Validation of the Withings Sleep Analyzer, an under-the-mattress device for the detection of moderate-severe sleep apnea syndrome

Reliable Contactless Monitoring of Heart Rate, Breathing Rate, and Breathing Disturbance During Sleep in Aging: Digital Health Technology Evaluation Study

BACKGROUND

Longitudinal monitoring of vital signs provides a method for identifying changes to general health in an individual, particularly in older adults. The nocturnal sleep period provides a convenient opportunity to assess vital signs. Contactless technologies that can be embedded into the bedroom environment are unintrusive and burdenless and have the potential to enable seamless monitoring of vital signs. To realize this potential, these technologies need to be evaluated against gold standard measures and in relevant populations.

OBJECTIVE

We aimed to evaluate the accuracy of heart rate and breathing rate measurements of 3 contactless technologies (2 undermattress trackers, Withings Sleep Analyzer [WSA] and Emfit QS [Emfit]; and a bedside radar, Somnofy) in a sleep laboratory environment and assess their potential to capture vital signs in a real-world setting.

METHODS

Data were collected from 35 community-dwelling older adults aged between 65 and 83 (mean 70.8, SD 4.9) years (men: n=21, 60%) during a 1-night clinical polysomnography (PSG) test in a sleep laboratory, preceded by 7 to 14 days of data collection at home. Several of the participants (20/35, 57%) had health conditions, including type 2 diabetes, hypertension, obesity, and arthritis, and 49% (17) had moderate to severe sleep apnea, while 29% (n=10) had periodic leg movement disorder. The undermattress trackers provided estimates of both heart rate and breathing rate, while the bedside radar provided only the breathing rate. The accuracy of the heart rate and breathing rate estimated by the devices was compared with PSG electrocardiogram-derived heart rate (beats per minute) and respiratory inductance plethysmography thorax-derived breathing rate (cycles per minute), respectively. We also evaluated breathing disturbance indexes of snoring and the apnea-hypopnea index, available from the WSA.

RESULTS

All 3 contactless technologies provided acceptable accuracy in estimating heart rate (mean absolute error <2.12 beats per minute and mean absolute percentage error <5%) and breathing rate (mean absolute error ≤1.6 cycles per minute and mean absolute percentage error <12%) at 1-minute resolution. All 3 contactless technologies were able to capture changes in heart rate and breathing rate across the sleep period. The WSA snoring and breathing disturbance estimates were also accurate compared with PSG estimates (WSA snore: r2=0.76; P<.001; WSA apnea-hypopnea index: r2=0.59; P<.001).

CONCLUSIONS

Contactless technologies offer an unintrusive alternative to conventional wearable technologies for reliable monitoring of heart rate, breathing rate, and sleep apnea in community-dwelling older adults at scale. They enable the assessment of night-to-night variation in these vital signs, which may allow the identification of acute changes in health, and longitudinal monitoring, which may provide insight into health trajectories.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.3390/clockssleep6010010.

Validation of the Sleepiz One + as a radar-based sensor for contactless diagnosis of sleep apnea

PURPOSE

The cardiorespiratory polysomnography (PSG) is an expensive and limited resource. The Sleepiz One + is a novel radar-based contactless monitoring device that can be used e.g. for longitudinal detection of nocturnal respiratory events. The present study aimed to compare the performance of the Sleepiz One + device to the PSG regarding the accuracy of apnea-hypopnea index (AHI).

METHODS

From January to December 2021, a total of 141 adult volunteers who were either suspected of having sleep apnea or who were healthy sleepers took part in a sleep study. This examination served to validate the Sleepiz One + device in the presence and absence of additional SpO2 information. The AHI determined by the Sleepiz One + monitor was estimated automatically and compared with the AHI derived from manual PSG scoring.

RESULTS

The correlation between the Sleepiz-AHI and the PSG-AHI with and without additional SpO2 measurement was rp = 0.94 and rp = 0,87, respectively. In general, the Bland-Altman plots showed good agreement between the two methods of AHI measurement, though their deviations became larger with increasing sleep-disordered breathing. Sensitivity and specificity for recordings without additional SpO2 was 85% and 88%, respectively. Adding a SpO2 sensor increased the sensitivity to 88% and the specificity to 98%.

CONCLUSION

The Sleepiz One + device is a valid diagnostic tool for patients with moderate to severe OSA. It can also be easily used in the home environment and is therefore beneficial for e.g. immobile and infectious patients. TRIAL REGISTRATION NUMBER AND DATE OF REGISTRATION FOR PROSPECTIVELY REGISTERED TRIALS: This study was registered on clinicaltrials.gov (NCT04670848) on 2020-12-09.

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

BACKGROUND

People with serious mental illnesses (SMIs) have three-fold higher rates of comorbid insomnia than the general population, which has downstream effects on cognitive, mental, and physical health. Cognitive Behavioral Therapy for Insomnia (CBT-i) is a safe and effective first-line treatment for insomnia, though the therapy's effectiveness relies on completing nightly sleep diaries which can be challenging for some people with SMI and comorbid cognitive deficits. Supportive technologies such as mobile applications and sleep sensors may aid with completing sleep diaries. However, commercially available CBT-i apps are not designed for individuals with cognitive deficits. To aid with this challenge, we have developed an integrated mobile application, named "Sleep Catcher," that will automatically incorporate data from a wearable fitness tracker and a bed sensor to track nightly sleep duration, overnight awakenings, bed-times, and wake-times to generate nightly sleep diaries for CBT-i.

METHODS

The application development process will be described-writing algorithms to generating useful data, creating a clinician web portal to oversee patients and the mobile application, and integrating sleep data from device platforms and user input.

RESULTS

The mobile and web applications were developed using Flutter, IBM Code Engine, and IBM Cloudant database. The mobile application was developed with a user-centered approach and incremental changes informed by a series of beta tests. Special user-interface features were considered to address the challenges of developing a simple and effective mobile application targeting people with SMI.

CONCLUSION

There is strong potential for synergy between engineering and mental health expertise to develop technologies for specific clinical populations. Digital health technologies allow for the development of multi-disciplinary solutions to existing health disparities in vulnerable populations, particularly in people with SMI.

Piezoelectric rubber sheet sensor: a promising tool for home sleep apnea testing

PURPOSE

This study aimed to develop an unobtrusive method for home sleep apnea testing (HSAT) utilizing micromotion signals obtained by a piezoelectric rubber sheet sensor.

METHODS

Algorithms were designated to extract respiratory and ballistocardiogram components from micromotion signals and to detect respiratory events as the characteristic separation of the fast envelope of the respiration component from the slow envelope. In 78 adults with diagnosed or suspected sleep apnea, micromotion signal was recorded with a piezoelectric rubber sheet sensor placed beneath the bedsheet during polysomnography. In a half of the subjects, the algorithms were optimized to calculate respiratory event index (REI), estimating apnea-hypopnea index (AHI). In the other half of subjects, the performance of REI in classifying sleep apnea severity was evaluated. Additionally, the predictive value of the frequency of cyclic variation in heart rate (Fcv) obtained from the ballistocardiogram was assessed.

