The DOI matches its Crossref record; 210 of the 210 references it cites were found in a registry or an index.
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
In the citing paper
“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]
In the citing paper
“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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Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009) · doi:10.1002/9780470743386 · Cited by 14,225
Duval, S., & Tweedie, R. (2000) · Biometrics · doi:10.1111/j.0006-341x.2000.00455.x · Cited by 14,099
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2021) · doi:10.1002/9781119558378 · Cited by 9,799
(2000) · Cited by 9,198
Moher, D., Cook, D. J., Eastwood, S., Olkin, I., Rennie, D., & Stroup, D. F. (1999) · The Lancet · doi:10.1016/s0140-6736(99)04149-5 · Cited by 4,826
Treating Tobacco Use and Dependence: 2008 Update
Panel, T. U. A. D. G. (2008) · PsycEXTRA Dataset · doi:10.1037/e566222009-001 · Cited by 3,864
Moher, D., Pham, B., Jones, A., Cook, D. J., Jadad, A. R., Moher, M., Tugwell, P., & Klassen, T. P. (1998) · The Lancet · doi:10.1016/s0140-6736(98)01085-x · Cited by 3,560
Boutron, I., Moher, D., Altman, D. G., Schulz, K. F., Ravaud, P., & for the CONSORT Group* (2008) · Annals of Internal Medicine · doi:10.7326/0003-4819-148-4-200802190-00008 · Cited by 2,292
Does tailoring matter? Meta-analytic review of tailored print health behavior change interventions
Noar, S. M., Benac, C. N., & Harris, M. S. (2007) · Psychological Bulletin · doi:10.1037/0033-2909.133.4.673 · Cited by 2,136
Clinical Practice Guideline: Otitis Media with Effusion (Update)
Rosenfeld, R. M., Shin, J. J., Schwartz, S. R., Coggins, R., Gagnon, L., Hackell, J. M., Hoelting, D., Hunter, L. L., Kummer, A. W., Payne, S. C., et al. (2016) · Otolaryngology–Head and Neck Surgery · doi:10.1177/0194599815623467 · Cited by 2,006
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Zhou, R., Shi, K., Li, S., & Zhou, W. (2023) · Research Square · doi:10.21203/rs.3.rs-3190890/v1
Smits, M., Bolman, C. A. W., Mesters, I., & Lechner, L. (2023) · Research Square · doi:10.21203/rs.3.rs-3649963/v1
Fernandes, G. G., Sánchez, X., Martin, J., & Ravenet, B. (2024) · Conference on l'Interaction Homme-Machine (IHM) · doi:10.1145/3649792.3649802
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Vaccines and Immunization Services During the Pandemic Era and Beyond
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Raussi, V., Kujala, S., Hörhammer, I., Savolainen, K., Autio, R., & Koskela, T. (2024) · Journal of Medical Internet Research · doi:10.2196/49802
Dantas, L. A. (2024) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.55.2024.tde-15012025-155856
Soto‐Ruiz, N., Escalada‐Hernández, P., de Murieta, A. B.-S., Leite, A. C. A. B., Pimentel-Parra, G. A., & García‐Vivar, C. (2025) · doi:10.2196/preprints.72654
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Whelan, M., & Esliger, D. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009391573.025
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DiMatteo, M. R., Martin, L. R., & Haskard‐Zolnierek, K. B. (2025) · Oxford University Press eBooks · doi:10.1093/oso/9780197778586.003.0005
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Rubyan, M. J., Weaver, A., Tang, X., Gorton, S., Kim, S. G., Bodie, N., Harris, E., Landry, C., Long, B., Whitted, R., et al. (2025) · Families in Society The Journal of Contemporary Social Services · doi:10.1177/10443894251357635
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Truin, L. S., Heger, I., Wimmers, S. C. P. M., Deckers, K., de Vugt, M. E., & Köhler, S. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877261440958
Ramos, S. R., Boerger, L. L., Restar, A., Phadke, M. A., Jeon, S., Johnson, C., Melkus, G. D., Kershaw, T. C., Reynolds, H., & Vorderstrasse, A. A. (2026) · Journal of Medical Internet Research · doi:10.2196/91145
van Keulen, H. M., Voogt, C. V., Kleinjan, M., Huizing, A., Andree, R., & van Empelen, P. (2026) · JMIR mhealth and uhealth · doi:10.2196/68468
Mensah, M. A., Kwasi, E., Abdulmawjood, M., & Oyibo, K. (2026) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3774935.3806188
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Mitková, Ľ., Musilová, M., Chrančoková, M., & Rentková, K. (2026) · Revista Sítio Novo · doi:10.47236/2594-7036.2026.v10.2083
Kang, B., Rimal, R. N., Lakew, Y., & Temesgen, H. T. (2026) · Health Communication · doi:10.1080/10410236.2026.2692533
Zhou, P., Lau, P. W. C., Lu, N., & Ma, M. (2026) · Springer eBooks · doi:10.1007/978-981-95-2175-3_59
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Sharvani, P. A. L., & Ghosh, S. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-3085-1.ch002
Sinvani, L., Diefenbach, M. A., Perrin, A., Gordon, S., D’Angelo, S., Groton, S., Ferdous, N.-E., Parker, M., Babar, H., Chiuzan, C. C., et al. (2026) · doi:10.2196/preprints.104737
Nagao, S., Nakamura, M., Sano, S., Oku, Y., Hattori, M., Hayamizu, S., & Terada, K. (2026) · ACM International Conference on Intelligent Virtual Agents (IVA) · doi:10.1145/3806774.3827994
Mellaert, S. V., Kreuzberg, M. S., ten Klooster, I., van Beijnum, B.-J. F., van Rijn, J. N., & Tabak, M. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001713
Dai, R., Zhou, D., Wan, X., Xu, X., He, W., Guo, X., Tang, H., & You, H. (2026) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.70410
Nagao, H. S., Shimazu, T., & Odawara, M. (2026) · doi:10.2196/preprints.113108
Obuobi-Donkor, G., Shalaby, R., Agyapong, B., Eboreime, E., Wozney, L., & Agyapong, V. I. O. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1949324
Khattak, M. W., Ross, V., Dirix, H., Gruden, C., Brijs, T., Adnan, M., & Brijs, K. (2026) · Accident Analysis & Prevention · doi:10.1016/j.aap.2026.108841
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
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