Development of a Recommendation Engine to University Student Mental Health Support Aligned With Stepped Care: Longitudinal Cohort Study.

IF 6 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Pedro Velmovitsky, Charles Keown-Stoneman, Kaylen J Pfisterer, Julia Hews-Girard, Joseph Saliba, Shumit Saha, Scott Patten, Nathan King, Anne Duffy, Quynh Pham
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引用次数: 0

Abstract

Background: Mental health challenges are prevalent among Canadian higher education students, with significant rates of depression and anxiety often going untreated due to reduced early detection, stigmatizing beliefs, and practical barriers. The U-Flourish longitudinal electronic survey study launched in 2018 engages new cohorts of incoming undergraduate students and repeatedly collects data about mental health and well-being and access to support.

Objective: U-Flourish survey data provide a unique opportunity to train evidence-based prediction risk models and a personalized recommendation engine to signpost students to indicated mental health support based on their own data.

Methods: Two approaches were integrated in developing the risk prediction models and recommendation engine: (1) clinically defined rules by experts in the field to detect current and predict the risk of future anxiety and depression and to signpost students to appropriate care using a stepped care approach and based on clinical factors (ie, self-harm and suicidal thoughts, symptom levels, and lifetime history); and (2) machine learning models, trained with additional data including family history, early adversity, and stress indicators, to predict future risks of clinically significant depression (9-item Patient Health Questionnaire) and anxiety (7-item Generalized Anxiety Disorder questionnaire). Models were created using the XGBoost algorithm and a 70:30 ratio for training and testing with 10-fold cross-validation.

Results: In total, 27.5% of students at entry to university from 2018 to 2023 were identified as having potentially clinically significant levels of anxiety and depression and signposted to university mental health services based on the clinically defined rules. Optimizing thresholds to reduce false negatives, the machine learning models predicted anxiety and depression over the year in students screening negative at baseline with accuracy comparable with reported clinical screening as evidenced by sensitivity ≥90% for all models trained. Models had high negative predictive value (≥89%), balanced against low specificity. Individuals identified at risk for anxiety or depression were signposted primarily to self-guided resources supporting proactive prevention. Model findings also demonstrated that abbreviated screens (2-item Patient Health Questionnaire [PHQ-2] and 2-item Generalized Anxiety Disorder Questionnaire [GAD-2]), with potential to reduce respondent burden and improve adherence, can be used without compromising sensitivity. Indeed, PHQ-2 displayed a 90% sensitivity and GAD-2 displayed a 92% sensitivity. Shapley additive explanations analyses revealed other predictive factors including childhood trauma, family history of mental illness, and functional impairment associated with reported depression and anxiety symptoms.

Conclusions: The risk prediction models and recommendation engine's dual approach rationalize support allocation and promote targeted early intervention and prevention, potentially improving capacity to address the increasing burden on university mental health services. Future directions include further refinement based on a larger harmonized and enriched dataset, independent validation, and implementation studies to estimate the complex factors that influence uptake, reach to services, and acceptability across more diverse student users.

从数据到护理:一个推荐引擎的开发,以大学生心理健康支持与阶梯护理相一致。
背景:心理健康挑战在加拿大高等教育学生中普遍存在,由于早期发现减少、污名化的信念和实际障碍,抑郁症和焦虑症往往得不到治疗的比例很高。u - thrive纵向电子调查研究于2018年启动,吸引了新的本科新生群体,并反复收集有关心理健康和福祉以及获得支持的数据。目的:u - thrive调查数据提供了一个独特的机会来训练基于证据的预测风险模型和个性化推荐引擎,根据学生自己的数据为他们提供指示性的心理健康支持。方法:在开发风险预测模型和推荐引擎时,采用并整合了两种方法:(i)由该领域的专家制定临床定义的规则,以检测当前和预测未来的焦虑和抑郁风险,并根据临床因素(即自残和自杀念头、症状水平、一生历史),使用阶梯式护理方法为学生提供适当的护理指引;(ii)机器学习(ML)模型,使用包括家族史、早期逆境和压力指标在内的额外数据进行训练,以预测临床显著抑郁症(PHQ-9)和焦虑症(GAD-7)的未来风险。使用XGBoost算法创建模型,并以70:30的比例进行训练和测试,并进行10倍交叉验证。结果:在2018-2023年进入大学的学生中,27.5%的学生被确定为具有潜在的临床显著水平的焦虑和抑郁,并根据临床定义的规则向大学心理健康服务机构通报。优化阈值以减少假阴性,ML模型预测在基线时筛选阴性学生的焦虑和抑郁,其准确性与报告的临床筛查相当(所有训练模型的灵敏度等于或高于90%)。模型具有较高的阴性预测值(89%或以上),与低特异性相平衡。被确定有焦虑或抑郁风险的个体主要被标记为支持主动预防的自我指导资源。模型研究结果还表明,缩短筛选(PHQ-2、GAD-2)可以在不影响灵敏度的情况下使用,具有减轻应答者负担和提高依从性的潜力。事实上,PHQ-2的灵敏度为90%,GAD-2的灵敏度为92%。SHAP分析还揭示了其他预测因素,包括童年创伤、精神疾病家族史以及与报告的抑郁和焦虑症状相关的功能障碍。结论:风险预测模型和推荐引擎的双重方法合理化了支持分配,促进了有针对性的早期干预和预防,有可能提高应对日益增加的大学心理卫生服务负担的能力。未来的方向包括基于更大的统一和丰富的数据集的进一步改进,独立验证和实施研究,以估计影响更多不同学生用户的吸收、服务范围和可接受性的复杂因素。临床试验:
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
14.40
自引率
5.40%
发文量
654
审稿时长
1 months
期刊介绍: The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.
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