Sleep in Depressed and Nondepressed Participants with Chronic Low Back Pain: Electroencephalographic and Behaviour Findings
Bibliographic record
Abstract
STUDY OBJECTIVES: To study the nature of sleep disturbance in depressed and nondepressed patients with chronic low back pain (CLBP). DESIGN: A controlled, consecutive 4-night polysomnographic study. PATIENTS: Participants were screened (psychologic, psychiatric, and physical) to determine their study group, and 21 participants (CLBP: 4 depressed, 6 nondepressed and 11 controls) were studied. MEASUREMENTS AND RESULTS: On all nights, standard polysomnographic sleep measures as well as midline occipital and frontal electroencephalography and respiration were recorded on a Grass Model 7 polygraph. Pain, sleep quality, and depression were also measured. Participants with CLBP reported significant levels of pain and sleep disturbance as compared to controls, but all groups had equivalent amounts of sleep and comparable sleep architecture. The electroencephalographic power spectral analyses revealed significant differences, with controls having more sigma across sites, more low beta activity occipitally and frontally than nondepressed patients with CLBP, and more occipital sigma and less high beta activity than depressed participants. Between pain subgroups, the depressed participants showed more occipital delta, more occipital and central alpha, and more high beta activity across all sites than did the nondepressed participants. CONCLUSIONS: Lower sigma power in participants with CLBP suggests less-effective sensorimotor gating that may contribute to poor sleep quality. Pain subgroup differences underscore the need to consider the influence of depression in the evaluation of sleep in clinical populations. This study controlled for many factors other than pain that may contribute to the sleep complaints in this population. Consequently, the absence of signs of major sleep disturbance must not be interpreted as evidence of a lack of a true sleep problem in CLBP but more likely reflects control of these factors as well as the difficulty in measuring sleep quality.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".