A Single-Center Validation of the Accuracy of a Photoplethysmography-Based Smartwatch for Screening Obstructive Sleep Apnea

Higher ambient temperatures may worsen obstructive sleep apnea: A nationwide smartwatch-based analysis of 6.2 million person-days

Obstructive sleep apnea (OSA) is a serious type of sleep disorder that can lead to cardiometabolic and neurocognitive diseases. We utilized smart device-based photoplethysmography technology to collect sleep data from the Chinese population from 2019 to 2022. Distributed lag nonlinear models combined with a generalized nonlinear model or a linear mixed effects model were used to investigate the short-term associations between daily temperature and indicators of OSA severity. We included a total of 6,232,056 d of sleep monitoring data from 51,842 participants with moderate to severe risk of OSA from 313 Chinese cities. The relationships between ambient temperature and OSA exacerbation, apnea-hypopnea index (AHI), and minimum oxygen saturation (MinSpO2) were almost linear and present only on the same day. Higher temperatures were associated with a greater risk of OSA exacerbation, with an 8.4% (95% confidence interval (CI): 7.6%-9.3%) increase per 10 °C increase in temperature. A 10 °C increase in daily temperature corresponded to an AHI increase of 0.70 events h-1 (95% CI: 0.65-0.76) and a MinSpO2 decrease of 0.18% (95% CI: 0.16%-0.19%). Exposure to elevated temperatures during the night can also lead to adverse effects. The effects of higher temperatures on OSA severity were stronger among men, participants with a body mass index ≥24 kg m-2, those aged 45 years and older, individuals with a history of hypertension and diabetes, and during the cold season. This large-scale, nationwide, longitudinal study provides robust evidence suggesting that higher ambient temperatures may immediately worsen OSA.

State of the science and recommendations for using wearable technology in sleep and circadian research

Wearable sleep-tracking technology is of growing use in the sleep and circadian fields, including for applications across other disciplines, inclusive of a variety of disease states. Patients increasingly present sleep data derived from their wearable devices to their providers and the ever-increasing availability of commercial devices and new-generation research/clinical tools has led to the wide adoption of wearables in research, which has become even more relevant given the discontinuation of the Philips Respironics Actiwatch. Standards for evaluating the performance of wearable sleep-tracking devices have been introduced and the available evidence suggests that consumer-grade devices exceed the performance of traditional actigraphy in assessing sleep as defined by polysomnogram. However, clear limitations exist, for example, the misclassification of wakefulness during the sleep period, problems with sleep tracking outside of the main sleep bout or nighttime period, artifacts, and unclear translation of performance to individuals with certain characteristics or comorbidities. This is of particular relevance when person-specific factors (like skin color or obesity) negatively impact sensor performance with the potential downstream impact of augmenting already existing healthcare disparities. However, wearable sleep-tracking technology holds great promise for our field, given features distinct from traditional actigraphy such as measurement of autonomic parameters, estimation of circadian features, and the potential to integrate other self-reported, objective, and passively recorded health indicators. Scientists face numerous decision points and barriers when incorporating traditional actigraphy, consumer-grade multi-sensor devices, or contemporary research/clinical-grade sleep trackers into their research. Considerations include wearable device capabilities and performance, target population and goals of the study, wearable device outputs and availability of raw and aggregate data, and data extraction, processing, and analysis. Given the difficulties in the implementation and utilization of wearable sleep-tracking technology in real-world research and clinical settings, the following State of the Science review requested by the Sleep Research Society aims to address the following questions. What data can wearable sleep-tracking devices provide? How accurate are these data? What should be taken into account when incorporating wearable sleep-tracking devices into research? These outstanding questions and surrounding considerations motivated this work, outlining practical recommendations for using wearable technology in sleep and circadian research.

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.

Scanning of obstructive sleep apnea syndrome using smartwatch: A comparison of smartwatch and polysomnography

BACKGROUND

Obstructive Sleep Apnea Syndrome (OSAS), which significantly impairs nighttime sleep quality and causes excessive daytime sleepiness, not only reduces the quality of life of patients, but also increases the social and socioeconomic burden. Wearable-noninvasive devices can provide faster OSAS screening and follow-up. Smartwatches as an objective, non-invasive, practical and relatively inexpensive method, they are attractive candidates for pre-evaluation of OSAS and referral to a physician. In this study, it was aimed to evaluate the effectiveness of a smart watch in detecting OSAS findings compared to the gold standard polysomnograhy (PSG).

