The Investigation and Measurement of Quality of Sleep in Individuals with Osteoarthritis in Taiwan: A cross-sectional survey
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".