What does this research mean for the field?

Specific bedroom environmental factors, including light, humidity, temperature, and PM2.5 levels, are significantly associated with distinct objective sleep metrics such as total sleep time, sleep efficiency, and wake after sleep onset in older adults. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

June 12, 2026

Associations between night-time bedroom environmental factors and objective sleep metrics of older adults

Key Points

This research aims to identify the associations between bedroom environmental factors and sleep quality in older adults.
Enrolled 29 older adults (17 cognitively normal, 12 with mild cognitive impairment),
Measured six sleep metrics and seven bedroom environmental factors over an average of 211 nights in personal residences.
Analyzed data using mixed-effects models, adjusting for age, sex, cognitive status, bed-sharing, and season.
Light levels showed the strongest negative association with total sleep time (effect size = -0.051, p < 0.0083).
Relative humidity had a significant negative association with wake after sleep onset (effect size = -0.101) and a positive association with sleep efficiency (effect size = 0.104).
Temperature was associated with toss-and-turn count (effect size = -0.118) and physiological metrics like average heart rate and respiratory rate.

Abstract

Poor sleep quality is associated with adverse outcomes in older adults. Identifying associations between bedroom environmental factors and sleep quality could potentially provide guidance on how to control these factors to improve older adults' sleep quality. Seventeen older adults with normal cognition (4 males and 13 females, mean SD age: 76.6 8.2 years) and 12 with mild cognitive impairment or dementia (11 males and 1 female, mean SD age: 77.1 8.5 years) living in Portland, Oregon were enrolled in the study. Night-time bedroom environmental factors and sleep metrics were objectively collected over a mean (SD) period of 211 (144) nights in participants' personal residences. Mixed-effects models were constructed with six sleep metrics (total sleep time TST, wake after sleep onset WASO, sleep efficiency SE, toss-and-turn count, average heart rate, and average respiratory rate) as outcomes and seven bedroom environmental factors (light, noise, temperature, humidity, particulate matter 2.5 PM2.5, total volatile organic compounds TVOC, and carbon dioxide CO2) (averaged per night) as predictors including covariates: age, sex, cognitive status, bed-sharing and season. Among all environmental factors, light level had the strongest association with TST (effect size ES = -0.051). Relative humidity was most strongly associated with WASO (ES = -0.101) and SE (ES = 0.104). Temperature was most strongly associated with toss-and-turn count (ES = -0.118), average heart rate (ES = 0.052), and average respiratory rate (ES = 0.083), while PM2.5 level was strongly associated with TST (ES = -0.050) and SE (ES = -0.058). All reported associations were statistically significant according to the Bonferroni corrected p-value threshold (p < 0.0083). The results provide insight into which environmental factors should be prioritized when attempting to optimize the sleep environment for older adults. Future work will examine whether environmental effects are different on the sleep of older adults with mild cognitive impairment or dementia versus those with normal cognition.

Demander à l'IA

Bookmark