Bed Sensor Research Articles

Implementing AI-Driven Bed Sensors: Perspectives from Interdisciplinary Teams in Geriatric Care

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.

In-Bed Monitoring: A Systematic Review of the Evaluation of In-Bed Movements Through Bed Sensors

The growing popularity of smart beds and devices for remote healthcare monitoring is based on advances in artificial intelligence (AI) applications. This systematic review aims to evaluate and synthesize the growing literature on the use of machine learning (ML) techniques to characterize patient in-bed movements and bedsore development. This review is conducted according to the principles of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and is registered in the International Prospective Register of Systematic Reviews (PROSPERO CRD42022314329). The search was performed through nine scientific databases. The review included 78 articles, including 142 ML models. The applied ML models revealed significant heterogeneity in the various methodologies used to identify and classify patient behaviors and postures. The assortment of ML models encompassed artificial neural networks, deep learning architectures, and multimodal sensor integration approaches. This review shows that the models for analyzing and interpreting in-bed movements perform well in experimental settings. Large-scale real-life studies are lacking in diverse patient populations.

A Review of Patient Bed Sensors for Monitoring of Vital Signs.

The analysis of biomedical signals is a very challenging task. This review paper is focused on the presentation of various methods where biomedical data, in particular vital signs, could be monitored using sensors mounted to beds. The presented methods to monitor vital signs include those combined with optical fibers, camera systems, pressure sensors, or other sensors, which may provide more efficient patient bed monitoring results. This work also covers the aspects of interference occurrence in the above-mentioned signals and sleep quality monitoring, which play a very important role in the analysis of biomedical signals and the choice of appropriate signal-processing methods. The provided information will help various researchers to understand the importance of vital sign monitoring and will be a thorough and up-to-date summary of these methods. It will also be a foundation for further enhancement of these methods.

Continuous Monitoring of Heart Rate Variability and Respiration for the Remote Diagnosis of Chronic Obstructive Pulmonary Disease: Prospective Observational Study.

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.

Cognitive behavioural therapy for insomnia (CBT-I) in schizophrenia and schizoaffective disorder: protocol for a randomised controlled trial

IntroductionInsomnia is a common symptom among patients with schizophrenia and schizoaffective disorder, negatively impacting symptom severity, functioning and well-being; however, it is rarely the direct focus of treatment. The main...

Medical Devices for the Management of Patients with Epilepsy

The increasing number of medical devices developed and marketed towards management of patients with epilepsy reflects the growing interest in translating technological advances and knowledge about epilepsy into better healthcare for this population. The objective of this narrative literature review is to analyze the available options of medical devices for detecting, treating, and recording epileptic seizures, and their potential clinical application. The included articles were selected from the PubMed database using the query “(Epilepsy[MeSH Terms]) AND (SUDEP)) AND (Medical Device)) AND (English[Language])”. The detection of epileptic seizures is essential for early intervention and to optimize the therapy for each patient. In outpatient settings, this detection is further challenging due to their unpredictability. Traditionally electroencephalography is the direct detection method used in a hospital environment. Indirect methods, such as electrocardiogram, photoplethysmography, oximeter, electrodermal activity, accelerometer, and electromyography, have shown potential for detecting seizures in the outpatient setting. Several medical devices have been developed based on the mentioned methods, with the aim of providing patients with solutions they can use in their daily lives. Behind-the-ear EEG, wristbands, armbands and bed sensors are some of the designs available. Equipped with different features, these devices can answer the need for early seizure detection and improve patients’ and caregivers’ quality of life. There are also devices available for the treatment of epileptic seizures. Through neuromodulation techniques such as vagus nerve stimulation, deep brain stimulation, and responsive neurostimulation, these devices are presented as solutions for patients with refractory epilepsy not eligible for ressective surgery. Patients with epilepsy have several apps available online for proper recording of seizures. These apps help doctors optimize therapy based on clinical evolution. The wide range of devices available creates the opportunity to personalize the approach to patient’s specific needs. Understanding each device’s characteristics can help clinicians improve management of patients with epilepsy.

