Comparing self-reported sleep quality and wearable-derived sleep metrics in middle-aged and older adults with chronic pain: a psychometric study
Bibliographic record
Abstract
Objectives Our primary aim is to evaluate the agreement between subjective and objective methods of measuring sleep quality in people with musculoskeletal pain and poor sleep. Our secondary aim is to explore the relationship between subjective and objective sleep quality in people with clinical and experimental pain, as well as its impact on function. Methods Participants with musculoskeletal pain (intensity >5/10 most days over the preceding 3 months) and poor sleep [Pittsburgh Sleep Quality Index (PSQI) total > 5] ( n = 33) completed the PSQI and wore a ring that characterizes sleep stages (i.e., Oura© ring). Responses to the PSQI over the preceding month were compared to the corresponding averaged Oura measures using zero-order correlations (primary aim). Partial Pearson correlations were used to assess sleep–pain relationships (second aim), controlling for age and sex. Statistical significance was set at α <0.05 with Bonferroni correction. Results PSQI responses for total bedtime ( p < 0.0005), sleep duration ( p < 0.0005), and the PSQI duration component ( p < 0.003) significantly correlated with their Oura-derived equivalents. In contrast, Oura measures of sleep latency, efficiency, and disturbances showed no alignment with PSQI metrics. The PSQI total score and its sleep latency component were significantly associated with pain measures, including Western Ontario and McMaster Universities Osteoarthritis Index—pain ( p = 0.022; latency p = 0.009), McGill Pain Questionnaire (total p = 0.026; latency p = 0.008; neuropathic p = 0.026, latency p = 0.011; continuous p = 0.026; intermittent p = 0.026, latency p = 0.008; affective—latency p = 0.008), and Graded Chronic Pain Scale pain intensity ( p = 0.026; latency p = 0.012) as well as interference (latency p = 0.008). By contrast, Oura-derived sleep measures showed no significant associations with pain, except for sleep latency, which correlated with conditioned pain modulation ( p = 0.049). All p -values are Bonferroni-corrected. Conclusions This preliminary study provides valuable insights into the complementary roles of subjective and objective sleep assessments in older adults with chronic pain. The findings underscore the importance of integrating both approaches to refine sleep evaluation in individuals with musculoskeletal pain. Future research should further examine the feasibility and clinical utility of combining subjective and objective assessments to enhance understanding of sleep- and pain-related health outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".