Cardiac Health Assessment Using a Wearable Device Before and After Transcatheter Aortic Valve Implantation: Prospective Study.

Background: Due to aging of the population, the prevalence of aortic valve stenosis will increase drastically in upcoming years. Consequently, transcatheter aortic valve implantation (TAVI) procedures will also expand worldwide. Optimal selection of patients who benefit with improved symptoms and prognoses is key, since TAVI is not without its risks. Currently, we are not able to adequately predict functional outcomes after TAVI. Quality of life measurement tools and traditional functional assessment tests do not always agree and can depend on factors unrelated to heart disease. Activity tracking using wearable devices might provide a more comprehensive assessment. Objective: This study aimed to identify objective parameters (eg, change in heart rate) associated with improvement after TAVI for severe aortic stenosis from a wearable device. Methods: In total, 100 patients undergoing routine TAVI wore a Philips Health Watch device for 1 week before and after the procedure. Watch data were analyzed offline-before TAVI for 97 patients and after TAVI for 75 patients. Results: Parameters such as the total number of steps and activity time did not change, in contrast to improvements in the 6-minute walking test (6MWT) and physical limitation domain of the transformed WHOQOL-BREF questionnaire. Conclusions: These findings, in an older TAVI population, show that watch-based parameters, such as the number of steps, do not change after TAVI, unlike traditional 6MWT and QoL assessments. Basic wearable device parameters might be less appropriate for measuring treatment effects from TAVI.

Monitoring Adolescent and Young Adult Patients With Cancer via a Smart T-Shirt: Prospective, Single-Cohort, Mixed Methods Feasibility Study (OncoSmartShirt Study).

Background: Wearables that measure vital parameters can be potential tools for monitoring patients at home during cancer treatment. One type of wearable is a smart T-shirt with embedded sensors. Initially, smart T-shirts were designed to aid athletes in their performance analyses. Recently however, researchers have been investigating the use of smart T-shirts as supportive tools in health care. In general, the knowledge on the use of wearables for symptom monitoring during cancer treatment is limited, and consensus and awareness about compliance or adherence are lacking. objectives: The aim of this study was to evaluate adherence to and experiences with using a smart T-shirt for the home monitoring of biometric sensor data among adolescent and young adult patients undergoing cancer treatment during a 2-week period. Methods: This study was a prospective, single-cohort, mixed methods feasibility study. The inclusion criteria were patients aged 18 to 39 years and those who were receiving treatment at Copenhagen University Hospital - Rigshospitalet, Denmark. Consenting patients were asked to wear the Chronolife smart T-shirt for a period of 2 weeks. The smart T-shirt had multiple sensors and electrodes, which engendered the following six measurements: electrocardiogram (ECG) measurements, thoracic respiration, abdominal respiration, thoracic impedance, physical activity (steps), and skin temperature. The primary end point was adherence, which was defined as a wear time of >8 hours per day. The patient experience was investigated via individual, semistructured telephone interviews and a paper questionnaire. Results: A total of 10 patients were included. The number of days with wear times of >8 hours during the study period (14 d) varied from 0 to 6 (mean 2 d). Further, 3 patients had a mean wear time of >8 hours during each of their days with data registration. The number of days with any data registration ranged from 0 to 10 (mean 6.4 d). The thematic analysis of interviews pointed to the following three main themes: (1) the smart T-shirt is cool but does not fit patients with cancer, (2) the technology limits the use of the smart T-shirt, and (3) the monitoring of data increases the feeling of safety. Results from the questionnaire showed that the patients generally had confidence in the device. Conclusions: Although the primary end point was not reached, the patients' experiences with using the smart T-shirt resulted in the knowledge that patients acknowledged the need for new technologies that improve supportive cancer care. The patients were positive when asked to wear the smart T-shirt. However, technical and practical challenges in using the device resulted in low adherence. Although wearables might have potential for home monitoring, the present technology is immature for clinical use.

Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study.

