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Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): Recruitment, retention, and data availability in a longitudinal remote measurement study

IntroductionMajor Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an exciting opportunity to transform the measurement and managem...

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Published in:European psychiatry 2022-06, Vol.65 (S1), p.S112-S112
Main Authors: Matcham, F., Leightley, D., Siddi, S., Lamers, F., White, K., Annas, P., De Girolamo, G., Difrancesco, S., Haro, J.M., Horsfall, M., Ivan, A., Lavelle, G., Li, Q., Lombardini, F., Mohr, D., Narayan, V., Oetzmann, C., Penninx, B., Simblett, S., Bruce, S., Nica, R., Wykes, T., Brasen, J., Myin-Germeys, I., Rintala, A., Conde, P., Dobson, R., Folarin, A., Stewart, C., Ranjan, Y., Rashid, Z., Cummins, N., Manyakov, N., Vairavan, S., Hotopf, M.
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container_end_page S112
container_issue S1
container_start_page S112
container_title European psychiatry
container_volume 65
creator Matcham, F.
Leightley, D.
Siddi, S.
Lamers, F.
White, K.
Annas, P.
De Girolamo, G.
Difrancesco, S.
Haro, J.M.
Horsfall, M.
Ivan, A.
Lavelle, G.
Li, Q.
Lombardini, F.
Mohr, D.
Narayan, V.
Oetzmann, C.
Penninx, B.
Simblett, S.
Bruce, S.
Nica, R.
Wykes, T.
Brasen, J.
Myin-Germeys, I.
Rintala, A.
Conde, P.
Dobson, R.
Folarin, A.
Stewart, C.
Ranjan, Y.
Rashid, Z.
Cummins, N.
Manyakov, N.
Vairavan, S.
Hotopf, M.
description IntroductionMajor Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an exciting opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks.ObjectivesTo describe the amount of data collected during a multimodal longitudinal RMT study, in an MDD population.MethodsRADAR-MDD is a multi-centre, prospective observational cohort study. People with a history of MDD were provided with a wrist-worn wearable, and several apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks and cognitive assessments and followed-up for a maximum of 2 years.ResultsA total of 623 individuals with a history of MDD were enrolled in the study with 80% completion rates for primary outcome assessments across all timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. Data availability across all RMT data types varied depending on the source of data and the participant-burden for each data type. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. 110 participants had > 50% data available across all data types, and thus able to contribute to multiparametric analyses.ConclusionsRADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible.DisclosureNo significant relationships.
doi_str_mv 10.1192/j.eurpsy.2022.315
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Remote Measurement Technologies (RMT) provide an exciting opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks.ObjectivesTo describe the amount of data collected during a multimodal longitudinal RMT study, in an MDD population.MethodsRADAR-MDD is a multi-centre, prospective observational cohort study. People with a history of MDD were provided with a wrist-worn wearable, and several apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks and cognitive assessments and followed-up for a maximum of 2 years.ResultsA total of 623 individuals with a history of MDD were enrolled in the study with 80% completion rates for primary outcome assessments across all timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. Data availability across all RMT data types varied depending on the source of data and the participant-burden for each data type. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. 110 participants had &gt; 50% data available across all data types, and thus able to contribute to multiparametric analyses.ConclusionsRADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible.DisclosureNo significant relationships.</description><identifier>ISSN: 0924-9338</identifier><identifier>EISSN: 1778-3585</identifier><identifier>DOI: 10.1192/j.eurpsy.2022.315</identifier><language>eng</language><publisher>Paris: Cambridge University Press</publisher><subject>Abstract ; longitudinal ; major depressive disorder ; Mental depression ; observational ; Oral Communication ; Questionnaires ; remote measurement technologies ; Smartphones</subject><ispartof>European psychiatry, 2022-06, Vol.65 (S1), p.S112-S112</ispartof><rights>The Author(s), 2022. Published by Cambridge University Press on behalf of the European Psychiatric Association. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2022 2022 The Author(s)</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2825-83cec58ca2be9889d1ba65c953e4ec1981cf1f4e10b078737d5db1e0f3ecefac3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2708682031/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2708682031?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,44590,53791,53793,75126</link.rule.ids></links><search><creatorcontrib>Matcham, F.</creatorcontrib><creatorcontrib>Leightley, D.</creatorcontrib><creatorcontrib>Siddi, S.</creatorcontrib><creatorcontrib>Lamers, F.