110023 research outputs found
Sort by
Association between smoking and glycemic control in men with newly diagnosed type 2 diabetes: a retrospective matched cohort study
No full text[[abstract]]Background: Longitudinal data on the association between smoking and glycemic control in men with newly diagnosed type 2 diabetes (T2DM) is scarce. Therefore, this study aimed to examine the extent of the association between smoking and glycemic control in this population.
Methods: The retrospective cohort study identified 3044 eligible men with T2DM in a medical centre in Taiwan between 2002 and 2017. Smokers (n = 757) were matched 1:1 with non-smokers using propensity score-matching. All of them were followed for one year. Glycated haemoglobin (HbA1c) levels were measured at 0, 3, 6, 9, and 12 months after enrolment. Generalised estimating equations were used to assess smoking status-by-time interaction to determine the difference in HbA1c reduction between the two cohorts. All analyses were performed in 2020.
Results: The estimated maximal difference in HbA1c reduction between smokers and non-smokers was 0.33% (95% CI, 0.05-0.62%) at 3 months of follow-up. For patients with body mass index (BMI) <25 kg/m2, the difference in HbA1c reduction between smokers and non-smokers was much larger (0.74%, 95% CI, 0.35-1.14%) than in those with a higher BMI.
Conclusions: Our findings show that smoking was independently associated with unfavourable glycemic control among men with newly diagnosed T2DM, and such a detrimental association could be stronger in men with a lower BMI
The impact of the covid-19 pandemic on anxiety, health literacy, and eHealth literacy in 2020 related to healthcare behavior in Thailand
No full text[[abstract]]Individual’s decision to cooperate with disease prevention varies based on their respective health beliefs and common factors that motivate actions. Previous research has found that pandemic anxiety, high health literacy, and eHealth literacy influenced healthcare behavior. Understanding how the pandemic affects people on modifying preventive health behavior is promising. Accordingly, this cross-sectional study focusing on health behavior utilized Structural Equation Modeling to characterize causative factors of anxiety, health literacy, eHealth literature, and protection in the new normal of COVID-19 pandemic in Thailand. Online surveys used a snowball sampling method through social media to recruit participants aged over 20 years in 8 provinces in Thailand. iGeneration and millennials were the top two, making up 75.0% of the 700-respondents in total. Independent variables: Health Literacy (p = .030); eHealth Literacy (p < .001); and anxiety (p = .040) significantly influenced the new normal. The new normal practices: hand hygiene, wearing hygienic masks and social distancing, maintaining good health, and preventing virus exposure by making digital payments could be indicated by 34% of Thai people by all those independent variables. This means that those who are more concerned and literate about health literacy and eHealth literacy, will make better health decisions and practice more preventive health care. Individuals may use health knowledge to make healthy decisions to protect themselves from the current pandemic. They can also use what they have learned to defend themselves from other emerging infectious illnesses in the future. Therefore, official institutions should provide helpful and timely health information that is easily accessible. Public health interventions should prioritize the availability of health information in the electronic form on various social media platforms to educate people to protect themselves from the spread of disease. The information should be comprehensible and practical for all socioeconomic groups
A Deep Learning Method for Foot Progression Angle Detection in Plantar Pressure Images
No full text[[abstract]]Foot progression angle (FPA) analysis is one of the core methods to detect gait pathologies as basic information to prevent foot injury from excessive in-toeing and out-toeing. Deep learning-based object detection can assist in measuring the FPA through plantar pressure images. This study aims to establish a precision model for determining the FPA. The precision detection of FPA can provide information with in-toeing, out-toeing, and rearfoot kinematics to evaluate the effect of physical therapy programs on knee pain and knee osteoarthritis. We analyzed a total of 1424 plantar images with three different You Only Look Once (YOLO) networks: YOLO v3, v4, and v5x, to obtain a suitable model for FPA detection. YOLOv4 showed higher performance of the profile-box, with average precision in the left foot of 100.00% and the right foot of 99.78%, respectively. Besides, in detecting the foot angle-box, the ground-truth has similar results with YOLOv4 (5.58 ± 0.10° vs. 5.86 ± 0.09°, p = 0.013). In contrast, there was a significant difference in FPA between ground-truth vs. YOLOv3 (5.58 ± 0.10° vs. 6.07 ± 0.06°, p < 0.001), and ground-truth vs. YOLOv5x (5.58 ± 0.10° vs. 6.75 ± 0.06°, p < 0.001). This result implies that deep learning with YOLOv4 can enhance the detection of FPA