BACKGROUND: Postpartum depression (PPD) poses a significant maternal health challenge. The current approach to detecting PPD relies on in-person postpartum visits, which contributes to underdiagnosis. Furthermore, recognizing PPD symptoms can be challenging. Therefore, we explored the potential of using digital biomarkers from consumer wearables for PPD recognition. OBJECTIVE: The main goal of this study was to showcase the viability of using machine learning (ML) and digital biomarkers related to heart rate, physical activity, and energy expenditure derived from consumer-grade wearables for the recognition of PPD. METHODS: Using the All of Us Research Program Registered Tier v6 data set, we performed computational phenotyping of women with and without PPD following childbirth. Intraindividual ML models were developed using digital biomarkers from Fitbit to discern between prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods. Models were built using generalized linear models, random forest, support vector machine, and k-nearest neighbor algorithms and evaluated using the κ statistic and multiclass area under the receiver operating characteristic curve (mAUC) to determine the algorithm with the best performance. The specificity of our individualized ML approach was confirmed in a cohort of women who gave birth and did not experience PPD. Moreover, we assessed the impact of a previous history of depression on model performance. We determined the variable importance for predicting the PPD period using Shapley additive explanations and confirmed the results using a permutation approach. Finally, we compared our individualized ML methodology against a traditional cohort-based ML model for PPD recognition and compared model performance using sensitivity, specificity, precision, recall, and F1-score. RESULTS: Patient cohorts of women with valid Fitbit data who gave birth included <20 with PPD and 39 without PPD. Our results demonstrated that intraindividual models using digital biomarkers discerned among prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods, with random forest (mAUC=0.85; κ=0.80) models outperforming generalized linear models (mAUC=0.82; κ=0.74), support vector machine (mAUC=0.75; κ=0.72), and k-nearest neighbor (mAUC=0.74; κ=0.62). Model performance decreased in women without PPD, illustrating the method's specificity. Previous depression history did not impact the efficacy of the model for PPD recognition. Moreover, we found that the most predictive biomarker of PPD was calories burned during the basal metabolic rate. Finally, individualized models surpassed the performance of a conventional cohort-based model for PPD detection. CONCLUSIONS: This research establishes consumer wearables as a promising tool for PPD identification and highlights personalized ML approaches, which could transform early disease detection strategies.

Perceptions of Wearable Health Tools Post the COVID-19 Emergency in Low-Income Latin Communities: Qualitative Study.

BACKGROUND: Mobile health (mHealth) wearable devices are increasingly being adopted by individuals to help manage and monitor physiological signals. However, the current state of wearables does not consider the needs of racially minoritized low-socioeconomic status (SES) communities regarding usability, accessibility, and price. This is a critical issue that necessitates immediate attention and resolution. OBJECTIVE: This study's aims were 3-fold, to (1) understand how members of minoritized low-SES communities perceive current mHealth wearable devices, (2) identify the barriers and facilitators toward adoption, and (3) articulate design requirements for future wearable devices to enable equitable access for these communities. METHODS: We performed semistructured interviews with low-SES Hispanic or Latine adults (N=19) from 2 metropolitan cities in the Midwest and West Coast of the United States. Participants were asked questions about how they perceive wearables, what are the current benefits and barriers toward use, and what features they would like to see in future wearable devices. Common themes were identified and analyzed through an exploratory qualitative approach. RESULTS: Through qualitative analysis, we identified 4 main themes. Participants' perceptions of wearable devices were strongly influenced by their COVID-19 experiences. Hence, the first theme was related to the impact of COVID-19 on the community, and how this resulted in a significant increase in interest in wearables. The second theme highlights the challenges faced in obtaining adequate health resources and how this further motivated participants' interest in health wearables. The third theme focuses on a general distrust in health care infrastructure and systems and how these challenges are motivating a need for wearables. Lastly, participants emphasized the pressing need for community-driven design of wearable technologies. CONCLUSIONS: The findings from this study reveal that participants from underserved communities are showing emerging interest in using health wearables due to the COVID-19 pandemic and health care access issues. Yet, the needs of these individuals have been excluded from the design and development of current devices.

Heterogeneous digital biomarker integration out-performs patient self-reports in predicting Parkinson's disease.

