Feasibility and Acceptability of a Smartphone-Delivered Mindfulness Intervention for Stress Reduction in Adult Singaporeans: Pilot Randomized Controlled Trial
Notice bibliographique
Résumé
Background Fully decentralized self-administered mindfulness interventions show promise for stress reduction, but rigorous evaluations of their feasibility, acceptability, and effectiveness using both self-report and physiological measures remain limited. In Singapore, where mental health concerns rank as the top health care priority (46%), ahead of cancer (38%) and stress-related issues (35%), accessible and scalable interventions are urgently needed to address the significant economic burden of mental health conditions. Objective This study aimed to evaluate a decentralized, 3-day self-administered mindfulness intervention with minimal supervision, compared with a sham control in Singaporean adults, examining effects on self-reported and physiologically measured stress responses. The inclusion of a sham control condition was intended to disentangle mindfulness-specific effects from demand characteristics and expectation effects, addressing a critical methodological gap in digital mindfulness research. Methods This was a purely smartphone-based, decentralized pilot trial with no face-to-face components. Participants were recruited using web-based methods and, after providing informed consent, 60 adults were randomized to either a mindfulness intervention or a structurally matched sham control. The daily 10-minute mindfulness sessions guided participants to focus on present-moment breath and body sensations, while the sham control omitted a focus on present-moment awareness. Outcomes were assessed remotely, using self-report questionnaires (State-Trait Anxiety Inventory-6) and physiological data from smartphone-based photoplethysmography (heart rate variability; HRV). The study incorporated three methodological innovations: (1) a structurally equivalent sham control to match expectancy and credibility, (2) remote collection of HRV as an objective physiological biomarker, and (3) full decentralization allowing unsupervised multiplatform delivery. Feasibility was evaluated through recruitment, retention rates, and data quality. Acceptability was assessed through quantitative ratings and qualitative feedback. Results The study demonstrated excellent feasibility, with near-perfect retention (59/60, 98.3%) and moderate HRV data quality (231/331, 69.8% valid signals). Acceptability ratings were high (mean 4.17, SD 0.53), with comfort/engagement receiving the highest scores (mean 4.27, SD 0.57) on a 5-point scale, exceeding established usability benchmarks for digital health interventions. Qualitative feedback identified technical challenges (HRV instability and device overheating) and scheduling difficulties. While Bayesian analyses did not detect significant group differences in stress reduction (Bayes factor10 [BF10]=0.03) or HRV improvement (BF10=0.2), both groups showed significant stress reductions (BF10=3.01×106), suggesting that observed benefits may stem from nonspecific factors common to both interventions. Conclusions This study demonstrates (1) the feasibility of conducting fully decentralized mindfulness trials with multimodal assessment, (2) the value of a mixed methods acceptability evaluation, and (3) the identification of key technical and control condition refinements necessary for future trials. By addressing methodological limitations through improved control conditions and objective measures, this study provides a foundation for more rigorous investigation of mindfulness-specific effects. Trial Registration ClinicalTrials.gov NCT06765889; https://clinicaltrials.gov/study/NCT06765889
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 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 | 0,004 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».