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

Summary

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.

1. INTRODUCTION

Sleep is a critical and modifiable factor that has a profound impact on health and wellbeing in older adults (Ravyts & Dzierzewski, 2022). As individuals age, they may experience a decline in both the quality and quantity of sleep, irrespective of the presence of sleep disorders (Ancoli‐Israel, 2009). With aging, older adults might encounter increased sleep‐onset latency (SOL) and wake after sleep onset (WASO), and a decrease in total sleep time (TST), sleep efficiency (SE), slow‐wave sleep and rapid eye movement (REM) sleep (Espiritu, 2008). Several studies have demonstrated the negative association between sleep and various health outcomes in this population, including frailty (Sun et al., 2020), cognitive function (Behrens et al., 2023; Miyata et al., 2013), cardiovascular disorders (von Ruesten et al., 2012) and other age‐related conditions (Teramoto et al., 2005). These outcomes can directly or indirectly affect the wellbeing of older adults. The World Health Organization (WHO) referred to wellbeing in older age as healthy aging. The WHO claims it to be “a process of developing and maintaining the functional ability that enables wellbeing in older age” (Fallon & Karlawish, 2019). Given these facts, effective sleep monitoring becomes important to maintain functional ability, thus promoting healthy aging (Ravyts & Dzierzewski, 2022) and human longevity (Mazzotti et al., 2014) in older adults.

Sleep monitoring can be performed using either subjective measures or objectively. Subjective monitoring is the self‐perception where the individuals provide information about their sleep based on their own experience. Objective sleep evaluation involves the use of specialized sleep‐monitoring devices to monitor and analyse one or more biological signals of an individual, which helps to evaluate the structure and status of their sleep (Chen et al., 2023).

Evidence suggests that objective and subjective assessments of sleep quality may differ, especially in older adults and/or in people with cognitive impairment (Hita‐Yañez et al., 2013; O'Donnell et al., 2009). Subjective assessments rely mostly on self‐report data, and are only valid with normal or very mild cognitive decline in older adults (Lorette et al., 2021). Older adults are less likely to complain about sleep than younger adults, even if they have objective sleep disturbances (Gooneratne & Vitiello, 2014; Landry et al., 2015). Accordingly, reliance on subjective measures of sleep quality may prevent appropriate care‐seeking and biased results in scientific studies, especially in older adults. Therefore, objective sleep monitoring is essential, and subjective assessments should not be used as a substitute for objective sleep monitoring but as a complement to it (Landry et al., 2015). Objective monitoring of sleep is important to identify physiological parameters of sleep and their relationship with health as well as diagnosis of sleep‐related disorders (McCarter et al., 2022). The most commonly used method in clinical settings and the gold‐standard for diagnosing sleeping disorders is polysomnography (PSG), which tracks sleep stages and cycles to identify disruptions in sleep quality. During PSG, electrodes are placed on the scalp to simultaneously record cortical activity (electroencephalogram [EEG]), submental muscle activity (electromyogram [EMG]) and eye movements (electrooculographic activity [EOG]; Butkov & Keenan, 2017). However, PSG has several limitations, including being a one‐time assessment in a sleep lab, requiring expert interpretation of signals, and lacking the ability to capture long‐term and night‐to‐night variability of sleep patterns. Even if PSG is performed at home with home‐based ambulatory PSG, the limitations regarding the requirement of an expert for interpretation and intrusiveness remain the same (Ong et al., 2023). PSG is mainly used to diagnose sleep disorders rather than for long‐term sleep monitoring. Even with sleep disorders, night‐to‐night variability is important to accurately diagnose and monitor its severity (Lechat, Scott, et al., 2023). Furthermore, sleep‐monitoring devices for older adults should be user‐friendly and non‐intrusive, with consideration of their unique needs to enhance device acceptance and compliance (LeBlanc et al., 2022; Starr et al., 1939). Additionally, sleep in a laboratory setting may differ from sleep at home (Rundo & Downey, 2019). Home‐based sleep monitoring is shown to be associated with improved SE, longer sleep duration (SD), shorter SOL and increased time spent in REM sleep than lab‐based sleep monitoring (Bruyneel et al., 2011). There are several other objective sleep‐monitoring methods and tools that can be performed in the home environment without the complexities and limitations of PSG. Previous studies and reviews have effectively summarized the subjective measures and instruments to monitor sleep (Fabbri et al., 2021; Ji & Liu, 2016). There are also scoping reviews conducted on objective sleep monitoring. However, they are either not specifically targeted for older adults (Baron et al., 2018), or their research questions do not include a comprehensive classification of technologies with their sensors and objective sleep parameters and their associations with different types of predictors and outcomes (Kim et al., 2010). Therefore, the primary objective of this study is to provide a comprehensive overview of the current state of the art in home‐based objective sleep monitoring for older adults. This includes an examination of the technologies, sensors and sleep parameters utilized. Additionally, the study aims to explore various predictors and outcomes associated with sleep to gain a comprehensive understanding of home‐based sleep monitoring and its determinants.

2. RESEARCH METHODOLOGY

2.1. Overview

In this study, we chose to perform a scoping review based on the framework proposed by Arksey & O'Malley (2007). A scoping review is a suitable option for examining a broad range of literature on a particular topic in order to determine the scope and diversity of the research activity. Arksey & O'Malley (2007) proposed the methodology of a scoping review to consist of the following steps:

1. Identifying the research question/s

2. Identifying relevant studies

3. Study selection

4. Charting the data

5. Collating, summarizing and reporting the results

The PRISMA flow diagram for the literature search was used for a systematic and transparent research process.

2.2. Step 1: Research questions

The main research questions that were identified for this review were as follows.

1. What technologies and sensors are used to monitor objective sleep at home in older adults?

2. Which objective parameters of sleep are used to measure sleep in older adults?

3. Which predictors and outcomes are measured in the studies of objective sleep monitoring in older adults?

4. What predictors and outcomes are associated with the most common objective parameters of sleep in the studies of objective sleep monitoring in older adults?

2.3. Step 2: Searching and identifying relevant studies

We developed a search strategy by identifying keywords using the Population, Concept, Context (PCC) framework (Pollock et al., 2023). Similar terms were explored using Mesh (PubMed), SvenskMesh, thesaurus.com and keywords used in similar articles searched through Google Scholar. Initial search strings were formulated and piloted in the relevant databases. The search strings were further refined by consulting with the librarian at the authors' affiliated institution. A systematic search was conducted in Scopus, Web of Science and PubMed databases. Keywords for search are available in Appendix A in Figure A1. The date of the final search was 27 February 2023. The full electronic search strategy for Pubmed is shown in Appendix B in Figure B2. The records were exported from each database into a web tool, Rayyan (Ouzzani et al., 2016), where duplicates were removed, and articles were screened by title and abstract for eligibility. After the final retrieval of selected articles, an AI‐based search was done on the Research Rabbit (RR) (Sharma et al., 2022) as an added check to ensure no important articles are missed to be included. This added check helped us to identify 10 more research studies. After applying the inclusion and exclusion criteria on these studies, five of these were included in the list of primary studies for this scoping review.

2.4. Step 3: Study selection

To select the primary studies, three independent reviewers evaluated the initial set of retrieved research studies, and applied inclusion and exclusion criteria developed based on the research questions. The step‐by‐step application of the study selection process is shown in Figure 1.

