Clinical Applications of Mobile Health Wearable-Based Sleep Monitoring: Systematic Review

Sex Differences in the Associations Between Chronic Diseases and Insomnia Symptoms Among Older Adults in India

Background

Sleep problems are a critical issue in the aging population, affecting quality of life, cognitive efficiency, and contributing to adverse health outcomes. The coexistence of multiple diseases is common among older adults, particularly women. This study examines the associations between specific chronic diseases, multimorbidity, and insomnia symptoms among older Indian men and women, with a focus on the interaction of sex in these associations.

Methods

Data were drawn from 31,464 individuals aged 60 and older in the Longitudinal Ageing Study in India, Wave-1 (2017-18). Insomnia symptoms were assessed using four questions adapted from the Jenkins Sleep Scale (JSS-4), covering difficulty falling asleep, waking up, waking too early, and feeling unrested during the day. Multivariable logistic regression models, stratified by sex, were used to analyze the associations between chronic diseases and insomnia symptoms.

Results

Older women had a higher prevalence of insomnia symptoms than men (44.73% vs 37.15%). Hypertension was associated with higher odds of insomnia in both men (AOR: 1.20) and women (AOR: 1.36). Women with diabetes had lower odds of insomnia (AOR: 0.80), while this association was not significant in men. Neurological or psychiatric disorders, stroke, and bone and joint diseases were linked to higher odds of insomnia in both sexes. Chronic lung disease was associated with insomnia in men (AOR: 1.65), but not in women. Additionally, having three or more chronic diseases significantly increased the odds of insomnia in both men (AOR: 2.43) and women (AOR: 2.01).

Conclusion

Hypertension, bone and joint diseases, lung diseases, stroke, neurological or psychiatric disorders, and multimorbidity are linked to insomnia symptoms in older Indian adults. Disease-specific management and routine insomnia screening are crucial for promoting healthy aging in this vulnerable population.

The Swiss Primary Hypersomnolence and Narcolepsy Cohort Study: feasibility of long-term monitoring with Fitbit smartwatches in central disorders of hypersomnolence and extraction of digital biomarkers in narcolepsy

The Swiss Primary Hypersomnolence and Narcolepsy Cohort Study (SPHYNCS) is a multicenter research initiative to identify new biomarkers in central disorders of hypersomnolence (CDH). Whereas narcolepsy type 1 (NT1) is well characterized, other CDH disorders lack precise biomarkers. In SPHYNCS, we utilized Fitbit smartwatches to monitor physical activity, heart rate, and sleep parameters over 1 year. We examined the feasibility of long-term ambulatory monitoring using the wearable device. We then explored digital biomarkers differentiating patients with NT1 from healthy controls (HC). A total of 115 participants received a Fitbit smartwatch. Using a adherence metric to evaluate the usability of the wearable device, we found an overall adherence rate of 80% over 1 year. We calculated daily physical activity, heart rate, and sleep parameters from 2 weeks of greatest adherence to compare NT1 (n = 20) and HC (n = 9) participants. Compared to controls, NT1 patients demonstrated findings consistent with increased sleep fragmentation, including significantly greater wake-after-sleep onset (p = .007) and awakening index (p = .025), as well as standard deviation of time in bed (p = .044). Moreover, NT1 patients exhibited a significantly shorter REM latency (p = .019), and sleep latency (p = .001), as well as a lower peak heart rate (p = .008), heart rate standard deviation (p = .039) and high-intensity activity (p = .009) compared to HC. This ongoing study demonstrates the feasibility of long-term monitoring with wearable technology in patients with CDH and potentially identifies a digital biomarker profile for NT1. While further validation is needed in larger datasets, these data suggest that long-term wearable technology may play a future role in diagnosing and managing narcolepsy.

Improving sleep health in paramedics through an app-based intervention: a randomised waitlist control pilot trial

BACKGROUND

Due to work commitments, shiftworkers often obtain inadequate sleep, consequently experiencing negative health, wellbeing, and safety outcomes. Given shiftworkers may have limited control over their work commitments, lifestyle and environmental factors within their control may present an intervention opportunity. However, such interventions require tailoring to ensure applicability for this sleep-vulnerable population.

METHODS

A randomised waitlist control pilot trial investigated the effectiveness of mobile health application Sleepfit, which delivered a tailored sleep health intervention aimed at improving sleep health and sleep hygiene outcomes amongst paramedic shiftworkers. Outcome measures of self-reported sleep health (sleep need, duration, and quality, fatigue, Insomnia Severity Index, Fatigue Severity Scale, and Epworth Sleepiness Scale scores) and sleep hygiene (Sleep Hygiene Index score) were collected at baseline, post-intervention, and 3-month follow-up.

RESULTS

Fifty-eight paramedics (aged 33.4 ± 8.0 years; 50% male) were recruited, and trialed Sleepfit for a 14-day intervention period between August 2021-January 2022. For all participants, there was a significant reduction in Insomnia Severity Index and Sleep Hygiene index scores after intervention engagement. Regression models demonstrated no significant intervention effect on sleep health or sleep hygiene outcomes (intervention versus waitlist control group). A high study drop-out rate (91.4%) prevented assessment of outcomes at 3-month follow-up.

CONCLUSIONS

Pilot trial findings demonstrate that Sleepfit may elicit improvements in sleep health and sleep hygiene outcomes amongst paramedic shiftworkers. However, low enrolment and retention means that findings should be interpreted with caution, further highlighting potential engagement challenges, especially among paramedics who are particularly in need of support for improved sleep.

TRIAL REGISTRATION

Prospectively registered with the Australian New Zealand Clinical Trial Registry 24/01/2020 (reference no. ACTRN12620000059965).

Deciphering Optimal Radar Ensemble for Advancing Sleep Posture Prediction through Multiview Convolutional Neural Network (MVCNN) Approach Using Spatial Radio Echo Map (SREM)

Assessing sleep posture, a critical component in sleep tests, is crucial for understanding an individual's sleep quality and identifying potential sleep disorders. However, monitoring sleep posture has traditionally posed significant challenges due to factors such as low light conditions and obstructions like blankets. The use of radar technolsogy could be a potential solution. The objective of this study is to identify the optimal quantity and placement of radar sensors to achieve accurate sleep posture estimation. We invited 70 participants to assume nine different sleep postures under blankets of varying thicknesses. This was conducted in a setting equipped with a baseline of eight radars-three positioned at the headboard and five along the side. We proposed a novel technique for generating radar maps, Spatial Radio Echo Map (SREM), designed specifically for data fusion across multiple radars. Sleep posture estimation was conducted using a Multiview Convolutional Neural Network (MVCNN), which serves as the overarching framework for the comparative evaluation of various deep feature extractors, including ResNet-50, EfficientNet-50, DenseNet-121, PHResNet-50, Attention-50, and Swin Transformer. Among these, DenseNet-121 achieved the highest accuracy, scoring 0.534 and 0.804 for nine-class coarse- and four-class fine-grained classification, respectively. This led to further analysis on the optimal ensemble of radars. For the radars positioned at the head, a single left-located radar proved both essential and sufficient, achieving an accuracy of 0.809. When only one central head radar was used, omitting the central side radar and retaining only the three upper-body radars resulted in accuracies of 0.779 and 0.753, respectively. This study established the foundation for determining the optimal sensor configuration in this application, while also exploring the trade-offs between accuracy and the use of fewer sensors.

Sleep assessment using EEG-based wearables - A systematic review

Polysomnography (PSG) is the reference standard of sleep measurement, but is burdensome for the participant and labor intensive. Affordable electroencephalography (EEG)-based wearables are easy to use and are gaining popularity, yet selecting the most suitable device is a challenge for clinicians and researchers. In this systematic review, we aim to provide a comprehensive overview of available EEG-based wearables to measure human sleep. For each wearable, an overview will be provided regarding validated population and reported measurement properties. A systematic search was conducted in the databases OVID MEDLINE, Embase.com and CINAHL. A machine learning algorithm (ASReview) was utilized to screen titles and abstracts for eligibility. In total, 60 papers were selected, covering 34 unique EEG-based wearables. Feasibility studies indicated good tolerance, high compliance, and success rates. The 42 included validation studies were conducted across diverse populations and showed consistently high accuracy in sleep staging detection. Therefore, the recent advancements in EEG-based wearables show great promise as alternative for PSG and for at-home sleep monitoring. Users should consider factors like user-friendliness, comfort, and costs, as these devices vary in features and pricing, impacting their suitability for individual needs.

Personalized interpretable prediction of perceived sleep quality: Models with meaningful cardiovascular and behavioral features

Understanding a person's perceived quality of sleep is an important problem, but hard due to its poor definition and high intra- as well as inter-individual variation. In the short term, sleep quality has an established impact on cognitive function during the following day as well as on fatigue. In the long term, good quality sleep is essential for mental and physical health and contributes to quality of life. Despite the need to better understand sleep quality as an early indicator for sleep disorders, perceived sleep quality has been rarely modeled for multiple consecutive days using biosignals. In this paper, we present novel insights on the association of cardiac activity and perceived sleep quality using an interpretable modeling approach utilizing the publicly available intensive-longitudinal study M2Sleep. Our method takes as input signals from commodity wearable devices, including motion and blood volume pulses. Despite processing only simple and clearly interpretable features, we achieve an accuracy of up to 70% with an AUC of 0.76 and reduce the error by up to 36% compared to related work. We further argue that collected biosignals and sleep quality labels should be normalized per-participant to enable a medically insightful analysis. Coupled with explainable models, this allows for the interpretations of effects on perceived sleep quality. Analysis revealed that besides higher skin temperature and sufficient sleep duration, especially higher average heart rate while awake and lower minimal activity of the parasympathetic and sympathetic nervous system while asleep increased the chances of higher sleep quality.

