Dropout, Nonusage Attrition, and Pretreatment Predictors of Nonusage Attrition in a Commercial Web-Based Weight Loss Program
Open accessAbstract
BACKGROUND: An understanding of the factors that predict retention and website use are critical to the development of effective Web-based weight loss interventions. However, poor retention (dropout attrition) and website utilization (nonusage attrition) are major inhibitors to the effectiveness of Web-based programs. OBJECTIVE: The study aimed to (1) describe the prevalence of dropout and nonusage attrition and (2) examine pretreatment predictors of nonusage attrition in a cohort of commercial Web-based weight loss program participants. METHODS: Participants enrolled in the online program, The Biggest Loser Club, Australia, from August 15, 2007, to May 31, 2008. Only those who subscribed for 12 or 52 weeks were included in this study. All data were collected by the program proprietors, SP Health Co Pty Ltd (Sydney, Australia), and provided in "deidentified" form. Data collected included responses to a pretreatment survey (sociodemographic and behavioral characteristics), subscription history (date of enrollment and subscription end), and website use (log-ins, food and exercise diary entries, weigh-ins, and forum posts). Participants were classified as a member of the program at 12 or 52 weeks if they held an active subscription plan at that point in time. Participants were classified as nonusers at 12 or 52 weeks if they had stopped using all of the website features and had not returned. Predictors of nonusage attrition were explored using Cox proportional hazards regression analysis. RESULTS: Of the 9599 eligible participants, 6943 (72%) subscribed to the program for 12 weeks, and 2656 (28%) subscribed for 52 weeks. Of all participants, 31% (2975/9599) were classified as overweight, 61% (5866/9599) were classified as obese, 86% (8279/9599) were female, and participants' mean (SD) age was 35.7 (9.5) years. The 12 week and 52 week subscribers' retention rates were 97% and 77% respectively. Of 12 week subscribers, 35% were classified as program "users" after 12 weeks, and 30% of 52 week subscribers were classified as "users" after 52 weeks. Significant predictors of nonusage attrition among 12 week subscribers included age (hazard ratio for 45 to 55 years of age = 0.83, 95% confidence interval [CI] 0.73 - 0.93, P = .001; hazard ratio for 55 to 65 years of age = 0.80, 95% CI 0.66 - 0.99, P = .04), exercise level (hazard ratio = 0.76, 95% CI 0.72 - 0.81, P < .001), emotional eating (hazard ratio = 1.11, 95% CI 1.04 - 1.18, P = .001), eating breakfast (hazard ratio = 0.88, 95% CI 0.82 - 0.95, P = .001), and skipping meals (hazard ratio = 1.12, 95% CI 1.04 -1.19, P = .001). For 52 week subscribers, eating breakfast (hazard ratio = 0.88, 95% CI 0.79 - 0.99, P = .04) and not drinking tea or coffee with sugar (hazard ratio = 1.23, 95% CI 1.11 - 1.37, P < .001) were the pretreatment characteristics that significantly decreased risk of nonusage attrition. CONCLUSIONS: The findings demonstrate a high prevalence of nonusage attrition among a cohort of commercial Web-based weight loss program participants. Several sociodemographic and behavioral factors were shown to independently predict nonusage attrition.
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Attrition is typically high in Web-based interventions.
“Attrition is a significant problem in Web-based interventions.”
From A Visualization Tool to Analyse Usage of Web-Based Interventions: The Example of Positive Online Weight Reduction (POWeR) (2015) · cites this paper as [5]
Higher use of website features may be associated with weight loss, but it is not clear which features improve this effect or reduce attrition.
“Although higher use of website features may be associated with weight loss, it is not clear which features improve this effect or reduce attrition [5].”
From A Visualization Tool to Analyse Usage of Web-Based Interventions: The Example of Positive Online Weight Reduction (POWeR) (2015) · cites this paper as [5]
The rapid attrition rate observed is similar to that reported in similar Web-based weight loss interventions.
“This is similar to the rapid attrition rate reported in similar Web-based weight loss interventions [3-5].”
From A Visualization Tool to Analyse Usage of Web-Based Interventions: The Example of Positive Online Weight Reduction (POWeR) (2015) · cites this paper as [5]
In a study of a commercial weight loss service, Neve et al found that 30% of participants could still be classified as users after 1 year.
“In a study of a commercial weight loss service, Neve et al [48] used criteria comparable to ours to define sustained usage. They found that 30% of participants could still be classified as users after 1 year.”
From Personal Health Technologies in Employee Health Promotion: Usage Activity, Usefulness, and Health-Related Outcomes in a 1-Year Randomized Controlled Trial (2013) · cites this paper as [48]
Many studies focus on whether respondents' characteristics can explain variations in adherence.
“Many studies focus on whether and which respondents’ characteristics can explain variations in adherence [11, 13, 16, 22].”
From Persuasive System Design Does Matter: a Systematic Review of Adherence to Web-based Interventions (2012) · cites this paper as [22]
A lack of human centeredness explains the incidence of usability problems and high attrition rates.
“This lack of human centeredness explains the incidence of usability problems [7-9], or high attrition rates [10-18].”
From A Holistic Framework to Improve the Uptake and Impact of eHealth Technologies (2011) · cites this paper as [15]
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- Authors
- Neve, M. J., Collins, C. E., & Morgan, P. J.
- Affiliations
- University of Newcastle Australia (AU)
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2010-12-14
- Volume
- 12
- Issue
- 4
- Pages
- e69
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC3056525
- OpenAlex
- W1981687717
- Cited by
- 137
- References
- 45
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 6 · 2023: 3 · 2022: 13 · 2021: 7 · 2020: 13 · 2019: 9 · 2018: 11 · 2017: 9 · 2016: 25 · 2015: 7 · 2014: 6 · 2013: 10 · 2012: 7
- FWCI
- 14.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion)
- NOVA (University of Newcastle Australia) (submittedVersion)
- Funders
- Medical Research Council
- National Health and Medical Research Council
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- online health interventions, participant retention, predictors of attrition, internet use, obesity, overweight, commercial weight loss programs, user engagement, online forums, weight loss outcomes, sociodemographic predictors, behavioral predictors
- MeSH terms
- Adult, Australia, Computer-Assisted Instruction, Female, Humans, Male, Middle Aged, Obesity, Outcome and Process Assessment, Health Care, Patient Compliance, Patient Dropouts, Patient Education as Topic, Self Care, Cohort Studies, Program Evaluation, Weight Loss, Prevalence, Confidence Intervals, Odds Ratio, Treatment Outcome, Internet, Young Adult
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