Combining continuous smartphone native sensors data capture and unsupervised data mining techniques for behavioral changes detection: a case series of the evidence-based behavior (eB2) study

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dc.contributor.author Berrouiguet, Sofian
dc.contributor.author Ramírez García, David
dc.contributor.author Barrigón Estévez, María Luisa
dc.contributor.author Moreno Muñoz, Pablo
dc.contributor.author Carmona Camacho, Rodrigo
dc.contributor.author Baca García, Enrique
dc.contributor.author Artés Rodríguez, Antonio
dc.date.accessioned 2020-11-25T08:58:48Z
dc.date.available 2020-11-25T08:58:48Z
dc.date.issued 2018-12-10
dc.identifier.bibliographicCitation JMIR Mhealth Uhealth, (2018), 6(12):e197.
dc.identifier.issn 2291-5222
dc.identifier.uri http://hdl.handle.net/10016/31472
dc.description.abstract Background: The emergence of smartphones, wearable sensor technologies, and smart homes allows the nonintrusive collection of activity data. Thus, health-related events, such as activities of daily living (ADLs; eg, mobility patterns, feeding, sleeping, ...) can be captured without patients' active participation. We designed a system to detect changes in the mobility patterns based on the smartphone's native sensors and advanced machine learning and signal processing techniques. Objective: The principal objective of this work is to assess the feasibility of detecting mobility pattern changes in a sample of outpatients with depression using the smartphone's sensors. The proposed method processed the data acquired by the smartphone using an unsupervised detection technique. Methods: In this study, 38 outpatients from the Hospital Fundacion Jimenez Diaz Psychiatry Department (Madrid, Spain) participated. The Evidence-Based Behavior (eB(2)) app was downloaded by patients on the day of recruitment and configured with the assistance of a physician. The app captured the following data: inertial sensors, physical activity, phone calls and message logs, app usage, nearby Bluetooth and Wi-Fi connections, and location. We applied a change-point detection technique to location data on a sample of 9 outpatients recruited between April 6, 2017 and December 14, 2017. The change-point detection was based only on location information, but the eB(2) platform allowed for an easy integration of additional data. The app remained running in the background on patients' smartphone during the study participation. Results: The principal outcome measure was the identification of mobility pattern changes based on an unsupervised detection technique applied to the smartphone's native sensors data. Here, results from 5 patients' records are presented as a case series. The eB(2) system detected specific mobility pattern changes according to the patients' activity, which may be used as indicators
dc.description.sponsorship This study was partly supported by Ministerio de Economía of Spain under project: AID (TEC2014-62194-EXP) and aMBITION (TEC2017-92552-EXP), the Ministerio de Economía of Spain jointly with the European Commission under projects ADVENTURE (TEC2015-69868-C2-1-R) and CAIMAN (TEC2017-86921-C2-2-R), the Comunidad de Madrid under project CASI-CAM-CM (S2013/ICE-2845), Fundación Jiménez Díaz Hospital, Instituto de Salud Carlos III (PI16/01852), Delegación del Gobierno para el Plan Nacional de Drogas (20151073), American Foundation for Suicide Prevention (LSRG-1-005-16), the French Embassy in Madrid, Spain, the fondation de l’Avenir, and the Fondation de France. The work of PMM has been supported by FPI grant BES-2016-077626.
dc.format.extent 15
dc.language.iso eng
dc.publisher JMIR
dc.rights ©Sofian Berrouiguet, David Ramírez, María Luisa Barrigón, Pablo Moreno-Muñoz, Rodrigo Carmona Camacho, Enrique Baca-García, Antonio Artés-Rodríguez. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 17.11.2018. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Behavioral changes
dc.subject.other Data mining
dc.subject.other Mental disorders
dc.subject.other Sensors
dc.subject.other Wearables
dc.subject.other Severity
dc.subject.other Privacy
dc.title Combining continuous smartphone native sensors data capture and unsupervised data mining techniques for behavioral changes detection: a case series of the evidence-based behavior (eB2) study
dc.type article
dc.description.status Publicado
dc.relation.publisherversion https://mhealth.jmir.org/2018/12/e197/
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.2196/mhealth.9472
dc.rights.accessRights openAccess
dc.relation.projectID Comunidad de Madrid. S2013/ICE-2845
dc.relation.projectID Gobierno de España. TEC2014-62194-EXP
dc.relation.projectID Gobierno de España. TEC2015-69868-C2-1-R
dc.relation.projectID Gobierno de España. BES-2016-077626
dc.relation.projectID Gobierno de España. TEC2017-86921-C2-2-R
dc.relation.projectID Gobierno de España. TEC2017-92552-EXP
dc.type.version publishedVersion
dc.identifier.publicationissue 12(e197)
dc.identifier.publicationtitle JMIR mHealth and uHealth
dc.identifier.publicationvolume 6
dc.identifier.uxxi AR/0000022618
dc.contributor.funder Comunidad de Madrid
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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