Search

• The mobile phone text-messaging is an effective and cost-effective method in improving glycemic control. • Text-messaging might be a valuable addition to standard treatment for diabetes care in low-resource settings. To evaluate the cost-effectiveness of a mobile phone text messaging program for people with type 2 diabetes mellitus. We performed a generalized cost-effectiveness analysis in a randomized controlled trial in Bangladesh. Patients with type 2 diabetes were randomized (1:1) to a text messaging intervention plus standard-care or standard-care alone. Intervention participants received a text message daily for 6 months encouraging healthy lifestyles. Costs to users and the health systems were measured. The EQ-5D-3L was used to measure improvements in health-related quality-adjusted life years (QALYs). Intervention costs were expressed as average cost-effectiveness ratios (cost-per 1% unit-reduction in glycated haemoglobin HbA1c and cost per QALY gained), based on the World Health Organization cost effectiveness and strategic planning (WHO-CHOICE) method. In 236 patients [mean age 48 (SD9.6) years] the adjusted difference in accumulated QALYs between the intervention and the control group over the 6-month period was 0.010 (95%CI: 0.000; 0.021). Additional costs per-patient averaged 24 international dollars (Intl.$), resulting in incremental cost-effectiveness ratios of 38 Intl.$ per % glycated haemoglobin (HbA1c) reduction and 2406 Intl.$ per QALY gained. The total intervention costs for the mobile phone text messaging program was 2842 Int.$. Text messaging might be a valuable addition to standard treatment for diabetes care in low-resource settings and predicted to lead an overall saving in health systems costs. Studies with longer follow-up and larger samples are needed to draw reliable conclusions. [ABSTRACT FROM AUTHOR]

• This population-based study is the first to examine the clustering patterns of cardiovascular disease behavioural risk factors among adolescents in Bangladesh. • About one out of five adolescents had three or more risk factors, with males having had higher prevalence than females. • Low fruit and vegetable intake clustered with physical inactivity in both male and female adolescents. • The risk factors in this study tended to co-occur more often than expected. • Intervention should target adolescents who are engaging in multiple risk behaviours. To examine the clustering of major behavioural risk factors for cardiovascular diseases (CVD) among adolescents in Bangladesh. Nationally representative data from the 2014 Bangladesh Global School-based Student Health Survey were analysed in 2,978 adolescents (mean age: 14.2 SD 0.99; 60.0% females). We compared the observed (O) to expected (E) prevalence ratios of 64 possible combinations of the six behavioural risk factors (i.e., physical inactivity, sedentary behaviour, tobacco smoking, alcohol drinking, low fruit and vegetable intake, and overweight/obesity) to determine their clustering patterns. Poisson regression was used to examine the associations. The most common single risk factor was low fruit and vegetable intake, followed by physical inactivity (87% and 59%, respectively). Overall, 18% of the adolescents had three or more risk factors, with males reporting higher prevalence than females (21% vs. 11%). The simultaneous occurrence of low fruit and vegetable intake, and physical inactivity was 30% greater in males (O/E ratio: 1.3, 95% CI: 1.2–1.4) and 20% greater in females (1.2, 1.2–1.3) than what was expected if there was no clustering. The analysis also demonstrated clustering of low fruit and vegetable, overweight/obesity, and high sedentary behaviour (2.5, 1.3–4.9), and low fruit and vegetable, overweight/obesity, and physical inactivity (1.4, 1.1–2.0) for males. This study fills the gap in the scientific literature by providing evidence that behavioural risk factors tend to cluster among adolescents in Bangladesh. Early prevention programs with evidence-based strategies need to target adolescents who are at a greater risk of engaging in multiple risk behaviours. [ABSTRACT FROM AUTHOR]

Prolonged social media usage is linked to poor sleep among adolescents; however, little is known about these associations in developing countries. The aim of this study was to examine the associations of social media use with sleep duration and sleep disturbance among adolescents in Bangladesh. We used a self-administered survey to collect data on sleep duration and social media use from secondary school students in Dhaka, Bangladesh. Parents reported about their child's sleep disturbance. Binary logistic regression models were estimated to examine the associations. For every 10 minutes/day increase in social media use during school days, the odds of reporting sleep disturbance was increased by 14% (95% CI: 1.08–1.21). The odds of reporting sleep ≤7 h/day was increased by 3% (1.01–1.06) for every 10 minutes increase in social media use during weekend days. Limiting social media use can be a pragmatic strategy to improve sleep, which can improve health and wellbeing of adolescents in urban Bangladesh. [ABSTRACT FROM AUTHOR]