RESULTS

In the training group, the optimized REI showed a strong correlation with the AHI (r = 0.93). Using the optimal cutoff of REI ≥ 14/h, subjects with an AHI ≥ 15 were identified with 77.8% sensitivity and 90.5% specificity. When applying this REI to the test group, it correlated closely with the AHI (r = 0.92) and identified subjects with an AHI ≥ 15 with 87.5% sensitivity and 91.3% specificity. While Fcv showed a modest correlation with AHI (r = 0.46 and 0.66 in the training and test groups), it lacked independent predictive power for AHI.

CONCLUSION

The analysis of respiratory component of micromotion using piezoelectric rubber sheet sensors presents a promising approach for HSAT, providing a practical and effective means of estimating sleep apnea severity.

Prospective cohort study of long-term neurological outcomes in retired elite athletes: the Advanced BiomaRker, Advanced Imaging and Neurocognitive (BRAIN) Health Study protocol

INTRODUCTION

Although limited, recent research suggests that contact sport participation might have an adverse long-term effect on brain health. Further work is required to determine whether this includes an increased risk of neurodegenerative disease and/or subsequent changes in cognition and behaviour. The Advanced BiomaRker, Advanced Imaging and Neurocognitive Health Study will prospectively examine the neurological, psychiatric, psychological and general health of retired elite-level rugby union and association football/soccer players.

METHODS AND ANALYSIS

400 retired athletes will be recruited (200 rugby union and 200 association football players, male and female). Athletes will undergo a detailed clinical assessment, advanced neuroimaging, blood testing for a range of brain health outcomes and neuropsychological assessment longitudinally. Follow-up assessments will be completed at 2 and 4 years after baseline visit. 60 healthy volunteers will be recruited and undergo an aligned assessment protocol including advanced neuroimaging, blood testing and neuropsychological assessment. We will describe the previous exposure to head injuries across the cohort and investigate relationships between biomarkers of brain injury and clinical outcomes including cognitive performance, clinical diagnoses and psychiatric symptom burden.

ETHICS AND DISSEMINATION

Relevant ethical approvals have been granted by the Camberwell St Giles Research Ethics Committee (Ref: 17/LO/2066). The study findings will be disseminated through manuscripts in clinical/academic journals, presentations at professional conferences and through participant and stakeholder communications.

A perspective on automated rapid eye movement sleep assessment

Rapid eye movement sleep is associated with distinct changes in various biomedical signals that can be easily captured during sleep, lending themselves to automated sleep staging using machine learning systems. Here, we provide a perspective on the critical characteristics of biomedical signals associated with rapid eye movement sleep and how they can be exploited for automated sleep assessment. We summarise key historical developments in automated sleep staging systems, having now achieved classification accuracy on par with human expert scorers and their role in the clinical setting. We also discuss rapid eye movement sleep assessment with consumer sleep trackers and its potential for unprecedented sleep assessment on a global scale. We conclude by providing a future outlook of computerised rapid eye movement sleep assessment and the role AI systems may play.

Effect of transcranial direct current stimulation on postoperative sleep disturbance in older patients undergoing lower limb major arthroplasty: a prospective, double-blind, pilot, randomised controlled trial

Background

Postoperative sleep disturbance (PSD) is a common and serious postoperative complication and is associated with poor postoperative outcomes.

Aims

This study aimed to investigate the effect of transcranial direct current stimulation (tDCS) on PSD in older patients undergoing lower limb major arthroplasty.

Methods

In this prospective, double-blind, pilot, randomised, sham-controlled trial, patients 65 years and over undergoing lower limb major arthroplasty were randomly assigned to receive active tDCS (a-tDCS) or sham tDCS (s-tDCS). The primary outcomes were the objective sleep measures on postoperative nights (N) 1 and N2.

Results

116 inpatients were assessed for eligibility, and a total of 92 patients were enrolled; 47 received a-tDCS and 45 received s-tDCS. tDCS improved PSD by altering the following sleep measures in the a-tDCS and s-tDCS groups; the respective comparisons were as follows: the promotion of rapid eye movement (REM) sleep time on N1 (64.5 (33.5-105.5) vs 19.0 (0.0, 45.0) min, F=20.10, p<0.001) and N2 (75.0 (36.0-120.8) vs 30.0 (1.3-59.3) min, F=12.55, p<0.001); the total sleep time on N1 (506.0 (408.0-561.0) vs 392.0 (243.0-483.5) min, F=14.13, p<0.001) and N2 (488.5 (455.5-548.5) vs 346.0 (286.5-517.5) min, F=7.36, p=0.007); the deep sleep time on N1 (130.0 (103.3-177.0) vs 42.5 (9.8-100.8) min, F=24.4, p<0.001) and N2 (103.5 (46.0-154.8) vs 57.5 (23.3-106.5) min, F=8.4, p=0.004); and the percentages of light sleep and REM sleep on N1 and N2 (p<0.05 for each). The postoperative depression and anxiety scores did not differ significantly between the two groups. No significant adverse events were reported.

Conclusion

In older patients undergoing lower limb major arthroplasty, a single session of anodal tDCS over the left dorsolateral prefrontal cortex showed a potentially prophylactic effect in improving postoperative short-term objective sleep measures. However, this benefit was temporary and was not maintained over time.

Citizen neuroscience: Wearable technology and open software to study the human brain in its natural habitat

Citizen science allows the public to participate in various stages of scientific research, including study design, data acquisition, and data analysis. Citizen science has a long history in several fields of the natural sciences, and with recent developments in wearable technology, neuroscience has also become more accessible to citizen scientists. This development was largely driven by the influx of minimal sensing systems in the consumer market, allowing more do-it-yourself (DIY) and quantified-self (QS) investigations of the human brain. While most subfields of neuroscience require sophisticated monitoring devices and laboratories, the study of sleep characteristics can be performed at home with relevant noninvasive consumer devices. The strong influence of sleep quality on waking life and the accessibility of devices to measure sleep are two primary reasons citizen scientists have widely embraced sleep research. Their involvement has evolved from solely contributing to data collection to engaging in more collaborative or autonomous approaches, such as instigating ideas, formulating research inquiries, designing research protocols and methodology, acting upon their findings, and disseminating results. In this article, we introduce the emerging field of citizen neuroscience, illustrating examples of such projects in sleep research. We then provide overviews of the wearable technologies for tracking human neurophysiology and various open-source software used to analyse them. Finally, we discuss the opportunities and challenges in citizen neuroscience projects and suggest how to improve the study of the human brain outside the laboratory.

A Protocol for Evaluating Digital Technology for Monitoring Sleep and Circadian Rhythms in Older People and People Living with Dementia in the Community

Sleep and circadian rhythm disturbance are predictors of poor physical and mental health, including dementia. Long-term digital technology-enabled monitoring of sleep and circadian rhythms in the community has great potential for early diagnosis, monitoring of disease progression, and assessing the effectiveness of interventions. Before novel digital technology-based monitoring can be implemented at scale, its performance and acceptability need to be evaluated and compared to gold-standard methodology in relevant populations. Here, we describe our protocol for the evaluation of novel sleep and circadian technology which we have applied in cognitively intact older adults and are currently using in people living with dementia (PLWD). In this protocol, we test a range of technologies simultaneously at home (7-14 days) and subsequently in a clinical research facility in which gold standard methodology for assessing sleep and circadian physiology is implemented. We emphasize the importance of assessing both nocturnal and diurnal sleep (naps), valid markers of circadian physiology, and that evaluation of technology is best achieved in protocols in which sleep is mildly disturbed and in populations that are relevant to the intended use-case. We provide details on the design, implementation, challenges, and advantages of this protocol, along with examples of datasets.

