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A framework for assessing neuropsychiatric phenotypes by using smartphone-based location data.
Translational Psychiatry ( IF 6.8 ) Pub Date : 2020-07-01 , DOI: 10.1038/s41398-020-00893-4
Niels Jongs 1 , Raj Jagesar 1 , Neeltje E M van Haren 2, 3 , Brenda W J H Penninx 4 , Lianne Reus 5 , Pieter J Visser 5 , Nic J A van der Wee 6, 7 , Ina M Koning 8 , Celso Arango 9 , Iris E C Sommer 3 , Marinus J C Eijkemans 10 , Jacob A Vorstman 11, 12 , Martien J Kas 1
Affiliation  

The use of smartphone-based location data to quantify behavior longitudinally and passively is rapidly gaining traction in neuropsychiatric research. However, a standardized and validated preprocessing framework for deriving behavioral phenotypes from smartphone-based location data is currently lacking. Here, we present a preprocessing framework consisting of methods that are validated in the context of geospatial data. This framework aims to generate context-enriched location data by identifying stationary, non-stationary, and recurrent stationary states in movement patterns. Subsequently, this context-enriched data is used to derive a series of behavioral phenotypes that are related to movement. By using smartphone-based location data collected from 245 subjects, including patients with schizophrenia, we show that the proposed framework is effective and accurate in generating context-enriched location data. This data was subsequently used to derive behavioral readouts that were sensitive in detecting behavioral nuances related to schizophrenia and aging, such as the time spent at home and the number of unique places visited. Overall, our results indicate that the proposed framework reliably preprocesses raw smartphone-based location data in such a manner that relevant behavioral phenotypes of interest can be derived.



中文翻译:

通过使用基于智能手机的位置数据评估神经精神病学表型的框架。

在神经精神病学研究中,使用基于智能手机的位置数据来纵向和被动地量化行为正在迅速获得关注。但是,目前缺乏用于从基于智能手机的位置数据中得出行为表型的标准化且经过验证的预处理框架。在这里,我们介绍了一个预处理框架,该框架由在地理空间数据环境中经过验证的方法组成。该框架旨在通过识别运动模式中的固定,非固定和循环固定状态来生成上下文丰富的位置数据。随后,此上下文丰富的数据用于导出与运动相关的一系列行为表型。通过使用从245位受试者(包括精神分裂症患者)收集的基于智能手机的位置数据,我们表明,所提出的框架在生成内容丰富的位置数据方面是有效且准确的。随后,该数据被用于导出行为读数,这些读数对于检测与精神分裂症和衰老相关的行为细微差别非常敏感,例如在家里度过的时间和所拜访的独特地点的数量。总体而言,我们的结果表明,所提出的框架能够可靠地预处理基于智能手机的原始位置数据,从而可以得出相关的相关行为表型。

更新日期:2020-07-01
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