The DOI matches its Crossref record; 102 of the 102 references it cites were found in a registry or an index.
A Meta-Analysis of Web-Delivered Tailored Health Behavior Change Interventions
Abstract
Web-based tailored intervention programs show considerable promise in effecting health-promoting behaviors and improving health outcomes across a variety of medical conditions and patient populations. This meta-analysis compares the effects of tailored versus nontailored web-based interventions on health behaviors and explores the influence of key moderators on treatment outcomes. Forty experimental and quasi-experimental studies (N =20,180) met criteria for inclusion and were analyzed using meta-analytic procedures. The findings indicated that web-based tailored interventions effected significantly greater improvement in health outcomes as compared with control conditions both at posttesting, d =.139 (95% CI = .111, .166, p <.001, k =40) and at follow-up, d =.158 (95% CI = .124, .192, p <.001, k =21). The authors found no evidence of publication bias. These results provided further support for the differential benefits of tailored web-based interventions over nontailored approaches. Analysis of participant/descriptive, intervention, and methodological moderators shed some light on factors that may be important to the success of tailored interventions. Implications of these findings and directions for future research are discussed.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Tailoring can have a positive impact on intervention outcomes and engagement, but this varies between studies and contexts.31,52”
From Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions (2016) · cites this paper as [52]
In the citing paper
“Compared to more traditional knowledge transfer methods in digital health (eg, tailored modules), the conversational methods in virtual assistance may be perceived as more personally relevant and humanistic, which is critical given that perceived relevance and the inclusion of social support are associated with increased effect sizes in digital behavior change interventions [7,8].”
From A Physical Activity and Diet Program Delivered by Artificially Intelligent Virtual Health Coach: Proof-of-Concept Study (2020) · cites this paper as [8]
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 [30]
In the citing paper
“A meta-analysis on tailored Web-based interventions in general found that expert input does not necessarily mean more efficacy [30].”
From Tailored web-based interventions for pain: Systematic review and meta-analysis (2017) · cites this paper as [30]
In the citing paper
“Hence, it has been shown that Web-based tailored interventions that are preventive in nature and targeting a general population, not a specific condition, were more successful [30].”
From Tailored web-based interventions for pain: Systematic review and meta-analysis (2017) · cites this paper as [30]
In the citing paper
“It is generally accepted that user engagement with DBCI is a precursor to improved health outcomes.”
From Understanding Health Behavior Technology Engagement: Pathway to Measuring Digital Behavior Change Interventions (2019) · cites this paper as [5]
In the citing paper
“Growing evidence supports that interaction with DBCI and adherence to intervention features improve the likelihood of reaching desired health outcomes [5].”
From Understanding Health Behavior Technology Engagement: Pathway to Measuring Digital Behavior Change Interventions (2019) · cites this paper as [5]
Citations
Cites
Yaşlı Kadınlarda Üreme Sağlığı
Kokanalı, D., & Engin Üstün, Y. (n.d.) · DergiPark (Istanbul University) · Cited by 170,090
Content Analysis: An Introduction to its Methodology.
Shelley, M., & Krippendorff, K. (1984) · Journal of the American Statistical Association · doi:10.2307/2288384 · Cited by 24,351
Duval, S., & Tweedie, R. (2000) · Biometrics · doi:10.1111/j.0006-341x.2000.00455.x · Cited by 14,099
Wasserman, S. S., Hedges, L. V., & Olkin, I. (1988) · Journal of Educational Statistics · doi:10.2307/1164953 · Cited by 10,377
Statistical Methods for Meta-Analysis.
Freeman, P. R., Hedges, L. V., & Olkin, I. (1986) · Biometrics · doi:10.2307/2531069 · Cited by 4,840
Meta-Analytic Procedures for Social Research
Rosenthal, R. (1991) · doi:10.4135/9781412984997 · Cited by 4,635
Meta-Analytic Procedures for Social Research.
