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Background: The transitional period from hospital to home is vital for stroke patients, but it poses serious challenges. Good self-management ability can optimize disease outcomes. However, stroke patients in China have a low level of self-management ability during the transitional period, and a lack of effective support may be the reason. With the rapid development of technology, using wearable monitors to achieve real-time and individualized support may be the key to solving this problem. This study uses a randomized controlled trial design to assess the efficacy of using wearable technology to realize real-time and individualized self-management support in stroke patients' self-management behavior during the transitional period following discharge from hospital. Methods: This parallel-group randomized controlled trial will be conducted in two hospitals and patients' homes. A total of 183 adult stroke patients will be enrolled in the study and randomly assigned to three groups in a 1:1:1 ratio. The smartwatch intervention group (n = 61) will receive Real-time and Individualized Self-management Support (RISS) program + routine care, the wristband group (n = 61) will wear a fitness tracker (self-monitoring) + routine care, and the control group (n = 61) will receive routine stroke care. The intervention will last for 6 months. The primary outcomes are neurological function status, self-management behavior, quality of life, biochemical indicators, recurrence rate, and unplanned readmission rate. Secondary outcomes are resilience, patient activation, psychological status, and caregiver assessments. The analysis is intention-to-treat. The intervention effect will be evaluated at baseline (T0), 2 months after discharge (T1), 3 months after discharge (T2), and 6 months after discharge (T3). Discussion: The cloud platform designed in this study not only has the function of real-time recording but also can push timely solutions when patients have abnormal conditions, as well as early warnings or alarms. This study could also potentially help patients develop good self-management habits through resilience theory, wearable devices, and individualized problem–solution library of self-management which can lay the foundation for long-term maintenance and continuous improvement of good self-management behavior in the future. Trial registration: The ethics approval has been granted by the Ethics Committee of West China Hospital, Sichuan University (2022–941). All patients will be informed of the study details and sign a written informed consent form before enrollment. The research results will be reported in conferences and peer-reviewed publications. The trial registration number is ChiCTR2300070384. Registered on 11 April 2023. [ABSTRACT FROM AUTHOR]

Background: Postoperative pulmonary complications (PPCs) following cardiac valvular surgery are characterized by high morbidity, mortality, and economic cost. This study leverages wearable technology and machine learning algorithms to preoperatively identify high-risk individuals, thereby enhancing clinical decision-making for the mitigation of PPCs., Methods: A prospective study was conducted at the Department of Cardiovascular Surgery of West China Hospital, Sichuan University, from August 2021 to December 2022. We examined 100 cardiac valvular surgery patients, where wearable technology was utilized to collect and analyze nocturnal physiological data at the 24-hour admission, in conjunction with clinical data extraction from the Hospital Information System's electronic records. We systematically evaluated three different input types (physiological, clinical, and both) and five classifiers (XGB, LR, RF, SVM, KNN) to identify the combination with strong predictive performance for PPCs. Feature selection was conducted using Recursive Feature Elimination with Cross-Validated (RFECV) for each model, yielding an optimal feature subset for each, followed by a grid search to tune hyperparameters. Stratified 5-fold cross-validation was used to evaluate the generalization performance. The significance of AUC differences between models was tested using the DeLong test to determine the optimal prognostic model comprehensively. Additionally, univariate logistic regression analysis was conducted on the features of the best-performing model to understand the impact of individual feature on PPCs., Results: In this study, 22 patients (22%) developed PPCs. Across classifiers, models combining both physiological and clinical features performed better than physiological or clinical features alone. Specifically, including physiological data in the classification model improved AUC, ACC, F1, and precision by an average of 8.32%, 1.80%, 3.28% and 6.06% compared to using clinical data only. The XGB classifier, utilizing both dataset, achieved the highest performance with an AUC of 0.82 (± 0.08) and identified eight significant features. The DeLong test indicated that the XGB model utilizing the both dataset significantly outperformed the XGB models trained on the physiological or clinical datasets alone. Univariate logistic regression analysis suggested that surgical methods, age, nni_50, and min_ven_in_mean are significantly associated with the occurrence of PPCs., Conclusion: The integration of continuous wearable physiological and clinical data significantly improves preoperative risk assessment for PPCs, which helps to optimize surgical management and reduce PPCs morbidity and mortality., Competing Interests: Declarations. Ethics approval and consent to participate: All participants provided written informed consent. This study was approved by the Ethics Committee of the West China Hospital of Sichuan University, Ethics No.20211023, Clinical Registration No. ChiCTR2100050005 ( http://www.chictr.org.cn ). Consent for publication: Not applicable. Competing interests: This work was done during ZY’s internship at Beijing SensEcho Science and Technology Co., Ltd., Beijing, China, when he was a Ph.D. candidate at University of California, Davis, CA, United states, and now he works in PAII Inc., Palo Alto, CA, United States. The authors declared that the sleep feature calculation algorithm described in this article is covered by a patent (Patent Title: Sleep Apnoea Event Detection Method, Patent Number: ZL 202110167659.6X, Holder: Chinese PLA General Hospital). Zhengbo Zhang is named as inventors on this patent. The remaining authors declared that they have no conflict of interest., (© 2025. The Author(s).)