Developing a novel mobile application for cognitive behavioral therapy for insomnia for people with schizophrenia: integration of wearable and environmental sleep sensors

BackgroundPeople with serious mental illnesses (SMIs) have three-fold higher rates of comorbid insomnia than the general population, which has downstream effects on cognitive, mental, and physical health. Cognitive Behavioral Therapy for Insomnia (CBT-i) is a safe and effective first-line treatment for insomnia, though the therapy’s effectiveness relies on completing nightly sleep diaries which can be challenging for some people with SMI and comorbid cognitive deficits. Supportive technologies such as mobile applications and sleep sensors may aid with completing sleep diaries. However, commercially available CBT-i apps are not designed for individuals with cognitive deficits. To aid with this challenge, we have developed an integrated mobile application, named “Sleep Catcher,” that will automatically incorporate data from a wearable fitness tracker and a bed sensor to track nightly sleep duration, overnight awakenings, bed-times, and wake-times to generate nightly sleep diaries for CBT-i.MethodsThe application development process will be described—writing algorithms to generating useful data, creating a clinician web portal to oversee patients and the mobile application, and integrating sleep data from device platforms and user input.ResultsThe mobile and web applications were developed using Flutter, IBM Code Engine, and IBM Cloudant database. The mobile application was developed with a user-centered approach and incremental changes informed by a series of beta tests. Special user-interface features were considered to address the challenges of developing a simple and effective mobile application targeting people with SMI.ConclusionThere is strong potential for synergy between engineering and mental health expertise to develop technologies for specific clinical populations. Digital health technologies allow for the development of multi-disciplinary solutions to existing health disparities in vulnerable populations, particularly in people with SMI.

Nonlinear Heart Rate Variability Analysis for Sleep Stage Classification Using Integration of Ballistocardiogram and Apple Watch.

Wearable or non-contact, non-intrusive devices present a practical alternative to traditional polysomnography (PSG) for daily assessment of sleep quality. Physiological signals have been known to be nonlinear and nonstationary as the body adapts to states of rest or activity. By integrating more sophisticated nonlinear methodologies, the accuracy of sleep stage identification using such devices can be improved. This advancement enables individuals to monitor and adjust their sleep patterns more effectively without visiting sleep clinics. Six participants slept for three cycles of at least three hours each, wearing PSG as a reference, along with an Apple Watch, an actigraphy device, and a ballistocardiography (BCG) bed sensor. The physiological signals were processed with nonlinear methods and trained with a long short-term memory (LSTM) model to classify sleep stages. Nonlinear methods, such as return maps with advanced techniques to analyze the shape and asymmetry in physiological signals, were used to relate these signals to the autonomic nervous system (ANS). The changing dynamics of cardiac signals in restful or active states, regulated by the ANS, were associated with sleep stages and quality, which were measurable. Approximately 73% agreement was obtained by comparing the combination of the BCG and Apple Watch signals against a PSG reference system to classify rapid eye movement (REM) and non-REM sleep stages. Utilizing nonlinear methods to evaluate cardiac dynamics showed an improved sleep quality detection with the non-intrusive devices in this study. A system of non-intrusive devices can provide a comprehensive outlook on health by regularly measuring sleeping patterns and quality over time, offering a relatively accessible method for participants. Additionally, a non-intrusive system can be integrated into a user's or clinic's bedroom environment to measure and evaluate sleep quality without negatively impacting sleep. Devices placed around the bedroom could measure user vitals over longer periods with minimal interaction from the user, representing their natural sleeping trends for more accurate health and sleep disorder diagnosis.