Metcalfe, A., & Rosenthal, R. (1994) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2348600 · Cited by 4,249
Answering the Call for a Standard Reliability Measure for Coding Data
Hayes, A. F., & Krippendorff, K. (2007) · Communication Methods and Measures · doi:10.1080/19312450709336664 · Cited by 4,225
Health promotion from the perspective of Social Cognitive Theory
Bandura, A. (1998) · Psychology & Health · doi:10.1080/08870449808407422 · Cited by 3,087
Webb, T. L., Joseph, J., Yardley, L., & Michie, S. (2010) · Journal of Medical Internet Research · doi:10.2196/jmir.1376 · Cited by 2,517
Research works printed but not linked: 14
- Davis S., 2008, Interface on the Internet
- 10.1111/j.1460–2466.2006. 00280.x · DOI
- Lipsey M. W., 2001, Practical meta-analysis: Applied social research methods
- 10.1001/archinte. 167.9.944 · DOI
- Noar S. M., 2009, Communication Yearbook 33, 73
- 10.3102/10769986008002157 · DOI · Paper page
- Shire N., 2002, Journal of Gender-Specific Medicine, 5, 48
- 10.2196/jmir.113 · DOI
- 10.1016/j.ypmed.2007. 06.009 · DOI
- 10.1093/her/cy1096 · DOI
- 10.1016/S0778–3991(98)00128–1 · DOI
- 10.1016/j. amepre.2007.12.024 · DOI
- 10.2307/4597943 · DOI
- 10.2307/20747900 · DOI
Cited by
Sorted by citations
Items 601–689 of 689
A Framework for Designing and Evaluating Internet Interventions to Improve Tinnitus Care
Beukes, E. W., Andersson, G., Manchaiah, V. K. C., Allen, P. M., & Baguley, D. (2021) · IGI Global eBooks · doi:10.4018/978-1-7998-8052-3.ch007
Cheung, K. L., Knapen, V., Durusu, D., Winkens, B., & de Vries, H. (2021) · doi:10.2196/preprints.29766
The effect of tailored Web-based interventions on pain in adults: a systematic review protocol
Martorella, G., Gélinas, C., Berube, M., Boitor, M., Fredericks, S., & May, S. L. (2021) · doi:10.32920/14668797.v1
The effect of tailored Web-based interventions on pain in adults: a systematic review protocol
Martorella, G., Gélinas, C., Berube, M., Boitor, M., Fredericks, S., & May, S. L. (2021) · doi:10.32920/14668797
Côté, J., Cossette, S., Ramirez-Garcìa, P., de Pokomandy, A., Worthington, C., Gagnon, M., Auger, P., Boudreau, F., Miranda, J., Guéhéneuc, Y., et al. (2021) · doi:10.32920/14668893
Wray, T. B., Kahler, C. W., Simpanen, E. M., & Operario, D. (2017) · doi:10.2196/preprints.9479
Côté, J., Cossette, S., Ramirez-Garcìa, P., de Pokomandy, A., Worthington, C., Gagnon, M., Auger, P., Boudreau, F., Miranda, J., Guéhéneuc, Y., et al. (2021) · doi:10.32920/14668893.v1
Khazaee-Pool, M., Pashaei, T., & Ponnet, K. S. (2019) · doi:10.2196/preprints.16183
Health recommender systems for behavior change
Hors-Fraile, S. (2022) · Research Publications (Maastricht University) · doi:10.26481/dis.20220324sh
Bol, N. (2022) · The International Encyclopedia of Health Communication · doi:10.1002/9781119678816.iehc0794
Huang, H. Y. (2022) · Wiley eBooks · doi:10.1002/9781119678816.iehc0762
Optimizing digital smoking cessation interventions
Elling, J. M. (2022) · doi:10.26481/dis.20221207je
Schroé, H., Dyck, D. V., Paepe, A. L. D., Braun, M., & Crombez, G. (2023) · Research Square · doi:10.21203/rs.3.rs-2459495/v1
Joyal‐Desmarais, K., Bacon, S., Bredin, S. S. D., Firth, W., Giacomantonio, N., Jones, J. M., Lavoie, K., Nooyen, J., Warburton, D. E. R., & OH, P. I. (2023) · doi:10.31219/osf.io/7yzsr
Analysis of Sexually Transmitted Infections Prevention Campaigns between 2008 and 2020
Stephanou, A. T., de Freitas, I. K., & Dias, A. C. G. (2023) · Psicologia Teoria e Pesquisa · doi:10.1590/0102.3772e39414.en
“Furthermore, communication strategies must clearly understand who the target audience is and how to access it, taking into account the social and economic factors of each group (Lustria et al., 2013).”
