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Objective: In this study, we built a mobile continuous Blood Oxygen Saturation (SpO2) monitor, and for the first time, explored key design principles towards daily applications. Methods: We firstly built a customized wearable computer that can sense two-channel photoplethysmogram (PPG) signals, and transmit the signals wirelessly to smartphone. Afterwards, we explored many SpO2 model building principles, focusing on linear/nonlinear models, different PPG parameter calculation methods, and different finger types. Moreover, we further compared PPG sensor placement principles by comparing different hand configurations and different finger configurations. Finally, a dataset collected from eleven human subjects was used to evaluate the mobile health monitor and explore all of the above design principles. Results: The experimental results show that the root mean square error of the SpO2 estimation is only 1.8, indicating the effectiveness of the system. Conclusion: These results indicate the effectiveness of the customized mobile SpO2 monitor and the selected design principles. Significance: This research is expected to facilitate the continuous SpO2 monitoring of patients with clinical indications.

Introduction A critical obstacle for circadian medicine is the lack of feasibility in measuring circadian phase in the clinic. Existing tools such as assessment of dim light melatonin onset (DLMO) are too resource intensive, especially in populations with extreme circadian disruption such as night shift workers. Recent studies have demonstrated the validity and feasibility of estimating circadian phase via mathematical modeling data collected with wearable technology. However, these studies have mostly relied on research grade devices (e.g., actigraphs) that have limited scalability. In this work, we validate the use of a consumer wearable (the Apple Watch) to predict DLMO in a population of night shift workers. Methods A sample of 21 fixed-night shift workers wore an Apple Watch for two weeks before completing DLMO in the lab. DLMO was assessed via hourly salivary melatonin samples collected in dim light (< 10 lux) for a period of 24 hours. Activity data was used as input into the Hannay model of the circadian clock to produce a predicted DLMO, which was then compared to in-lab DLMO. Results Model predictions of DLMO showed high correlation with in-lab DLMO, with a Lin’s concordance correlation coefficient (CCC) of 0.81, with a mean absolute error (MAE) of 2.10. This was comparable to the previously published validation using research grade actigraphy (CCC = 0.70; MAE = 2.88). Conclusion This study is the first to provide evidence suggesting that estimates of circadian phase using the Apple Watch have comparable validity with research grade actigraphy. These results have significant implications for the scalability of circadian medicine, particularly as consumer-based wearable technology is already commonplace. Apple Watches are also the most common consumer-based wearable device. Future research should extend findings to other wearable devices, especially devices at a lower price-point to increase accessibility. Support (if any) Support for this study was provided from the American Academy of Sleep Medicine Foundation (245-SR-21) awarded to Dr. Philip Cheng.

Introduction Precision measurement of sleep metrics like sleep onset latency (SOL) and total sleep time (TST) has long been a challenge. Multiple studies have demonstrated significant discrepancies between sleep diaries and wearable device derived sleep metrics especially in patients with subjective-objective sleep discrepancy (SOSD; e.g., some patients with insomnia). Typically SOSD manifests as longer than expected self-reported SOL and shorter than expected TST. Without a method of reconciliation between sleep diaries and wearable devices, the current practice is to rely on sleep diary data which is suboptimal in the context of SOSD, and could contribute to blunted or nonsignificant effects in clinical trials. The current study investigated the effects of providing wearable device data feedback contemporaneously with the completion of the sleep diary, on a daily basis across 288 nights. We expected that by providing wearable device data feedback, participants with SOSD could be identified and potentially self-correct their SOSD. Methods A 3 phase, randomized, crossover design study was used in which 24 undergraduate college students without a diagnosed sleep disorder completed week-long periods of control condition (digital sleep diary without wearable device data feedback), washout, and then test phase (diary with device feedback). Participants were randomized to start with control or test conditions. Results Within and between subjects analyses were performed to understand the effect of the wearable device data feedback on sleep diary responses. Two participants (8.3%) with SOSD were identified and had marked differences in their estimated SOL (~47 min) that were corrected when provided with their wearable device data. Importantly no subjects copied their wearable device data into their sleep diary. Between subjects analyses revealed no differences in the average or variability of sleep metrics. Conclusion These preliminary results suggest that presenting wearable device data during sleep diary completion may impact self-reporting by individuals with SOSD but not by those without SOSD. In the setting of a clinical trial, participants who have significant SOSD may be contributing to non-disease/non-treatment related variability that may be ameliorated by providing wearable device data during sleep diary completion. Support (if any)

\"... Pull back the curtain on making fun and innovative costumes and accessories incorporating technologies like low-cost microprocessors, sensors and programmable LEDs. Fashion tech can require skills in design, pattern-making, sewing, electronics, and maybe 3D printing. Besides the tech skills, making a good costume or accessory also requires knowledge of the intangibles of what makes a good costume. This book is a collaboration between two technologists and a veteran teacher, costumer, and choreographer. Regardless of whether you are coming at this from the theater costuming, sewing, or electronics side, the authors will help you get started with the other skills you need. More than just a book of projects (although it has those too), Practical Fashion Tech teaches why things are done a certain way to impart the authors? collective wealth of experience. Whether you need a book for a wearable tech class or you just want to get started making fantastic costumes and wearables on your own, Practical Fashion Tech will get you there.\" -- back cover.

