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Siddi S, Bailon R, Giné-Vázquez I, Matcham F, Lamers F, Kontaxis S, Laporta E, Garcia E, Lombardini F, Annas P, Hotopf M, Penninx BWJH, Ivan A, White KM, Difrancesco S, Locatelli P, Aguiló J, Peñarrubia-Maria MT, Narayan VA, Folarin A, Leightley D, Cummins N, Vairavan S, Ranjan Y, Rintala A, de Girolamo G, Simblett SK, Wykes T, Myin-Germeys I, Dobson R, Haro JM. The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity. Psychol Med 2023; 53:3249-3260. [PMID: 37184076 DOI: 10.1017/s0033291723001034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
BACKGROUND Alterations in heart rate (HR) may provide new information about physiological signatures of depression severity. This 2-year study in individuals with a history of recurrent major depressive disorder (MDD) explored the intra-individual variations in HR parameters and their relationship with depression severity. METHODS Data from 510 participants (Number of observations of the HR parameters = 6666) were collected from three centres in the Netherlands, Spain, and the UK, as a part of the remote assessment of disease and relapse-MDD study. We analysed the relationship between depression severity, assessed every 2 weeks with the Patient Health Questionnaire-8, with HR parameters in the week before the assessment, such as HR features during all day, resting periods during the day and at night, and activity periods during the day evaluated with a wrist-worn Fitbit device. Linear mixed models were used with random intercepts for participants and countries. Covariates included in the models were age, sex, BMI, smoking and alcohol consumption, antidepressant use and co-morbidities with other medical health conditions. RESULTS Decreases in HR variation during resting periods during the day were related with an increased severity of depression both in univariate and multivariate analyses. Mean HR during resting at night was higher in participants with more severe depressive symptoms. CONCLUSIONS Our findings demonstrate that alterations in resting HR during all day and night are associated with depression severity. These findings may provide an early warning of worsening depression symptoms which could allow clinicians to take responsive treatment measures promptly.
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Affiliation(s)
- S Siddi
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - R Bailon
- Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain
- Centros de investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid, Spain
| | - I Giné-Vázquez
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - F Matcham
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- School of Psychology, University of Sussex, Falmer, UK
| | - F Lamers
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - S Kontaxis
- Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain
- Centros de investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid, Spain
| | - E Laporta
- Centros de investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid, Spain
| | - E Garcia
- Centros de investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid, Spain
- Microelectrónica y Sistemas Electrónicos, Universidad Autónoma de Barcelona, CIBERBBN, Barcelona, Spain
| | - F Lombardini
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - P Annas
- H. Lundbeck A/S, Valby, Denmark
| | - M Hotopf
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - B W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - A Ivan
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - K M White
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - S Difrancesco
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - P Locatelli
- Department of Engineering and Applied Science, University of Bergamo, Bergamo, Italy
| | - J Aguiló
- Centros de investigación biomédica en red en el área de bioingeniería, biomateriales y nanomedicina (CIBER-BBN), Madrid, Spain
- Microelectrónica y Sistemas Electrónicos, Universidad Autónoma de Barcelona, CIBERBBN, Barcelona, Spain
| | - M T Peñarrubia-Maria
- Catalan Institute of Health, Primary Care Research Institute (IDIAP Jordi Gol), CIBERESP, Barcelona, Spain
| | - V A Narayan
- Research and Development Information Technology, Janssen Research & Development, LLC, Titusville, NJ, USA
| | - A Folarin
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - D Leightley
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - N Cummins
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - S Vairavan
- Research and Development Information Technology, Janssen Research & Development, LLC, Titusville, NJ, USA
| | - Y Ranjan
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - A Rintala
- Department for Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
- Faculty of Social Services and Health Care, LAB University of Applied Sciences, Lahti, Finland
| | - G de Girolamo
- IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - S K Simblett
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - T Wykes
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
- South London and Maudsley NHS Foundation Trust, London, UK
| | - I Myin-Germeys
- Department for Neurosciences, Center for Contextual Psychiatry, Katholieke Universiteit Leuven, Leuven, Belgium
| | - R Dobson
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - J M Haro
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
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Matcham F, Carr E, White KM, Leightley D, Lamers F, Siddi S, Annas P, de Girolamo G, Haro JM, Horsfall M, Ivan A, Lavelle G, Li Q, Lombardini F, Mohr DC, Narayan VA, Penninx BWHJ, Oetzmann C, Coromina M, Simblett SK, Weyer J, Wykes T, Zorbas S, Brasen JC, Myin-Germeys I, Conde P, Dobson RJB, Folarin AA, Ranjan Y, Rashid Z, Cummins N, Dineley J, Vairavan S, Hotopf M. Predictors of engagement with remote sensing technologies for symptom measurement in Major Depressive Disorder. J Affect Disord 2022; 310:106-115. [PMID: 35525507 DOI: 10.1016/j.jad.2022.05.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/28/2022] [Accepted: 05/02/2022] [Indexed: 01/10/2023]