</creatorcontrib><creatorcontrib>White, K.</creatorcontrib><creatorcontrib>Annas, P.</creatorcontrib><creatorcontrib>De Girolamo, G.</creatorcontrib><creatorcontrib>Difrancesco, S.</creatorcontrib><creatorcontrib>Haro, J.M.</creatorcontrib><creatorcontrib>Horsfall, M.</creatorcontrib><creatorcontrib>Ivan, A.</creatorcontrib><creatorcontrib>Lavelle, G.</creatorcontrib><creatorcontrib>Li, Q.</creatorcontrib><creatorcontrib>Lombardini, F.</creatorcontrib><creatorcontrib>Mohr, D.</creatorcontrib><creatorcontrib>Narayan, V.</creatorcontrib><creatorcontrib>Oetzmann, C.</creatorcontrib><creatorcontrib>Penninx, B.</creatorcontrib><creatorcontrib>Simblett, S.</creatorcontrib><creatorcontrib>Bruce, S.</creatorcontrib><creatorcontrib>Nica, R.</creatorcontrib><creatorcontrib>Wykes, T.</creatorcontrib><creatorcontrib>Brasen, J.</creatorcontrib><creatorcontrib>Myin-Germeys, I.</creatorcontrib><creatorcontrib>Rintala, A.</creatorcontrib><creatorcontrib>Conde, P.</creatorcontrib><creatorcontrib>Dobson, R.</creatorcontrib><creatorcontrib>Folarin, A.</creatorcontrib><creatorcontrib>Stewart, C.</creatorcontrib><creatorcontrib>Ranjan, Y.</creatorcontrib><creatorcontrib>Rashid, Z.</creatorcontrib><creatorcontrib>Cummins, N.</creatorcontrib><creatorcontrib>Manyakov, N.</creatorcontrib><creatorcontrib>Vairavan, S.</creatorcontrib><creatorcontrib>Hotopf, M.</creatorcontrib><title>Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): Recruitment, retention, and data availability in a longitudinal remote measurement study</title><title>European psychiatry</title><description>IntroductionMajor Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an exciting opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks.ObjectivesTo describe the amount of data collected during a multimodal longitudinal RMT study, in an MDD population.MethodsRADAR-MDD is a multi-centre, prospective observational cohort study. People with a history of MDD were provided with a wrist-worn wearable, and several apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks and cognitive assessments and followed-up for a maximum of 2 years.ResultsA total of 623 individuals with a history of MDD were enrolled in the study with 80% completion rates for primary outcome assessments across all timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. Data availability across all RMT data types varied depending on the source of data and the participant-burden for each data type. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. 110 participants had &gt; 50% data available across all data types, and thus able to contribute to multiparametric analyses.ConclusionsRADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible.DisclosureNo significant relationships.</description><subject>Abstract</subject><subject>longitudinal</subject><subject>major depressive disorder</subject><subject>Mental depression</subject><subject>observational</subject><subject>Oral Communication</subject><subject>Questionnaires</subject><subject>remote measurement 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measurement study</atitle><jtitle>European psychiatry</jtitle><date>2022-06-01</date><risdate>2022</risdate><volume>65</volume><issue>S1</issue><spage>S112</spage><epage>S112</epage><pages>S112-S112</pages><issn>0924-9338</issn><eissn>1778-3585</eissn><abstract>IntroductionMajor Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an exciting opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks.ObjectivesTo describe the amount of data collected during a multimodal longitudinal RMT study, in an MDD population.MethodsRADAR-MDD is a multi-centre, prospective observational cohort study. People with a history of MDD were provided with a wrist-worn wearable, and several apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks and cognitive assessments and followed-up for a maximum of 2 years.ResultsA total of 623 individuals with a history of MDD were enrolled in the study with 80% completion rates for primary outcome assessments across all timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. Data availability across all RMT data types varied depending on the source of data and the participant-burden for each data type. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. 110 participants had &gt; 50% data available across all data types, and thus able to contribute to multiparametric analyses.ConclusionsRADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible.DisclosureNo significant relationships.</abstract><cop>Paris</cop><pub>Cambridge University Press</pub><doi>10.1192/j.eurpsy.2022.315</doi><oa>free_for_read</oa></addata></record>
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1778-3585
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subjects Abstract
longitudinal
major depressive disorder
Mental depression
observational
Oral Communication
Questionnaires
remote measurement technologies
Smartphones
title Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): Recruitment, retention, and data availability in a longitudinal remote measurement study
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