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. 2025 Aug;34(4):e14436.
doi: 10.1111/jsr.14436. Epub 2024 Dec 9.

Objective sleep monitoring at home in older adults: A scoping review

Sarah Nauman Ghazi  1 , Anders Behrens  1 , Jessica Berner  1 , Johan Sanmartin Berglund  1 , Peter Anderberg  1   2
Affiliations

Objective sleep monitoring at home in older adults: A scoping review

Sarah Nauman Ghazi et al. J Sleep Res. 2025 Aug.

Abstract

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 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 polysomnography. 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, wakeup after sleep onset and sleep efficiency, with only six studies evaluating sleep architecture in terms of sleep stages. Additionally, six categories of predictors and outcomes associated with sleep were analysed, including Health-related, Environmental, Interventional, Behavioural, Time and Place, and Social associations. These associations correlate with total sleep time, wakeup after sleep onset and sleep efficiency, and include in-bed behaviours, exterior housing conditions, aerobic exercise, living place, relationship status, and seasonal thermal environments.

Keywords: actigraphy; healthy aging; objective sleep monitoring; sensors; sleep; technology.

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Conflict of interest statement

The authors declare no potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
PRISMA‐ScR flow chart of study selection.
FIGURE 2
FIGURE 2
Percentage of studies based on locations.
FIGURE 3
FIGURE 3
The number of studies over the years.
FIGURE 4
FIGURE 4
Types of sensors. The size of each bubble represents the number of studies in which the sensors are used. HR, heart rate; RR, respiratory rate. Note that one of the studies used two devices to track sleep, so the total here becomes 55 instead of 54 studies.
FIGURE 5
FIGURE 5
Objective parameters of sleep. NREM, non‐rapid eye movement; REM, rapid eye movement.
FIGURE 6
FIGURE 6
The 10 predictors and outcomes that are associated with the most common objective parameters of sleep i.e. TST, WASO and SE. The figure illustrates that TST, WASO and SE are associated with, for example, grip strength, partnership status, frailty status, etc. SE, sleep efficiency; TST, total sleep time; WASO, wake after sleep onset.
FIGURE A1
FIGURE A1
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