Sleep Health Analysis Through Sleep Symptoms in 35,808 Individuals Across Age and Sex Differences: Comparative Symptom Network Study

BACKGROUND

Sleep health is a multidimensional construct that includes objective and subjective parameters and is influenced by individual sleep-related behaviors and sleep disorders. Symptom network analysis allows modeling of the interactions between variables, enabling both the visualization of relationships between different factors and the identification of the strength of those relationships. Given the known influence of sex and age on sleep health, network analysis can help explore sets of mutually interacting symptoms relative to these demographic variables.

OBJECTIVE

This study aimed to study the centrality of symptoms and compare age and sex differences regarding sleep health using a symptom network approach in a large French population that feels concerned about their sleep.

METHODS

Data were extracted from a questionnaire provided by the Réseau Morphée health network. A network analysis was conducted on 39 clinical variables related to sleep disorders and sleep health. After network estimation, statistical analyses consisted of calculating inferences of centrality, robustness (ie, testifying to a sufficient effect size), predictability, and network comparison. Sleep clinical variable centralities within the networks were analyzed by both sex and age using 4 age groups (18-30, 31-45, 46-55, and >55 years), and local symptom-by-symptom correlations determined.

RESULTS

Data of 35,808 participants were obtained. The mean age was 42.7 (SD 15.7) years, and 24,964 (69.7%) were women. Overall, there were no significant differences in the structure of the symptom networks between sexes or age groups. The most central symptoms across all groups were nonrestorative sleep and excessive daytime sleepiness. In the youngest group, additional central symptoms were chronic circadian misalignment and chronic sleep deprivation (related to sleep behaviors), particularly among women. In the oldest group, leg sensory discomfort and breath abnormality complaint were among the top 4 central symptoms. Symptoms of sleep disorders thus became more central with age than sleep behaviors. The high predictability of central nodes in one of the networks underlined its importance in influencing other nodes.

CONCLUSIONS

The absence of structural difference between networks is an important finding, given the known differences in sleep between sexes and across age groups. These similarities suggest comparable interactions between clinical sleep variables across sexes and age groups and highlight the implication of common sleep and wake neural circuits and circadian rhythms in understanding sleep health. More precisely, nonrestorative sleep and excessive daytime sleepiness are central symptoms in all groups. The behavioral component is particularly central in young people and women. Sleep-related respiratory and motor symptoms are prominent in older people. These results underscore the importance of comprehensive sleep promotion and screening strategies tailored to sex and age to impact sleep health.

Application of portable sleep monitoring devices in pregnancy: a comprehensive review

Objective.The physiological, hormonal and biomechanical changes during pregnancy may trigger sleep disordered breathing (SDB) in pregnant women. Pregnancy-related sleep disorders may associate with adverse fetal and maternal outcomes including gestational diabetes, preeclampsia, preterm birth and gestational hypertension. Most of the screening and diagnostic studies that explore SDB during pregnancy were based on questionnaires which are inherently limited in providing definitive conclusions. The current gold standard in diagnostics is overnight polysomnography (PSG) involving the comprehensive measurements of physiological changes during sleep. However, applying the overnight laboratory PSG on pregnant women is not practical due to a number of challenges such as patient inconvenience, unnatural sleep dynamics, and expenses due to highly trained personnel and technology. Parallel to the progress in wearable sensors and portable electronics, home sleep monitoring devices became indispensable tools to record the sleep signals of pregnant women at her own sleep environment. This article reviews the application of portable sleep monitoring devices in pregnancy with particular emphasis on estimating the perinatal outcomes.Approach.The advantages and disadvantages of home based sleep monitoring systems compared to subjective sleep questionnaires and overnight PSG for pregnant women were evaluated.Main Results.An overview on the efficiency of the application of home sleep monitoring in terms of accuracy and specificity were presented for particular fetal and maternal outcomes.Significance.Based on our review, more homogenous and comparable research is needed to produce conclusive results with home based sleep monitoring systems to study the epidemiology of SDB in pregnancy and its impact on maternal and neonatal health.

Assessment of Stress and Well-Being of Japanese Employees Using Wearable Devices for Sleep Monitoring Combined With Ecological Momentary Assessment: Pilot Observational Study

BACKGROUND

Poor sleep quality can elevate stress levels and diminish overall well-being. Japanese individuals often experience sleep deprivation, and workers have high levels of stress. Nevertheless, research examining the connection between objective sleep assessments and stress levels, as well as overall well-being, among Japanese workers is lacking.

OBJECTIVE

This study aims to investigate the correlation between physiological data, including sleep duration and heart rate variability (HRV), objectively measured through wearable devices, and 3 states (sleepiness, mood, and energy) assessed through ecological momentary assessment (EMA) and use of rating scales for stress and well-being.

METHODS

A total of 40 office workers (female, 20/40, 50%; mean age 40.4 years, SD 11.8 years) participated in the study. Participants were asked to wear a wearable wristband device for 8 consecutive weeks. EMA regarding sleepiness, mood, and energy levels was conducted via email messages sent by participants 4 times daily, with each session spaced 3 hours apart. This assessment occurred on 8 designated days within the 8-week timeframe. Participants' stress levels and perception of well-being were assessed using respective self-rating questionnaires. Subsequently, participants were categorized into quartiles based on their stress and well-being scores, and the sleep patterns and HRV indices recorded by the Fitbit Inspire 2 were compared among these groups. The Mann-Whitney U test was used to assess differences between the quartiles, with adjustments made for multiple comparisons using the Bonferroni correction. Furthermore, EMA results and the sleep and HRV indices were subjected to multilevel analysis for a comprehensive evaluation.

RESULTS

The EMA achieved a total response rate of 87.3%, while the Fitbit Inspire 2 wear rate reached 88.0%. When participants were grouped based on quartiles of well-being and stress-related scores, significant differences emerged. Specifically, individuals in the lowest stress quartile or highest subjective satisfaction quartile retired to bed earlier (P<.001 and P=.01, respectively), whereas those in the highest stress quartile exhibited greater variation in the midpoint of sleep (P<.001). A multilevel analysis unveiled notable relationships: intraindividual variability analysis indicated that higher energy levels were associated with lower deviation of heart rate during sleep on the preceding day (β=-.12, P<.001), and decreased sleepiness was observed on days following longer sleep durations (β=-.10, P<.001). Furthermore, interindividual variability analysis revealed that individuals with earlier midpoints of sleep tended to exhibit higher energy levels (β=-.26, P=.04).

CONCLUSIONS

Increased sleep variabilities, characterized by unstable bedtime or midpoint of sleep, were correlated with elevated stress levels and diminished well-being. Conversely, improved sleep indices (eg, lower heart rate during sleep and earlier average bedtime) were associated with heightened daytime energy levels. Further research with a larger sample size using these methodologies, particularly focusing on specific phenomena such as social jet lag, has the potential to yield valuable insights.

TRIAL REGISTRATION

UMIN-CTR UMIN000046858; https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000053392.

Attributes, Methods, and Frameworks Used to Evaluate Wearables and Their Companion mHealth Apps: Scoping Review

BACKGROUND

Wearable devices, mobile technologies, and their combination have been accepted into clinical use to better assess the physical fitness and quality of life of patients and as preventive measures. Usability is pivotal for overcoming constraints and gaining users' acceptance of technology such as wearables and their companion mobile health (mHealth) apps. However, owing to limitations in design and evaluation, interactive wearables and mHealth apps have often been restricted from their full potential.

OBJECTIVE

This study aims to identify studies that have incorporated wearable devices and determine their frequency of use in conjunction with mHealth apps or their combination. Specifically, this study aims to understand the attributes and evaluation techniques used to evaluate usability in the health care domain for these technologies and their combinations.

METHODS

We conducted an extensive search across 4 electronic databases, spanning the last 30 years up to December 2021. Studies including the keywords "wearable devices," "mobile apps," "mHealth apps," "physiological data," "usability," "user experience," and "user evaluation" were considered for inclusion. A team of 5 reviewers screened the collected publications and charted the features based on the research questions. Subsequently, we categorized these characteristics following existing usability and wearable taxonomies. We applied a methodological framework for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.

RESULTS

A total of 382 reports were identified from the search strategy, and 68 articles were included. Most of the studies (57/68, 84%) involved the simultaneous use of wearables and connected mobile apps. Wrist-worn commercial consumer devices such as wristbands were the most prevalent, accounting for 66% (45/68) of the wearables identified in our review. Approximately half of the data from the medical domain (32/68, 47%) focused on studies involving participants with chronic illnesses or disorders. Overall, 29 usability attributes were identified, and 5 attributes were frequently used for evaluation: satisfaction (34/68, 50%), ease of use (27/68, 40%), user experience (16/68, 24%), perceived usefulness (18/68, 26%), and effectiveness (15/68, 22%). Only 10% (7/68) of the studies used a user- or human-centered design paradigm for usability evaluation.

CONCLUSIONS

Our scoping review identified the types and categories of wearable devices and mHealth apps, their frequency of use in studies, and their implementation in the medical context. In addition, we examined the usability evaluation of these technologies: methods, attributes, and frameworks. Within the array of available wearables and mHealth apps, health care providers encounter the challenge of selecting devices and companion apps that are effective, user-friendly, and compatible with user interactions. The current gap in usability and user experience in health care research limits our understanding of the strengths and limitations of wearable technologies and their companion apps. Additional research is necessary to overcome these limitations.

