Conectar
To investigate associations between body composition indices and metabolic status among normal-weight adults.
Cross-sectional study using data from the Tehran Lipid and Glucose Study (phaseVII: 2019–2021).
Primary care and community health services in an urban Tehran population.
1298 adults (40.5% men, 59.5% women), aged 18–80years, body mass index (BMI) 18.5–24.9 kg/m². Exclusions: known diabetes, cardiovascular disease, kidney failure, malignancy, pregnancy or lactation, diuretic or glucocorticoid use. Participants were classified as metabolically healthy normal weight (MHNW) or unhealthy (MUHNW).
The primary outcome was the association between body composition and anthropometric indices with metabolic status. The secondary outcome was identification of the strongest predictors of MUHNW. Body composition was assessed by bioelectrical impedance analysis to obtain fat mass (FM), body fat percentage (BFP), skeletal muscle mass percentage (SMM%), fat mass index (FMI), fat-free mass index, skeletal muscle indices and the fat-to-muscle mass ratio (FMR). Anthropometric measures included waist circumference (WC) and waist-to-hip ratio (WHR). Associations were examined using logistic regression adjusted for age, smoking and physical activity.
Mean age: 37.5±12.8 y; MUHNW participants were older than MHNW (44.5±13.2 vs 35.8±12.1 years, p
BMI, WC, WHR and body fat indices were positively associated with metabolically unhealthy status among normal-weight adults of both sexes. WHR was the strongest predictor, highlighting its value for identifying at-risk individuals where advanced body composition tools are unavailable.
This study aimed to use wearable technology to predict the sleep quality of family caregivers of people with dementia among underrepresented groups. Caregivers of people with dementia often experience high levels of stress and poor sleep, and those from underrepresented communities face additional burdens, such as language barriers and cultural adaptation challenges. Participants, consisting of 29 dementia caregivers from underrepresented populations, wore smartwatches that tracked various physiological and behavioral markers, including stress level, heart rate, steps taken, sleep duration and stages, and overall daily wellness. The study spanned 529 days and analyzed data using 70 features. Three machine learning algorithms—random forest, k nearest neighbor, and XGBoost classifiers—were developed for this purpose. The random forest classifier was shown to be the most effective, boasting an area under the curve of 0.86, an F1 score of 0.87, and a precision of 0.84. Key findings revealed that factors such as wake-up stress, wake-up heart rate, sedentary seconds, total distance traveled, and sleep duration significantly correlated with the caregivers' sleep quality. This research highlights the potential of wearable technology in assessing and predicting sleep quality, offering a pathway to creating targeted support measures for dementia caregivers from underserved groups. The study suggests that such technology can be instrumental in enhancing the well-being of these caregivers across diverse populations. 
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