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

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 promisin...

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Autores principales: Menculini, G., Pigliautile, I., Moretti, P., Cotana, F., Pisello, A.L., Tortorella, A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9471617/
http://dx.doi.org/10.1192/j.eurpsy.2021.49
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author Menculini, G.
Pigliautile, I.
Moretti, P.
Cotana, F.
Pisello, A.L.
Tortorella, A.
author_facet Menculini, G.
Pigliautile, I.
Moretti, P.
Cotana, F.
Pisello, A.L.
Tortorella, A.
author_sort Menculini, G.
collection PubMed
description 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.
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spelling pubmed-94716172022-09-29 The ecological momentary assessment approach and the use of big data to analyse possible effects of urbanisation on mental health Menculini, G. Pigliautile, I. Moretti, P. Cotana, F. Pisello, A.L. Tortorella, A. Eur Psychiatry Abstract 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. Cambridge University Press 2021-08-13 /pmc/articles/PMC9471617/ http://dx.doi.org/10.1192/j.eurpsy.2021.49 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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.
spellingShingle Abstract
Menculini, G.
Pigliautile, I.
Moretti, P.
Cotana, F.
Pisello, A.L.
Tortorella, A.
The ecological momentary assessment approach and the use of big data to analyse possible effects of urbanisation on mental health
title The ecological momentary assessment approach and the use of big data to analyse possible effects of urbanisation on mental health
title_full The ecological momentary assessment approach and the use of big data to analyse possible effects of urbanisation on mental health
title_fullStr The ecological momentary assessment approach and the use of big data to analyse possible effects of urbanisation on mental health
title_full_unstemmed The ecological momentary assessment approach and the use of big data to analyse possible effects of urbanisation on mental health
title_short The ecological momentary assessment approach and the use of big data to analyse possible effects of urbanisation on mental health
title_sort ecological momentary assessment approach and the use of big data to analyse possible effects of urbanisation on mental health
topic Abstract
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9471617/
http://dx.doi.org/10.1192/j.eurpsy.2021.49
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