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Self-Administered Interventions Based on Natural Language Processing Models for Reducing Depressive and Anxiety
David Villarreal-Zegarra1,2, C Mahony Reategui-Rivera2, Jackeline García-Serna1
1Instituto Peruano de Orientación Psicológica, Lima, Peru.
JMIR Mental Health
|August 21, 2024
Summary
Self-administered natural language processing (NLP) interventions show promise in reducing depression and anxiety symptoms. Further high-quality trials are needed due to low certainty of evidence.
Area of Science:
- Mental Health Technology
- Computational Linguistics
- Digital Therapeutics
Background:
- Natural Language Processing (NLP) enhances self-administered interventions for anxiety and depression.
- Generative Artificial Intelligence (AI) models show promise, but evidence of effectiveness is limited.
Purpose of the Study:
- To determine if NLP-based self-administered interventions reduce depressive and anxiety symptoms.
- To synthesize existing evidence through systematic review and meta-analysis.
Main Methods:
- Systematic review and meta-analysis of randomized controlled trials and quasi-experimental studies.
- Searched multiple databases (Web of Science, Scopus, MEDLINE, etc.) up to November 2023.
- Analyzed data using standardized mean differences (SMDs) and random effects models.
Main Results:
- NLP interventions significantly reduced depressive (SMD 0.819) and anxiety (SMD 0.272) symptoms.
- AI-based and rule-based NLP models demonstrated effectiveness in symptom reduction.
- Overall certainty of evidence was very low due to high risk of bias and heterogeneity.
Conclusions:
- NLP-based interventions are effective for alleviating depressive and anxiety symptoms.
- These interventions can improve mental healthcare accessibility and reduce costs.
- Further high-quality research is essential to confirm findings and assess implementation.
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