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A meta-analysis of computer-tailored interventions for health behavior change
Open accessAbstract
Objective Computer-tailored interventions have become increasingly common for facilitating improvement in behaviors related to chronic disease and health promotion. A sufficient number of outcome studies from these interventions are now available to facilitate the quantitative analysis of effect sizes, permitting moderator analyses that were not possible with previous systematic reviews.Method The present study employs meta-analytic techniques to assess the mean effect for 88 computer-tailored interventions published between 1988 and 2009 focusing on four health behaviors: smoking cessation, physical activity, eating a healthy diet, and receiving regular mammography screening. Effect sizes were calculated using Hedges g. Study, tailoring, and demographic moderators were examined by analyzing between-group variance and meta-regression.Results Clinically and statistically significant overall effect sizes were found across each of the four behaviors. While effect sizes decreased after intervention completion, dynamically tailored interventions were found to have increased efficacy over time as compared with tailored interventions based on one assessment only. Study effects did not differ across communication channels nor decline when up to three behaviors were identified for intervention simultaneously.Conclusion This study demonstrates that computer-tailored interventions have the potential to improve health behaviors and suggests strategies that may lead to greater effectiveness of these techniques.
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“The effect sizes on behavior were however in line with findings of two meta-analyses on computer-delivered interventions (range between 0.13 and 0.22 (Krebs, Prochaska, & Rossi 2010); and range between 0.04 and 0.35 (Portnoy, Scott-Sheldon, Johnson, & Carey 2008)). Hence, serious games for the promotion of healthy lifestyles may be considered as effective as other computer-delivered interventions.”
From A meta-analysis of serious digital games for healthy lifestyle promotion (2014) · cites this paper as Krebs, Prochaska, & Rossi 2010
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“Similarly, in computer-tailored interventions, tailoring showed the strongest effects when based on both theoretical concepts (e.g. stages of change, decisional balance, attitudes) and user characteristics (Noar et al. 2007) and performed via dynamic tailoring, in which tailoring is continuously adjusted during the intervention (Krebs, Prochaska, & Rossi 2010).”
From A meta-analysis of serious digital games for healthy lifestyle promotion (2014) · cites this paper as Krebs, Prochaska, & Rossi 2010
In the citing paper
“Experts in health behavior change have shown that conveying health information without considering individual differences may inhibit behavior change [20-26].”
From Tailored web-based interventions for pain: Systematic review and meta-analysis (2017) · cites this paper as [20]
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“Clinically relevant results, such as adopting a healthy lifestyle or adhering to medication, and statistically significant effect sizes of tailored Web-based interventions have been recognized for health behavior change among diverse populations facing chronic disease [20,29,30].”
From Tailored web-based interventions for pain: Systematic review and meta-analysis (2017) · cites this paper as [20]
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Metadata
- Authors
- Krebs, P., Prochaska, J. O., & Rossi, J. S.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- University of Rhode Island (US)
- Corresponding author
- Krebs, Paul
- Venue
- Preventive Medicine
- Publisher
- Elsevier BV
- Published
- 2010-09
- Volume
- 51
- Issue
- 3-4
- Pages
- 214-221
- ISSN
- 0091-7435 (ISSN-L)
- PMC
- PMC2939185
- OpenAlex
- W2020292934
- Cited by
- 854
- References
- 209
- Citations by year
- 2026: 27 · 2025: 43 · 2024: 29 · 2023: 42 · 2022: 58 · 2021: 61 · 2020: 54 · 2019: 65 · 2018: 74 · 2017: 71 · 2016: 64 · 2015: 64 · 2014: 70 · 2013: 53 · 2012: 53
- FWCI
- 4.46
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Preventive Medicine (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Journal of Media Literacy Education (submittedVersion, cc-by)
- Funders
- National Cancer Institute (P30CA008748, T32CA009461)
- Topics
- Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Communication › Media Influence and Health
- Keywords
- computer-tailored intervention, health behavior change, smoking cessation, physical activity, healthy eating, mammography screening, chronic disease prevention, health promotion, communication channels
- MeSH terms
- Decision Making, Computer-Assisted, Diet, Female, Health Promotion, Humans, Male, Mammography, Motor Activity, Patient Compliance, Health Behavior, Smoking Cessation, Reminder Systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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