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Sleep is a crucial aspect of geriatric assessment for hospitalized older adults, and implementing AI-driven technology for sleep monitoring can significantly enhance the rehabilitation process. Sleepsense, an AI-driven sleep-tracking device, provides real-time data and insights, enabling healthcare professionals to tailor interventions and improve sleep quality. This study explores the perspectives of an interdisciplinary hospital team on implementing Sleepsense in geriatric hospital care. Using the interpretive description approach, we conducted focus groups with physicians, nurses, care aides, and an activity worker. The Consolidated Framework for Implementation Research (CFIR) informed our thematic analysis to identify barriers and facilitators to implementation. Among 27 healthcare staff, predominantly female (88.89%) and Asian (74.1%) and mostly aged 30–50 years, themes emerged that Sleepsense is perceived as a timesaving and data-driven tool that enhances patient monitoring and assessment. However, barriers such as resistance to change and concerns about trusting the device for patient comfort and safety were noted, while facilitators included training and staff engagement. The CFIR framework proved useful for analyzing implementation barriers and facilitators, suggesting future research should prioritize effective strategies for interdisciplinary team support to enhance innovation adoption and patient outcomes in rehabilitation settings.

Abstract Study Objectives To compare the 24-hour sleep assessment capabilities of two contactless sleep technologies (CSTs) to actigraphy in community-dwelling older adults. Methods We collected 7–14 days of data at home from 35 older adults (age: 65–83), some with medical conditions, using Withings Sleep Analyser (WSA, n = 29), Emfit QS (Emfit, n = 17), a standard actigraphy device (Actiwatch Spectrum [AWS, n = 34]), and a sleep diary (n = 35). We compared nocturnal and daytime sleep measures estimated by the CSTs and actigraphy without sleep diary information (AWS-A) against sleep-diary-assisted actigraphy (AWS|SD). Results Compared to sleep diary, both CSTs accurately determined the timing of nocturnal sleep (intraclass correlation [ICC]: going to bed, getting out of bed, time in bed >0.75), whereas the accuracy of AWS-A was much lower. Compared to AWS|SD, the CSTs overestimated nocturnal total sleep time (WSA: +92.71 ± 81.16 minutes; Emfit: +101.47 ± 75.95 minutes) as did AWS-A (+46.95 ± 67.26 minutes). The CSTs overestimated sleep efficiency (WSA: +9.19% ± 14.26%; Emfit: +9.41% ± 11.05%), whereas AWS-A estimate (−2.38% ± 10.06%) was accurate. About 65% (n = 23) of participants reported daytime naps either in bed or elsewhere. About 90% in-bed nap periods were accurately determined by WSA while Emfit was less accurate. All three devices estimated 24-hour sleep duration with an error of ≈10% compared to the sleep diary. Conclusions CSTs accurately capture the timing of in-bed nocturnal sleep periods without the need for sleep diary information. However, improvements are needed in assessing parameters such as total sleep time, sleep efficiency, and naps before these CSTs can be fully utilized in field settings. Graphical Abstract Graphical Abstract

Sleep disorders are a growing threat nowadays as they are linked to neurological, cardiovascular and metabolic diseases. The gold standard methodology for sleep study is polysomnography (PSG), an intrusive and onerous technique that can disrupt normal routines. In this perspective, m-Health technologies offer an unobtrusive and rapid solution for home monitoring. We developed a multi-scale method based on motion signal extracted from an unobtrusive device to evaluate sleep behavior. Data used in this study were collected during two different acquisition campaigns by using a Pressure Bed Sensor (PBS). The first one was carried out with 22 subjects for sleep problems, and the second one comprises 11 healthy shift workers. All underwent full PSG and PBS recordings. The algorithm consists of extracting sleep quality and fragmentation indexes correlating to clinical metrics. In particular, the method classifies sleep windows of 1-s of the motion signal into: displacement (DI), quiet sleep (QS), disrupted sleep (DS) and absence from the bed (ABS). QS proved to be positively correlated (0.72±0.014) to Sleep Efficiency (SE) and DS/DI positively correlated (0.85±0.007) to the Apnea-Hypopnea Index (AHI). The work proved to be potentially helpful in the early investigation of sleep in the home environment. The minimized intrusiveness of the device together with a low complexity and good performance might provide valuable indications for the home monitoring of sleep disorders and for subjects’ awareness.

