Nicholas C Jacobson1, Elad Yom-Tov2, Damien Lekkas3, Michael Heinz4, Lili Liu5, Paul J Barr6. 1. Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, 46 Centerra Parkway, EverGreen Center, Suite 315, Lebanon, NH, 03756, United States; Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Williamson Building, 3rd Floor, 1 Medical Center Drive, Lebanon, NH, 03756, United States; Department of Psychiatry, Geisel School of Medicine, Dartmouth College, 1 Medical Center Drive, Lebanon, NH, 03756, United States; Quantitative Biomedical Sciences Program, Dartmouth College, NH, United States. Electronic address: Nicholas.C.Jacobson@dartmouth.edu. 2. Microsoft Research, 13 Shenkar Street, Herzeliya, 4672513, Israel; Faculty of Industrial Engineering and Management, Technion - Israel Institute of Technology, Haifa, 3200003, Israel. Electronic address: eladyt@microsoft.com. 3. Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, 46 Centerra Parkway, EverGreen Center, Suite 315, Lebanon, NH, 03756, United States; Quantitative Biomedical Sciences Program, Dartmouth College, NH, United States. Electronic address: Damien.Lekkas.GR@dartmouth.edu. 4. Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, 46 Centerra Parkway, EverGreen Center, Suite 315, Lebanon, NH, 03756, United States; Dartmouth-Hitchcock Medical Center, 1 Medical Center Drive, Lebanon, NH, 03756, United States. Electronic address: Michael.V.Heinz@hitchcock.org. 5. Quantitative Biomedical Sciences Program, Dartmouth College, NH, United States. Electronic address: Lili.Liu.GR@dartmouth.edu. 6. Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, 46 Centerra Parkway, EverGreen Center, Suite 315, Lebanon, NH, 03756, United States; The Dartmouth Institute, Geisel School of Medicine, Dartmouth College, Williamson Building, 5th Floor, 1 Medical Center Drive, Lebanon, NH, 03756, United States. Electronic address: Paul.J.Barr@dartmouth.edu.
Abstract
INTRODUCTION: Most people with psychiatric illnesses do not receive treatment for almost a decade after disorder onset. Online mental health screens reflect one mechanism designed to shorten this lag in help-seeking, yet there has been limited research on the effectiveness of screening tools in naturalistic settings. MATERIAL AND METHODS: We examined a cohort of persons directed to a mental health screening tool via the Bing search engine (n = 126,060). We evaluated the impact of tool content on later searches for mental health self-references, self-diagnosis, care seeking, psychoactive medications, suicidal ideation, and suicidal intent. Website characteristics were evaluated by pairs of independent raters to ascertain screen type and content. These included the presence/absence of a suggestive diagnosis, a message on interpretability, as well as referrals to digital treatments, in-person treatments, and crisis services. RESULTS: Using machine learning models, the results suggested that screen content predicted later searches with mental health self-references (AUC = 0·73), mental health self-diagnosis (AUC = 0·69), mental health care seeking (AUC = 0·61), psychoactive medications (AUC = 0·55), suicidal ideation (AUC = 0·58), and suicidal intent (AUC = 0·60). Cox-proportional hazards models suggested individuals utilizing tools with in-person care referral were significantly more likely to subsequently search for methods to actively end their life (HR = 1·727, p = 0·007). DISCUSSION: Online screens may influence help-seeking behavior, suicidal ideation, and suicidal intent. Websites with referrals to in-person treatments could put persons at greater risk of active suicidal intent. Further evaluation using large-scale randomized controlled trials is needed.
INTRODUCTION: Most people with psychiatric illnesses do not receive treatment for almost a decade after disorder onset. Online mental health screens reflect one mechanism designed to shorten this lag in help-seeking, yet there has been limited research on the effectiveness of screening tools in naturalistic settings. MATERIAL AND METHODS: We examined a cohort of persons directed to a mental health screening tool via the Bing search engine (n = 126,060). We evaluated the impact of tool content on later searches for mental health self-references, self-diagnosis, care seeking, psychoactive medications, suicidal ideation, and suicidal intent. Website characteristics were evaluated by pairs of independent raters to ascertain screen type and content. These included the presence/absence of a suggestive diagnosis, a message on interpretability, as well as referrals to digital treatments, in-person treatments, and crisis services. RESULTS: Using machine learning models, the results suggested that screen content predicted later searches with mental health self-references (AUC = 0·73), mental health self-diagnosis (AUC = 0·69), mental health care seeking (AUC = 0·61), psychoactive medications (AUC = 0·55), suicidal ideation (AUC = 0·58), and suicidal intent (AUC = 0·60). Cox-proportional hazards models suggested individuals utilizing tools with in-person care referral were significantly more likely to subsequently search for methods to actively end their life (HR = 1·727, p = 0·007). DISCUSSION: Online screens may influence help-seeking behavior, suicidal ideation, and suicidal intent. Websites with referrals to in-person treatments could put persons at greater risk of active suicidal intent. Further evaluation using large-scale randomized controlled trials is needed.
Authors: Ellen E Fitzsimmons-Craft; Katherine N Balantekin; Dawn M Eichen; Andrea K Graham; Grace E Monterubio; Shiri Sadeh-Sharvit; Neha J Goel; Rachael E Flatt; Kristina Saffran; Anna M Karam; Marie-Laure Firebaugh; Mickey Trockel; C Barr Taylor; Denise E Wilfley Journal: Int J Eat Disord Date: 2019-07-03 Impact factor: 4.861
Authors: Xianglong Xu; Zongyuan Ge; Eric P F Chow; Zhen Yu; David Lee; Jinrong Wu; Jason J Ong; Christopher K Fairley; Lei Zhang Journal: J Clin Med Date: 2022-03-25 Impact factor: 4.241