0095 Sleep Enhancement Technology in 2021: An Updated Survey of Apps
Bibliographic record
Abstract
Abstract Introduction Commercially available smartphone apps that claim to improve sleep quantity and/or quality represent an ever-evolving and fast-growing market. Although a large body of work has validated the performance of sleep tracking technologies, there is little information regarding potential sleep enhancement technologies. Our study systematically surveyed currently available commercial sleep enhancement smartphone apps to provide details to inform both providers and patients alike, in addition to the healthy consumer market. Methods We systematically searched the Google Play Store (Android) on 30 JUN 2021 and the App Store (Apple) on 30 JUN 2021 and 26 JUL 2021 in the US using the keyword “sleep.” The Android search was conducted via the Google Play Store website. The Apple search was conducted via third-party websites linked to the App Store due to restrictions on searching the App Store online. This survey was conducted using Google Chrome web browsers and is inclusive of all smartphone applications found. Results We identified 550 apps: 59.5% on Android (N=327) and 40.5% on Apple (N=223). Ninety-four percent of apps offered a free version. The majority of sleep apps were intended for use during wake (72.7% exclusively during wake; 25.1% during both wake and sleep), with only 2.2% intended to be used during sleep alone. Most apps purport to enhance rather than measure sleep (87.8% versus 0.5%). The vast majority of apps claim to enhance sleep via reductions in sleep latency (92.9%). Reduced sleep latency is primarily achieved using auditory stimuli (74.5%). Conclusion Most current sleep apps are designed to be used while awake, prior to sleep, and focus on the enhancement of sleep, rather than measurement, by targeting sleep latency. Given the evidence that supports sleep latency as an important target for sleep promoting interventions and the multitude of available sleep enhancement apps across both Android and Apple platforms, sleep apps could be considered a possible strategy for patients and consumers to improve their sleep, although validation of these apps is required. Support (If Any) Department of Defense Military Operational Medicine Research Program (MOMRP)
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.008 | 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".