FIGURE 1.

PRISMA‐ScR flow chart of study selection.

2.4.1. Inclusion criteria

The inclusion criteria for the scoping review were focused on studies that examined the use of wearable and non‐wearable devices for sleep monitoring in older adults, specifically those aged 60 years and above, in a home setting. The criteria were expanded to include studies where the majority of participants were aged 60 years or older. Eligible publications were required to have been published in English from 2008 to 2023, with the search concluding on 27 February 2023. The scope of included articles was limited to those focusing on sleep health or sleep quality, rather than sleep disturbances. Additionally, the studies were to emphasize objective sleep monitoring over technology specifications.

2.4.2. Exclusion criteria

In this scoping review, specific exclusion criteria were applied to refine the study's scope. Grey literature, systematic literature reviews (SLRs) and other forms of literature reviews were excluded. Additionally, studies that lacked age specifications for participants, used PSG as the sleep measurement tool, or were conducted outside a home setting were deemed ineligible. Moreover, studies not primarily focused on sleep parameters and their associations with various predictors and outcomes, or those that concentrated on technological specifications rather than sleep monitoring, were also excluded. The aim of these criteria is to ensure a focused analysis of the use of wearable and non‐wearable devices for sleep monitoring in older adults in a home setting.

2.5. Step 4: Charting the data

In this data extraction process, a data charting form was developed by identifying variables corresponding to the research questions. Data charting was done by the first author but, during the pilot run, the first three authors had discussions back and forth to finalize the template. Table C1 in Appendix C shows the charting of data done during this scoping review.

2.6. Step 5: Collating, summarizing and reporting the results

The collating and summarizing of the data was performed after the data charting. A general summarizing of the number of studies according to the location and year of publication was performed. The sample size was summarized from small to large, and a median was calculated. In addition to general summarizing, a specific collating and summarizing of the data was conducted in accordance with each research question. The technologies were categorized into wearables and non‐wearables, while sensors used in the studies were categorized into different categories. The objective parameters of sleep were listed, and each study was vigilantly read to understand the definition of each parameter. Thereafter, several different parameters were collated into one to avoid any redundancy. For example, SD or total time of sleeping was collated into TST. For the third question, the data were collated into four categories and summarized. Consequently, this led to the fourth question, where the most common objective markers of sleep were chosen and identified common predictors and outcomes associated with them. The results were then reported to present a comprehensive view. The descriptive numerical summary was presented to provide key quantitative information, such as the number of studies reviewed and the distribution of sleep parameters across various studies. In addition, common themes/categories were reported to highlight recurring patterns or trends, such as types of technologies, predictors and outcomes associated with sleep parameters in the studies. Finally, a data charting table was included to organize and report the extracted data from each study.

3. RESULTS AND ANALYSIS

3.1. General overview

Our search identified 2020 articles, of which 54 articles met the inclusion criteria. A PRISMA flow diagram of the scientific literature and selection criteria is presented in Figure 1. A total of 12 countries were represented in the studies. The two most common locations where the studies took place were the USA (n = 30) and Japan (n = 10). A total of three studies were conducted in Switzerland, two in Australia, and two in Canada, while all the other countries, as mentioned in Figure 2, had only one study each.

FIGURE 2.

Percentage of studies based on locations.

This scoping review showed a fluctuating publication trend with a surge in publications around 2021 (n = 10). The graphical representation of this trend is shown in Figure 3. The sample size in the studies ranged from n = 8 (Tsuzuki et al., 2015) to n = 6107 (Patel et al., 2008), with a median of 103. In 35 studies, most of the sample population was female, and only one study had an equal number of both genders (Obayashi, Saeki, Iwamoto, et al., 2014). In most of the studies (n = 21), the majority of the population was Caucasians (white), and in only three studies, the majority of the population was African or African American (black). All the studies were quantitative because of the nature of the research questions. Fifty‐one studies were published in a medicine, health or public health journal.

FIGURE 3.

The number of studies over the years.

3.2. Technologies and sensors

All the technologies and sensors used to monitor sleep are mentioned in the data charting Table C1 in Appendix C. The data were collated into wearables and non‐wearables. Note that one of the studies used two devices to monitor sleep (Seelye et al., 2015). Among the 48 studies on wearables, 43 studies used wrist‐worn with actigraphy sensors, while three studies used headbands with EEG sensors (Altendahl et al., 2020; Lustenberger et al., 2022; Scullin, 2013) and two used actigraphy sensors around the waist/hip (Full et al., 2020; Miyazaki et al., 2021). Among the non‐wearable devices, three studies used near‐bed devices, including in‐home sensor networks using motion sensors (Seelye et al., 2015) and passive infrared sensors (Reynolds et al., 2022), as well as bedside monitors using radiofrequency sensors (Mahajan et al., 2021). Furthermore, four studies used under‐mattress mats to monitor sleep with ballistocardiography (BCG; Gao et al., 2021; Kholghi et al., 2021; Rawtaer et al., 2020; Schütz et al., 2021), while one study used a motion sensor under the mattress along with in‐home sensor networks using motion sensors. (Seelye et al., 2015). The study (Rawtaer et al., 2020) did not mention the brand name, except that it used fibre‐optic technology with BCG sensors placed under the mattress. All the sensors used are summarized in Figure 4, where the size of each bubble represents the number of studies in which the sensors are used. Most of the studies used manufacturers' software to analyse the raw data from the device (n = 39), while five studies did not mention how the analysis of raw data was conducted. Thirteen studies utilized algorithm‐based analysis or a combination of the manufacturer's software and algorithms to determine specific parameters like sleep onset and offset.

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.

3.3. Objective parameters of sleep

Figure 5 shows all the objective parameters used in the studies to evaluate sleep. Our results show that the most common parameters used in the studies are TST (n = 44), WASO (n = 33) and SE (n = 32).

FIGURE 5.

Objective parameters of sleep. NREM, non‐rapid eye movement; REM, rapid eye movement.

Six studies evaluated the sleep architecture with sleep stages (Altendahl et al., 2020; Lustenberger et al., 2022; Mahajan et al., 2021; Schütz et al., 2021; Scullin, 2013; Tadokoro et al., 2020). These parameters were obtained by using different kinds of sensors, for example, EEG (Altendahl et al., 2020; Lustenberger et al., 2022), BCG (Schütz et al., 2021), Radiofrequency (Mahajan et al., 2021) and Actigraphy sensors (Tadokoro et al., 2020).

Scullin (2013) used a headband device that measured only slow‐wave sleep. The studies (Altendahl et al., 2020; Lustenberger et al., 2022; Schütz et al., 2021) assessed non‐rapid eye movement (NREM) sleep, encompassing light‐stage sleep (Stages 1 and 2), deep‐stage sleep (slow‐wave sleep, Stage 3), and REM sleep, serving as objective markers of sleep stages. Another study (Tadokoro et al., 2020) assessed the sleep architecture with the same parameters as in studies (Altendahl et al., 2020; Lustenberger et al., 2022; Schütz et al., 2021) in addition to REM latency.

3.4. Types of predictors and outcomes measured in studies of objective sleep monitoring

The total number of associations measured in 54 studies was 75. To understand the overall landscape of outcomes that are studied with sleep parameters, we collated the outcomes in different categories, as mentioned in Table 1.

