The recent COVID-19 pandemic has imposed threats on both physical and mental health since its outbreak. This study aimed to explore the impact of the COVID-19 pandemic on mental health among a ...representative sample of home-quarantined Bangladeshi adults. A cross-sectional design was used with an online survey completed by a convenience sample recruited via social media. A total of 1,427 respondents were recruited, and their mental health was assessed by the DASS-21 measure. The prevalence of anxiety symptoms and depressive symptoms was 33.7% and 57.9%, respectively, and 59.7% reported mild to extremely severe levels of stress. Perceptions that the pandemic disrupted life events, affected mental health, jobs, the economy and education, predictions of a worsening situation, and uncertainty of the health care system capacities were significantly associated with poor mental health outcomes. Multivariate logistic regressions showed that sociodemographic factors and perceptions of COVID-19 significantly predict mental health outcomes. These findings warrant the consideration of easily accessible low-intensity mental health interventions during and beyond this pandemic.
•This is the first few studies exploring the perceptions of Bangladeshi students towards the COVID-19 pandemic.•Female students showed higher stress levels, greater anxiety symptoms and, depressive ...symptoms.•Younger students had a greater psychological impact due to COVID-19 than senior students.•The psychological impact is higher among those students who have at least oneCOVID-19 like symptoms.
Although the COVID-19 pandemic triggered commination on both physical and mental wellbeing since its outbreak, the impact of the pandemic on mental health difficulties among Bangladeshi students is still lack in substantial evidence. The study aimed to explore such an impact on mental health among Bangladeshi students and their perception towards the COVID-19 pandemic. A web-based cross-sectional study was conducted among 589 students from Bangladesh in between April 29th to 7th May 2020. Data was collected by using an online questionnaire on demographic status, perceptions towards COVID-19, and mental health symptoms by using the Depression, Anxiety and Stress Scale (DASS 21). About 26.66% and 61.97% of students reported mild to extremely severe anxiety symptoms, and depressive symptoms, respectively, and 57.05% reported mild to extremely severe levels of stress. Multivariate logistic regression reported that students’ age, gender, family income, residence, and family size are associated with mental health difficulties. Negative perceptions on the effect of the pandemic on life events, mental health, disruptions in education, and health care system, existing physical health conditions, and COVID-19 like symptoms were significantly associated with poor mental outcomes. It is suggested that students' mental health difficulties should be monitored to provide adequate support and services during this ongoing pandemic.
Objectives: Overweight and obesity has an adverse effect on public health, and emerging adulthood (18-25 years) is recognized as a potential period for the onset of obesity, thus, this study aimed to ...explore risk factors associated with overweight and obesity in Bangladeshi university students. Participants: Bangladeshi university students (n = 280). Methods: A case-control study of 140 students with overweight and obesity and 140 students with normal weight. Results: Multiple logistic regression revealed having at least one overweight or obese parent (AOR = 4.2; 95% CI: 2.2-8.4) and participating in no physical activity (AOR = 3.2; 95% CI: 1.0-9.9) were potential risk factors. Reported consumption ≥4 meals in a day, junk food/fast food, and soft drinks for ≥3 days a week were potential determinants of overweight and obesity in this population. Conclusion: Increased awareness on the importance of regular physical activity and a healthy diet may reduce the risk of overweight and obesity among Bangladeshi university students.
