The Investigation and Measurement of Quality of Sleep in Individuals with Osteoarthritis in Taiwan: A cross-sectional survey
Notice bibliographique
Résumé
Introduction: Osteoarthritis (OA), a degenerative musculoskeletal disease affecting joints, is characterised by pain and poor physical functioning, resulting in poor health related quality of life (HRQoL), emotional well-being and quality of sleep. There are few studies in this area in Taiwan.Aim and objectives: The aim of the study was to investigate how quality of sleep impacts on quality of life in individuals with OA in Taiwan. Specific objectives were to measure quality of sleep; to measure pain, physical function, emotional health and quality of life, and investigate their associations with quality of sleep; to investigate predictors of quality of sleep; and to investigate the association between subjective sleep perceptions and objective sleep outcomes.Methods: In a cross-sectional study, 192 OA patients aged over 40, diagnosed by radiology, fluent in Mandarin or Taiwanese, and residing in the community were recruited from musculoskeletal or rehabilitation outpatient departments in a university hospital in Taiwan from October 2010 to March 2011. A supervised self-completion questionnaire was used to collect data. Four validated Mandarin Chinese versions of questionnaires were used: the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) to measure pain and physical functioning; the Short Form-36 Health Survey (SF-36) to measure HRQoL; the Hospital Anxiety and Depression Scale (HADS) to measure emotional health; and the Pittsburgh Sleep Quality Index (PSQI) to measure subjective quality of sleep. A sub-sample of 30 individuals was recruited to measure objective sleep quality using an Actigraph wrist monitor. Data were encoded, entered onto computer and analysed with SPSS 16.0 software.Results: Most participants had poor subjective quality of sleep (70.3%), but only 19.8% were taking sleep medication. Global quality of sleep was poorer in participants who were older, female, had a low educational level and had more severe OA. Pain was mild-to-moderate but 47.4% and 25.5% of participants reported no or poor self-management of OA symptoms respectively, and 66.7% never used a walking aid. Poor quality of sleep was associated with pain, poor physical function, anxiety, depression and low scores on the physical and mental components of HRQoL (Pearson correlations 0.27 to 0.87), although most participants did not present problems with anxiety or depression. Regression showed that taking sleep medication, SF-36 role physical and social functioning, high HADS anxiety, a lack of secondary education, high WOMAC pain and taking analgesics significantly contributed to poor global quality of sleep. Path analysis identified four components potentially causing poor quality of sleep: an OA component (pain and physical function), a sleep medication component, a psychological component (anxiety) and a sociodemographic component (low education and poor social functioning), where being female was causally related to the last two. From the objective measurements, participants overestimated the actual time to fall asleep and underestimated their sleep duration and efficiency. Those with poor subjective quality of sleep were woken more often during the night and awake for longer during the night (both p < 0.027).Conclusion: Global quality of sleep was poor in individuals with OA in Taiwan; pain, physical function and emotional health negatively influenced quality of sleep and HRQoL. A hypothesised causal model for quality of sleep had components related not only to OA but also to psychological distress, socio-demographics and taking sleep medication. Objective measurements indicated that sleep disturbance was associated with poor perceived quality of sleep. The study suggests that better support and guidance on self-management of OA in Taiwan is required to allow patients more control over their health, well being and quality of sleep.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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) | 0,001 | 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 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 ».