Using mobile meditation app data to predict future app engagement: an observational study.
Rylan Fowers, Vincent Berardi, Jennifer Huberty, Chad Stecher
Journal of the American Medical Informatics Association : JAMIA November 14, 2022 DOI: 10.1093/jamia/ocac169 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational cohort with predictive modeling Peer reviewed |
|---|---|
| Sample size | 2,600 |
| Population | New subscribers to a 1-year Calm mobile meditation app membership who started using the app between July and November 2018 |
| Duration | 365 days of app usage data |
| Measures | Area under the receiver operator characteristic curve (AUC) |
| Topics | Meditation |
| Keywords | App engagement Dynamic time warping Habit formation Mhealth Mindfulness meditation Mobile apps |
| Key findings | Among 2,600 new Calm subscribers, 83.1% used the app on at least one additional day, yet 58.0% had abandoned it by day 350. The authors report that about 64 days of usage data are needed to predict future app engagement accurately, a period they say is consistent with existing estimates of habit formation. |
Abstract
Meditation with mobile apps has been shown to improve mental and physical health. However, regular, long-term meditation app use is needed to maintain these health benefits, and many people have a difficult time maintaining engagement with meditation apps over time. Our goal was to determine the length of the timeframe over which usage data must be collected before future app abandonment can be predicted accurately in order to better target additional behavioral support to those who are most likely to stop using the app. Data were collected from a randomly drawn sample of 2600 new subscribers to a 1-year membership of the mobile app Calm, who started using the app between July and November of 2018. App usage data contained the duration and start time of all meditation sessions with the app over 365 days. We used these data to construct the following predictive model features: total daily sessions, total daily duration, and a measure of temporal similarity between consecutive days based on the dynamic time warping (DTW) distance measure. We then fit random forest models using increasingly longer periods of data after users subscribed to Calm to predict whether they performed any meditation sessions over 2-week intervals in the future. Model fit was assessed using the area under the receiver operator characteristic curve (AUC), and an exponential growth model was used to determine the minimal amount of data needed to reach an accurate prediction (95% of max AUC) of future engagement. After first subscribing to Calm, 83.1% of the sample used the Calm app on at least 1 more day. However, by day 350 after subscribing, 58.0% of users abandoned their use of the app. For the persistent users, the average number of daily sessions was 0.33 (SD = 0.02), the average daily duration of meditating was 3.93 minutes (SD = 0.25), and the average DTW distance to the previous day was 1.50 (SD = 0.17). The exponential growth models revealed that an average of 64 days of observations after subscribing to Calm are needed to reach an accurate prediction of future app engagement. Our results are consistent with existing estimates of the time required to develop a new habit. Additionally, this research demonstrates how to use app usage data to quickly and accurately predict the likelihood of users' future app abandonment. This research allows future researchers to better target just-in-time interventions towards users at risk of abandonment.
Comparable studies
Other observational and cohort studies on meditation, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Using Self-Report Assessment Methods to Explore Facets of Mindfulness Undergraduate students | 2006 | Observational cohort | |
| Effectiveness of a meditation-based stress reduction program in the treatment of anxiety disorders Patients with generalized anxiety disorder or panic disorder with or without agoraphobia | 1992 | Observational cohort | n = 22 |
| Meditation experience is associated with increased cortical thickness Participants with extensive Insight meditation experience and matched controls | 2005 | Observational cohort | n = 20 |
| Meditation experience is associated with differences in default mode network activity and connectivity Experienced meditators and matched meditation-naive controls | 2011 | Observational cohort | |
| Long-term meditators self-induce high-amplitude gamma synchrony during mental practice Long-term Buddhist practitioners and controls | 2004 | Observational cohort |
Citations in the library
Cited by 4
- What predicts meditation app engagement? an evaluation of user characteristics and motivation. Psychology and Health April 7, 2026
- Integrated online fantasy football platform promotes meditation activity among male-identifying undergraduate students Discover Psychology January 4, 2026
- Rates of attrition and engagement in randomized controlled trials of mindfulness apps: Systematic review and meta-analysis. Behaviour Research and Therapy October 14, 2023
- Mindfulness-Based Interventions for Patients with Cardiovascular Disease: A Focused Review for Practicing Clinicians. Current cardiology reports March 2, 2023
Cites 7
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- Cost-effectiveness of Mindfulness-based Stress Reduction Versus Cognitive Behavioral Therapy or Usual Care Among Adults With Chronic Low Back Pain. Spine October 15, 2017
and 2 more in the library