Wigman Johanna T W, Ching Ann Ee, Chung Yoonho, Eichi Habiballah Rahimi, Lane Erlend, Langholm Carsten, Vaidyam Aditya, Byun Andrew Jin Soo, Haidar Anastasia, Hartmann Jessica, Nunez Angela, Dwyer Dominic, Nasarudin Adibah Amani, Borders Owen, Scott Isabelle, Tamayo Zailyn, Matneja Priya, Cho Kang-Ik, Addington Jean, Alameda Luis K, Arango Celso, Breitborde Nicholas J K, Broome Matthew R, Cadenhead Kristin S, Calkins Monica E, Chen Eric Yu Hai, Choi Jimmy, Conus Philippe, Corcoran Cheryl M, Cornblatt Barbara A, Diaz-Caneja Covadonga M, Ellman Lauren M, Fusar-Poli Paolo, Gaspar Pablo A, Gerber Carla, Glenthøj Louise Birkedal, Horton Leslie E, Hui Christy Lai Ming, Kambeitz Joseph, Kambeitz-Ilankovic Lana, Keshavan Matcheri S, Kim Sung-Wan, Koutsouleris Nikolaos, Langbein Kerstin, Mamah Daniel, Mathalon Daniel H, Mittal Vijay A, Nordentoft Merete, Pearlson Godfrey D, Perez Jesus, Perkins Diana O, Powers Albert R, Rogers Jack, Sabb Fred W, Schiffman Jason, Shah Jai L, Silverstein Steven M, Smesny Stefan, Yassin Walid, Stone William S, Strauss Gregory P, Thompson Judy L, Upthegrove Rachel, Verma Swapna, Wang Jijun, Wolf Daniel H, Wolff Phillip, Rowland Laura M, D'Alfonso Simon, Pasternak Ofer, Bouix Sylvain, McGorry Patrick D, Kahn Rene S, Kane John M, Bearden Carrie E, Woods Scott W, Shenton Martha E, Nelson Barnaby, Baker Justin T, Torous John
University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Centre of Psychopathology and Emotion regulation, Groningen, The Netherlands.
Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia.
Schizophrenia (Heidelb). 2025 Jun 3;11(1):83. doi: 10.1038/s41537-025-00599-w.
Although meta-analytic studies have shown that 25-33% of those at Clinical High Risk (CHR) for psychosis transition to a first episode of psychosis within three years, less is known about estimating the risk of transition at an individual level. Digital phenotyping offers a novel approach to explore the nature of CHR and may help to improve personalized risk prediction. Specifically, digital data enable detailed mapping of experiences, moods and behaviors during longer periods of time (e.g., weeks, months) and offer more insight into patterns over time at the individual level across their routine daily life. However, while novel digital health technologies open up many new avenues of research, they also come with specific challenges, including replicability of results and the adherence of participants. This paper outlines the design of the digital component of the Accelerating Medicines Partnership® Schizophrenia Program (AMP SCZ) project, a large international collaborative project that follows individuals at CHR for psychosis over a period of two years. The digital component comprises one-year smartphone-based digital phenotyping and actigraphy. Smartphone-based digital phenotyping includes 30-item short daily self-report surveys and voice diaries as well as passive data capture (geolocation, on/off screen state, and accelerometer). Actigraphy data are collected via an Axivity wristwatch. The aim of this paper is to describe the design and the three goals of the digital measures used in AMP SCZ to: (i) better understand the symptoms, real-life experiences, and behaviors of those at CHR for psychosis, (ii) improve the prediction of transition to psychosis and other health outcomes in this population based on digital phenotyping and, (iii) serve as an example for replicable and ethical research across geographically diverse regions and cultures. Accordingly, we describe the rationale, protocol and implementation of these digital components of the AMP SCZ project. **Link to video interview: https://vimeo.com/1060935583 *.
尽管荟萃分析研究表明，处于精神病临床高危（CHR）状态的人群中，有25%-33%会在三年内发展为首次精神病发作，但在个体层面评估转化风险方面，我们了解得还较少。数字表型分析提供了一种探索CHR本质的新方法，可能有助于改善个性化风险预测。具体而言，数字数据能够详细描绘较长时间段（如数周、数月）内的经历、情绪和行为，并能更深入地洞察个体在日常生活中的长期模式。然而，尽管新型数字健康技术开辟了许多新的研究途径，但它们也带来了一些特定的挑战，包括结果的可重复性和参与者的依从性。本文概述了加速药物合作组织精神分裂症项目（AMP SCZ）数字部分的设计，该项目是一个大型国际合作项目，对处于CHR状态的个体进行为期两年的跟踪研究。数字部分包括为期一年的基于智能手机的数字表型分析和活动记录仪监测。基于智能手机的数字表型分析包括每日30项简短的自我报告调查、语音日记以及被动数据捕获（地理位置、屏幕开关状态和加速度计）。活动记录仪数据通过Axivity手表收集。本文的目的是描述AMP SCZ中使用的数字测量方法的设计及其三个目标：（i）更好地了解处于CHR状态的人群的症状、现实生活经历和行为；（ii）基于数字表型分析改善对该人群向精神病及其他健康结局转化的预测；（iii）为跨地域不同地区和文化的可重复且符合伦理的研究提供范例。因此，我们描述了AMP SCZ项目这些数字部分的基本原理、方案和实施情况。**视频访谈链接：https://vimeo.com/1060935583 *