Evaluating reliability in wearable devices for sleep staging

Sleep is crucial for physical and mental health, but traditional sleep quality assessment methods have limitations. This scoping review analyzes 35 articles from the past decade, evaluating 62 wearable setups with varying sensors, algorithms, and features. Our analysis indicates a trend towards combining accelerometer and photoplethysmography (PPG) data for out-of-lab sleep staging. Devices using only accelerometer data are effective for sleep/wake detection but fall short in identifying multiple sleep stages, unlike those incorporating PPG signals. To enhance the reliability of sleep staging wearables, we propose five recommendations: (1) Algorithm validation with equity, diversity, and inclusion considerations, (2) Comparative performance analysis of commercial algorithms across multiple sleep stages, (3) Exploration of feature impacts on algorithm accuracy, (4) Consistent reporting of performance metrics for objective reliability assessment, and (5) Encouragement of open-source classifier and data availability. Implementing these recommendations can improve the accuracy and reliability of sleep staging algorithms in wearables, solidifying their value in research and clinical settings.

Validation of sleep-staging accuracy for an in-home sleep electroencephalography device compared with simultaneous polysomnography in patients with obstructive sleep apnea

Efforts to simplify standard polysomnography (PSG) in laboratories, especially for obstructive sleep apnea (OSA), and assess its agreement with portable electroencephalogram (EEG) devices are limited. We aimed to evaluate the agreement between a portable EEG device and type I PSG in patients with OSA and examine the EEG-based arousal index's ability to estimate apnea severity. We enrolled 77 Japanese patients with OSA who underwent simultaneous type I PSG and portable EEG monitoring. Combining pulse rate, oxygen saturation (SpO2), and EEG improved sleep staging accuracy. Bland-Altman plots, paired t-tests, and receiver operating characteristics curves were used to assess agreement and screening accuracy. Significant small biases were observed for total sleep time, sleep latency, awakening after falling asleep, sleep efficiency, N1, N2, and N3 rates, arousal index, and apnea indexes. All variables showed > 95% agreement in the Bland-Altman analysis, with interclass correlation coefficients of 0.761-0.982, indicating high inter-instrument validity. The EEG-based arousal index demonstrated sufficient power for screening AHI ≥ 15 and ≥ 30 and yielded promising results in predicting apnea severity. Portable EEG device showed strong agreement with type I PSG in patients with OSA. These suggest that patients with OSA may assess their condition at home.

Feasibility of wearable activity tracking devices to measure physical activity and sleep change among adolescents with chronic pain-a pilot nonrandomized treatment study

Purpose

Personal informatics devices are being used to measure engagement in health behaviors in adults with chronic pain and may be appropriate for adolescent use. The aim of this study was to evaluate the utilization of a wearable activity tracking device to measure physical activity and sleep among adolescents attending a three-week, intensive interdisciplinary pain treatment (IIPT) program. We also assessed changes in physical activity and sleep from baseline to the treatment phase.

Methods

Participants (57.1% female, average age 15.88, SD = 1.27) wore an activity tracking device three weeks prior to starting and during the treatment program.

Results

Of 129 participants contacted, 47 (36.4%) agreed to participate. However, only 30 (64%) complied with the instructions for using the device prior to programming and during program participation. Preliminary analyses comparing averages from 3-weeks pre-treatment to 3-weeks during treatment indicated increases in daily overall activity minutes, daily step counts, and minutes of moderate to vigorous physical activity (by 353%), as well as a corresponding decrease in sedentary minutes. There was more missing data for sleep than anticipated.

Conclusions

Wearable activity tracking devices can be successfully used to measure adolescent physical activity in-person, with more difficulty obtaining this information remotely. Adolescents with chronic pain experience improvements in objective measurements of physical activity over the course of a 3-week IIPT program. Future studies may want to spend more time working with pediatric patients on their understanding of how to use trackers for sleep and physical activity.

The Two Fundamental Shapes of Sleep Heart Rate Dynamics and Their Connection to Mental Health in College Students

Introduction

Wearable devices are rapidly improving our ability to observe health-related processes for extended durations in an unintrusive manner. In this study, we use wearable devices to understand how the shape of the heart rate curve during sleep relates to mental health.

Methods

As part of the Lived Experiences Measured Using Rings Study (LEMURS), we collected heart rate measurements using the Oura ring (Gen3) for over 25,000 sleep periods and self-reported mental health indicators from roughly 600 first-year university students in the USA during the fall semester of 2022. Using clustering techniques, we find that the sleeping heart rate curves can be broadly separated into two categories that are mainly differentiated by how far along the sleep period the lowest heart rate is reached.

Results

Sleep periods characterized by reaching the lowest heart rate later during sleep are also associated with shorter deep and REM sleep and longer light sleep, but not a difference in total sleep duration. Aggregating sleep periods at the individual level, we find that consistently reaching the lowest heart rate later during sleep is a significant predictor of (1) self-reported impairment due to anxiety or depression, (2) a prior mental health diagnosis, and (3) firsthand experience in traumatic events. This association is more pronounced among females.

Conclusion

Our results show that the shape of the sleeping heart rate curve, which is only weakly correlated with descriptive statistics such as the average or the minimum heart rate, is a viable but mostly overlooked metric that can help quantify the relationship between sleep and mental health.

Ethical considerations in using sensors to remotely assess pediatric health behaviors

Sensors, including accelerometer-based and electronic adherence monitoring devices, have transformed health data collection. Sensors allow for unobtrusive, real-time sampling of health behaviors that relate to psychological health, including sleep, physical activity, and medication-taking. These technical strengths have captured scholarly attention, with far less discussion about the level of human touch involved in implementing sensors. Researchers face several subjective decision points when collecting health data via sensors, with these decisions posing ethical concerns for users and the public at large. Using examples from pediatric sleep, physical activity, and medication adherence research, we pose critical ethical questions, practical dilemmas, and guidance for implementing health-based sensors. We focus on youth given that they are often deemed the ideal population for digital health approaches but have unique technology-related vulnerabilities and preferences. Ethical considerations are organized according to Belmont principles of respect for persons (e.g., when sensor-based data are valued above the subjective lived experiences of youth and their families), beneficence (e.g., with sensor data management and sharing), and justice (e.g., with sensor access and acceptability among minoritized pediatric populations). Recommendations include the need to increase transparency about the extent of subjective decision making with sensor data management. Without greater attention to the human factors involved in sensor research, ethical risks could outweigh the scientific promise of sensors, thereby negating their potential role in improving child health and care. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Advancements in Wearable EEG Technology for Improved Home-Based Sleep Monitoring and Assessment: A Review

Sleep is a fundamental aspect of daily life, profoundly impacting mental and emotional well-being. Optimal sleep quality is vital for overall health and quality of life, yet many individuals struggle with sleep-related difficulties. In the past, polysomnography (PSG) has served as the gold standard for assessing sleep, but its bulky nature, cost, and the need for expertise has made it cumbersome for widespread use. By recognizing the need for a more accessible and user-friendly approach, wearable home monitoring systems have emerged. EEG technology plays a pivotal role in sleep monitoring, as it captures crucial brain activity data during sleep and serves as a primary indicator of sleep stages and disorders. This review provides an overview of the most recent advancements in wearable sleep monitoring leveraging EEG technology. We summarize the latest EEG devices and systems available in the scientific literature, highlighting their design, form factors, materials, and methods of sleep assessment. By exploring these developments, we aim to offer insights into cutting-edge technologies, shedding light on wearable EEG sensors for advanced at-home sleep monitoring and assessment. This comprehensive review contributes to a broader perspective on enhancing sleep quality and overall health using wearable EEG sensors.

Association between Self-reported Sleep Quality and Single-task Gait in Young Adults: A Study Using Machine Learning

Objective  The objective of the present study was to find biomechanical correlates of single-task gait and self-reported sleep quality in a healthy, young population by replicating a recently published study. Materials and Methods  Young adults ( n  = 123) were recruited and were asked to complete the Pittsburgh Sleep Quality Inventory to assess sleep quality. Gait variables ( n  = 53) were recorded using a wearable inertial measurement sensor system on an indoor track. The data were split into training and test sets and then different machine learning models were applied. A post-hoc analysis of covariance (ANCOVA) was used to find statistically significant differences in gait variables between good and poor sleepers. Results  AdaBoost models reported the highest correlation coefficient (0.77), with Support-Vector classifiers reporting the highest accuracy (62%). The most important features associated with poor sleep quality related to pelvic tilt and gait initiation. This indicates that overall poor sleepers have decreased pelvic tilt angle changes, specifically when initiating gait coming out of turns (first step pelvic tilt angle) and demonstrate difficulty maintaining gait speed. Discussion  The results of the present study indicate that when using traditional gait variables, single-task gait has poor accuracy prediction for subjective sleep quality in young adults. Although the associations in the study are not as strong as those previously reported, they do provide insight into how gait varies in individuals who report poor sleep hygiene. Future studies should use larger samples to determine whether single task-gait may help predict objective measures of sleep quality especially in a repeated measures or longitudinal or intervention framework.

Exploring Variations in Sleep Perception: Comparative Study of Chatbot Sleep Logs and Fitbit Sleep Data

BACKGROUND

Patient-generated health data are important in the management of several diseases. Although there are limitations, information can be obtained using a wearable device and time-related information such as exercise time or sleep time can also be obtained. Fitbits can be used to acquire sleep onset, sleep offset, total sleep time (TST), and wakefulness after sleep onset (WASO) data, although there are limitations regarding the depth of sleep and satisfaction; therefore, the patient's subjective response is still important information that cannot be replaced by wearable devices.

OBJECTIVE

To effectively use patient-generated health data related to time such as sleep, it is first necessary to understand the characteristics of the time response recorded by the user. Therefore, the aim of this study was to analyze the characteristics of individuals' time perception in comparison with wearable data.

