July 17, 2020Open Access

A Machine Learning Approach to Understanding Patterns of Engagement With Internet-Delivered Mental Health Interventions

Key Points

Key points are not available for this paper at this time.

Abstract

The findings of this study may facilitate tailoring interventions according to specific subtypes of engagement for individuals with depression and anxiety. Informing clinical decision needs of supporters may be a route to successful adoption of machine learning insights, thus improving clinical outcomes overall.

Demander à l'IA

Bookmark

View Full Paper

Cite This Study

Chien et al. (Fri,) studied this question.

synapsesocial.com/papers/69d8fdff7e3358c846d17d7d https://doi.org/https://doi.org/10.1001/jamanetworkopen.2020.10791

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1A Guide to Treatments that Work2007 · 1,087 citations
2Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 20102013 · 6,078 citations
3Integrative analysis using coupled latent variable models for individualizing prognoses2016 · 20 citations
4Internet-based vs. face-to-face cognitive behavior therapy for psychiatric and somatic disorders: an updated systematic review and meta-analysis2017 · 1,319 citations
5Computer-based psychological treatments for depression: A systematic review and meta-analysis2012 · 1,550 citations

Demander à l'IA

Bookmark

View Full Paper