Conventional daytime monitoring in a single day may be influenced by factors such as motion artifacts and emotions, and continuous monitoring of nighttime heart rate variability (HRV) and respiration to assist in chronic obstructive pulmonary disease (COPD) diagnosis has not been reported yet. The aim of this study was to explore and compare the effects of continuously monitored HRV, heart rate (HR), and respiration during night sleep on the remote diagnosis of COPD. We recruited patients with different severities of COPD and healthy controls between January 2021 and November 2022. Vital signs such as HRV, HR, and respiration were recorded using noncontact bed sensors from 10 PM to 8 AM of the following day, and the recordings of each patient lasted for at least 30 days. We obtained statistical means of HRV, HR, and respiration over time periods of 7, 14, and 30 days by continuous monitoring. Additionally, the effects that the statistical means of HRV, HR, and respiration had on COPD diagnosis were evaluated at different times of recordings. In this study, 146 individuals were enrolled: 37 patients with COPD in the case group and 109 participants in the control group. The median number of continuous night-sleep monitoring days per person was 56.5 (IQR 32.0-113.0) days. Using the features regarding the statistical means of HRV, HR, and respiration over 1, 7, 14, and 30 days, binary logistic regression classification of COPD yielded an accuracy, Youden index, and area under the receiver operating characteristic curve of 0.958, 0.904, and 0.989, respectively. The classification performance for COPD diagnosis was directionally proportional to the monitoring duration of vital signs at night. The importance of the features for diagnosis was determined by the statistical means of respiration, HRV, and HR, which followed the order of respiration > HRV > HR. Specifically, the statistical means of the duration of respiration rate faster than 21 times/min (RRF), high frequency band power of 0.15-0.40 Hz (HF), and respiration rate (RR) were identified as the top 3 most significant features for classification, corresponding to cutoff values of 0.1 minute, 1316.3 nU, and 16.3 times/min, respectively. Continuous monitoring of nocturnal vital signs has significant potential for the remote diagnosis of COPD. As the duration of night-sleep monitoring increased from 1 to 30 days, the statistical means of HRV, HR, and respiration showed a better reflection of an individual's health condition compared to monitoring the vital signs in a single day or night, and better was the classification performance for COPD diagnosis. Further, the statistical means of RRF, HF, and RR are crucial features for diagnosing COPD, demonstrating the importance of monitoring HRV and respiration during night sleep.

Purpose Nurses routinely assess respiration of hospitalized children; however, respiratory rate measurements are technically difficult due to rapid and small chest wall movements. The aim of this study is to reveal the respiratory status of small children undergoing minor surgery with load cells placed under the bed legs, and to test the hypothesis that respiratory rate (primary variable) is slower immediately after arrival to the ward and recovers in 2 h. Methods Continuous recordings of the load cell signals were performed and stable respiratory waves within the 10 discriminative perioperative timepoints were used for respiratory rate measurements. Apnea frequencies were calculated at pre and postoperative nights and 2 h immediately after returning to the ward after surgery. Results Continuous recordings of the load cell signals were successfully performed in 18 children (13 to 119 months). Respiratory waves were appraisable for more than 70% of nighttime period and 40% of immediate postoperative period. There were no statistically significant differences of respiratory rate in any timepoint comparisons ( p  = 0.448), thereby not supporting the study hypothesis. Respiratory rates changed more than 5 breaths per minute postoperatively in 5 out of 18 children (28%) while doses of fentanyl alone did not explain the changes. Apnea frequencies significantly decreased 2 h immediately after returning to the ward and during the operative night compared to the preoperative night. Conclusion Respiratory signal extracted from load cell sensors under the bed legs successfully revealed various postoperative respiratory pattern change in small children undergoing minor surgery. Clinical Trail Registration: UMIN (University Hospital Information Network) Clinical Registry: UMIN000045579 ( https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000052039 ).

Sleep is essential for overall health and plays a critical role in the diagnosis of psychiatric disorders. Although polysomnography remains the gold standard for measuring sleep, its reliance on laboratory settings limits its feasibility for long-term, naturalistic monitoring, particularly for patients with mental disorders. This study assesses sleep-tracking reliability and alignment in healthy individuals and patients with mood disorders using wearables, nearables, and ecological momentary assessment, while examining measurement biases and the impact of seasonal and demographic factors on discrepancies across methods. We conducted a 14-day study in Finland and enrolled a total of 201 participants, comprising patients with a major depressive episode and healthy controls. Of these, 169 participants with sufficient observations were retained for further analyses. Participants' sleep patterns (onset, offset, and total sleep time [TST]) were gathered daily from an actigraph (Actiwatch 2), a bed sensor (Murata SCA11H), mobile screen events, and a daily survey. The alignment between sleep measurement methods was evaluated using Bland-Altman plots and Pearson correlation. Linear mixed models were used to assess the effects of demographics, season, and disorder type on the sleep measures alignment. Patients exhibited greater variability in sleep measures than healthy controls. For sleep onset, mean biases between devices were small and not statistically significant in either group, with moderate to strong correlations. In contrast, sleep offset showed significantly larger biases in patients: actigraph versus bed (+34.9 minutes; P=.01), smartphone versus bed (-45.3 minutes; P=.004), and actigraph versus smartphone (+78.7 minutes; P<.001), while controls exhibited minimal and nonsignificant differences. For TST, smartphone underestimates sleep compared to both bed sensors (-0.71 minutes; P<.001) and actigraphs (-1.35 minutes; P<.001). Across devices, TST correlations remained low, spanning r=0.12 (P=.58) to r=0.55 (P=.10) in controls and r=0.17 (P=.19) to r=0.43 (P=.002) in patients. Mixed models showed that older age was linked to better sleep offset alignment between actigraphy and bed sensors (β=-0.02, 95% CI -0.04 to 0.00; P=.048), as well as smartphone and bed sensor (β=-0.03, 95% CI -0.06 to 0.00; P=.03). Patients with bipolar/borderline personality disorder showed lower TST alignment, and alignment between smartphone and bed sensor was worse in females (β=-1.03, 95% CI -1.74 to -0.33, P=.004). Longer daylight duration was also associated with improved alignment in sleep offset and TST. This study demonstrates the feasibility of using actigraphy, smartphone data, and bed sensors for sleep tracking in naturalistic settings with patients. It highlights measurement biases across devices, the impact of seasonal variations on sleep research in unique geographical regions like Finland, and key demographic factors influencing sleep measurement discrepancies.

