A co-segmentation algorithm to predict emotional stress from passively sensed mHealth data

TL;DR

This paper introduces a novel co-segmentation algorithm that identifies emotionally stressful states from passively sensed data by detecting change points and segment-specific associations, improving stress detection accuracy in patients with mood disorders.

Contribution

The paper presents a new data-driven segmentation approach that captures short-term, time-varying relationships between passive and active variables for stress prediction.

Findings

Segmentation improves stress detection accuracy.

Method outperforms traditional global ML approaches.

Effective in real patient data from ALACRITY study.

Abstract

We develop a data-driven co-segmentation algorithm of passively sensed and self-reported active variables collected through smartphones to identify emotionally stressful states in middle-aged and older patients with mood disorders undergoing therapy, some of whom also have chronic pain. Our method leverages the association between the different types of time series. These data are typically non-stationary, with meaningful associations often occurring only over short time windows. Traditional machine learning (ML) methods, when applied globally on the entire time series, often fail to capture these time-varying local patterns. Our approach first segments the passive sensing variables by detecting their change points, then examines segment-specific associations with the active variable to identify co-segmented periods that exhibit distinct relationships between stress and passively sensed measures. We then use these periods to predict future emotional stress states using standard ML methods. By shifting the unit of analysis from individual time points to data-driven segments of time and allowing for different associations in different segments, our algorithm helps detect patterns that only exist within short-time windows. We apply our method to detect periods of stress in patient data collected during ALACRITY Phase I study. Our findings indicate that the data-driven segmentation algorithm identifies stress periods more accurately than traditional ML methods that do not incorporate segmentation.