Introduction: Wearable devices, including smart wristbands and watches, are often used with e-health applications (apps). The users' characteristics of wrist wearable devices currently lack description, and the cardiovascular disease (CVD) high-risk rate of users remains unknown. Purpose: This study aimed to (1) describe the basic characteristics and habits of users of the "Amazfit Health" app and Huami wrist wearable devices and (2) analyze the proportion and define the population characteristics of users with a high risk of developing CVD. Subjects and Methods: This study included users >18 years of age, residing in mainland China, using the "Amazfit Health" app and Huami wearable devices. Devices data and users' self-reported information were collected in the app. The risk stratification was based on WHO/ISH cardiovascular risk prediction charts for the Western Pacific Region. Subjects with CVD history, total cholesterol ≥8 mmol/L, or ≥10% predicted CVD risk and those with <10% predicted CVD risk were considered to be at high and low risk of developing CVD, respectively. Results: Data were obtained from 80,098 (total users) and 10,866 users (subjects) for risk stratification. The age of the total users and subjects were 45.6 ± 15.4 and 50.7 ± 14.0 years, respectively. The number of male and female users was 50,024, and 30,074 in total users, and 7,284, and 3,582 in subjects, respectively. The body mass index of total users and subjects was 24.0 ± 4.6 kg/m2 and 24.6 ± 3.8 kg/m2, respectively. By classifying users' residences into first-tier cities, municipalities and provincial capitals, and other areas, the numbers of total users were 20,179, 28,213, and 31,137, and subjects were 2,587, 3,966, and 4,269, respectively. The number of subjects with high CVD risk was 1,161, accounting for 10.7% of all subjects. Conclusions: Users with high CVD risk only accounted for a small proportion of the population of wearable devices users. [ABSTRACT FROM AUTHOR]

Background: The paucity of evidence on longitudinal and consecutive recordings of physical activity (PA) and blood pressure (BP) under real-life conditions and their relationships is a vital research gap that needs to be addressed., Objective: This study aims to (1) investigate the short-term relationship between device-measured step volume and BP; (2) explore the joint effects of step volume and variability on BP; and (3) examine whether the association patterns between PA and BP varied across sex, hypertension status, and chronic condition status., Methods: This study used PA data of a prospective cohort of 3070 community-dwelling older adults derived from a mobile health app. Daily step counts, as a proxy of step volume, were derived from wearable devices between 2018 and 2022 and categorized into tertiles (low, medium, and high). Step variability was assessed using the SD of daily step counts. Consecutive daily step count recordings within 0 to 6 days preceding each BP measurement were analyzed. Generalized estimation equation models were used to estimate the individual and joint associations of daily step volume and variability with BP. Stratified analyses by sex, the presence of hypertension, and the number of morbidities were further conducted., Results: A total of 3070 participants, with a median age of 72 (IQR 67-77) years and 71.37% (2191/3070) women, were included. Participants walked a median of 7580 (IQR 4972-10,653) steps and 5523 (IQR 3590-7820) meters per day for a total of 592,597 person-days of PA monitoring. Our results showed that higher levels of daily step volume were associated with lower BP (systolic BP, diastolic BP, mean arterial pressure, and pulse pressure). Compared with participants with low step volume (daily step counts <6000/d) and irregular steps, participants with high step volume (≥9500/d) and regular steps showed the strongest decrease in systolic BP (-1.69 mm Hg, 95% CI -2.2 to -1.18), while participants with medium step volume (6000/d to <9500/d) and regular steps were associated with the lowest diastolic BP (-1.067 mm Hg, 95% CI -1.379 to -0.755). Subgroup analyses indicated generally greater effects on women, individuals with normal BP, and those with only 1 chronic disease, but the effect pattern was varied and heterogeneous between participants with different characteristics., Conclusions: Increased step volume demonstrated a substantial protective effect on BP among older adults with chronic conditions. Furthermore, the beneficial association between step volume and BP was enhanced by regular steps, suggesting potential synergistic protective effects of both increased step volume and step regularity. Targeting both step volume and variability through PA interventions may yield greater benefits in BP control, particularly among participants with hypertension and a higher chronic disease burden., (©Han Xiao, Zechen Zhou, Yujia Ma, Xiaoyi Li, Kexin Ding, Xiaotong Dai, Dafang Chen. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.08.2024.)

