Contactless Sleep Monitoring for Early Detection of Health Deteriorations in Community-Dwelling Older Adults: Exploratory Study

Abstract

Background Population aging is posing multiple social and economic challenges to society. One such challenge is the social and economic burden related to increased health care expenditure caused by early institutionalizations. The use of modern pervasive computing technology makes it possible to continuously monitor the health status of community-dwelling older adults at home. Early detection of health issues through these technologies may allow for reduced treatment costs and initiation of targeted preventive measures leading to better health outcomes. Sleep is a key factor when it comes to overall health and many health issues manifest themselves with associated sleep deteriorations. Sleep quality and sleep disorders such as sleep apnea syndrome have been extensively studied using various wearable devices at home or in the setting of sleep laboratories. However, little research has been conducted evaluating the potential of contactless and continuous sleep monitoring in detecting early signs of health problems in community-dwelling older adults. Objective In this work we aim to evaluate which contactlessly measurable sleep parameter is best suited to monitor perceived and actual health status changes in older adults. Methods We analyzed real-world longitudinal (up to 1 year) data from 37 community-dwelling older adults including more than 6000 nights of measured sleep. Sleep parameters were recorded by a pressure sensor placed beneath the mattress, and corresponding health status information was acquired through weekly questionnaires and reports by health care personnel. A total of 20 sleep parameters were analyzed, including common sleep metrics such as sleep efficiency, sleep onset delay, and sleep stages but also vital signs in the form of heart and breathing rate as well as movements in bed. Association with self-reported health, evaluated by EuroQol visual analog scale (EQ-VAS) ratings, were quantitatively evaluated using individual linear mixed-effects models. Translation to objective, real-world health incidents was investigated through manual retrospective case-by-case analysis. Results Using EQ-VAS rating based self-reported perceived health, we identified body movements in bed—measured by the number toss-and-turn events—as the most predictive sleep parameter (t score=–0.435, P value [adj]=<.001). Case-by-case analysis further substantiated this finding, showing that increases in number of body movements could often be explained by reported health incidents. Real world incidents included heart failure, hypertension, abdominal tumor, seasonal flu, gastrointestinal problems, and urinary tract infection. Conclusions Our results suggest that nightly body movements in bed could potentially be a highly relevant as well as easy to interpret and derive digital biomarker to monitor a wide range of health deteriorations in older adults. As such, it could help in detecting health deteriorations early on and provide timelier, more personalized, and precise treatment options.

From luxury to necessity: Progress of touchless interaction technology

“…By using remote sensing, a touchless system was introduced for early detection of eye diseases to control blindness [ 51 ], however its main purpose is early detection of eye diseases instead of sanitation measures. Similar studies were conducted for Contactless Sleep Monitoring [ 52 ], Contactless analysis of heart rate variability [ 53 ] and Early Diagnosis of COVID-19 [ 54 ]. As this progress will continue on its way even after vaccination, educating healthcare customers is important for medical professionals to know about how and where touch free interaction can be applied to allow for an increase in protection of patients and safety of what can be costly equipment [ 55 ].…”

A systems approach towards remote health-monitoring in older adults: Introducing a zero-interaction digital exhaust

“…Note that measures derived from PIR and door sensors stem from one sensor system (meaning that technical failure usually affect both sensor types, except for instances were an individual sensor unit failed, which happened rarely), while sleep stems from another sensor; thus, for a bi-weekly segment to be valid, at least 30% of measures from both sensor systems must be valid. This led to a significant reduction in the number of bi-weekly segments, as a large number of sleep sensor data were missing due to technical issues, as has been discussed in prior work 39 . These two issues — lacking sensor data from both PIR/door and bed sensors and the unavailability of respective assessments — are responsible for the generally lower numbers of participants who could be included in this analysis (the exact numbers with regards to each assessment are given in Table 6 ).…”

“…As a result, many older adults that are affected by the digital divide 31 may be excluded, this may be even more problematic in those of lower socio-economic background [32][33][34] , those living in harder to access rural areas 32 , or those living with certain conditions like cognitive impairments or late-life depression 33 . Not too surprisingly, most successful real-world, long-term research using sensor technology with older adults has focused on contactless, zero-interaction approaches 9,13,28,[35][36][37][38][39][40] . Such technologies include passive infrared (PIR) motion sensors that capture an individual's activity in a given room 9,28,35,36,38,41,42 , contact door sensors that can signal when a person leaves or enters the home [36][37][38]43 , pressure sensors on or under a mattress that capture sleep measures 9,36,39 , and electronic pillboxes to track medication adherence 44 , along with more obtrusive depth-sensing cameras that track silhouettes to detect falls and monitor gait parameters 9 .…”

“…Not too surprisingly, most successful real-world, long-term research using sensor technology with older adults has focused on contactless, zero-interaction approaches 9,13,28,[35][36][37][38][39][40] . Such technologies include passive infrared (PIR) motion sensors that capture an individual's activity in a given room 9,28,35,36,38,41,42 , contact door sensors that can signal when a person leaves or enters the home [36][37][38]43 , pressure sensors on or under a mattress that capture sleep measures 9,36,39 , and electronic pillboxes to track medication adherence 44 , along with more obtrusive depth-sensing cameras that track silhouettes to detect falls and monitor gait parameters 9 .…”

Unobtrusive Monitoring of Sleep Cycles: A Technical Review

“…That is, the collection of longitudinal sleep data on a large scale can boost epidemiological studies that examine the influence of sleep on health and disease [11]. There are also less cumbersome approaches to sleep monitoring owing to the advancement, adoption, and integration of technology into healthcare in the form of non-contact systems, wearables, and mobile systems [11][12][13][14][15]. These systems capitalize on the strong correlation between bio-vital signs and sleep.…”