Quality of life (QoL) focuses on a person’s ability to live a fulfilling life. It helps in determining successful aging in the elderly population. Because of a scarcity of information regarding predictors of QoL in the elderly population in developing countries, this study was undertaken to assess the overall QoL and its predictors in the elderly population in an urban setting of Bangladesh. In this cross-sectional study, 275 participants were enrolled by systematic sampling at the outpatient department of a tertiary care hospital in Dhaka, Bangladesh. Data were collected by using a pretested semi-structured standard questionnaire for QoL (OPQOL-35). Pearson’s correlation was used to assess the association between QoL and sociodemographic factors. Multivariate linear regression was conducted to identify predictors of QoL, after controlling for potential confounders. The median age of the participants (n = 275) was 65 years (range, 60 to 85; 25th and 75th percentile, 60 and 68, respectively). The majority (78%) of them were married, and 20% were widowed or divorced. The median score of QoL was 113 (25th and 75th percentile, 101 and 124, respectively). Most of the participants had very poor (bad as can be) or poor (bad) QoL. Only 7.3% were observed to have “good” QoL (scores 140 or more). Females had significantly poorer QoL scores compared to males (p < 0.001). Age, gender, educational status, smoking, and sleep duration significantly predicted QoL in a multiple regression analysis. In conclusion, the QoL of the elderly individuals in Bangladesh was very poor or poor. Future research should focus on service-oriented interventions, especially targeting women. Counseling elderly people to enhance their quality of life may include improving sleep patterns, healthy diets, regular exercise, and caring for their mental health.

Low back pain (LBP) is common among adolescent cricketers, often due to repetitive lumbar stress. This study investigated LBP among 450 adolescent cricketers in Dhaka City, Bangladesh, considering a range of factors, including sociodemographic characteristics, game-related activities, preventive practices, and LBP-related history. Various ML algorithms applied to LBP severity classification included K-Nearest Neighbors, Random Forest, Logistic Regression, and Support Vector Machine (SVM). LBP severity was categorized into three classes as no pain, mild pain, and moderate pain because there was an insufficient amount of data for the severe pain category. The SVM using the sigmoid kernel of the models considered gave the best performance as it produced the best performance metrics of test accuracy (87.6%), precision (90%), recall (87.6%), and F1-score (87.1%). In addition, regression analysis was also applied to identify the predictors of LBP. Key correlates included female gender (adjusted odds ratio [AOR] = 2.52), higher educational attainment (e.g., undergraduate: AOR = 5.38), elevated family income (e.g., > 60,000 BDT: AOR = 4.36), longer weekly practice duration (>20 hours: higher prevalence of 81.7%), inconsistent warm-up (often/sometimes: AOR = 12.48-14.07) and cool-down practices (sometimes: AOR = 2.86), and prior LBP history (AOR = 6.92), all significantly associated with increased LBP risk (p < 0.05). The findings show the importance of early intervention and prevention protocols for minimizing LBP occurrence among junior cricket players. In short, this work demonstrates the effectiveness of ML and regression models for ascertaining sports injury patterns of risk, data-informed prevention and management protocols, and providing a foundation for future studies on this subject. Limitations include the exclusion of a severe pain category due to insufficient data, which reduces the model's capacity to triage urgent cases requiring immediate intervention.

Knee osteoarthritis (OA) is a disabling joint condition that leads to extreme morbidityand quality of life impairment, particularly among older adults. This study aimed to investigate the socio-demographic factors and comorbid conditions influencing the severity of symptoms of knee OA using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Data were derived from 622 patients across 9 months from the major healthcare facilities of Dhaka. This study found age, sex, educational status, obesity, diabetes mellitus, and cardiovascular disease (CVD) were predictors for the severity of symptoms of OA of the knee. Female participants were more prone to have severe symptoms compared to males, and those who were more than 70-years-old were at greater risk of severe symptoms. Low educational status, obesity, diabetes mellitus, and CVD were also predictors for severe OA of the knee. Age (p < 0.001), obesity (p < 0.001), and diabetes (p < 0.001) were the best predictors of severity of symptoms based on the multinomial logistic regression analysis. The findings from the study highlighted the associated factors of knee OA and the need for integral healthcare measures that address both the socio-economic and the physical determinants. Focused interventions need to be employed, particularly for high-risk groups such as the elderly, women, and the comorbid, to minimize the incidence of OA of the knee and maximize the outcomes for patients in settings such as that of Bangladesh, where resources may not be available.

