Characteristics and Usage Patterns Among 12,151 Paid Subscribers of the Calm Meditation App: Cross-Sectional Survey
Notice bibliographique
Résumé
Background Meditation has become increasingly popular due to its health benefits; however, barriers to delivering meditation programs in traditional group-based formats limit the accessibility of these benefits. Smartphone-based meditation may increase the availability of these programs to larger, more diverse audiences; however, research on subscriber characteristics and usage patterns in meditation mobile apps is lacking. Objective This study aimed to describe the demographics, clinical characteristics, and usage patterns of a convenience sample of Calm subscribers and explore the relationship between self-reported app usage and changes in health, stress, and sleep. Methods Participants were 12,151 paying Calm subscribers (response rate=12.08%, 12,151/100,594) who completed an anonymous Web-based survey with 11 quantitative questions related to user engagement, reasons for starting Calm, and changes after using the app. Demographic characteristics, chronic health diagnoses, and sleep difficulties were also assessed. Chi-square tests were used to examine differences in app usage. Logistic regression models were used to examine demographic and health characteristics that may predict changes in health, stress, and sleep. Results Respondents were 18-96 years old (mean 48.57 [SD 13.79]), primarily female (79.94%, 8778/10,981), white (81.41%, 8959/11,005), and most reported a chronic health diagnosis (56.86%, 6289/11,061). Mental health diagnoses (41.13%, 4549/11,061) were more common than physical health diagnoses (32.19%, 3560/11,061). Most respondents (76.31%, 8684/11,360) reported difficulties falling or staying asleep. On average, respondents had been using Calm for 11.49 months (SD 10.49), and 60.03% (7281/12,129) used it 5 or more times per week. Meditations (used by 80.02%, 9497/11,841) and Sleep Stories (55.66%, 6591/11,841) were the most popular components. The frequency of using Calm was associated with incremental increases in the likelihood of noticing changes in mental health (χ22=136.8; P<.001), physical health (χ22=102.8; P<.001), stress (χ22=128.1; P<.001), and sleep (χ22=141.4; P<.001). Respondents who had used Calm longer were also more likely to notice changes in mental health (OR 1.06 [95% CI 1.05 to 1.06]), physical health (OR 1.01 [95% CI 1.01 to 1.02]), stress (OR 1.04 [95% CI 1.04 to 1.05]), and sleep (OR 1.004 [95% CI 1.00 to 1.01]). Subscribers with sleep difficulties used Calm more frequently (χ82=11.5; P=.003), were more likely to use Sleep Stories (χ12=1590.2; P<.001), and were more likely to notice changes in their physical health (χ12=49.2; P<.001) and sleep (χ12=2391.1; P<.001). Conclusions Results highlight important demographic characteristics and usage patterns among a self-selected sample of Calm subscribers. Mental health concerns and sleep appear to be top reasons for downloading Calm. Sleep Stories and meditations are the most popular app components. The frequency of using Calm was associated with incremental changes in outcomes. Findings support future randomized controlled trials testing the efficacy of Calm for health, stress, and sleep. Studies should also explore strategies to attract a more diverse sample of subscribers.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».