METHODS

Sleep data were acquired for 2 weeks using a Fitbit. Participants' sleep records were collected daily through chatbot conversations while wearing the Fitbit, and the two sets of data were statistically compared.

RESULTS

In total, 736 people aged 30-59 years were recruited for this study, and the sleep data of 543 people who wore a Fitbit and responded to the chatbot for more than 7 days on the same day were analyzed. Research participants tended to respond to sleep-related times on the hour or in 30-minute increments, and each participant responded within the range of 60-90 minutes from the value measured by the Fitbit. On average for all participants, the chat responses and the Fitbit data were similar within a difference of approximately 15 minutes. Regarding sleep onset, the participant response was 8 minutes and 39 seconds (SD 58 minutes) later than that of the Fitbit data, whereas with respect to sleep offset, the response was 5 minutes and 38 seconds (SD 57 minutes) earlier. The participants' actual sleep time (AST) indicated in the chat was similar to that obtained by subtracting the WASO from the TST measured by the Fitbit. The AST was 13 minutes and 39 seconds (SD 87 minutes) longer than the time WASO was subtracted from the Fitbit TST. On days when the participants reported good sleep, they responded 19 (SD 90) minutes longer on the AST than the Fitbit data. However, for each sleep event, the probability that the participant's AST was within ±30 and ±60 minutes of the Fitbit TST-WASO was 50.7% and 74.3%, respectively.

CONCLUSIONS

The chatbot sleep response and Fitbit measured time were similar on average and the study participants had a slight tendency to perceive a relatively long sleep time if the quality of sleep was self-reported as good. However, on a participant-by-participant basis, it was difficult to predict participants' sleep duration responses with Fitbit data. Individual variations in sleep time perception significantly affect patient responses related to sleep, revealing the limitations of objective measures obtained through wearable devices.

Human-centred artificial intelligence for mobile health sensing: challenges and opportunities

Advances in wearable sensing and mobile computing have enabled the collection of health and well-being data outside of traditional laboratory and hospital settings, paving the way for a new era of mobile health. Meanwhile, artificial intelligence (AI) has made significant strides in various domains, demonstrating its potential to revolutionize healthcare. Devices can now diagnose diseases, predict heart irregularities and unlock the full potential of human cognition. However, the application of machine learning (ML) to mobile health sensing poses unique challenges due to noisy sensor measurements, high-dimensional data, sparse and irregular time series, heterogeneity in data, privacy concerns and resource constraints. Despite the recognition of the value of mobile sensing, leveraging these datasets has lagged behind other areas of ML. Furthermore, obtaining quality annotations and ground truth for such data is often expensive or impractical. While recent large-scale longitudinal studies have shown promise in leveraging wearable sensor data for health monitoring and prediction, they also introduce new challenges for data modelling. This paper explores the challenges and opportunities of human-centred AI for mobile health, focusing on key sensing modalities such as audio, location and activity tracking. We discuss the limitations of current approaches and propose potential solutions.

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

BACKGROUND

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.

METHOD

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.

RESULTS

In a care-as-usual setting, EMFIT QS and actigraphy assessment show little agreement on sleep-wake variables.

CONCLUSION

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.

Effect of sleep report feedback using information and communication technology combined with health guidance on improving sleep indicators in community-dwelling older people: a pilot trial

BACKGROUND

This study evaluated the preliminary effect of an integrated novel intervention comprising visualised sleep report feedback using information and communication technology and periodic health guidance on improving sleep indicators among community-dwelling older people.

METHODS

The intervention was implemented among 29 older people in Sakai City, Japan, in a 3 months pilot trial. Non-worn actigraph devices were placed under participants' bedding to continuously measure their sleep state, and they received monthly sleep reports in writing. Sleep efficiency, total sleep time, sleep latency, and the number of times away from bed were recorded. A trained nurse expertly interpreted participants' sleep data and provided telephone health guidance. The first month's data were used as the baseline (T1), the second month provided data for the first intervention (T2), and the third month provided data for the second intervention (T3). Friedman tests and Wilcoxon signed-rank tests were used to examine differences in sleep outcomes between different time points.

RESULTS

Participants' mean age was 78.97 ± 5.15 years, and 51.72% (15/29) were female. Comparison of T2 and T1 showed the intervention decreased participants' sleep latency at T2 (P = 0.038). Compared with T1, the intervention significantly decreased sleep latency (P = 0.004), increased total sleep time (P < 0.001), and improved sleep efficiency (P < 0.001) at T3. When T3 was compared with T2, only total sleep time was significantly increased (P < 0.001). There were no significant differences in the number of times away from bed across the three time points (P > 0.05).

CONCLUSIONS

This visualised sleep report feedback and periodic health guidance intervention for community-dwelling older people showed promising, albeit small preliminary effects on sleep. A fully powered randomised controlled trial is required to verify the significance of this effect.

A 2022 Survey of Commercially Available Smartphone Apps for Sleep: Most Enhance Sleep

Commercially available smartphone apps represent an ever-evolving and fast-growing market. Our review systematically surveyed currently available commercial sleep smartphone apps to provide details to inform both providers and patients alike, in addition to the healthy consumer market. Most current sleep apps offer a free version and are designed to be used while awake, prior to sleep, and focus on the enhancement of sleep, rather than measurement, by targeting sleep latency using auditory stimuli. Sleep apps could be considered a possible strategy for patients and consumers to improve their sleep, although further validation of specific apps is recommended.

Perspective of Artificial Intelligence in Disease Diagnosis: A Review of Current and Future Endeavours in the Medical Field

Artificial intelligence (AI) has demonstrated significant promise for the present and future diagnosis of diseases. At the moment, AI-powered diagnostic technologies can help physicians decipher medical pictures like X-rays, magnetic resonance imaging, and computed tomography scans, resulting in quicker and more precise diagnoses. In order to make a prospective diagnosis, AI algorithms may also examine patient information, symptoms, and medical background. The application of AI in disease diagnosis is anticipated to grow as the field develops. In the future, AI may be used to find patterns in enormous volumes of medical data, aiding in disease prediction and prevention before symptoms appear. Additionally, by combining genetic data, lifestyle data, and environmental variables, AI may help in the diagnosis of complicated diseases. It is crucial to remember that while AI can be a powerful tool, it cannot take the place of qualified medical personnel. Instead, AI ought to support and improve diagnostic procedures, enhancing patient care and healthcare results. Future research and the use of AI for disease diagnosis must take ethical issues, data protection, and ongoing model validation into account.

Digital health in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) can be prevented and treated through effective care, reducing exacerbations and hospitalizations. Early identification of individuals at high risk of COPD exacerbation is an opportunity for preventive measures. However, many patients struggle to follow their treatment plans because of a lack of knowledge about the disease, limited access to resources, and insufficient clinical support. The growth of digital health-which encompasses advancements in health information technology, artificial intelligence, telehealth, the Internet of Things, mobile health, wearable technology, and digital therapeutics-offers opportunities for improving the early diagnosis and management of COPD. This study reviewed the field of digital health in terms of COPD. The findings showed that despite significant advances in digital health, there are still obstacles impeding its effectiveness. Finally, we highlighted some of the major challenges and possibilities for developing and integrating digital health in COPD management.

At-home wireless sleep monitoring patches for the clinical assessment of sleep quality and sleep apnea

Although many people suffer from sleep disorders, most are undiagnosed, leading to impairments in health. The existing polysomnography method is not easily accessible; it's costly, burdensome to patients, and requires specialized facilities and personnel. Here, we report an at-home portable system that includes wireless sleep sensors and wearable electronics with embedded machine learning. We also show its application for assessing sleep quality and detecting sleep apnea with multiple patients. Unlike the conventional system using numerous bulky sensors, the soft, all-integrated wearable platform offers natural sleep wherever the user prefers. In a clinical study, the face-mounted patches that detect brain, eye, and muscle signals show comparable performance with polysomnography. When comparing healthy controls to sleep apnea patients, the wearable system can detect obstructive sleep apnea with an accuracy of 88.5%. Furthermore, deep learning offers automated sleep scoring, demonstrating portability, and point-of-care usability. At-home wearable electronics could ensure a promising future supporting portable sleep monitoring and home healthcare.

The past, present, and future of sleep quality assessment and monitoring

Sleep quality is considered to be an individual's self-satisfaction with all aspects of the sleep experience. Good sleep not only improves a person's physical, mental and daily functional health, but also improves the quality-of-life level to some extent. In contrast, chronic sleep deprivation can increase the risk of diseases such as cardiovascular diseases, metabolic dysfunction and cognitive and emotional dysfunction, and can even lead to increased mortality. The scientific evaluation and monitoring of sleep quality is an important prerequisite for safeguarding and promoting the physiological health of the body. Therefore, we have compiled and reviewed the existing methods and emerging technologies commonly used for subjective and objective evaluation and monitoring of sleep quality, and found that subjective sleep evaluation is suitable for clinical screening and large-scale studies, while objective evaluation results are more intuitive and scientific, and in the comprehensive evaluation of sleep, if we want to get more scientific monitoring results, we should combine subjective and objective monitoring and dynamic monitoring.

Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review

Many studies have used Digital Phenotyping of Mental Health (DPMH) to complement classic methods of mental health assessment and monitoring. This research area proposes innovative methods that perform multimodal sensing of multiple situations of interest (e.g., sleep, physical activity, mobility) to health professionals. In this paper, we present a Systematic Literature Review (SLR) to recognize, characterize and analyze the state of the art on DPMH using multimodal sensing of multiple situations of interest to professionals. We searched for studies in six digital libraries, which resulted in 1865 retrieved published papers. Next, we performed a systematic process of selecting studies based on inclusion and exclusion criteria, which selected 59 studies for the data extraction phase. First, based on the analysis of the extracted data, we describe an overview of this field, then presenting characteristics of the selected studies, the main mental health topics targeted, the physical and virtual sensors used, and the identified situations of interest. Next, we outline answers to research questions, describing the context data sources used to detect situations, the DPMH workflow used for multimodal sensing of situations, and the application of DPMH solutions in the mental health assessment and monitoring process. In addition, we recognize trends presented by DPMH studies, such as the design of solutions for high-level information recognition, association of features with mental states/disorders, classification of mental states/disorders, and prediction of mental states/disorders. We also recognize the main open issues in this research area. Based on the results of this SLR, we conclude that despite the potential and continuous evolution for using these solutions as medical decision support tools, this research area needs more work to overcome technology and methodological rigor issues to adopt proposed solutions in real clinical settings.

Exercise, diet, and sleep habits of nurses working full-time during the COVID-19 pandemic: An observational study

BACKGROUND

Healthy diet, exercise, and sleep practices may mitigate stress and prevent illness. However, lifestyle behaviors of acute care nurses working during stressful COVID-19 surges are unclear.

PURPOSE

To quantify sleep, diet, and exercise practices of 12-hour acute care nurses working day or night shift during COVID-19-related surges.

METHODS

Nurses across 10 hospitals in the United States wore wrist actigraphs and pedometers to quantify sleep and steps and completed electronic diaries documenting diet over 7-days.

FINDINGS

Participant average sleep quantity did not meet national recommendations; night shift nurses (n = 23) slept significantly less before on-duty days when compared to day shift nurses (n = 34). Proportionally more night shift nurses did not meet daily step recommendations. Diet quality was low on average among participants.

DISCUSSION

Nurses, especially those on night shift, may require resources to support healthy sleep hygiene, physical activity practices, and diet quality to mitigate stressful work environments.

The effect of wearable-delivered sleep interventions on sleep outcomes among adults: A systematic review and meta-analysis of randomized controlled trials

The aims of the review were to (i) evaluate the effectiveness of wearable-delivered sleep interventions on sleep outcomes among adults, and (ii) explore the effect of factors affecting total sleep time. Eight databases were searched to identify relevant studies in English from inception until December 23, 2021. The Cochrane Risk of Bias tool version 2.0 and Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) criteria were used to assess the risk of bias and certainty of the evidence, respectively. Twenty randomized controlled trials (RCTs) were included, involving 1608 adults across nine countries. Wearable-delivered sleep interventions elicited significant improvement of 1.96 events/h for the oxygen desaturation index and 3.13 events/h for the respiratory distress index. Meta-analyses found that wearable-delivered sleep interventions significantly decreased sleep disturbance (Hedges' g [g] = -0.37, 95% confidence interval [CI]: -0.59, -0.15) and sleep-related impairment (g = -1.06, 95% CI: -1.99, -0.13) versus the comparators. The wearable-delivered sleep interventions may complement usual care to improve sleep outcomes. More rigorous RCTs with a long-term assessment in a wide range of populations are warranted.

The Development of a Novel mHealth Tool for Obstructive Sleep Apnea: Tracking Continuous Positive Airway Pressure Adherence as a Percentage of Time in Bed

BACKGROUND

Continuous positive airway pressure (CPAP) is the mainstay obstructive sleep apnea (OSA) treatment; however, poor adherence to CPAP is common. Current guidelines specify 4 hours of CPAP use per night as a target to define adequate treatment adherence. However, effective OSA treatment requires CPAP use during the entire time spent in bed to optimally treat respiratory events and prevent adverse health effects associated with the time spent sleeping without wearing a CPAP device. Nightly sleep patterns vary considerably, making it necessary to measure CPAP adherence relative to the time spent in bed. Weight loss is an important goal for patients with OSA. Tools are required to address these clinical challenges in patients with OSA.

OBJECTIVE

This study aimed to develop a mobile health tool that combined weight loss features with novel CPAP adherence tracking (ie, percentage of CPAP wear time relative to objectively assessed time spent in bed) for patients with OSA.

METHODS

We used an iterative, user-centered process to design a new CPAP adherence tracking module that integrated with an existing weight loss app. A total of 37 patients with OSA aged 20 to 65 years were recruited. In phase 1, patients with OSA who were receiving CPAP treatment (n=7) tested the weight loss app to track nutrition, activity, and weight for 10 days. Participants completed a usability and acceptability survey. In phase 2, patients with OSA who were receiving CPAP treatment (n=21) completed a web-based survey about their interpretations and preferences for wireframes of the CPAP tracking module. In phase 3, patients with recently diagnosed OSA who were CPAP naive (n=9) were prescribed a CPAP device (ResMed AirSense10 AutoSet) and tested the integrated app for 3 to 4 weeks. Participants completed a usability survey and provided feedback.

RESULTS

During phase 1, participants found the app to be mostly easy to use, except for some difficulty searching for specific foods. All participants found the connected devices (Fitbit activity tracker and Fitbit Aria scale) easy to use and helpful. During phase 2, participants correctly interpreted CPAP adherence success, expressed as percentage of wear time relative to time spent in bed, and preferred seeing a clearly stated percentage goal ("Goal: 100%"). In phase 3, participants found the integrated app easy to use and requested push notification reminders to wear CPAP before bedtime and to sync Fitbit in the morning.

CONCLUSIONS

We developed a mobile health tool that integrated a new CPAP adherence tracking module into an existing weight loss app. Novel features included addressing OSA-obesity comorbidity, CPAP adherence tracking via percentage of CPAP wear time relative to objectively assessed time spent in bed, and push notifications to foster adherence. Future research on the effectiveness of this tool in improving OSA treatment adherence is warranted.

Identifying Sleep Disorders From Search Engine Activity: Combining User-Generated Data With a Clinically Validated Questionnaire

Background

Sleep disorders are experienced by up to 40% of the population but their diagnosis is often delayed by the availability of specialists.

Objective

We propose the use of search engine activity in conjunction with a validated web-based sleep questionnaire to facilitate wide-scale screening of prevalent sleep disorders.

Methods

Search advertisements offering a web-based sleep disorder screening questionnaire were shown on the Bing search engine to individuals who indicated an interest in sleep disorders. People who clicked on the advertisements and completed the sleep questionnaire were identified as being at risk for 1 of 4 common sleep disorders. A machine learning algorithm was applied to previous search engine queries to predict their suspected sleep disorder, as identified by the questionnaire.

Results

A total of 397 users consented to participate in the study and completed the questionnaire. Of them, 132 had sufficient past query data for analysis. Our findings show that diurnal patterns of people with sleep disorders were shifted by 2-3 hours compared to those of the controls. Past query activity was predictive of sleep disorders, approaching an area under the receiver operating characteristic curve of 0.62-0.69, depending on the sleep disorder.

Conclusions

Targeted advertisements can be used as an initial screening tool for people with sleep disorders. However, search engine data are seemingly insufficient as a sole method for screening. Nevertheless, we believe that evaluable web-based information, easily collected and processed with little effort on part of the physician and with low burden on the individual, can assist in the diagnostic process and possibly drive people to seek sleep assessment and diagnosis earlier than they currently do.

Coordination of glucose monitoring, self-care behaviour and mental health: achieving precision monitoring in diabetes

Monitoring of glucose plays an essential role in the management of diabetes. However, to fully understand and meaningfully interpret glucose levels, additional information on context is necessary. Important contextual factors include data on behaviours such as eating, exercise, medication-taking and sleep, as well as data on mental health aspects such as stress, affect, diabetes distress and depressive symptoms. This narrative review provides an overview of the current state and future directions of precision monitoring in diabetes. Precision monitoring of glucose has made great progress over the last 5 years with the emergence of continuous glucose monitoring (CGM), automated analysis of new glucose variables and visualisation of CGM data via the ambulatory glucose profile. Interestingly, there has been little progress in the identification of subgroups of people with diabetes based on their glycaemic profile. The integration of behavioural and mental health data could enrich such identification of subgroups to stimulate precision medicine. There are a handful of studies that have used innovative methodology such as ecological momentary assessment to monitor behaviour and mental health in people's everyday life. These studies indicate the importance of the interplay between behaviour, mental health and glucose. However, automated integration and intelligent interpretation of these data sources are currently not available. Automated integration of behaviour, mental health and glucose could lead to the identification of certain subgroups that, for example, show a strong association between mental health and glucose in contrast to subgroups that show independence of mental health and glucose. This could inform precision diagnostics and precision therapeutics. We identified just-in-time adaptive interventions as a potential means by which precision monitoring could lead to precision therapeutics. Just-in-time adaptive interventions consist of micro-interventions that are triggered in people's everyday lives when a certain problem is identified using monitored behaviour, mental health and glucose variables. Thus, these micro-interventions are responsive to real-life circumstances and are adaptive to the specific needs of an individual with diabetes. We conclude that, with current developments in big data analysis, there is a huge potential for precision monitoring in diabetes.

A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications

Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used for modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges. This review presents the non-deep learning models that are commonly used for time series classification in biomedical applications that can achieve high performance. Objective: We performed a systematic review to characterize the techniques that are used in time series classification of digital clinical measures throughout all the stages of data processing and model building. Methods: We conducted a literature search on PubMed, as well as the Institute of Electrical and Electronics Engineers (IEEE), Web of Science, and SCOPUS databases using a range of search terms to retrieve peer-reviewed articles that report on the academic research about digital clinical measures from a five-year period between June 2016 and June 2021. We identified and categorized the research studies based on the types of classification algorithms and sensor input types. Results: We found 452 papers in total from four different databases: PubMed, IEEE, Web of Science Database, and SCOPUS. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely used machine learning classifiers were the most commonly used technique, and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also the second-best classification technique. Conclusions: In this review paper, summaries of the time series classification models and interpretation methods for biomedical applications are summarized and categorized. While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation, however many different methods have proven successful.

