Associations of lower-limb muscle strength performance with static and dynamic balance control among older adults in Taiwan.

Background: Aging is an inevitable process of life development. These physical changes can cause a decline in the functional adaptability and health status of older adult individuals. Aims: The purpose of this study was to investigate the association of lower-limb muscle strength performance with static and dynamic balance control among older adults in Taiwan. Methods: We conducted a cross-sectional study and reviewed data derived from the National Physical Fitness Survey in Taiwan 2015-2016. A total of 20,846 Taiwanese older adult individuals aged 65 years old or older were recruited as study participants. Demographic characteristics, anthropometric assessments, lifestyle habits, and health-related physical fitness measurements from this dataset were analyzed using the chi-square test, one-way analysis of variance, and linear regression analysis. Lower-limb muscle strength performance was classified into 4 groups based on quartile (Quartile 1 [Q1], Quartile 2 [Q2], Quartile 3 [Q3], and Quartile 4 [Q4]) values. Results: Increased lower-limb muscle strength levels were significantly associated with static balance in men (Q2: ß = 2.539, p < 0.0001; Q3: ß = 4.590, p < 0.0001; Q4: ß = 7.700, p < 0.0001) and women (Q2: ß = 2.022, p < 0.0001; Q3: ß = 4.179, p < 0.0001; Q4: ß = 6.920, p < 0.0001) after adjusting for potential confounders. In addition, we observed that increased lower-limb muscle strength levels were significantly associated with dynamic balance in men (Q2: ß = -1.661, p < 0.0001; Q3: ß = -2.434, p < 0.0001; Q4: ß = -3.091, p < 0.0001) and women (Q2: ß = -1.660, p < 0.0001; Q3: ß = -2.548, p < 0.0001; Q4: ß = -3.196, p < 0.0001) after adjusting for potential confounders. Conclusion: Lower-limb muscle strength was the most important factor, as it was an improved method for static and dynamic balance control in both genders.

Impact of Pre-Transplant Parathyroidectomy on Graft Survival: A Comparative Study of Renal Transplant Patients (2005-2015).

BACKGROUND Hyperparathyroidism poses significant risks for patients prior to kidney transplantation. However, the outcomes of patients who undergo parathyroidectomy before renal transplantation compared to those without such a procedure remain uncertain. This real-world data study aimed to examine the clinical outcomes of both patient groups. MATERIAL AND METHODS Using the Taiwan National Health Insurance Research Database, we conducted a retrospective cohort study on patients who underwent renal transplantation between January 2005 and December 2015. The patients were divided into two groups: a case group (n=294) with parathyroidectomy and a control group (n=588) without the need for parathyroidectomy before kidney transplantation. The groups were matched based on age, sex, dialysis vintage, and baseline characteristics at a 1:2 ratio. Hazard ratios (HR) were estimated using the Cox regression model. The main outcomes assessed were graft failure, mortality, and major adverse cardiovascular events (MACE) recorded until December 2019. RESULTS During a mean follow-up period of 6 years, a significant difference was observed in graft failure (HR 1.40; 95% confidence interval 1.10-1.79, p=0.007) between the two groups. After further adjustment, graft failure remained significant (HR 1.52; 95% CI 1.07-2.15, p=0.019). Additionally, machine learning-based feature selection identified the importance of parathyroidectomy (ranked 9 out of 11) before kidney transplantation in predicting subsequent graft failure. CONCLUSIONS Our study demonstrates that severe hyperparathyroidism requiring parathyroidectomy before kidney transplantation may contribute to poor post-transplant graft outcomes compared to patients who do not require parathyroidectomy.

Health Informatics: The Foundations of Public Health.

As technology continues to evolve, vast amounts of diverse digital data are becoming more easily generated and collected [...].

A Hybrid Risk Factor Evaluation Scheme for Metabolic Syndrome and Stage 3 Chronic Kidney Disease Based on Multiple Machine Learning Techniques.

