The purpose of this study is to understand the relationship among leisure physical activity, sedentary lifestyle, physical fitness, and happiness in healthy elderly adults aged over 65 years old in Taiwan. Data were recruited from the National Physical Fitness Survey in Taiwan, which was proposed in the Project on the Establishment of Physical Fitness Testing Stations by the Sports Administration of the Ministry of Education. Participants were recruited from fitness testing stations set up in 22 counties and cities from October 2015 to May 2016. A total of 20,111 healthy older adults aged 65–102 years were recruited as research participants. The fitness testing procedure was described to all participants, who were provided with a standardized structured questionnaire. Participants’ data included sex, city or county of residence, living status (living together with others or living alone), education level, and income. Physical fitness testing was conducted in accordance with The Fitness Guide for Older Adults published by the Sports Administration of the Ministry of Education. The testing involved cardiorespiratory endurance, muscle strength, muscle endurance, flexibility, balance, and body composition. The t-test was used to evaluate the differences between continuous and grade variables under the two classification variables of sex, city or county of residence, and living status. We used the MARS (multivariate adaptive regression splines) model to analyze the effects of physical fitness variables and leisure physical activity variables on happiness. Among healthy elderly adults, sex, age, living status, body mass index, and leisure physical activity habits proved to be related to happiness. Aerobic endurance (2-min step test), muscular strength and endurance (30-s arm curl and 30-s chair stand tests), flexibility (back stretch and chair sit-and-reach tests), and balance ability (8-foot up-and-go tests and one-leg stance with eyes open tests) were found to be related to happiness. The results of this study indicate that increased physical activity and intensity, as well as physical fitness performance, are associated with improved happiness.

The economic burden of diabetes has increased over time with disease severity. Previous publications investigating the effects of physical activity (PA) on medical costs have made use of small sample sizes. We assessed the relationship between PA and 1-year medical expenditure among Taiwanese patients with type-2 diabetes mellitus (T2DM). Data were recruited from three governmental databases, including the 2012 adult preventive health service database. Participants were grouped as inactive (no exercise), insufficiently active (exercise < 150 minutes/week), and sufficiently active (exercise >150 minutes/week) individuals. Patients were stratified according to age and Charlson score. Multivariate linear regression models were used to determine β-coefficients and their P values. Overall, 218,960 individuals were identified with diabetes. The prevalence of the disease was 13.1% among sufficiently active, 35% among insufficiently active, and 51.9% among physically inactive adults. In general, patients who had exercise >150 minutes/week had lower health care spending (i.e., US$ 755.83) followed by those who had less than 150 minutes/week (US$ 880.08) when compared with inactive patients ( P < .0001). Moreover, health care costs derived from outpatient or inpatient care were lower for sufficiently active than inactive participants ( P < .0001). Compared with being sedentary, PA was associated with lower health care costs of Taiwanese adults with diabetes mellitus.

The relationships between different functional fitness performance and abdominal obesity among the elderly have not been widely discussed in the literature. The present study aimed to investigate the associations between functional fitness test performance and abdominal obesity in Taiwanese elderly people. A total of 22,399 items of data from the National Physical Fitness Survey Databases in Taiwan (NPFSD 2014-15) were reviewed and analyzed. The quartiles of functional fitness test results were identified as the dependent variable in the multiple linear regression analysis to examine the association between functional fitness and abdominal obesity status. The results showed that body balance (odds ratios (ORs) listed from 1.18 to 2.29, p < 0.05) and flexibility (ORs listed from 1.23 to 2.16, p < 0.05) were critically associated with abdominal obesity. However, measurements related to muscle strength revealed the limited effect sizes for abdominal obesity. From a disability prevention perspective, the development of muscle strength in the elderly should be emphasized and encouraged to maintain their daily capabilities instead of satisfaction with a lean body.

Both decreased insulin sensitivity, and impaired insulin secretion are 2 major pathophyisologies for type 2 diabetes (T2DM). There are two phases of ISEC-the first (1st ISEC) and second phase (2 nd ISEC). In this study, we tried to build an equation to predict 2 nd ISEC.Totally, 82 subjects, including 15 with normal fasting glucose, 26 with pre-diabetes and 41 with T2DM were enrolled. They received a modified low dose graded glucose infusion (M-LDGGI). The M-LDGGI is a simplified version of Polonsky's method. The results were interpreted as the slope of the changes of plasma insulin against the glucose levels. The slopes of these curves were regarded as the 2 nd ISEC.If only metabolic syndrome (MetS) components were analyzed, the equation was built as the following: log (2 nd ISEC) = -2.400-0.088 • (fasting plasma glucose, FPG + 0.072 • (body mass index, BMI). After fasting plasma insulin (FPI) was added , the equation was shown as the following: log (2 nd ISEC) = -2.316-0.093˙FPG + 0.049 • BMI+ 0.434 • log(FPI). The second equation provided a greater accuracy to determine 2nd ISEC than first one in the external validation group (r 2 = 0.545 vs r 2 = 0.423).Using MetS components, 2 nd ISEC could be predicted with good accuracy. After adding FPI into the equation, the predictive power further increases. These equations could be widely used in daily practice and clinical settings.

Cybersickness is a critical problem of virtual reality (VR) applications. This study found an objective method to quantize the cybersickness level using the bio-signals, including electroencephalography (EEG) and electrocardiography (ECG). These results demonstrated a higher level of cybersickness caused power of gamma band (>32Hz) stronger and enhanced subjects' heart rate as well. The variations of these bio-signals could be an indicator to quantize people's cybersickness level and be applied for a warning system for VR devices.