Regular snoring is associated with uncontrolled hypertension

Snoring may be a risk factor for cardiovascular disease independent of other co-morbidities. However, most prior studies have relied on subjective, self-report, snoring evaluation. This study assessed snoring prevalence objectively over multiple months using in-home monitoring technology, and its association with hypertension prevalence. In this study, 12,287 participants were monitored nightly for approximately six months using under-the-mattress sensor technology to estimate the average percentage of sleep time spent snoring per night and the estimated apnea-hypopnea index (eAHI). Blood pressure cuff measurements from multiple daytime assessments were averaged to define uncontrolled hypertension based on mean systolic blood pressure≥140 mmHg and/or a mean diastolic blood pressure ≥90 mmHg. Associations between snoring and uncontrolled hypertension were examined using logistic regressions controlled for age, body mass index, sex, and eAHI. Participants were middle-aged (mean ± SD; 50 ± 12 y) and most were male (88%). There were 2467 cases (20%) with uncontrolled hypertension. Approximately 29, 14 and 7% of the study population snored for an average of >10, 20, and 30% per night, respectively. A higher proportion of time spent snoring (75th vs. 5th; 12% vs. 0.04%) was associated with a ~1.9-fold increase (OR [95%CI]; 1.87 [1.63, 2.15]) in uncontrolled hypertension independent of sleep apnea. Multi-night objective snoring assessments and repeat daytime blood pressure recordings in a large global consumer sample, indicate that snoring is common and positively associated with hypertension. These findings highlight the potential clinical utility of simple, objective, and noninvasive methods to detect snoring and its potential adverse health consequences.

Comparison of OPPO Watch Sleep Analyzer and Polysomnography for Obstructive Sleep Apnea Screening

Objective

To evaluate the clinical performance of the OPPO Watch (OW) Sleep Analyzer (OWSA) on OSA screening with polysomnography reference.

Methods

We recruited 350 participants using OWSA and PSG simultaneously in a sleep laboratory. The respiratory event index (REI) derived from OWSA and the apnea-hypopnea index (AHI) provided by PSG were compared. SHapley Additive exPlanation (SHAP) values were calculated to explain the model of OWSA.

Results

The OWSA-REI (26.5±18.5 events/h) correlated well with PSG-AHI (33.2±25.7 events/h; r = 0.91, p < 0.001), with an intraclass correlation coefficient (ICC) of 0.83. Using a threshold of AHI ≥15 events/h, the sensitivity, specificity, accuracy, and area under the curve (AUC) were 86.1%, 86.7%, 86.3%, and 0.94, respectively. Bland-Altman analysis showed that OWSA-REI and PSG-AHI were in good agreement (Mean Difference: -6.7, 95% CI:16.0 to -29.3 events/h). In addition, the effectiveness of the models in OWSA were also explained by visualizing SHAP values.

Conclusion

The OWSA demonstrated a reasonable performance for OSA screening in the clinical setting. In light of this, it is possible for smartwatches to become a complementary tool to PSG, which is particularly useful for larger-scale preliminary screenings.

Are we getting enough sleep? Frequent irregular sleep found in an analysis of over 11 million nights of objective in-home sleep data

OBJECTIVES

Evidence-based guidelines recommend that adults should sleep 7-9 h/night for optimal health and function. This study used noninvasive, multinight, objective sleep monitoring to determine average sleep duration and sleep duration variability in a large global community sample, and how often participants met the recommended sleep duration range.

METHODS

Data were analyzed from registered users of the Withings under-mattress Sleep Analyzer (predominantly located in Europe and North America) who had ≥28 nights of sleep recordings, averaging ≥4 per week. Sleep durations (the average and standard deviation) were assessed across a ∼9-month period. Associations between age groups, sex, and sleep duration were assessed using linear and logistic regressions, and proportions of participants within (7-9 hours) or outside (<7 hours or >9 hours) the recommended sleep duration range were calculated.

RESULTS

The sample consisted of 67,254 adults (52,523 males, 14,731 females; aged mean ± SD 50 ± 12 years). About 30% of adults demonstrated an average sleep duration outside the recommended 7-9 h/night. Even in participants with an average sleep duration within 7-9 hours, about 40% of nights were outside this range. Only 15% of participants slept between 7 and 9 hours for at least 5 nights per week. Female participants had significantly longer sleep durations than male participants, and middle-aged participants had shorter sleep durations than younger or older participants.

CONCLUSIONS

These findings indicate that a considerable proportion of adults are not regularly sleeping the recommended 7-9 h/night. Even among those who do, irregular sleep is prevalent. These novel data raise several important questions regarding sleep requirements and the need for improved sleep health policy and advocacy.

Estimating vigilance from the pre-work shift sleep using an under-mattress sleep sensor

Predicting vigilance impairment in high-risk shift work occupations is critical to help to reduce workplace errors and accidents. Current methods rely on multi-night, often manually entered, sleep data. This study developed a machine learning model for predicting vigilance errors based on a single prior sleep period, derived from an under-mattress sensor. Twenty-four healthy volunteers (mean [SD] age = 27.6 [9.5] years, 12 male) attended the laboratory on two separate occasions, 1 month apart, to compare wake performance and sleep under two different lighting conditions. Each condition occurred over an 8 day protocol comprising a baseline sleep opportunity from 10 p.m. to 7 a.m., a 27 h wake period, then daytime sleep opportunities from 10 a.m. to 7 p.m. on days 3-7. From 12 a.m. to 8 a.m. on each of days 4-7, participants completed simulated night shifts that included six 10 min psychomotor vigilance task (PVT) trials per shift. Sleep was assessed using an under-mattress sensor. Using extra-trees machine learning models, PVT performance (reaction times <500 ms, reaction, and lapses) during each night shift was predicted based on the preceding daytime sleep. The final extra-trees model demonstrated moderate accuracy for predicting PVT performance, with standard errors (RMSE) of 19.9 ms (reaction time, 359 [41.6]ms), 0.42 reactions/s (reaction speed, 2.5 [0.6] reactions/s), and 7.2 (lapses, 10.5 [12.3]). The model also correctly classified 84% of trials containing ≥5 lapses (Matthews correlation coefficient = 0.59, F1 = 0.83). Model performance is comparable to current fatigue prediction models that rely upon self-report or manually entered data. This efficient approach may help to manage fatigue and safety in non-standard work schedules.