Background Pressure ulcer prophylaxis is a central topic in clinical care. Pressure-relieving repositioning is strongly recommended for all pressure-sensitive patients. The Mobility Monitor (MoMo) is a technical device that records a patient’s movements and transmits the data to a monitor. This study investigated the extent to which the MoMo sensor system, which records and visualises patients’ movements in bed, supports nurses in performing pressure-relieving repositioning in neurological and neurosurgical intensive care units (ICU). Methods This stepped-wedge cluster-randomised trial involved two clusters: one neurological and one neurosurgical ICU. The study was carried out in two steps over three periods between November 2018 and May 2019, with a two-month interval between each step. At the beginning of the study, we equipped 33 beds across the two ICUs with a MoMo system. Our primary endpoint was the immobility rate, which is defined as the patient’s inactive time in bed exceeding two hours without pressure-relieving movements divided by the time the MoMo was in the bed. The immobility rate ranges from 0 to below 1, with higher values indicating lower mobility. Secondary endpoints were the rate of new pressure ulcers and the rate of relevant pressure-relieving repositionings. Relevant repositionings are defined as the number of repositionings identified by the MoMo as a pressure-relieving repositioning divided by the total number of repositionings, Results 808 patients were included in the study, of whom 403 were in the control group and 405 were in the intervention group. The mean immobility rate was 0.171 during the control phase and 0.144 during the intervention phase. The estimated intervention effect was -0.0018 (95% confidence interval [-0.0471, 0.0436], p =0.94). The number of new pressure ulcers was 5/405 in the intervention phase and 15/403 in the control phase. We noted a small difference in the mean rate of relevant repositioningswith an estimated intervention effect of 0.046 (95% confidence interval [-0.018, 0.110], p =0.16). Conclusion Our results are insufficient to recommend the standardised use of mobility monitors in neurological or neurosurgical ICUs. Clinical trial registration The primary analysis was prespecified and the trial was registered in the German Clinical Trials Register (DRKS) under the reference number DRKS00015492 (31/10/2018).

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. The aim of this study was to investigate the feasibility of a bed sensor for continuous sleep monitoring overnight in a clinical rehabilitation center. 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. 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 (t =-2.1, P=.04) and movement activity (t =-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. 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.

To develop a non-invasive and clinically practical method for a long-term monitoring of infant sleep cycling in the intensive care unit. Forty three infant polysomnography recordings were performed at 1-18 weeks of age, including a piezo element bed mattress sensor to record respiratory and gross-body movements. The hypnogram scored from polysomnography signals was used as the ground truth in training sleep classifiers based on 20,022 epochs of movement and/or electrocardiography signals. Three classifier designs were evaluated in the detection of deep sleep (N3 state): support vector machine (SVM), Long Short-Term Memory neural network, and convolutional neural network (CNN). Deep sleep was accurately identified from other states with all classifier variants. The SVM classifier based on a combination of movement and electrocardiography features had the highest performance (AUC 97.6%). A SVM classifier based on only movement features had comparable accuracy (AUC 95.0%). The feature-independent CNN resulted in roughly comparable accuracy (AUC 93.3%). Automated non-invasive tracking of sleep state cycling is technically feasible using measurements from a piezo element situated under a bed mattress. An open source infant deep sleep detector of this kind allows quantitative, continuous bedside assessment of infant's sleep cycling.

This article reports the qualitative element of an observational study that examined whether an extended alarm service using fall detectors and bed occupancy sensors could reduce fear of falling among community-dwelling older people who had recurrent falls. The 17 participants in the intervention group used the extended alarm service while the 18 in the control group used a standard pendant alarm. Individual interviews were tape-recorded and transcribed. The participants' fall history and whether they were afraid of falling were also explored. Interview questions were grounded in theories relating to falls and queried participants in the intervention group about their expectations of and experiences with the use of telemonitoring devices; those in the control group were asked whether they would consider using such devices in the future. Key themes from the analysis were expectations, feelings of security, call center support, barriers to using assistive devices, and adherence and likelihood of using telemonitoring devices. Older people found that the use of telemonitoring gave them \"a greater sense of security\" and enabled them to remain in their home. However, some found the devices \"intrusive\" and did not feel they were in control of alerting the call center, which played a key role in their adherence to using the devices.