ArticleJMIR mHealth and uHealth2022
Nonusage Attrition of Adolescents in an mHealth Promotion Intervention and the Role of Socioeconomic Status: Secondary Analysis of a 2-Arm Cluster-Controlled Trial.
Article in JMIR mHealth and uHealth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Testing the Effectiveness of the mHealth Intervention #LIFEGOALS Targeting Health Behaviors in Early Adolescents for Promoting Mental Well-being: a Group-Randomized Controlled Trial
Smartphone Based Health Behaviour Intervention for Adolescents; Usage and Daily Attrition Rates.
Who cites it
13 citing papers in PubMed, 28 citations in OpenAlex.
- Usage of an App-Based Addiction Prevention Program for German Vocational Students: Secondary Analysis of Data From a Cluster Randomized Controlled Trial.Journal of medical Internet research · 2025Trial
- Effective Communication Supported by an App for Pregnant Women: Quantitative Longitudinal Study.JMIR human factors · 2024Trial
- A mobile healthy lifestyle intervention to promote mental health in adolescence: a mixed-methods evaluation.BMC public health · 2024Trial
- Usage and Daily Attrition of a Smartphone-Based Health Behavior Intervention: Randomized Controlled Trial.JMIR mHealth and uHealth · 2023Trial
- Prediction of Adherence to an Online Wellness Program for People with Mobility Limitations: A Machine Learning Approach.Healthcare (Basel, Switzerland) · 2026Article
- Adolescents' Engagement With an mHealth Multiple Health Behavior Change Intervention (LIFE4YOUth): Mixed Methods and Qualitative Comparative Analysis.JMIR mHealth and uHealth · 2026Article
- Autonomous motivation moderates the relationship between fitness application usage and exercise behavior through flow experience: a cross-sectional study among Chinese university students.Frontiers in sports and active living · 2026Article
- Utilizing Mobile Health Technology to Enhance Brace Compliance: Feasibility and Effectiveness of an App-Based Monitoring System for Adolescents with Idiopathic Scoliosis.Journal of personalized medicine · 2025Article
- Adolescent Engagement With a Multicomponent mHealth Tool: Identifying Usage Patterns, Determinants, and Health Behavior Change in an Intervention Trial.JMIR mHealth and uHealth · 2025Article
- Assessing the Feasibility and Acceptability of the Daybreak Drink Tracker: Prospective Observational Study.JMIR formative research · 2024Observational
- Process evaluation of the digital Health4Life intervention among a sample of disadvantaged adolescents and teachers.Health promotion international · 2024Article
- Randomized Clinical Trial to Increase Self-Monitoring of Physical Activity and Eating Behaviors in Youth: A Feasibility Study.Translational journal of the American College of Sports Medicine · 2024Article
- The Appa Health App for Youth Mental Health: Development and Usability Study.JMIR formative research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMobile health (mHealth) interventions may help adolescents adopt healthy lifestyles. However, attrition in these interventions is high. Overall, there is a lack of research on nonusage attrition in adolescents, particularly regarding the role of socioeconomic status (SES).
objectiveThe aim of this study was to focus on the role of SES in the following three research questions (RQs): When do adolescents stop using an mHealth intervention (RQ1)? Why do they report nonusage attrition (RQ2)? Which intervention components (ie, self-regulation component, narrative, and chatbot) prevent nonusage attrition among adolescents (RQ3)?
methodsA total of 186 Flemish adolescents (aged 12-15 years) participated in a 12-week mHealth program. Log data were monitored to measure nonusage attrition and usage duration for the 3 intervention components. A web-based questionnaire was administered to assess reasons for attrition. A survival analysis was conducted to estimate the time to attrition and determine whether this differed according to SES (RQ1). Descriptive statistics were performed to map the attrition reasons, and Fisher exact tests were used to determine if these reasons differed depending on the educational track (RQ2). Mixed effects Cox proportional hazard regression models were used to estimate the associations between the use duration of the 3 components during the first week and attrition. An interaction term was added to the regression models to determine whether associations differed by the educational track (RQ3).
resultsAfter 12 weeks, 95.7% (178/186) of the participants stopped using the app. 30.1% (56/186) of the adolescents only opened the app on the installation day, and 44.1% (82/186) stopped using the app in the first week. Attrition at any given time during the intervention period was higher for adolescents from the nonacademic educational track compared with those from the academic track. The other SES indicators (family affluence and perceived financial situation) did not explain attrition. The most common reasons for nonusage attrition among participants were perceiving that the app did not lead to behavior change, not liking the app, thinking that they already had a sufficiently healthy lifestyle, using other apps, and not being motivated by the environment. Attrition reasons did not differ depending on the educational track. More time spent in the self-regulation and narrative components during the first week was associated with lower attrition, whereas chatbot use duration was not associated with attrition rates. No moderating effects of SES were observed in the latter association.
conclusionsNonusage attrition was high, especially among adolescents in the nonacademic educational track. The reported reasons for attrition were diverse, with no statistical differences according to the educational level. The duration of the use of the self-regulation and narrative components during the first week may prevent attrition for both educational tracks.
trial registrationClinicalTrials.gov NCT04719858; http://clinicaltrials.gov/ct2/show/NCT04719858.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.