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The ecological momentary assessment approach and the use of big data to analyse possible effects of urbanisation on mental health

Published online by Cambridge University Press:  13 August 2021

G. Menculini ,
I. Pigliautile ,
P. Moretti ,
F. Cotana ,
A.L. Pisello  and
A. Tortorella
G. Menculini
Affiliation:
Department Of Psychiatry, University of Perugia, Perugia, Italy
I. Pigliautile
Affiliation:
Department Of Engineering And Ciriaf Interuniversity Research Centre On Pollution And Environment Mauro Felli, University of Perugia, Perugia, Italy
P. Moretti
Affiliation:
Department Of Psychiatry, University of Perugia, Perugia, Italy
F. Cotana
Affiliation:
Department Of Engineering And Ciriaf Interuniversity Research Centre On Pollution And Environment Mauro Felli, University of Perugia, Perugia, Italy
A.L. Pisello
Affiliation:
Department Of Engineering And Ciriaf Interuniversity Research Centre On Pollution And Environment Mauro Felli, University of Perugia, Perugia, Italy
A. Tortorella*
Affiliation:
Department Of Psychiatry, University of Perugia, Perugia, Italy
*
*Corresponding Author.

Abstract

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Introduction

Smart healthcare monitoring allows detecting health conditions using Big Data, namely aggregated data concerning physiological and behavioral parameters. The continuous collection of data from smart-devices performed by the Ecological Momentary Assessment approach represents a promising application of Big Data.

Objectives

This preliminary study was aimed at developing a research protocol focused on the use of Big Data in evaluating the impact of urban environment, affected by a variety of potentially damaging anthropogenic actions, on illness relapses in Bipolar Disorders (BD).

Methods

This pilot study was designed by researchers from Departments of Psychiatry and Engineering (CIRIAF), University of Perugia. Environmental, physiological, and behavioral parameters and smart-devices aimed at collecting Big Data were identified. Subjects aged 18-65, affected by BD in current euthymic state referring to the University/General Hospital of Perugia will be recruited.

Results

Subjects will undergo a baseline visit and three monitoring visits during one year. Wearable devices will be provided for collecting data about environmental and physiological parameters. Behavioral data will be collected through smartphone accelerometers, GPS, and overall smartphone use. Big data will be stored into an online platform that will provide real-time feedback concerning the recorded variables. Clinical information concerning BD relapses will be collected. Machine learning techniques, integrated to deterministic analysis of urban environmental conditions, will be used to create possible predictive models for BD relapses.

Conclusions

The present project could allow the creation of a new operative platform for a better health management system correlating real-time Big Data to specific clinical features of BD.

Disclosure

No significant relationships.

Keywords

bipolar disorderenvironmentBig Dataurbanisation
Type
Abstract
Information
European Psychiatry , Volume 64 , Special Issue S1: Abstracts of the 29th European Congress of Psychiatry , April 2021 , pp. S10 - S11
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the European Psychiatric Association
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