Obstructive sleep apnea diagnosis and beyond using portable monitors

Obstructive sleep apnea (OSA) is a chronic sleep and breathing disorder with significant health complications, including cardiovascular disease and neurocognitive impairments. To ensure timely treatment, there is a need for a portable, accurate and rapid method of diagnosing OSA. This review examines the use of various physiological signals used in the detection of respiratory events and evaluates their effectiveness in portable monitors (PM) relative to gold standard polysomnography. The primary objective is to explore the relationship between these physiological parameters and OSA, their application in calculating the apnea hypopnea index (AHI), the standard metric for OSA diagnosis, and the derivation of non-AHI metrics that offer additional diagnostic value. It is found that increasing the number of parameters in PMs does not necessarily improve OSA detection. Several factors can cause performance variations among different PMs, even if they extract similar signals. The review also highlights the potential of PMs to be used beyond OSA diagnosis. These devices possess parameters that can be utilized to obtain endotypic and other non-AHI metrics, enabling improved characterization of the disorder and personalized treatment strategies. Advancements in PM technology, coupled with thorough evaluation and validation of these devices, have the potential to revolutionize OSA diagnosis, personalized treatment, and ultimately improve health outcomes for patients with OSA. By identifying the key factors influencing performance and exploring the application of PMs beyond OSA diagnosis, this review aims to contribute to the ongoing development and utilization of portable, efficient, and effective diagnostic tools for OSA.

In-Home Smartphone-Based Prediction of Obstructive Sleep Apnea in Conjunction With Level 2 Home Polysomnography

Importance

Consumer-level sleep analysis technologies have the potential to revolutionize the screening for obstructive sleep apnea (OSA). However, assessment of OSA prediction models based on in-home recording data is usually performed concurrently with level 1 in-laboratory polysomnography (PSG). Establishing the predictability of OSA using sound data recorded from smartphones based on level 2 PSG at home is important.

Objective

To validate the performance of a prediction model for OSA using breathing sound recorded from smartphones in conjunction with level 2 PSG at home.

Design, Setting, and Participants

This diagnostic study followed a prospective design, involving participants who underwent unattended level 2 home PSG. Breathing sounds were recorded during sleep using 2 smartphones, one with an iOS operating system and the other with an Android operating system, simultaneously with home PSG in participants' own home environment. Participants were 19 years and older, slept alone, and had either been diagnosed with OSA or had no previous diagnosis. The study was performed between February 2022 and February 2023.

Main Outcomes and Measures

Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the predictive model based on the recorded breathing sounds.

Results

Of the 101 participants included during the study duration, the mean (SD) age was 48.3 (14.9) years, and 51 (50.5%) were female. For the iOS smartphone, the sensitivity values at apnea-hypopnea index (AHI) levels of 5, 15, and 30 per hour were 92.6%, 90.9%, and 93.3%, respectively, with specificities of 84.3%, 94.4%, and 94.4%, respectively. Similarly, for the Android smartphone, the sensitivity values at AHI levels of 5, 15, and 30 per hour were 92.2%, 90.0%, and 92.9%, respectively, with specificities of 84.0%, 94.4%, and 94.3%, respectively. The accuracy for the iOS smartphone was 88.6%, 93.3%, and 94.3%, respectively, and for the Android smartphone was 88.1%, 93.1%, and 94.1% at AHI levels of 5, 15, and 30 per hour, respectively.

Conclusions and Relevance

This diagnostic study demonstrated the feasibility of predicting OSA with a reasonable level of accuracy using breathing sounds obtained by smartphones during sleep at home.

Predicting neurodegeneration from sleep related biofluid changes

Sleep-wake disturbances are common in neurodegenerative diseases and may occur years before the clinical diagnosis, potentially either representing an early stage of the disease itself or acting as a pathophysiological driver. Therefore, discovering biomarkers that identify individuals with sleep-wake disturbances who are at risk of developing neurodegenerative diseases will allow early diagnosis and intervention. Given the association between sleep and neurodegeneration, the most frequently analyzed fluid biomarkers in people with sleep-wake disturbances to date include those directly associated with neurodegeneration itself, such as neurofilament light chain, phosphorylated tau, amyloid-beta and alpha-synuclein. Abnormalities in these biomarkers in patients with sleep-wake disturbances are considered as evidence of an underlying neurodegenerative process. Levels of hormonal sleep-related biomarkers such as melatonin, cortisol and orexin are often abnormal in patients with clinical neurodegenerative diseases, but their relationships with the more standard neurodegenerative biomarkers remain unclear. Similarly, it is unclear whether other chronobiological/circadian biomarkers, such as disrupted clock gene expression, are causal factors or a consequence of neurodegeneration. Current data would suggest that a combination of fluid biomarkers may identify sleep-wake disturbances that are most predictive for the risk of developing neurodegenerative disease with more optimal sensitivity and specificity.

Multi-night measurement for diagnosis and simplified monitoring of obstructive sleep apnoea

Substantial night-to-night variability in obstructive sleep apnoea (OSA) severity has raised misdiagnosis and misdirected treatment concerns with the current prevailing single-night diagnostic approach. In-home, multi-night sleep monitoring technology may provide a feasible complimentary diagnostic pathway to improve both the speed and accuracy of OSA diagnosis and monitor treatment efficacy. This review describes the latest evidence on night-to-night variability in OSA severity, and its impact on OSA diagnostic misclassification. Emerging evidence for the potential impact of night-to-night variability in OSA severity to influence important health risk outcomes associated with OSA is considered. This review also characterises emerging diagnostic applications of wearable and non-wearable technologies that may provide an alternative, or complimentary, approach to traditional OSA diagnostic pathways. The required evidence to translate these devices into clinical care is also discussed. Appropriately sized randomised controlled trials are needed to determine the most appropriate and effective technologies for OSA diagnosis, as well as the optimal number of nights needed for accurate diagnosis and management. Potential risks versus benefits, patient perspectives, and cost-effectiveness of these novel approaches should be carefully considered in future trials.

Perioperative management of Obstructive Sleep Apnoea: Present themes and future directions

PURPOSE OF REVIEW

Obstructive sleep apnoea (OSA) is an increasingly common pathology that all those involved in perioperative care will come across. Patients with the condition present a challenge at many stages along the perioperative journey, not least because many patients living with OSA are unaware of their diagnosis.Key interventions can be made pre, intra-, and postoperatively to improve outcomes. Knowledge of screening tools, diagnostic tests, and the raft of treatment options are important for anyone caring for these patients.

RECENT FINDINGS

Recent literature has highlighted the increasing complexity of surgical patients and significant underdiagnosis of OSA in this patient population. Work has demonstrated how and why patients with OSA are at a higher perioperative risk and that effective positive airways pressure (PAP) therapy can reduce these risks, alongside evidencing how best to optimise adherence to therapy, a key issue in OSA.

SUMMARY

OSA, and particularly undiagnosed OSA, presents a huge problem in the perioperative period. Perioperative PAP reduces the risk of postoperative complications but adherence remains an issue. Bespoke perioperative pathways should be developed to identify and optimise high risk patients, although at present evidence on how best to achieve this is lacking.

Circadian-like patterns in electrochemical skin conductance measured from home-based devices: a retrospective study

In this study, we investigated the potential of electrochemical skin conductance (ESC) measurements gathered from home-based devices to detect circadian-like patterns. We analyzed data from 43,284 individuals using the Withings Body Comp or Body Scan scales, which provide ESC measurements. Our results highlighted a circadian pattern of ESC values across different age groups and countries. Our findings suggest that home-based ESC measurements could be used to evaluate circadian rhythm disorders associated with neuropathies and contribute to a better understanding of their pathophysiology. However, further controlled studies are needed to confirm these results. This study highlights the potential of digital health devices to generate new scientific and medical knowledge.

