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Machine Learning-based Classification of Symptom Clusters and Online CBT

NCT06350201 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 380

Last updated 2026-05-12

No results posted yet for this study

Summary

To breakthrough the bottleneck identified, we will conduct a cross-sectional study to develop a symptom clustering model for depression and anxiety. A wide range of statistical methods as well as machine learning approaches were explored, and a cohesive hierarchical clustering algorithm will be used. After developing the model, a symptom-matched intervention program based on problem solving therapy will be formulated. We are supposed to examine whether its use for personalizing symptom-matched psychological treatment can lead to improved patient outcomes, compared with usual care. This project is expected to provide a new and precise method for the emotion management, which will provide a standardized intervention pathway combining screening with treatment for the management of depression symptom and anxiety symptom. A preciser intervention matched to individual symptoms may provide important insight in improving patient outcome as well as a standardized mood management pathway targeting to the early detection and intervention for community residents.

Conditions

  • Depression and Anxiety Symptom

Interventions

BEHAVIORAL

problem solving therapy

Problem-solving therapy-based holistic emotion management interventions matched to individual symptoms

OTHER

control group

Routine psychological care and guidance on mood management

Sponsors & Collaborators

  • National Natural Science Foundation of China

    collaborator OTHER_GOV

  • Wuhan Mental Health Centre

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
SUPPORTIVE_CARE
Masking
DOUBLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Max Age
64 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-09-01
Primary Completion
2026-05-01
Completion
2026-12-01

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06350201 on ClinicalTrials.gov