@Article{info:doi/10.2196/36056,
author="Storman, Dawid
and Jemio{\l}o, Pawe{\l}
and Swierz, Mateusz Jan
and Sawiec, Zuzanna
and Antonowicz, Ewa
and Prokop-Dorner, Anna
and Gotfryd-Burzy{\'{n}}ska, Marcelina
and Bala, Malgorzata M",
title="Meeting the Unmet Needs of Individuals With Mental Disorders: Scoping Review on Peer-to-Peer Web-Based Interactions",
journal="JMIR Ment Health",
year="2022",
month="Dec",
day="5",
volume="9",
number="12",
pages="e36056",
keywords="scoping review; peer-to-peer interactions; mental disorders; web-based interactions",
abstract="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 $\phi$ 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; $\phi$=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. ",
issn="2368-7959",
doi="10.2196/36056",
url="https://mental.jmir.org/2022/12/e36056",
url="https://doi.org/10.2196/36056",
url="http://www.ncbi.nlm.nih.gov/pubmed/36469366"
}