Aims To investigate the clinical features of patients with type 2 diabetes on oral medication and determine the complications and risk factors in these patients. Methods A cross-sectional was conducted among 515 patients with type 2 diabetes at the outpatient clinics of Bangladesh Institute of Health Science (BIHS) hospital from September to December 2013. We collected data on socio-economic characteristics, clinical status, risk factors, complications, anthropometric measurements and blood tests. Univariate and multivariate logistic regression was performed to identify risk factors associated with diabetes complications. Results The mean(±SD) age of the participants was 50.0(±10.1) years and 15.3% were less than 40 years. The mean HbA1c was 8.3(±2.1). Only 28.7% of the participants achieved targets for HbA1c. The overall prevalence of hypertension, obesity and dyslipidemia was 57.5%, 62.6% and 72.7%, respectively. Eye problems were the most common complication (68.9%) followed by chronic kidney diseases (21.3%) and cardiovascular diseases (11.8%). There were significant associations between the complications and age, duration of diabetes and duration of hypertension. In the multivariate analysis adjusting for other confounding variables, only systolic blood pressure was found to be significantly associated with complications [OR 0.809, 95% CI 0.666–0.981 ( p -value 0.031)]. Conclusion Results of the study confirm that even under best clinical settings a great majority Bangladeshi adults with type 2 diabetes have uncontrolled diabetes and a high prevalence of risk factors that might contribute to early development of complications. Early screening of high risk groups and proper management of diabetes is recommended to avoid early complications. [ABSTRACT FROM AUTHOR]

• COVID-19 related lockdown policies and stay at home strategy may lead to the overuse or excessive usage of smartphones. • Excessive use of smartphones can potentially lead to gaming disorders and internet use disorders and eventually be considered to cause psychosocial crisis. • Without proper initiative, problematic smartphone usage can turn into an emerging public health challenge to annihilate lives by perpetuating the socio-psychological problems. • It will be a crucial step to monitor smartphone overuse and take necessary action to minimise the problem through protective policies and family support during and after this COVID 19 pandemic. [ABSTRACT FROM AUTHOR]

• We ourselves collected the data. So, the dataset is new. • Proposed method was tested on six public datasets and the results are better than most of state of the art papers. • We showed that using the Sobel filter can improve the performance of CNN. • We did not use pre-trained networks unlike most of the other researches. We ourselves designed the network. • Our method performance was better than the compared references. The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filter (CNN-SVM + Sobel) achieved the highest classification accuracy, sensitivity and specificity of 99.02%, 100% and 95.23%, respectively in automated detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application. [ABSTRACT FROM AUTHOR]

Background: Regular physical activity is effective for preventing and managing depression; however, only a few studies have assessed physical activity using device-based measures. We aimed to examine the association between device-based total physical activity and late-life depressive symptoms and explore which factors may explain this relationship.Methods: Data from 10 European countries who participated in wave 8 of the Survey of Health, Ageing and Retirement in Europe (SHARE) were analyzed. Triaxial accelerometers (Axivity AX3; Axivity, Newcastle UK) were used to assess total physical activity. Depressive symptoms were assessed through the EURO-D scale. Possion regression models and mediation analysis were performed.Results: The final sample included 785 older adults (70 participants had missing data in one or more variables and were excluded from the analysis) [mean (SD): age 68.6 (8.8) years; 59.2 % female]. After adjusting for several confounders, a 10 % increase in the mean acceleration was associated with lower depressive symptoms (B = -0.0228; 95%CI = -0.0395, -0.0061). This association was partly explained by limitations in activities of daily living and quality of life.Limitations: Cross-sectional design, convenience sample, bi-directionality, lack of cutoff points for classification of the Axivity AX3 placed in the thigh, into intensities.Conclusions: Higher device-based total physical activity was linked with lower depressive symptoms in older European adults. Future studies employing device-based measures that allow to better capture important physical activity dimensions (i.e., intensity) will better inform about dose-response relationships. [ABSTRACT FROM AUTHOR]