Parkinson's disease (PD) is one of the first diseases where digital biomarkers demonstrated excellent performance in differentiating disease from healthy individuals. However, no study has systematically compared and leveraged multiple types of digital biomarkers to predict PD. Particularly, machine learning works on the fine-motor skills of PD are limited. Here, we developed deep learning methods that achieved an AUC (Area Under the receiver operator characteristic Curve) of 0.933 in identifying PD patients on 6418 individuals using 75048 tapping accelerometer and position records. Performance of tapping is superior to gait/rest and voice-based models obtained from the same benchmark population. Assembling the three models achieved a higher AUC of 0.944. Notably, the models not only correlated strongly to, but also performed better than patient self-reported symptom scores in diagnosing PD. This study demonstrates the complementary predictive power of tapping, gait/rest and voice data and establishes integrative deep learning-based models for identifying PD.

Association between behavioral phenotypes and sustained use of smartphones and wearable devices to remotely monitor physical activity.

Smartphones and wearable devices can be used to remotely monitor health behaviors, but little is known about how individual characteristics influence sustained use of these devices. Leveraging data on baseline activity levels and demographic, behavioral, and psychosocial traits, we used latent class analysis to identify behavioral phenotypes among participants randomized to track physical activity using a smartphone or wearable device for 6 months following hospital discharge. Four phenotypes were identified: (1) more agreeable and conscientious; (2) more active, social, and motivated; (3) more risk-taking and less supported; and (4) less active, social, and risk-taking. We found that duration and consistency of device use differed by phenotype for wearables, but not smartphones. Additionally, "at-risk" phenotypes 3 and 4 were more likely to discontinue use of a wearable device than a smartphone, while activity monitoring in phenotypes 1 and 2 did not differ by device type. These findings could help to better target remote-monitoring interventions for hospitalized patients.

Interpretation of Electrocardiogram Heartbeat by CNN and GRU.

The diagnosis of electrocardiogram (ECG) is extremely onerous and inefficient, so it is necessary to use a computer-aided diagnosis of ECG signals. However, it is still a challenging problem to design high-accuracy ECG algorithms suitable for the medical field. In this paper, a classification method is proposed to classify ECG signals. Firstly, wavelet transform is used to denoise the original data, and data enhancement technology is used to overcome the problem of an unbalanced dataset. Secondly, an integrated convolutional neural network (CNN) and gated recurrent unit (GRU) classifier is proposed. The proposed network consists of a convolution layer, followed by 6 local feature extraction modules (LFEM), a GRU, and a Dense layer and a Softmax layer. Finally, the processed data were input into the CNN-GRU network into five categories: nonectopic beats, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unknown beats. The MIT-BIH arrhythmia database was used to evaluate the approach, and the average sensitivity, accuracy, and F1-score of the network for 5 types of ECG were 99.33%, 99.61%, and 99.42%. The evaluation criteria of the proposed method are superior to other state-of-the-art methods, and this model can be applied to wearable devices to achieve high-precision monitoring of ECG.

Skin-electrode iontronic interface for mechanosensing.

Electrodermal devices that capture the physiological response of skin are crucial for monitoring vital signals, but they often require convoluted layered designs with either electronic or ionic active materials relying on complicated synthesis procedures, encapsulation, and packaging techniques. Here, we report that the ionic transport in living systems can provide a simple mode of iontronic sensing and bypass the need of artificial ionic materials. A simple skin-electrode mechanosensing structure (SEMS) is constructed, exhibiting high pressure-resolution and spatial-resolution, being capable of feeling touch and detecting weak physiological signals such as fingertip pulse under different skin humidity. Our mechanical analysis reveals the critical role of instability in high-aspect-ratio microstructures on sensing. We further demonstrate pressure mapping with millimeter-spatial-resolution using a fully textile SEMS-based glove. The simplicity and reliability of SEMS hold great promise of diverse healthcare applications, such as pulse detection and recovering the sensory capability in patients with tactile dysfunction.

A catalog of validity indices for step counting wearable technologies during treadmill walking: the CADENCE-Kids study.

