# Wearables and behavioral coding show promise for measuring and predicting severe emotional outbursts in children

## TL;DR

Wearables and behavioral data can help predict severe emotional outbursts in children, offering new ways to measure and manage emotional dysregulation.

## Contribution

A novel multimodal approach combining EHR analysis and wearable technology to predict and quantify emotional outbursts in children.

## Key findings

- Observed rough behaviors at the start of the day strongly predict later emotional outbursts.
- Wearable sensors showed high tolerability and minimal data loss in children.
- Heart rate, heart rate variability, and skin temperature from wearables may indicate distress and outbursts.

## Abstract

Temper tantrums are common in early childhood. Severe emotional outbursts, however, are transdiagnostic, disruptive, and difficult to measure across settings, highlighting the need for better methods to identify and predict these components of emotion dysregulation. To address major methodological gaps, we propose a multimodal approach combining a retrospective electronic health record (EHR) analysis (Study 1) and a pilot wearable feasibility study (Study 2) to explore new ways of predicting and quantifying emotional outbursts in children enrolled in a therapeutic day program (TDP).

In Study 1, we explored retrospective data collected from the EHR (historical patient data and hourly behavioral observations), trying to understand which variables might predict an outburst. In Study 2, wearable technology was employed to characterize outbursts leveraging free-living data collected during a typical day at a TDP. Moreover, we used these data to assess the future of possible outburst predictions among a clinical sample by analyzing the feasibility of such a technology.

An EHR analysis of 45 patients aged 4–8 years revealed that observed rough behaviors at the beginning of the day were associated with an increased likelihood of subsequent outbursts (p < .001), from 6% for those with zero rough behaviors to 68% for those with two or more such behaviors. Wearable sensor data demonstrated high tolerability (all four children assented each of 3–5 days of participation for 5 h of wear) and minimal data loss (<4%). Case studies of wearable-derived heart rate, heart rate variability, and skin temperature suggested that these factors might serve as promising indicators for detecting distress and outbursts.

Our results suggest that behavioral observation has the potential of predicting outbursts, and that wearable sensors are tolerable and feasible for children to wear. Overall, multiple methodologies should be studied concurrently and may be required to predict outbursts in the future.