With the rapid development of medicine and technology, machine learning (ML) techniques are extensively applied to medical informatics and the suboptimal health field to identify critical predictor variables and risk factors. Metabolic syndrome (MetS) and chronic kidney disease (CKD) are important risk factors for many comorbidities and complications. Existing studies that utilize different statistical or ML algorithms to perform CKD data analysis mostly analyze the early-stage subjects directly, but few studies have discussed the predictive models and important risk factors for the stage-III CKD high-risk health screening population. The middle stages 3a and 3b of CKD indicate moderate renal failure. This study aims to construct an effective hybrid important risk factor evaluation scheme for subjects with MetS and CKD stages III based on ML predictive models. The six well-known ML techniques, namely random forest (RF), logistic regression (LGR), multivariate adaptive regression splines (MARS), extreme gradient boosting (XGBoost), gradient boosting with categorical features support (CatBoost), and a light gradient boosting machine (LightGBM), were used in the proposed scheme. The data were sourced from the Taiwan health examination indicators and the questionnaire responses of 71,108 members between 2005 and 2017. In total, 375 stage 3a CKD and 50 CKD stage 3b CKD patients were enrolled, and 33 different variables were used to evaluate potential risk factors. Based on the results, the top five important variables, namely BUN, SBP, Right Intraocular Pressure (R-IOP), RBCs, and T-Cho/HDL-C (C/H), were identified as significant variables for evaluating the subjects with MetS and CKD stage 3a or 3b.

Use of Preoperative FDG PET/CT and Survival of Patients with Resectable Non-Small Cell Lung Cancer.

Background The added value of preoperative PET/CT for the overall survival of patients with resectable non-small cell lung cancer (NSCLC) is unknown. Purpose To investigate the association of the use of preoperative PET/CT on survival of patients with resectable stage I-IIIB NSCLC. Materials and Methods In this retrospective study, patients with resectable stage I-IIIB NSCLC who underwent thoracic surgery from January 1, 2009, to December 31, 2018, from the Taiwan Cancer Registry were included. The last follow-up date was December 31, 2019. Patients were categorized into two groups according to whether they underwent preoperative metabolic imaging with fluorine 18 fluorodeoxyglucose PET/CT. Patients who did not undergo preoperative imaging were used as the control group. The primary outcome of interest was all-cause mortality. Patients in both groups were propensity score matched at a ratio of 1:1. Matching variables used were sex, age, histologic findings, American Joint Committee on Cancer clinical stage, cT stage, cN stage, current and past smoker history, adjuvant chemotherapy, adjuvant chemoradiation, Charlson comorbidity index, and hospital type. Survival curves were generated using the Kaplan-Meier method and compared using the log-rank test. Results In the matched cohort, 6754 patients (3349 men, mean age ± SD: 64 years ± 11) underwent PET/CT and 6754 did not (3362 men, mean age: 64 years ± 11). In adjusted analysis, patients with stage IIIA or IIIB NSCLC and preoperative PET/CT had a lower risk of death versus those without PET/CT (for stage IIIA: hazard ratio [HR] = 0.90 [95% CI: 0.79, 0.94], P = .02; for stage IIIB: HR = 0.80 [95% CI: 0.71, 0.90], P < .01). There was no improvement in a lower risk of death for patients with stage I-II NSCLC (after multivariable adjustment, the HR was 1.19 [95% CI: 0.89, 1.30], P = .65). Conclusion Use of preoperative PET/CT was associated with lower risk of death in patients with stage IIIA-IIIB non-small cell lung cancer compared with those without preoperative PET/CT. © RSNA, 2022 Online supplemental material is available for this article.

Balance factors affecting the quality of life in patients with knee osteoarthritis.

Background: Knee osteoarthritis (OA) affects the quality of life (QOL) and balance control of elderly people; our study explored the balance factors that affected the QOL in patients with knee OA. Objectives: To determine the balance factors that affected the QOL of patients with knee OA who attended general clinics. Method: A total of 30 healthy controls and 60 patients with mild-to-moderate bilateral knee OA, all aged 55-75 years, were enrolled in our cross-sectional study. All participants were interviewed; the Medical Outcomes Study 36-Item Short-Form Health Survey was used to assess their QOL in eight dimensions, and the Balance Master System was used to evaluate their balance control according to six parameters. Descriptive statistics were used to reduce the data; an independent t-test determined differences between the two groups, and a multiple regression analysis was undertaken to establish associations between variables from the balance control test and SH36 physical and mental health components. The level of statistical significance was set at 5%. Results: In the OA group, significant negative correlations were observed between sway velocity and the physical health component (p = 0.003) and between sway velocity and the mental health component (p = 0.006). Thus, sway velocity had a major impact on the QOL of patients with knee OA. Conclusions: The sway velocity at the centre of gravity in balance control was a crucial factor for determining the QOL of patients with bilateral knee OA. Clinical implications: Sway velocity is a key factor affecting the QOL and may provide a basis to formulate preventive actions and design treatment goals for patients with knee OA.