BackgroundResultvan der Gaag, M. (2023) · doi:10.26481/dis.20230928mg
Smits, M., Bolman, C. A. W., Mesters, I., & Lechner, L. (2023) · Research Square · doi:10.21203/rs.3.rs-3649963/v1
Nelson, B. (2023) · Digital Commons - Michigan Tech (Michigan Technological University) · doi:10.37099/mtu.dc.etdr/1699
Active and passive choices concerning lifestyle and medication
Landais, L. L. (2024) · doi:10.5463/thesis.552
Parveen, S., Pereira, A. G., Orjuela, N. G., McHugh, P., Surendran, A., Vornhagen, H., & Vellinga, A. (2024) · doi:10.2196/preprints.59687
Djurtoft, C., Sørensen, K., Odgaard, C., Hoegh, M., Rathleff, M. S., & Johansen, S. K. (2024) · medRxiv · doi:10.1101/2024.05.17.24307464
“GPs, on the other hand, were more focused on exploring eHealth tailoring to ensure that information was delivered within teachable moments 42,43 and that patients remained motivated to explore their own conditions, as described by Lusteria et al 45 .”
BackgroundUnderstanding Organ Donation Messaging: A Qualitative Inquiry
Britt, R. K., & Ritchart, A. (2024) · The Qualitative Report · doi:10.46743/2160-3715/2024.5825
Davis, A., Martin, J., Cooks, E., Vilaro, M. J., Wilson-Howard, D. S., Tang, K., & Krieger, J. L. R. (2024) · Journal of Participatory Medicine · doi:10.2196/57528
James, P., & Reilly, R. (2024) · Mental Health Practice · doi:10.7748/mhp.2024.e1712
Lim, I.-J., Hwang, A., & Keum, H. (2024) · Korean Journal of Journalism & Communication Studies · doi:10.20879/kjjcs.2024.68.6.003
Hommes, S., Vromans, R., de Jongh, M. A. C., Houwen, T., Bol, N., de Groot, E., Antheunis, M. L., & Krahmer, E. (2025) · Disability and Rehabilitation · doi:10.1080/09638288.2025.2453638
“…effect on a range of health outcomes such as screening intentions, or (intentions of) behavioral change (smoking cessation or eating healthier) [15–17]. there is also evidence that people understand, process, relate to, and recall personalized information better than generic information…”
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
McAlister, K. L., Lawrence‐Sidebottom, D., Roots, M., McCutchen, D., & Huberty, J. (2025) · doi:10.2196/preprints.72282
Bonnevie, E., Smith, S., Kapur, M., Smyser, J., & Castrucci, B. C. (2025) · Journal of Public Health Management and Practice · doi:10.1097/phh.0000000000002132
Persuasive Prevention Strategies in Health Communication
Crano, W. D. (2025) · Wiley eBooks · doi:10.1002/9781394179909.ch32
Lessons from Digital Health Research
Reno, J. E., Wombacher, K., & Southwell, B. G. (2025) · doi:10.1002/9781394179909.ch31
The Social Life of Health-Promotion Messages: Alcohol Craving in Complex Information EnvironmentsPreprint
Scholz, C., Chan, H., Hendriks, H., & van Reijmersdal, E. A. (2025) · doi:10.31234/osf.io/bakpd_v2
Optimising Physical Education Teachers’ Motivating Style: An Online Video Annotation Tool
Bouten, A., Haerens, L. L., Doren, N. V., Compernolle, S., & Cocker, K. A. D. (2025) · Springer eBooks · doi:10.1007/978-3-031-86908-2_10
Smartphone interventions to promote sustainable food choices
Azzena, I., & Carfora, V. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33140-4.00017-8
Sektorenübergreifendes beanspruchungsgesteuertes Blended-Therapy-Konzept für Post-COVID-19-Patienten
Urban, T., Lohse, P., Spitzer, S. G., & Reinhardt, F. (2025) · Prävention und Gesundheitsförderung · doi:10.1007/s11553-025-01236-1
“Um die Adhärenz und Ef-fektstärke bei den Patienten zu erhöhen, müssen digitale Therapieangebote indi-vidualisiert auf die Patienten und ihre Symptome zugeschnitten sein [8, 9, 30, 32, 37, 45].”