The usage of dietary supplement (DS) such as vitamins, minerals, and fish oil has expanded, but there is limited data on their use by sub-populations such as university students. The study was aimed ...to investigate the prevalence of DS use among Bangladeshi university students and its associated factors. A cross-sectional survey of 390 students was conducted from two public universities from Barishal Division in Bangladesh using a structured questionnaire with 72 questions divided into five sections: sociodemographic, knowledge, opinions, and attitudes, types of DS, reasons and sources for using DS, and adverse reactions after taking DS. Descriptive statistics and logistic regression were utilized to estimate the results. Among all the students, 15.6% students were using DS where only 7.7% of them used DS according to physicians' recommendation. Additionally, students used DS for general health and well-being, weight gaining and as a source of energy for physical and sporting activities, etc. The use of DS was significantly associated with female sex (AOR = 5.44, 95% CI: 2.18-13.52), greater than or equal to25 years age (AOR = 0.08, 95% CI: 0.01-0.67), underweight (AOR = 5.86, 95% CI: 1.95-17.62), having major illness (AOR = 6.99, 95% CI: 1.98-24.70) and good knowledge of DS (AOR = 2.64, 95% CI: 1.23-5.64). This study provides new findings on DS use and its correlates in Bangladeshi students which may be used by the policymakers to improve DS usage among students. Adaptation of an appropriate program is recommended to educate students on proper and safer ways of using DS.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Fall causes trauma or critical injury among the geriatric population which is a second leading accidental cause of post-injury mortality around the world. It is crucial to keep elderly people under ...supervision by ensuring proper privacy and comfort. Thus the elderly fall detection and prediction using wearable/ non-wearable sensors become an active field of research. In this work, a novel pipeline for fall detection based on wearable accelerometer data has been proposed. Three publicly available datasets have been used to validate our proposed method, and more than 7700 cross-disciplinary time-series features were investigated for each of the datasets. After following a series of feature reduction techniques such as mutual information, removing highly correlated features using the Pearson correlation coefficient, Boruta algorithm, we have obtained the dominant features for each dataset. Different classical machine learning (ML) algorithms were utilized to detect falls based on the obtained features. For individual datasets, the simple ML classifiers achieved very good accuracy. We trained our pipeline with two of the three datasets and tested with the remaining one dataset until all three datasets were used as the test set to show the generalization capability of our proposed pipeline. A set of 39 high-performing features is selected, and the classifiers were trained with them. For all the cases, the proposed pipeline showed excellent efficiency in detecting falls. This architecture performed better than most of the existing works in all the used publicly available datasets, proving the supremacy of the proposed data analysis pipeline.
An earthquake is a tremor felt on the surface of the earth created by the movement of the major pieces of its outer shell. Till now, many attempts have been made to forecast earthquakes, which saw ...some success, but these attempted models are specific to a region. In this paper, an earthquake occurrence and location prediction model is proposed. After reviewing the literature, long short-term memory (LSTM) is found to be a good option for building the model because of its memory-keeping ability. Using the Keras tuner, the best model was selected from candidate models, which are composed of combinations of various LSTM architectures and dense layers. This selected model used seismic indicators from the earthquake catalog of Bangladesh as features to predict earthquakes of the following month. Attention mechanism was added to the LSTM architecture to improve the model's earthquake occurrence prediction accuracy, which was 74.67%. Additionally, a regression model was built using LSTM and dense layers to predict the earthquake epicenter as a distance from a predefined location, which provided a root mean square error of 1.25.
Much scholarly debate has centered on Bangladesh's family planning program (FPP) in lowering the country's fertility rate. This study aimed to investigate the prevalence of using modern and ...traditional contraceptive methods and to determine the factors that explain the contraceptive methods use.
The study used data from the 2017-18 Bangladesh Demographic and Health Survey (BDHS), which included 11,452 (weighted) women aged 15-49 years in the analysis. Multilevel multinomial logistic regression was used to identify the factors associated with the contraceptive method use.
The prevalence of using modern contraceptive methods was 72.16%, while 14.58% of women used traditional methods in Bangladesh. In comparison to women in the 15-24 years age group, older women (35-49 years) were more unwilling to use modern contraceptive methods (RRR: 0.28, 95% CI: 0.21-0.37). Women who had at least a living child were more likely to use both traditional and modern contraceptive methods (RRR: 4.37, 95% CI: 3.12-6.11). Similarly, given birth in the previous 5 years influenced women 2.41 times more to use modern method compared to those who had not given birth (RRR: 2.41, 95% CI: 1.65-3.52). Husbands'/partners' decision for using/not using contraception were positively associated with the use of both traditional (RRR: 4.49, 95% CI: 3.04-6.63) and modern methods (RRR: 3.01, 95% CI: 2.15-4.17) rather than using no method. This study suggests rural participants were 21% less likely to utilize modern methods than urban participants (RRR: 0.79, 95% CI: 0.67-0.94).
