Serveur d'exploration sur la COVID chez les séniors

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Using Smartphones and Wearable Devices to Monitor Behavioral Changes During COVID-19.

Identifieur interne : 000083 ( Main/Exploration ); précédent : 000082; suivant : 000084

Using Smartphones and Wearable Devices to Monitor Behavioral Changes During COVID-19.

Auteurs : Shaoxiong Sun [Royaume-Uni] ; Amos A. Folarin [Royaume-Uni] ; Yatharth Ranjan [Royaume-Uni] ; Zulqarnain Rashid [Royaume-Uni] ; Pauline Conde [Royaume-Uni] ; Callum Stewart [Royaume-Uni] ; Nicholas Cummins [Allemagne] ; Faith Matcham [Royaume-Uni] ; Gloria Dalla Costa [Italie] ; Sara Simblett [Royaume-Uni] ; Letizia Leocani [Italie] ; Femke Lamers [Pays-Bas] ; Per Soelberg S Rensen [Danemark] ; Mathias Buron [Danemark] ; Ana Zabalza [Espagne] ; Ana Isabel Guerrero Pérez [Espagne] ; Brenda Wjh Penninx [Pays-Bas] ; Sara Siddi [Espagne] ; Josep Maria Haro [Espagne] ; Inez Myin-Germeys [Belgique] ; Aki Rintala [Belgique] ; Til Wykes [Royaume-Uni] ; Vaibhav A. Narayan [États-Unis] ; Giancarlo Comi [Italie] ; Matthew Hotopf [Royaume-Uni] ; Richard Jb Dobson [Royaume-Uni]

Source :

RBID : pubmed:32877352

Descripteurs français

English descriptors

Abstract

BACKGROUND

In the absence of a vaccine or effective treatment for COVID-19, countries have adopted nonpharmaceutical interventions (NPIs) such as social distancing and full lockdown. An objective and quantitative means of passively monitoring the impact and response of these interventions at a local level is needed.

OBJECTIVE

We aim to explore the utility of the recently developed open-source mobile health platform Remote Assessment of Disease and Relapse (RADAR)-base as a toolbox to rapidly test the effect and response to NPIs intended to limit the spread of COVID-19.

METHODS

We analyzed data extracted from smartphone and wearable devices, and managed by the RADAR-base from 1062 participants recruited in Italy, Spain, Denmark, the United Kingdom, and the Netherlands. We derived nine features on a daily basis including time spent at home, maximum distance travelled from home, the maximum number of Bluetooth-enabled nearby devices (as a proxy for physical distancing), step count, average heart rate, sleep duration, bedtime, phone unlock duration, and social app use duration. We performed Kruskal-Wallis tests followed by post hoc Dunn tests to assess differences in these features among baseline, prelockdown, and during lockdown periods. We also studied behavioral differences by age, gender, BMI, and educational background.

RESULTS

We were able to quantify expected changes in time spent at home, distance travelled, and the number of nearby Bluetooth-enabled devices between prelockdown and during lockdown periods (P<.001 for all five countries). We saw reduced sociality as measured through mobility features and increased virtual sociality through phone use. People were more active on their phones (P<.001 for Italy, Spain, and the United Kingdom), spending more time using social media apps (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), particularly around major news events. Furthermore, participants had a lower heart rate (P<.001 for Italy and Spain; P=.02 for Denmark), went to bed later (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), and slept more (P<.001 for Italy, Spain, and the United Kingdom). We also found that young people had longer homestay than older people during the lockdown and fewer daily steps. Although there was no significant difference between the high and low BMI groups in time spent at home, the low BMI group walked more.

CONCLUSIONS

RADAR-base, a freely deployable data collection platform leveraging data from wearables and mobile technologies, can be used to rapidly quantify and provide a holistic view of behavioral changes in response to public health interventions as a result of infectious outbreaks such as COVID-19. RADAR-base may be a viable approach to implementing an early warning system for passively assessing the local compliance to interventions in epidemics and pandemics, and could help countries ease out of lockdown.