Hand foot and mouth disease (HFMD) is caused by a variety of enteroviruses, and occurs in large outbreaks in which a small proportion of children deteriorate rapidly with cardiopulmonary failure. Determining which children are likely to deteriorate is difficult and health systems may become overloaded during outbreaks as many children require hospitalization for monitoring. Heart rate variability (HRV) may help distinguish those with more severe diseases but requires simple scalable methods to collect ECG data.We carried out a prospective observational study to examine the feasibility of using wearable devices to measure HRV in 142 children admitted with HFMD at a children's hospital in Vietnam. ECG data were collected in all children. HRV indices calculated were lower in those with enterovirus A71 associated HFMD compared to those with other viral pathogens.HRV analysis collected from wearable devices is feasible in a low and middle income country (LMIC) and may help classify disease severity in HFMD., (© 2024. The Author(s).)

Background and Objective Capital market and consumer market interest in wearable devices has surged in recent years, however, their actual acceptance in the field of health monitoring is somewhat not as expected. This study aims to understand the perceptions of wearable devices of general consumers, analyze the review of the devices by users, and find existing problems associated with current wearable devices. Methods Sojump.com, an on-line questionnaire tool, was used to generate the questionnaire, which focused on four aspects. The snowball sampling method was employed to collect questionnaires by making use of the author's social network. Results (1) A total of 2058 valid questionnaires were received from the respondents from every province in China; of the respondents, 52.4% have used a wearable device. (2) The respondents had a low level of knowledge about wearable devices (2.79/5) but were optimistic with regard to the devices’ future (3.86/5), and 84% recognized an acceptable price of less than 2000 RMB. Nearly half of the respondents were unwilling to continuously wear the device (47.1%) and share their health data (44.7%). (3) The functions of wearable devices that the respondents expected were mainly health management (63.5%), mobile phone accessories (61.9%), and location tracking (61.2%), and the promising hot future functions were mainly data analysis (74.2%), exercise coaching (60.5%), and child tracking (58.8%). Regarding the health monitoring functions, the respondents were most interested in heart health monitoring. (4) The respondents had different levels of emphasis regarding the existing problems of wearable devices at different use stages. Being easily damaged or lost (49.7%), being incapable of providing health recommendations based on data analysis (46.7%), and being uncomfortable to wear (45.8%) likely lead consumers to abandon the use of wearable devices. Conclusions Consumers are optimistic about the prospects of wearable devices; however, there is a large gap between the reliability of the measurement data, the ease of use, and the interpretation of measurement data of current wearable products and consumer expectations. Consumer demand for health management functions is higher than that for daily auxiliary-type functions, which is an issue that should be properly addressed and resolved by manufacturers. [ABSTRACT FROM AUTHOR]

Recently, increasing numbers of people have realized the harm that particulate matter (PM) causes to health, especially those with a diameter less than 2.5 μm (PM2.5). With the increasing popularity of wearable devices in recent years, it is believed that wearable technology can contribute feasible solutions to prevent health hazards caused by PM2.5. In order to better understand the public's needs regarding wearable devices, this study aimed to determine what kinds of PM2.5 wearable devices were needed by the public and the factors that may influence these needs. An online survey was conducted in the Beijing metropolitan area of China of a total of 894 subjects. The results showed that the public's overall need for wearable PM2.5 purifiers was higher than for wearable PM2.5 trackers. The public's needs for wearable breathing-zone PM2.5 devices were significantly higher than for any other type, indicating that people care about the quality of the air they actually breathe. It was also found that education, income level, and attitude toward PM2.5 positively affected their needs for wearable devices. In contrast, age had a negative influence on their needs. The results of this study are expected to serve as a valuable reference for related academic and industrial research. [ABSTRACT FROM AUTHOR]

