Identifying Common Patterns in the Time of Day of Mindfulness Meditation Associated with Long-Term Maintenance.
Rylan Fowers, Aurel Coza, Yunro Chung, Hassan Ghasemzadeh, Sara Cloonan, Jennifer Huberty, Vincent Berardi, Chad Stecher
Behavioral sciences (Basel, Switzerland) March 18, 2025 DOI: 10.3390/bs15030381 via PubMed
Summary
AI-generated from the abstractTemporal consistency in the time of day of meditation sessions is associated with long-term meditation app use for fewer than half of users. Among 4205 annual subscribers to a commercial meditation app, 39.5% showed consistent timing, 55.3% inconsistent timing, and 5.23% were indeterminate. Panel models confirmed that temporal consistency had contrasting relationships with meditation maintenance across these three groups. This suggests that other behavioral mechanisms besides temporally consistent habits can support sustained meditation app use, with implications for promoting maintenance of complex health behaviors like physical activity and diet.
Study at a glance
| Characteristics | Observational cohort Peer reviewed |
|---|---|
| Sample size | 4,205 |
| Population | Users who had paid for an annual membership to a commercial meditation app in 2017 |
| Keywords | App engagement Behavior maintenance Habit formation Mindfulness meditation Temporal consistency |
| Key finding | Temporal consistency in meditation timing was associated with long-term app use for only 39.5% of users, indicating other behavioral mechanisms also support maintenance. |
Abstract
Forming a habit of practicing mindfulness meditation around the same time of day is one strategy that may support long-term maintenance and in turn improve physical and mental health. The purpose of this study was to identify common patterns in the time of day of meditation associated with long-term meditation app use to assess the importance of temporal consistency for maintaining meditation over time. App usage data were collected from a random sample of 15,000 users who had paid for an annual membership to a commercial meditation app in 2017. We constructed three measures of temporal consistency in the time of day of meditation sessions in order to categorize users into one of three behavioral phenotypes: Consistent, Inconsistent, or Indeterminate. Panel data models were used to compare temporal consistency across the three phenotypes. Of the 4205 users (28.0%) in the final analytic sample, 1659 (39.5%) users were Consistent, 2326 (55.3%) were Inconsistent, and 220 users (5.23%) were Indeterminate. Panel models confirmed that temporal consistency had contrasting relationships with meditation maintenance among these three phenotypes (p < 0.01). These findings revealed that temporal consistency was associated with meditation maintenance for less than half of app users, which suggests that other behavioral mechanisms in addition to temporally consistent habits can support meditation app use over time. This has important implications for researchers and policymakers trying to promote the maintenance of meditation and other complex health behaviors, such as increased physical activity and healthier diets.