METHODS

PSG data of the study group were compared with data such as SpO2, heart rate and saturation obtained by smartwatch from both sides, and the Cohen's kappa was used to measure for two methods and predictive values were evaluated.

RESULTS

A total of 115 participants [44 female (38.3%), mean age (SD): 49.24 (11.39)] were enrolled. 75 (65.22%) of the participants were diagnosed with OSAS, of which 29 (25.22%) participants have severe OSAS. The smartwatch showed good sensitivity (75% to 96%), specificity (79% to 91%), and diagnostic accuracy (AUC: 0.84 to 0.93) in predicting apnea and severe apnea, respectively. The highest agreement between PSG and smartwatch and the diagnostic ability of smartwatch were found in persons with severe OSAS.

CONCLUSION

The high PPV-NPV values in our study and the good compatibility coefficient of the smart watch with the PSG device can contribute to the expansion of the usage areas of smart watches that come into the lives of many people in daily practice.

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.

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

Air pollution may increase the sleep apnea severity: A nationwide analysis of smart device-based monitoring

Obstructive sleep apnea (OSA) can lead to sleep deprivation, accidents, and cardiovascular diseases. However, research on the short-term effects of air pollutants on OSA severity is limited and inconsistent. We conducted a novel case time series analysis using a nationwide dataset among Huawei smart device users to assess the association between air pollution and OSA severity in a population at moderate-to-severe risk of OSA. Fixed-effects regression models were used to assess the associations between air pollution and the risk of OSA exacerbation, apnea-hypopnea index (AHI), and oxygen saturation. A total of 51,842 participants who were at moderate-to-severe risk of OSA (mean age [SD]: 45.4 [11.0], 95.5% male) were included, with 6,232,056 person-days of monitoring. The associations of fine particulate matter, nitrogen dioxide, carbon monoxide, and sulfur dioxide with OSA severity could occur during the sleep period, and last for 2 days. An increase of 1 interquartile range in the moving average concentrations of air pollution during the sleep period and the 2 previous days was associated with a 1.14%-4.31% increase in the risk of OSA exacerbation, an increase in AHI by 0.05-0.17 events/h, and a decrease in oxygen saturation (%) by 0.003-0.014. The exposure-response curves were almost linear. The associations between air pollutants and OSA were consistently stronger in participants aged 45 years or older. By virtue of the smart device-based technology, this large-scale, nationwide, longitudinal study provides compelling evidence that short-term exposure to air pollution may worsen sleep apnea. Our findings highlight the significance of ongoing efforts to improve air quality in mitigating OSA severity and the relevant disease burden in an aging era.

Smartwatches in healthcare medicine: assistance and monitoring; a scoping review

Smartwatches have become increasingly popular in recent times because of their capacity to track different health indicators, including heart rate, patterns of sleep, and physical movements. This scoping review aims to explore the utilisation of smartwatches within the healthcare sector. According to Arksey and O'Malley's methodology, an organised search was performed in PubMed/Medline, Scopus, Embase, Web of Science, ERIC and Google Scholar. In our search strategy, 761 articles were returned. The exclusion/inclusion criteria were applied. Finally, 35 articles were selected for extracting data. These included six studies on stress monitoring, six on movement disorders, three on sleep tracking, three on blood pressure, two on heart disease, six on covid pandemic, three on safety and six on validation. The use of smartwatches has been found to be effective in diagnosing the symptoms of various diseases. In particular, smartwatches have shown promise in detecting heart diseases, movement disorders, and even early signs of COVID-19. Nevertheless, it should be emphasised that there is an ongoing discussion concerning the reliability of smartwatch diagnoses within healthcare systems. Despite the potential advantages offered by utilising smartwatches for disease detection, it is imperative to approach their data interpretation with prudence. The discrepancies in detection between smartwatches and their algorithms have important implications for healthcare use. The accuracy and reliability of the algorithms used are crucial, as well as high accuracy in detecting changes in health status by the smartwatches themselves. This calls for the development of medical watches and the creation of AI-hospital assistants. These assistants will be designed to help with patient monitoring, appointment scheduling, and medication management tasks. They can educate patients and answer common questions, freeing healthcare providers to focus on more complex tasks.