Risk Factors for Female Breast Cancer: A Population Cohort Study.

BACKGROUND: The incidence of female BC among the Eastern and Southeastern Asian populations has gradually increased in recent years. However, epidemiological studies on the relationship between a sedentary lifestyle and female BC are insufficient. In order to determine the association between this lifestyle and the incidence of female BC, we conducted a population-based cohort study on women in Taiwan. METHODS: We followed a prospective cohort of 5879 women aged 30 years and over enrolled in the 2001 National Health Interview Survey (NHIS), who developed female BC over a period of 72,453 person years, and we estimated the hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) using the Cox proportional hazards model. RESULTS: RFs associated with female BC incidence included parity (adjusted HR = 0.63; 95% CI: 0.44-0.91), body mass index (adjusted HR = 1.34; 95% CI: 1.04-1.71), and ≥3 h/day spent sitting (adjusted HR = 1.89; 95% CI: 1.08-3.32). The incidence of female BC in participants who sat for ≥3 h/day and consumed sugary drinks was 2.5 times greater than that in those who sat for <3 h/day and did not consume sugary drinks (adjusted HR = 2.51; 95% CI: 1.01-6.23). CONCLUSIONS: The findings of this study indicate that sedentary behavior and sugary drink intake may increase the risk of developing female BC. These are modifiable RFs; therefore, a healthy lifestyle and diet can reduce the incidence of female BC.

Adjuvant postmastectomy radiotherapy might be associated with better survival in women with heart failure receiving total mastectomy.

BACKGROUND: To date, no data on the effect of adjuvant postmastectomy radiotherapy (PMRT) on oncologic outcomes, such as all-cause death, locoregional recurrence (LRR), and distant metastasis (DM), are available in women with left-side breast invasive ductal carcinoma (IDC) and heart failure with reduced ejection fraction (HFrEF). PATIENTS AND METHODS: We enrolled 646 women with left-breast IDC at clinical stages I-IIIC and HFrEF receiving radical total mastectomy (TM) followed by adjuvant PMRT or non-adjuvant PMRT. We categorized them into two groups based on their adjuvant PMRT status and compared their overall survival (OS), LRR, and DM outcomes. We calculated the propensity score and applied inverse probability of treatment weighting (IPTW) to create a pseudo-study cohort. Furthermore, we performed a multivariate analysis of the propensity score-weighted population to obtain hazard ratios (HRs). RESULTS: In the IPTW-adjusted model, adjuvant PMRT (adjusted HR [aHR]: 0.52; 95% confidence interval [CI]: 0.37-0.74) was a significant independent prognostic factor for all-cause death (P = 0.0003), and the aHR (95% CI) of LRR and DM for adjuvant PMRT was 0.90 (0.79-0.96; P = 0.0356) and 0.89 (0.54-1.50; P = 0.6854), respectively, compared with the nonadjuvant PMRT group. CONCLUSION: Adjuvant PMRT was associated with a decrease in all-cause death, and LRR in women with left IDC and HFrEF compared with nonadjuvant PMRT.

Adjuvant Whole Breast Radiotherapy Improve Survival in Women with Heart Failure with Reduced Ejection Fraction Receiving Breast-Conserving Surgery.

BACKGROUND: to date, no data on the effect of adjuvant whole breast radiotherapy (WBRT) on oncologic outcomes, such as all-cause death, locoregional recurrence (LRR), and distant metastasis (DM), are available in women with left-side breast invasive ductal carcinoma (IDC) and heart failure with reduced ejection fraction (HFrEF). PATIENTS AND METHODS: we included 294 women with left-breast IDC at clinical stages IA-IIIC and HFrEF receiving breast-conserving surgery (BCS) followed by adjuvant WBRT or non-adjuvant WBRT. We categorized them into two groups based on their adjuvant WBRT status and compared their overall survival (OS), LRR, and DM outcomes. We calculated the propensity score and applied inverse probability of treatment weighting (IPTW) to create a pseudo-study cohort. Furthermore, we performed a multivariate analysis of the propensity score-weighted population to obtain hazard ratios (HRs). RESULTS: in the IPTW-adjusted model, adjuvant WBRT (adjusted HR [aHR]: 0.60; 95% confidence interval [CI]: 0.44-0.94) was a significant independent prognostic factor for all-cause death (p = 0.0424), and the aHR (95% CI) of LRR and DM for adjuvant WBRT was 0.33 (0.24-0.71; p = 0.0017) and 0.37 (0.22-0.63; p = 0.0004), respectively, compared with the non-adjuvant WBRT group. CONCLUSION: Adjuvant WBRT was associated with a decrease in all-cause death, LRR, and DM in women with left IDC and HFrEF compared with non-adjuvant WBRT.

