Engagement and Nonusage Attrition With a Free Physical Activity Promotion Program: The Case of 10,000 Steps Australia
Open accessAbstract
BACKGROUND: Data from controlled trials indicate that Web-based interventions generally suffer from low engagement and high attrition. This is important because the level of exposure to intervention content is linked to intervention effectiveness. However, data from real-life Web-based behavior change interventions are scarce, especially when looking at physical activity promotion. OBJECTIVE: The aims of this study were to (1) examine the engagement with the freely available physical activity promotion program 10,000 Steps, (2) examine how the use of a smartphone app may be helpful in increasing engagement with the intervention and in decreasing nonusage attrition, and (3) identify sociodemographic- and engagement-related determinants of nonusage attrition. METHODS: Users (N=16,948) were grouped based on which platform (website, app) they logged their physical activity: Web only, app only, or Web and app. Groups were compared on sociodemographics and engagement parameters (duration of usage, number of individual and workplace challenges started, and number of physical activity log days) using ANOVA and chi-square tests. For a subsample of users that had been members for at least 3 months (n=11,651), Kaplan-Meier survival curves were estimated to plot attrition over the first 3 months after registration. A Cox regression model was used to determine predictors of nonusage attrition. RESULTS: In the overall sample, user groups differed significantly in all sociodemographics and engagement parameters. Engagement with the program was highest for Web-and-app users. In the subsample, 50.00% (5826/11,651) of users stopped logging physical activity through the program after 30 days. Cox regression showed that user group predicted nonusage attrition: Web-and-app users (hazard ratio=0.86, 95% CI 0.81-0.93, P<.001) and app-only users (hazard ratio=0.63, 95% CI 0.58-0.68, P<.001) showed a reduced attrition risk compared to Web-only users. Further, having a higher number of individual challenges (hazard ratio=0.62, 95% CI 0.59-0.66, P<.001), workplace challenges (hazard ratio=0.94, 95% CI 0.90-0.97, P<.001), physical activity logging days (hazard ratio=0.921, 95% CI 0.919-0.922, P<.001), and steps logged per day (hazard ratio=0.99999, 95% CI 0.99998-0.99999, P<.001) were associated with reduced nonusage attrition risk as well as older age (hazard ratio=0.992, 95% CI 0.991-0.994, P<.001), being male (hazard ratio=0.85, 95% CI 0.82-0.89, P<.001), and being non-Australian (hazard ratio=0.87, 95% CI 0.82-0.91, P<.001). CONCLUSIONS: Compared to other freely accessible Web-based health behavior interventions, the 10,000 Steps program showed high engagement. The use of an app alone or in addition to the website can enhance program engagement and reduce risk of attrition. Better understanding of participant reasons for reducing engagement can assist in clarifying how to best address this issue to maximize behavior change.
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Being younger than 44 years was associated with a higher level of uptake and engagement than older adults.
“Being younger than 44 years was associated with a higher level of uptake and engagement [38,41,42,44,49,53,55,63,64] than older adults.”
From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [49]
The Discussion (Sociodemographics, Engagement, and Physical Activity as Determinants of Nonusage Attrition; page unavailable) says older age was associated with engagement and reduced nonusage attrition—the opposite of the claimed age–engagement relationship. The Results (User Characteristics; page unavailable) show that app users were younger than Web-only users, but do not establish higher uptake or engagement among people younger than 44. (LLM, conf=0.85)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredAvailability and accessibility of a smartphone facilitate both uptake of and engagement with health and well-being smartphone apps by having a behavior change device in close proximity.
“The availability and accessibility of a smartphone [37,40,45,49,52,57,72,78] facilitate both uptake and engagement by having a behavior change device in close proximity (Q25).”
From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [49]
The Introduction (page unavailable) says smartphone technology makes interventions more convenient to access, and the Discussion (page unavailable) associates use of the 10,000 Steps app with longer engagement. The paper does not establish that keeping a behavior-change device close at hand increases app uptake, and its engagement findings concern one physical-activity program rather than health and well-being apps generally. (LLM, conf=0.85)
Experimental — accuracy not yet measuredApps were more frequently downloaded by women than men, with the percentage ranging from 59% to 74%.
“Apps were more frequently downloaded by women than men, with the percentage ranging from 59% to 74% [38,41,49,53,55,63], although 1 study found that being male was associated with using an app to manage alcohol consumption [65].”
From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [49]
The Results (User Characteristics, Table 1; page unavailable) show that women made up 72.43% of app-only users and 73.48% of Web-and-app users, both within the cited range. The Methods (Smartphone App; page unavailable) give a total download count but no gender breakdown, so the paper supports a claim about app users, not specifically about who downloaded the app. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe comparatively higher attrition rate in pure online intervention studies was also evident in this review, where the blended group scored higher in the risk of bias assessment regarding withdrawals and dropouts compared to the online group.
“Generally, the comparatively higher attrition rate in pure online intervention studies (84–88) was also evident in this review where the blended group scored higher in the risk of bias assessment regarding withdrawals and dropouts compared to the online group.”
From Digital resilience interventions for healthcare workers: a systematic review (2025) · cites this paper as [86]
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Experimental — accuracy not yet measuredIn a digital behavior change intervention study examining engagement and nonusage attrition with a physical activity program, when the app was used together with the accompanying website, a higher engagement rate was observed compared with app-only or web-only versions.
“Furthermore, the results of a digital behavior change intervention study examining engagement and nonusage attrition with a physical activity program suggest that when the app was used together with the accompanying website, a higher engagement rate was observed compared with those who used the app-only or the web-only versions [49].”
From Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review (2020) · cites this paper as [49]
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- Authors
- Guertler, D., Vandelanotte, C., Kirwan, M., & Duncan, M. J.
- Affiliations
- Universitätsmedizin Greifswald (DE)
- Central Queensland University (AU)
- Western Sydney University (AU)
- Corresponding author
- Guertler, Diana
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2015-07-15
- Volume
- 17
- Issue
- 7
- Pages
- e176
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC4526999
- OpenAlex
- W1500501013
- Cited by
- 147
- References
- 59
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 9 · 2023: 12 · 2022: 24 · 2021: 17 · 2020: 16 · 2019: 22 · 2018: 9 · 2017: 13 · 2016: 6 · 2015: 1
- FWCI
- 17.67
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- other-oa
- Locations
- Journal of Medical Internet Research (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Acquire (CQUniversity) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- NOVA (University of Newcastle Australia) (submittedVersion)
- Funders
- Queensland Health
- National Heart Foundation of Australia (ID: 100029, 100029)
- Central Queensland University
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- physical activity promotion, online intervention, mobile application, user engagement, employment problems, individual challenges, sociodemographic factors, user retention
- MeSH terms
- Smartphone, Adult, Australia, Data Collection, Female, Humans, Male, Motor Activity, Internet, Kaplan-Meier Estimate
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 ›
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