Parveen, S., McHugh, P., Surendran, A., Matthews, J., & Vellinga, A. (2025) · medRxiv · doi:10.1101/2025.07.13.25331473
“Passive health messaging without personal relevance has been shown to be ineffective because people dismiss generic health advice [32, 33].”
Criddle, J. M., Malvini, W., Jasper, H., & Bordieri, M. J. (2025) · Behavioral Sciences · doi:10.3390/bs15081095
“Such interventions may include motivational interviewing and programs or smartphone applications (apps) with tailored feedback (Lustria et al., 2013; Morton et al., 2014).”
Kowalski, L., Finnes, A., Koch, S., & Bujacz, A. (2025) · doi:10.2196/preprints.83753
McAlister, K. L., Lawrence‐Sidebottom, D., Roots, M., McCutchen, D., & Huberty, J. (2025) · JMIR Pediatrics and Parenting · doi:10.2196/72282
Szypula, J., Jarvstad, A., Jones, L., & Tapper, K. (2025) · JMIR Formative Research · doi:10.2196/72645
“Tailoring interventions has been shown to increase engagement, which may lead to better outcomes [20-22], possibly because personalized advice feels more relevant and persuasive [23-26].”
Haddad, J., Hendrie, G. A., & Golley, R. K. (2025) · Research Square · doi:10.21203/rs.3.rs-7647393/v1
Penfornis, K. M., Nooren, N., Meijer, E., Gebhardt, W. A., Janssen, V. R., & Ludden, G. (2025) · Journal of Imagery Research in Sport and Physical Activity · doi:10.1515/jirspa-2025-0014
“Tailoring has shown to enhance the uptake and e ff ectiveness of behavior change interventions [65] and is expected to bene fi t digital apps as well.”
Gosetto, L., Falquet, G., & Ehrler, F. (2025) · Digital Health · doi:10.1177/20552076251393402
Online-Gesundheitskommunikation. Von Gesundheitsportalen über soziale Medien bis mobile Health
Stehr, P., & Rossmann, C. (2025) · Springer eBooks · doi:10.1007/978-3-658-18017-1_15-2
Patel, N., Waldrop‐Valverde, D., & Ownby, R. L. (2023) · Distance Learning · doi:10.1108/dl-09-2023-0002 · Cited by 6
Lebeaut, A., Pedersen, E. R., Francis, D. J., Zvolensky, M. J., & Vujanovic, A. A. (2025) · Behaviour Research and Therapy · doi:10.1016/j.brat.2025.104935
Haddad, J., Hendrie, G. A., & Golley, R. K. (2025) · Discover Social Science and Health · doi:10.1007/s44155-025-00337-8 · Cited by 1
Kreis, A., Klimesch, J., Beutel, M. E., Dahn, I., Schiller, A., Schulz, D., Loy, G., Zajac, H., Kosmuetzky, G., Mikus, H., et al. (2025) · Die Rehabilitation · doi:10.1055/a-2707-9142
Evans, H. E. L., Forbes, C. C., Galvão, D. A., Vandelanotte, C., Newton, R. U., Wittert, G., Chambers, S. K., Vincent, A. D., Kichenadasse, G., Brook, N. R., et al. (2021) · Figshare · doi:10.6084/m9.figshare.13558815
Vromans, R. D., Pauws, S. C., Bol, N., van de Poll-Franse, L. V., & Krahmer, E. J. (2020) · Figshare · doi:10.6084/m9.figshare.13152019
Additional file 1 of Content and quality of physical activity ontologies: a systematic review