BACKGROUND: Wearable technologies play an important role in measuring physical activity (PA) and promoting health. Standardized validation indices (i.e., accuracy, bias, and precision) compare performance of step counting wearable technologies in young people. PURPOSE: To produce a catalog of validity indices for step counting wearable technologies assessed during different treadmill speeds (slow [0.8-3.2 km/h], normal [4.0-6.4 km/h], fast [7.2-8.0 km/h]), wear locations (waist, wrist/arm, thigh, and ankle), and age groups (children, 6-12 years; adolescents, 13-17 years; young adults, 18-20 years). METHODS: One hundred seventeen individuals (13.1 ± 4.2 years, 50.4% female) participated in this cross-sectional study and completed 5-min treadmill bouts (0.8 km/h to 8.0 km/h) while wearing eight devices (Waist: Actical, ActiGraph GT3X+, NL-1000, SW-200; Wrist: ActiGraph GT3X+; Arm: SenseWear; Thigh: activPAL; Ankle: StepWatch). Directly observed steps served as the criterion measure. Accuracy (mean absolute percentage error, MAPE), bias (mean percentage error, MPE), and precision (correlation coefficient, r; standard deviation, SD; coefficient of variation, CoV) were computed. RESULTS: Five of the eight tested wearable technologies (i.e., Actical, waist-worn ActiGraph GT3X+, activPAL, StepWatch, and SW-200) performed at < 5% MAPE over the range of normal speeds. More generally, waist (MAPE = 4%), thigh (4%) and ankle (5%) locations displayed higher accuracy than the wrist location (23%) at normal speeds. On average, all wearable technologies displayed the lowest accuracy across slow speeds (MAPE = 50.1 ± 35.5%), and the highest accuracy across normal speeds (MAPE = 15.9 ± 21.7%). Speed and wear location had a significant effect on accuracy and bias (P < 0.001), but not on precision (P > 0.05). Age did not have any effect (P > 0.05). CONCLUSIONS: Standardized validation indices focused on accuracy, bias, and precision were cataloged by speed, wear location, and age group to serve as important reference points when selecting and/or evaluating device performance in young people moving forward. Reduced performance can be expected at very slow walking speeds (0.8 to 3.2 km/h) for all devices. Ankle-worn and thigh-worn devices demonstrated the highest accuracy. Speed and wear location had a significant effect on accuracy and bias, but not precision. TRIAL REGISTRATION: Clinicaltrials.gov NCT01989104 . Registered November 14, 2013.

Trifluralin recognition using touch-based fingertip: Application of wearable glove-based sensor toward environmental pollution and human health control.

Monitoring of herbicides and pesticides in water, food, and the environment is essential for human health, and this requires low-cost, portable devices for widespread deployment of this technology. For the first time, a wearable glove-based electrochemical sensor based on conductive Ag nano-ink was developed for the on-site monitoring of trifluralin residue on the surface of various substrates. Three electrode system with optimal thicknesses was designed directly on the finger surface of a rubber glove. Then, fabricated electrochemical sensor used for the direct detection of trifluralin in the range of 0.01 µM to 1 mM on the surface of tomato and mulberry leaves using square wave voltammetry (SWV) and difference pulse voltammetry technique. The obtained LLOQ was 0.01 µM, which indicates the suitable sensitivity of this sensor. On the other hand, this sensor is portable, easy to use, and has a high environmental capability that can be effective in detecting other chemical threats in the soil and water environment.

Willingness to use a wearable device capable of detecting and reversing overdose among people who use opioids in Philadelphia.

