Bed Sensor Technology for Objective Sleep Monitoring Within the Clinical Rehabilitation Setting: Observational Feasibility Study

Abstract

Background Since adequate sleep is essential for optimal inpatient rehabilitation, there is an increased interest in sleep assessment. Unobtrusive, contactless, portable bed sensors show great potential for objective sleep analysis. Objective The aim of this study was to investigate the feasibility of a bed sensor for continuous sleep monitoring overnight in a clinical rehabilitation center. Methods Patients with incomplete spinal cord injury (iSCI) or stroke were monitored overnight for a 1-week period during their in-hospital rehabilitation using the Emfit QS bed sensor. Feasibility was examined based on missing measurement nights, coverage percentages, and missing periods of heart rate (HR) and respiratory rate (RR). Furthermore, descriptive data of sleep-related parameters (nocturnal HR, RR, movement activity, and bed exits) were reported. Results In total, 24 participants (12 iSCI, 12 stroke) were measured. Of the 132 nights, 5 (3.8%) missed sensor data due to Wi-Fi (2), slipping away (1), or unknown (2) errors. Coverage percentages of HR and RR were 97% and 93% for iSCI and 99% and 97% for stroke participants. Two-thirds of the missing HR and RR periods had a short duration of ≤120 seconds. Patients with an iSCI had an average nocturnal HR of 72 (SD 13) beats per minute (bpm), RR of 16 (SD 3) cycles per minute (cpm), and movement activity of 239 (SD 116) activity points, and had 86 reported and 84 recorded bed exits. Patients with a stroke had an average nocturnal HR of 61 (SD 8) bpm, RR of 15 (SD 1) cpm, and movement activity of 136 (SD 49) activity points, and 42 reported and 57 recorded bed exits. Patients with an iSCI had significantly higher nocturnal HR (t18=−2.1, P=.04) and movement activity (t18=−1.2, P=.02) compared to stroke patients. Furthermore, there was a difference between self-reported and recorded bed exits per night in 26% and 38% of the nights for iSCI and stroke patients, respectively. Conclusions It is feasible to implement the bed sensor for continuous sleep monitoring in the clinical rehabilitation setting. This study provides a good foundation for further bed sensor development addressing sleep types and sleep disorders to optimize care for rehabilitants.

Sleep monitoring challenges in patients with neurocognitive disorders: A cross‐sectional analysis of missing data from activity trackers

“…22 As a result, increased movements at night in the neurocognitive disorder group could have contributed to the disproportionate missing sleep data between the two groups. 24 There are study limitations worth noting. First, we were unable to compare the data collected from the Fitbit ® Alta 2 with the goldstandard method of PSG.…”

“…Finally, alternate methods of monitoring sleep in the inpatient setting could be further investigated with bed sensor technology, which measures sleep through ballistocardiography. This technology could be promising method of measuring sleep for patients with neurocognitive disorders as it may better capture restlessness and awake movements in bed and are is less prone to missing data 24 …”

Sleep–wake monitoring of people with intellectual disability: Examining the agreement of EMFIT QS and actigraphy

“…Notably, in our study, we also found that it is often unclear whether a bed exit is true or false because it is difficult to differentiate bed exits from missing data. Another explanation for differences between our results and those of Piantino et al (2021) likely to actually represent sleep for a person with the intention to go to sleep than for a person who is put to bed but not yet tired (Hendriks et al, 2021). Further, irregular sleeping patterns, known to influence sleeping measurement (Kholghi et al, 2022;Sadeh, 2011), are common among people with intellectual disabilities (Böhmer et al, 2020;Evenhuis, 2014;Smits, 2021), which also compromise the validity of sleep-wake variables assessment.…”

“…EMFIT QS is a contactless sleep assessment device that has the potential to provide access to objective sleep-wake monitoring in people with intellectual disabilities for whom wrist actigraphy is unsuitable. However, an entirely sufficient measure to determine sleepwake patterns does not exist (Hendriks et al, 2021;Ibáñez et al, 2018), and EMFIT QS is no different. To date, there is no convincing evidence concerning the validity and reliability of the information displayed by EMFIT QS' dashboard regarding sleep-wake patterns (Kholghi et al, 2022).…”

“…Recent studies have been conducted in which EMFIT QS was compared with different methods to study sleep (i.e., sleep questionnaires, PSG, and actigraphy). Hendriks et al (2021) investigated to what extent EMFIT QS is a feasible instrument for continuous sleep monitoring of people with spinal cord injuries or stroke in a clinical rehabilitation center. They compared the bed sensor with sleep questionnaires and used the percentage of missing nights as the primary outcome.…”