Pizzoli Silvia Francesca Maria, Monzani Dario, Conti Lorenzo, Ferraris Giulia, Grasso Roberto, Pravettoni Gabriella
Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy.
Department of Psychology, Catholic University of the Sacred Heart,, Milan, Italy.
Front Psychol. 2023 Jun 27;14:1103703. doi: 10.3389/fpsyg.2023.1103703. eCollection 2023.
Digital phenotyping refers to the collection of real-time biometric and personal data on digital tools, mainly smartphones, and wearables, to measure behaviors and variables that can be used as a proxy for complex psychophysiological conditions. Digital phenotyping might be used for diagnosis, clinical assessment, predicting changes and trajectories in psychological clinical conditions, and delivering tailored interventions according to individual real-time data. Recent works pointed out the possibility of using such an approach in the field of suicide risk in high-suicide-risk patients. Among the possible targets of such interventions, adolescence might be a population of interest, since they display higher odds of committing suicide and impulsive behaviors. The present work systematizes the available evidence of the data that might be used for digital phenotyping in the field of adolescent suicide and provides insight into possible personalized approaches for monitoring and treating suicidal risk or predicting risk trajectories. Specifically, the authors first define the field of digital phenotyping and its features, secondly, they organize the available literature to gather all the digital indexes (active and passive data) that can provide reliable information on the increase in the suicidal odds, lastly, they discuss the challenges and future directions of such an approach, together with its ethical implications.
数字表型分析是指在数字工具(主要是智能手机和可穿戴设备)上收集实时生物特征和个人数据,以测量可作为复杂心理生理状况替代指标的行为和变量。数字表型分析可用于诊断、临床评估、预测心理临床状况的变化和轨迹,以及根据个体实时数据提供量身定制的干预措施。最近的研究指出了在高自杀风险患者的自杀风险领域使用这种方法的可能性。在这类干预措施的可能目标人群中,青少年可能是一个值得关注的群体,因为他们自杀和冲动行为的几率更高。本研究系统整理了青少年自杀领域可用于数字表型分析的数据的现有证据,并深入探讨了监测和治疗自杀风险或预测风险轨迹的可能个性化方法。具体而言,作者首先定义了数字表型分析领域及其特征,其次,他们整理现有文献,收集所有能够提供关于自杀几率增加的可靠信息的数字指标(主动和被动数据),最后,他们讨论了这种方法的挑战和未来方向,以及其伦理意义。