BACKGROUND: The incidence of opioid-related overdose deaths has been rising for 30 years and has been further exacerbated amidst the COVID-19 pandemic. Naloxone can reverse opioid overdose, lower death rates, and enable a transition to medication for opioid use disorder. Though current formulations for community use of naloxone have been shown to be safe and effective public health interventions, they rely on bystander presence. We sought to understand the preferences and minimum necessary conditions for wearing a device capable of sensing and reversing opioid overdose among people who regularly use opioids. METHODS: We conducted a combined cross-sectional survey and semi-structured interview at a respite center, shelter, and syringe exchange drop-in program in Philadelphia, Pennsylvania, USA, during the COVID-19 pandemic in August and September 2020. The primary aim was to explore the proportion of participants who would use a wearable device to detect and reverse overdose. Preferences regarding designs and functionalities were collected via a questionnaire with items having Likert-based response options and a semi-structured interview intended to elicit feedback on prototype designs. Independent variables included demographics, opioid use habits, and previous experience with overdose. RESULTS: A total of 97 adults with an opioid use history of at least 3 months were interviewed. A majority of survey participants (76%) reported a willingness to use a device capable of detecting an overdose and automatically administering a reversal agent upon initial survey. When reflecting on the prototype, most respondents (75.5%) reported that they would wear the device always or most of the time. Respondents indicated discreetness and comfort as important factors that increased their chance of uptake. Respondents suggested that people experiencing homelessness and those with low tolerance for opioids would be in greatest need of the device. CONCLUSIONS: The majority of people sampled with a history of opioid use in an urban setting were interested in having access to a device capable of detecting and reversing an opioid overdose. Participants emphasized privacy and comfort as the most important factors influencing their willingness to use such a device. TRIAL REGISTRATION: NCT04530591.

Identification of Arrhythmia by Using a Decision Tree and Gated Network Fusion Model.

In recent years, deep learning (DNN) based methods have made leapfrogging level breakthroughs in detecting cardiac arrhythmias as the cost effectiveness of arithmetic power, and data size has broken through the tipping point. However, the inability of these methods to provide a basis for modeling decisions limits clinicians' confidence on such methods. In this paper, a Gate Recurrent Unit (GRU) and decision tree fusion model, referred to as (T-GRU), was designed to explore the problem of arrhythmia recognition and to improve the credibility of deep learning methods. The fusion model multipathway processing time-frequency domain featured the introduction of decision tree probability analysis of frequency domain features, the regularization of GRU model parameters and weight control to improve the decision tree model output weights. The MIT-BIH arrhythmia database was used for validation. Results showed that the low-frequency band features dominated the model prediction. The fusion model had an accuracy of 98.31%, sensitivity of 96.85%, specificity of 98.81%, and precision of 96.73%, indicating its high reliability and clinical significance.

Wearable Wireless Body Area Networks for Medical Applications.

In recent times, there has been a significant growth in networks known as the wireless body area networks (WBANs). A WBAN connects distributed nodes throughout the human body, which can be placed on the skin, under the skin, or on clothing and can use the human body's electromagnetic waves. An approach to reduce the size of different telecommunication equipment is constantly being sought; this allows these devices to be closer to the body or even glued and embedded within the skin without making the user feel uncomfortable or posing as a danger for the user. These networks promise new medical applications; however, these are always based on the freedom of movement and the comfort they offer. Among the advantages of these networks is that they can significantly increase user's quality of life. For example, a person can carry a WBAN with built-in sensors that calculate the user's heart rate at any given time and send these data over the internet to user's doctor. This study provides a systematic review of WBAN, describing the applications and trends that have been developed with this type of network and, in addition, the protocols and standards that must be considered.

A New Measure of Pulse Rate Variability and Detection of Atrial Fibrillation Based on Improved Time Synchronous Averaging.

