Bedtime App–Guided Mindfulness Meditation in Patients With Insomnia: Mixed Methods Feasibility and Acceptability Pilot Study
Notice bibliographique
Résumé
Background: While mindfulness meditation (MM) apps have gained popularity as a tool for promoting sleep, research focusing on bedtime mindfulness practice and app usage is limited. Objective: As the first step toward understanding the efficacy and mechanisms of such bedtime practice and to inform future investigations, the goal of this pilot study was to explore the feasibility of app-guided bedtime MM practice with both in-lab and at-home physiological and self-report sleep remote assessments. Methods: We conducted a single-arm, prospective mixed methods pilot study that included both standard in-lab sleep studies and remote at-home assessments of individuals with insomnia disorder with self-reported difficulty falling asleep. Participants practiced MM guided by a commercially available smartphone app at bedtime for 4 weeks. Pre-post assessments included a battery of sleep-related and psychological health questionnaires, objective physiological sleep measures (polysomnography and actigraphy), and daily sleep logs. We also conducted qualitative exit interviews to further assess feasibility and acceptability. Transcripts were analyzed for dominant themes using inductive and deductive qualitative methods. Results: We recruited 13 participants with chronic insomnia (symptoms ≥3 nights weekly for ≥3 months) to complete the study protocol within 8 months (retention rate 77%). We were able to collect analyzable physiological and psychometric data with overall completion rates of more than 90%. The study was deemed feasible, meeting a priori benchmarks including recruitment, retention, completion, and adherence. The 10 participants retained in the program had excellent engagement (95% completion of in-lab studies, 100% completion of questionnaires, and 91% compliance with use of the app). Our preliminary analysis of subjective measures indicated improvement in sleep quality, insomnia severity, and presleep arousal, including Pittsburgh Sleep Quality Index change of -3.7 (95% CI -6.7 to -0.7), Insomnia Severity Index change of -4.5 (95% CI -7.7 to -1.4), Pre-Sleep Arousal Scale change of -7.7 (95% CI -13.1 to -2.3), and trend toward improvement in the Ford Insomnia Response to Stress Test indicated by a change of -2.5 (95% CI -5.9 to 0.9). From qualitative data, we identified domains that inform the feasibility and acceptability of the study, including (1) barriers to sleep prior to the study, (2) benefits and skills imparted by mindfulness, and (3) feedback on app use. Benefits and skills imparted by mindfulness included decreased catastrophizing, acceptance and nonreactivity, body awareness and relaxation, self-kindness, awareness of sleep hygiene and bedtime routine, earlier defusing of stress, increased focus and presence, and calm throughout the day. Conclusions: Bedtime app-guided MM as an intervention in patients with insomnia and the hybrid study design with in-lab and at-home assessments are feasible and acceptable. This study informs the design of future clinical and mechanistic research examining app-guided MM to impact insomnia severity and presleep arousal.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».