The DOI matches its Crossref record, with a small difference in title or year; 25 of the 26 references it cites were found in a registry or an index, and 1 are not checked yet.
Web-Based Smoking-Cessation Programs
Open accessAbstract
Background Initial trials of web-based smoking-cessation programs have generally been promising. The active components of these programs, however, are not well understood. This study aimed to (1) identify active psychosocial and communication components of a web-based smoking-cessation intervention and (2) examine the impact of increasing the tailoring depth on smoking cessation.Design Randomized fractional factorial design.Setting Two HMOs: Group Health in Washington State and Henry Ford Health System in Michigan.Participants 1866 smokers.Intervention A web-based smoking-cessation program plus nicotine patch. Five components of the intervention were randomized using a fractional factorial design: high- versus low-depth tailored success story, outcome expectation, and efficacy expectation messages; high- versus low-personalized source; and multiple versus single exposure to the intervention components.Measurements Primary outcome was 7 day point-prevalence abstinence at the 6-month follow-up.Findings Abstinence was most influenced by high-depth tailored success stories and a high-personalized message source. The cumulative assignment of the three tailoring depth factors also resulted in increasing the rates of 6-month cessation, demonstrating an effect of tailoring depth.Conclusions The study identified relevant components of smoking-cessation interventions that should be generalizable to other cessation interventions. The study also demonstrated the importance of higher-depth tailoring in smoking-cessation programs. Finally, the use of a novel fractional factorial design allowed efficient examination of the study aims. The rapidly changing interfaces, software, and capabilities of eHealth are likely to require such dynamic experimental approaches to intervention discovery.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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
“The experimental approaches used for optimization include full or fractional factorial experiments 38, 39, the sequential multiple-assignment randomized trial (SMART), 40 and system identification techniques 41, 42.”
From Evaluating Digital Health Interventions (2016) · cites this paper as [39]
In the citing paper
“Recently, behavioral scientists have noticed that a factorial experiment can be an attractive alternative to an observational study or an RCT (e.g., Collins et al. 2011; Strecher et al. 2008), when the objective is to estimate the individual effects of several different aspects of an intervention.”
From Translational research in South Africa: evaluating implementation quality using a factorial design (2012) · cites this paper as Strecher et al. 2008
In the citing paper
“As our project and a few others (e.g. (55)) have demonstrated, it is possible to obtain funding for MOST and similar approaches from the National Institutes of Health.”
From The Multiphase Optimization Strategy for Engineering Effective Tobacco Use Interventions (2011) · cites this paper as [55]
In the citing paper
“The possibilities are legion with viable models ranging from clinic staff merely referring to a state quitline or an eHealth intervention (54, 55), to a chronic care model approach to disease management in which staff take primary responsibility for assessment, intervening, and patient tracking (7).”
From The Multiphase Optimization Strategy for Engineering Effective Tobacco Use Interventions (2011) · cites this paper as [55]
In the citing paper
“Attempts to focus more directly on interactive technological elements of an intervention that may facilitate engagement have reported positive correlations between interactive feature inclusion and outcomes [21-24].”
From Analyzing Engagement in a Web-Based Intervention Platform Through Visualizing Log-Data (2014) · cites this paper as [24]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hao (2015) · doi:10.1002/14651858 · Cited by 11,321
Health Promotion by Social Cognitive Means
Bandura, A. (2004) · Health Education & Behavior · doi:10.1177/1090198104263660 · Cited by 7,238
Social Learning Theory and the Health Belief Model
Rosenstock, I. M., Strecher, V. J., & Becker, M. H. (1988) · Health Education Quarterly · doi:10.1177/109019818801500203 · Cited by 5,564
Self-citationThe role of transportation in the persuasiveness of public narratives.
Green, M. C., & Brock, T. C. (2000) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.79.5.701 · Cited by 4,861
Statistics for experimenters. An introduction to design, data analysis, and model building.
Box, G. E. P., Hunter, W. G., & Hunter, J. S. (1978) · Cited by 4,021
Nicotine replacement therapy for smoking cessation
Stead, L. F., Perera, R., Bullen, C., Mant, D., Hartmann‐Boyce, J., Cahill, K., & Lancaster, T. (2012) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd000146.pub4 · Cited by 2,564
(2017) · doi:10.5040/9798400682018.ch-003 · Cited by 2,248
Statistics for Experimenters: An Introduction to Design, Data Analysis and Model Building.
Pearce, S. C., Box, G. E. P., Hunter, W. G., & Hunter, J. S. (1979) · Biometrics · doi:10.2307/2530361 · Cited by 2,003
The validity of self-reported smoking: a review and meta-analysis.
