Langholm Carsten, Breitinger Scott, Gray Lucy, Goes Fernando, Walker Alex, Xiong Ashley, Stopel Cindy, Zandi Peter, Frye Mark A, Torous John
Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA.
Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, 55902, USA.
NPJ Digit Med. 2023 Dec 21;6(1):238. doi: 10.1038/s41746-023-00977-7.
Differentiating between bipolar disorder and major depressive disorder can be challenging for clinicians. The diagnostic process might benefit from new ways of monitoring the phenotypes of these disorders. Smartphone data might offer insight in this regard. Today, smartphones collect dense, multimodal data from which behavioral metrics can be derived. Distinct patterns in these metrics have the potential to differentiate the two conditions. To examine the feasibility of smartphone-based phenotyping, two study sites (Mayo Clinic, Johns Hopkins University) recruited patients with bipolar I disorder (BPI), bipolar II disorder (BPII), major depressive disorder (MDD), and undiagnosed controls for a 12-week observational study. On their smartphones, study participants used a digital phenotyping app (mindLAMP) for data collection. While in use, mindLAMP gathered real-time geolocation, accelerometer, and screen-state (on/off) data. mindLAMP was also used for EMA delivery. MindLAMP data was then used as input variables in binary classification, three-group k-nearest neighbors (KNN) classification, and k-means clustering. The best-performing binary classification model was able to classify patients as control or non-control with an AUC of 0.91 (random forest). The model that performed best at classifying patients as having MDD or bipolar I/II had an AUC of 0.62 (logistic regression). The k-means clustering model had a silhouette score of 0.46 and an ARI of 0.27. Results support the potential for digital phenotyping methods to cluster depression, bipolar disorder, and healthy controls. However, due to inconsistencies in accuracy, more data streams are required before these methods can be applied to clinical practice.
对临床医生来说，区分双相情感障碍和重度抑郁症可能具有挑战性。诊断过程可能会受益于监测这些疾病表型的新方法。智能手机数据在这方面可能会提供一些见解。如今，智能手机收集密集的多模态数据，从中可以得出行为指标。这些指标中的不同模式有可能区分这两种疾病。为了检验基于智能手机的表型分析的可行性，两个研究地点（梅奥诊所、约翰·霍普金斯大学）招募了患有双相I型障碍（BPI）、双相II型障碍（BPII）、重度抑郁症（MDD）的患者以及未确诊的对照者，进行了一项为期12周的观察性研究。研究参与者在他们的智能手机上使用一款数字表型分析应用程序（mindLAMP）来收集数据。在使用过程中，mindLAMP收集实时地理位置、加速度计和屏幕状态（开/关）数据。mindLAMP还用于进行电子移动评估（EMA）。然后，mindLAMP数据被用作二元分类、三组k近邻（KNN）分类和k均值聚类中的输入变量。表现最佳的二元分类模型能够以0.91的曲线下面积（AUC）（随机森林）将患者分类为对照或非对照。在将患者分类为患有MDD或双相I/II型方面表现最佳的模型的AUC为0.62（逻辑回归）。k均值聚类模型的轮廓系数为0.46，调整兰德指数为0.27。结果支持数字表型分析方法对抑郁症、双相情感障碍和健康对照进行聚类的潜力。然而，由于准确性存在不一致性，在这些方法能够应用于临床实践之前，还需要更多的数据流。
