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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.
Matcham, Faith; Leightley, Daniel; Siddi, Sara; Lamers, Femke; White, Katie M; Annas, Peter; de Girolamo, Giovanni; Difrancesco, Sonia; Haro, Josep Maria; Horsfall, Melany; Ivan, Alina; Lavelle, Grace; Li, Qingqin; Lombardini, Federica; Mohr, David C; Narayan, Vaibhav A; Oetzmann, Carolin; Penninx, Brenda W J H; Bruce, Stuart; Nica, Raluca; Simblett, Sara K; Wykes, Til; Brasen, Jens Christian; Myin-Germeys, Inez; Rintala, Aki; Conde, Pauline; Dobson, Richard J B; Folarin, Amos A; Stewart, Callum; Ranjan, Yatharth; Rashid, Zulqarnain; Cummins, Nick; Manyakov, Nikolay V; Vairavan, Srinivasan; Hotopf, Matthew.
Afiliação
  • Matcham F; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK. Faith.Matcham@kcl.ac.uk.
  • Leightley D; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  • Siddi S; Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain.
  • Lamers F; Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
  • White KM; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  • Annas P; H. Lundbeck A/S, Valby, Denmark.
  • de Girolamo G; IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
  • Difrancesco S; Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
  • Haro JM; Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain.
  • Horsfall M; Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
  • Ivan A; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  • Lavelle G; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  • Li Q; Janssen Research and Development, LLC, Titusville, NJ, USA.
  • Lombardini F; Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain.
  • Mohr DC; Center for Behavioral Intervention Technologies, Department of Preventative Medicine, Northwestern University, Chicago, IL, USA.
  • Narayan VA; Janssen Research and Development, LLC, Titusville, NJ, USA.
  • Oetzmann C; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  • Penninx BWJH; Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
  • Bruce S; RADAR-CNS Patient Advisory Board, King's College London, London, UK.
  • Nica R; RADAR-CNS Patient Advisory Board, King's College London, London, UK.
  • Simblett SK; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  • Wykes T; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  • Brasen JC; H. Lundbeck A/S, Valby, Denmark.
  • Myin-Germeys I; Department for Neurosciences, Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium.
  • Rintala A; Department for Neurosciences, Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium.
  • Conde P; Faculty of Social and Health Care, LAB University of Applied Sciences, Lahti, Finland.
  • Dobson RJB; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  • Folarin AA; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  • Stewart C; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  • Ranjan Y; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  • Rashid Z; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  • Cummins N; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  • Manyakov NV; Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  • Vairavan S; Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany.
  • Hotopf M; Janssen Pharmaceutica NV, Beerse, Belgium.
BMC Psychiatry ; 22(1): 136, 2022 02 21.
Article em En | MEDLINE | ID: mdl-35189842
BACKGROUND: Major 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 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. A key question for the field is the extent to which participants can adhere to research protocols and the completeness of data collected. We aimed to describe drop out and data completeness in a naturalistic multimodal longitudinal RMT study, in people with a history of recurrent MDD. We further aimed to determine whether those experiencing a depressive relapse at baseline contributed less complete data. METHODS: Remote Assessment of Disease and Relapse - Major Depressive Disorder (RADAR-MDD) is a multi-centre, prospective observational cohort study conducted as part of the Remote Assessment of Disease and Relapse - Central Nervous System (RADAR-CNS) program. People with a history of MDD were provided with a wrist-worn wearable device, and smartphone apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks, and cognitive assessments. Participants were followed-up for a minimum of 11 months and maximum of 24 months. RESULTS: Individuals with a history of MDD (n = 623) were enrolled in the study,. We report 80% completion rates for primary outcome assessments across all follow-up timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. In total, 110 participants had > 50% data available across all data types. CONCLUSIONS: RADAR-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. We found comparable levels of data availability in active and passive forms of data collection, demonstrating that both are feasible in this patient group.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Transtorno Depressivo Maior / Aplicativos Móveis Tipo de estudo: Diagnostic_studies / Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Transtorno Depressivo Maior / Aplicativos Móveis Tipo de estudo: Diagnostic_studies / Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article