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ddr-globem-analysis

Analyze GLOBEM participant data and generate QA pairs on mental health changes.

128|12|Updated May 21, 2025
Search Tags:#mental health#behavioral analysis#longitudinal data#GLOBEM dataset#EMA analysis
One-click install
npx skills add https://github.com/zjunlp/DataMind --skill ddr-globem-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ddr-globem-analysis
Source: https://github.com/zjunlp/DataMind/tree/main/datacope/report_task/skill/checklist/globem-checklist-skills/globem-checklist-skill-2-1/ddr-globem-analysis
Command: npx skills add https://github.com/zjunlp/DataMind --skill ddr-globem-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill analyzes specific participant's longitudinal passive-sensing and psychological data from the GLOBEM digital depression research dataset, providing insights into mental health and behavioral changes.

Core Features & Use Cases

  • Data Analysis: Analyze user's mental health or behavioral data from wearables/smartphones.
  • QA Generation: Generate QA pairs about behavioral/psychological changes over time.
  • EMA Analysis: Analyze EMA (Experience Sampling Method) data for mood assessment.
  • Use Case: Analyze a participant's data to identify patterns in mental health or behavior over time.

Quick Start

Analyze the mental health data for participant 'INS-W_011' using the ddr-globem-analysis skill.

Frequently Asked Questions about ddr-globem-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze longitudinal passive-sensing data for mental health changes?▼

To analyze longitudinal passive-sensing data for mental health changes, you can use this Skill to process the GLOBEM digital depression research dataset. It generates QA pairs that provide insights into a specific participant's behavioral and psychological patterns over time.

What is EMA analysis in digital depression research?▼

EMA (Experience Sampling Method) analysis in digital depression research evaluates mood assessments collected over time. This Skill analyzes EMA data alongside passive-sensing inputs to identify patterns and generate insights regarding a participant's psychological changes.

How do I generate QA pairs from wearable behavioral data?▼

You can generate QA pairs from wearable behavioral data by feeding the GLOBEM dataset into this Skill. It processes the longitudinal passive-sensing and psychological inputs to produce question and answer pairs detailing behavioral changes over time.

Can I analyze a specific participant's data in the GLOBEM dataset?▼

Yes, you can analyze a specific participant's data in the GLOBEM dataset. By providing a participant identifier like 'INS-W_011', the Skill analyzes their unique longitudinal passive-sensing and psychological records to identify behavioral patterns.

Does this Skill require passive-sensing and psychological data to work?▼

Yes, this Skill requires both passive-sensing and psychological data to function properly. It relies on these multiple data sources from the GLOBEM dataset to accurately analyze mental health and behavioral changes over time.

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