TABLE 1.

Predictors and outcomes analysed in the studies against sleep.

Predictors and outcomes category	Number of related associations
Health‐related	37
Environmental	16
Interventional	9
Behavioural	7
Time and place	4
Social	2

Health‐related predictors and outcomes include human immunodeficiency virus (HIV) status (Redman et al., 2022), cognitive performance (Bernstein et al., 2022), autonomic regulation through heart rate variability (Huang et al., 2022), daily functioning (Lustenberger et al., 2022; Parsey et al., 2015), peritoneal dialyses specific factors like haemoglobin and phosphorous level (Zhang et al., 2021), mild cognitive impairment (MCI; Gao et al., 2021; Rawtaer et al., 2020), Alzheimer's disease (Gao et al., 2021; Lim et al., 2013; Wams et al., 2017), perceived health status (Schütz et al., 2021), markers of cardiometabolic health (Full et al., 2020), neural structure, i.e. white matter of the brain (Altendahl et al., 2020), daytime cortisol level (Morgan et al., 2017), amnesic MCI (aMCI; Wams et al., 2017), inflammatory burden (Smagula et al., 2016), APOE e4 (a risk factor allele for Alzheimer's disease; Drogos et al., 2016), peripheral arterial disease (Nagayoshi et al., 2017), working memory (Miyata et al., 2013; Seelye et al., 2015), number of falls (Stone et al., 2014), obesity (Patel et al., 2014, 2008), episodic memory (Scullin, 2013), sustained attention and vigilance (Miyata et al., 2013), instrumental activities of daily living (IADL) impairment (Spira et al., 2012), grip strength (Spira et al., 2012), gait speed (Spira et al., 2012), subjective pain (Dzierzewski et al., 2010), frailty status (Ensrud et al., 2009), anxiety (Spira et al., 2009), testosterone levels (Barrett‐Connor et al., 2008), cognitive decline (Blackwell et al., 2014; Lim et al., 2013) and depression (Maglione et al., 2008; Paudel et al., 2009).

Environmental predictors and outcomes that were measured in the studies for association with sleep parameters are seasonal variations in patients with MCI (Reynolds et al., 2022), perceived household environment (Johnson et al., 2021), interior housing conditions (Okoye et al., 2021), exterior housing conditions (Okoye et al., 2021) neighbourhood disorder (Okoye et al., 2021), evening indoor temperature (Saeki et al., 2015), initial bed temperature (Okamoto‐Mizuno & Tsuzuki, 2010; Saeki et al., 2015), seasonal thermal environment (Okamoto‐Mizuno & Tsuzuki, 2010; Tsuzuki et al., 2015), illuminance (Obayashi, Saeki, & Kurumatani, 2014; Tsuzuki et al., 2015), evening light exposure (Obayashi, Saeki, Iwamoto, et al., 2014), daytime‐light exposure (McCurry et al., 2011), at‐night skin temperature (Okamoto‐Mizuno & Tsuzuki, 2010) and relative humidity (Okamoto‐Mizuno & Tsuzuki, 2010).

Interventional predictors and outcomes are few that involved intraocular lens placement in cataract patients (Chellappa et al., 2022), auditory deep stimulation (Lustenberger et al., 2022), interval training with sleep hygiene (Mahajan et al., 2021), 3‐month light to moderate intensity aerobic exercise (Miyazaki et al., 2021), CogniFit intervention (a cognitive training program; Haimov & Shatil, 2013), yoga intervention (Taibi & Vitiello, 2011), guided sleep education (NITE‐D; McCurry et al., 2011), sleep hygiene program with bright and dim light exposure in morning or evening (Friedman et al., 2009), and daytime light exposure (Martin et al., 2008).

Behavioural predictors and outcomes consisted of mobility in terms of daily steps (Gao et al., 2021), behavioural and psychological symptoms of dementia (Cho et al., 2021), in‐bed behaviours (Johnson et al., 2021), number of baths in 3 days (Tai et al., 2021), bathing duration (Tai et al., 2021), time interval between bathing and bedtime (Tai et al., 2021), and walking (McCurry et al., 2011).

Predictors and outcomes regarding time and place are measured by comparing sleep between two different times or places. These include sleep measures pre‐ and post‐lockdown (Kholghi et al., 2021), nursing home residents versus community‐dwelling residents (Kume et al., 2016; Olsen et al., 2016), and sleep in assisted living facilities versus sleep at home (Martin et al., 2008). Social predictors and outcomes are measured only in one study where partnership status and relationship quality are assessed in association with sleep parameters (Chen et al., 2015).

3.5. Predictors and outcomes associated with objective parameters of sleep

In our 54 studies, we found that several objective parameters of sleep were associated with several diverse predictors and outcomes mentioned in the previous section. Table 2 shows the predictors and outcomes that were associated with TST, WASO and SE. TST was associated with 14 health‐related, four environmental, two interventions, three behavioural, two time and place, and one social predictors and outcomes. WASO was associated with nine health‐related, four environmental, four interventions, three behavioural, one time and place, and one social predictors and outcomes. SE was associated with 10 health‐related, four environmental, two interventions, two behavioural, one time and place, and one social predictors and outcomes.

TABLE 2.

Association of objective parameters of sleep with health, environmental, interventions, behavioural, time and place, and social predictors and outcomes.

Predictors and outcomes	TST	WASO	SE
Health‐related	HIV status, HR variability, effectiveness of peritoneal dialysis (Hb, Ph levels), markers of cardiometabolic health, aMCI, inflammatory burden, peripheral arterial disease, number of falls, obesity, working memory capacity, IADL impairment, grip strength, subjective pain, frailty status	HR variability, daytime cortisol levels, inflammatory burden, grip strength, frailty status, anxiety, depression, testosterone levels, cognitive functional decline	Effectiveness of peritoneal dialysis (Hb, Ph levels), APOE e4, number of falls, working memory capacity, IADL impairment, grip strength, frailty status, anxiety, testosterone levels, cognitive functional decline
Environmental predictors and outcomes	Exterior housing conditions, seasonal thermal environment, illuminance, outside and inside temperature	Exterior housing conditions, seasonal thermal environment, illuminance, day‐time light exposure	Exterior housing conditions, seasonal thermal environment, illuminance, outside and inside temperature
Interventions	3‐month light to moderate intensity aerobic exercise, sleep hygiene and exposure to bright light	Interval training with sleep hygiene, 3‐month light to moderate intensity aerobic exercise, CogniFit intervention, guided sleep education; NITE‐D	3‐month light to moderate intensity aerobic exercise, CogniFit intervention
Behavioural	Mobility, BPSD, in‐bed behaviours	Mobility, in‐bed behaviours, walking	Mobility, in‐bed behaviours
Time and place	COVID‐19 pre‐ and post‐lockdown, living place	Living place	Living place
Social	Partnership status	Partnership status	Partnership status

aMCI, amnestic mild cognitive impairment; BPSD, behavioural and psychological symptoms of dementia; Hb, haemoglobin; HIV, human immunodeficiency virus; HR, heart rate; IADL, instrumental activities of daily living; Ph, phosphorous; SE, sleep efficiency; TST, total sleep time; WASO, wake after sleep onset.