Braun, M., Carlier, S., Backere, F. D., Paepe, A. L. D., Velde, M. V. D., Van Dyck, D., Marques, M. M., De Turck, F., & Crombez, G. (2023) · Figshare · doi:10.6084/m9.figshare.22619863
Additional file 2 of Content and quality of physical activity ontologies: a systematic review
Braun, M., Carlier, S., Backere, F. D., Paepe, A. L. D., Velde, M. V. D., Van Dyck, D., Marques, M. M., De Turck, F., & Crombez, G. (2023) · Figshare · doi:10.6084/m9.figshare.22619866
Curry, J., Lind, M., Short, C. E., Vandelanotte, C., Evans, H. E. L., Pearson, M., & Forbes, C. C. (2022) · Figshare · doi:10.6084/m9.figshare.20486885
Landais, L. L., Damman, O. C., Jelsma, J. G. M., Verhagen, E. A. L. M., & Timmermans, D. R. M. (2022) · Figshare · doi:10.6084/m9.figshare.19670057
Landais, L. L., Damman, O. C., Jelsma, J. G. M., Verhagen, E. A. L. M., & Timmermans, D. R. M. (2022) · Figshare · doi:10.6084/m9.figshare.19670060
Curry, J., Lind, M., Short, C. E., Vandelanotte, C., Evans, H. E. L., Pearson, M., & Forbes, C. C. (2022) · Figshare · doi:10.6084/m9.figshare.20486882
Kollamparambil, U., Oyenubi, A., & Nwosu, C. (2021) · Figshare · doi:10.6084/m9.figshare.17037747
Evans, H. E. L., Forbes, C. C., Galvão, D. A., Vandelanotte, C., Newton, R. U., Wittert, G., Chambers, S. K., Vincent, A. D., Kichenadasse, G., Brook, N. R., et al. (2021) · Figshare · doi:10.6084/m9.figshare.13558812
Wright, C., Kelly, J. T., Byrnes, J., Campbell, K. L., Healy, R., Musial, J., & Hamilton, K. (2024) · Figshare · doi:10.6084/m9.figshare.26627761
Evans, H. E. L., Forbes, C. C., Galvão, D. A., Vandelanotte, C., Newton, R. U., Wittert, G., Chambers, S. K., Vincent, A. D., Kichenadasse, G., Brook, N. R., et al. (2021) · Figshare · doi:10.6084/m9.figshare.13558818
de Vries, R., Neufingerl, N., & Zandstra, E. H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5511590
Blanco, L. R., Klomhaus, A. M., Dudovitz, R., Marquez, D., Lopez-Chang, R., Aleman, E., Weerman, B., Moldoff, M., Norris, K., & Castellon‐Lopez, Y. (2025) · Journal of Medical Internet Research · doi:10.2196/78103
Schlicht, J. A., van Woudenberg, T. J., Blok, D. J., Simoski, B., & Buijzen, M. (2026) · Appetite · doi:10.1016/j.appet.2026.108773
Eitze, S., Maslov, V., & Otten, L. (2025) · BMC Public Health · doi:10.1186/s12889-025-25470-6
Creating a digital segmentation tool to tailor weight management advice: the Diet Types Survey
Brindal, E., & Hendrie, G. A. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1611946
Djurtoft, C., Sørensen, K. S., Odgaard, C., Hoegh, M., Rathleff, M. S., & Johansen, S. K. (2026) · Disability and Rehabilitation · doi:10.1080/09638288.2025.2611577
Chew, J., Bartlett, G., Hopkins, L., & Frings, D. (2026) · Journal of Participatory Medicine · doi:10.2196/62748
“Personalized mHealth interventions are largely acceptable [21-23] and more effective for prompting behavior change (eg, increasing physical activity and reducing smoking) compared to nonpersonalized interventions [20,24,25].”
