Situating Meditation Apps Within the Ecosystem of Meditation Practice: Population-Based Survey Study
Bibliographic record
Abstract
BACKGROUND: Meditation apps have the potential to increase access to evidence-based strategies to promote mental health. However, it is currently unclear how meditation apps are situated within the broader landscape of meditation practice and what factors may influence engagement with them. OBJECTIVE: This study aimed to clarify the prevalence and correlates of meditation app use in a population-based sample of individuals with lifetime exposure to meditation in the United States. In addition, we sought to identify the concerns and desired features of meditation apps among those with lifetime exposure to meditation. METHODS: A total of 953 participants completed an initial screening survey. Of these 953 participants, 434 (45.5%) reported lifetime exposure to meditation and completed a follow-up survey (434/470, 92.3% response rate) assessing their meditation app use, anxiety, depression, loneliness, initial motivation for meditation, and concerns about and desired features of meditation apps. RESULTS: Almost half (434/953, 45.5%) of the participants who completed the screening survey reported lifetime exposure to meditation. Among those with lifetime exposure to meditation (ie, meditators), more than half (255/434, 58.8%) had used meditation apps at least once in their lives, and 21.7% (94/434) used meditation apps weekly or daily (ie, active users). Younger age, higher anxiety, and a mental health motivation for practicing meditation were associated with lifetime exposure to meditation apps. Among meditators, those with lifetime exposure to meditation apps were more likely to report concerns about apps, including concerns regarding the cost and effectiveness of apps, time required for use, technical issues with apps, and app user-friendliness. Meditators who used meditation apps weekly or daily (ie, active users) were younger, less likely to be men and non-Latinx White individuals and have lower income, and more likely to have an initial spiritual motivation for meditation. Active users reported more concerns regarding usability and technical problems and were less likely to report disinterest in apps. Headspace and Calm were the most frequently used apps. Tips and reminders for practice, encouragement of "mini" practices, and mental health content were the most desired features. Participants were less interested in social features (eg, the ability to communicate with other users or teachers). CONCLUSIONS: Meditation apps are commonly used by meditators in the United States, with a higher use among certain demographic groups. Future studies may increase user engagement in meditation apps by addressing concerns (eg, cost and effectiveness) and incorporating desired features (eg, tips and reminders for practice).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".