The 10 common predictors and outcomes associated with all three most common parameters are shown in Figure 6. Two of these were health‐related, frailty (Ensrud et al., 2009) and grip strength (Spira et al., 2012); three were environmental, which included exterior housing conditions (Okoye et al., 2021), seasonal thermal environment (Okamoto‐Mizuno & Tsuzuki, 2010; Tsuzuki et al., 2015) and illuminance (Obayashi, Saeki, & Kurumatani, 2014; Tsuzuki et al., 2015); two were behavioural associations, like in‐bed behaviours (Johnson et al., 2021) and mobility (Gao et al., 2021). One intervention, 3‐month light to moderate intensity aerobic exercise intervention (Miyazaki et al., 2021), living place, i.e. nursing homes versus community‐dwelling homes (Kume et al., 2016; Olsen et al., 2016), and a social outcome of partnership status (Chen et al., 2015) were associated with TST, WASO and SE. The objective parameters with sleep stages are associated with auditory deep sleep stimulation (Lustenberger et al., 2022), daily functioning (Lustenberger et al., 2022), Alzheimer's disease (Tadokoro et al., 2020), MCI (Tadokoro et al., 2020), neural structure, i.e. the white matter of the brain (Altendahl et al., 2020), and episodic memory (Scullin, 2013).

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.

4. DISCUSSION

Key findings

• The predominant method for home‐based objective sleep monitoring in older adults involved using a wearable, wrist‐worn device with actigraphy sensors.

• Among the various objective parameters of sleep, TST, WASO and SE were the most frequently measured parameters in older adults. In the limited number of studies that measured sleep architecture, the parameters of sleep commonly used were slow‐wave sleep (N3 or deep sleep) and REM sleep.

• Predictors and outcomes assessed in studies of objective sleep monitoring in older adults were health‐related, environmental conditions, interventions, behavioural aspects, time and place considerations, and social associations.

• In older adults, sleep parameters like TST, WASO and SE were associated with in‐bed behaviours, exterior housing conditions, 3‐month light to moderate intensity aerobic exercise, living place, relationship status, seasonal thermal environment, illuminance, grip strength, frailty status and mobility. Specific sleep stages were associated with auditory deep sleep stimulation, daily functioning, Alzheimer's disease, MCI, neural structure and episodic memory.

Our findings provide knowledge of the overview of home‐based objective sleep monitoring in older adults. The majority of studies on objective sleep monitoring at home in older adults were conducted in the USA. Among the European states, only Switzerland, the UK and Norway were represented, indicating a research gap in these regions. In recent years, especially during and after 2021, there has been an increase in studies focusing on home‐based objective sleep monitoring in older adults. The need for telehealth solutions and home‐based monitoring systems rose with the implementation of social distancing measures and the trend towards remote service. This change was necessary to maintaining healthcare delivery while lowering the risk of viral transmission, and it increased interest in creating and evaluating new technology that could facilitate these adjustments (Warrier & Sood, 2021). These immediate adaptations within healthcare research during the pandemic might be the reason for the reflected surge of research articles during 2021.

The review pointed out that actigraphy is the most used in‐home sleep‐monitoring method for older adults. The actigraphy technology collects data generated by movement or activity, and is either a wrist‐worn or hip/waist‐worn device. Although actigraphy is convenient and comfortable, a study shows that sleep parameters derived from actigraphy are less accurate than EEG (Rocknathan et al., 2017). In a study, it was found that actigraphy tends to overestimate light sleep bias more in the older age group than in the younger age group (Ong et al., 2023). EEG sensors monitor changes in brain activity, which in our review was performed through a headband device. The headband devices are comfortable and patient‐friendly, have a high level of accuracy, and provide accurate physiological signals (Arnal et al., 2020). Compared with the gold‐standard PSG, EEG headbands provided the lowest discrepancies in sleep parameters among other sleep monitors. These headbands are also recommended for evaluating poor sleep quality or when the highest degree of accuracy in sleep staging is required (Ong et al., 2023). There are other technologies than headbands that use EEG for sleep monitoring, such as ear‐EEG sleep‐monitoring plugs and patches (Mohamed et al., 2023). Our scoping review revealed a research gap in the use of these devices for older adults in their home environment. Another method of sleep monitoring we found in our studies is BCG. A BCG measures “the recoil force of the body in reaction to cardiac ejection of blood into the aorta and provides information on the timing of each heartbeat” (Starr et al., 1939). All of the BCG sensors, according to our review, are installed as under‐mattress mats. The use of these devices has the potential to empower individuals to improve their sleep quality. On one hand, the use of screens and connectivity is generally perceived as disruptive to sleep. On the other hand, novel digital technologies could potentially facilitate sleep health and aid in the identification of disruptions of sleep parameters and sleep disorders (Jaiswal et al., 2019). A further review study is required that investigates the user‐friendliness, non‐intrusiveness, acceptance and compliance of these technologies in older adults (LeBlanc et al., 2022; Sadek & Mohktari, 2018).

The objective parameters that were most commonly found in our study were TST, WASO and SE. Sleep quality is measured using various technologies that assess different aspects of sleep. These technologies generate diverse parameters used to determine the objective parameters of sleep, such as TST, WASO and SE. TST refers to the total duration of sleep, WASO measures the total amount of time a person spends awake after falling asleep and is measured in minutes, and SE is the percentage of time spent asleep while in bed (O'Donnell et al., 2009). The literature supports that TST decreases with age from childhood to late adulthood, but then plateaus after 60 years old (Li et al., 2022). WASO, which is a measure of sleep maintenance, also worsens as aging occurs but remains unchanged after 60 years (Ohayon et al., 2004). SE remains consistent from childhood to adolescence and declines significantly with age in adulthood, and it declines very slowly further on with advancing age (Ohayon et al., 2004). There is significant night‐to‐night variability in objective sleep parameters in older adults (McCarter et al., 2022; Parsey et al., 2015). Even among those averaging the recommended 7–9 hr of sleep, 40% of nights fell outside this range (Scott et al., 2024). Daily monitoring of sleep parameters can be a powerful tool for older adults and their healthcare providers. It can help identify potential sleep‐related disorders (Lechat, Scott, et al., 2023) at an early stage, and health factors that affect the variability in these parameters (Lechat, Loffler, et al., 2023). Single‐night parameters may result in misdiagnosis and undertreatment of patients with mild sleep disorder symptoms during the monitoring period (Lechat et al., 2022). Therefore, these sleep parameters, through daily monitoring, provide a more comprehensive and accurate picture of an individual's sleep health.

