ScholarWorks@성균관대학교

초록

Objective Adolescence is a critical developmental stage during which mental health vulnerabilities often emerge. Traditional self-report methods are insufficient to capture the complexity of emotional and physiological responses, underscoring the need for data-driven, personalized mental health strategies. This study aimed to develop and validate a structured multimodal data collection system for adolescents to support the future advancement of precision mental health care. Methods This study was conducted as the baseline phase of a longitudinal panel study designed to construct and validate a structured multimodal dataset for adolescent mental health research. A total of 74 adolescents aged 11-15 years from schools and community facilities in Korea was selected through convenience sampling. Multimodal data were collected by integrating six data types: self-reported surveys, electroencephalography (EEG), heart rate variability (HRV), genotyping, microbiome data, and video-based psychological counseling. Data collection was standardized through a three-phase protocol (pre-, on-site, and post-assessment), and participant privacy was protected via pseudonymization based on international standards. Variables were systematically labeled and structured to enable cross-modality analysis. Statistical analyses, including correlation and descriptive statistics, were performed to examine preliminary relationships across modalities. Results The study successfully constructed a comprehensive dataset encompassing biological and psychosocial indicators from 74 adolescents. Preliminary analysis revealed statistically significant associations between survey-based BMI and both genomic data (rho = 0.30, p < 0.01) and microbiome-based obesity indicators (rho = 0.27, p < 0.05), whereas other psychological constructs (e.g., stress, resilience) showed non-significant cross-modal correlations. Conclusions This study presents a replicable framework for collecting rich, multimodal data from adolescents in real-world settings. By enabling integrative analysis of biological and psychosocial variables, the dataset lays the groundwork for personalized mental health prediction and intervention strategies. Future research should expand longitudinally and optimize context alignment to improve predictive precision and clinical utility.

키워드