Monthly Disposable Income Is a Crucial Factor Affecting the Quality of Life in Patients with Knee Osteoarthritis.

Knee osteoarthritis (OA) affects the quality of life (QOL) of elderly people; this study examines the demographic characteristics and QOL of patients with knee OA and identifies demographic characteristics that affect the QOL of these patients. In this cross-sectional study, 30 healthy controls and 60 patients with mild-to-moderate bilateral knee OA aged between 55 and 75 years were enrolled. All participants completed a questionnaire containing questions on 10 demographic characteristics and the Medical Outcome Study 36-Item Short-Form Health Survey (SF-36), and their QOL scores in the eight dimensions of the SF-36 were evaluated. In the OA group, significant correlations were observed between monthly disposable income and physical and mental health components. Monthly disposable income was found to considerably affect the QOL of patients with bilateral knee OA (i.e., it is a crucial factor affecting these patients). The findings of this study may provide a reference for formulating preventive strategies for healthy individuals and for future confirmatory research.

Hazardous Effect of Low-Dose Aspirin in Patients with Predialysis Advanced Chronic Kidney Disease Assessed by Machine Learning Method Feature Selection.

Background: Low-dose aspirin (100 mg) is widely used in preventing cardiovascular disease in chronic kidney disease (CKD) because its benefits outweighs the harm, however, its effect on clinical outcomes in patients with predialysis advanced CKD is still unclear. This study aimed to assess the effect of aspirin use on clinical outcomes in such group. Methods: Patients were selected from a nationwide diabetes database from January 2009 to June 2017, and divided into two groups, a case group with aspirin use (n = 3021) and a control group without aspirin use (n = 9063), by propensity score matching with a 1:3 ratio. The Cox regression model was used to estimate the hazard ratio (HR). Moreover, machine learning method feature selection was used to assess the importance of parameters in the clinical outcomes. Results: In a mean follow-up of 1.54 years, aspirin use was associated with higher risk for entering dialysis (HR, 1.15 [95%CI, 1.10-1.21]) and death before entering dialysis (1.46 [1.25-1.71]), which were also supported by feature selection. The renal effect of aspirin use was consistent across patient subgroups. Nonusers and aspirin users did not show a significant difference, except for gastrointestinal bleeding (1.05 [0.96-1.15]), intracranial hemorrhage events (1.23 [0.98-1.55]), or ischemic stroke (1.15 [0.98-1.55]). Conclusions: Patients with predialysis advanced CKD and anemia who received aspirin exhibited higher risk of entering dialysis and death before entering dialysis by 15% and 46%, respectively.

Health Data-Driven Machine Learning Algorithms Applied to Risk Indicators Assessment for Chronic Kidney Disease.

PURPOSE: As global aging progresses, the health management of chronic diseases has become an important issue of concern to governments. Influenced by the aging of its population and improvements in the medical system and healthcare in general, Taiwan's population of patients with chronic kidney disease (CKD) has tended to grow year by year, including the incidence of high-risk cases that pose major health hazards to the elderly and middle-aged populations. METHODS: This study analyzed the annual health screening data for 65,394 people from 2010 to 2015 sourced from the MJ Group - a major health screening center in Taiwan - including data for 18 risk indicators. We used five prediction model analysis methods, namely, logistic regression (LR) analysis, C5.0 decision tree (C5.0) analysis, stochastic gradient boosting (SGB) analysis, multivariate adaptive regression splines (MARS), and eXtreme gradient boosting (XGboost), with estimated glomerular filtration rate (e-GFR) data to determine G3a, G3b & G4 stage CKD risk factors. RESULTS: The LR analysis (AUC=0.848), SGB analysis (AUC=0.855), and XGboost (AUC=0.858) generated similar classification performance levels and all outperformed the C5.0 and MARS methods. The study results showed that in terms of CKD risk factors, blood urea nitrogen (BUN) and uric acid (UA) were identified as the first and second most important indicators in the models of all five analysis methods, and they were also clinically recognized as the major risk factors. The results for systolic blood pressure (SBP), SGPT, SGOT, and LDL were similar to those of a related study. Interestingly, however, socioeconomic status-related education was found to be the third important indicator in all three of the better performing analysis methods, indicating that it is more important than the other risk indicators of this study, which had different levels of importance according to the different methods. CONCLUSION: The five prediction model methods can provide high and similar classification performance in this study. Based on the results of this study, it is recommended that education as the socioeconomic status should be an important factor for CKD, as high educational level showed a negative and highly significant correlation with CKD. The findings of this study should also be of value for further discussions and follow-up research.

