Sleep, Health Care–Seeking Behaviors, and Perceptions Associated With the Use of Sleep Wearables in Canada: Results From a Nationally Representative Survey
Bibliographic record
Abstract
Background: The popularity of sleep-tracking wearables has surged worldwide. Yet, there are significant gaps in understanding the real-life implications of this phenomenon. While wearables may offer insights about sleep and promote sleep health awareness, evidence remains mixed on whether they lead to improved sleep outcomes or fuel sleep anxiety. Objective: This study aims to (1) determine the prevalence and sociodemographic predictors of using sleep wearables in Canada, (2) evaluate the perceived effects of wearable use on sleep and stress, (3) compare sleep and health care-seeking behaviors in users and nonusers, and (4) investigate the moderating effects of wearable use on the association between sleep and anxiety. Methods: An online survey investigating sleep and mental health was distributed to a representative sample of 1200 Canadians. The survey included questions on demographics, wearables use, sleep patterns, health care-seeking behaviors, insomnia (ISI-3 [Insomnia Severity Index-3]), and anxiety (GAD-7 [Generalized Anxiety Disorder-7]) symptoms. Analyses relied on descriptive statistics and logistic regression (aims 1 and 2), multivariate analyses of covariance and chi-squared analyses (aim 3), and multiple regression (aim 4). Results: Among the 1200 respondents (n=636, 53% female; aged 16 to 88 years), 19.3% (n=231) reported using a wearable device to monitor sleep. Several sociodemographic variables were associated with an increased likelihood of using wearables including: youth, being retired, being part of a racialized minority group, earning a higher income, having greater health care coverage, having a sleep disorder, and having a mental disorder (χ214=110.2, P<.001). Of all wearable users, nearly 45% felt that using sleep wearables had a positive effect on their sleep (n=102) and stress levels (n=97), while 4.5% (n=10) noted a negative effect. Compared to nonusers, wearable users reported 13 minutes longer sleep onset latency (F1,1151=5.21, P=.02, ƞp2=0.005), slept about 1 hour less (F1,1143=31.60, P<.001, ƞp2=0.027), and endorsed more severe insomnia symptoms (F1,1119=4.04, P<.05, ƞp2=0.004). After adjusting for the presence of sleep disorders, only the differences in sleep duration remained. The proportion of wearable users was almost twice as high in those having informed a health care provider about sleep difficulties (χ22=35.4, P<.001) and in those having used sleep medications (χ23=38.7, P<.001). Wearable use was identified as a moderator of the effect of anxiety symptoms on sleep duration, with wearable users showing a steeper decline in total sleep time as anxiety increased compared to nonusers (F1,1165=17.5, P<.001). Conclusions: One in 5 Canadians acknowledged having used sleep wearables. Predictors include younger age, higher income, and having a sleep or mental disorder. Although many individuals reported positive effects of sleep wearables, wearables use strengthened the link between short sleep and anxiety. Expanding our understanding of the factors associated with beneficial versus detrimental use of sleep wearables may help support more informed applications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".