Abstract
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.
Highlights
The ability to objectively quantify different aspects of human behavior is essential for studies that aim to understand variations in human behavior and their underlying biological mechanisms
Traveling from work to home or from work to a supermarket are examples of non-stationary states. We identified these stationary states by employing a stay point detection algorithm[23] on raw location data that is filtered on accuracy
User confirmed validation of stationary states from five subjects collected over a period of 2 weeks showed that the accuracy of the stay point detection algorithm[23] in correctly identifying stationary states is 94%(μ) ± 8%(sd)
Summary
The ability to objectively quantify different aspects of human behavior is essential for studies that aim to understand variations in human behavior and their underlying biological mechanisms To date, such studies predominantly rely on subjective research methods such as in-person interviews, questionnaires and self- or proxyrated measures. These behavioral phenotypic measures are used to examine interactions with an array of biological parameters, such as genotypes, brain activity patterns or structural brain data to study the biological underpinnings of the observed behavior While such studies have led to numerous important insights, the current methods for behavioral phenotyping have their limitations that preclude their objectivity. Observational assessments are real-time, but they occur nearly always in a nonnatural (e.g., clinical) setting
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
