1306 Sleep Routines and Sleep Disturbances After Spinal Cord Injury: Insights from a Community Survey
Bibliographic record
Abstract
Abstract Introduction Many individuals with spinal cord injury (SCI) face a constellation of sleep disturbances that interfere with sleep initiation and/or continuity. While poor sleep is widely documented post-SCI, sleep management is often deprioritized for clinical attention. Given the potential for reciprocal impacts between sleep disturbances and additive effects on sleep outcomes, we aimed to characterise sleep routines and elucidate relationships between sleep disturbances and sleep outcomes in individuals living with SCI. Methods We conducted an online survey with community partner SCI British Columbia for Canadians (≥19 years old) living with SCI, inclusive of all lesion levels and sensorimotor-completeness. Survey questions pertained to sleep routines, support, and disturbances assessed by frequency, severity, and management. Established questionnaires evaluated poor sleep quality (Pittsburg Sleep Quality Index [PSQI]; score≥5), daytime sleepiness (Epworth Sleepiness Scale [ESS]; score≥10), and fatigue (Fatigue Severity Score [FSS]; score≥36). Results We report responses from 170 individuals with SCI (aged 43.4±13.6 years, 122 male, 14.5±11.8 years injured). Most (74%) participants manage their sleep independently. However, 28.8% use non-prescription substances to support sleep, which may reflect that 67.1% of participants have not reviewed their sleep care with a healthcare provider since initial discharge. In the past 6 months, 73.5% experienced ≥1 regular sleep disturbance, 55.2% of whom reported ≥3 disturbances. These included nociceptive pain (45.6%), anxiety (44.8%), bladder care (40.0%), spasticity (37.6%), turn routines (34.4%), neuropathic pain (30.4%), thermal discomfort (29.6%), autonomic dysreflexia (episodic hypertension; 23.1%), sleep apnea (20.8%), and bowel care (19.2%). Poor quality sleep was reported by 75.3% of respondents (PSQI 8.3±4.1), with 37.7% experiencing high fatigue (FSS 30.4±15.3), and 29.4% experiencing excessive daytime sleepiness (ESS 7.1±4.6). Compared to those without, individuals with ≥1 sleep disturbance reported higher PSQI (OR=14.9, p< 0.001), FSS (OR=6.0, p< 0.001), and ESS (OR=2.0, p< 0.05) scores, with all scores highly correlated with each other, with sleep duration, and the number of sleep disturbances experienced (p< 0.005). Conclusion Sleep disturbances post-SCI are highly prevalent, often occur in combination, and emerge as a determinant of global sleep health. Thus, sleep care presents as a clinical and research target with potential to improve quality of life for those living with SCI. Support (if any)
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".