Three Contactless Sleep Technologies Compared With Actigraphy and Polysomnography in a Heterogeneous Group of Older Men and Women in a Model of Mild Sleep Disturbance: Sleep Laboratory Study

BACKGROUND

Contactless sleep technologies (CSTs) hold promise for longitudinal, unobtrusive sleep monitoring in the community and at scale. They may be particularly useful in older populations wherein sleep disturbance, which may be indicative of the deterioration of physical and mental health, is highly prevalent. However, few CSTs have been evaluated in older people.

OBJECTIVE

This study evaluated the performance of 3 CSTs compared to polysomnography (PSG) and actigraphy in an older population.

METHODS

Overall, 35 older men and women (age: mean 70.8, SD 4.9 y; women: n=14, 40%), several of whom had comorbidities, including sleep apnea, participated in the study. Sleep was recorded simultaneously using a bedside radar (Somnofy [Vital Things]: n=17), 2 undermattress devices (Withings sleep analyzer [WSA; Withings Inc]: n=35; Emfit-QS [Emfit; Emfit Ltd]: n=17), PSG (n=35), and actigraphy (Actiwatch Spectrum [Philips Respironics]: n=18) during the first night in a 10-hour time-in-bed protocol conducted in a sleep laboratory. The devices were evaluated through performance metrics for summary measures and epoch-by-epoch classification. PSG served as the gold standard.

RESULTS

The protocol induced mild sleep disturbance with a mean sleep efficiency (SEFF) of 70.9% (SD 10.4%; range 52.27%-92.60%). All 3 CSTs overestimated the total sleep time (TST; bias: >90 min) and SEFF (bias: >13%) and underestimated wake after sleep onset (bias: >50 min). Sleep onset latency was accurately detected by the bedside radar (bias: <6 min) but overestimated by the undermattress devices (bias: >16 min). CSTs did not perform as well as actigraphy in estimating the all-night sleep summary measures. In an epoch-by-epoch concordance analysis, the bedside radar performed better in discriminating sleep versus wake (Matthew correlation coefficient [MCC]: mean 0.63, SD 0.12, 95% CI 0.57-0.69) than the undermattress devices (MCC of WSA: mean 0.41, SD 0.15, 95% CI 0.36-0.46; MCC of Emfit: mean 0.35, SD 0.16, 95% CI 0.26-0.43). The accuracy of identifying rapid eye movement and light sleep was poor across all CSTs, whereas deep sleep (ie, slow wave sleep) was predicted with moderate accuracy (MCC: >0.45) by both Somnofy and WSA. The deep sleep duration estimates of Somnofy correlated (r2=0.60; P<.01) with electroencephalography slow wave activity (0.75-4.5 Hz) derived from PSG, whereas for the undermattress devices, this correlation was not significant (WSA: r2=0.0096, P=.58; Emfit: r2=0.11, P=.21).

CONCLUSIONS

These CSTs overestimated the TST, and sleep stage prediction was unsatisfactory in this group of older people in whom SEFF was relatively low. Although it was previously shown that CSTs provide useful information on bed occupancy, which may be useful for particular use cases, the performance of these CSTs with respect to the TST and sleep stage estimation requires improvement before they can serve as an alternative to PSG in estimating most sleep variables in older individuals.

Using home monitoring technology to study the effects of traumatic brain injury in older multimorbid adults

Internet of things (IOT) based in-home monitoring systems can passively collect high temporal resolution data in the community, offering valuable insight into the impact of health conditions on patients' day-to-day lives. We used this technology to monitor activity and sleep patterns in older adults recently discharged after traumatic brain injury (TBI). The demographics of TBI are changing, and it is now a leading cause of hospitalisation in older adults. However, research in this population is minimal. We present three cases, showcasing the potential of in-home monitoring systems in understanding and managing early recovery in older adults following TBI.

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.

Evaluating an under-mattress sleep monitor compared to a peripheral arterial tonometry home sleep apnea test device in the diagnosis of obstructive sleep apnea

STUDY OBJECTIVES

To evaluate whether or not the apnea-hypopnea index (AHI) from a peripheral arterial tonometry (PAT) home sleep apnea test (HSAT) is equivalent to the AHI provided by the mean of one, three, or seven nights from the Withings Sleep Analyzer (WSA) under-mattress device.

METHODS

We prospectively enrolled patients with suspected OSA in whom a PAT-HSAT was ordered. Eligible patients used the WSA for seven to nine nights. PAT data were scored using the device's intrinsic machine learning algorithms to arrive at the AHI using both 3% and 4% desaturation criteria for hypopnea estimations (PAT3%-AHI and PAT4%-AHI, respectively). These were then compared with the WSA-estimated AHI (WSA-AHI).

RESULTS

Of 61 patients enrolled, 35 completed the study with valid PAT and WSA data. Of the 35 completers 16 (46%) had at least moderately severe OSA (PAT3%-AHI ≥ 15). The seven-night mean WSA-AHI was 2.13 (95%CI =  - 0.88, 5.14) less than the PAT3%-AHI, but 5.64 (95%CI = 2.54, 8.73) greater than the PAT4%-AHI. The accuracy and area under the receiver operating curve (AUC) using the PAT3%-AHI ≥ 15 were 77% and 0.87 and for PAT4%-AHI ≥ 15 were 77% and 0.85, respectively. The one-, three-, or seven-night WSA-AHI were not equivalent to either the 3% or 4% PAT-AHI (equivalency threshold of ± 2.5 using the two one-sided t-test method).

CONCLUSIONS

The WSA derives estimates of the AHI unobtrusively over many nights, which may prove to be a valuable clinical tool. However, the WSA-AHI over- or underestimates the PAT-AHI in clinical use, and the appropriate use of the WSA in clinical practice will require further evaluation.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04778748.

Using home monitoring technology to study the effects of traumatic brain injury on older multimorbid adults: protocol for a feasibility study

INTRODUCTION

The prevalence of traumatic brain injury (TBI) among older adults is increasing exponentially. The sequelae can be severe in older adults and interact with age-related conditions such as multimorbidity. Despite this, TBI research in older adults is sparse. Minder, an in-home monitoring system developed by the UK Dementia Research Institute Centre for Care Research and Technology, uses infrared sensors and a bed mat to passively collect sleep and activity data. Similar systems have been used to monitor the health of older adults living with dementia. We will assess the feasibility of using this system to study changes in the health status of older adults in the early period post-TBI.

METHODS AND ANALYSIS

The study will recruit 15 inpatients (>60 years) with a moderate-severe TBI, who will have their daily activity and sleep patterns monitored using passive and wearable sensors over 6 months. Participants will report on their health during weekly calls, which will be used to validate sensor data. Physical, functional and cognitive assessments will be conducted across the duration of the study. Activity levels and sleep patterns derived from sensor data will be calculated and visualised using activity maps. Within-participant analysis will be performed to determine if participants are deviating from their own routines. We will apply machine learning approaches to activity and sleep data to assess whether the changes in these data can predict clinical events. Qualitative analysis of interviews conducted with participants, carers and clinical staff will assess acceptability and utility of the system.

