# Identifying Predictors of Gambling Episodes and Craving Using Ecological Momentary Assessment and Smartwatch-Based Physiological Measures: Protocol for a Longitudinal Observational Study

## TL;DR

This study uses smartwatches and surveys to identify factors that predict gambling episodes, aiming to improve digital interventions for problem gambling.

## Contribution

The study introduces a novel approach combining ecological momentary assessments and smartwatch data to predict gambling episodes.

## Key findings

- Data collection from 109 at-risk gamblers across Switzerland and Korea was completed in April 2025.
- Machine learning and multilevel modeling will be used to identify predictors of gambling episodes.
- Findings could improve digital interventions by enabling just-in-time tailored support.

## Abstract

Like other addictive behaviors, problem gambling is often chronic and relapsing. While digital interventions offer low-threshold treatment and support, their effectiveness is often limited by small effect sizes, low adherence, and high dropout rates. Progress in digital technology has enabled the development of ecological momentary interventions (EMIs), which provide just-in-time support tailored to users’ needs. However, EMIs for addictive behaviors have hardly been developed so far.

This study aimed to explore and identify relevant predictors of gambling episodes assessed by ecological momentary assessments and physiological smartwatch (Apple Watch) data, which in turn may be used for the further development of EMI-based interventions.

A total of 109 at-risk gamblers were recruited online in a collaborative study between Switzerland and Korea. Over a period of 28 days, participants were asked to complete brief ecological momentary assessment surveys 3 times a day (morning, afternoon, and evening) asking about their gambling behavior and their levels of craving intensity, sleep quality, physical activity, boredom, vitality, depression, and anxiety. They were instructed to wear an Apple Watch that continuously and passively recorded several physiological indicators (eg, heart rate [variability], sleep metrics, and physical activity). Machine learning techniques and multilevel modeling approaches will be used to develop prediction models for gambling episodes and to identify relevant predictors.

Data collection has been completed since April 2025. In total, 109 participants have been enrolled in both countries (52 in Switzerland, 57 in Korea), and datasets are currently being prepared for analysis. The collected data are expected to enable the development of prediction models for gambling episodes.

Incorporating the relevant predictors found in this study into digital intervention programs and providing just-in-time individually tailored intervention elements could improve program engagement and effectiveness. The approach used in this study is transferable to other digital interventions for addictive behaviors and holds promise to exploit their potential.