DOI: 10.2196/19992
PubMed: 32877352


Affiliations:


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<nlm:affiliation>The Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.</nlm:affiliation>
<country xml:lang="fr">Royaume-Uni</country>
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<nlm:affiliation>Institute of Health Informatics, University College London, London, United Kingdom.</nlm:affiliation>
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<title level="j">Journal of medical Internet research</title>
<idno type="eISSN">1438-8871</idno>
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<term>Adolescent (MeSH)</term>
<term>Adult (MeSH)</term>
<term>Aged (MeSH)</term>
<term>Aged, 80 and over (MeSH)</term>
<term>Body Mass Index (MeSH)</term>
<term>Coronavirus Infections (epidemiology)</term>
<term>Coronavirus Infections (prevention & control)</term>
<term>Coronavirus Infections (psychology)</term>
<term>Coronavirus Infections (transmission)</term>
<term>Data Collection (MeSH)</term>
<term>Denmark (epidemiology)</term>
<term>Female (MeSH)</term>
<term>Humans (MeSH)</term>
<term>Italy (epidemiology)</term>
<term>Male (MeSH)</term>
<term>Middle Aged (MeSH)</term>
<term>Mobile Applications (MeSH)</term>
<term>Monitoring, Physiologic (MeSH)</term>
<term>Netherlands (epidemiology)</term>
<term>Pandemics (prevention & control)</term>
<term>Pneumonia, Viral (epidemiology)</term>
<term>Pneumonia, Viral (prevention & control)</term>
<term>Pneumonia, Viral (psychology)</term>
<term>Pneumonia, Viral (transmission)</term>
<term>Smartphone (MeSH)</term>
<term>Social Isolation (MeSH)</term>
<term>Social Media (MeSH)</term>
<term>Spain (epidemiology)</term>
<term>Telemedicine (MeSH)</term>
<term>United Kingdom (epidemiology)</term>
<term>Wearable Electronic Devices (MeSH)</term>
<term>Young Adult (MeSH)</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr">
<term>Adolescent (MeSH)</term>
<term>Adulte (MeSH)</term>
<term>Adulte d'âge moyen (MeSH)</term>
<term>Applications mobiles (MeSH)</term>
<term>Collecte de données (MeSH)</term>
<term>Danemark (épidémiologie)</term>
<term>Dispositifs électroniques portables (MeSH)</term>
<term>Espagne (épidémiologie)</term>
<term>Femelle (MeSH)</term>
<term>Humains (MeSH)</term>
<term>Indice de masse corporelle (MeSH)</term>
<term>Infections à coronavirus (prévention et contrôle)</term>
<term>Infections à coronavirus (psychologie)</term>
<term>Infections à coronavirus (transmission)</term>
<term>Infections à coronavirus (épidémiologie)</term>
<term>Isolement social (MeSH)</term>
<term>Italie (épidémiologie)</term>
<term>Jeune adulte (MeSH)</term>
<term>Monitorage physiologique (MeSH)</term>
<term>Mâle (MeSH)</term>
<term>Médias sociaux (MeSH)</term>
<term>Ordiphone (MeSH)</term>
<term>Pandémies (prévention et contrôle)</term>
<term>Pays-Bas (épidémiologie)</term>
<term>Pneumopathie virale (prévention et contrôle)</term>
<term>Pneumopathie virale (psychologie)</term>
<term>Pneumopathie virale (transmission)</term>
<term>Pneumopathie virale (épidémiologie)</term>
<term>Royaume-Uni (épidémiologie)</term>
<term>Sujet âgé (MeSH)</term>
<term>Sujet âgé de 80 ans ou plus (MeSH)</term>
<term>Télémédecine (MeSH)</term>
</keywords>
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<term>Denmark</term>
<term>Italy</term>
<term>Netherlands</term>
<term>Spain</term>
<term>United Kingdom</term>
</keywords>
<keywords scheme="MESH" qualifier="epidemiology" xml:lang="en">
<term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="prevention & control" xml:lang="en">
<term>Coronavirus Infections</term>
<term>Pandemics</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="prévention et contrôle" xml:lang="fr">
<term>Infections à coronavirus</term>
<term>Pandémies</term>
<term>Pneumopathie virale</term>
</keywords>
<keywords scheme="MESH" qualifier="psychologie" xml:lang="fr">
<term>Infections à coronavirus</term>
<term>Pneumopathie virale</term>
</keywords>
<keywords scheme="MESH" qualifier="psychology" xml:lang="en">
<term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="transmission" xml:lang="en">
<term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr">
<term>Danemark</term>
<term>Espagne</term>
<term>Infections à coronavirus</term>
<term>Italie</term>
<term>Pays-Bas</term>
<term>Pneumopathie virale</term>
<term>Royaume-Uni</term>
</keywords>
<keywords scheme="MESH" xml:lang="en">
<term>Adolescent</term>
<term>Adult</term>
<term>Aged</term>