BACKGROUND: Pulse rate variability monitoring and atrial fibrillation detection algorithms have been widely used in wearable devices, but the accuracies of these algorithms are restricted by the signal quality of pulse wave. Time synchronous averaging is a powerful noise reduction method for periodic and approximately periodic signals. It is usually used to extract single-period pulse waveforms, but has nothing to do with pulse rate variability monitoring and atrial fibrillation detection traditionally. If this method is improved properly, it may provide a new way to measure pulse rate variability and to detect atrial fibrillation, which may have some potential advantages under the condition of poor signal quality. OBJECTIVE: The objective of this paper was to develop a new measure of pulse rate variability by improving existing time synchronous averaging and to detect atrial fibrillation by the new measure of pulse rate variability. METHODS: During time synchronous averaging, two adjacent periods were regarded as the basic unit to calculate the average signal, and the difference between waveforms of the two adjacent periods was the new measure of pulse rate variability. 3 types of distance measures (Euclidean distance, Manhattan distance, and cosine distance) were tested to measure this difference on a simulated training set with a capacity of 1000. The distance measure, which can accurately distinguish regular pulse rate and irregular pulse rate, was used to detect atrial fibrillation on the testing set with a capacity of 62 (11 with atrial fibrillation, 8 with premature contraction, and 43 with sinus rhythm). The receiver operating characteristic curve was used to evaluate the performance of the indexes. RESULTS: The Euclidean distance between waveforms of the two adjacent periods performs best on the training set. On the testing set, the Euclidean distance in atrial fibrillation group is significantly higher than that of the other two groups. The area under receiver operating characteristic curve to identify atrial fibrillation was 0.998. With the threshold of 2.1, the accuracy, sensitivity, and specificity were 98.39%, 100%, and 98.04%, respectively. This new index can detect atrial fibrillation from pulse wave signal. CONCLUSION: This algorithm not only provides a new perspective to detect AF but also accomplishes the monitoring of PRV and the extraction of single-period pulse wave through the same technical route, which may promote the popularization and application of pulse wave.

Remote Monitoring of a Work-From-Home Employee to Identify Stress: A Case Report.

How do you assess the mental wellness of your work-from-home employees? This case study reports on how an occupational health nurse used work-from-home employee's own phone and Fitbit™ smartwatch to obtain heart rate data to screen for high periods of stress. Telemedicine and telemetry allowed the occupational health nurses to screen an employee when the nurse could not assess the employee face-to-face. When the occupational health nurses identified an at-risk employee, the occupational health nurses referred the employee to the Employee Assistance Program (EAP) for counseling. Leveraging heart rate data on a smartwatch is a free intervention that is scalable and has a demonstrated outcome measure with a positive return on investment.

Wearable Biosensors: An Alternative and Practical Approach in Healthcare and Disease Monitoring.

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.

Wearable Devices to Monitor and Reduce the Risk of Cardiovascular Disease: Evidence and Opportunities.

There is a growing interest in using wearable devices to improve cardiovascular risk factors and care. This review evaluates how wearable devices are used for cardiovascular disease monitoring and risk reduction. Wearables have been evaluated for detecting arrhythmias (e.g., atrial fibrillation) as well as monitoring physical activity, sleep, and blood pressure. Thus far, most interventions for risk reduction have focused on increasing physical activity. Interventions have been more successful if the use of wearable devices is combined with an engagement strategy such as incorporating principles from behavioral economics to integrate social or financial incentives. As the technology continues to evolve, wearable devices could be an important part of remote-monitoring interventions but are more likely to be effective at improving cardiovascular care if integrated into programs that use an effective behavior change strategy.

Association Between Wearable Device-Based Measures of Physical Frailty and Major Adverse Events Following Lower Extremity Revascularization.

