Machine learning to match depression and anxiety symptom clusters with online problem-solving therapy
Machine Learning-based Classification of Symptom Clusters and Matched Online Cognitive Behavior Intervention for Depression Symptom and Anxiety Symptom
This project will test whether grouping depression and anxiety symptoms with machine learning and delivering a matched online problem-solving therapy helps adults aged 18–64 with moderate to severe symptoms feel better than usual care.
Quick facts
| Phase | Not applicable |
|---|---|
| Study type | Interventional |
| Enrollment | 380 (estimated) |
| Ages | 18 Years to 64 Years |
| Sex | All |
| Sponsor | Wuhan Mental Health Centre Academic / other |
| Locations | 1 site (Wuhan) |
| Trial ID | NCT06350201 on ClinicalTrials.gov |
What this trial studies
Researchers will use cross-sectional symptom data and a hierarchical clustering algorithm to define symptom clusters for depression and anxiety. They will develop a symptom-matched online intervention program based on problem-solving therapy. Eligible adults (PHQ-9 ≥10 and/or GAD-7 ≥8, ages 18–64) will be allocated to the personalized online program or a control/usual-care group to compare outcomes. The trial excludes people currently in psychological therapy, those with recent suicide attempts, bipolar disorder, psychosis, substance dependence, or unstable medical illness.
Who should consider this trial
Good fit: Adults aged 18–64 with clinically significant depressive or anxiety symptoms (PHQ-9 ≥10 and/or GAD-7 ≥8) who are not currently receiving psychological therapy and do not have exclusionary psychiatric or medical conditions are ideal candidates.
Not a fit: People who are acutely suicidal, have bipolar disorder or psychosis, are substance dependent, have unstable medical illnesses, or are currently in therapy are excluded and unlikely to benefit from this intervention.
Why it matters
Potential benefit: If successful, this approach could deliver more precise, standardized online emotional care and improve symptom reduction by matching therapy content to individual symptom patterns.
How similar studies have performed: Internet-delivered CBT has shown benefit in many studies, but using machine learning to create symptom-matched psychological treatments is relatively novel and has limited large-scale evidence so far.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Aged between 18 and 64 years. PHQ-9 ≥10 and/or GAD-7 ≥8 at baseline assessment defined as the threshold for caseness. Exclusion Criteria: * People will be excluded if they meet any of the following criteria: 1. They are receiving psychological therapy during an interview for any mental health issue; 2. currently acutely suicidal or have attempted suicide in the past 2 months, as indicated by PHQ-9 item 9; 3. cognitively impaired or diagnosed with bipolar disorder or psychosis or experiencing psychotic symptoms; d) dependent on alcohol or drugs; e) living with an unstable or acute medical illness that would interfere with trial participation.
Where this trial is running
Wuhan
- Renmin Hospital of Wuhan University — Wuhan, China (Recruiting)
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.