Assistive technology to enable sleep function in patients with acquired brain injury: Issues and opportunities
Bibliographic record
Abstract
Introduction Sleep disorders in patients with acquired brain injury are highly burdensome and associated with disability. An assistive technology framework emphasises the need to develop and apply a broad range of devices, strategies, and practices to ameliorate disabilities. We aimed to summarise scientific evidence regarding the utility of assistive technology in managing sleep disorders in patients with various causes of acquired brain injury. Method We retrieved articles before January 2016, through database searches of Medline, Embase, PsycINFO, CINAHL, and various bibliographies. The person–environment–occupation framework was used to analyse complex data pertaining to technology application and utility. Results We found 21 studies that described seven assistive technologies (continuous positive airway pressure, adaptive servo ventilator, nasotracheal suction mechanical ventilation, positioning devices, cognitive behavioural therapy, light therapy, and acupuncture) utilised in patients with acquired brain injury to manage sleep disorders. Conclusion Assistive technologies demonstrated effectiveness in alleviating and/or managing sleep disorders after acquired brain injury. Adherence to using the technology is limited by the level of injury-induced cognitive and physical impairment, technological regime, and environmental support. Development of user-friendly sleep-assistive technologies that take into consideration functional limitations and practice guidelines on structural communication between the occupational therapist, patient, and caregiver may facilitate patients’ self-determination in managing sleep disorders.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".