Personalized mediation effect model for heterogeneous mobile health data on stress

Music City Center 

Pregnancy is a significant period in a woman's life, often accompanied by both mental and physical stressors. Identifying mediators in these associations is crucial for early intervention and improved maternal health outcomes. The growing use of wearable devices enables continuous monitoring of heart rate variability (HRV), sleep patterns, and physical activity.

This study aims to assess the heterogeneity introduced by individual behavioral patterns in wearable device data. Specifically, our research investigates potential mediators between stress and age (≥30), as well as stress and BMI (≥25), during the second and third trimesters of pregnancy. An individualized mediation effect approach incorporating subgrouping is proposed to identify relevant mediators, including daily step count, deep sleep, REM sleep, and a weekly negative emotions score derived from an EMA questionnaire. Additionally, time-varying mediation models are used to capture dynamic changes in the mediation effects. By integrating these methods, this research aims to enhance our understanding of stress-related health disparities during pregnancy and support the development of more personalized interventions.

mediation effect model

heterogeneous data

individualized model

wearable device

subgroup analysis

mobile health 

Biometrics Section