The Prediction Model of Medical Expenditure Appling Machine Learning Algorithm in CABG Patients.

Most patients face expensive healthcare management after coronary artery bypass grafting (CABG) surgery, which brings a substantial financial burden to the government. The National Health Insurance Research Database (NHIRD) is a complete database containing over 99% of individuals' medical information in Taiwan. Our research used the latest data that selected patients who accepted their first CABG surgery between January 2014 and December 2017 (n = 12,945) to predict which factors will affect medical expenses, and built the prediction model using different machine learning algorithms. After analysis, our result showed that the surgical expenditure (X4) and 1-year medical expenditure before the CABG operation (X14), and the number of hemodialysis (X15), were the key factors affecting the 1-year medical expenses of CABG patients after discharge. Furthermore, the XGBoost and SVR methods are both the best predictive models. Thus, our research suggests enhancing the healthcare management for patients with kidney-related diseases to avoid costly complications. We provide helpful information for medical management, which may decrease health insurance burdens in the future.

Treatment-Emergent Co-Morbidities and Survival in Patients With Metastatic Castration-Resistant Prostate Cancer Receiving Abiraterone or Enzalutamide.

Secondary hormone therapy, abiraterone and enzalutamide, has improved outcomes for metastatic castration-resistant prostate cancer (mCRPC) and prolonged patients' lives significantly. Various studies have compared the cancer-related outcomes, adverse effects, and drug-induced comorbidities in patients with mCRPC who are treated with abiraterone or enzalutamide. However, few studies have explored associations between survival and comorbidities or comprehensive analyzed newly developed comorbidities during and after secondary hormone therapy. We attempted to clarify whether the Charlson comorbidity index (CCI) overall or itemized is predictive for overall survival, and we compared newly developed comorbidities between abiraterone and enzalutamide groups. We extracted data about expenses and comorbidities for patients who have mCRPC, received abiraterone and enzalutamide and met pre-examination operation criteria between September 2016 and December 2017 from the Taiwan National Health Insurance database. A total of 1153 patients with mCRPC who received abiraterone (n = 782) or enzalutamide (n = 371) with or without previous chemotherapy were included. We used the propensity score to match confounding factors, including age, pre-existing comorbidities, and precipitating factors for comorbidity (e.g., hypertension, hyperlipidemia), to eliminate selection bias in the comparison of newly developed comorbidities. Cox regression analysis was used for overall survival. We found that enzalutamide is superior to abiraterone with regard to overall survival. Our study revealed that there is no statistically significant difference in development of new comorbidities between abiraterone and enzalutamide group. Moreover, the CCI score, rather than any single item of the CCI, was a statistically significant predictor for overall survival.

Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients.

Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicting elements. This research used different machine-learning methods to select the features and predict older adults' survival (more than 65 years old). This nationwide population-based cohort study used the National Health Insurance Research Database (NHIRD), the largest and most complete dataset in Taiwan. We extracted the data of older patients who had received their first CABG surgery criteria between January 2008 and December 2009 (n = 3728), and we used five different machine-learning methods to select the features and predict survival rates. The results show that, without variable selection, XGBoost had the best predictive ability. Upon selecting XGBoost and adding the CHA2DS score, acute pancreatitis, and acute kidney failure for further predictive analysis, MARS had the best prediction performance, and it only needed 10 variables. This study's advantages are that it is innovative and useful for clinical decision making, and machine learning could achieve better prediction with fewer variables. If we could predict patients' survival risk before a CABG operation, early prevention and disease management would be possible.

Hybrid Basketball Game Outcome Prediction Model by Integrating Data Mining Methods for the National Basketball Association.