Patrick, D. L., Cheadle, A. D., Thompson, D. C., Diehr, P. H., Koepsell, T. D., & Kinne, S. (1994) · American Journal of Public Health · doi:10.2105/ajph.84.7.1086 · Cited by 1,737
Collins, L. M., Murphy, S. A., & Strecher, V. (2007) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2007.01.022 · Cited by 1,082
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Items 201–266 of 266
Bur, O. T., Krieger, T., Moritz, S., Klein, J. P., & Berger, T. (2021) · JMIR Research Protocols · doi:10.2196/21207 · Cited by 7
Schwartz, J., Bottorff, J. L., Ratner, P. A., Gotay, C. C., Johnson, K. C., Memetovic, J., & Richardson, C. G. (2014) · JMIR Research Protocols · doi:10.2196/resprot.3282 · Cited by 7
Challenges, Opportunities, and Methods for Large-Scale Evaluations
Derzon, J. H. (2018) · Evaluation & the Health Professions · doi:10.1177/0163278718762292 · Cited by 7
Reactance to a Tailored Multimedia Intervention Encouraging Teachers to Promote Cover-the-Cough
David, P., Henry, A., Srivastava, J., Orcena, J. E., & Thrush, J. (2012) · Journal of Health Communication · doi:10.1080/10810730.2011.650826 · Cited by 8
Protocol of a randomized controlled trial of hearing protection interventions for farm operators
McCullagh, M. C., & Ronis, D. L. (2015) · BMC Public Health · doi:10.1186/s12889-015-1743-0 · Cited by 6
Hasson, D., & Villaume, K. (2013) · Springer eBooks · doi:10.1007/978-94-007-6470-5_12 · Cited by 6
Suzuki, A., Ginsborg, J., Phillips, M., & Franklin, Z. C. (2024) · Music & Science · doi:10.1177/20592043241262612 · Cited by 5
Artificial Intelligence-Based Decision Support Systems for Precision and Digital Health
Deliu, N., & Chakraborty, B. (2025) · Springer eBooks · doi:10.1007/978-981-96-0742-6_1 · Cited by 15
Self-citationEfficacy of an SMS-Based Smoking Intervention Using Message Self-Authorship: A Pilot Study
DeStasio, K. L., Hill, A. P., & Berkman, E. T. (2016) · The Journal of Smoking Cessation · doi:10.1017/jsc.2016.27 · Cited by 5
An appraisal of smoking cessation aids
Mundey, K. (2009) · Current Opinion in Pulmonary Medicine · doi:10.1097/mcp.0b013e328321862e · Cited by 5
Neurobiological Bases of Self-Reference and Deliberate Processing in Tailored Health Communication
Ho, S. S., & Chua, H. F. (2013) · Springer eBooks · doi:10.1007/978-1-4614-6852-3_5 · Cited by 6
Kim, M., Moon, Y.-K., & Chung, K.-M. (2024) · JMIR mHealth and uHealth · doi:10.2196/50006 · Cited by 5
Akers, L. G., Andrews, J. A., & Gordon, J. S. (2018) · JMIR Formative Research · doi:10.2196/formative.9948 · Cited by 4
Bayesian likelihood-based regression for estimation of optimal dynamic treatment regimes
Yu, W., & Bondell, H. D. (2023) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1093/jrsssb/qkad016 · Cited by 3
Jacobsen, H. B., Lyby, P. S., Johansen, T., Rème, S. E., & Klungsøyr, O. (2023) · Psychotherapy Research · doi:10.1080/10503307.2023.2221808 · Cited by 3
Social comparison and persuasion in health communications
Suls, J. M., & Bruchmann, K. (2013) · Scholar Commons (Santa Clara University) · Cited by 3
Ultraviolet photography as a skin cancer risk assessment and intervention tool
Hornung, R. L., & Strecher, V. J. (2012) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2012.02.016 · Cited by 3
Self-citationThe Development of the Tobacco Tactics Website
Duffy, S. A., Fowler, K. E., Flanagan, P. S., Ronis, D. L., Ewing, L. A., & Waltje, A. H. (2013) · JMIR Research Protocols · doi:10.2196/resprot.2445 · Cited by 3
An Epidemiologic Framework for the Delivery of Health Care Services
Oleske, D. M. (2009) · Springer eBooks · doi:10.1007/978-1-4419-0164-4_1 · Cited by 3
Collins, L. M. (2018) · Statistics for social and behavioral sciences · doi:10.1007/978-3-319-72206-1_6 · Cited by 3
Self-citationAzak, M., Mutlu, B., & Tamay, Z. (2020) · Children s Health Care · doi:10.1080/02739615.2020.1821376 · Cited by 3
Levin, M. E., & Lillis, J. (2010) · Springer eBooks · doi:10.1007/978-1-4419-6510-3_7 · Cited by 3
Evolutionary processes and mother-child attachment in intentional change
