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Villarreal-Zegarra D, García-Serna J, Quispe-Callo G, Lázaro-Cruz G, Centeno-Terrazas G, Galvez-Arevalo R, Escobar-Agreda S, Dominguez-Rodriguez A, Reategui-Rivera CM, Finkelstein J
Self-Administered Interventions Based on Natural Language Processing Models for Reducing Depressive and Anxiety Symptoms: Systematic Review and Meta-Analysis
Self-administered interventions based on natural language processing models for reducing depressive and anxious symptoms: Systematic review and meta-analysis
David Villarreal-Zegarra;
Jackeline García-Serna;
Gleni Quispe-Callo;
Gabriel Lázaro-Cruz;
Gianfranco Centeno-Terrazas;
Ricardo Galvez-Arevalo;
Stefan Escobar-Agreda;
Alejandro Dominguez-Rodriguez;
C Mahony Reategui-Rivera;
Joseph Finkelstein
ABSTRACT
Background:
The introduction of Natural Language Processing (NLP) technologies has significantly enhanced the potential of self-directed interventions for treating anxiety and depression by improving human-computer interactions. Despite these advancements, particularly in AI and Large Language Models (LLMs), robust evidence validating their effectiveness remains sparse.
Objective:
To determine whether interventions based on NLP models can reduce depressive and anxiety symptoms.
Methods:
Our study was a systematic review, and the protocol was registered in PROSPERO (CRD42023472120). The databases we used for the systematic review are Web of Science, SCOPUS, MEDLINE (via PubMed), PsycINFO (via EBSCO), IEEE Xplore, EMBASE (via EBSCO), and Cochrane Library. The quality of the included studies was assessed using the JBI Critical Appraisal Tools.
Results:
21 articles were selected for review, and 16 were included in the meta-analysis for each outcome. The overall meta-analysis showed that self-administered interventions based on NLP models were significantly more effective in reducing depressive symptoms (SMD=0.819; 95%CI: 0.389-1.250; p<0.001) and anxiety symptoms (SMD=0.272; 95% CI: 0.116-0.428; p=0.001) compared with various control conditions. In subgroup analysis, AI-based NLP was shown to be effective in reducing depressive (SMD=1.059 [0.520 to 1.597]; p<0.001) and anxiety symptoms (SMD=0.302 [0.073 to 0.532]; p=0.010) compared with pooled control conditions. Also, NLP-based interventions overall outperform psychoeducation and bibliotherapy in reducing both depressive (SMD=1.481 [0.368 to 2.594]; p=0.009) and anxiety symptoms (SMD=0.561 [0.195 to 0.927]; p=0.003). In addition, these interventions are more effective than waitlist or no intervention in reducing anxious symptoms (SMD=0.196 [0.042 to 0.351]; p=0.013).
Conclusions:
Our findings support the usefulness of self-applied NLP-based interventions in alleviating widely prevalent mental health problems such as depressive and anxious symptoms. Clinical Trial: Protocol was registered in PROSPERO (CRD42023472120)
Citation
Please cite as:
Villarreal-Zegarra D, García-Serna J, Quispe-Callo G, Lázaro-Cruz G, Centeno-Terrazas G, Galvez-Arevalo R, Escobar-Agreda S, Dominguez-Rodriguez A, Reategui-Rivera CM, Finkelstein J
Self-Administered Interventions Based on Natural Language Processing Models for Reducing Depressive and Anxiety Symptoms: Systematic Review and Meta-Analysis