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.
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“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
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“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]
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“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]
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“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]
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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
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