Effects of Smart Goggles Used at Bedtime on Objectively Measured Sleep and Self-Reported Anxiety, Stress, and Relaxation: Pre-Post Pilot Study
Bibliographic record
Abstract
BACKGROUND: Insufficient sleep is a problem affecting millions. Poor sleep can instigate or worsen anxiety and, conversely, anxiety can lead to or exacerbate poor sleep. Advances in innovative consumer products designed to promote relaxation and support healthy sleep are emerging and their effectiveness can be evaluated accurately using sleep measurement technologies in the home environment. OBJECTIVE: This pilot study examined the effects of smart goggles used before bed to deliver gentle, slow vibration to the eyes and temples. The hypothesis was that objective sleep, perceived sleep, and self-reported stress, anxiety, relaxation, and sleepiness would improve after using the smart goggles. METHODS: A within-subjects, pre-post design was implemented. Healthy adults with subclinical threshold sleep problems (N=20) tracked their sleep nightly using a PSG-validated non-contact biomotion device and completed daily questionnaires (3 weeks baseline, 3 weeks intervention). During the baseline period, participants slept at home as usual. During the intervention period, participants used Therabody SmartGoggles in Sleep mode before bed. This mode, designed for relaxation, delivers gentle eye and temple massage through the inflation of internal compartments to create a kneading sensation and vibrating motors. At night, participants completed questionnaires assessing relaxation, stress, anxiety, and sleepiness immediately before and after goggle use. Daily questionnaires assessed perceived sleep each morning, complementing the objective sleep measurement. RESULTS: Multilevel regression analysis of 676 nights of objective data showed improvements during nights when using the goggles, relative to baseline, in sleep duration (+12 minutes, P=.014); deep sleep, measured in duration (+6 minutes, P=.002), proportion of the night (7% relative increase, P=.020), and BodyScore, an age- and gender-normalized measure of deep sleep (4% increase, P=.002); number of nighttime awakenings (7% decrease, P=.021); total time awake at night after sleep onset (-6 minutes, P=.047); and SleepScore, a measure of overall sleep quality (3% increase, P=.020). Questionnaire data showed that, compared to baseline, participants felt they had better sleep quality (P<.001) and felt more well-rested upon waking (P<.001). Furthermore, immediately after using the goggles each night, compared to immediately before, participants reported feeling sleepier, less stressed, less anxious, and more relaxed (all Ps<.05). A standardized inventory administered before and after the 3-week intervention period indicated reduced anxiety, confirming the nightly analysis (P=.03). CONCLUSIONS: Objectively measured sleep quality and duration, as well as perceived sleep, improved when using the goggles before bed compared to baseline. Participants also reported increased feelings of relaxation along with reduced stress and anxiety. Future research expanding on this pilot study is warranted to confirm the preliminary evidence presented in this brief report.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".