A semi-supervised approach to unobtrusively predict abnormality in breathing patterns using hydraulic bed sensor data in older adults aging in place

Shortness of breath is often considered a repercussion of aging in older adults, as respiratory illnesses like COPD11C2SHIP/CERT Website: https://www.eldertech.missouri.edu/. or respiratory illnesses due to heart-related issues are often misdiagnosed, under-diagnosed or ignored at early stages. Continuous health monitoring using ambient sensors has the potential to ameliorate this problem for older adults at aging-in-place facilities. In this paper, we leverage continuous respiratory health data collected by using ambient hydraulic bed sensors installed in the apartments of older adults in aging-in-place Americare facilities to find data-adaptive indicators related to shortness of breath. We used unlabeled data collected unobtrusively over the span of three years from a COPD-diagnosed individual and used data mining to label the data. These labeled data are then used to train a predictive model to make future predictions in older adults related to shortness of breath abnormality. To pick the continuous changes in respiratory health we make predictions for shorter time windows (60-s). Hence, to summarize each day’s predictions we propose an abnormal breathing index (ABI) in this paper. To showcase the trajectory of the shortness of breath abnormality over time (in terms of days), we also propose trend analysis on the ABI quarterly and incrementally. We have evaluated six individual cases retrospectively to highlight the potential and use cases of our approach.

Blood pressure monitoring with piezoelectric bed sensor systems

ObjectiveBallistocardiography (BCG) measures vital signals without direct contact, which shows great potential for continuous sleep monitoring. This study proposed a BCG-based blood pressure (BP) estimation algorithm framework using piezoelectric bed sensor systems. Methods: To derive BP, a combination of morphological features, spectral features, and fractal dimensions of the BCG signal were utilized. Bayesian neural networks were employed to weigh the contribution of each feature and generate input-dependent coefficients. Two data balancing procedures were tested, and the proposed system's effectiveness was evaluated, including in-hospital patients and healthy subjects. Transfer learning techniques were employed to further improve the system's performance and showcase the similarity between bed systems with different piezoelectric sensors. Results: The combination of morphological and spectral features significantly improves BP estimation accuracy. The fractal dimension captures short-term BP fluctuations, improving intra-subject BP trend estimation. For young and healthy subjects, calibration-free mean absolute error (MAE) for systolic BP (SBP) and diastolic BP (DBP) is 4.20/4.25 mmHg at a 5-second time resolution. In the case of in-hospital patients, the best MAE for overnight SBP/DBP is 9.96/7.59 mmHg. Transfer learning, combined with data balancing techniques, substantially enhances BP estimation accuracy for in-hospital patients, providing insights for future algorithm designs. Conclusion: The study concludes that bed-sensor systems can track BP changes in relatively healthy subjects. However, BCG alone may not be sufficient for subjects with severe cardiovascular dysfunctions to obtain reliable BP readings. Transfer learning and proper data balancing may facilitate the fast development of new bed sensor systems with similar technologies.

Differential responses to artificial photoperiods of the rising and falling phases of human melatonin rhythm are consistent with a dual oscillator hypothesis.

Circadian rhythms and sleep-wake cycles were measured in volunteers staying singly in temporal isolation unit where they were exposed to artificial short and long light-dark (LD) cycles for 7 days. The long day consisted of 16-h light and 8-h dark (LD 16:8) and the short day consisted of 8-h light and 16-h dark (LD 8:16). During the light period, bright light of approximately 5,000 lux was given from the ceiling and during the dark period there was no illumination. Sleep was monitored by bed sensors, wrist actiwatch, and polysomnography (PSG) on the first and last nights of the schedule. Sleep length was significantly longer under LD 8:16 than under LD 16:8 and the sleep quality estimated by PSG was worse under LD 8:16 than under LD 16:8, which were comparable to natural seasonality in sleep. The circadian rhythm in plasma melatonin was measured in dim light (10 lux) before and after the LD exposures. The nocturnal melatonin secretion (NMS) was significantly longer after LD 8:16 than after LD 16:8 due to differential phase shifts of the rising and falling phases of NMS. After LD 8:16, the falling phase was much advanced than the rising phase, whereas after LD 16:8 the rising phase was much delayed than the falling phase, resulting in the NMS compression. These results indicate that the light sensitivity in terms of phase shifting is different in the two circadian phases, supporting a dual oscillator hypothesis with different phase-response curves for light in the human circadian system.NEW & NOTEWORTHY The present study demonstrated differential light responsiveness of the rising and falling phases of nocturnal melatonin secretion in human subjects exposed to artificial long (LD 16:8) and short days (LD 8:16) and suggested the involvement of different oscillators under these phases. The findings well mimicked the seasonality of the circadian rhythms in nature and consisted with the evening/morning dual oscillator hypothesis proposed originally for nocturnal rodents, providing a new concept for the human circadian system.

