270 Sleep Enhancement Technology: A Survey of Devices
Bibliographic record
Abstract
Abstract Introduction Innovations in consumer sleep technologies have risen exponentially and providers struggle to keep up with patient expectations in this arena. Although there is high quality data on the validity on commercial sleep monitoring devices, there has been a rise in devices for the explicit purpose of sleep enhancement. We sought to identify existing consumer sleep devices that claim to enhance sleep, provide a comprehensive review of the main characteristics of these devices, and look into the types of evidence the developers offered to support their claims. Methods Using a scoping review framework we identified and mapped out the main characteristics of sleep enhancement devices in the consumer market. We systematically used a common search engine and the FDA database using various combinations of sleep-related search terms, such as “sleep enhancement device”. Through an iterative process, we identified and categorized devices based on the intervention target. Devices that were exclusively for clinical use and required a prescription, such as for the treatment of obstructive sleep apnea or diagnosed insomnia, were excluded. Results We identified 34 sleep enhancement devices, all 34 were found via web search and one was also found in the FDA Database. We defined the following overlapping categories: reduce sleep latency (94.1%), increase restorative sleep (17.6%), and/or “other” (32.4%). About half of the devices use sound (44.1%), 26.5% use visual stimuli, and 11.8% use vibration. Additionally, roughly a third of all devices claim to entrain brain signals associated with sleep. Half of devices found operate near the bed without being in contact with the consumer, 44.1% are worn on the body, and the remaining 5.9% operate in bed, near the consumer. Conclusion For the most part, commercial sleep enhancement devices target sleep latency or claim to increase the restorative power of sleep. These devices generally use auditory, visual, and vibratory stimuli, and half are worn on the body. Lack of evidence supporting whether these devices actually improve sleep questions the utility of such devices and demonstrates the need for validation standards for consumer sleep enhancement devices. Support (if any) Department of Defense Military Operational Medicine Research Program (MOMRP)
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".