Advanced Cross-Correlation Function Application to Identify Arterial Baroreflex Sensitivity Variations From Healthy to Diabetes Mellitus
No full text[[abstract]]Diabetes mellitus (DM) is a chronic disease characterized by elevated blood glucose levels, which leads over time to serious damage to the heart, blood vessels, eyes, kidneys, and nerves. DM is of two types–types 1 or 2. In type 1, there is a problem with insulin secretion, and in type 2–insulin resistance. About 463 million people worldwide have diabetes, and 80% of the majority live in low- and middle-income countries, and 1.5 million deaths are directly attributed to diabetes each year. Autonomic neuropathy (AN) is one of the common diabetic complications, leading to failure in blood pressure (BP) control and causing cardiovascular disease. Therefore, early detection of AN becomes crucial to optimize treatment. We propose an advanced cross-correlation function (ACCF) between BP and heart rate with suitable threshold parameters to analyze and detect early changes in baroreflex sensitivity (BRS) in DM with AN (DM+). We studied heart rate (HR) and systolic BP responses during tilt in 16 patients with diabetes mellitus only (DM?), 19 diabetes mellitus with autonomic dysfunction (DM+), and 10 healthy subjects. The ACCF analysis revealed that the healthy and DM groups had different filtered percentages of significant maximum cross-correlation function (CCF) value (p < 0.05), and the maximum CCF value after thresholds was significantly reduced during tilt in the DM+ group (p < 0.05). The maximum CCF index, a parameter for the phase between HR and BP, separated the healthy group from the DM groups (p < 0.05). Due to the maximum CCF index in DM groups being located in the positive range and significantly different from healthy ones, it could be speculated that BRS dysfunction in DM and AN could cause a phase change from lead to lag. ACCF could detect and separate DM+ from DM groups. This fact could represent an advantage of the ACCF algorithm. A common cross-correlation analysis was not easy to distinguish between DM? and DM+. This pilot study demonstrates that ACCF analysis with suitable threshold parameters could explore hidden changes in baroreflex control in DM+ and DM?. Furthermore, the superiority of this ACCF algorithm is useful in distinguishing whether AN is present or not in DM
Incidence and Risk of Fatal Vehicle Crashes Among Professional Drivers: A Population-Based Study in Taiwan
No full text[[abstract]]Fatal vehicle crashes (FVCs) are among the leading causes of death worldwide. Professional drivers often drive under dangerous conditions; however, knowledge of the risk factors for FVCs among professional drivers remain scant. We investigated whether professional drivers have a higher risk of FVCs than non-professional drivers and sought to clarify potential risk factors for FVCs among professional drivers. We analyzed nationwide incidence rates of FVCs as preliminary data. Furthermore, by using these data, we created a 1:4 professionals/non-professionals preliminary study to compare with the risk factors between professional and non-professional drivers. In Taiwan, the average crude incidence rate of FVCs for 2003-2016 among professional drivers was 1.09 per 1,000 person-years; professional drivers had a higher percentage of FVCs than non-professional drivers among all motor vehicle crashes. In the 14-year preliminary study with frequency-matched non-professional drivers, the risk of FVCs among professional drivers was significantly associated with a previous history of involvement in motor vehicle crashes (adjustment odds ratio [OR] = 2.157; 95% confidence interval [CI], 1.896-2.453), previous history of benzodiazepine use (adjustment OR = 1.385; 95% CI, 1.215-1.579), and speeding (adjustment OR = 1.009; 95% CI, 1.006-1.013). The findings have value to policymakers seeking to curtail FVCs
Maternal diabetes and childhood cancer risks in offspring: two population-based studies
No full text[[abstract]]Background: The effect of maternal diabetes on childhood cancer has not been widely studied.