There were several predictors and outcomes that were found to be associated with the most common objective parameters of sleep in older adults. They were health‐related, environmental, interventions, behavioural, time and place, and social. Home‐based sleep monitoring can be used to explore the link between sleep and various factors by measuring night‐to‐night sleep variability and objective measures of sleep across multiple nights. Analysis of sleep parameters and their association with multidimensional associations reveals a complex interplay that impacts healthy aging. Health‐related associations such as frailty and grip strength are associated with TST, SE and WASO. Grip strength is often used as a measure of overall physical health and functional ability, while frailty, which encompasses physical, cognitive and social elements, has also been linked to reduced grip strength (Pana et al., 2021). Sleep quality and grip strength have a bidirectional association, with sleep quality affecting grip strength and vice versa (Han et al., 2023). The interconnectedness of objective sleep, frailty and grip strength highlights the importance of sleep in the overall wellbeing of older adults (Ravyts & Dzierzewski, 2022). Environmental associations such as exterior housing conditions, seasonal thermal environment, and illuminance are also found to be associated with sleep parameters TST, SE and WASO. Several environmental factors, both indoors and outdoors, play an important role in sleep quality. Exterior housing conditions, such as poorer housing quality, were found to be associated with shorter SD (Troxel et al., 2020). Being exposed to continuous ambient noise levels of 39 dBA or higher is connected to more frequent awakenings at night, shorter duration of sleep, diminished sleep quality, and a decrease in REM sleep (Marks & Griefahn, 2007). Light at night affects the sleep quality through increased awakenings and reduced slow‐wave sleep (Cho et al., 2013). By facilitating an environment conducive to healthy sleep, public health workers can contribute to better sleep for older adults. Apart from environmental associations, in‐bed behaviours such as watching TV or using screens have been found to be associated with TST, WASO and SE. A recent review highlighted that the relationship between technology use and sleep may be bidirectional. Technology use can affect sleep by displacing bedtime and delaying sleep onset, while sleep problems may lead to increased technology use as a way to fill time, provide cognitive distraction or aid in emotional regulation (Bauducco et al., 2024). These results about the association of objective parameters and sleep in older adults indicate that different behaviours, the environment, and the time before and after the pandemic can affect brain activity and, thus, inevitably affect sleep. Objective sleep monitoring, simultaneously with other technologies that help in relaxation and maintaining sleep hygiene, can be useful in improving sleep quality (Leonidis et al., 2021). The result of the analysis provides valuable insights to guide the development of future research. They can guide interventions to address key factors that significantly impact the quality of sleep among older adults. For example, interventions of changing in‐bed behaviours or exterior environmental conditions may improve the SD, SE and sleep maintenance of older adults (Koch et al., 2006). Recognizing the associations between specific sleep stages and conditions such as Alzheimer's disease, MCI, neural structure and episodic memory can have significant implications for cognitive health. Addressing sleep disturbances in older adults could help maintain cognitive function and potentially lower the risk of cognitive decline (Nyholm et al., 2024).

There has been limited research on daily monitoring of sleep stages in older adults in home environments. Monitoring sleep stages, such as REM sleep and NREM sleep (N1, N2 and N3 or deep sleep), provides detailed insight into the quality and depth of your sleep (McCarter et al., 2022). The disorders in specific stages of sleep might be an indicator of a potential health condition. For example, increased time in stage N1 sleep and less time in stage REM sleep are associated with worsening cognitive performance in older men over time (Song et al., 2015). Disturbances in NREM sleep are associated with the development of Alzheimer's disease. This is due to the insufficient functioning of the brain's glymphatic system, leading to the accumulation of amyloid beta, a hallmark of Alzheimer's disease (Astara et al., 2023). Therefore, distinguishing sleep stages is important in older adults to monitor and diagnose sleep disturbances and health outcomes. Non‐EEG‐based technologies are currently not accurate enough to reliably distinguish sleep stages when compared with the gold‐standard of PSG (Imtiaz, 2021; Yuan et al., 2024). Recent advances in wearables using EEG technology have shown high accuracy, especially in sleep staging, supported by feasibility studies and validation across populations. These devices offer the potential for more accurate at‐home sleep monitoring, with high compliance and user‐friendliness (de et al., 2024).

There are several strengths of this review. This review entailed a comprehensive search, which was reviewed and improved with the help of a librarian. The meticulous following of the methodology led to a detailed extraction process, which was discussed on a number of occasions among the three authors. To our knowledge, this is the first scoping review regarding home‐based objective sleep monitoring at home in older adults, and its associations with diverse predictors and outcomes.

There are some limitations of our study that are also important to consider. Our search of the articles was limited to English, which may have led to the exclusion of important studies. The exclusion of the articles after 27 February 2023 and articles that published algorithms based on machine learning or described technologies without exploring the predictors and outcomes related to sleep parameters might have led to leaving out recent technologies. Furthermore, the exclusion of studies focused on sleep disturbances and clinical sleep disorders also limits the applicability of the findings to populations with sleep disorders. One limitation of this review is the inability to provide validation of the technologies provided, as this was out of the scope of the review. Future studies should comprehensively study validations, and disclose funding and conflicts of interest to enhance clarity and depth of information.

5. CONCLUSIONS

This scoping review adds to the expanding body of knowledge on objective sleep monitoring at home in older adults, highlighting various predictors and outcomes associated with sleep parameters. In summary, the predominant method for home‐based objective sleep monitoring involves using a wearable, wrist‐worn device with actigraphy sensors. Among the various objective parameters of sleep, TST, WASO and SE are the most frequently measured parameters in older adults. In older adults, sleep parameters like TST, WASO and SE are associated with in‐bed behaviours, exterior housing conditions, 3‐month light to moderate intensity aerobic exercise, living place, relationship status, seasonal thermal environment, illuminance, grip strength, frailty status, and mobility. The results of our study hold significant implications for tailored interventions, environmental modifications, early detection of sleep irregularities, and a comprehensive understanding of the interplay between sleep and the many factors that impact the aging population. The gaps identified, particularly exploring sleep architecture using home‐based objective sleep monitoring in older adults, offer a roadmap for future research.

FUNDING INFORMATION

This study has not received any external funding.

CONFLICT OF INTEREST STATEMENT

The authors declare no potential conflict of interest.

Supporting information

TABLE S1. Charting of data with ethnic background, gender and publishing venue.

APPENDIX A.

KEYWORDS FOR SEARCH

FIGURE A1.

Search keywords.

APPENDIX B.

EXAMPLE OF SEARCH STRING IN PUBMED

FIGURE B2.

Search string example from PubMed.

APPENDIX C.

DATA CHARTING

TABLE C1.

Data charting Table‐List of studies included in the review.

