The Role of Emotion Regulation, Affect, and Sleep in Individuals With Sleep Bruxism and Those Without: Protocol for a Remote Longitudinal Observational Study
Notice bibliographique
Résumé
BACKGROUND: Sleep bruxism (SB) is an oral behavior characterized by high levels of repetitive jaw muscle activity during sleep, leading to teeth grinding and clenching, and may develop into a disorder. Despite its prevalence and negative outcomes on oral health and quality of life, there is currently no cure for SB. The etiology of SB remains poorly understood, but recent research suggests a potential role of negative emotions and maladaptive emotion regulation (ER). OBJECTIVE: This study's primary aim investigates whether ER is impaired in individuals with SB, while controlling for affective and sleep disturbances. The secondary aim tests for the presence of cross-sectional and longitudinal mediation pathways in the bidirectional relationships among SB, ER, affect, and sleep. METHODS: The study used a nonrandomized repeated-measures observational design and was conducted remotely. Participants aged 18-49 years underwent a 14-day ambulatory assessment. Data collection was carried out using electronic platforms. We assessed trait and state SB and ER alongside affect and sleep variables. We measured SB using self-reported trait questionnaires, ecological momentary assessment (EMA) for real-time reports of SB behavior, and portable electromyography for multinight assessment of rhythmic masticatory muscle activity. We assessed ER through self-reported trait questionnaires, EMA for real-time reports of ER strategies, and heart rate variability derived from an electrocardiography wireless physiological sensor as an objective physiological measure. Participants' trait affect and real-time emotional experiences were obtained using self-reported trait questionnaires and EMA. Sleep patterns and quality were evaluated using self-reported trait questionnaires and sleep diaries, as well as actigraphy as a physiological measure. For the primary objective, analyses will test for maladaptive ER in terms of strategy use frequency and effectiveness as a function of SB using targeted contrasts in the general linear model. Control analyses will be conducted to examine the persistence of the SB-ER relationship after adjusting for affective and sleep measures, as well as demographic variables. For the secondary objective, cross-sectional and longitudinal mediation analyses will test various competing models of directional effects among self-reported and physiological measures of SB, ER, affect, and sleep. RESULTS: This research received funding in April 2017. Data collection took place from August 2020 to March 2022. In all, 237 participants were eligible and completed the study. Data analysis has not yet started. CONCLUSIONS: We hope that the effort to thoroughly measure SB and ER using gold standard methods and cutting-edge technology will advance the knowledge of SB. The findings of this study may contribute to a better understanding of the relationship among SB, ER, affect, and sleep disturbances. By identifying the role of ER in SB, the results may pave the way for the development of targeted interventions for SB management to alleviate the pain and distress of those affected. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/41719.
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 | 0,011 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,003 |
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 ».