Methods: We examined this in two population-based studies in Denmark (N = 6420 cancer cases, 160,484 controls) and Taiwan (N = 2160 cancer cases, 2,076,877 non-cases) using logistic regression and Cox proportional hazard regression adjusted for birth year, child's sex, maternal age and birth order.
Results: Gestational diabetes in Denmark [odds ratio (OR) = 0.98, 95% confidence interval (CI): 0.71-1.35] or type II and gestational diabetes in Taiwan (type II: hazard ratio (HR) = 0.81, 95% CI: 0.63-1.05; gestational diabetes: HR = 1.06, 95% CI: 0.92-1.22) were not associated with cancer (all types combined). In Denmark, maternal type I diabetes was associated with the risk of glioma (OR = 2.33, 95% CI: 1.04-5.22), while in Taiwan, the risks of glioma (HR = 1.59, 95% CI: 1.01-2.50) were elevated among children whose mothers had gestational diabetes. There was a twofold increased risk for hepatoblastoma with maternal type II diabetes (HR = 2.02, 95% CI: 1.02-4.00).
Conclusions: Our results suggest that maternal diabetes is an important risk factor for certain types of childhood cancers, emphasising the need for effective interventions targeting maternal diabetes to prevent serious health effects in offspring
Sleep duration predicts subsequent long-term mortality in patients with type 2 diabetes: A large single-center cohort study
No full text[[abstract]]Background
Sleep duration is associated with mortality. However, prior studies exploring whether sleep duration predicts subsequent long-term mortality in patients with diabetes are limited. This study aims to examine whether metabolic factors affect the associations between baseline sleep duration and subsequent risks of all-cause, expanded, and non-expanded cardiovascular disease (CVD) mortalities among patients with type 2 diabetes (T2D).
Methods
A total of 12,526 T2D patients aged 30 years and older, with a follow-up period???3 years, were identified from the Diabetes Case Management Program of a medical center in Taiwan. Sleep duration was measured using computerized questionnaires by case managers, and the time frame for this question was 1 month prior to the interview date. Sleep duration in relation to subsequent mortality from all causes, expanded CVD, and non-expanded CVD was examined using Cox proportional hazard models.
Results
Within 10 years of follow-up, 2918 deaths (1328 CVD deaths and 1590 non-CVD deaths) were recorded. A J-shaped association was observed for all-cause, expanded CVD, and non-expanded CVD mortalities, and the lowest risks were observed for patients with 5–7 h of sleep. The significant joint effects included sleep duration of more or less than 7 h with age???65 years [adjusted HRs: 4.00 (3.49–4.60)], diabetes duration???5 years [1.60 (1.40–1.84)], age at diabetes diagnosis???45 years [1.69 (1.38–2.07)], insulin use [1.76 (1.54–2.03)], systolic blood pressure/diastolic blood pressure?>?130/85 mmHg [1.24 (1.07–1.43)], triglyceride???150 mg/dL [1.38 (1.22–1.56)], HbA1c???7% [1.31 (1.13–1.52)], and body mass index?<?27 kg/m2 [1.31 (1.17–1.45)] for all-cause mortality.