ETHICS AND DISSEMINATION

Ethical approval for this study has been granted by the London-Camberwell St Giles Research Ethics Committee (REC) (REC number: 17/LO/2066). Results will be submitted for publication in peer-reviewed journals, presented at conferences and inform the design of a larger trial assessing recovery after TBI.

Evaluation of the Accuracy of Contactless Consumer Sleep-Tracking Devices Application in Human Experiment: A Systematic Review and Meta-Analysis

Compared with the gold standard, polysomnography (PSG), and silver standard, actigraphy, contactless consumer sleep-tracking devices (CCSTDs) are more advantageous for implementing large-sample and long-period experiments in the field and out of the laboratory due to their low price, convenience, and unobtrusiveness. This review aimed to examine the effectiveness of CCSTDs application in human experiments. A systematic review and meta-analysis (PRISMA) of their performance in monitoring sleep parameters were conducted (PROSPERO: CRD42022342378). PubMed, EMBASE, Cochrane CENTRALE, and Web of Science were searched, and 26 articles were qualified for systematic review, of which 22 provided quantitative data for meta-analysis. The findings show that CCSTDs had a better accuracy in the experimental group of healthy participants who wore mattress-based devices with piezoelectric sensors. CCSTDs' performance in distinguishing waking from sleeping epochs is as good as that of actigraphy. Moreover, CCSTDs provide data on sleep stages that are not available when actigraphy is used. Therefore, CCSTDs could be an effective alternative tool to PSG and actigraphy in human experiments.

Digital health technology to support care and improve outcomes of chronic kidney disease patients: as a case illustration, the Withings toolkit health sensing tools

Cardiovascular disease (CVD) is a major burden in dialysis-dependent chronic kidney disease (CKD5D) patients. Several factors contribute to this vulnerability including traditional risk factors such as age, gender, life style and comorbidities, and non-traditional ones as part of dialysis-induced systemic stress. In this context, it appears of utmost importance to bring a closer attention to CVD monitoring in caring for CKD5D patients to ensure early and appropriate intervention for improving their outcomes. Interestingly, new home-used, self-operated, connected medical devices offer convenient and new tools for monitoring in a fully automated and ambulatory mode CKD5D patients during the interdialytic period. Sensoring devices are installed with WiFi or Bluetooth. Some devices are also available in a cellular version such as the Withings Remote Patient Monitoring (RPM) solution. These devices analyze the data and upload the results to Withings HDS (Hybrid data security) platform servers. Data visualization can be viewed by the patient using the Withings Health Mate application on a smartphone, or with a web interface. Health Care Professionals (HCP) can also visualize patient data via the Withings web-based RPM interface. In this narrative essay, we analyze the clinical potential of pervasive wearable sensors for monitoring ambulatory dialysis patients and provide an assessment of such toolkit digital medical health devices currently available on the market. These devices offer a fully automated, unobtrusive and remote monitoring of main vital functions in ambulatory subjects. These unique features provide a multidimensional assessment of ambulatory CKD5D patients covering most physiologic functionalities, detecting unexpected disorders (i.e., volume overload, arrhythmias, sleep disorders) and allowing physicians to judge patient's response to treatment and recommendations. In the future, the wider availability of such pervasive health sensing and digital technology to monitor patients at an affordable cost price will improve the personalized management of CKD5D patients, so potentially resulting in improvements in patient quality of life and survival.

Mattress-Based Non-Influencing Sleep Apnea Monitoring System

A mattress-type non-influencing sleep apnea monitoring system was designed to detect sleep apnea-hypopnea syndrome (SAHS). The pressure signals generated during sleep on the mattress were collected, and ballistocardiogram (BCG) and respiratory signals were extracted from the original signals. In the experiment, wavelet transform (WT) was used to reduce noise and decompose and reconstruct the signal to eliminate the influence of interference noise, which can directly and accurately separate the BCG signal and respiratory signal. In feature extraction, based on the five features commonly used in SAHS, an innovative respiratory waveform similarity feature was proposed in this work for the first time. In the SAHS detection, the binomial logistic regression was used to determine the sleep apnea symptoms in the signal segment. Simulation and experimental results showed that the device, algorithm, and system designed in this work were effective methods to detect, diagnose, and assist the diagnosis of SAHS.

High night-to-night variability in sleep apnea severity is associated with uncontrolled hypertension

Obstructive sleep apnea (OSA) severity can vary markedly from night-to-night. However, the impact of night-to-night variability in OSA severity on key cardiovascular outcomes such as hypertension is unknown. Thus, the primary aim of this study is to determine the effects of night-to-night variability in OSA severity on hypertension likelihood. This study uses in-home monitoring of 15,526 adults with ~180 nights per participant with an under-mattress sleep sensor device, plus ~30 repeat blood pressure measures. OSA severity is defined from the mean estimated apnea-hypopnoea index (AHI) over the ~6-month recording period for each participant. Night-to-night variability in severity is determined from the standard deviation of the estimated AHI across recording nights. Uncontrolled hypertension is defined as mean systolic blood pressure ≥140 mmHg and/or mean diastolic blood pressure ≥90 mmHg. Regression analyses are performed adjusted for age, sex, and body mass index. A total of 12,287 participants (12% female) are included in the analyses. Participants in the highest night-to-night variability quartile within each OSA severity category, have a 50-70% increase in uncontrolled hypertension likelihood versus the lowest variability quartile, independent of OSA severity. This study demonstrates that high night-to-night variability in OSA severity is a predictor of uncontrolled hypertension, independent of OSA severity. These findings have important implications for the identification of which OSA patients are most at risk of cardiovascular harm.

Measuring Sleep Quality in the Hospital Environment with Wearable and Non-Wearable Devices in Adults with Stroke Undergoing Inpatient Rehabilitation

Sleep disturbances are common after stroke and may affect recovery and rehabilitation outcomes. Sleep monitoring in the hospital environment is not routine practice yet may offer insight into how the hospital environment influences post-stroke sleep quality while also enabling us to investigate the relationships between sleep quality and neuroplasticity, physical activity, fatigue levels, and recovery of functional independence while undergoing rehabilitation. Commonly used sleep monitoring devices can be expensive, which limits their use in clinical settings. Therefore, there is a need for low-cost methods to monitor sleep quality in hospital settings. This study compared a commonly used actigraphy sleep monitoring device with a low-cost commercial device. Eighteen adults with stroke wore the Philips Actiwatch to monitor sleep latency, sleep time, number of awakenings, time spent awake, and sleep efficiency. A sub-sample (n = 6) slept with the Withings Sleep Analyzer in situ, recording the same sleep parameters. Intraclass correlation coefficients and Bland-Altman plots indicated poor agreement between the devices. Usability issues and inconsistencies were reported between the objectively measured sleep parameters recorded by the Withings device compared with the Philips Actiwatch. While these findings suggest that low-cost devices are not suitable for use in a hospital environment, further investigations in larger cohorts of adults with stroke are needed to examine the utility and accuracy of off-the-shelf low-cost devices to monitor sleep quality in the hospital environment.

