Sleep After Concussion: A Scoping Review of Sensor Technologies
Bibliographic record
Abstract
Sleep disturbances following a concussion/mild traumatic brain injury are associated with longer recovery times and more comorbidities. Sensor technologies can directly monitor sleep-related physiology and provide objective sleep metrics. This scoping review determines how sensor technologies are currently used to monitor sleep following a concussion. We searched Ovid (Medline, Embase), Web of Science, CINAHL, Compendex Engineering Village, and PsycINFO from inception to June 20, 2022, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for scoping reviews. Included studies objectively monitored sleep in participants with concussion. We screened 1081 articles and included 37 in the review. A total of 17 studies implemented polysomnography (PSG) months to years after injury for a median of two nights and provided a wide range of sleep metrics, including sleep-wake times, sleep stages, arousal indices, and periodic limb movements. Twenty-two studies used actigraphy days to weeks after injury for a median of 10 days and nights and provided information limited to sleep-wake times. Sleep stages were most reported in PSG studies, and sleep efficiency was most reported in actigraphy studies. For both technologies there was high variability in reported outcome measures. Sleep sensing technologies may be used to identify how sleep affects concussion recovery. However, high variability in sensor deployment methodologies makes cross-study comparisons difficult and highlights the need for standardization. Consensus on how sleep sensing technologies are used post-concussion may lead to clinical integration with subjective methods for improved sleep monitoring during the recovery period.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".