Background: The COVID-19 outbreak has not only changed the lifestyles of people globally but has also resulted in other challenges, such as the requirement of self-isolation and distance learning. Moreover, people are unable to venture out to exercise, leading to reduced movement, and therefore, the demand for exercise at home has increased. Objective: We intended to investigate the relationships between a Nintendo Ring Fit Adventure (RFA) intervention and improvements in running time, cardiac force index (CFI), sleep quality (Chinese version of the Pittsburgh Sleep Quality Index score), and mood disorders (5-item Brief Symptom Rating Scale score). Methods: This was a randomized prospective study and included 80 students who were required to complete a 1600-meter outdoor run before and after the intervention, the completion times of which were recorded in seconds. They were also required to fill out a lifestyle questionnaire. During the study, 40 participants (16 males and 24 females, with an average age of 23.75 years) were assigned to the RFA group and were required to exercise for 30 minutes 3 times per week (in the adventure mode) over 4 weeks. The exercise intensity was set according to the instructions given by the virtual coach during the first game. The remaining 40 participants (30 males and 10 females, with an average age of 22.65 years) were assigned to the control group and maintained their regular habits during the study period. Results: The study was completed by 80 participants aged 20 to 36 years (mean 23.20, SD 2.96 years). The results showed that the running time in the RFA group was significantly reduced. After 4 weeks of physical training, it took females in the RFA group 19.79 seconds (P=.03) and males 22.56 seconds (P=.03) less than the baseline to complete the 1600-meter run. In contrast, there were no significant differences in the performance of the control group in the run before and after the fourth week of intervention. In terms of mood disorders, the average score of the RFA group increased from 1.81 to 3.31 for males (difference=1.50, P=.04) and from 3.17 to 4.54 for females (difference=1.38, P=.06). In addition, no significant differences between the RFA and control groups were observed for the CFI peak acceleration (CFIPA)_walk, CFIPA_run, or sleep quality. Conclusions: RFA could either maintain or improve an individual’s physical fitness, thereby providing a good solution for people involved in distance learning or those who have not exercised for an extended period. Trial Registration: ClinicalTrials.gov NCT05227040; https://clinicaltrials.gov/ct2/show/NCT05227040

According to a WHO report, nearly 15% of adults aged 60 and over suffer from a mental disorder, constituting 6.6% of the total disability for this age group. Taipei City faces rapid transformation towards an aging society, with the proportion of elderly in the total population rising from 12% in 2008 to 16% in 2016. The aim of this study is to identify the prevalence of mental disorders among the elderly in Taipei City and to elucidate risk factors contributing to mental disorders. The elderly health examination database was obtained from the Department of Health, Taipei City government, from 2005 to 2012. A total of 86,061 people underwent publicly funded health examinations, with 348,067 visits. Each year, there are around 43,000 elderly persons in Taipei City using this service. We used a mental health questionnaire including five questions to estimated relative risks among potential risk factors with the generalized estimating equations (GEE) model to measure the mental health status of the elderly. Mood disorders were measured with the Brief Symptom Rating Scale (BSRS-5) questionnaire. Age, education level, gender, marital status, living alone, drinking milk, eating vegetables and fruits, long-term medication, smoking status, frequency of alcohol consumption, frequency of physical activity, BMI, and number of chronic diseases were included as covariates. The results show that being male (odds ratio (OR) 0.57; 95% CI = 0.56, 0.59), higher education (OR 0.88; 95% CI = 0.82, 0.95), no long-term medication (OR 0.57; 95% CI = 0.56, 0.58), and exercising three or more times per week (OR 0.94; 95% CI = 0.91, 0.98) were all positively correlated with better emotional status. However, being divorced (OR = 1.22, 95% CI = 1.09, 1.36), not drinking milk (OR = 1.12, 95% CI = 1.09, 1.14), not eating enough vegetables and fruits every day (OR = 1.78, 95% CI = 1.73, 1.83), daily smoking (OR = 1.15, 95% CI = 1.01, 1.32), and having more chronic diseases (OR = 1.02, 95% CI = 1.01, 1.03) were all correlated with poor mental status among the elderly. The findings of this research can both estimate the prevalence of mood disorders at the community level, and identify risk factors of mood disorders at the personal level.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Background: The successful completion of medical practices often relies on information collection and analysis. Government agencies and medical institutions have encouraged people to use medical information technology (MIT) to manage their conditions and promote personal health. In 2014, Taiwan established the first electronic personal health record (PHR) platform, My Health Bank (MHB), which allows people to access and manage their PHRs at any time. In the face of the COVID-19 pandemic in 2020, Taiwan has used MIT to effectively prevent the spread of COVID-19 and undertaken various prevention measures before the onset of the outbreak. Using MHB to purchase masks in an efficient and orderly way and thoroughly implementing personal protection efforts is highly important to contain disease spread. Objective: This study aims to understand people’s intention to use the electronic PHR platform MHB and to investigate the factors affecting their intention to use this platform. Methods: From March 31 to April 9, 2014, in a promotion via email and Facebook, participants were asked to fill out a structured questionnaire after watching an introductory video about MHB on YouTube. The questionnaire included seven dimensions: perceived usefulness, perceived ease of use, health literacy, privacy and security, computer self-efficacy, attitude toward use, and behavioral intention to use. Each question was measured on a 5-point Likert scale ranging from “strongly disagree” (1 point) to “strongly agree” (5 points). Descriptive statistics and structural equation analysis were performed using SPSS 21 and AMOS 21 software. Results: A total of 350 valid questionnaire responses were collected (female: 219/350, 62.6%; age: 21-30 years: 238/350, 68.0%; university-level education: 228/350, 65.1%; occupation as student: 195/350, 56.6%; average monthly income <NT $30,000 [<US $1054.89]: 230/350, 65.7%; residence in northern Taiwan: 236/350, 67.4%; and health status perceived as “good”: 171/350, 48.9%). Five indicators, including chi-square test (X2310=2.63), goodness-of-fit index (0.85), adjusted goodness-of-fit index (0.81), comparative fit index (0.91), and root mean square error of approximation (0.07), were calculated. The results indicated a good fit. Further analysis indicated that the most important factor affecting respondents’ behavioral intention to use MHB was their attitude toward use (0.78), followed by perceived ease of use (0.65), perceived usefulness (0.41), health literacy (0.10), and privacy and security (0.07). Conclusions: From the perspective of the populace, this study explored the factors affecting the use of MHB and constructed an interpretation model with a strong goodness of fit. The results of our analysis are consistent with the technology acceptance model. Through the diverse value-added services of MHB, Taiwan's experience in pandemic prevention with smart technology can facilitate future responses to unknown, emerging infectious diseases.

