Identifying Common Patterns in the Time of Day of Mindfulness Meditation Associated with Long-Term Maintenance

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 (<i>p</i> < 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.

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Publication Details

Journal
Behavioral Sciences
Published
2025-03-18
DOI
https://doi.org/10.3390/bs15030381
Primary Topic
Digital Mental Health Interventions
Type
article
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article

Identifying Common Patterns in the Time of Day of Mindfulness Meditation Associated with Long-Term Maintenance

Sara Cloonan, Chad Stecher, Hassan Ghasemzadeh, Yunro Chung et al.
Behavioral Sciences
Digital Mental Health Interventions
article

Identifying Common Patterns in the Time of Day of Mindfulness Meditation Associated with Long-Term Maintenance

Sara Cloonan, Chad Stecher, Hassan Ghasemzadeh, Yunro Chung, Aurel Coza, Jennifer Huberty, Vincent Berardi, Rylan Fowers
article en

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 (<i>p</i> < 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.

Behavioral SciencesVol. 15(3)
University of Georgia (US), Chapman University (US), Phoenix (United States) (US), Arizona State University (US)
Openalex Percentile: Top 95%
Digital Mental Health Interventions
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