Technologies for sleep monitoring at home: wearables and nearables

Sleep is an essential part of our lives and daily sleep monitoring is crucial for maintaining good health and well-being. Traditionally, the gold standard method for sleep monitoring is polysomnography using various sensors attached to the body; however, it is limited with regards to long-term sleep monitoring in a home environment. Recent advancements in wearable and nearable technology have made it possible to monitor sleep at home. In this review paper, the technologies that are currently available for sleep stages and sleep disorder monitoring at home are reviewed using wearable and nearable devices. Wearables are devices that are worn on the body, while nearables are placed near the body. These devices can accurately monitor sleep stages and sleep disorder in a home environment. In this study, the benefits and limitations of each technology are discussed, along with their potential to improve sleep quality.

A consumer wearable device for tracking sleep respiratory events

PURPOSE

The diagnosis of obstructive sleep apnea (OSA) is instrument, operator, and time-dependent and therefore requires long waiting times. In recent decades, technological development has produced useful devices to monitor the health status of the population, including sleep. Therefore, the aim of this study was to evaluate a wearable device (WD) in a group of individuals at high risk of OSA.

METHODS

The study was conducted on consecutive subjects with high risk of OSA assessed by sleep questionnaires and clinical evaluation. All subjects performed cardio-respiratory monitoring (CRM) and WD simultaneously on a single night, after which the parameters of the two sleep investigations were compared.

RESULTS

Of 20 individuals enrolled, 60% were men and mean age was 57.3 ± 10.7 years. The apnea-hypopnea index (AHI) for the CRM was 23.1 ± 19.6 events·h-1 while it was 10.3 ± 8.3 events·h-1 for the WD. Correlation analysis between the results of the two investigations showed r = 0.19 (p = 0.40) for AHI and r = 0.4076 (p = 0.07) for sO2%. The accuracy for different stages of OSA severity was 70% in OSA cases and 60% in moderate to severe cases with sensitivity and specificity varying a great deal.

CONCLUSION

Small and low-cost devices may prove to be a valuable resource to reduce costs and waiting times for a sleep investigation in suspected OSA. However, diagnosis of sleep apnea requires valid and reliable instruments, so validation tests are necessary before a device can be commercialized.

The Role of Novel Digital Clinical Tools in the Screening or Diagnosis of Obstructive Sleep Apnea: Systematic Review

BACKGROUND

Digital clinical tools are a new technology that can be used in the screening or diagnosis of obstructive sleep apnea (OSA), notwithstanding the crucial role of polysomnography, the gold standard.

OBJECTIVE

This study aimed to identify, gather, and analyze the most accurate digital tools and smartphone-based health platforms used for OSA screening or diagnosis in the adult population.

METHODS

We performed a comprehensive literature search of PubMed, Scopus, and Web of Science databases for studies evaluating the validity of digital tools in OSA screening or diagnosis until November 2022. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal tool for diagnostic test accuracy studies. The sensitivity, specificity, and area under the curve (AUC) were used as discrimination measures.

RESULTS

We retrieved 1714 articles, 41 (2.39%) of which were included in the study. From these 41 articles, we found 7 (17%) smartphone-based tools, 10 (24%) wearables, 11 (27%) bed or mattress sensors, 5 (12%) nasal airflow devices, and 8 (20%) other sensors that did not fit the previous categories. Only 8 (20%) of the 41 studies performed external validation of the developed tool. Of these, the highest reported values for AUC, sensitivity, and specificity were 0.99, 96%, and 92%, respectively, for a clinical cutoff of apnea-hypopnea index (AHI)≥30. These values correspond to a noncontact audio recorder that records sleep sounds, which are then analyzed by a deep learning technique that automatically detects sleep apnea events, calculates the AHI, and identifies OSA. Looking at the studies that only internally validated their models, the work that reported the highest accuracy measures showed AUC, sensitivity, and specificity values of 1.00, 100%, and 96%, respectively, for a clinical cutoff AHI≥30. It uses the Sonomat-a foam mattress that, aside from recording breath sounds, has pressure sensors that generate voltage when deformed, thus detecting respiratory movements, and uses it to classify OSA events.

