1305 Validation of a Novel Method for Identifying Sleep-disordered Breathing in Spinal Cord Injury
Bibliographic record
Abstract
Abstract Introduction Spinal cord injury (SCI) is associated with complex health outcomes and can result in wide-ranging autonomic dysfunctions, many of which may complicate sleep. Indeed, most individuals with SCI experience poor sleep and some form of symptomatic sleep-disordered breathing (SDB: involuntary breath-holds during sleep causing transient hypoxia). Unmanaged SDB can progressively worsen functions of daily living and overall health, and there remains significant need for improved SDB evaluation in populations with SCI. Clinical sleep disorder diagnoses are necessary for treatment, but current assessments are prohibitive and require specialized in-laboratory testing that can be uncomfortable, impractical, and inaccessible for individuals with SCI. Methods We aimed to develop an at-home sleep testing protocol to assess SDB using two novel wearable technologies which noninvasively record overnight vital signs and sleep staging. Our validations compared Astroskin (form-fitting vest and headband) monitoring to simultaneous recordings using relevant gold-standard criteria. We then paired this with Fitbit (smartwatch) sleep staging to enable alignment of vital signs and sleep stages. Our design was initially evaluated in overnight sleep recordings from matched pairs of four individuals with SCI and four healthy controls. Results In healthy controls, the monitoring technology shows promise in providing dynamic readings of nocturnal blood oxygen saturation (SpO2; bias -2.95±2.7%; r=0.943; p< 0.0001), skin temperature (bias -0.3±0.4°C; r=0.997; p< 0.0001), respiration (r=0.967; p< 0.0001), 3-lead ECG, and body position. Over longer duration recordings, baseline blood pressure values met industry standards (bias -4.46±7.7mmHg; r=0.117; p=0.004), but dynamic blood pressure responses were captured poorly. In a case series examining preliminary results of one night of sleep in matched pairs, participants with SCI spent a greater proportion of their sleep in desaturated states (SpO2 ≤94%) compared to their matched controls. Interestingly, these nocturnal desaturations did not culminate in differences in time spent in varying sleep stages. Conclusion This study provides valuable insight into the use of novel wearable technologies to address known challenges and limitations of current home sleep apnea assessment methods. Our preliminary findings successfully track nocturnal desaturations and changes in sleep staging. These data show the utility of providing more accessible at-home evaluation of SDB in people with SCI. Support (if any)
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".