Article	Year	Sample size	Location	Sample age in years	Type of monitoring technology	Sensors	Data analysis technique	Objective parameters of sleep	Predictors/outcomes	Parameters associated with predictors and outcomes
1. (Redman et al., 2022)	2022	172	South Africa	45–93 (66.7 ± 11.5)	ActTrust. Condor instruments (wrist‐worn)	Actigraphy	Manually marked: sleep onset and offset, automated algorithm, Cole/Kripke algorithm in ActStudio	Sleep onset and offset, WASO, TST, SE (% time spent asleep between sleep onset and offset)	HIV status	Sleep onset and offset and TST
2. (Bernstein et al., 2022)	2022	91	USA	73.7 ± 5.7	Withings steel (wrist‐worn)	Actigraphy	Not mentioned	Mean nightly sleep hours, variability in nightly sleep hours, mean time to wake up, variability in time to wake up, mean time of going to bed, variability in time of going to bed, and percentage of night data recorded	Cognitive performance	Percentage of night data recorded Greater variability in wake time
3. (Huang et al., 2022)	2022	68	USA	69 ± 2	Actiwatch SpectrumPro, Phillips Respironics (wrist‐worn)	Actigraphy	Actiwatch software (version 6.0)	TST, SE, WASO	Heart rate variability (autonomic regulation)	TST, WASO
4. (Chellappa et al., 2022)	2022	29	Switzerland	55–80 (69.8 ± 6.2 experiment, 63.6 ± 5.6)	Acti‐watch‐L, Cambridge Neurotechnology (wrist‐worn)	Actigraphy	Not mentioned	TIB, TST, SE, mean wake about time	Intraocular lens placement with previous cataract	None
5. (Reynolds et al., 2022)	2022	128	USA	≥ 60 (85.2 ± 7.7)	In‐home passive monitoring technology (NYCE, Vancouver, BC)	Infrared presence sensors	An algorithm was written in MATLAB to estimate total rest time from the firing timestamps of the bedroom sensors and sensors in neighbouring rooms	Total rest time in bedroom (TRTiB)	MCI older adults seasonal variation	TRTiB
6. (Lustenberger et al., 2022)	2022	16	Switzerland	62–78 (69.5 ± 1.3)	MHSL‐SleepBandv2 (Headband)	EEG, EMG, EOG	Algorithm was written in MATLAB to classify sleep as NREM and REM	NREM sleep (sleep stages N2 and N3 characterized by SW and sleep spindles) or not‐NREM sleep (awake, N1 and REM)	Auditory deep sleep stimulation and daily functioning	Slow‐wave activity (NREM)
7. (Gao et al., 2021)	2021	36	Australia	≥ 65 (83.5 ± 7.0)	EMFIT QS sensor (mattress‐based)	BCG	Not mentioned	TST, SOL, WASO, TIB, NoA, SE	Mobility (daily step)	SE, WASO, TST
8. (Mahajan et al., 2021)	2021	13	India	≥ 60 (74.16 ± 6.17)	S+ Sleep monitor (manu. by ResMed.) (bedside, connected to a smartphone through Bluetooth)	Directional radiofrequency sensor that collects data based on respiratory patterns of the participant during sleep	ResMed app for android v 1.2.0 (ResMed San Diego, CA)	Light sleep, deep sleep, REM sleep, wake time (WASO), TST, number of disruptions	Interval training with sleep hygiene	Wake time in minutes (WASO)
9. (Cho et al., 2021)	2021	204	South Korea	≥ 65 (81.23 ± 6.01) with dementia	wGT3X‐BT activity monitor; ActiGraph, LLC (wrist‐worn)	Actigraphy	ActiLife software (version 6.13.3; ActiGraph, LLC) Cole–Kripke algorithm to score a 1‐min epoch as asleep or awake	TST, WASO	Behavioural and psychological symptoms of dementia	TST
10. (Zhang et al., 2021)	2021	50	China	≥ 60 (70.4 ± 7.2) with end‐stage renal disease undergoing peritoneal dialysis	ActiGraph GT3X, Pensacola, FL, USA (wrist‐worn)	Actigraphy	ActiLife V6.13.3 software, wear time through Choi algorithm, sleep parameters through Cole–Kripke algorithm	TST, SE	Haemoglobin levels, serum phosphorus levels (disease peritoneal dialysis specific factor)	TST, SE
11. (Johnson et al., 2021)	2021	231	USA	66.3 ± 10.8	ActiGraph GT3X, Pensacola, FL, USA (wrist‐worn)	Actigraphy	ActiLife V6.13.3 software use Cole–Kripke algorithm	TST, SE, WASO	a. Perceived household environment; b. in‐bed behaviours	a. No associated marker; b. TST, SE, WASO
12. (Kholghi et al., 2021)	2021	31	Australia	≥ 65 (846.8)	EMFIT QS sensor (mattress‐based)	BCG	Cloud‐based analysis platform	TST, SOL, WASO, SE	Sleep measures pre‐ and during lockdown	TST in women
13. (Okoye et al., 2021)	2021	136	USA	≥ 65 (76 ± 0)	Actiwatch‐2 wrist actigraph (Philips Respironics, Bend, OR) (wrist‐worn)	Actigraphy	Actiware Software 6.0.9	TST, WASO, SE	a. Interior housing conditions; b. exterior housing conditions; c. neighbourhood disorder	a. Not statistically significant association; b. TST, WASO, SE; c. not statistically significant association
14. (Tai et al., 2021)	2021	1094	Japan	≥ 60 (72 ± 7.1)	Actiwatch‐2 wrist actigraph (Respironics, Murrysville, PA) (wrist‐worn)	Actigraphy	Actiware Software 5.5 Respironics	SOL	Day of bath (both, 1st day, 2nd day, neither), bathing duration, time interval between bathing and bedtime	SOL
15. (Miyazaki et al., 2021)	2021	49	Japan	65.7 ± 5.7	Lifecorder GS (LC, Suzuken, Nagoya, Japan) (worn on hip)	Actigraphy	Sleep‐SignAct software program (version 2.0, Kissei Comtec, Matsumoto, Japan) with a scoring algorithm	TST, wake episode (NoA), WASO, SE, TIB	3‐month light‐to‐moderate intensity aerobic exercise intervention	TST, WASO, SE, NoA
16. (Rawtaer et al., 2020)	2020	49	Singapore	≥ 65 (HC: 73 ± 5.3; MCI 75.1 ± 6.3)	Fibre‐optic technology placed under participants mattress	BCG	Not mentioned	SD (TST) interruptions	Sleep interruptions in MCI compared with healthy controls	Sleep interruptions were associated with MCI
17. (Tadokoro et al., 2020)	2020	63	Japan	NC: 74.0 ± 7.8; MCI: 77.6 ± 6.5; AD: 75.5 ± 6.2	WatchPAT (Itamar Medical, Caesarea, Israel)	Actigraphy	Algorithms	TST, REM, light sleep, deep sleep, SOL, REM sleep latency	Normal cognition, AD, MCI	REM, NREM
18. (Schütz et al., 2021)	2021	37	Switzerland	70–101 (87 ± 7)	EMFIT QS, Emfit (mattress‐based)	Respiration, heart rate	Proprietary algorithms from the device manufacturer	Number bed exits (count), number toss‐and‐turn events (larger movements, count), TST REM, light sleep, deep sleep, duration awake (WASO), SE, SOL	Health status EQVAS (equation 5D 3L)	Number of toss‐and‐turn events (larger movements, count)
19. (Full et al., 2020)	2019	3329 women	USA	63–99 (78.5 ± 6)	ActiGraph GT3X+; Pensacola, FL (worn on the waist)	Actigraphy	ActiLife version 6.11	TST	Markers of cardiometabolic health	TST
20. (Altendahl et al., 2020)	2020	43	USA	66–84 (75 ± 4.51)	Zeo (headband)	EEG	Proprietary algorithms from the device manufacturer	TST, SE, light sleep (LS) (stages 1 and 2 NREM sleep), deep sleep (DS) (Stage 3 NREM sleep) and REM sleep (RS)	White matter microstructure through DTI using fractional anisotropy & mean diffusivity	REM SLEEP
21. (Morgan et al., 2017)	2017	672	USA	66–78 (71.5 ± 0.0)	Actiwatch spectrum model, Philips, Respironics	Actigraphy	Actiware Software 5.5 Respironics	TST, sleep fragmentation score, and WASO	Daytime cortisol level	Sleep fragmentation score, WASO
22. (Wams et al., 2017)	2017	24	United Kingdom	77.1 ± 4.0	Actiwatch 7, CamNTech (wrist‐worn)	Actigraphy	Actiwatch activity and Sleep analysis (version 7, CamNTech)	Number of wake bouts, fragmentation index, TIB, sleep period, TST, SE, sleep start, sleep end	Sleep in healthy versus aMCI (AD patients)	TST, TIB, sleep period, sleep start
23. (Kume et al., 2016)	2016	17	Japan	82.2 ± 4.2	Actiwatch 2; Philips Respironics, USA (wrist‐worn)	Actigraphy	Actiware System, version 6.08 (Philips Respironics, USA)	TST, SOL, SE, awakening time, awakening frequency	Nursing home residents with or without dementia versus community‐dwelling without dementia	SE, awakening time, awakening frequency
24. (Olsen et al., 2016)	2016	115 (dementia)	Norway	82.6 ± 6.84	ActiSleep+, ActiGraph, Pensacola, USA (wrist‐worn)	Actigraphy	ActiLife, software Version 6.11.2 (ActiGraph, Pensacola, USA)	TST, WASO, NoA, SE	Differences in sleep parameters in home‐dwelling persons and nursing home residents with dementia	Nursing home residents have lower TST, WASO and sleep during the night time than community dwelling individuals
25. (Smagula et al., 2016)	2016	2531men	USA	75.3 ± 5.01	The Octagonal Sleep Watch actigraphy, or SleepWatch‐O, (Ambulatory Monitoring, Ardsley, NY) (wrist‐worn)	Actigraphy	ActionW‐2 software (Ambulatory Monitoring, Ardsley, NY)	TST, SL, WASO	Inflammatory burden	TST, WASO
26. (Drogos et al., 2016)	2016	35	Canada	≥ 55 (64.9 ± 5.1)	AW‐2 Actiwatch (Minimitter; Philips Respironics, Murrysville, PA) (wrist‐worn)	Actigraphy	Actiware program (Philips Actiware 6.0.4)	TST, NoA, WASO, SE, SOL	APOE e4 (risk factor allele for AD)	SE
