res. 1982;20:161–173.
- Reyna VF, Farley F. Risk and Rationality in Adolescent Decision Making: Implications for Theory, Practice, and Public Policy. Psychol Sci Public Interest. 2006;7:1–44.
- Roberto AJ, Zimmerman RS, Carlyle KE, Abner EL. A computer-based approach to preventing pregnancy, STD, and HIV in rural adolescents. J Health Commun. 2007;12:53–76. [PubMed]
- Rose G. The strategy of preventive medicine. Oxford: Oxford University Press; 1992.
- 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. [PubMed]
- Ryan P, Lauver RD. The efficacy of tailored interventions. J Nurs Scholarship. 2002;34:331–337. [PubMed]
- Sánchez-Meca J, Marín-Martínez F, Chacón-Moscoso S. Effect-size indices for dichotomized outcomes in meta-analysis. Psychol Methods. 2003;8:448–467. [PubMed]
- Schinke S, Schwinn T. Gender-specific computer-based intervention for preventing drug abuse among girls. Am J Drug Alcohol Ab. 2005;31:609–616. [PMC free article] [PubMed]
- Schinke SP, Di Noia J, Glassman JR. Computer-mediated intervention to prevent drug abuse and violence among high-risk youth. Addict Behav. 2004a;29:225–229. [PMC free article] [PubMed]
- Schinke SP, Moncher MS, Singer BR. Native American youths and cancer risk reduction: Effects of software intervention. J Adoles Health. 1994;15:105–110. [PubMed]
- Schinke SP, Schwinn TM, Di Noia J, Cole KC. Reducing the risks of alcohol use among urban youth: three-year effects of a computer-based intervention with and without parent involvement. J Stud Alcohol. 2004b;65:443–449. [PMC free article] [PubMed]
- Schinke SP, Schwinn TM, Ozanian AJ. Alcohol Abuse Prevention Among High-Risk Youth: Computer-Based Intervention. J Prevent Intervention Commun. 2005;29:117–130. [PMC free article] [PubMed]
- Schopp LH, Demiris G, Glueckauf RL. Rural backwaters or front runners? Rural telehealth in the vanguard of psychology practice. Prof Psychol-Res Pr. 2006;37:165–173.
- Skinner CS, Campbell MK, Rimer BK, Curry S, Prochaska JO. How effective is tailored print communication? Ann Behav Med. 1999;21:290–298. [PubMed]
- Smoak ND, Scott-Sheldon LAJ, Johnson BT, Carey MP. Do sexual risk reduction interventions inadvertently increase the overall frequency of sexual behavior? Answers from a meta-analysis of 174 studies with 116,735 participants. JAIDS. 2006;43:374–384. [PMC free article] [PubMed]
- Southard DR, Southard BH. Promoting physical activity in children with MetaKenkoh. Clin Invest Med. 2006;29:293–297. [PubMed]
- Sparks P, Conner M, James R, Shepherd R, Povey R. Ambivalence about health-related behaviours: An exploration in the domain of food choice. Brit J Health Psych. 2001;6:53–63. [PubMed]
- StataCorp. Statistical Software: Release 10.0. College Station, TX: StataCorp; 2007.
- Steele R, Mummery KW, Dwyer T. Development and process evaluation of an Internet-based physical activity behaviour change program. Patient Educ Couns. 2007;67:127–136. [PubMed]
- Strecher VJ, Shiffman S, West R. Randomized controlled trial of a web-based computer-tailored smoking cessation program as a supplement to nicotine patch therapy. Addiction. 2005;100:682–688. [PubMed]
- Swartz LH, Noell JW, Schroeder SW, Ary DV. A randomised control study of a fully automated internet based smoking cessation programme. Tob Control. 2006;15:7–12. [PMC free article] [PubMed]
- Taylor CB, Bryson S, Luce KH, Cunning D, Doyle AC, Abascal LB, Rockwell R, Dev P, Winzelberg AJ, Wilfley DE. Prevention of eating disorders in at-risk college-age women. Arch Gen Psychiat. 2006;63:881–888. [PMC free article] [PubMed]
- Tessaro I, Rye S, Parker L, Mangone C, McCrone S. Effectiveness of a nutrition intervention with rural low-income women. Am J Health Behav. 2007;31:35–43. [PubMed]
- Turnin M, Beddok R, Clottes J, Martini P, Abadie R, Buisson J, Soule-Dupuy C, Bonneu M, Camare R, Anton J, et al. Telematic expert system Diabeto. New tool for diet self-monitoring for diabetic patients. Diabetes Care. 1992;15:204–212. [PubMed]
- U.S. Census Bureau. Computer and Internet Use in the United States: 2003. 2005. [Online]. Available from: http://www.census.gov/prod/2005pubs/p23-208.pdf [cited 6 July 2007]
- U.S. Department of Health and Human Services. Leading health indicators: Priorities for action. 2007. Available from http://www.healthypeople.gov/LHI/LHIPrioritiesforAction.pdf [cited 6 July 2007]
- Verheijden M, Bakx JC, Akkermans R, van den Hoogen H, Godwin NM, Rosser W, van Staveren W, van Weel C. Web-Based Targeted Nutrition Counselling and Social Support for Patients at Increased Cardiovascular Risk in General Practice: Randomized Controlled Trial. J Med Internet Res. 2004;6 [PMC free article] [PubMed]
- Walters ST, Wright JA, Shegog A. A review of computer and internet based interventions for smoking behavior. Addict Behav. 2006;31:264–277. [PubMed]
- 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 Internet Res. 2004;6:e40. [PMC free article] [PubMed]
