Inadequate sleep in older adults is linked to health issues such as frailty, cognitive impairment, and cardiovascular disorders. Maintaining regular sleep patterns is important for healthy aging, making effective sleep monitoring essential. While polysomnography (PSG) is the gold standard for diagnosing sleep disorders, its regular use in home settings is limited. Alternative objective monitoring methods in the home can offer insights into natural sleep patterns and factors affecting them without the limitations of PSG.

This scoping review aims to examine current technologies, sensors, and sleep parameters used for home-based sleep monitoring in older adults. It also aims to explore various predictors and outcomes associated with sleep to understand the factors of sleep monitoring at home. 

We identified 54 relevant articles using PubMed, Scopus, Web of Science, and an AI tool (Research Rabbit), with 48 studies using wearable technologies and eight studies using non-wearable technologies. Further, six types of sensors were utilized. The most common technology employed was actigraphy wearables, while ballistocardiography and electroencephalography were less common. The most frequent objective parameters of sleep measured were Total Sleep Time (TST), Wakeup After Sleep Onset (WASO), and Sleep Efficiency (SE), with only six studies evaluating sleep architecture in terms of sleep stages. Additionally, six categories of predictors and outcomes associated with sleep were analyzed, including Health-related, Environmental, Interventional, Behavioral, Time and Place, and Social associations. These associations correlate with TST, WASO, and SE and include in-bed behaviors, exterior housing conditions, aerobic exercise, living place, relationship status, and seasonal thermal environments.

Introduction: Exploring the association between technology use and sleep health in older adults is important as digital engagement becomes integrated into society.

Objective: This study aimed to examine sleep health and its association with technology use in a population-based cohort of 60 years and older.

Methods: This cross-sectional, population-based study (2023) included 436 older adults from the Swedish National Study on Aging and Care, Blekinge (SNAC-B) population. These participants were sent questionnaires about their sleep, internet usage, and information and communication technology. We used a multidimensional instrument, SATED, to measure sleep health. In this study, we conducted statistical analyses using the chi2 test, T-test, Pearson correlation, and backward logistic regression.

Results: Our study found that older adults (60 years+) have a good mean sleep health score of 7.40 (SD = 2.03). We found that males showed significantly better sleep satisfaction (66.5%), timing (90.8%), and duration (78.4%) than females, while females have significantly better alertness during the day (59.4%). 60 - 74 years old experienced significantly better alertness and sleep duration than those over 85 years. Technology Enthusiasm (TE) and digital social participation (DSP) are linked to better sleep, while technology anxiety (TA) is associated with poorer sleep health. Frequent internet use and engaging with screens before bedtime is significantly associated with better sleep health. The most common digital determinants of sleep dimensions were TA and DSP.

Conclusions: The study highlights both positive and negative effects of technology use on sleep health and encourages further research on this among older adults.