27. (Nagayoshi et al., 2017)	2016	650	USA	(63 ± 0.00)	Actiwatch Spectrum (Philips Respironics, Murrysville, PA) (wrist‐worn)	Actigraphy	Actiware‐Sleep version 5.59 software	SD (TST)	Peripheral arterial disease	TST
28. (Chen et al., 2015)	2015	727	USA	62–90 (71.79)	Actiwatch (Philips/Respironics 2010) (wrist‐worn)	Actigraphy	Actiware software version 5.59 (Philips/Respironics 2010)	TST, percent sleep, sleep fragmentation index, WASO	a. Partnership status (married or unmarried); b. relationship quality	a. TST, WASO; b. no association
29. (Seelye et al., 2015)	2015	63	USA	87.2 ± 6.2	Home sensor network in the bedroom and pressure mats under beds (BAM Labs, Los Gatos, CA)	Passive motion sensor	Self‐created algorithms	Total movement in bed at night (MIB), times up at night, and TST	Neuropsychological performance (working memory)	Mean MIB
30. (Saeki et al., 2015)	2015	861	Japan	72.1 ± 7.1	Actiwatch 2; Respironics, Murrysville, PA, USA	Actigraphy	Actiware version 5.5 software	Sleep onset, sleep offset, SOL, SE	a. Evening indoor temperature; b. initial bed temperature	a. SOL; b. SOL
31. (Tsuzuki et al., 2015)	2015	8 Men	Japan	64 ± 1	Micro‐mini motionlogger actigraph; Ambulatory Monitoring, NY, USA and Actiwatch‐L; Mini Mitter (wrist‐worn)	Actigraphy	Action‐W, 2.4.20, Ambulatory Monitoring using the Cole–Kripke algorithm for scoring sleeping and waking	SOL, TST, SE	a. Seasonal thermal environment; b. illuminance	a. SE, WASO, TST; b. TST, WASO, SE
32. (Parsey et al., 2015)	2015	85	USA	50–86 (67.69 ± 9.90)	Micro‐Mini Motionlogger, Ambulatory Monitoring (wrist‐worn)	Actigraphy	Act Millennium software version 3.31 (Ambulatory Monitoring) and the Cole–Kripke default scoring algorithm	TST, SOL, SE, WASO	Everyday functioning (direct observation, self‐report and paper‐and‐pencil‐based problem‐solving tasks)	None
33. (Obayashi, Saeki, & Kurumatani, 2014)	2014	857	Japan	≥ 60, mean 72.2	Actiwatch 2; Respironics, (Murrysville, PA) (wrist‐worn)	Actigraphy	Actiware version 5.5 (Respironics) with the default algorithm (Philips Respironics Actiware Tutorials, 2013)	SE, SOL, WASO, TST, sleep‐mid time, mid‐time between sleep onset and sleep offset	LAN	SE, SOL, WASO, TST, sleep mid‐time
34. (Stone et al., 2014)	2014	3101Men	USA	≥ 67 (76 ± 0)	SleepWatch‐O, Ambulatory Monitoring, Ardsley, NY (wrist‐worn)	Actigraphy	Action W‐2 software, Cole–Kripke algorithm, University of California, San Diego (UCSD) scoring algorithm	TST, SE, TIB (time spent sleeping after lights off), nap time (number)	Number of falls (recurrent/non‐recurrent)	TST, SE, nap time
35. (Patel et al., 2014)	2014	6038	USA	≥ 65	Sleepwatch‐O, Ambulatory Monitoring, Ardsley NY (wrist‐worn)	Actigraphy	Action W‐2 software (Ambulatory Monitoring) raw data, the University of California San Diego (UCSD) scoring algorithm to determine sleep/wake status	TST (variability), nap time	BMI Obesity was defined as a BMI ≥ 30 kg m−2 in men and women	TST variability, nap time
36. (Obayashi, Saeki, Iwamoto, et al., 2014)	2013	192	Japan	≥ 60 (69.9 ± 0)	(Actiwatch 2; Respironics, Murrysville, PA) (wrist‐worn)	Actigraphy	Actiware software (version 5.5, Respironics, Murrysville, PA)	SOL	Evening light exposure	SOL
37. (Haimov & Shatil, 2013)	2013	51	USA	65–85	Mini Motionlogger, Ambulatory Monitoring, Ardsley, New York, USA (wrist‐worn)	Actigraphy	Actigraphic Scoring Analysis program for an IBM‐compatible personal computer (W2 scoring algorithm) provided by the manufacturer	TST, SOL, SE, WASO, NoA	CogniFit intervention, a cognitive training program	SOL, SE, WASO, NoA
38. (Scullin, 2013)	2013	41	USA	(70.66 ± 5.41)	Zeo (headband)	EEG	Proprietary algorithms from the device manufacturer	Slow‐wave sleep	Episodic memory	Amount of slow‐wave sleep
39. (Miyata et al., 2013)	2013	78	Japan	≥ 60 (72.2 ± 5.9)	Ambulatory Monitoring, New York, NY, USA (wrist‐worn)	Actigraphy	Algorithm supplied by the ActionW‐2 clinical sleep analysis software package for Windows (Ambulatory Monitoring) Sleep and activity were scored according to the Cole–Kripke formula	TST, SE, SOL and WASO	a. CPT (for sustained attention and vigilance); b. working memory capacity	a. No significance; b. TST, SE
40. (Spira et al., 2012)	2013	817 women	USA	82.4 ± 00.00	SleepWatch‐O, Ambulatory Monitoring, Ardsley, NY (wrist‐worn)	Actigraphy	Action W‐2 software (Ambulatory Monitoring)	TST, WASO, SE	a. IADL impairment; b. grip strength; c. gait speed at baseline and 5‐year follow‐up	a. TST, SE; b. TST, WASO and SE; c. no significant association
41. (Taibi & Vitiello, 2011)	2011	13 women	USA	55–85 (65.2 ± 6.9)	Mini‐mitter Company, Bend, OR (wrist‐worn)	Actigraphy	Actiware version 5.57 (Mini‐mitter Company, Bend, OR)	SOL, WASO, TST, SE	Yoga intervention	no associations
42. (McCurry et al., 2011)	2011	132	USA	82.2 ± 8.5	Micro‐Mini Motionlogger actigraph (Ambulatory Monitoring, Ardcsley, NY) (wrist‐worn)	Actigraphy	Action4 software package (Ambulatory Monitoring), which incorporates Cole–Kripke's sleep scoring algorithm	WASO, TST, TIB, daytime sleep/inactivity	Walking, light exposure, guided sleep education (NITE‐AD)	WASO
43. (Dzierzewski et al., 2010)	2011	50	USA	60–90 (69.10 ± 7.02)	Actiwatch‐L monitors	Actigraphy	Actiware‐Sleep v. 3.3	WASO, TST	Subjective pain	TST
44. (Okamoto‐Mizuno & Tsuzuki, 2010)	2010	19	JAPAN	≥ 62 (65.8 ± 2.6)	Actigraph Basic, Ambulatory Monitoring	Actigraph	The software Action W (AMI)	Bedtime, wakeup time, TIB, TST, SE, SOL	Outside temperature and the temperature and RH, at night skin temperature (Tsk), microclimate and bed climate were continuously measured at 1‐min intervals	TST, SE
45. (Ensrud et al., 2009)	2009	3133 men	USA	≥ 65	Ambulatory Monitoring, Ardsley, NY	Actigraphy	ActionW‐2 software (Ambulatory Monitoring, Ardsley, NY)	TST, SE, SOL, WASO, NoA	Frailty status	SE, SOL, TST, NoA, WASO
46. (Friedman et al., 2009)	2009	51 insomnia patients	USA	≥ 55 (63.6 ± 7.1)	Actiwatch‐L (MiniMitter, Bend OR)	Actigraphy	Actiware‐Sleep v.3.1 software program (Mini‐ Mitter, Bend OR)	WASO, TIB, TST, SE	A 12‐week program of sleep hygiene and exposure either to a. bright (≥ 4000 lux) or b. dim light (≈65 lux) scheduled daily in the morning or evening for 45 min	a. TIB, TST; b. no sign association
47. (Spira et al., 2009)	2009	3040 women	USA	≥ 65 (83.6)	(SleepWatch‐O; Ambulatory Monitoring, Ardsley, NY) (wrist‐worn)	Actigraphy	ActionW‐2 software (Ambulatory Monitoring)	TST, SE, WASO, SOL, nap time	ANX	SE, WASO
48. (Barrett‐Connor et al., 2008)	2008	1312 men	USA	≥ 65	The Octagonal Sleep Watch actigraph (Ambulatory Monitoring, Ardsley, NY)	Actigraphy	Action W software, based on an algorithm developed at the University of California, San Diego	TST, SE, SOL, WASO	Testosterone	SE, WASO
49. (Martin et al., 2008)	2008	38	USA	≥ 65	(Octagonal‐L; Ambulatory Monitoring [AMI], Ardsley, NY)	Actigraphy	Action4 software; (AMI)	TIB (sleep time and wakeup time), NoA, night time percentage sleep, daytime percentage sleep	Sleep in assisted living facility, sleep at home, daytime light exposure, 1000 lux	NoA, TIB
50. (Lim et al., 2013)	2013	737	CANADA	81.6 (7.2)	Actical (Phillips Respironics, Bend, OR) The Actical is a wristwatch‐like accelerometer	Actigraphy	Using algorithms implemented in the MATLAB language (Mathworks, Natick, MA)	Sleep fragmentation	Incident AD and cognitive decline	Sleep fragmentation
51. (Blackwell et al., 2014)	2014	2822 men	USA	≥ 65 (76.0 ± 5.3)	SleepWatch‐O, Ambulatory Monitoring, Ardsley, NY (wrist‐worn)	Actigraphy	ActionW‐2 software (Ambulatory Monitoring, Ardsley, NY)	TST, SE, WASO, NoA	Cognitive function decline	WASO, NoA, SE
52. (Misti L. Paudel et al., 2009)	2008	3051 men	USA	≥ 67	SleepWatch‐O, Ambulatory Monitoring, Ardsley, NY (wrist‐worn)	Actigraphy	ActionW‐2 software (Ambulatory Monitoring, Ardsley, NY)	SE, SOL, WASO, NoA, TST	Depressive symptoms assessed using the 15‐item GDS	SOL
53. (Patel et al., 2008)	2008	3055 men and 3052 women = 6107	USA	≥ 65 (67–99)	SleepWatch‐O, Ambulatory Monitoring, Ardsley, NY (wrist‐worn)	Actigraphy	ActionW‐2 software (Ambulatory Monitoring, Ardsley, NY)	TST	Obesity	TST
54. (Maglione et al., 2008)	2012	3045 women	USA	≥ 70	SleepWatch‐O, Ambulatory Monitoring, Ardsley, NY (wrist‐worn)	Actigraphy	ActionW‐2 software (Ambulatory Monitoring, Ardsley, NY)	TST, SOL, WASO, SE, NoA, number of naps	GDS	WASO, NoA, number of naps