The Apple Watch for Monitoring Mental Health-Related Physiological Symptoms: Literature Review

BACKGROUND

An anticipated surge in mental health service demand related to COVID-19 has motivated the use of novel methods of care to meet demand, given workforce limitations. Digital health technologies in the form of self-tracking technology have been identified as a potential avenue, provided sufficient evidence exists to support their effectiveness in mental health contexts.

OBJECTIVE

This literature review aims to identify current and potential physiological or physiologically related monitoring capabilities of the Apple Watch relevant to mental health monitoring and examine the accuracy and validation status of these measures and their implications for mental health treatment.

METHODS

A literature review was conducted from June 2021 to July 2021 of both published and gray literature pertaining to the Apple Watch, mental health, and physiology. The literature review identified studies validating the sensor capabilities of the Apple Watch.

RESULTS

A total of 5583 paper titles were identified, with 115 (2.06%) reviewed in full. Of these 115 papers, 19 (16.5%) were related to Apple Watch validation or comparison studies. Most studies showed that the Apple Watch could measure heart rate acceptably with increased errors in case of movement. Accurate energy expenditure measurements are difficult for most wearables, with the Apple Watch generally providing the best results compared with peers, despite overestimation. Heart rate variability measurements were found to have gaps in data but were able to detect mild mental stress. Activity monitoring with step counting showed good agreement, although wheelchair use was found to be prone to overestimation and poor performance on overground tasks. Atrial fibrillation detection showed mixed results, in part because of a high inconclusive result rate, but may be useful for ongoing monitoring. No studies recorded validation of the Sleep app feature; however, accelerometer-based sleep monitoring showed high accuracy and sensitivity in detecting sleep.

CONCLUSIONS

The results are encouraging regarding the application of the Apple Watch in mental health, particularly as heart rate variability is a key indicator of changes in both physical and emotional states. Particular benefits may be derived through avoidance of recall bias and collection of supporting ecological context data. However, a lack of methodologically robust and replicated evidence of user benefit, a supportive health economic analysis, and concerns about personal health information remain key factors that must be addressed to enable broader uptake.

A systematic review of movement monitoring devices to aid the prediction of pressure ulcers in at-risk adults

The present study sought to explore the impact of movement monitoring devices on risk prediction and prevention of pressure ulcers (PU) among adults. Using systematic review methodology, we included original research studies using a prospective design, written in English, assessing adult patients' movement in bed, using a movement monitoring device. The search was conducted in March 2021, using PubMed, CINAHL, Scopus, Cochrane, and EMBASE databases, and returned 1537 records, of which 25 met the inclusion criteria. Data were extracted using a pre-designed extraction tool and quality appraisal was undertaken using the evidence-based librarianship (EBL). In total, 19 different movement monitoring devices were used in the studies, using a range of physical sensing principles. The studies focused on quantifying the number and types of movements. In four studies the authors compared the monitoring system with PU risk assessment tools, with a variety of high and low correlations observed. Four studies compared the relationship between movement magnitude and frequency and the development of PUs, with variability in results also identified. Two of these studies showed, as expected, that those who made less movements developed more PU; however, the two studies also unexpectedly found that PUs occurred in both low movers and high movers. In the final two studies, the authors focused on the concordance with recommended repositioning based on the results of the monitoring device. Overall, concordance with repositioning increased with the use of a monitoring device. The synthesis of the literature surrounding bed monitoring technologies for PU risk prediction showed that a range of physical sensors can be used to detect the frequency of movement. Clinical studies showed some correlation between parameters of movement and PU risk/incidence, although the heterogeneity of approaches limits generalisable recommendations.

Emerging Artificial Intelligence–Empowered mHealth: Scoping Review

Background

Artificial intelligence (AI) has revolutionized health care delivery in recent years. There is an increase in research for advanced AI techniques, such as deep learning, to build predictive models for the early detection of diseases. Such predictive models leverage mobile health (mHealth) data from wearable sensors and smartphones to discover novel ways for detecting and managing chronic diseases and mental health conditions.

Objective

Currently, little is known about the use of AI-powered mHealth (AIM) settings. Therefore, this scoping review aims to map current research on the emerging use of AIM for managing diseases and promoting health. Our objective is to synthesize research in AIM models that have increasingly been used for health care delivery in the last 2 years.

Methods

Using Arksey and O’Malley’s 5-point framework for conducting scoping reviews, we reviewed AIM literature from the past 2 years in the fields of biomedical technology, AI, and information systems. We searched 3 databases, PubsOnline at INFORMS, e-journal archive at MIS Quarterly, and Association for Computing Machinery (ACM) Digital Library using keywords such as “mobile healthcare,” “wearable medical sensors,” “smartphones”, and “AI.” We included AIM articles and excluded technical articles focused only on AI models. We also used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) technique for identifying articles that represent a comprehensive view of current research in the AIM domain.

Results

We screened 108 articles focusing on developing AIM models for ensuring better health care delivery, detecting diseases early, and diagnosing chronic health conditions, and 37 articles were eligible for inclusion, with 31 of the 37 articles being published last year (76%). Of the included articles, 9 studied AI models to detect serious mental health issues, such as depression and suicidal tendencies, and chronic health conditions, such as sleep apnea and diabetes. Several articles discussed the application of AIM models for remote patient monitoring and disease management. The considered primary health concerns belonged to 3 categories: mental health, physical health, and health promotion and wellness. Moreover, 14 of the 37 articles used AIM applications to research physical health, representing 38% of the total studies. Finally, 28 out of the 37 (76%) studies used proprietary data sets rather than public data sets. We found a lack of research in addressing chronic mental health issues and a lack of publicly available data sets for AIM research.

Conclusions

The application of AIM models for disease detection and management is a growing research domain. These models provide accurate predictions for enabling preventive care on a broader scale in the health care domain. Given the ever-increasing need for remote disease management during the pandemic, recent AI techniques, such as federated learning and explainable AI, can act as a catalyst for increasing the adoption of AIM and enabling secure data sharing across the health care industry.

Exploring Wearables to Focus on the “Sweet Spot” of Physical Activity and Sleep After Hospitalization: Secondary Analysis

Background

Inadequate sleep and physical activity are common during and after hospitalization, but their impact on patient-reported functional outcomes after discharge is poorly understood. Wearable devices that measure sleep and activity can provide patient-generated data to explore ideal levels of sleep and activity to promote recovery after hospital discharge.

Objective

This study aimed to examine the relationship between daily sleep and physical activity with 6 patient-reported functional outcomes (symptom burden, sleep quality, physical health, life space mobility, activities of daily living, and instrumental activities of daily living) at 13 weeks after hospital discharge.

Methods

This secondary analysis sought to examine the relationship between daily sleep, physical activity, and patient-reported outcomes at 13 weeks after hospital discharge. We utilized wearable sleep and activity trackers (Withings Activité wristwatch) to collect data on sleep and activity. We performed descriptive analysis of device-recorded sleep (minutes/night) with patient-reported sleep and device-recorded activity (steps/day) for the entire sample with full data to explore trends. Based on these trends, we performed additional analyses for a subgroup of patients who slept 7-9 hours/night on average. Differences in patient-reported functional outcomes at 13 weeks following hospital discharge were examined using a multivariate linear regression model for this subgroup.

Results

For the full sample of 120 participants, we observed a “T-shaped” distribution between device-reported physical activity (steps/day) and sleep (patient-reported quality or device-recorded minutes/night) with lowest physical activity among those who slept <7 or >9 hours/night. We also performed a subgroup analysis (n=60) of participants that averaged the recommended 7-9 hours of sleep/night over the 13-week study period. Our key finding was that participants who had both adequate sleep (7-9 hours/night) and activity (>5000 steps/day) had better functional outcomes at 13 weeks after hospital discharge. Participants with adequate sleep but less activity (<5000 steps/day) had significantly worse symptom burden (z-score 0.93, 95% CI 0.3 to 1.5; P=.02), community mobility (z-score –0.77, 95% CI –1.3 to –0.15; P=.02), and perceived physical health (z-score –0.73, 95% CI –1.3 to –0.13; P=.003), compared with those who were more physically active (≥5000 steps/day).

Conclusions

Participants within the “sweet spot” that balances recommended sleep (7-9 hours/night) and physical activity (>5000 steps/day) reported better functional outcomes after 13 weeks compared with participants outside the “sweet spot.” Wearable sleep and activity trackers may provide opportunities to hone postdischarge monitoring and target a “sweet spot” of recommended levels for both sleep and activity needed for optimal recovery.

Trial Registration

ClinicalTrials.gov NCT03321279; https://clinicaltrials.gov/ct2/show/NCT03321279

Wearables in Schizophrenia: Update on Current and Future Clinical Applications

Schizophrenia affects 1% of the world population and is associated with a reduction in life expectancy of 20 years. The increasing prevalence of both consumer technology and clinical-grade wearable technology offers new metrics to guide clinical decision-making remotely and in real time. Herein, recent literature is reviewed to determine the potential utility of wearables in schizophrenia, including their utility in diagnosis, first-episode psychosis, and relapse prevention and their acceptability to patients. Several studies have further confirmed the validity of various devices in their ability to track sleep—an especially useful metric in schizophrenia, as sleep disturbances may be predictive of disease onset or the acute worsening of psychotic symptoms. Through machine learning, wearable-obtained heart rate and motor activity were used to differentiate between controls and patients with schizophrenia. Wearables can capture the autonomic dysregulation that has been detected when patients are actively experiencing paranoia, hallucinations, or delusions. Multiple platforms are currently being researched, such as Health Outcomes Through Positive Engagement and Self-Empowerment, Mobile Therapeutic Attention for Treatment-Resistant Schizophrenia, and Sleepsight, that may ultimately link patient data to clinicians. The future is bright for wearables in schizophrenia, as the recent literature exemplifies their potential to offer real-time insights to guide diagnosis and management.