<term>Aged, 80 and over</term>
<term>Body Mass Index</term>
<term>Data Collection</term>
<term>Female</term>
<term>Humans</term>
<term>Male</term>
<term>Middle Aged</term>
<term>Mobile Applications</term>
<term>Monitoring, Physiologic</term>
<term>Smartphone</term>
<term>Social Isolation</term>
<term>Social Media</term>
<term>Telemedicine</term>
<term>Wearable Electronic Devices</term>
<term>Young Adult</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr">
<term>Adolescent</term>
<term>Adulte</term>
<term>Adulte d'âge moyen</term>
<term>Applications mobiles</term>
<term>Collecte de données</term>
<term>Dispositifs électroniques portables</term>
<term>Femelle</term>
<term>Humains</term>
<term>Indice de masse corporelle</term>
<term>Isolement social</term>
<term>Jeune adulte</term>
<term>Monitorage physiologique</term>
<term>Mâle</term>
<term>Médias sociaux</term>
<term>Ordiphone</term>
<term>Sujet âgé</term>
<term>Sujet âgé de 80 ans ou plus</term>
<term>Télémédecine</term>
</keywords>
<keywords scheme="Wicri" type="geographic" xml:lang="fr">
<term>Danemark</term>
<term>Italie</term>
<term>Pays-Bas</term>
<term>Espagne</term>
<term>Royaume-Uni</term>
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<front>
<div type="abstract" xml:lang="en">
<p>
<b>BACKGROUND</b>
</p>
<p>In the absence of a vaccine or effective treatment for COVID-19, countries have adopted nonpharmaceutical interventions (NPIs) such as social distancing and full lockdown. An objective and quantitative means of passively monitoring the impact and response of these interventions at a local level is needed.</p>
</div>
<div type="abstract" xml:lang="en">
<p>
<b>OBJECTIVE</b>
</p>
<p>We aim to explore the utility of the recently developed open-source mobile health platform Remote Assessment of Disease and Relapse (RADAR)-base as a toolbox to rapidly test the effect and response to NPIs intended to limit the spread of COVID-19.</p>
</div>
<div type="abstract" xml:lang="en">
<p>
<b>METHODS</b>
</p>
<p>We analyzed data extracted from smartphone and wearable devices, and managed by the RADAR-base from 1062 participants recruited in Italy, Spain, Denmark, the United Kingdom, and the Netherlands. We derived nine features on a daily basis including time spent at home, maximum distance travelled from home, the maximum number of Bluetooth-enabled nearby devices (as a proxy for physical distancing), step count, average heart rate, sleep duration, bedtime, phone unlock duration, and social app use duration. We performed Kruskal-Wallis tests followed by post hoc Dunn tests to assess differences in these features among baseline, prelockdown, and during lockdown periods. We also studied behavioral differences by age, gender, BMI, and educational background.</p>
</div>
<div type="abstract" xml:lang="en">
<p>
<b>RESULTS</b>
</p>
<p>We were able to quantify expected changes in time spent at home, distance travelled, and the number of nearby Bluetooth-enabled devices between prelockdown and during lockdown periods (P<.001 for all five countries). We saw reduced sociality as measured through mobility features and increased virtual sociality through phone use. People were more active on their phones (P<.001 for Italy, Spain, and the United Kingdom), spending more time using social media apps (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), particularly around major news events. Furthermore, participants had a lower heart rate (P<.001 for Italy and Spain; P=.02 for Denmark), went to bed later (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), and slept more (P<.001 for Italy, Spain, and the United Kingdom). We also found that young people had longer homestay than older people during the lockdown and fewer daily steps. Although there was no significant difference between the high and low BMI groups in time spent at home, the low BMI group walked more.</p>
</div>
<div type="abstract" xml:lang="en">
<p>
<b>CONCLUSIONS</b>
</p>
<p>RADAR-base, a freely deployable data collection platform leveraging data from wearables and mobile technologies, can be used to rapidly quantify and provide a holistic view of behavioral changes in response to public health interventions as a result of infectious outbreaks such as COVID-19. RADAR-base may be a viable approach to implementing an early warning system for passively assessing the local compliance to interventions in epidemics and pandemics, and could help countries ease out of lockdown.</p>
</div>
</front>
</TEI>
<pubmed>
<MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM">
<PMID Version="1">32877352</PMID>
<DateCompleted>
<Year>2020</Year>
<Month>10</Month>
<Day>05</Day>
</DateCompleted>
<DateRevised>
<Year>2020</Year>
<Month>10</Month>
<Day>05</Day>
</DateRevised>
<Article PubModel="Electronic">
<Journal>
<ISSN IssnType="Electronic">1438-8871</ISSN>
<JournalIssue CitedMedium="Internet">
<Volume>22</Volume>
<Issue>9</Issue>
<PubDate>
<Year>2020</Year>
<Month>09</Month>
<Day>25</Day>
</PubDate>
</JournalIssue>
<Title>Journal of medical Internet research</Title>
<ISOAbbreviation>J Med Internet Res</ISOAbbreviation>
</Journal>
<ArticleTitle>Using Smartphones and Wearable Devices to Monitor Behavioral Changes During COVID-19.</ArticleTitle>
<Pagination>
<MedlinePgn>e19992</MedlinePgn>
</Pagination>
<ELocationID EIdType="doi" ValidYN="Y">10.2196/19992</ELocationID>
<Abstract>
<AbstractText Label="BACKGROUND">In the absence of a vaccine or effective treatment for COVID-19, countries have adopted nonpharmaceutical interventions (NPIs) such as social distancing and full lockdown. An objective and quantitative means of passively monitoring the impact and response of these interventions at a local level is needed.</AbstractText>
<AbstractText Label="OBJECTIVE">We aim to explore the utility of the recently developed open-source mobile health platform Remote Assessment of Disease and Relapse (RADAR)-base as a toolbox to rapidly test the effect and response to NPIs intended to limit the spread of COVID-19.</AbstractText>
<AbstractText Label="METHODS">We analyzed data extracted from smartphone and wearable devices, and managed by the RADAR-base from 1062 participants recruited in Italy, Spain, Denmark, the United Kingdom, and the Netherlands. We derived nine features on a daily basis including time spent at home, maximum distance travelled from home, the maximum number of Bluetooth-enabled nearby devices (as a proxy for physical distancing), step count, average heart rate, sleep duration, bedtime, phone unlock duration, and social app use duration. We performed Kruskal-Wallis tests followed by post hoc Dunn tests to assess differences in these features among baseline, prelockdown, and during lockdown periods. We also studied behavioral differences by age, gender, BMI, and educational background.</AbstractText>
<AbstractText Label="RESULTS">We were able to quantify expected changes in time spent at home, distance travelled, and the number of nearby Bluetooth-enabled devices between prelockdown and during lockdown periods (P<.001 for all five countries). We saw reduced sociality as measured through mobility features and increased virtual sociality through phone use. People were more active on their phones (P<.001 for Italy, Spain, and the United Kingdom), spending more time using social media apps (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), particularly around major news events. Furthermore, participants had a lower heart rate (P<.001 for Italy and Spain; P=.02 for Denmark), went to bed later (P<.001 for Italy, Spain, the United Kingdom, and the Netherlands), and slept more (P<.001 for Italy, Spain, and the United Kingdom). We also found that young people had longer homestay than older people during the lockdown and fewer daily steps. Although there was no significant difference between the high and low BMI groups in time spent at home, the low BMI group walked more.</AbstractText>
<AbstractText Label="CONCLUSIONS">RADAR-base, a freely deployable data collection platform leveraging data from wearables and mobile technologies, can be used to rapidly quantify and provide a holistic view of behavioral changes in response to public health interventions as a result of infectious outbreaks such as COVID-19. RADAR-base may be a viable approach to implementing an early warning system for passively assessing the local compliance to interventions in epidemics and pandemics, and could help countries ease out of lockdown.</AbstractText>
<CopyrightInformation>©Shaoxiong Sun, Amos A Folarin, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Callum Stewart, Nicholas Cummins, Faith Matcham, Gloria Dalla Costa, Sara Simblett, Letizia Leocani, Femke Lamers, Per Soelberg Sørensen, Mathias Buron, Ana Zabalza, Ana Isabel Guerrero Pérez, Brenda WJH Penninx, Sara Siddi, Josep Maria Haro, Inez Myin-Germeys, Aki Rintala, Til Wykes, Vaibhav A Narayan, Giancarlo Comi, Matthew Hotopf, Richard JB Dobson, RADAR-CNS Consortium. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 25.09.2020.</CopyrightInformation>