Real-Time Detection of Sleep Apnea Based on Breathing Sounds and Prediction Reinforcement Using Home Noises: Algorithm Development and Validation

BACKGROUND

Multinight monitoring can be helpful for the diagnosis and management of obstructive sleep apnea (OSA). For this purpose, it is necessary to be able to detect OSA in real time in a noisy home environment. Sound-based OSA assessment holds great potential since it can be integrated with smartphones to provide full noncontact monitoring of OSA at home.

OBJECTIVE

The purpose of this study is to develop a predictive model that can detect OSA in real time, even in a home environment where various noises exist.

METHODS

This study included 1018 polysomnography (PSG) audio data sets, 297 smartphone audio data sets synced with PSG, and a home noise data set containing 22,500 noises to train the model to predict breathing events, such as apneas and hypopneas, based on breathing sounds that occur during sleep. The whole breathing sound of each night was divided into 30-second epochs and labeled as "apnea," "hypopnea," or "no-event," and the home noises were used to make the model robust to a noisy home environment. The performance of the prediction model was assessed using epoch-by-epoch prediction accuracy and OSA severity classification based on the apnea-hypopnea index (AHI).

RESULTS

Epoch-by-epoch OSA event detection showed an accuracy of 86% and a macro F₁-score of 0.75 for the 3-class OSA event detection task. The model had an accuracy of 92% for "no-event," 84% for "apnea," and 51% for "hypopnea." Most misclassifications were made for "hypopnea," with 15% and 34% of "hypopnea" being wrongly predicted as "apnea" and "no-event," respectively. The sensitivity and specificity of the OSA severity classification (AHI≥15) were 0.85 and 0.84, respectively.

CONCLUSIONS

Our study presents a real-time epoch-by-epoch OSA detector that works in a variety of noisy home environments. Based on this, additional research is needed to verify the usefulness of various multinight monitoring and real-time diagnostic technologies in the home environment.

Examining clinician choice to follow-up (or not) on automated notifications of medication non-adherence by clinical decision support systems

BACKGROUND

Maintaining medication adherence can be challenging for people living with mental ill-health. Clinical decision support systems (CDSS) based on automated detection of problematic patterns in Electronic Health Records (EHRs) have the potential to enable early intervention into non-adherence events ("flags") through suggesting evidence-based courses of action. However, extant literature shows multiple barriers-perceived lack of benefit in following up low-risk cases, veracity of data, human-centric design concerns, etc.-to clinician follow-up in real-world settings. This study examined patterns in clinician decision making behaviour related to follow-up of non-adherence prompts within a community mental health clinic.

METHODS

The prompts for follow-up, and the recording of clinician responses, were enabled by CDSS software (AI²). De-identified clinician notes recorded after reviewing a prompt were analysed using a thematic synthesis approach-starting with descriptions of clinician comments, then sorting into analytical themes related to design and, in parallel, a priori categories describing follow-up behaviours. Hypotheses derived from the literature about the follow-up categories' relationships with client and medication-subtype characteristics were tested.

RESULTS

The majority of clients were Not Followed-up (n = 260; 78%; Followed-up: n = 71; 22%). The analytical themes emerging from the decision notes suggested contextual factors-the clients' environment, their clinical relationships, and medical needs-mediated how clinicians interacted with the CDSS flags. Significant differences were found between medication subtypes and follow-up, with Anti-depressants less likely to be followed up than Anti-Psychotics and Anxiolytics (χ² = 35.196, 44.825; p < 0.001; v = 0.389, 0.499); and between the time taken to action Followed-up₀ and Not-followed up₁ flags (M₀ = 31.78; M₁ = 45.55; U = 12,119; p < 0.001; η² = .05).

CONCLUSION

These analyses encourage actively incorporating the input of consumers and carers, non-EHR data streams, and better incorporation of data from parallel health systems and other clinicians into CDSS designs to encourage follow-up.

Single-Night Diagnosis of Sleep Apnea Contributes to Inconsistent Cardiovascular Outcome Findings

BACKGROUND

Single-night disease misclassification of OSA due to night-to-night variability may contribute to inconsistent findings in OSA trials.

RESEARCH QUESTION

Does multinight quantification of OSA severity provide more precise estimates of associations with incident hypertension?

STUDY DESIGN AND METHODS

A total of 3,831 participants without hypertension at baseline were included in simulation analyses. Included participants had ≥ 28 days of nightly apnea-hypopnea index (AHI) recordings via an under-mattress sensor and ≥ 3 separate BP measurements over a 3-month baseline period followed by ≥ 3 separate BP measurements 6 to 9 months postbaseline. Incident hypertension was defined as a mean systolic BP ≥ 140 mm Hg or a mean diastolic BP ≥ 90 mm Hg. Simulated trials (1,000) were performed, using bootstrap methods to investigate the effect of variable numbers of nights (x = 1-56 per participant) to quantify AHI and the ability to detect associations between OSA and incident hypertension via logistic regression adjusted for age, sex, and BMI.

RESULTS

Participants were middle-aged (mean ± SD, 52 ± 12 y), mostly men (91%), and overweight (BMI, 28 ± 5 kg/m²). Single-night quantification of OSA failed to detect an association with hypertension risk in 42% of simulated trials (α = 0.05). Conversely, 100% of trials detected an association when AHI was quantified over ≥ 28 nights. Point estimates of hypertension risk were also 50% higher and uncertainty was 5 times lower during multinight vs single-night simulation trials.

INTERPRETATION

Multinight monitoring of OSA allows for better estimates of hypertension risk and potentially other adverse health outcomes associated with OSA. These findings have important implications for clinical care and OSA trial design.

Portable evaluation of obstructive sleep apnea in adults: A systematic review

Obstructive sleep apnea (OSA) is a significant healthcare burden affecting approximately one billion people worldwide. The prevalence of OSA is rising with the ongoing obesity epidemic, a key risk factor for its development. While in-laboratory polysomnography (PSG) is the gold standard for diagnosing OSA, it has significant drawbacks that prevent widespread use. Portable devices with different levels of monitoring are available to allow remote assessment for OSA. To better inform clinical practice and research, this comprehensive systematic review evaluated diagnostic performances, study cost and patients' experience of different levels of portable sleep studies (type 2, 3, and 4), as well as wearable devices and non-contact systems, in adults. Despite varying study designs and devices used, portable diagnostic tests are found to be sufficient for initial screening of patients at risk of OSA. Future studies are needed to evaluate cost effectiveness with the incorporation of portable diagnostic tests into the diagnostic pathway for OSA, as well as their application in patients with chronic respiratory diseases and other comorbidities that may affect test performance.

Paper-Based Humidity Sensor for Respiratory Monitoring

Flexible respiratory monitoring devices have become available for outside-hospital application scenarios attributable to their improved system wearability. However, the complex fabrication process of such flexible devices results in high prices, limiting their applications in real-life scenarios. This study proposes a flexible, low-cost, and easy-processing paper-based humidity sensor for sleep respiratory monitoring. A paper humidity sensing model was established and sensors under different design parameters were processed and tested, achieving high sensitivity of 5.45 kΩ/%RH and good repeatability with a matching rate of over 85.7%. Furthermore, the sensor patch with a dual-channel 3D structure was designed to distinguish between oral and nasal breathing from origin signals proved in the simulated breathing signal monitoring test. The sensor patch was applied in the sleep respiratory monitoring of a healthy volunteer and an obstruct sleep apnea patient, demonstrating its ability to distinguish between different respiratory patterns as well as various breathing modes.