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

Gaining insight into sleep-wake patterns of persons with intellectual disabilities is commonly done using wrist actigraphy. For some people, contactless alternatives are needed. This study compares a contactless bed sensor with wrist actigraphy to monitor sleep-wake patterns of people with moderate to profound intellectual disabilities. Data were collected with EMFIT QS (activity and presence) and MotionWatch 8/Actiwatch 2 (activity, ambient light, and event marker/sleep diary) for 14 nights in 13 adults with moderate-profound intellectual disabilities residing in intramural care. In a care-as-usual setting, EMFIT QS and actigraphy assessment show little agreement on sleep-wake variables. Currently, EMFIT QS should not be considered an alternative to wrist actigraphy for sleep-wake monitoring. Further research is needed into assessing sleep-wake variables using (contactless) technological devices and how the data should be interpreted within the care context to achieve reliable and valid information on sleep-wake patterns of people with intellectual disabilities.

Consumer sleep technology for the screening of obstructive sleep apnea and snoring: current status and a protocol for a systematic review and meta-analysis of diagnostic test accuracy.

There are concerns about the validation and accuracy of currently available consumer sleep technology for sleep-disordered breathing. The present report provides a background review of existing consumer sleep technologies and discloses the methods and procedures for a systematic review and meta-analysis of diagnostic test accuracy of these devices and apps for the detection of obstructive sleep apnea and snoring in comparison with polysomnography. The search will be performed in four databases (PubMed, Scopus, Web of Science, and the Cochrane Library). Studies will be selected in two steps, first by an analysis of abstracts followed by full-text analysis, and two independent reviewers will perform both phases. Primary outcomes include apnea-hypopnea index, respiratory disturbance index, respiratory event index, oxygen desaturation index, and snoring duration for both index and reference tests, as well as the number of true positives, false positives, true negatives, and false negatives for each threshold, as well as for epoch-by-epoch and event-by-event results, which will be considered for the calculation of surrogate measures (including sensitivity, specificity, and accuracy). Diagnostic test accuracy meta-analyses will be performed using the Chu and Cole bivariate binomial model. Mean difference meta-analysis will be performed for continuous outcomes using the DerSimonian and Laird random-effects model. Analyses will be performed independently for each outcome. Subgroup and sensitivity analyses will evaluate the effects of the types (wearables, nearables, bed sensors, smartphone applications), technologies (e.g., oximeter, microphone, arterial tonometry, accelerometer), the role of manufacturers, and the representativeness of the samples.

Smart Home Tech: an interface to provide feedback to caregivers of persons living with cognitive impairment.

Home-based sensor technologies can detect information on daily activities, such as sleep, activity level, and time spent together. This is relevant information for care partners of individuals with cognitive impairment as it can detect early changes in daily activities and cognition. The challenge is how to present thousands of data points to care partners in real-time, allowing them to make adjustments in their daily routine to help reduce burden. The Collaborative Aging Research using Technology (CART) sensor platform was installed in 4 Ottawa homes of persons with cognitive impairment and their care partner. This system includes contact and motion sensors to assess home activity, a smart watch to record steps and sleep patterns, a bed sensor, and medication tracking pillboxes. Questions regarding areas of stress, the types of information that caregivers would like to receive, and how to receive their data were posed in focus group discussions involving caregivers with and without the sensor platform. An interface was designed based on participant input. Eight care partners (75% female) aged 30 to 81 participated in focus groups that obtained their opinions on the usefulness of a feedback system, its content, layout, and frequency in which care partners would like to receive their sensor information. The majority of participants wished to be sent their sensor information on a weekly basis in easy to interpret graphical formats that summarized information for them. An algorithm that collects sensor data, parses appropriate measures; steps, sleep, time together in a room, and displays it in graphical representation, was developed. The user interface displays the daily summary compared to the weekly average, with the ability to navigate between day and weekly views. Sensor-based data needs to be displayed in an efficient and convenient manner if it is going to be used by care partners to help guide changes in behavior to reduce stress. This project designed an interface, based on user feedback. This system also has the potential to be used by clinicians to evaluate longitudinal cognitive decline. Next, we will provide participants with the CART system with sample visual reports and gather their feedback.

