Abstract：
BACKGROUND: Less than 10% of the individuals seeking behavioral health care receive measurement-based care (MBC). Technology has the potential to implement MBC in a secure and efficient manner. To test this idea, a mobile health (mHealth) platform was developed with the goal of making MBC easier to deliver by clinicians and more accessible to patients within integrated behavioral health care. Data from over 3000 users of the mHealth platform were used to develop an output severity score, a robust screening measure for depression and anxiety.
OBJECTIVE: The aim of this study is to compare severity scores with scores from validated assessments for depression and anxiety and scores from clinician review to evaluate the potential added value of this new measure.
METHODS: The severity score uses patient-reported and passively collected data related to behavioral health on an mHealth platform. An artificial intelligence-derived algorithm was developed that condenses behavioral health data into a single, quantifiable measure for longitudinal tracking of an individual\'s depression and anxiety symptoms. Linear regression and Bland-Altman analyses were used to evaluate the relationships and differences between severity scores and Personal Health Questionnaire-9 (PHQ-9) or Generalized Anxiety Disorder-7 (GAD-7) scores from over 35,000 mHealth platform users. The severity score was also compared with a review by a panel of expert clinicians for a subset of 250 individuals.
RESULTS: Linear regression results showed a strong correlation between the severity score and PHQ-9 (r=0.74; P<.001) and GAD-7 (r=0.80; P<.001) changes. A strong positive correlation was also found between the severity score and expert panel clinical review (r=0.80-0.84; P<.001). However, Bland-Altman analysis and the evaluation of outliers on regression analysis showed that the severity score was significantly different from the PHQ-9.
CONCLUSIONS: Clinicians can reliably use the mHealth severity score as a proxy measure for screening and monitoring behavioral health symptoms longitudinally. The severity score may identify at-risk individuals who are not identified by the PHQ-9. Further research is warranted to evaluate the sensitivity and specificity of the severity score.
OBJECTIVE: The aim of this study is to compare severity scores with scores from validated assessments for depression and anxiety and scores from clinician review to evaluate the potential added value of this new measure.
METHODS: The severity score uses patient-reported and passively collected data related to behavioral health on an mHealth platform. An artificial intelligence-derived algorithm was developed that condenses behavioral health data into a single, quantifiable measure for longitudinal tracking of an individual\'s depression and anxiety symptoms. Linear regression and Bland-Altman analyses were used to evaluate the relationships and differences between severity scores and Personal Health Questionnaire-9 (PHQ-9) or Generalized Anxiety Disorder-7 (GAD-7) scores from over 35,000 mHealth platform users. The severity score was also compared with a review by a panel of expert clinicians for a subset of 250 individuals.
RESULTS: Linear regression results showed a strong correlation between the severity score and PHQ-9 (r=0.74; P<.001) and GAD-7 (r=0.80; P<.001) changes. A strong positive correlation was also found between the severity score and expert panel clinical review (r=0.80-0.84; P<.001). However, Bland-Altman analysis and the evaluation of outliers on regression analysis showed that the severity score was significantly different from the PHQ-9.
CONCLUSIONS: Clinicians can reliably use the mHealth severity score as a proxy measure for screening and monitoring behavioral health symptoms longitudinally. The severity score may identify at-risk individuals who are not identified by the PHQ-9. Further research is warranted to evaluate the sensitivity and specificity of the severity score.
摘要：
背景：不到10%的寻求行为保健的个体接受基于测量的护理（MBC）。技术有可能以安全有效的方式实施MBC。为了测试这个想法，我们开发了一个移动健康(mHealth)平台,目的是使临床医生更容易提供MBC,并且在综合行为健康护理中患者更容易获得MBC.来自mHealth平台的3000多名用户的数据用于开发输出严重性评分，对抑郁和焦虑的强有力的筛查措施。
目的：本研究的目的是将严重程度评分与经过验证的抑郁和焦虑评估评分以及临床医生评估评分进行比较，以评估这一新指标的潜在附加值。
方法：严重程度评分使用mHealth平台上患者报告和被动收集的行为健康相关数据。开发了一种人工智能衍生算法，将行为健康数据浓缩为一个单一的，纵向跟踪个体抑郁和焦虑症状的可量化措施。线性回归和Bland-Altman分析用于评估来自35,000多个mHealth平台用户的严重程度评分与个人健康问卷-9（PHQ-9）或广泛性焦虑症-7（GAD-7）评分之间的关系和差异。还将严重程度评分与专家临床医生小组对250名个体的评价进行了比较。
结果：线性回归结果显示严重程度评分与PHQ-9（r=0.74；P<.001）和GAD-7（r=0.80；P<.001）变化之间存在很强的相关性。严重程度评分与专家小组临床评估之间也存在强正相关（r=0.80-0.84；P<.001）。然而,Bland-Altman分析和回归分析的异常值评估显示，严重程度评分与PHQ-9有显著差异。
结论：临床医生可以可靠地使用mHealth严重程度评分作为纵向筛查和监测行为健康症状的替代指标。严重性评分可以识别未被PHQ-9识别的有风险的个体。需要进一步的研究来评估严重程度评分的敏感性和特异性。
目的：本研究的目的是将严重程度评分与经过验证的抑郁和焦虑评估评分以及临床医生评估评分进行比较，以评估这一新指标的潜在附加值。
方法：严重程度评分使用mHealth平台上患者报告和被动收集的行为健康相关数据。开发了一种人工智能衍生算法，将行为健康数据浓缩为一个单一的，纵向跟踪个体抑郁和焦虑症状的可量化措施。线性回归和Bland-Altman分析用于评估来自35,000多个mHealth平台用户的严重程度评分与个人健康问卷-9（PHQ-9）或广泛性焦虑症-7（GAD-7）评分之间的关系和差异。还将严重程度评分与专家临床医生小组对250名个体的评价进行了比较。
结果：线性回归结果显示严重程度评分与PHQ-9（r=0.74；P<.001）和GAD-7（r=0.80；P<.001）变化之间存在很强的相关性。严重程度评分与专家小组临床评估之间也存在强正相关（r=0.80-0.84；P<.001）。然而,Bland-Altman分析和回归分析的异常值评估显示，严重程度评分与PHQ-9有显著差异。
结论：临床医生可以可靠地使用mHealth严重程度评分作为纵向筛查和监测行为健康症状的替代指标。严重性评分可以识别未被PHQ-9识别的有风险的个体。需要进一步的研究来评估严重程度评分的敏感性和特异性。
