ABSTRACT
Introduction: In elderly persons, due to physiological, anatomical, and functional changes, body mass index (BMI) may not be suitable for the assessment of nutritional status. Mid-upper arm circumference (MUAC) can be a proxy indicator to identify underweight and overweight/obesity among elderly persons. This study aimed to estimate the correlation between MUAC and BMI, and the cutoffs for MUAC using receiver operating characteristic (ROC) analysis. Material and Methods: This survey was carried out in a resettlement colony of Delhi. The participants were residents of the area who were aged 60 years or older, and selected by a simple random sampling technique. The arm span, weight, and MUAC of the participants were measured. The correlation between MUAC and BMI for gender and age group was calculated. The ROC curve was also constructed. Results: A total of 946 eligible participants were enrolled. The correlation between MUAC and BMI was 0.67 (P < 0.001) and 0.76 (P < 0.001) among men and women, respectively. The MUAC cutoff for underweight was <25 cm with a sensitivity of 68.8% and specificity of 84.9%. The area under the curve (AUC) was 0.84 (0.80–0.88). The MUAC cutoff for overweight/obesity was ?27 cm with a sensitivity of 83.9% and specificity of 64.9%, and AUC was 0.78 (0.75–0.82). Conclusion: The MUAC of the participants increased as the BMI of the participants increased. MUAC cutoff was determined using the ROC curve for underweight and overweight/obesity among elderly persons.
ABSTRACT
Screen-based media usage among young people is blooming rapidly due to technological and digital revolution. We conducted community-based cross-sectional study to determine the prevalence of excess screen time and its association with sociodemographic and behavioral patterns in a rural block of Haryana, India. Asemi-structured interview schedule was administered by trained physicians to ascertain screen time in a typical day and various socioeconomic and behavioral factors among a random sample of 860 young men aged 18–24 years. The prevalence of excess screen time was 61.8% (95% confidence interval [CI] 58.4–65.1). It was significantly associated with education (adjusted odds ratio [AOR] 1.7, 95% CI 1.1–2.6) and occupation (AOR 2.2, 95% CI 1.2–3.9) of the father and their sleep duration of ?8 h (AOR 1.6, 95% CI 1.2–2.3). Limiting the screen time as per international standards and behavioral interventions are needed for this young population.