Sleeping in an Inclined Position to Reduce Snoring and Improve Sleep: In-home Product Intervention Study

BACKGROUND

Accurately and unobtrusively testing the effects of snoring and sleep interventions at home has become possible with recent advances in digital measurement technologies.

OBJECTIVE

The aim of this study was to examine the effectiveness of using an adjustable bed base to sleep with the upper body in an inclined position to reduce snoring and improve sleep, measured at home using commercially available trackers.

METHODS

Self-reported snorers (N=25) monitored their snoring and sleep nightly and completed questionnaires daily for 8 weeks. They slept flat for the first 4 weeks, then used an adjustable bed base to sleep with the upper body at a 12-degree incline for the next 4 weeks.

RESULTS

Over 1000 nights of data were analyzed. Objective snoring data showed a 7% relative reduction in snoring duration (P=.001) in the inclined position. Objective sleep data showed 4% fewer awakenings (P=.04) and a 5% increase in the proportion of time spent in deep sleep (P=.02) in the inclined position. Consistent with these objective findings, snoring and sleep measured by self-report improved.

CONCLUSIONS

New measurement technologies allow intervention studies to be conducted in the comfort of research participants' own bedrooms. This study showed that sleeping at an incline has potential as a nonobtrusive means of reducing snoring and improving sleep in a nonclinical snoring population.

Importance of Getting Enough Sleep and Daily Activity Data to Assess Variability: Longitudinal Observational Study

Background

The gold standard measurement for recording sleep is polysomnography performed in a hospital environment for 1 night. This requires individuals to sleep with a device and several sensors attached to their face, scalp, and body, which is both cumbersome and expensive. Self-trackers, such as wearable sensors (eg, smartwatch) and nearable sensors (eg, sleep mattress), can measure a broad range of physiological parameters related to free-living sleep conditions; however, the optimal duration of such a self-tracker measurement is not known. For such free-living sleep studies with actigraphy, 3 to 14 days of data collection are typically used.

Objective

The primary goal of this study is to investigate if 3 to 14 days of sleep data collection is sufficient while using self-trackers. The secondary goal is to investigate whether there is a relationship among sleep quality, physical activity, and heart rate. Specifically, we study whether individuals who exhibit similar activity can be clustered together and to what extent the sleep patterns of individuals in relation to seasonality vary.

Methods

Data on sleep, physical activity, and heart rate were collected over 6 months from 54 individuals aged 52 to 86 years. The Withings Aura sleep mattress (nearable; Withings Inc) and Withings Steel HR smartwatch (wearable; Withings Inc) were used. At the individual level, we investigated the consistency of various physical activities and sleep metrics over different time spans to illustrate how sensor data from self-trackers can be used to illuminate trends. We used exploratory data analysis and unsupervised machine learning at both the cohort and individual levels.

Results

Significant variability in standard metrics of sleep quality was found between different periods throughout the study. We showed specifically that to obtain more robust individual assessments of sleep and physical activity patterns through self-trackers, an evaluation period of >3 to 14 days is necessary. In addition, we found seasonal patterns in sleep data related to the changing of the clock for daylight saving time.

Conclusions

We demonstrate that >2 months’ worth of self-tracking data are needed to provide a representative summary of daily activity and sleep patterns. By doing so, we challenge the current standard of 3 to 14 days for sleep quality assessment and call for the rethinking of standards when collecting data for research purposes. Seasonal patterns and daylight saving time clock change are also important aspects that need to be taken into consideration when choosing a period for collecting data and designing studies on sleep. Furthermore, we suggest using self-trackers (wearable and nearable ones) to support longer-term evaluations of sleep and physical activity for research purposes and, possibly, clinical purposes in the future.

Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson's Disease

Rapid eye movement (REM) sleep behavior disorder (RBD) is associated with Parkinson's disease (PD). In this study, a smartwatch-based sensor is utilized as a convenient tool to detect the abnormal RBD phenomenon in PD patients. Instead, a questionnaire with sleep quality assessment and sleep physiological indices, such as sleep stage, activity level, and heart rate, were measured in the smartwatch sensors. Therefore, this device can record comprehensive sleep physiological data, offering several advantages such as ubiquity, long-term monitoring, and wearable convenience. In addition, it can provide the clinical doctor with sufficient information on the patient's sleeping patterns with individualized treatment. In this study, a three-stage sleep staging method (i.e., comprising sleep/awake detection, sleep-stage detection, and REM-stage detection) based on an accelerometer and heart-rate data is implemented using machine learning (ML) techniques. The ML-based algorithms used here for sleep/awake detection, sleep-stage detection, and REM-stage detection were a Cole-Kripke algorithm, a stepwise clustering algorithm, and a k-means clustering algorithm with predefined criteria, respectively. The sleep staging method was validated in a clinical trial. The results showed a statistically significant difference in the percentage of abnormal REM between the control group (1.6 ± 1.3; n = 18) and the PD group (3.8 ± 5.0; n = 20) (p = 0.04). The percentage of deep sleep stage in our results presented a significant difference between the control group (38.1 ± 24.3; n = 18) and PD group (22.0 ± 15.0, n = 20) (p = 0.011) as well. Further, our results suggested that the smartwatch-based sensor was able to detect the difference of an abnormal REM percentage in the control group (1.6 ± 1.3; n = 18), PD patient with clonazepam (2.0 ± 1.7; n = 10), and without clonazepam (5.7 ± 7.1; n = 10) (p = 0.007). Our results confirmed the effectiveness of our sensor in investigating the sleep stage in PD patients. The sensor also successfully determined the effect of clonazepam on reducing abnormal REM in PD patients. In conclusion, our smartwatch sensor is a convenient and effective tool for sleep quantification analysis in PD patients.

Recent Advances in Wearable Optical Sensor Automation Powered by Battery versus Skin-like Battery-Free Devices for Personal Healthcare-A Review

Currently, old-style personal Medicare techniques rely mostly on traditional methods, such as cumbersome tools and complicated processes, which can be time consuming and inconvenient in some circumstances. Furthermore, such old methods need the use of heavy equipment, blood draws, and traditional bench-top testing procedures. Invasive ways of acquiring test samples can potentially cause patient discomfort and anguish. Wearable sensors, on the other hand, may be attached to numerous body areas to capture diverse biochemical and physiological characteristics as a developing analytical tool. Physical, chemical, and biological data transferred via the skin are used to monitor health in various circumstances. Wearable sensors can assess the aberrant conditions of the physical or chemical components of the human body in real time, exposing the body state in time, thanks to unintrusive sampling and high accuracy. Most commercially available wearable gadgets are mechanically hard components attached to bands and worn on the wrist, with form factors ultimately constrained by the size and weight of the batteries required for the power supply. Basic physiological signals comprise a lot of health-related data. The estimation of critical physiological characteristics, such as pulse inconstancy or variability using photoplethysmography (PPG) and oxygen saturation in arterial blood using pulse oximetry, is possible by utilizing an analysis of the pulsatile component of the bloodstream. Wearable gadgets with “skin-like” qualities are a new type of automation that is only starting to make its way out of research labs and into pre-commercial prototypes. Flexible skin-like sensing devices have accomplished several functionalities previously inaccessible for typical sensing devices due to their deformability, lightness, portability, and flexibility. In this paper, we studied the recent advancement in battery-powered wearable sensors established on optical phenomena and skin-like battery-free sensors, which brings a breakthrough in wearable sensing automation.

Stride Velocity 95th Centile: Insights into Gaining Regulatory Qualification of the First Wearable-Derived Digital Endpoint for use in Duchenne Muscular Dystrophy Trials

In 2019, stride velocity 95th centile (SV95C) became the first wearable-derived digital clinical outcome assessment (COA) qualified by the European Medicines Agency (EMA) for use as a secondary endpoint in trials for Duchenne muscular dystrophy. SV95C was approved via the EMA's qualification pathway for novel methodologies for medicine development, which is a voluntary procedure for assessing the regulatory acceptability of innovative methods used in pharmaceutical research and development. SV95C is an objective, real-world digital ambulation measure of peak performance, representing the speed of the fastest strides taken by the wearer over a recording period of 180 hours. SV95C is correlated with traditional clinic-based assessments of motor function and has greater sensitivity to clinical change over 6 months than other wearable-derived stride variables, for example, median stride length or velocity. SV95C overcomes many limitations of episodic, clinic-based motor function testing, allowing the assessment of ambulation ability between clinic visits and under free-living conditions. Here we highlight considerations and challenges in developing SV95C using evidence generated by a high-performance wearable sensor. We also provide a commentary of the device's technical capabilities, which were a determining factor in the regulatory approval of SV95C. This article aims to provide insights into the methods employed, and the challenges faced, during the regulatory approval process for researchers developing new digital tools for patients with diseases that affect motor function.

Guiding principles for determining work shift duration and addressing the effects of work shift duration on performance, safety, and health: guidance from the American Academy of Sleep Medicine and the Sleep Research Society

CITATION

Risks associated with fatigue that accumulates during work shifts have historically been managed through working time arrangements that specify fixed maximum durations of work shifts and minimum durations of time off. By themselves, such arrangements are not sufficient to curb risks to performance, safety, and health caused by misalignment between work schedules and the biological regulation of waking alertness and sleep. Science-based approaches for determining shift duration and mitigating associated risks, while addressing operational needs, require: (1) a recognition of the factors contributing to fatigue and fatigue-related risks; (2) an understanding of evidence-based countermeasures that may reduce fatigue and/or fatigue-related risks; and (3) an informed approach to selecting workplace-specific strategies for managing work hours. We propose a series of guiding principles to assist stakeholders with designing a shift duration decision-making process that effectively balances the need to meet operational demands with the need to manage fatigue-related risks.

