TY  - JOUR
AU  - Storman, Dawid
AU  - Jemioło, Paweł
AU  - Swierz, Mateusz Jan
AU  - Sawiec, Zuzanna
AU  - Antonowicz, Ewa
AU  - Prokop-Dorner, Anna
AU  - Gotfryd-Burzyńska, Marcelina
AU  - Bala, Malgorzata M
PY  - 2022
DA  - 2022/12/5
TI  - Meeting the Unmet Needs of Individuals With Mental Disorders: Scoping Review on Peer-to-Peer Web-Based Interactions
JO  - JMIR Ment Health
SP  - e36056
VL  - 9
IS  - 12
KW  - scoping review
KW  - peer-to-peer interactions
KW  - mental disorders
KW  - web-based interactions
AB  - Background: An increasing number of online support groups are providing advice and information on topics related to mental health. Objective: This study aimed to investigate the needs that internet users meet through peer-to-peer interactions. Methods: A search of 4 databases was performed until August 15, 2022. Qualitative or mixed methods (ie, qualitative and quantitative) studies investigating interactions among internet users with mental disorders were included. The φ coefficient was used and machine learning techniques were applied to investigate the associations between the type of mental disorders and web-based interactions linked to seeking help or support. Results: Of the 13,098 identified records, 44 studies (analyzed in 54 study-disorder pairs) that assessed 82,091 users and 293,103 posts were included. The most frequent interactions were noted for people with eating disorders (14/54, 26%), depression (12/54, 22%), and psychoactive substance use disorders (9/54, 17%). We grouped interactions between users into 42 codes, with the empathy or compassion code being the most common (41/54, 76%). The most frequently coexisting codes were request for information and network (35 times; φ=0.5; P<.001). The algorithms that provided the best accuracy in classifying disorders by interactions were decision trees (44/54, 81%) and logistic regression (40/54, 74%). The included studies were of moderate quality. Conclusions: People with mental disorders mostly use the internet to seek support, find answers to their questions, and chat. The results of this analysis should be interpreted as a proof of concept. More data on web-based interactions among these people might help apply machine learning methods to develop a tool that might facilitate screening or even support mental health assessment. 
SN  - 2368-7959
UR  - https://mental.jmir.org/2022/12/e36056
UR  - https://doi.org/10.2196/36056
UR  - http://www.ncbi.nlm.nih.gov/pubmed/36469366
DO  - 10.2196/36056
ID  - info:doi/10.2196/36056
ER  -