Ho, S.-H. S., Torres-García, A., & Swain, J. E. (2014) · Behavioral and Brain Sciences · doi:10.1017/s0140525x13003336 · Cited by 2
Mohr, D. C., Burns, M. N., Schueller, S. M., Clarke, G. N., & Klinkman, M. S. (2013) · Cited by 2
Heryadi, D. Y., Sundari, R. S., Agustini, R., & Hidayat, A. (2021) · Jurnal Agrinika Jurnal Agroteknologi dan Agribisnis · doi:10.30737/agrinika.v5i2.1601 · Cited by 2
Wang, Z., Shen, Y. L., Wu, T., Ni, H., Zhou, Y. Q., & Wang, W. (2024) · International Journal of Pediatric Otorhinolaryngology · doi:10.1016/j.ijporl.2024.112123 · Cited by 3
Level 2: Services Without In-Person Contact Between Provider and Client
Wetter, T. C. (2015) · Health informatics · doi:10.1007/978-3-319-19590-2_5 · Cited by 2
Modeling and Twitter-based Surveillance of Smoking Contagion
Tuli, G. (2016) · VTechWorks (Virginia Tech) · Cited by 2
The Twenty Five Most Important Studies in Health Promotion
Terry, P. E. (2022) · American Journal of Health Promotion · doi:10.1177/08901171221120716 · Cited by 2
Neural Mechanisms of Memory Formation for Tailored Messages: A Neuroimaging Study
Casado‐Aranda, L., Sánchez‐Fernández, J., Porcu, L., & Özer, İ. (2025) · Journal of Advertising · doi:10.1080/00913367.2025.2499446 · Cited by 1
The Data: Observational Studies and Sequentially Randomized Trials
Chakraborty, B., & Moodie, E. E. M. (2013) · Statistics in the health sciences · doi:10.1007/978-1-4614-7428-9_2 · Cited by 1
Self-citationWatti, J., Millner, M., Siklósi, K., Kiss, H., Kelemen, O., & Pócs, D. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19169983 · Cited by 1
Haviari, S., & Mentré, F. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02191-9 · Cited by 1
Computational Techniques for Public Health Surveillance
Burton, S. H. (2013) · ScholarsArchive (Brigham Young University) · Cited by 1
Statistical issues in designing and interpreting clinical trials in older adults
Allore, H. G., Espeland, M. A., & Miller, M. E. (2015) · doi:10.1002/9781118323434.ch7 · Cited by 1
Adolescent Social Skills Intervention: Theory, Design, and Implementation
Spaans, R. E. (2021) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3-1yt1-yx70 · Cited by 1
Chakraborty, B. (2009) · Deep Blue (University of Michigan) · Cited by 1
Self-citationSURVIVAL ANALYSIS OF SHARED-PATH ADAPTIVE TREATMENT STRATEGIES
Kidwell, K. (2012) · D-Scholarship@Pitt (University of Pittsburgh)
Brouwer, W. (2011) · RePub (Erasmus University Rotterdam)
Experimental Design Issues in Addiction Research
West, R. (2010) · Wiley eBooks · doi:10.1002/9781444318852.ch4
The Role of Interactive Communication Technologies in Behavioral Medicine
Strecher, V. J. (2010) · Springer eBooks · doi:10.1007/978-0-387-09488-5_64
Self-citationHow an internet intervention works to achieve benefit for patients with coronary heart disease
Kerr, C. (2010) · UCL Discovery (University College London)
Making the Case for Funding of Smoking Cessation Treatment Programs in Alabama
McCallum, D. M. (2009)
e-Health Resources to Support Lifestyle Medicine and Management
Ahern, D. K., & Phalen, J. M. (2013) · CRC Press eBooks · doi:10.1201/b13781-119
David, S. P., McClure, J. B., & Swan, G. E. (2012) · Wiley eBooks · doi:10.1002/9781118133880.hop209007
Self-citationTerapie internetowe oparte na dowodach naukowych
Cunningham, J. A. (2013) · Wydawnictwa Uniwersytetu Warszawskiego eBooks · doi:10.31338/uw.9788323513759.pp.375-394
Wieling, E. A., Solheim, C. A., & Dwanyen, L. (2019)
Kumar, R., Kant, R., Yadav, P., Rodney, T. W., & Bairwa, M. C. (2021) · Research Square · doi:10.21203/rs.3.rs-318627/v2
Characterizing Autonomy Supportiveness in Diabetes Peer Health Coaches
Cedarbaum, J., Brown, B., Wilson, S., Mase, R., & Heisler, M. (2021) · Michigan Journal of Medicine · doi:10.3998/mjm.1432
Wright, A. G. C., & Hallquist, M. N. (2020) · Cambridge University Press eBooks · doi:10.1017/9781316995808.024
Chakraborty, B., Maiti, R., & Strecher, V. J. (2017) · doi:10.2196/preprints.9555.a
