When and Why Adults Abandon Lifestyle Behavior and Mental Health Mobile Apps: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: With 1 in 3 adults globally living with chronic conditions and the rise in smartphone ownership, mobile health apps have become a prominent tool for managing lifestyle-related health behaviors and mental health. However, high rates of app abandonment pose challenges to their effectiveness. OBJECTIVE: We explored the abandonment of apps used for managing physical activity, diet, alcohol, smoking, and mental health in free-living conditions, examining the duration of app use before abandonment and the underlying reasons. METHODS: A scoping review was conducted based on the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines and eligibility criteria were designed according to the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research type) framework. In total, 4 databases were searched (MEDLINE, Scopus, Embase, and PsycINFO) to identify quantitative and qualitative studies with outcome measures related to app abandonment in adults with free-living conditions, including reasons for abandonment and duration of use, for mobile apps related to WHO (World Health Organization) modifiable health behaviors and mental health. The included studies' risk of bias was appraised based on the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) and COREQ (Consolidated Criteria for Reporting Qualitative Research) checklists. To enable data synthesis across different methodologies, app domains, demographic data, and outcome measures were categorized. Results are presented in 2 sections: quantitatively in a scatterplot to understand when users abandon apps and qualitatively through basic qualitative content analysis to identify the underlying reasons. RESULTS: Eighteen eligible studies (525,824 participants) published between 2014 and 2022, predominantly from the United States, Canada, the United Kingdom, and Germany, were identified. Findings revealed a curvilinear pattern of app abandonment, with sharper abandonment soon after acquisition, followed by a slowing rate of abandonment over time. Taken together, a median of 70% of users discontinued use within the first 100 days. The abandonment rate appeared to vary by app domain, with apps focusing on alcohol and smoking exhibiting faster abandonment, and physical activity and mental health exhibiting longer usage durations. In total, 22 unique reasons for abandonment were organized into six categories: (1) technical and functional issues, (2) privacy concerns, (3) poor user experience, (4) content and features, (5) time and financial costs, and (6) evolving user needs and goals. CONCLUSIONS: This study highlights the complex nature of health app abandonment and the need for an improved understanding of user engagement over time, underscoring the importance of addressing various factors contributing to abandonment, from technical issues to evolving user needs. Our findings also emphasize the need for longitudinal studies and a consistent definition of app abandonment to better understand and mitigate this phenomenon, thereby enhancing the effectiveness of health apps in supporting public health initiatives.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,080 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,008 |
| Bibliométrie | 0,015 | 0,013 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».