Comparing consumer grade sleep trackers for research purposes: A field study

Sleep tracking has been rapidly developing alongside wearable technologies and digital trackers are increasingly being used in research, replacing diaries and other more laborious methods. In this work, we describe the user expectations and experiences of four different sleep tracking devices used simultaneously during week-long field deployment. The sensor-based data collection was supplemented with qualitative data from a 2-week long daily questionnaire period which overlapped with device usage for a period of 1 week. We compare the sleep data on each of the tracking nights between all four devices, and showcase that while each device has been validated with the polysomnography (PSG) gold standard, the devices show highly varying results in everyday use. Differences between devices for measuring sleep duration or sleep stages on a single night can be up to an average of 1 h 36 min. Study participants provided their expectations and experiences with the devices, and provided qualitative insights into their usage throughout the daily questionnaires. The participants assessed each device according to ease of use, functionality and reliability, and comfortability and effect on sleep disturbances. We conclude the work with lessons learned and recommendations for researchers who wish to conduct field studies using digital sleep trackers, and how to mitigate potential challenges and problems that might arise regarding data validity and technical issues.

Association of Obstructive Sleep Apnea Syndrome (OSA/OSAHS) with Coronary Atherosclerosis Risk: Systematic Review and Meta-Analysis

Objective

Obstructive sleep apnea syndrome (OSA) is the most common type of sleep disorders. This study aimed to systematically review the correlation between OSA and the risk of coronary atherosclerosis.

Methods

Literature on case-control studies on the relationship between coronary heart disease (CHD) and sleep apnea syndrome was collected and collated, and the incidence of SAS in CHD and non-CHD patients was observed and compared. RevMan 5.2 analysis software and Stata12SE analysis software were used for heterogeneity test and combination analysis of the included studies. The results were expressed with odds ratio (OR), 95% confidence intervals (CI) were calculated, and publication bias and sensitivity tests were evaluated.

Results

There was a statistical difference in OSA associated with the risk of coronary atherosclerosis between the experimental group and the control group [OR = 1.38, 95% CI (1.18, 1.62), P < 0.0001, I ² = 0%, Z = 3.93]. OSA associated with vascular endothelial injury [OR = 3.59, 95% CI (3.00, 4.29), P < 0.00001, I ² = 90%, Z = 14.09]. OSA is associated with vascular oxidation emergency [OR = 2.19, 95% CI (2.05, 2.33), P < 0.00001, I ² = 94%, Z = 23.40]; OSA is associated with chronic vascular inflammation [OR = 1.70, 95% CI (1.39, 2.07), P < 0.00001, I ² = 16%, Z = 5.18].

Conclusion

The incidence of obstructive sleep apnea in patients with CHD was higher than that in non-CHD patients, and obstructive sleep apnea was a risk factor for CHD.

Sleep and Mental Health Issues in Current and Former Athletes: A Mini Review

Sleep and mental health are important aspects of human health that work concurrently. However, sleep and mental health disorders are often overlooked and undiagnosed in sport due to the negative stigma associated with them. Evidence suggests that athletes are disproportionately affected by mental health issues and sleep problems. Internal and external pressures contribute to psychological distress. Variable competition times, travel and stress are detrimental to sleep quality. Retirement from sport can deteriorate sleep and psychological wellbeing, particularly for those who retired involuntarily and identify strongly with their athletic role. When untreated, these issues can manifest into a range of clinical disorders. This is concerning, not only for compromised athletic performance, but for general health and wellbeing beyond sport. Previous research has focussed on sleep and health independently among currently competing, or former, athletes. To date, no research has comprehensively assessed and compared sleep complaints and mental health issues between these two cohorts. Moreover, research has failed to obtain data across a variety of different competition levels, sports, and genders, leaving the current scope of the literature narrow. Comorbid conditions (e.g., concussion history, obesity), ex-college athletes, and mental health has been the focus of existing literature post-retirement. Future research would benefit from employing both quantitative and qualitative methodologies to comprehensively assess the prevalence and severity of sleep and mental health disorders across current and retired athletes. Research outcomes would inform education strategies, safeguarding athletes from these issues by reducing negative stigmas associated with help-seeking in sport and ultimately increase self-guided treatment.

Cost and Effort Considerations for the Development of Intervention Studies Using Mobile Health Platforms: Pragmatic Case Study

BACKGROUND

The research marketplace has seen a flood of open-source or commercial mobile health (mHealth) platforms that can collect and use user data in real time. However, there is a lack of practical literature on how these platforms are developed, integrated into study designs, and adopted, including important information around cost and effort considerations.

OBJECTIVE

We intend to build critical literacy in the clinician-researcher readership into the cost, effort, and processes involved in developing and operationalizing an mHealth platform, focusing on Intui, an mHealth platform that we developed.

METHODS

We describe the development of the Intui mHealth platform and general principles of its operationalization across sites.

RESULTS

We provide a worked example in the form of a case study. Intui was operationalized in the design of a behavioral activation intervention in collaboration with a mental health service provider. We describe the design specifications of the study site, the developed software, and the cost and effort required to build the final product.

CONCLUSIONS

Study designs, researcher needs, and technical considerations can impact effort and costs associated with the use of mHealth platforms. Greater transparency from platform developers about the impact of these factors on practical considerations relevant to end users such as clinician-researchers is crucial to increasing critical literacy around mHealth, thereby aiding in the widespread use of these potentially beneficial technologies and building clinician confidence in these tools.

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.

Multi-night Prevalence, Variability, and Diagnostic Misclassification of Obstructive Sleep Apnea

Rationale Recent studies suggest that obstructive sleep apnea (OSA) severity can vary markedly from night to night which may have important implications for diagnosis and management. Objectives This study aimed to assess OSA prevalence from multi-night in-home recordings and the impact of night-to-night variability in OSA severity on diagnostic classification in a large, global, non-randomly selected community sample from a consumer database of people that purchased a novel, validated, under-mattress sleep analyzer. Methods 67,278 individuals aged between 18 and 90 years underwent in-home nightly monitoring over an average of ~170 nights per participant between July 2020 to March 2021. OSA was defined as a nightly mean apnea-hypopnea index (AHI) >15 events/h. Outcomes were multi-night global prevalence and likelihood of OSA misclassification from a single night AHI value. Measurements and Main Results Over 11.6 million nights of data were collected and analyzed. OSA global prevalence was 22.6% (95% CI: 20.9-24.3%). The likelihood of misdiagnosis in people with OSA based on a single night ranged between ~20% and 50%. Misdiagnosis error rates decreased with increased monitoring nights (e.g. 1-night F1-score=0.77 vs. 0.94 for 14-nights); and remained stable after 14-nights of monitoring. Conclusions Multi-night in-home monitoring using novel non-invasive under mattress sensor technology indicates a global prevalence of moderate to severe OSA of ~20%, and that ~20% of people diagnosed with a single night study may be misclassified. These findings highlight the need to consider night-to-night variation on OSA diagnosis and management. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).