Identifying Body Composition Measures That Correlate with Self-Compassion and Social Support Within The Lived Experiences Measured Using Rings Study (LEMURS)

2026-02-08

AI Summary

This study investigates physiological biomarkers reflective of psychosocial well-being, focusing on the associations between body composition metrics and dimensions of self-compassion and social support among college students. By integrating seasonal body composition data collected via the InBody770 system with psychometric measures from the LEMURS study, the authors employ canonical correlation analysis (CCA) to examine the interrelationships between these domains. Notably, this work introduces trunk and leg impedance—derived body composition indicators—as novel potential biomarkers of psychosocial well-being, revealing stable moderate correlations with facets such as mindfulness, over-identification, and both emotional and instrumental social support. These findings offer a new empirical foundation and scalable perspective for modeling mental health through objective physiological measures.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Security and Privacy: Large-scale security measurements

Abstract

This study explores the relationship between body composition metrics, self-compassion, and social support among college students. Using seasonal body composition data from the InBody770 system and psychometric measures from the Lived Experiences Measured Using Rings Study (LEMURS) (n=156; freshmen=66, sophomores=90), Canonical Correlation Analysis (CCA) reveals body composition metrics exhibit moderate correlation with self-compassion and social support. Certain physiological and psychological features showed strong and consistent relationships with well-being across the academic year. Trunk and leg impedance stood out as key physiological indicators, while \textit{mindfulness}, \textit{over-identification}, \textit{affectionate support}, and \textit{tangible support} emerged as recurring psychological and social correlates. This demonstrates that body composition metrics can serve as valuable biomarkers for indicating self-perceived psychosocial well-being, offering insights for future research on scalable mental health modeling and intervention strategies.

Problem

• body composition
• self-compassion
• social support
• psychosocial well-being
• biomarkers

Innovation

• body composition
• self-compassion
• social support
• Canonical Correlation Analysis
• biomarkers