Smart Home Tech: an interface to provide feedback to caregivers of persons living with cognitive impairment

AbstractBackgroundHome‐based sensor technologies can detect information on daily activities, such as sleep, activity level, and time spent together. This is relevant information for care partners of individuals with cognitive impairment as it can detect early changes in daily activities and cognition. The challenge is how to present thousands of data points to care partners in real‐time, allowing them to make adjustments in their daily routine to help reduce burden.MethodThe Collaborative Aging Research using Technology (CART) sensor platform was installed in 4 Ottawa homes of persons with cognitive impairment and their care partner. This system includes contact and motion sensors to assess home activity, a smart watch to record steps and sleep patterns, a bed sensor, and medication tracking pillboxes. Questions regarding areas of stress, the types of information that caregivers would like to receive, and how to receive their data were posed in focus group discussions involving caregivers with and without the sensor platform. An interface was designed based on participant input.ResultEight care partners (75% female) aged 30 to 81 participated in focus groups that obtained their opinions on the usefulness of a feedback system, its content, layout, and frequency in which care partners would like to receive their sensor information. The majority of participants wished to be sent their sensor information on a weekly basis in easy to interpret graphical formats that summarized information for them. An algorithm that collects sensor data, parses appropriate measures; steps, sleep, time together in a room, and displays it in graphical representation, was developed. The user interface displays the daily summary compared to the weekly average, with the ability to navigate between day and weekly views.ConclusionSensor‐based data needs to be displayed in an efficient and convenient manner if it is going to be used by care partners to help guide changes in behavior to reduce stress. This project designed an interface, based on user feedback. This system also has the potential to be used by clinicians to evaluate longitudinal cognitive decline. Next, we will provide participants with the CART system with sample visual reports and gather their feedback.

Abstract 15880: Detection of an Acute Pulmonary Embolism in the Home Bed Using Adherence-Independent Home Monitoring

Acute pulmonary embolism classically presents with dyspnea, tachypnea, and hypoxia, but self-recognition of symptoms and clinical findings is often challenging. This is particularly true in elder patients or those with cognitive impairments. Digital health technologies offer opportunities to remotely detect pre-symptomatic illness. We present a prodrome of an acute pulmonary embolism as it emerges in the home during continuous respiratory monitoring using a non-contact adherence-independent home bed sensor. The patient is an 85-year-old woman who is morbidly obese, has limited mobility, and multiple comorbidities including a recent diagnosis of atrial fibrillation for which she was prescribed anticoagulation but has not yet initiated. As part of an observational study, nocturnal respiratory rates (NRR) were longitudinally monitored in her home bed using a non-contact, adherence-independent bed sensor. Eight days prior to admission, increasing NRR prompted a patient call to family who noted no acute symptomatic changes. During the call it was discovered she had not been started on apixaban. On subsequent days, persistently elevated NRR prompted a second phone call 4 days prior to admission (PTA), where again no acute symptoms were noted. Anticoagulation still had not been started at that time. NRR worsened further on days following the second call prompting a home visit by an RN who found the patient dyspneic, tachypneic in the high 20s, and hypoxic with oxygen saturations in the 80s, prompting a transfer to the emergency department for further evaluation. She was eventually diagnosed with an acute pulmonary embolism. After a week-long hospitalization, the patient was discharged on adequate anticoagulation therapy.This case suggests that adherence-independent home bed monitoring of nocturnal respiratory rate may enable early detection of chronic volume overload and acute pulmonary embolism, potentially facilitating early intervention.