CONCLUSIONS

These clinical tools presented promising results with high discrimination measures (best results reached AUC>0.99). However, there is still a need for quality studies comparing the developed tools with the gold standard and validating them in external populations and other environments before they can be used in clinical settings.

TRIAL REGISTRATION

PROSPERO CRD42023387748; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387748.

Automatic monitoring of obstructive sleep apnea based on multi-modal signals by phone and smartwatch

Obstructive Sleep Apnea (OSA) is the most common sleep-related breathing disorder, with an overall population prevalence ranging from 9% to 38%, and it is associated with many cardiovascular diseases. The diagnosis of OSA requires polysomnography (PSG) testing, which is unsuitable for large-scale preliminary screening due to its high cost and discomfort to wear. Therefore, a simple and inexpensive screening method would be of great value. This study presents a novel at-home OSA screening method using a smartwatch and a smartphone to obtain several physiological signals, snoring segments, and questionnaire information during a whole night's sleep. The proposed method can distinguish four OSA risk levels based on machine learning (ML) classifications; the system was validated by conducting an in-hospital study on 350 subjects with sleep disorders. The estimated OSA risk levels are in good agreement with the OSA severity diagnosed by PSG (correlation with apnea-hypopnea index (AHI) = 0.92), and an encouraging classification performance is achieved (accuracy = 88.1%, 84.5%, 85.1%, sensitivity = 89.1%, 84.2%, 85.6% for mild, moderate and severe OSA). These findings reveal that wearable devices have the potential for large-scale OSA screening.

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

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

Consumer-Led Screening for Atrial Fibrillation: A Report From the mAFA-II Trial Long-Term Extension Cohort

BACKGROUND

There are limited data on mobile health detection of prevalent atrial fibrillation (AF) and its related risk factors over time.

OBJECTIVES

This study aimed to report the trends on prevalent AF detection over time and risk factors, with a consumer-led photoplethysmography screening approach.

METHODS

3,499,461 subjects aged over 18 years, who use smart devices (Huawei Technologies Co.) were enrolled between October 26, 2018, and December 1, 2021.

RESULTS

Among 2,852,217 subjects for AF screening, 12,244 subjects (0.43%; 83.2% male, mean age 57 ± 15 years) detected AF episodes. When compared with 2018, the risk (adjusted HRs, 95% CI) for monitored prevalent AF increased significantly for subjects when monitoring started in 2020 (adjusted HR: 1.34; 95% CI: 1.27-1.40; P < .001) or in 2021 (adjusted HR: 1.67; 95% CI: 1.59-1.76; P < 0.001). Of the 961,931 subjects who screening for both AF and OSA, 18,032 (1.9%, 97.8% male, mean age 44 ±17 years) were identified as high risk for OSA, which resulted in a 1.5-fold increase (95% CI: 1.30-fold to 1.75-fold) in the prevalent AF. A total of 5,227 (53.3%, 5,227/9,797) subjects were effectively followed up, from which 4,903 (93.8%, 4,903/5,227) subjects were confirmed with the diagnosis of AF, by the mAFA Telecare Team health providers.

CONCLUSIONS

Photoplethysmography-based smart devices can facilitate screening for AF with >93% confirmation of detected AF episodes even for the low-risk general population, highlighting the increased risk for detecting prevalent AF and the need for modification of OSA that increase AF susceptibility. (Mobile Health [mHealth] Technology for Improved Screening, Patient Involvement and Optimizing Integrated Care in Atrial Fibrillation [mAFA (mAF-App) II study]; ChiCTR-OOC-17014138).

Commercial smartwatch with pulse oximeter detects short-time hypoxemia as well as standard medical-grade device: Validation study

OBJECTIVE

We investigated how a commercially available smartwatch that measures peripheral blood oxygen saturation (SpO₂) can detect hypoxemia compared to a medical-grade pulse oximeter.