Wearable electronics with great breathability enable a comfortable wearing experience and facilitate continuous biosignal monitoring over extended periods 1 – 3 . However, current research on permeable electronics is predominantly at the stage of electrode and substrate development, which is far behind practical applications with comprehensive integration with diverse electronic components (for example, circuitry, electronics, encapsulation) 4 – 8 . Achieving permeability and multifunctionality in a singular, integrated wearable electronic system remains a formidable challenge. Here we present a general strategy for integrated moisture-permeable wearable electronics based on three-dimensional liquid diode (3D LD) configurations. By constructing spatially heterogeneous wettability, the 3D LD unidirectionally self-pumps the sweat from the skin to the outlet at a maximum flow rate of 11.6 ml cm −2  min −1 , 4,000 times greater than the physiological sweat rate during exercise, presenting exceptional skin-friendliness, user comfort and stable signal-reading behaviour even under sweating conditions. A detachable design incorporating a replaceable vapour/sweat-discharging substrate enables the reuse of soft circuitry/electronics, increasing its sustainability and cost-effectiveness. We demonstrated this fundamental technology in both advanced skin-integrated electronics and textile-integrated electronics, highlighting its potential for scalable, user-friendly wearable devices. Incorporation of a ‘liquid diode’ into a wearable electronic platform enhances comfort and stability by shunting away sweat as it accumulates.

IntroductionThe increasing prevalence of arrhythmias, particularly atrial fibrillation (AF), presents a significant public health challenge. Early detection of arrhythmias is essential to mitigate the risks of stroke and other cardiovascular complications. Wearable devices, especially smartwatches, have emerged as effective tools for continuous monitoring of arrhythmias, providing a valuable solution for detecting infrequent events. These devices facilitate early detection by reversing the traditional healthcare model, where medical review precedes diagnostic monitoring. This shift helps bridge the gap between clinical visits and enables real-time data sharing between patients and healthcare providers.MethodsThis review examines the current landscape of smartwatches for arrhythmia detection. It focuses on the technologies utilized in these devices, such as photoplethysmography (PPG) and electrocardiography (ECG), their applications in arrhythmia detection, costs, licences, and clinical effectiveness, as evidenced by various studies and trials. Smartwatches from companies like Apple, Garmin, Samsung, and Huawei, incorporating PPG and ECG technologies, are assessed for their performance in detecting arrhythmias, particularly AF.ResultsSmartwatches equipped with PPG and ECG sensors have shown substantial accuracy in detecting AF, with sensitivity and specificity values exceeding 90%. For example, the Apple heart study showed Apple Watch’s positive predictive value (PPV) of 84% in detecting AF. Other smartwatches, such as the Garmin Venu series has PPV of 90%, Galaxy Watch by Samsung proved PPV of 95%, and Huawei Watch showed PPV of 91.6% in mAFA-II trial. Despite their effectiveness, these devices can lead to false positives and overdiagnosis, which may result in unnecessary anxiety and healthcare visits. While their performance in detecting AF is well-established, smartwatches have yet to be extensively validated for detecting other arrhythmias, limiting their broader clinical application.ConclusionsSmartwatches represent a transformative shift in arrhythmia detection and management, and their diagnostic accuracy for AF is promising. However, challenges such as the risk of overdiagnosis, limited validation for a broader spectrum of arrhythmias, and difficulties with integration into established healthcare systems remain. To fully realize their potential, future advancements should focus on enhancing detection algorithms, expanding the range of detectable arrhythmias, and driving cost reductions. Additionally, the inclusion of measurements for blood pressure, cardiac output, or suitable surrogates would significantly improve the evaluation of arrhythmias and provide critical insights for diagnosing syncope.Abstract 4-001 Table 1 Manufac-turer MODEL* FDA CE Cost** AF Detection Automatic-ally*** Core Study PPG ECG Apple Inc • Series 4,5,6,7,8,9,10• ULTRA 1 & 2 £300 -£800 Apple Heart Study Samsung electronics • Galaxy watch Active 2.• Galaxy Watch 3,4,5,6,7 £250 -£400 - Huawei Technologies Co • GT 3 Pro• Gt 5 pro• 4 pro space Edition £300 - £500 mAFA-II Trial Garmin Ltd • Venu 2 plus• Venu 3 Series• Epix Pro• Fenix 7 Pro £350 - £700 - Abstract 4-001 Figure 1[Image Omitted. See PDF.]

With the increasing prevalence of growing population, aging and chronic diseases continuously rising healthcare costs, the healthcare system is undergoing a vital transformation from the traditional hospital-centered system to an individual-centered system. Since the 20th century, wearable sensors are becoming widespread in healthcare and biomedical monitoring systems, empowering continuous measurement of critical biomarkers for monitoring of the diseased condition and health, medical diagnostics and evaluation in biological fluids like saliva, blood, and sweat. Over the past few decades, the developments have been focused on electrochemical and optical biosensors, along with advances with the non-invasive monitoring of biomarkers, bacteria and hormones, etc. Wearable devices have evolved gradually with a mix of multiplexed biosensing, microfluidic sampling and transport systems integrated with flexible materials and body attachments for improved wearability and simplicity. These wearables hold promise and are capable of a higher understanding of the correlations between analyte concentrations within the blood or non-invasive biofluids and feedback to the patient, which is significantly important in timely diagnosis, treatment, and control of medical conditions. However, cohort validation studies and performance evaluation of wearable biosensors are needed to underpin their clinical acceptance. In the present review, we discuss the importance, features, types of wearables, challenges and applications of wearable devices for biological fluids for the prevention of diseased conditions and real-time monitoring of human health. Herein, we summarize the various wearable devices that are developed for healthcare monitoring and their future potential has been discussed in detail.