The integration of technology in clinical care is growing rapidly and has become especially relevant during the global COVID-19 pandemic. Smartphone-based digital phenotyping, or the use of integrated sensors to identify patterns in behavior and symptomatology, has shown potential in detecting subtle moment-to-moment changes. These changes, often referred to as anomalies, represent significant deviations from an individual’s baseline, may be useful in informing the risk of relapse in serious mental illness. Our investigation of smartphone-based anomaly detection resulted in 89% sensitivity and 75% specificity for predicting relapse in schizophrenia. These results demonstrate the potential of longitudinal collection of real-time behavior and symptomatology via smartphones and the clinical utility of individualized analysis. Future studies are necessary to explore how specificity can be improved, just-in-time adaptive interventions utilized, and clinical integration achieved.

技术在临床护理中的集成正在迅速增长，并且在全球COVID-19大流行期间变得尤为重要。基于智能手机的数字表型，或使用集成的传感器来识别行为和症状的模式，已显示出检测微妙的瞬间变化的潜力。这些变化（通常称为异常）代表与个人基线的显着偏离，可能有助于告知严重精神疾病的复发风险。我们对基于智能手机的异常检测进行的调查得出，预测精神分裂症复发的敏感性为89％，特异性为75％。这些结果证明了通过智能手机纵向收集实时行为和症状的潜力以及个性化分析的临床实用性。