- Williams C, Griffin KW, Macaulay AP, West TL, Gronewold E. Efficacy of a drug prevention CD-ROM intervention for adolescents. Subst Use Misuse. 2005;40:869–878. [PubMed]
- Winett RA, Moore JF, Wagner JL, Hite LA, Leahy M, Neubauer TE, Walberg JL, Walker WB, Lombard D, Geller ES, Mundy L. Altering shoppers' supermarket purchases to fit nutritional guidelines: An interactive information system. J Appl Behav Anal. 1991;24:95–105. [PMC free article] [PubMed]
- Wing RR, Tate DF, Gorin AA, Raynor HA, Fava JL. A self-regulation program for maintenance of weight loss. New Engl J Med. 2006;355:1563–1571. [PubMed]
- Winzelberg AJ, Eppstein D, Eldredge KL, Wilfley D, Dasmahapatra R, Dev P, Taylor CB. Effectiveness of an Internet-based program for reducing risk factors for eating disorders. J Consult Clin Psych. 2000;68:346–350. [PubMed]
- Winzelberg AJ, Taylor CB, Sharpe T, Eldredge KL, Dev P, Constantinou PS. Evaluation of a computer-mediated eating disorder intervention program. Int J Eat Disorder. 1998;24:339–349. [PubMed]
- Wofford JL, Smith ED, Miller DP. The multimedia computer for office based patient education: A systematic review. Patient Educ Couns. 2005;59:148–157. [PubMed]
- Yates S. Worldwide PC Adoption Forecast, 2007 To 2015. 2007. [Online]. Available from: http://www.forrester.com/Research/Document/Excerpt/0,7211,42496,00.html[cited 13 July 2007]
- Zabinski MF, Pung MA, Wilfley DE, Eppstein DL, Winzelberg AJ, Celio A, Taylor CB. Reducing risk factors for eating disorders: Targeting at-risk women with a computerized psychoeducational program. Int J Eat Disorder. 2001;29:401–408. [PubMed]
- Zabinski MF, Wilfley DE, Calfas KJ, Winzelberg AJ, Taylor CB. An Interactive Psychoeducational Intervention for Women at Risk of Developing an Eating Disorder. J Consult Clin Psych. 2004;72:914–919. [PubMed]
Supplemental References for the Studies Included in the Meta-Analysis
- Brown JLB. PhD thesis. Stanford University; 2002. An evaluation of an internet-delivered eating disorder prevention program for adolescents and their parents. [Additional study information for Brown et al. 2004]
- Feil EG, Glasgow RE, Boles S, McKay HG. Who participates in internet-based self-management programs? A study among novice computer users in a primary care setting. Diabetes Educator. 2000;26:806–811. [Additional study information for Glasgow et al. 2003] [PubMed]
- Hawkins RP, Gustafson DH, Chewning B, Bosworth K, Day PM. Reaching hard-to-reach populations. Interactive computer programs as public information campaigns for adolescents. J Commun. 1987;37:8–28. [Additional study information for Bosworth et al. 1994]
- Kosma M. PhD thesis. Oregon State University; 2003. Interactive vs. non-interactive electronically delivered motivational materials for physical activity initiation and enhancement among adults with physical disabilities. [Additional study information for Kosma et al. 2005]
- Lawrence T, Aveyard P, Cheng KK, Griffin C, Johnson C, Croghan E. Does stage-based smoking cessation advice in pregnancy result in long-term quitters? An 18-month postpartum follow-up of a randomized controller trial. Addiction. 2005;100:107–116. [Additional measurement occasion for Lawrence et al 2003] [PubMed]
- Ozanian AJ. PhD thesis. Columbia University; 2003. The influence of multimedia-based parent and adolescent interventions on substance abuse among poor youth. [Additional study information for Schinke et al. 2005]
- Paschall MJ, Bersamin M, Fearnow-Kenney M, Wyrick D, Currey D. Short-term evaluation of a web-based college alcohol misuse and harm prevention course (College Alc) J Alcohol Drug Educ. 2006;50:49–65. [Additional measurement occasion for Bersamin et al 2007]
- Schinke S, Schwinn T, Cole K. Preventing alcohol abuse among early adolescents through family and computer-based interventions: Four-year outcomes and mediating variables. J Dev Phys Disabil. 2006;18:149–161. [Additional measurement occasion for Schinke et al. 2004] [PMC free article] [PubMed]
- Squires DD, Hester RK. Development of a computer-based, brief intervention for drinkers: The increasing role for computer in the assessment and treatment of addictive behaviors. Behavior Therapist. 2002;3:56–65. [Additional study information for Hester et al. 2005]
- Winett RA, Anderson ES, Bickley PG, Walberg-Rankin J, Moore JF, Leahy M, Harris CE, Gerkin RE. Nutrition for a lifetime system: A multimedia system for altering food supermarket shoppers’ purchases to meet nutritional guidelines. Comput Hum Behav. 1997;13:371–392. [Additional study information for Anderson et al 1997]
- Winzelberg AJ. PhD thesis. Stanford University; 1998. Evaluation of a computer-mediated eating disorder prevention program. [Additional study information for Winzelberg et al. 1998]
- Zabinski MF. PhD thesis. San Diego: University of California; 2003. An interactive Psychoeducational intervention for women at-risk of developing an eating disorder. [Additional study information for Zabinski et al. 2004]