McIntyre Roger S, Lee Yena, Rong Carola, Rosenblat Joshua D, Brietzke Elisa, Pan Zihang, Park Caroline, Subramaniapillai Mehala, Ragguett Renee-Marie, Mansur Rodrigo B, Lui Leanna M W, Nasri Flora, Gill Hartej, Berriah Said
Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada; Brain and Cognition Discovery Foundation, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Department of Pharmacology, University of Toronto, Toronto, ON, Canada.
Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
J Psychiatr Res. 2021 Mar;135:311-317. doi: 10.1016/j.jpsychires.2021.01.012. Epub 2021 Jan 11.
Ecological momentary assessment (EMA) for mental disorders, using application-based (app) technology capable of passive and ambient data collection, has been insufficiently evaluated and validated with rigorous, adequately-powered, high-quality studies. Herein, we sought to validate the mind.me application for the assessment of depressive symptoms in adults. Adults (ages 18-65) who self-identified as having clinically significant depressive symptoms [i.e. Patient Health Questionnaire 9 (PHQ-9) ≥ 5] utilized the mind.me app-a mobile phone technology that collects data passively and continuously, and is capable of integrating broad multimodal data [e.g., location variance (e.g. GPS), behavioural (e.g. social network activity), and communication data (e.g. SMS texting, phone calls)]. The primary outcome was predictive accuracy (i.e. convergent validity with depressive symptom measurement, as captured by the PHQ-9). 200 subjects were enrolled in the study (mean age 46 ± 12.71). The average PHQ-9 score was 12.8 ± 6.9. The predictive accuracy of the mind.me app was 0.91 ± 0.06. The sensitivity was 0.98 and the specificity was 0.93. The mind.me app was rated by 200 users as highly usable and informative to their illness. The mind.me app exhibits robust predictive accuracy in detecting depressive symptoms in adults with clinically relevant depressive symptoms. The mind.me app more specifically demonstrates convergence with the PHQ-9.
对于精神障碍的生态瞬时评估(EMA),使用能够进行被动和环境数据收集的基于应用程序(app)的技术,尚未通过严格、有足够效力的高质量研究进行充分评估和验证。在此,我们试图验证mind.me应用程序在评估成年人抑郁症状方面的有效性。自我认定有临床显著抑郁症状[即患者健康问卷9(PHQ-9)≥5]的成年人(年龄在18 - 65岁之间)使用了mind.me应用程序——一种能够被动且持续收集数据、并能够整合广泛多模态数据[例如,位置变化(如全球定位系统)、行为(如社交网络活动)和通信数据(如短信、电话)]的移动电话技术。主要结果是预测准确性(即与PHQ-9所捕获的抑郁症状测量的收敛效度)。200名受试者参与了该研究(平均年龄46±12.71)。平均PHQ-9得分是12.8±6.9。mind.me应用程序的预测准确性为0.91±0.06。敏感性为0.98,特异性为0.93。200名用户对mind.me应用程序的评价是非常实用且对他们的病情有参考价值。mind.me应用程序在检测有临床相关抑郁症状的成年人的抑郁症状方面表现出强大的预测准确性。更具体地说,mind.me应用程序显示出与PHQ-9的趋同性。