User perceptions of individually-tailored health information in digital apps: development of a scale
Ownby, R. L., Davenport, R., & Caballero, J. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1731948
Eneslätt, M., Dupont, C., Savolainen, E., Fjällström, P., & Goliath, I. (2026) · JMIR Research Protocols · doi:10.2196/88452
Hondmann, S. M., van Vliet, M. H. M., van Eersel, R. A., Breeman, L. D., Keesman, M., de Buisonjé, D. R., Haroon, G., Kraaijenhagen, R. A., Janssen, V. R., & Evers, A. W. M. (2026) · Obesity Reviews · doi:10.1111/obr.70124
Vakili, N., Cassidy, C., Chawla, S., Curran, J., Munro, S., Phillips, D., Pho, N. S. H., Walls, R. A., Wood, A., & Wozney, L. (2026) · JMIR Formative Research · doi:10.2196/91834
Hohendorf, L., Dekkers, T., Kip, H., Alpay, L., & Kelders, S. M. (2026) · Journal of Medical Internet Research · doi:10.2196/86596
de Visser, R. O., Esselink, A., Rozema, A. D., Bovens, R. H. L. M., & Piper, R. (2026) · Psychology and Health · doi:10.1080/08870446.2026.2652425
Social Context Considerations for Future HIV Vaccine Introduction and Implementation
Bhushan, N. L., Gonzalez, R., & Southwell, B. G. (2026) · Vaccines · doi:10.3390/vaccines14050450
Zhang, L., Xu, M., Zhang, X., Li, X., Wang, S., Zhou, F., & Han, J. (2026) · JMIR mhealth and uhealth · doi:10.2196/86429
“Prior studies have emphasized that tailoring health information to a defined target group is a key determinant of usability and engagement, as users are more likely to interact with information they perceive as personally relevant [50].”
He, H.-Z., Yao, S., Ruiz, M., Paro, F., Zhang, W., & Chu, H.-R. (2026) · Patient Education and Counseling · doi:10.1016/j.pec.2026.109807
Pivka, J., Šabeder, R., & Bogataj, D. (2026) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit70676.2026.11631053
“Meta-analyses of online and mobile behavioural interventions show that personalised digital interventions are statistically more effective than generic programmes [6].”
Jeong, M.-A., & Kim, S.-D. (2026) · Applied Sciences · doi:10.3390/app16167899
Searching for Answers: Health Literacy and Digital Navigation in Midlife and Older Adults
Proctor, M. A., Pagán, C. R., Rahman, S., & Schmitter‐Edgecombe, M. (2026) · The International Journal of Aging and Human Development · doi:10.1177/00914150261480662
Brack, F. (2026) · Business Strategy and the Environment · doi:10.1002/bse.71565
Nagao, H. S., Shimazu, T., & Odawara, M. (2026) · doi:10.2196/preprints.113108
Metadata
- Authors
- Lustria, M. L. A., Noar, S. M., Cortese, J., Van Stee, S. K., Glueckauf, R. L., & Lee, J.
- Affiliations
- Florida State University (US)
- University of North Carolina at Chapel Hill (US)
- Yonsei University (KR)
- Corresponding author
- Lustria, Mia Liza A.
- Venue
- Journal of Health Communication
- Publisher
- Informa UK Limited
- Published
- 2013-09
- Volume
- 18
- Issue
- 9
- Pages
- 1039-1069
- ISSN
- 1081-0730 (ISSN-L)
- 1087-0415
- OpenAlex
- W2057902960
- Cited by
- 689
- References
- 102
- Citations by year
- 2026: 25 · 2025: 63 · 2024: 45 · 2023: 62 · 2022: 68 · 2021: 69 · 2020: 61 · 2019: 68 · 2018: 52 · 2017: 57 · 2016: 41 · 2015: 49 · 2014: 25 · 2013: 3
- FWCI
- 4.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Health Communication (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- personalized interventions, online intervention, health behavior change, health-promoting behaviors, health outcomes, treatment moderators, online health interventions
- MeSH terms
- Adolescent, Adult, Behavior Therapy, Child, Female, Follow-Up Studies, Health Promotion, Humans, Male, Middle Aged, Program Evaluation, Health Behavior, Randomized Controlled Trials as Topic, Treatment Outcome, Internet, Young Adult
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 ›
Are you an author, or do you see a problem with this page? Request a review