PREFACE: A SPECIAL SELECTION ON EMERGING TECHNIQUES FOR BIOMECHANICS–PART II

PREFACE: A SPECIAL SELECTION ON EMERGING TECHNIQUES FOR BIOMECHANICS–PART II

SLEEP MONITORING FOR INDIVIDUALS WITH SPINAL CORD INJURY USING CONTACT-FREE BED SENSORS

Sleep disorders are common in individuals with spinal cord injury (SCI), and contact-free bed sensors can detect these problems. This study examined whether these sensors apply to these individuals, especially at the cervical level, with a high degree of paralysis and limited mobility. First, we investigated whether their motion characteristics affect heart rate (HR), sleep latency, and sleep stage. We compared three commercial contact-free sensors: Emfit QS (Emfit Ltd., Finland), EarlySense Live (EarlySense Ltd., Israel), and SCA11H (Murata Manufacturing Ltd., Japan) with polysomnography (PSG)—a standard for sleep assessment. Then, we monitored the sleep on hospital and home beds and observed the percentages of timestamps and average HR and respiration rate (RR) recorded on the three devices. Compared with PSG, Emfit QS and EarlySense Live recorded an earlier sleep onset. Furthermore, the mean HR recorded by Emfit QS, EarlySense Live, and SCA11H differed by 0 to 8 beats per minute (BMP). In hospital beds, the collection rate for all three sensors was higher than 98% and HR, and RR differed by only 1.1[Formula: see text]BPM and 0.5[Formula: see text]BPM, respectively. Contrastingly, the rates differed by 13.7[Formula: see text]BPM and 9.9[Formula: see text]BMP at home, respectively. While these sensors differed from PSG and their data coverage rate was low, they can help clarify sleep disorders. Continuous sleep monitoring is essential for individuals with SCI, and sleep-monitoring devices are expected to contribute to this and help improve their quality of life.

Device-based and subjective measurements of sleep in children with cerebral palsy: a comparison of sleep diary, actigraphy, and bed sensor data.

To investigate how subjective assessments and device-based measurements of sleep relate to each other in children with cerebral palsy (CP). Sleep of children with CP, classified at Gross Motor Function Classification System levels I-III, was measured during 7 consecutive nights using 1 subjective (ie, sleep diary) and 2 device-based (ie, actigraphy and bed sensor) instruments. The agreement between the instruments was assessed for all nights and separately for school- and weekend nights, using intraclass correlation coefficients (ICC) and Bland-Altman plots. A total of 227 nights from 38 children with CP (53% male; median age [range] 6 [2-12] years), were included in the analyses. Sleep parameters showed poor agreement between the 3 instruments, except for total time in bed, which showed satisfactory agreement between (1) actigraphy and sleep diary (ICC > 0.86), (2) actigraphy and bed sensor (ICC > 0.84), and (3) sleep diary and bed sensor (ICC > 0.83). Furthermore, agreement between sleep diary and bed sensor was also satisfactory for total sleep time (ICC > 0.70) and wakefulness after sleep onset (ICC = 0.55; only during weekend nights). Researchers and clinicians need to be aware of the discrepancies between instruments for sleep monitoring in children with CP. We recommend combining both subjective and device-based measures to provide information on the perception as well as an unbiased estimate of sleep. Further research needs to be conducted on the use of a bed sensor for sleep monitoring in children with CP. van Rijssen IM, Hulst RY, Gorter JW, etal. Device-based and subjective measurements of sleep in children with cerebral palsy: a comparison of sleep diary, actigraphy, and bed sensor data. J Clin Sleep Med. 2023;19(1):35-43.

Multi-Scale Evaluation of Sleep Quality Based on Motion Signal from Unobtrusive Device.

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 () to Sleep Efficiency (SE) and DS/DI positively correlated () 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.