Conclusion
A J-shaped association was observed between sleep duration and all-cause and expanded CVD mortality, and a sleep duration of 5–7 h had the lowest mortality risk. Sleep duration also showed significant synergistic interactions with diabetes duration but shared an antagonistic interaction with age and obesity
Statins Associated with Better Long-Term Outcomes in Aged Hospitalized Patients with COPD: A Real-World Experience from Pay-for-Performance Program
No full text[[abstract]]Chronic obstructive pulmonary disease (COPD) is the third leading cause of death globally. Previous studies have addressed the impact of comorbidity on short-term mortality in patients with COPD. However, the prevalence of cardiovascular disease (CVD) and the association of statins prescription with mortality for aged COPD patients remains unclear. We enrolled 296 aged, hospitalized patients who were monitored in the pay-for-performance (P-4-P) program of COPD. Factors associated with long-term mortality were identified by Cox regression analysis. The median age of the study cohort was 80 years old, and the prevalence of coronary artery disease (CAD) and statins prescriptions were 16.6% and 31.4%, respectively. The mortality rate of the median 3-year follow-up was 51.4%. Through multivariate analysis, body mass index (BMI), statin prescription, and events of respiratory failure were associated with long-term mortality. A Cox analysis showed that statins prescription was associated with lower mortality (hazard ratio (HR): 0.5, 95% Confident interval, 95% CI: 0.34-0.73, p = 0.0004) and subgroup analysis showed that rosuvastatin prescription had protective effect on long-term mortality (HR: 0.44; 95% CI: 0.20-0.97; p < 0.05). Statin prescriptions might be associated with better long-term survival in aged COPD patients, especially those who experienced an acute exacerbation of COPD (AECOPD) who require hospitalization
Stress on caregivers providing prolonged mechanical ventilation patient care in different facilities: a cross-sectional study
No full text[[abstract]]Purpose: Taiwan has implemented an integrated prospective payment program (IPP) for prolonged mechanical ventilation (PMV) patients that consists of four stages of care: intensive care unit (ICU), respiratory care center (RCC), respiratory care ward (RCW), and respiratory home care (RHC). We aimed to investigate the life impact on family caregivers of PMV patients opting for a payment program and compared different care units.
Method: A total of 610 questionnaires were recalled. Statistical analyses were conducted by using the chi-square test and multivariate logistic regression model.
Results: The results indicated no associations between caregivers' stress levels and opting for a payment program. Participants in the non-IPP group spent less time with friends and family owing to caregiver responsibilities. The results of the family domain show that the RHC group (OR = 2.54) had worsened family relationships compared with the ICU group; however, there was less psychological stress in the RCC (OR = 0.54) and RCW (OR = 0.16) groups than in the ICU group. In the social domain, RHC interviewees experienced reduced friend and family interactivity (OR = 2.18) and community or religious activities (OR = 2.06) than the ICU group. The RCW group felt that leisure and work time had less effect (OR = 0.37 and 0.41) than the ICU group. Furthermore, RCW interviewees (OR = 0.43) were less influenced by the reduced family income than the ICU group in the economic domain.
Conclusions: RHC family caregivers had the highest level of stress, whereas family caregivers in the RCW group had the lowest level of stress
Ten existing osteoporosis prediction tools for the successful application of National Health Insurance-reimbursed anti-osteoporosis medications in long-term care residents in Taiwan
No full text[[abstract]]Background/purpose: Osteoporotic fracture introduce enormous societal and economic burden, especially for long-term care residents (LTCRs). Although osteoporosis prevention for LTCRs is urgently needed, obstacles such as frail status and inconvenient hospital visits hurdled them from necessary examinations and diagnoses. We aimed to test 10 existing osteoporosis screening tools (OSTs), which can be easily used in institutions and serve as a prediction, for accurately determining the outcome of a Taiwan's National Health Insurance (NHI)-reimbursed anti-osteoporosis medications (AOMs) application for LTCRs.
Methods: This prospective analysis recruited 444 patients from LTC institutions between October 2018 and November 2019. Predictions of whether the NHI-reimbursed AOMs criteria was met were tested for 10 OSTs. The results of OSTs categorized into self-reported or validated based on previous fracture history were self-reported by LTCRs or validated by imaging data and medical records, respectively. The receiver operating characteristic curve and the optimal cut-off points for LTCRs based on Youden's index were explored.
Results: Overall, the validated OSTs had a higher positive predictive value (PPV) and negative predictive value (NPV) summation than the corresponding reported OSTs. The validated FRAX-Major was the best OST (PPV = 63.6%, NPV = 82.4% for the male group and, PPV = 78.8%, NPV = 90.0% for the female group). After applying the optimum cut-off derived from Youden's index, the validated FRAX-Major (PPV = 75.4%, NPV = 92.0%)) remained performed best for men. In female population, validated FRAX-Major (PPV = 87.2%, NPV = 84.1%) and validated osteoporosis prescreening risk assessment (OPERA; PPV = 96.1%, NPV = 79.7%)) both provided good prediction results.
Conclusion: FRAX-Major and OPERA have better prediction ability for LTCRs to acquire NHI-reimbursed AOMs. The validated fracture history and adjusted cut-off points could prominently increase the PPV during prediction