Exploring the Effects of Technology-Enabled Mindfulness and Meditation on Stress Management
Notice bibliographique
Résumé
Background: Studies suggest that certain technologies may help regulate emotional states and reduce stress. Spire is a wearable device that measures breathing patterns to identify distinct emotional states—calm, tense or focus—to send feedback to the user. Muse is a meditation device that gives real time user audio-feedback based on the EEG wave patterns. Objective: The objective of our study was to test the effect of each device on managing stress. Methods: There were 126 participants recruited remotely and randomized to one of two interventions evaluating the Spire (group 1) and Muse (group 2) devices. Each study involved a 2-week baseline period followed by a 6-week intervention. All participants wore the Spire device in the baseline period where it collected data on the frequency and duration of calm, focus, or tense breathing patterns. Then, in group 1, feedback from the Spire device was turned on during the intervention period alerting participants of unfavorable breathing patterns on a companion app. In group 2, feedback from the Spire device remained off for the 6-week intervention period. Additionally, participants in group 2 meditated with the Muse device for 3 to 5 times per week during the intervention period. The Perceived Stress Survey (PSS-14) and Connor-Davidson Stress Resilience Scale (CD-Risc) were administered to all participants at enrollment (Week 1) and closeout (Week 8). Questionnaire and device data from group 1 and 2 were analyzed to determine the effect of each device on mental health outcomes from enrollment to closeout, and baseline to intervention, respectively. Results: In group 1, perceived stress significantly decreased from 23.59 to 20.24 (P=.001), but there was no significant change in stress resilience [(68.25 to 69.5 (P=.44)]. In group 2, perceived stress significantly decreased from 22.49 to 19.15 (P<.001), and stress resilience scores significantly increased from 70.00 to 73.44 (P=.014). Average calm minutes per day using Spire did not significantly change for Group 1 or 2 [61.75 (SD 27.25) to 59.22 (SD 35.24) (P=.69) and 69.72 (SD 29.41) to 63.89 (SD 29.71) (P=.13), respectively]. In Group 1, average focus minutes per day using Spire significantly decreased from 64.15 (SD 31.2) to 50.79 (SD 28.73) (P=.001) minutes, but in group 2, the average focus minutes per day as measured Spire did not significantly change [82.38 (SD) to 71.29 (SD 46.55) (P=.08)]. In Group 1, average tense streaks did not significantly decrease [17.63 (SD 16.99) to 14.08 (SD 10.63) (P=.15)]. However, in Group 2, average tense streaks significantly decreased from 18.67 (SD 16.38) to 13.78 (SD 11.71) (P=.004). After controlling for age, minutes of Spire use, baseline depression, and baseline anxiety in group 1 participants, each day of Spire use was significantly associated with 53.04 (P=.02) more minutes of calm and 62.69 (P=.04) more minutes of focus mind-states. Group 2 participants had 2.9 more tense mind-state minutes (P=.02) with each day of using the Muse device after also controlling for minutes of Muse use, baseline depression, baseline anxiety and age. Conclusions: Emotion sensing technologies may have a positive role in improving stress management.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 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) | 0,000 | 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 ».