Please use this identifier to cite or link to this item: https://doi.org/10.2196/14645
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dc.titleUser Engagement and Attrition in an App-Based Physical Activity Intervention: Secondary Analysis of a Randomized Controlled Trial
dc.contributor.authorEdney, Sarah
dc.contributor.authorRyan, Jillian C
dc.contributor.authorOlds, Tim
dc.contributor.authorMonroe, Courtney
dc.contributor.authorFraysse, Francois
dc.contributor.authorVandelanotte, Corneel
dc.contributor.authorPlotnikoff, Ronald
dc.contributor.authorCurtis, Rachel
dc.contributor.authorMaher, Carol
dc.date.accessioned2022-06-08T07:49:44Z
dc.date.available2022-06-08T07:49:44Z
dc.date.issued2019-11-27
dc.identifier.citationEdney, Sarah, Ryan, Jillian C, Olds, Tim, Monroe, Courtney, Fraysse, Francois, Vandelanotte, Corneel, Plotnikoff, Ronald, Curtis, Rachel, Maher, Carol (2019-11-27). User Engagement and Attrition in an App-Based Physical Activity Intervention: Secondary Analysis of a Randomized Controlled Trial. JOURNAL OF MEDICAL INTERNET RESEARCH 21 (11). ScholarBank@NUS Repository. https://doi.org/10.2196/14645
dc.identifier.issn14388871
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/226745
dc.description.abstractBackground: The success of a mobile phone app in changing health behavior is thought to be contingent on engagement, commonly operationalized as frequency of use. Objective: This subgroup analysis of the 2 intervention arms from a 3-group randomized controlled trial aimed to examine user engagement with a 100-day physical activity intervention delivered via an app. Rates of engagement, associations between user characteristics and engagement, and whether engagement was related to intervention efficacy were examined. Methods: Engagement was captured in a real-time log of interactions by users randomized to either a gamified (n=141) or nongamified version of the same app (n=160). Physical activity was assessed via accelerometry and self-report at baseline and 3-month follow-up. Survival analysis was used to assess time to nonuse attrition. Mixed models examined associations between user characteristics and engagement (total app use). Characteristics of super users (top quartile of users) and regular users (lowest 3 quartiles) were compared using t tests and a chi-square analysis. Linear mixed models were used to assess whether being a super user was related to change in physical activity over time. Results: Engagement was high. Attrition (30 days of nonuse) occurred in 32% and 39% of the gamified and basic groups, respectively, with no significant between-group differences in time to attrition (P=.17). Users with a body mass index (BMI) in the healthy range had higher total app use (mean 230.5, 95% CI 190.6-270.5; F2=8.67; P<.001), compared with users whose BMI was overweight or obese (mean 170.6, 95% CI 139.5-201.6; mean 132.9, 95% CI 104.8-161.0). Older users had higher total app use (mean 200.4, 95% CI 171.9-228.9; F1=6.385; P=.01) than younger users (mean 155.6, 95% CI 128.5-182.6). Super users were 4.6 years older (t297=3.6; P<.001) and less likely to have a BMI in the obese range (χ22=15.1; P<.001). At the 3-month follow-up, super users were completing 28.2 (95% CI 9.4-46.9) more minutes of objectively measured physical activity than regular users (F1,272=4.76; P=.03). Conclusions: Total app use was high across the 100-day intervention period, and the inclusion of gamified features enhanced engagement. Participants who engaged the most saw significantly greater increases to their objectively measured physical activity over time, supporting the theory that intervention exposure is linked to efficacy. Further research is needed to determine whether these findings are replicated in other app-based interventions, including those experimentally evaluating engagement and those conducted in real-world settings.
dc.language.isoen
dc.publisherJMIR PUBLICATIONS, INC
dc.sourceElements
dc.subjectScience & Technology
dc.subjectLife Sciences & Biomedicine
dc.subjectHealth Care Sciences & Services
dc.subjectMedical Informatics
dc.subjectphysical activity
dc.subjectsmartphone
dc.subjectbehavior
dc.subjectHEALTH-BENEFITS
dc.subjectBEHAVIOR
dc.subjectADULTS
dc.subjectWALK
dc.typeArticle
dc.date.updated2022-06-07T05:24:31Z
dc.contributor.departmentSAW SWEE HOCK SCHOOL OF PUBLIC HEALTH
dc.description.doi10.2196/14645
dc.description.sourcetitleJOURNAL OF MEDICAL INTERNET RESEARCH
dc.description.volume21
dc.description.issue11
dc.published.statePublished
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