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.
Microsoft Research, 13 Shenkar Street, Herzeliya, 4672513, Israel; Faculty of Industrial Engineering and Management, Technion - Israel Institute of Technology, Haifa, 3200003, Israel.
J Psychiatr Res. 2022 Jan;145:276-283. doi: 10.1016/j.jpsychires.2020.11.010. Epub 2020 Nov 9.
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.
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.
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).
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.
大多数精神疾病患者在发病后近十年内都未接受治疗。在线心理健康筛查反映了一种旨在缩短寻求帮助的滞后时间的机制,但在自然环境中,对筛查工具的有效性的研究有限。
我们研究了通过 Bing 搜索引擎(n=126060)被引导至心理健康筛查工具的人群。我们评估了工具内容对后来搜索心理健康自我参照、自我诊断、寻求治疗、精神活性药物、自杀意念和自杀意图的影响。网站特征由两名独立评估者评估,以确定屏幕类型和内容。这些内容包括是否存在暗示性诊断、关于可解释性的信息,以及对数字治疗、面对面治疗和危机服务的推荐。
使用机器学习模型,结果表明屏幕内容预测了与心理健康自我参照(AUC=0.73)、心理健康自我诊断(AUC=0.69)、心理健康治疗寻求(AUC=0.61)、精神活性药物(AUC=0.55)、自杀意念(AUC=0.58)和自杀意图(AUC=0.60)的后续搜索。Cox 比例风险模型表明,使用有面对面治疗推荐的工具的个体随后更有可能搜索主动结束生命的方法(HR=1.727,p=0.007)。
在线筛查可能会影响寻求帮助的行为、自杀意念和自杀意图。有面对面治疗推荐的网站可能会使患者面临更大的主动自杀意图风险。需要使用大规模随机对照试验进行进一步评估。