The Role of Engagement in a Tailored Web-Based Smoking Cessation Program: Randomized Controlled Trial
Open accessAbstract
BACKGROUND: Web-based programs for health promotion, disease prevention, and disease management often experience high rates of attrition. There are 3 questions which are particularly relevant to this issue. First, does engagement with program content predict long-term outcomes? Second, which users are most likely to drop out or disengage from the program? Third, do particular intervention strategies enhance engagement? OBJECTIVE: To determine: (1) whether engagement (defined by the number of Web sections opened) in a Web-based smoking cessation intervention predicts 6-month abstinence, (2) whether particular sociodemographic and psychographic groups are more likely to have lower engagement, and (3) whether particular components of a Web-based smoking cessation program influence engagement. METHODS: A randomized trial of 1866 smokers was used to examine the efficacy of 5 different treatment components of a Web-based smoking cessation intervention. The components were: high- versus low-personalized message source, high- versus low-tailored outcome expectation, efficacy expectation, and success story messages. Moreover, the timing of exposure to these sections was manipulated, with participants randomized to either a single unified Web program with all sections available at once, or sequential exposure to each section over a 5-week period of time. Participants from 2 large health plans enrolled to receive the online behavioral smoking cessation program and a free course of nicotine replacement therapy (patch). The program included: an introduction section, a section focusing on outcome expectations, 2 sections focusing on efficacy expectations, and a section with a narrative success story (5 sections altogether, each with multiple screens). Most of the analyses were conducted with a stratification of the 2 exposure types. Measures included: sociodemographic and psychosocial characteristics, Web sections opened, perceived message relevance, and smoking cessation 6-months following quit date. RESULTS: The total number of Web sections opened was related to subsequent smoking cessation. Participants who were younger, were male, or had less formal education were more likely to disengage from the Web-based cessation program, particularly when the program sections were delivered sequentially over time. More personalized source and high-depth tailored self-efficacy components were related to a greater number of Web sections opened. A path analysis model suggested that the impact of high-depth message tailoring on engagement in the sequentially delivered Web program was mediated by perceived message relevance. CONCLUSIONS: Results of this study suggest that one of the mechanisms underlying the impact of Web-based smoking cessation interventions is engagement with the program. The source of the message, the degree of message tailoring, and the timing of exposure appear to influence Web-based program engagement.
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The number of modules completed was found to correlate with outcomes in interventions that targeted cigarette smoking, depression, and anxiety disorders.
“The number of modules completed was found to correlate with outcomes in interventions that targeted cigarette smoking [42,46,47], depression [45,48-51], and anxiety disorders [52-57].”
From A Systematic Review of the Impact of Adherence on the Effectiveness of e-Therapies (2011) · cites this paper as [46]
The evidence that usage is associated with intended outcomes is mixed, and largely correlational.
“Firstly, the evidence that usage is associated with intended outcomes is mixed, and largely correlational.[21][22][23]”
From Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions (2016) · cites this paper as [22]
DBCIs have the potential to generate datasets sufficiently large to reliably model and experimentally test mediation of outcomes by engagement with particular intervention components and to statistically control for confounding moderator effects such as baseline motivation levels.
“DBCIs have the potential to generate datasets sufficiently large to be able to reliably model and experimentally test 34 mediation of outcomes by engagement with particular intervention components and to statistically control for confounding moderator effects such as baseline motivation levels.22,26,35,36”
From Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions (2016) · cites this paper as [22]
Characteristics of the intervention that may improve adherence include a strong theoretical foundation, perceived personal relevance, perceived effectiveness, tailoring, persuasive technologies, credibility, social networking, and regular "push factors" including human support and/or periodic prompts.
“Characteristics of the intervention that may improve adherence to the intervention include a strong theoretical foundation [41], perceived personal relevance to the user [65,66], perceived effectiveness [67,68], tailoring [69,70], persuasive technologies [71], credibility [72,73], social networking [74,75], and regular “push factors” including human support [76-78] and/or periodic prompts (eg, by”
From Web-Based Interventions for Behavior Change and Self-Management: Potential, Pitfalls, and Progress (2012) · cites this paper as [65]
Many researchers have found that women, older people, and well-educated people are all more likely to demonstrate adherence to Web-based interventions than males, younger people, and less-educated people.
“Although many researchers have found that women, older people, and well-educated people are all more likely to demonstrate adherence to Web-based interventions than males, younger people, and less-educated people [65,66,68,69]”
From Web-Based Interventions for Behavior Change and Self-Management: Potential, Pitfalls, and Progress (2012) · cites this paper as [65]
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- Authors
- Strecher, V., McClure, J., Alexander, G., Chakraborty, B., Nair, V., Konkel, J., Greene, S., Couper, M., Carlier, C., Wiese, C., Little, R., Pomerleau, C., & Pomerleau, O.
- Affiliations
- University of Michigan (US)
- Group Health Cooperative (US)
- Henry Ford Health (US)
- Corresponding author
- Strecher, Victor
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2008-11-04
- Volume
- 10
- Issue
- 5
- Pages
- e36
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC2630833
- OpenAlex
- W2103945120
- Cited by
- 264
- References
- 20
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 10 · 2023: 9 · 2022: 11 · 2021: 15 · 2020: 12 · 2019: 25 · 2018: 19 · 2017: 22 · 2016: 28 · 2015: 16 · 2014: 18 · 2013: 19 · 2012: 10
- FWCI
- 9.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Physiology › 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, user engagement, attrition, message tailoring, personalized messages, outcome expectations, self-efficacy, success stories, sequential exposure, nicotine replacement therapy, smoking abstinence, health behavior change, health plans, disease prevention, disease management
- MeSH terms
- Computer User Training, Female, Health Maintenance Organizations, Humans, Male, Online Systems, Patient Dropouts, Patient Education as Topic, Perception, Photography, Regression Analysis, Sex Characteristics, Social Support, Therapy, Computer-Assisted, Tobacco Use Disorder, User-Computer Interface, Smoking Cessation, Treatment Outcome, Patient Selection
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