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<Identifier Source="ORCID">0000-0003-3652-5266</Identifier>
<AffiliationInfo>
<Affiliation>The Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.</Affiliation>
</AffiliationInfo>
</Author>
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<LastName>Folarin</LastName>
<ForeName>Amos A</ForeName>
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<Identifier Source="ORCID">0000-0002-0333-1927</Identifier>
<AffiliationInfo>
<Affiliation>The Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.</Affiliation>
</AffiliationInfo>
<AffiliationInfo>
<Affiliation>Institute of Health Informatics, University College London, London, United Kingdom.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Ranjan</LastName>
<ForeName>Yatharth</ForeName>
<Initials>Y</Initials>
<Identifier Source="ORCID">0000-0003-3079-3120</Identifier>
<AffiliationInfo>
<Affiliation>The Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.</Affiliation>
</AffiliationInfo>
</Author>
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<LastName>Rashid</LastName>
<ForeName>Zulqarnain</ForeName>
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<Identifier Source="ORCID">0000-0002-6843-9920</Identifier>
<AffiliationInfo>
<Affiliation>The Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.</Affiliation>
</AffiliationInfo>
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<LastName>Conde</LastName>
<ForeName>Pauline</ForeName>
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<Identifier Source="ORCID">0000-0003-0513-0915</Identifier>
<AffiliationInfo>
<Affiliation>The Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.</Affiliation>
</AffiliationInfo>
</Author>
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<LastName>Stewart</LastName>
<ForeName>Callum</ForeName>
<Initials>C</Initials>
<Identifier Source="ORCID">0000-0001-9947-8677</Identifier>
<AffiliationInfo>
<Affiliation>The Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Cummins</LastName>
<ForeName>Nicholas</ForeName>
<Initials>N</Initials>
<Identifier Source="ORCID">0000-0002-1178-917X</Identifier>
<AffiliationInfo>
<Affiliation>Chair of Embedded Intelligence for Health Care & Wellbeing, University of Augsburg, Augsburg, Germany.</Affiliation>
</AffiliationInfo>
</Author>
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<LastName>Matcham</LastName>
<ForeName>Faith</ForeName>
<Initials>F</Initials>
<Identifier Source="ORCID">0000-0002-4055-904X</Identifier>
<AffiliationInfo>
<Affiliation>The Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.</Affiliation>
</AffiliationInfo>
</Author>
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<LastName>Dalla Costa</LastName>
<ForeName>Gloria</ForeName>
<Initials>G</Initials>
<Identifier Source="ORCID">0000-0002-8761-9337</Identifier>
<AffiliationInfo>
<Affiliation>Neurorehabilitation Unit and Institute of Experimental Neurology, University Vita Salute San Raffaele, Istituto Di Ricovero e Cura a Carattere Scientifico Ospedale San Raffaele, Milan, Italy.</Affiliation>
</AffiliationInfo>
</Author>
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<LastName>Simblett</LastName>
<ForeName>Sara</ForeName>
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<Identifier Source="ORCID">0000-0002-8075-8238</Identifier>
<AffiliationInfo>
<Affiliation>The Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.</Affiliation>
</AffiliationInfo>
</Author>
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<LastName>Leocani</LastName>
<ForeName>Letizia</ForeName>
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<AffiliationInfo>
<Affiliation>Neurorehabilitation Unit and Institute of Experimental Neurology, University Vita Salute San Raffaele, Istituto Di Ricovero e Cura a Carattere Scientifico Ospedale San Raffaele, Milan, Italy.</Affiliation>
</AffiliationInfo>
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<LastName>Lamers</LastName>
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<AffiliationInfo>
<Affiliation>Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ inGeest, Amsterdam, Netherlands.</Affiliation>
</AffiliationInfo>
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<LastName>Sørensen</LastName>
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<Affiliation>Danish Multiple Sclerosis Centre, Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.</Affiliation>