The sports market has grown rapidly over the last several decades. Sports outcomes prediction is an attractive sports analytic challenge as it provides useful information for operations in the sports market. In this study, a hybrid basketball game outcomes prediction scheme is developed for predicting the final score of the National Basketball Association (NBA) games by integrating five data mining techniques, including extreme learning machine, multivariate adaptive regression splines, k-nearest neighbors, eXtreme gradient boosting (XGBoost), and stochastic gradient boosting. Designed features are generated by merging different game-lags information from fundamental basketball statistics and used in the proposed scheme. This study collected data from all the games of the NBA 2018-2019 seasons. There are 30 teams in the NBA and each team play 82 games per season. A total of 2460 NBA game data points were collected. Empirical results illustrated that the proposed hybrid basketball game prediction scheme achieves high prediction performance and identifies suitable game-lag information and relevant game features (statistics). Our findings suggested that a two-stage XGBoost model using four pieces of game-lags information achieves the best prediction performance among all competing models. The six designed features, including averaged defensive rebounds, averaged two-point field goal percentage, averaged free throw percentage, averaged offensive rebounds, averaged assists, and averaged three-point field goal attempts, from four game-lags have a greater effect on the prediction of final scores of NBA games than other game-lags. The findings of this study provide relevant insights and guidance for other team or individual sports outcomes prediction research.

Associations of sleep duration with physical fitness performance and self-perception of health: a cross-sectional study of Taiwanese adults aged 23-45.

BACKGROUND: The relationship between sleep duration and physical fitness is one aspect of sleep health. Potential factors associated with sleep duration interfere with physical fitness performance, but the impact trends on physical fitness indicators remain unclear. METHODS: This study examined associations between sleep duration and physical fitness among young to middle-aged adults in Taiwan. A total of 42,781 Taiwanese adults aged 23-45 participated in the National Physical Fitness Examination Survey 2013 (NPFES-2013) in Taiwan between October 2013 and March 2014. A standardized structural questionnaire was used to record participants' sleep duration, which was stratified as short (< 6 h/day (h/d)), moderate (6-7 h/d; 7-8 h/d; 8-9 h), and long (≥ 9 h/d) sleep duration groups. Physical fitness was assessed based on four components: body composition (body mass index [BMI], waist-to-height ratio [WHtR], and waist-to-hip ratio [WHR]), muscle strength and endurance (1-min bent-leg sit-up test [BS]), flexibility (sit-and-reach test [SR]), and cardiorespiratory endurance index (3-min step test [CEI]). RESULTS: By using analysis of covariance (ANCOVA), after sex grouping and age adjustment, we observed that sleep duration was significantly associated with obesity, functional fitness, and self-perception of health. The sleep duration for low obesity-related values (BMI, WHtR, and WHR) for men was 7-9 h/d, and that for women was 7-8 h/d. Sleeping more than 8 h/d showed poor functional fitness performances (BS and SR). For both sexes, sleep duration of 8-9 h/d was the optimal sleep duration for self-perceptions of health. CONCLUSIONS: Our research found that there were wide and different associations of sleep duration with physical fitness and self-perception of health among Taiwanese adults aged 23-45, and there were differences in these associated manifestations between men and women. This study could be of great importance in regional public health management in Taiwan, and provide inspirations for clinical research on physical fitness.

Prevalence and Risk Factors of Metabolic Syndrome among the Homeless in Taipei City: A Cross-Sectional Study.

The safety and health of homeless people are important social issues. Metabolic syndrome (MetS) is a sub-health-risk phenomenon that has been severely aggravated worldwide in recent years. The purpose of this study was to investigate the prevalence and risk factors of MetS among the homeless in Taipei City, Taiwan. In this study, a convenience sampling was conducted at homeless counseling agencies in Taipei City from April 2018 to September 2018. A total of 297 homeless participants were recruited, from whom clinical indicators and questionnaire information were collected. Through statistical verification, analysis of variance (ANOVA), and logistic regression, we found the following main conclusions for homeless adults in Taipei: (1) The prevalence of MetS was estimated to be 53%, with 50% meeting four or more diagnostic conditions. (2) Dyslipidemia (high-density lipoprotein (HDL) deficiency and elevated triglyceride (TG)) showed the strongest association with the prevalence of MetS; more than 83% of people with HDL deficiency or hypertriglyceridemia had MetS. For the patient groups meeting more MetS diagnostic conditions, the values of high-density lipoprotein cholesterol (HDL-C), TG, and total cholesterol (TC) increased significantly. (3) The deterioration of MetS was significantly related to the high prevalence of hyperlipidemia (HL). (4) The homeless who were divorced, separated or widowed were more likely to suffer from MetS.

Relationships among Leisure Physical Activity, Sedentary Lifestyle, Physical Fitness, and Happiness in Adults 65 Years or Older in Taiwan.

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.