Importance: Physical frailty is a key risk factor associated with higher rates of major adverse events (MAEs) after surgery. Assessing physical frailty is often challenging among patients with chronic limb-threatening ischemia (CLTI) who are often unable to perform gait-based assessments because of the presence of plantar wounds. Objective: To test a frailty meter (FM) that does not rely on gait to determine the risk of occurrence of MAEs after revascularization for patients with CLTI. Design, Setting, and Participants: This cohort study included 184 consecutively recruited patients with CLTI at 2 tertiary care centers. After 32 individuals were excluded, 152 participants were included in the study. Data collection was conducted between May 2018 and June 2019. Exposures: Physical frailty measurement within 1 week before limb revascularization and incidence of MAEs for as long as 1 month after surgery. Main Outcomes and Measures: The FM works by quantifying weakness, slowness, rigidity, and exhaustion during a 20-second repetitive elbow flexion-extension exercise using a wrist-worn sensor. The FM generates a frailty index (FI) ranging from 0 to 1; higher values indicate progressively greater severity of physical frailty. Results: Of 152 eligible participants (mean [SD] age, 67.0 [11.8] years; 59 [38.8%] women), 119 (78.2%) were unable to perform the gait test, while all could perform the FM test. Overall, 53 (34.9%), 58 (38.1%), and 41 (27.0%) were classified as robust (FI <0.20), prefrail (FI ≥0.20 to <0.35), or frail (FI ≥0.35), respectively. Within 30 days after surgery, 24 (15.7%) developed MAEs, either major adverse cardiovascular events (MACE; 8 [5.2%]) or major adverse limb events (MALE; 16 [10.5%]). Baseline demographic characteristics were not significantly different between frailty groups. In contrast, the FI was approximately 30% higher in the group that developed MAEs (mean [SD] score, 0.36 [0.14]) than those who were MAE free (mean [SD] score, 0.26 [0.13]; P = .001), with observed MAE rates of 4 patients (7.5%), 7 patients (12.1%), and 13 patients (31.7%) in the robust, prefrail and frail groups, respectively (P = .004). The FI distinguished individuals who developed MACE and MALE from those who were MAE free (MACE: mean [SD] FI score, 0.38 [0.16]; P = .03; MALE: mean [SD] FI score, 0.35 [0.13]; P = .004) after adjusting by body mass index. Conclusions and Relevance: In this cohort study, measuring physical frailty using a wrist-worn sensor during a short upper extremity test was a practical method for stratifying the risk of MAEs following revascularization for CLTI when the administration of gait-based tests is often challenging.

Quantitative detection of sleep apnea with wearable watch device.

The spread of wearable watch devices with photoplethysmography (PPG) sensors has made it possible to use continuous pulse wave data during daily life. We examined if PPG pulse wave data can be used to detect sleep apnea, a common but underdiagnosed health problem associated with impaired quality of life and increased cardiovascular risk. In 41 patients undergoing diagnostic polysomnography (PSG) for sleep apnea, PPG was recorded simultaneously with a wearable watch device. The pulse interval data were analyzed by an automated algorithm called auto-correlated wave detection with adaptive threshold (ACAT) which was developed for electrocardiogram (ECG) to detect the cyclic variation of heart rate (CVHR), a characteristic heart rate pattern accompanying sleep apnea episodes. The median (IQR) apnea-hypopnea index (AHI) was 17.2 (4.4-28.4) and 22 (54%) subjects had AHI ≥15. The hourly frequency of CVHR (Fcv) detected by the ACAT algorithm closely correlated with AHI (r = 0.81), while none of the time-domain, frequency-domain, or non-linear indices of pulse interval variability showed significant correlation. The Fcv was greater in subjects with AHI ≥15 (19.6 ± 12.3 /h) than in those with AHI <15 (6.4 ± 4.6 /h), and was able to discriminate them with 82% sensitivity, 89% specificity, and 85% accuracy. The classification performance was comparable to that obtained when the ACAT algorithm was applied to ECG R-R intervals during the PSG. The analysis of wearable watch PPG by the ACAT algorithm could be used for the quantitative screening of sleep apnea.

The Implication of Wearables and the Factors Affecting Their Usage among Recreationally Active People.

Regular physical activity (PA) is associated with health and well-being. Recent findings show that PA tracking using technological devices can enhance PA behavior. Consumer devices can track many different parameters affecting PA (e.g., number of steps, distance, and heart rate). However, it remains unclear what factors affect the usage of such devices. In this study, we evaluated whether there was a change in usage behavior across the first weeks of usage. Further we investigated whether external factors such as weather and day of the week influence usage behavior. Thirty nine participants received a Fitbit Charge 2 fitness tracker for a nine-week period. All participants were asked to wear the device according to their wishes. The usage time and amount of PA were assessed, and the influencing factors, such as weather conditions and day of the week, were analyzed. The results showed that usage behavior differed largely between individuals and decreased after five weeks of usage. Moreover, the steps per worn hour did not change significantly, indicating a similar amount of activity across the nine-week period when wearing the device. Further influencing factors were the day of the week (the tracker was used less on Sundays) and the temperature (usage time was lower with temperatures >25°). Tracking peoples' activity might have the potential to evaluate different interventions to increase PA.