globem-user-analysis

This Skill analyzes smartphone sensor data for user behavior and mental health trends by processing activity, sleep, communication, and phone usage inputs, correlating them with mental health surveys for individual college student participants.

The best way to identify correlations between physical activity and mental health surveys is to process smartphone sensor data to track long-term behavioral trends. This Skill maps daily activities directly to mental health outcomes for individual users.

Yes, you can analyze individual participants in the GLOBEM dataset by running this Skill with a specific user ID. It focuses on extracting behavioral patterns and mental health insights for that single participant, such as college student INS-W_002.

To start analyzing a specific user ID in a sensor dataset, run the Skill with the target participant's identifier. The tool will immediately process the available smartphone sensor data to generate comprehensive behavioral and mental health insights for that individual.

Yes, this approach handles long-term trend analysis for college student mental health by examining continuous smartphone sensor data. It focuses on identifying sustained behavioral patterns and correlating them with mental health survey results over extended periods.

Behavioral pattern analysis requires smartphone sensor data including activity, sleep, communication, and phone usage metrics. The Skill uses these data types to identify daily behavioral trends and establish correlations with mental health survey outcomes.