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A framework for assessing neuropsychiatric phenotypes by using smartphone-based location data.

Niels Jongs¹, Raj Jagesar¹, Neeltje E M van Haren^2,3

¹Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands.

Translational Psychiatry

|July 3, 2020

Summary

This study introduces a new framework to analyze smartphone location data for neuropsychiatric research. It accurately extracts behavioral patterns, aiding in the detection of conditions like schizophrenia and aging effects.

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Area of Science:

Digital Phenotyping
Neuropsychiatric Research
Geospatial Data Analysis

Background:

Smartphone location data offers passive, longitudinal behavioral quantification for neuropsychiatric research.
A lack of standardized, validated preprocessing frameworks hinders the use of this data.
Existing methods require robust frameworks for deriving meaningful behavioral phenotypes.

Purpose of the Study:

To present a validated preprocessing framework for smartphone-based location data.
To generate context-enriched location data by identifying movement patterns (stationary, non-stationary, recurrent).
To derive movement-related behavioral phenotypes for neuropsychiatric applications.

Main Methods:

Developed a preprocessing framework using validated geospatial data methods.
Identified stationary, non-stationary, and recurrent stationary states in movement data.
Applied the framework to smartphone location data from 245 subjects, including schizophrenia patients.

Main Results:

The framework effectively generated context-enriched location data from raw smartphone data.
Derived behavioral readouts, such as time spent at home and unique places visited, were sensitive.
The data successfully detected behavioral nuances associated with schizophrenia and aging.

Conclusions:

The proposed framework reliably preprocesses smartphone location data for behavioral phenotype derivation.
This approach enables accurate quantification of behaviors relevant to neuropsychiatric conditions.
The validated framework supports the advancement of digital phenotyping in clinical research.