ArticleJMIR mHealth and uHealth2020
Assessing User Retention of a Mobile App: Survival Analysis.
Article in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Telehealth Use and Health Equity for Mental Health and Substance Use Disorder During the COVID-19 Pandemic: A Systematic Review.Telemedicine journal and e-health : the official journal of the American Telemedicine Association · 2024Pooled it
- Digital Health Technologies for Long-term Self-management of Osteoporosis: Systematic Review and Meta-analysis.JMIR mHealth and uHealth · 2022Pooled it
- Characteristics of Mobile Health Platforms for Depression and Anxiety: Content Analysis Through a Systematic Review of the Literature and Systematic Search of Two App Stores.Journal of medical Internet research · 2022Pooled it
- Predicting Long-Term Engagement in mHealth Apps: Comparative Study of Engagement Indices.Journal of medical Internet research · 2024Trial
- Exploring engagement patterns in a reproductive health app using the Technology Acceptance Model.Frontiers in reproductive health · 2026Article
- A comparison of self-reported COVID-19 symptoms between android and iOS CoronaCheck app users.NPJ digital medicine · 2025Article
- An app to keep: smartphone-based dispatch of community first responder to cardiac arrest.BMC cardiovascular disorders · 2025Article
- The BLV App Arcade: a new curated repository and evaluation rubric for mobile applications supporting blindness and low vision.Disability and rehabilitation. Assistive technology · 2024Article
- Developing a Machine Learning Algorithm to Predict the Probability of Medical Staff Work Mode Using Human-Smartphone Interaction Patterns: Algorithm Development and Validation Study.Journal of medical Internet research · 2023Article
- Dispel the Clouds and See the Sun: Influencing Factors and Multiple Paths of User Retention Intention Formation.Behavioral sciences (Basel, Switzerland) · 2023Article
- Development of Digital Biomarkers of Mental Illness via Mobile Apps for Personalized Treatment and Diagnosis.Journal of personalized medicine · 2022Review
- Real-world Implementation of a Smartphone-Based Psychoeducation Program for Bipolar Disorder: Observational Ecological Study.Journal of medical Internet research · 2022Observational
- Investigation of the Role of Anxiety and Depression on the Formation of Phantom Vibration and Ringing Syndrome Caused by Working Stress during Medical Internship.International journal of environmental research and public health · 2020Article
- Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA mobile app generates passive data, such as GPS data traces, without any direct involvement from the user. These passive data have transformed the manner of traditional assessments that require active participation from the user. Passive data collection is one of the most important core techniques for mobile health development because it may promote user retention, which is a unique characteristic of a software medical device.
objectiveThe primary aim of this study was to quantify user retention for the "Staff Hours" app using survival analysis. The secondary aim was to compare user retention between passive data and active data, as well as factors associated with the survival rates of user retention.
methodsWe developed an app called "Staff Hours" to automatically calculate users' work hours through GPS data (passive data). "Staff Hours" not only continuously collects these passive data but also sends an 11-item mental health survey to users monthly (active data). We applied survival analysis to compare user retention in the collection of passive and active data among 342 office workers from the "Staff Hours" database. We also compared user retention on Android and iOS platforms and examined the moderators of user retention.
resultsA total of 342 volunteers (224 men; mean age 33.8 years, SD 7.0 years) were included in this study. Passive data had higher user retention than active data (P=.011). In addition, user retention for passive data collected via Android devices was higher than that for iOS devices (P=.015). Trainee physicians had higher user retention for the collection of active data than trainees from other occupations, whereas no significant differences between these two groups were observed for the collection of passive data (P=.700).
conclusionsOur findings demonstrated that passive data collected via Android devices had the best user retention for this app that records GPS-based work hours.
Indexed as
Identifiers
What OpenQuestion holds
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.