METHODS

We recruited 24 healthy participants. Each participant wore a smartwatch (Apple Watch Series 6) on the left wrist and a pulse oximeter sensor (Masimo Radical-7) on the left middle finger. The participants breathed via a breathing circuit with a three-way non-rebreathing valve in three phases. First, in the 2-minute initial stabilization phase, the participants inhaled the ambient air. Then in the 5-minute desaturation phase, the participants breathed the oxygen-reduced gas mixture (12% O₂), which temporarily reduced their blood oxygen saturation. In the final stabilization phase, the participants inhaled the ambient air again until SpO₂ returned to normal values. Measurements of SpO₂ were taken from the smartwatch and the pulse oximeter simultaneously in 30-s intervals.

RESULTS

There were 642 individual pairs of SpO₂ measurements. The bias in SpO₂ between the smartwatch and the oximeter was 0.0% for all the data points. The bias for SpO₂ less than 90% was 1.2%. The differences in individual measurements between the smartwatch and oximeter within 6% SpO₂ can be expected for SpO₂ readings 90%-100% and up to 8% for SpO₂ readings less than 90%.

CONCLUSIONS

Apple Watch Series 6 can reliably detect states of reduced blood oxygen saturation with SpO₂ below 90% when compared to a medical-grade pulse oximeter. The technology used in this smartwatch is sufficiently advanced for the indicative measurement of SpO₂ outside the clinic.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04780724.

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.

Smart Pillow Based on Flexible and Breathable Triboelectric Nanogenerator Arrays for Head Movement Monitoring during Sleep

Sleep quality plays an essential role in human health and has become an index for assessing physical health. Self-powered, sensitive, noninvasive, comfortable, and low-cost sleep monitoring sensors for monitoring sleep behavior are still in high demand. Here, a pressure-sensitive, noninvasive, and comfortable smart pillow is developed based on a flexible and breathable triboelectric nanogenerator (FB-TENG) sensor array, which can monitor head movement in real time during sleep. The FB-TENG is based on flexible and breathable porous poly(dimethylsiloxane) (PDMS) with a fluorinated ethylene propylene (FEP) powder and exhibits pressure sensitivity and durability. The electrical output of the FB-TENG is further optimized by modifying the porous structure and the FEP powder. Combining the FB-TENG and the flexible printed circuit (FPC), a self-powered pressure sensor array is fabricated to realize touch sensing and motion track monitoring. The smart pillow is formed by laying the self-powered pressure sensor array on an ordinary pillow to realize real-time monitoring of the head position in a static state and head movement trajectory in a dynamic state during sleep. Additionally, the smart pillow also has an early warning function for falling out of bed. This work not only provides a viable sensing device for sleep monitoring but also could be extended to real-time monitoring of some diseases, such as brain diseases and cervical spondylosis, in the future. It is expected to introduce a practical strategy in the real-time mobile healthcare field for disease management.

Impacts on Context Aware Systems in Evidence-Based Health Informatics: A Review

Background: The application of Context Aware Computing (CAC) can be an effective, useful, feasible, and acceptable way to advance medical research and provide health services. Methods: This review was conducted in accordance with the principles of the development of a mixed methods review and existing knowledge in the field via the Synthesis Framework for the Assessment of Health Information Technology to evaluate CAC implemented by Evidence-Based Health Informatics (EBHI). A systematic search of the literature was performed during 18 November 2021–22 January 2022 in Cochrane Library, IEEE Xplore, PUBMED, Scopus and in the clinical registry platform Clinicaltrials.gov. The author included the articles in the review if they were implemented by EBHI and concerned with CAC technologies. Results: 29 articles met the inclusion criteria and refer to 26 trials published between 2011 and 2022. The author noticed improvements in healthcare provision using EBHI in the findings of CAC application. She also confirmed that CAC systems are a valuable and reliable method in health care provision. Conclusions: The use of CAC systems in healthcare is a promising new area of research and development. The author presented that the evaluation of CAC systems in EBHI presents positive effects on the state of health and the management of long-term diseases. These implications are presented in this article in a detailed, clear, and reliable manner.