Bangladesh remains a focus for contraceptive use, as it is one of the most populous countries in South Asia. To lower the fertility rate, policymakers may design interventions to improve awareness especially targeting uneducated, and rural reproductive women in Bangladesh. The study also highlights the importance of male partners' decision-making regarding women's contraceptive use.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The study aimed to determine the associated factors of household food security (HFS) and household dietary diversity (HDD) during the COVID-19 pandemic in Bangladesh.
Both online survey and ...face-to-face interviews were employed in this cross-sectional study. The Household Food Security Scale and Household Dietary Diversity Score were used to access HFS and HDD, respectively. The HDD scores were derived from a 24-h recall of food intake from 12 groups.
Bangladesh.
A total sample of 1876 households were recruited.
The overall mean scores of HFS and HDD were 31·86 (sd 2·52) and 6·22 (sd 5·49), respectively. Being a rural resident, having no formal education, occupation of household head other than government job and low monthly income were potential determinants of lower HFS and HDD. Approximately 45 % and 61 % of Bangladeshi households did not get the same quantity and same type of food, respectively, as they got before the pandemic. Over 10 % of respondents reported that they lost their job or had to close their businesses, and income reduction was reported by over 70 % of household income earners during the COVID-19 pandemic, which in turn was negatively associated with HFS and HDD.
Household socio-economic variables and COVID-19 effects on occupation and income are potential predictors of lower HFS and HDD scores. HFS and HDD deserve more attention during this pandemic particularly with reference to low-earning households and the households whose earning persons' occupation has been negatively impacted during the COVID-19 pandemic.
•The prevalence of Facebook addiction was 36.9%.•FA was 1.67 and 2.51 times higher among the students with depressive symptoms and a domestic violence history respectively.•Participants who did not ...fail or fall into love were less likely to be at risk of FA than those who did.•Appropriate behavioral interventions targeting these factors should be envisaged to reduce FA among university students.
Facebook addiction (FA) has been suggested as a potential behavioral addiction. Data about FA among university students in Bangladesh has been scarce despite being a research topic of growing interest. This study aimed to determine the prevalence of FA and its related factors amongst university students in Bangladesh. A cross-sectional study was conducted between February to March 2020 within two Bangladeshi universities (i.e., Patuakhali Science and Technology University and Barishal University) residing in the southern territory of the country. Data were collected on socio-demographic characteristics, behavioral and online activities. The Bergen Facebook Addiction Scale and the Beck Depression Inventory-21 Scale were used to access the risk of FA, and depressive symptoms of students. About 36.9% of the students were identified as at risk of Facebook addiction using 18 as the cutoff score out of 30 on the Bergen Facebook Addiction Scale. The risk factors of Facebook addiction were predicted to be failure in love, having history of domestic violence, having stressful life event, sufferings from sleep disturbance (i.e., more than 8 h sleep status compared to 6–8 h normal status), spending more than 5 h daily time on Facebook, and having symptoms of depression. Future research should use longitudinal designs to identify FA contributing factors among university students in Bangladesh.
The novel coronavirus disease (COVID-19) pandemic is provoking a prevalent consequence on mental health because of less interaction among people, economic collapse, negativity, fear of losing jobs, ...and death of the near and dear ones. To express their mental state, people often are using social media as one of the preferred means. Due to reduced outdoor activities, people are spending more time on social media than usual and expressing their emotion of anxiety, fear, and depression. On a daily basis, about 2.5 quintillion bytes of data are generated on social media. Analyzing this big data can become an excellent means to evaluate the effect of COVID-19 on mental health. In this work, we have analyzed data from Twitter microblog (tweets) to find out the effect of COVID-19 on people's mental health with a special focus on depression. We propose a novel pipeline, based on recurrent neural network (in the form of long short-term memory or LSTM) and convolutional neural network, capable of identifying depressive tweets with an accuracy of 99.42%. Preprocessed using various natural language processing techniques, the aim was to find out depressive emotion from these tweets. Analyzing over 571 thousand tweets posted between October 2019 and May 2020 by 482 users, a significant rise in depressing tweets was observed between February and May of 2020, which indicates as an impact of the long ongoing COVID-19 pandemic situation.
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