Diaphragmatic Breathing Interfaces to Promote Relaxation for Mitigating Insomnia: Pilot Study
Notice bibliographique
Résumé
BACKGROUND: Brief behavioral treatment for insomnia is an effective short-term therapy focusing on stimulus control and sleep restriction to enhance sleep quality. As a crucial part of this therapy, diaphragmatic breathing is often recommended when patients fail to fall asleep within 30 minutes. With the rise of health apps and gamification, these tools are increasingly seen as effective ways to boost self-efficacy and user engagement; however, traditional games tend to increase attention, which can negatively impact sleep and contradicts the aim of sleep therapy. This study thus explored the potential for gamification techniques to promote relaxation without disrupting sleep processes. OBJECTIVE: The study developed 4 breathing guidance mechanisms, ranging from concrete to abstract: number countdown, zoom-in/out, up/down, and color gradients. The objective was to explore the relationship between game mechanics, cognitive load, relaxation effects, and attention as well as to understand how different designs impact users with varying levels of insomnia. METHODS: The study was conducted in 2 phases. The first phase involved a questionnaire on the 4 guidance mechanisms. In the second phase, 33 participants classified by insomnia severity completed a Sleep Self-Efficacy Scale. They then engaged in 5 minutes of diaphragmatic breathing using each of the 4 interfaces. Relaxation effects were measured using heart rate variability via a smartwatch, attention and relaxation levels via an electroencephalogram device, and respiratory rate via a smartphone. Participants also completed the Game Experience Questionnaire and NASA Task Load Index, followed by user interviews. RESULTS: The results indicated that competence, immersion, and challenge significantly influenced cognitive load. Specifically, competence and immersion reduced cognitive load, while challenge, negative affect, and positive affect were correlated with relaxation. Negative affect showed a positive correlation with the mean root mean square of successive differences, while positive affect exhibited a negative correlation with the mean root mean square of successive differences. Cognitive load was found to affect both relaxation and attention, with a negative correlation between mental demand and attention and a positive correlation between temporal demand and respiratory rate. Sleep self-efficacy was negatively correlated with temporal demand and negative affect and positively correlated with competence and immersion. CONCLUSIONS: Interfaces offering moderate variability and neither overly abstract nor too concrete guidance are preferable. The up/down interface was most effective, showing the best overall relaxation effect. Conversely, the number countdown interface was stress-inducing, while the zoom-in/out interface had a significant impact on insomnia-related issues, making them less suitable for insomnia-related breathing exercises. Participants showed considerable variability in their response to the color gradient interface. These findings underscore the importance of carefully considering game design elements in relaxation training. It is essential that breathing guidance designs account for the impact of the game experience to effectively promote relaxation in users.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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 ».