The Taiwan government has been promoting Electronic Health Records (EHRs) to primary care physicians. How to extend EHRs adoption rate by measuring physicians’ perspective of importance and performance of EHRs has become one of the critical issues for healthcare organizations. We conducted a comprehensive survey in 2010 in which a total of 1034 questionnaires which were distributed to primary care physicians. The project was sponsored by the Department of Health to accelerate the adoption of EHRs. 556 valid responses were analyzed resulting in a valid response rate of 53.77%. The data were analyzed based on a data-centered analytical framework (5-point Likert scale). The mean of importance and satisfaction of four dimensions were 4.16, 3.44 (installation and maintenance), 4.12, 3.51 (product effectiveness), 4.10, 3.31 (system function) and 4.34, 3.70 (customer service) respectively. This study provided a direction to government by focusing on attributes which physicians found important but were dissatisfied with, to close the gap between actual and expected performance of the EHRs. The authorities should emphasize the potential advantages in meaningful use and provide training programs, conferences, technical assistance and incentives to enhance the national level implementation of EHRs for primary physicians.

Background: Long-distance running can be a form of stress to the heart. Technological improvements combined with the public’s gradual turn toward mobile health (mHealth), self-health, and exercise effectiveness have resulted in the widespread use of wearable exercise products. The monitoring of dynamic cardiac function changes during running and running performance should be further studied. Objective: We investigated the relationship between dynamic cardiac function changes and finish time for 3000-meter runs. Using a wearable device based on a novel cardiac force index (CFI), we explored potential correlations among 3000-meter runners with stronger and weaker cardiac functions during running. Methods: This study used the American product BioHarness 3.0 (Zephyr Technology Corporation), which can measure basic physiological parameters including heart rate, respiratory rate, temperature, maximum oxygen consumption, and activity. We investigated the correlations among new physiological parameters, including CFI = weight * activity / heart rate, cardiac force ratio (CFR) = CFI of running / CFI of walking, and finish times for 3000-meter runs. Results: The results showed that waist circumference, smoking, and CFI were the significant factors for qualifying in the 3000-meter run. The prediction model was as follows: ln (3000 meters running performance pass probability / fail results probability) = –2.702 – 0.096 × [waist circumference] – 1.827 × [smoke] + 0.020 × [ACi7]. If smoking and the ACi7 were controlled, contestants with a larger waist circumference tended to fail the qualification based on the formula above. If waist circumference and ACi7 were controlled, smokers tended to fail more often than nonsmokers. Finally, we investigated a new calculation method for monitoring cardiac status during exercise that uses the CFI of walking for the runner as a reference to obtain the ratio between the cardiac force of exercise and that of walking (CFR) to provide a standard for determining if the heart is capable of exercise. A relationship is documented between the CFR and the performance of 3000-meter runs in a healthy 22-year-old person. During the running period, data are obtained while participant slowly runs 3000 meters, and the relationship between the CFR and time is plotted. The runner’s CFR varies with changes in activity. Since the runner’s acceleration increases, the CFR quickly increases to an explosive peak, indicating the runner’s explosive power. At this period, the CFI revealed a 3-fold increase (CFR=3) in a strong heart. After a time lapse, the CFR is approximately 2.5 during an endurance period until finishing the 3000-meter run. Similar correlation is found in a runner with a weak heart, with the CFR at the beginning period being 4 and approximately 2.5 thereafter. Conclusions: In conclusion, the study results suggested that measuring the real-time CFR changes could be used in a prediction model for 3000-meter running performance.