Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Cardiorespiratory fitness (CRF), an important index of physical fitness, is the ability to take in and provide oxygen to the exercising muscle. However, despite its importance, the current gold standard for measuring CRF is impractical, requiring a maximal exercise from the participants. Objective: This study aimed to develop a convenient and practical estimation model for CRF using data collected from daily life with a wristwatch-type device. Methods: A total of 191 subjects, aged 20 to 65 years old, participated in this study. Maximal oxygen uptake (VO2max), a standard measure of CRF, was measured with a maximal exercise test. Heart rate (HR) and physical activity data were collected using a commercial wristwatch-type fitness tracker (Fitbit) for 3 consecutive days. Maximal activity energy expenditure (aEEmax) and slope between HR and physical activity were calculated by a linear regression. A VO2max estimation model was built using multiple linear regression with data on age, sex, height, percentage body fat, aEEmax, and the slope. The result was validated with two different cross-validation methods. Results: aEEmax showed a moderate correlation with VO2max (r = 0.50). The correlation coefficient for the multiple linear regression model was 0.81, and the standard error of estimate (SEE) was 3.518 mL/kg/min. The regression model was cross-validated through the predicted residual error sum of square (PRESS). The PRESS correlation coefficient was 0.79, and the PRESS SEE was 3.667 mL/kg/min. The model was further validated by dividing into different subgroups and calculating the constant error (CE) where, the low CE showed that the model does not significantly overestimate or underestimate VO2max. Conclusions: This study proposes a CRF estimation method using data collected by a wristwatch-type fitness tracker without any specific protocol for a wide range of population.

Many people, although they may recognise the positive effects of exercise, do not exercise regularly owing to lack of time. This study aimed to investigate the effects of prolonged single-session exercise and multiple short sessions of exercise on the risk of metabolic syndrome and the atherogenic index in middle-aged obese women. Thirty-six participants were divided into the single-session group, multiple-session group, and control group. The single-session group engaged in one session of treadmill exercise for 30 min a day; the multiple-session group had three sessions of 10 min a day. Both groups exercised 3 days/week for 12 weeks. The control group did not perform any exercise. The single-session group showed decreases in weight (0.97 kg [95% C.I. = 0.09–1.83], p < .05), body mass index (0.43 kg/m2 [95% C.I. = 0.03–0.81], p < .05), and fat mass (1.65 kg, [95% C.I. = 0.78–2.51], p < .01). Systolic blood pressure dropped in the single-session group (6.66 mmHg, [95% C.I. = 1.44–11.88], p < .05), and diastolic blood pressure dropped in the multiple-session group (3.38 mmHg, [95% C.I. = 1.44–5.88], p < .01). High-density lipoprotein cholesterol rose in the single-session group (4.08 mg/dL, [95% C.I. = −8.08–(−)0.07], p < .05) and dropped in the control group (10.75 mg/dL [95% C.I. = 1.95–19.54], p < .01). According to post hoc analysis, high-density lipoprotein cholesterol increased more in the single-session group than the control group (95% C.I. = 0.61–21.88, p < .05). Glucose levels decreased in both the single-session group (16 mg/dL [95% C.I. = 5.64–26.35], p < .01) and the multiple-session group (12.16 mg/dL, [95% C.I. = 2.18–22.14], p < .05). Waist circumference decreased in the single-session group (2.65 cm [95% C.I. = 1.46–3.83], p < .001) and multiple-session group (2.04 cm, [95% C.I. = 1.51–2.73], p < .001). Low-density lipoprotein cholesterol levels rose in both the multiple-session group (−15.79 mg/dL [95% C.I. = −34.24–(−)3.78], p < .05) and the control group (−22.94 mg/dL [95% C.I. = −44.63–(−)1.24], p < .05). The atherogenic index increased in the control group (−1.06 [95% C.I. = −1.69–(−)0.41], p < .01). The findings indicate that prolonged exercise is superior to multiple short sessions for improving the risk of metabolic syndrome and the atherogenic index in middle-aged obese women. However, multiple short sessions can be recommended as an alternative to prolonged exercise when the goal is to decrease blood glucose or waist circumference.

Objectives: The purpose of this study was to explore new ways of creating value in the medical field and to derive recommendations for the role of medical institutions and the government.Methods: In this paper, based on expert discussion, we classified Internet of Things (IoT) technologies into four categories according to the type of information they collect (location, environmental parameters, energy consumption, and biometrics), and investigated examples of application.Results: Biometric IoT diagnoses diseases accurately and offers appropriate and effective treatment. Environmental parameter measurement plays an important role in accurately identifying and controlling environmental factors that could be harmful to patients. The use of energy measurement and location tracking technology enabled optimal allocation of limited hospital resources and increased the efficiency of energy consumption. The resulting economic value has returned to patients, improving hospitals’ cost-effectiveness.Conclusions: Introducing IoT-based technology to clinical sites, including medical institutions, will enhance the quality of medical services, increase patient safety, improve management efficiency, and promote patient-centered medical services. Moreover, the IoT is expected to play an active role in the five major tasks of facility hygiene in medical fields, which are all required to deal with the COVID-19 pandemic: social distancing, contact tracking, bed occupancy control, and air quality management. Ultimately, the IoT is expected to serve as a key element for hospitals to perform their original functions more effectively. Continuing investments, deregulation policies, information protection, and IT standardization activities should be carried out more actively for the IoT to fulfill its expectations.

Objectives: The purpose of this study was to identify any difference in user experience between tablet- and augmented reality (AR) glasses-based tele-exercise programs in elderly women.Methods: Participants in the AR group (n = 14) connected Nreal glasses with smartphones to display a pre-recorded exercise program, while each member of the tablet group (n = 13) participated in the same exercise program using an all-in-one personal computer. The program included sitting or standing on a chair, bare-handed calisthenics, and muscle strengthening using an elastic band. The exercise movements were presented first for the upper and then the lower extremities, and the total exercise time was 40 minutes (5 minutes of warm-up exercises, 30 minutes of main exercises, and 5 minutes of cool-down exercises). To evaluate the user experience, a questionnaire consisting of a 7-point Likert scale was used as a measurement tool. In addition, the Wilcoxon rank-sum test was used to assess differences between the two groups.Results: Of the six user experience scales, attractiveness (p = 0.114), stimulation (p = 0.534), and novelty (p = 0.916) did not differ significantly between the groups. However, efficiency (p = 0.006), perspicuity (p = 0.008), and dependability (p = 0.049) did vary significantly between groups.Conclusions: When developing an AR glasses-based exercise program for the elderly, the efficiency, clarity, and stability of the program must be considered to meet the participants’ needs.