Bangladesh is experiencing rapid urbanization and a growing elderly population, particularly in urban areas. Cognitive decline, ranging from mild cognitive impairment to dementia, is a prevalent issue among elderly populations globally. Understanding the current state of cognitive functioning in this demographic is essential for informing effective healthcare plans and programs. This study aims to investigate the prevalence of cognitive decline and its associated factors among urban-dwelling elderly adults in Bangladesh, using the Rowland Universal Dementia Assessment Scale (RUDAS) to assess cognitive function. This cross-sectional study employed systematic random sampling among 150 elderly participants (aged 60–85 years) from the outpatient department of the National Institute of Traumatology and Orthopaedic Rehabilitation (NITOR) in Dhaka, Bangladesh. The mean age of participants was 67.41 ± 6.31 years, with a male predominance (53.3%). Cognitive function was impaired in a majority of participants, with 53.3% classified as having dementia, 38.7% with MNCD, and only 8% showing normal cognitive function. Significant predictors of cognitive function included age (r = -0.451, P < 0.001), educational level (P = 0.009), and diabetes (P = 0.038). Female participants had lower mean cognitive function scores compared to males (21.16 ± 5.25 vs. 22.03 ± 4.36, P = 0.271). Cognitive impairment is highly prevalent among elderly individuals in urban Bangladesh, with age, educational level, and diabetes being key predictors. These findings highlight the need for public health interventions and policies focused on early screening and targeted healthcare for cognitive decline in this demographic.

Objectives: This umbrella review synthesized and critically evaluated evidence from systematic reviews examining the effectiveness of Robot-Assisted Therapy (RAT) and Computerized Training (CT) for individuals with dementia and Mild Cognitive Impairment (MCI), with a focus on cognitive, psychosocial, and functional outcomes. Methods: A systematic search was conducted across six databases: PubMed, IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library, and EBSCOhost. The review protocol was prospectively registered in PROSPERO (CRD420251117930). Included reviews were appraised using AMSTAR-2 and ROBIS, while the methodological limitations of primary studies were synthesized from the limitations reported by the included reviews. Narrative synthesis, overlap analysis using the Corrected Covered Area (CCA), and GRADE assessments were performed. Results: Sixteen systematic reviews published between 2017 and 2025, encompassing 324 unique primary studies, met the inclusion criteria. Methodological quality was generally poor: twelve reviews were rated as having “Critically Low” confidence using AMSTAR-2, while nine were judged to be at ‘High Risk of Bias’ using ROBIS; methodological shortcomings included inadequate or absent assessment of risk of bias in the included primary studies in several reviews. Overlap analysis (CCA = 0.53%) identified two distinct evidence structures. The RAT literature demonstrated a concentrated citation pattern, with recurrent evidence for selected psychosocial outcomes. In contrast, the CT literature was highly fragmented, with divergent findings regarding specific cognitive domains including processing speed and memory recall, largely attributable to non-overlapping evidence bases rather than true scientific disagreement. Consequently, the umbrella-level certainty of evidence was rated as ‘Very Low’ for both RAT and CT across the broad psychosocial and cognitive outcome domains, reflecting serious concerns regarding risk of bias, inconsistency, and imprecision. Conclusions: RAT showed potential benefits for selected psychosocial outcomes, particularly depression, anxiety, positive emotion, and social interaction. However, the current evidence remains insufficient to establish the clinical effectiveness of either RAT or CT across broader cognitive, psychosocial, and functional outcomes. Future research should prioritize large-scale, pre-registered, methodologically rigorous trials with standardized core outcome measures to generate reliable evidence for clinical decision-making and dementia care.

Bangladesh is experiencing rapid urbanization and an increasing elderly population, particularly in urban areas. Cognitive decline is a common issue among elderly populations worldwide. This study aims to investigate the current state of cognitive decline among urban-dwelling older adults in Bangladesh to inform effective healthcare plans and programs. This cross-sectional study employed systematic random sampling among 150 participants at the outpatient department of NITOR in Dhaka, Bangladesh. Data were collected using a pretested structured standard questionnaire, and cognitive function was assessed with the Rowland Universal Dementia Assessment Scale (RUDAS). The study revealed a linear association between cognitive function and factors such as level of educational status, and history of eye sight problem. Conversely, age and diabetes showed an adverse correlation with cognitive function. Moreover, factors such as age, educational level and diabetes remained significant predictors of cognitive function in multivariate analysis. Cognitive impairment is significantly more common among the elderly in Bangladesh, with cognitive abilities being affected by a range of factors such as age, education level, diabetes, and vision issues.