Self-citationAn App-based CBT Program tailored for College Students: A Randomized Controlled Trial (Preprint)Preprint
Kim, M. H., & Chung, K. (2024) · doi:10.2196/preprints.50006
Meeting people where they are: Crowdsourcing goal-specific personalized wellness practices
Hidalgo, J. E., Kim, J., Llorin, J., Stanton, K., Cherian, J., Bloomfield, L. S. P., Fudolig, M. I., Price, M., Ha, J., Noble, N., et al. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000650 · Cited by 1
Wu, J., Hanby, E. P., Ye, Q., Applegate, J. M., Obedin‐Maliver, J., Lunn, M. R., Potter, J. E., & Tan, A. S. L. (2025) · Health Communication · doi:10.1080/10410236.2025.2530738
Jasinska, A. J. (2011) · Deep Blue (University of Michigan)
Chen, J., Houston, T. K., Faro, J. M., Nagawa, C. S., Orvek, E., Blok, A. C., Allison, J. J., Person, S. D., Smith, B. M., & Sadasivam, R. S. (2021) · Figshare · doi:10.6084/m9.figshare.16681286
Bayesian Machine Learning for Estimating Optimal Dynamic Treatment Regimes With Ordinal Outcomes
Wang, X., Chakraborty, T., & Chakraborty, B. (2026) · Statistics in Medicine · doi:10.1002/sim.70714
Self-citationChen, J., Houston, T. K., Faro, J. M., Nagawa, C. S., Orvek, E., Blok, A. C., Allison, J. J., Person, S. D., Smith, B. M., & Sadasivam, R. S. (2021) · Figshare · doi:10.6084/m9.figshare.16681283
Chen, J., Houston, T. K., Faro, J. M., Nagawa, C. S., Orvek, E., Blok, A. C., Allison, J. J., Person, S. D., Smith, B. M., & Sadasivam, R. S. (2021) · Figshare · doi:10.6084/m9.figshare.16681292
Chen, J., Houston, T. K., Faro, J. M., Nagawa, C. S., Orvek, E., Blok, A. C., Allison, J. J., Person, S. D., Smith, B. M., & Sadasivam, R. S. (2021) · Figshare · doi:10.6084/m9.figshare.16681289
Hoang, V. L., Nguyen, N. T., Bui, H. T., Hoang, T. H., & Liamputtong, P. (2025) · Springer eBooks · doi:10.1007/978-981-97-0821-5_133-1
Presant, C. A., Gascon, B., Yeung, S., Macalintal, J., Sandoval, A., Raz, D. J., Erhunmwunsee, L., Cronkhite, J., Davy, K., Cianfrocca, M. E., et al. (2026) · Supportive Care in Cancer · doi:10.1007/s00520-026-10971-w
Hoang, V. L., Nguyen, N. T., Bui, H. T., Hoang, T. H., & Liamputtong, P. (2026) · Springer eBooks · doi:10.1007/978-981-92-1288-0_133
Özer, İ., Sánchez‐Fernández, J., Casado‐Aranda, L., & Hernández-Vergara, N.-M. (2026) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2026.109585
Metadata
- Authors
- Strecher, V. J., McClure, J. B., Alexander, G. L., Chakraborty, B., Nair, V. N., Konkel, J. M., Greene, S. M., Collins, L. M., Carlier, C. C., Wiese, C. J., Little, R. J., Pomerleau, C. S., & Pomerleau, O. F.
- Affiliations
- University of Michigan (US)
- Group Health Cooperative (US)
- Henry Ford Health (US)
- Pennsylvania State University (US)
- Corresponding author
- Strecher, Victor J.
- ORCID
- Venue
- American Journal of Preventive Medicine
- Publisher
- Elsevier BV
- Published
- 2008-05
- Volume
- 34
- Issue
- 5
- Pages
- 373-381
- ISSN
- 0749-3797 (ISSN-L)
- PMC
- PMC2697448
- OpenAlex
- W1930054094
- Cited by
- 266
- References
- 25
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 10 · 2023: 5 · 2022: 6 · 2021: 15 · 2020: 10 · 2019: 12 · 2018: 12 · 2017: 11 · 2016: 17 · 2015: 19 · 2014: 24 · 2013: 29 · 2012: 21
- FWCI
- 14.44
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- American Journal of Preventive Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (P50CA101451, R01CA101843)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Smoking Behavior and Cessation
- Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
- Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
- Keywords
- smoking cessation, online intervention, e-health, personalized messages, success stories, outcome expectations, efficacy expectations, nicotine patch, abstinence, intervention components, fractional factorial design, health maintenance organizations
- MeSH terms
- Adult, Aged, Female, Humans, Male, Michigan, Middle Aged, Smoking Cessation, Treatment Outcome, Internet
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