Circadian Rhythm Analysis Using Wearable Device Data: Novel Penalized Machine Learning Approach

BACKGROUND

Wearable devices have been widely used in clinical studies to study daily activity patterns, but the analysis remains a major obstacle for researchers.

OBJECTIVE

This study proposes a novel method to characterize sleep-activity rhythms using actigraphy and further use it to describe early childhood daily rhythm formation and examine its association with physical development.

METHODS

We developed a machine learning-based Penalized Multiband Learning (PML) algorithm to sequentially infer dominant periodicities based on the Fast Fourier Transform (FFT) algorithm and further characterize daily rhythms. We implemented and applied the algorithm to Actiwatch data collected from a cohort of 262 healthy infants at ages 6, 12, 18, and 24 months, with 159, 101, 111, and 141 participants at each time point, respectively. Autocorrelation analysis and Fisher test in harmonic analysis with Bonferroni correction were applied for comparison with the PML. The association between activity rhythm features and early childhood motor development, assessed using the Peabody Developmental Motor Scales-Second Edition (PDMS-2), was studied through linear regression analysis.

RESULTS

The PML results showed that 1-day periodicity was most dominant at 6 and 12 months, whereas one-day, one-third-day, and half-day periodicities were most dominant at 18 and 24 months. These periodicities were all significant in the Fisher test, with one-fourth-day periodicity also significant at 12 months. Autocorrelation effectively detected 1-day periodicity but not the other periodicities. At 6 months, PDMS-2 was associated with the assessment seasons. At 12 months, PDMS-2 was associated with the assessment seasons and FFT signals at one-third-day periodicity (P<.001) and half-day periodicity (P=.04), respectively. In particular, the subcategories of stationary, locomotion, and gross motor were associated with the FFT signals at one-third-day periodicity (P<.001).

CONCLUSIONS

The proposed PML algorithm can effectively conduct circadian rhythm analysis using time-series wearable device data. The application of the method effectively characterized sleep-wake rhythm development and identified the association between daily rhythm formation and motor development during early childhood.

Feasibility and Effectiveness of Assessing Subhealth Using a Mobile Health Management App (MibyeongBogam) in Early Middle-Aged Koreans: Randomized Controlled Trial

BACKGROUND

Mobile health (mHealth) is a major source of health management systems. Moreover, the demand for mHealth, which is in need of change due to the COVID-19 pandemic, is increasing worldwide. Accordingly, interest in health care in everyday life and the importance of mHealth are growing.

OBJECTIVE

We developed the MibyeongBogam (MBBG) app that evaluates the user's subhealth status via a smartphone and provides a health management method based on that user's subhealth status for use in everyday life. Subhealth is defined as a state in which the capacity to recover to a healthy state is diminished, but without the presence of clinical disease. The objective of this study was to compare the awareness and status of subhealth after the use of the MBBG app between intervention and control groups, and to evaluate the app's practicality.

METHODS

This study was a prospective, open-label, parallel group, randomized controlled trial. The study was conducted at two hospitals in Korea with 150 healthy people in their 30s and 40s, at a 1:1 allocation ratio. Participants visited the hospital three times as follows: preintervention, intermediate visit 6 weeks after the intervention, and final visit 12 weeks after the intervention. Key endpoints were measured at the first visit before the intervention and at 12 weeks after the intervention. The primary outcome was the awareness of subhealth, and the secondary outcomes were subhealth status, health-promoting behaviors, and motivation to engage in healthy behaviors.

RESULTS

The primary outcome, subhealth awareness, tended to slightly increase for both groups after the uncompensated intervention, but there was no significant difference in the score between the two groups (intervention group: mean 23.69, SD 0.25 vs control group: mean 23.1, SD 0.25; P=.09). In the case of secondary outcomes, only some variables of the subhealth status showed significant differences between the two groups after the intervention, and the intervention group showed an improvement in the total scores of subhealth (P=.03), sleep disturbance (P=.02), depression (P=.003), anger (P=.01), and anxiety symptoms (P=.009) compared with the control group.

CONCLUSIONS

In this study, the MBBG app showed potential for improving the health, especially with regard to sleep disturbance and depression, of individuals without particular health problems. However, the effects of the app on subhealth awareness and health-promoting behaviors were not clearly evaluated. Therefore, further studies to assess improvements in health after the use of personalized health management programs provided by the MBBG app are needed. The MBBG app may be useful for members of the general public, who are not diagnosed with a disease but are unable to lead an optimal daily life due to discomfort, to seek strategies that can improve their health.

TRIAL REGISTRATION

Clinical Research Information Service KCT0003488; https://cris.nih.go.kr/cris/search/search_result_st01.jsp?seq=14379.

A Feasibility Study on Smart Mattresses to Improve Sleep Quality

Good sleep quality is essential, especially for clinical users. Sleep disorders not only impair the success rate of treatment but also delay recovery. They can seriously interfere with treatment outcomes and even endanger a user's life. In this study, we created a smart mattress containing 10 × 18 air packs and control units. Each air pack contains a set of pressure and height sensors and two air valves. Each row control unit can detect and adjust the pressure and height of each air bag in the row. When the bed body is turned on, it automatically initializes, adjusts the state of each air bag to the same height and pressure, and enters a slow scanning state. When perceived objects or people are lying on the bed, the bed automatically perceives the human body structure and body pressure matrix, increases the scanning speed for more timely and accurate measurements of the digital matrix and forming pressure by matrix-normalized processing, and then uses local pressure variance detection to automatically adjust to the sleeping position of the human body and thus achieve a uniform force distribution and a comfortable state. Finally, pressure matrix binarization was used to match sleeping position templates to identify the best template for automatic recognition of the sleeping position. The experimental results show that the sleeping position recognition method has high accuracy, recall, and precision. Our mattress is designed with interfaces for external devices. In future research, the smart mattress can connect to an auxiliary part of a smart ecosystem consisting of a smart pill box, a smart lighting system, and a microclimate system, which is expected to yield a more comprehensive intelligent ward to explore the possibility of improving sleep quality.

Guiding principles for determining work shift duration and addressing the effects of work shift duration on performance, safety, and health: guidance from the American Academy of Sleep Medicine and the Sleep Research Society

Risks associated with fatigue that accumulates during work shifts have historically been managed through working time arrangements that specify fixed maximum durations of work shifts and minimum durations of time off. By themselves, such arrangements are not sufficient to curb risks to performance, safety, and health caused by misalignment between work schedules and the biological regulation of waking alertness and sleep. Science-based approaches for determining shift duration and mitigating associated risks, while addressing operational needs, require: (1) a recognition of the factors contributing to fatigue and fatigue-related risks; (2) an understanding of evidence-based countermeasures that may reduce fatigue and/or fatigue-related risks; and (3) an informed approach to selecting workplace-specific strategies for managing work hours. We propose a series of guiding principles to assist stakeholders with designing a shift duration decision-making process that effectively balances the need to meet operational demands with the need to manage fatigue-related risks.

Sleep Detection for Younger Adults, Healthy Older Adults, and Older Adults Living With Dementia Using Wrist Temperature and Actigraphy: Prototype Testing and Case Study Analysis

BACKGROUND

Sleep is essential for one's health and quality of life. Wearable technologies that use motion and temperature sensors have made it possible to self-monitor sleep. Although there is a growing body of research on sleep monitoring using wearable devices for healthy young-to-middle-aged adults, few studies have focused on older adults, including those living with dementia.

OBJECTIVE

This study aims to investigate the impact of age and dementia on sleep detection through movement and wrist temperature.

METHODS

A total of 10 younger adults, 10 healthy older adults, and 8 older adults living with dementia (OAWD) were recruited. Each participant wore a Mi Band 2 (accemetry-based sleep detection) and our custom-built wristband (actigraphy and wrist temperature) 24 hours a day for 2 weeks and was asked to keep a daily sleep journal. Sleep parameters detected by the Mi Band 2 were compared with sleep journals, and visual analysis of actigraphy and temperature data was performed.

RESULTS

The absolute differences in sleep onset and offset between the sleep journals and Mi Band 2 were 39 (SD 51) minutes and 31 (SD 52) minutes for younger adults, 49 (SD 58) minutes and 33 (SD 58) minutes for older adults, and 253 (SD 104) minutes and 161 (SD 94) minutes for OAWD. The Mi Band 2 was unable to accurately detect sleep in 3 healthy older adults and all OAWDs. The average sleep and wake temperature difference of OAWD (1.26 °C, SD 0.82 °C) was significantly lower than that of healthy older adults (2.04 °C, SD 0.70 °C) and healthy younger adults (2.48 °C, SD 0.88 °C). Actigraphy data showed that older adults had more movement during sleep compared with younger adults and that this trend appears to increase for those with dementia.

CONCLUSIONS

The Mi Band 2 did not accurately detect sleep in older adults who had greater levels of nighttime movement. As more nighttime movement appears to be a phenomenon that increases in prevalence with age and even more so with dementia, further research needs to be conducted with a larger sample size and greater diversity of commercially available wearable devices to explore these trends more conclusively. All participants, including older adults and OAWD, had a distinct sleep and wake wrist temperature contrast, which suggests that wrist temperature could be leveraged to create more robust and broadly applicable sleep detection algorithms.