Sleep health · 2026
Bedtime technology activities and multidimensional sleep health in rural Appalachian adults: Findings from the REST-KY study.
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Mitu Maliha Mehnaz, Chung Joon, Slade Emily, Moga Daniela C, Grandner Michael A, Moloney Mairead E
Аннотация
OBJECTIVES: Bedtime technology use is frequently associated with poor sleep outcomes, yet evidence examining the impacts of specific technologies on multidimensional sleep health remains limited, particularly in health disparity populations. We examined associations between bedtime technology activities and sleep health in rural Appalachian adults.
METHODS: The sample comprised 375 adults from 12 economically distressed counties in Eastern Kentucky. Participants completed online surveys assessing bedtime technology use in the hour before sleep and sleep health across 8 dimensions (satisfaction, alertness, timing, efficiency, duration, duration irregularity, social jetlag, and wake after sleep onset), operationalized as an extended sleep health score (range: 0-8). Linear regression models examined associations between technology use frequency (frequent vs. infrequent) and overall sleep health, adjusting for sociodemographic factors, mental health symptoms, chronotype, and sleep medication use. Logistic regression explored dimension-specific associations for various technology types.
RESULTS: Most participants (73.3%) were female and White (97.3%) (mAge 45.6 [range 18-81: SD = 13.1]); mean extended sleep health score was 5.1 (SD = 1.7). In adjusted models, less frequent work-related computer use was associated with higher extended sleep health scores (mean difference = 0.70, 95% CI: 0.26-1.14). Dimension-specific analyses linked infrequent work-related computer use to favorable alertness (OR = 2.33, 95% CI: 1.21-4.49) and wake after sleep onset (OR = 3.15, 95% CI: 1.41-6.98).
CONCLUSIONS: Differential associations across technology types and sleep dimensions suggest that cognitively engaging, work-related technology use (e.g., work-related computer use) may be particularly detrimental to sleep, highlighting the importance of examining specific technology use patterns in sleep health interventions for rural, health disparate populations.
METHODS: The sample comprised 375 adults from 12 economically distressed counties in Eastern Kentucky. Participants completed online surveys assessing bedtime technology use in the hour before sleep and sleep health across 8 dimensions (satisfaction, alertness, timing, efficiency, duration, duration irregularity, social jetlag, and wake after sleep onset), operationalized as an extended sleep health score (range: 0-8). Linear regression models examined associations between technology use frequency (frequent vs. infrequent) and overall sleep health, adjusting for sociodemographic factors, mental health symptoms, chronotype, and sleep medication use. Logistic regression explored dimension-specific associations for various technology types.
RESULTS: Most participants (73.3%) were female and White (97.3%) (mAge 45.6 [range 18-81: SD = 13.1]); mean extended sleep health score was 5.1 (SD = 1.7). In adjusted models, less frequent work-related computer use was associated with higher extended sleep health scores (mean difference = 0.70, 95% CI: 0.26-1.14). Dimension-specific analyses linked infrequent work-related computer use to favorable alertness (OR = 2.33, 95% CI: 1.21-4.49) and wake after sleep onset (OR = 3.15, 95% CI: 1.41-6.98).
CONCLUSIONS: Differential associations across technology types and sleep dimensions suggest that cognitively engaging, work-related technology use (e.g., work-related computer use) may be particularly detrimental to sleep, highlighting the importance of examining specific technology use patterns in sleep health interventions for rural, health disparate populations.