</AffiliationInfo>
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<LastName>Buron</LastName>
<ForeName>Mathias</ForeName>
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<AffiliationInfo>
<Affiliation>Danish Multiple Sclerosis Centre, Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.</Affiliation>
</AffiliationInfo>
</Author>
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<LastName>Zabalza</LastName>
<ForeName>Ana</ForeName>
<Initials>A</Initials>
<Identifier Source="ORCID">0000-0003-3860-5251</Identifier>
<AffiliationInfo>
<Affiliation>Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Barcelona, Spain.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Guerrero Pérez</LastName>
<ForeName>Ana Isabel</ForeName>
<Initials>AI</Initials>
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<AffiliationInfo>
<Affiliation>Multiple Sclerosis Centre of Catalonia (Cemcat), Department of Neurology/Neuroimmunology, Hospital Universitari Vall d'Hebron, Barcelona, Spain.</Affiliation>
</AffiliationInfo>
</Author>
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<LastName>Penninx</LastName>
<ForeName>Brenda Wjh</ForeName>
<Initials>BW</Initials>
<Identifier Source="ORCID">0000-0001-7779-9672</Identifier>
<AffiliationInfo>
<Affiliation>Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam University Medical Centre, Vrije Universiteit and GGZ inGeest, Amsterdam, Netherlands.</Affiliation>
</AffiliationInfo>
</Author>
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<LastName>Siddi</LastName>
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<Identifier Source="ORCID">0000-0002-1494-9028</Identifier>
<AffiliationInfo>
<Affiliation>Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain.</Affiliation>
</AffiliationInfo>
<AffiliationInfo>
<Affiliation>Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain.</Affiliation>
</AffiliationInfo>
<AffiliationInfo>
<Affiliation>Universitat de Barcelona, Barcelona, Spain.</Affiliation>
</AffiliationInfo>
</Author>
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<LastName>Haro</LastName>
<ForeName>Josep Maria</ForeName>
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<AffiliationInfo>
<Affiliation>Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Barcelona, Spain.</Affiliation>
</AffiliationInfo>
<AffiliationInfo>
<Affiliation>Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain.</Affiliation>
</AffiliationInfo>
<AffiliationInfo>
<Affiliation>Universitat de Barcelona, Barcelona, Spain.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Myin-Germeys</LastName>
<ForeName>Inez</ForeName>
<Initials>I</Initials>
<Identifier Source="ORCID">0000-0002-3731-4930</Identifier>
<AffiliationInfo>
<Affiliation>Centre for Contextual Psychiatry, Department of Neurosciences, Katholieke Universiteit Leuven, Leuven, Belgium.</Affiliation>
</AffiliationInfo>
</Author>
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<LastName>Rintala</LastName>
<ForeName>Aki</ForeName>
<Initials>A</Initials>
<Identifier Source="ORCID">0000-0002-0066-4697</Identifier>
<AffiliationInfo>
<Affiliation>Centre for Contextual Psychiatry, Department of Neurosciences, Katholieke Universiteit Leuven, Leuven, Belgium.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Wykes</LastName>
<ForeName>Til</ForeName>
<Initials>T</Initials>
<Identifier Source="ORCID">0000-0002-5881-8003</Identifier>
<AffiliationInfo>
<Affiliation>The Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.</Affiliation>
</AffiliationInfo>
<AffiliationInfo>
<Affiliation>South London and Maudsley National Health Services Foundation Trust, London, United Kingdom.</Affiliation>
</AffiliationInfo>
</Author>
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<LastName>Narayan</LastName>
<ForeName>Vaibhav A</ForeName>
<Initials>VA</Initials>
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<AffiliationInfo>
<Affiliation>Janssen Research and Development LLC, Titusville, NJ, United States.</Affiliation>
</AffiliationInfo>
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<LastName>Comi</LastName>
<ForeName>Giancarlo</ForeName>
<Initials>G</Initials>
<Identifier Source="ORCID">0000-0002-6989-1054</Identifier>
<AffiliationInfo>
<Affiliation>Institute of Experimental Neurology, Istituto Di Ricovero e Cura a Carattere Scientifico Ospedale San Raffaele, Milan, Italy.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y">
<LastName>Hotopf</LastName>
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