%0 Journal Article
%@ 2368-7959
%I JMIR Publications
%V 9
%N 12
%P e36056
%T Meeting the Unmet Needs of Individuals With Mental Disorders: Scoping Review on Peer-to-Peer Web-Based Interactions
%A Storman,Dawid
%A Jemioło,Paweł
%A Swierz,Mateusz Jan
%A Sawiec,Zuzanna
%A Antonowicz,Ewa
%A Prokop-Dorner,Anna
%A Gotfryd-Burzyńska,Marcelina
%A Bala,Malgorzata M
%+ Chair of Epidemiology and Preventive Medicine, Department of Hygiene and Dietetics, Jagiellonian University Medical College, Kopernika 7a, Krakow, 31-034, Poland, 48 502846363, dawid.storman@doctoral.uj.edu.pl
%K scoping review
%K peer-to-peer interactions
%K mental disorders
%K web-based interactions
%D 2022
%7 5.12.2022
%9 Review
%J JMIR Ment Health
%G English
%X 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. 
%M 36469366
%R 10.2196/36056
%U https://mental.jmir.org/2022/12/e36056
%U https://doi.org/10.2196/36056
%U http://www.ncbi.nlm.nih.gov/pubmed/36469366