AD, Alzheimer's disease; aMCI, amnestic mild cognitive impairment; ANX, Goldberg Anxiety Scale; BCG, ballistocardiography; BMI, body mass index; CPT, continuous performance test; DTI, diffusion tensor imaging; EEG, electroencephalography; EMG, electromyography; EOG, electrooculography; EQVAS, EuroQol visual analogue scale; GDS, Geriatric Depression Scale; HC, Healthy control ; HIV, human immunodeficiency virus; IADL, instrumental activities of daily living; LAN, light at night; MCI, mild cognitive impairment; MIB, Movement in bed ; NITE‐AD, nighttime insomnia treatment and education in Alzheimer's disease; NoA, number of awakenings; NREM, non‐rapid eye movement; REM, rapid eye movement; RH, relative humidity; SD, sleep duration; SE, sleep efficiency; SL, Sleep Latency; SOL, sleep‐onset latency; SW, Slow wave ; TIB, time in bed; TST, total sleep time; WASO, wakeup after sleep onset.

Ghazi, S. N. , Behrens, A. , Berner, J. , Sanmartin Berglund, J. , & Anderberg, P. (2025). Objective sleep monitoring at home in older adults: A scoping review. Journal of Sleep Research, 34(4), e14436. 10.1111/jsr.14436

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.