Background: Chronotype is the propensity for a person to sleep at a particular time during 24 hours. It is largely regulated by the circadian clock but constrained by work obligations to a specific sleep schedule. The discrepancy between biological and social time can be described as social jetlag (SJL), which is highly prevalent in modern society and associated with health problems. SJL and chronotypes have been widely studied in Western countries but have never been described in China.Objective: We characterized the chronotypes and SJL in mainland China objectively by analyzing a database of Chinese sleep-wake pattern recorded by up-to-date wearable devices.Methods: We analyzed 71,176 anonymous Chinese people who were continuously recorded by wearable devices for at least one week between April and July in 2017. Chronotypes were assessed (N=49,573) by the adjusted mid-point of sleep on free days (MSFsc). Early, intermediate, and late chronotypes were defined by arbitrary cut-offs of MSFsc <3 hours, between 3-5 hours, and >5 hours. In all subjects, SJL was calculated as the difference between mid-points of sleep on free days and work days. The correlations between SJL and age/body mass index/MSFsc were assessed by Pearson correlation. Random forest was used to characterize which factors (ie, age, body mass index, sex, nocturnal and daytime sleep durations, and exercise) mostly contribute to SJL and MSFsc.Results: The mean total sleep duration of this Chinese sample is about 7 hours, with females sleeping on average 17 minutes longer than males. People taking longer naps sleep less during the night, but they have longer total 24-hour sleep durations. MSFsc follows a normal distribution, and the percentages of early, intermediate, and late chronotypes are approximately 26.76% (13,266/49,573), 58.59% (29,045/49,573), and 14.64% (7257/49,573). Adolescents are later types compared to adults. Age is the most important predictor of MSFsc suggested by our random forest model (relative feature importance: 0.772). No gender differences are found in chronotypes. We found that SJL follows a normal distribution and 17.07% (12,151/71,176) of Chinese have SJL longer than 1 hour. Nearly a third (22,442/71,176, 31.53%) of Chinese have SJL<0. The results showed that 53.72% (7127/13,266), 25.46% (7396/29,045), and 12.71% (922/7257) of the early, intermediate, and late chronotypes have SJL<0, respectively. SJL correlates with MSFsc (r=0.54, P<.001) but not with body mass index (r=0.004, P=.30). Random forest model suggests that age, nocturnal sleep, and daytime nap durations are the features contributing to SJL (their relative feature importance is 0.441, 0.349, and 0.204, respectively).Conclusions: Our data suggest a higher proportion of early compared to late chronotypes in Chinese. Chinese have less SJL than the results reported in European populations, and more than half of the early chronotypes have negative SJL. In the Chinese population, SJL is not associated with body mass index. People of later chronotypes and long sleepers suffer more from SJL. [ABSTRACT FROM AUTHOR]

Overwork-related disorders, such as cerebrovascular/cardiovascular diseases (CCVD) and mental disorders due to overwork, are a major occupational and public health issue worldwide, particularly in East Asian countries. Since wearable smart devices are inexpensive, convenient, popular and widely available today, we were interested in investigating the possibility of using wearable smart electrocardiogram (ECG) devices to detect the mental fatigue state. In total, 35 healthy participants were recruited from a public university in East China. Throughout the entire experiment, each participant wore a wearable device that was further linked to a smartphone to upload the data based on Bluetooth transmission. To manipulate the fatigue state, each participant was asked to finish a quiz, which lasted for approximately 80 min, with 30 logical referential and computing problems and 25 memory tests. Eight heart rate variability (HRV) indicators namely NN.mean (mean of normal to normal interval), rMSSD (root mean square of successive differences), PNN50 (the proportion of NN50 divided by total number of NNs), TP (total spectral power), HF (high frequency from 0.15 Hz to 0.4 Hz), LF (low frequency from 0.04 Hz to 0.15 Hz), VLF (very low frequency from 0.0033 Hz to 0.04 Hz) and the LF/HF ratio were collected at intervals of 5 min throughout the entire experiment. After the feature selection was performed, six indicators remained for further analysis, which were the NN.mean, rMSSD, PNN50, TP, LF, and VLF. Four algorithms, support vector machine (SVM), K-nearest neighbor (KNN), naïve Bayes (NB), and logistic regression (LR), were used to build classifiers that automatically detected the fatigue state. The best performance was achieved by KNN, which had a CV accuracy of 75.5%. The NN.mean, PNN50, TP and LF were the most important HRV indicators for mental fatigue detection. KNN performed the best among the four algorithms and had an average CV accuracy of 65.37% for all of the possible feature combinations., (Copyright © 2018 Elsevier B.V. All rights reserved.)