Abstract
BACKGROUND Remote sensing for the measurement and management of long-term conditions such as Major Depressive Disorder (MDD) is becoming more prevalent. User-engagement is essential to yield any benefits. We tested three hypotheses examining associations between clinical characteristics, perceptions of remote sensing, and objective user engagement metrics. METHODS The Remote Assessment of Disease and Relapse - Major Depressive Disorder (RADAR-MDD) study is a multicentre longitudinal observational cohort study in people with recurrent MDD. Participants wore a FitBit and completed app-based assessments every two weeks for a median of 18 months. Multivariable random effects regression models pooling data across timepoints were used to examine associations between variables. RESULTS A total of 547 participants (87.8% of the total sample) were included in the current analysis. Higher levels of anxiety were associated with lower levels of perceived technology ease of use; increased functional disability was associated with small differences in perceptions of technology usefulness and usability. Participants who reported higher system ease of use, usefulness, and acceptability subsequently completed more app-based questionnaires and tended to wear their FitBit activity tracker for longer. All effect sizes were small and unlikely to be of practical significance. LIMITATIONS Symptoms of depression, anxiety, functional disability, and perceptions of system usability are measured at the same time. These therefore represent cross-sectional associations rather than predictions of future perceptions. CONCLUSIONS These findings suggest that perceived usability and actual use of remote measurement technologies in people with MDD are robust across differences in severity of depression, anxiety, and functional impairment.
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Affiliation(s)
- F Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - E Carr
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - K M White
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - D Leightley
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - F Lamers
- Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - S Siddi
- Parc Sanitari Sant Joan de Déu, Fundació San Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - P Annas
- H. Lundbeck A/S, Valby, Denmark
| | - G de Girolamo
- IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - J M Haro
- Parc Sanitari Sant Joan de Déu, Fundació San Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - M Horsfall
- Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - A Ivan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - G Lavelle
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Q Li
- Janssen Research and Development, LLC, Titusville, NJ, USA
| | - F Lombardini
- Parc Sanitari Sant Joan de Déu, Fundació San Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - D C Mohr
- Center for Behavioral Intervention Technologies, Department of Preventative Medicine, Northwestern University, Chicago, IL, USA
| | - V A Narayan
- Janssen Research and Development, LLC, Titusville, NJ, USA
| | - B W H J Penninx
- Department of Psychiatry and Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - C Oetzmann
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - M Coromina
- Parc Sanitari Joan de Déu, Barcelona, Spain
| | - S K Simblett
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - J Weyer
- RADAR-CNS Patient Advisory Board
| | - T Wykes
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; South London and Maudsley NHS Foundation Trust, London, UK
| | - S Zorbas
- RADAR-CNS Patient Advisory Board
| | | | - I Myin-Germeys
- Department for Neurosciences, Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium
| | - P Conde
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - R J B Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - A A Folarin
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Y Ranjan
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Z Rashid
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - N Cummins
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - J Dineley
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
| | - S Vairavan
- Janssen Research and Development, LLC, Titusville, NJ, USA
| | - M Hotopf
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; South London and Maudsley NHS Foundation Trust, London, UK
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Matcham F, Leightley D, Siddi S, Lamers F, White K, Annas P, De Girolamo G, Difrancesco S, Haro J, 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. Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): Recruitment, retention, and data availability in a longitudinal remote measurement study. Eur Psychiatry 2022. [PMCID: PMC9564033 DOI: 10.1192/j.eurpsy.2022.315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Introduction
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 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.
Objectives
To describe the amount of data collected during a multimodal longitudinal RMT study, in an MDD population.
Methods
RADAR-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.
Results
A 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.
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.
Disclosure
No significant relationships.
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Matcham F, Barattieri di San Pietro C, Bulgari V, de Girolamo G, Dobson R, Eriksson H, Folarin AA, Haro JM, Kerz M, Lamers F, Li Q, Manyakov NV, Mohr DC, Myin-Germeys I, Narayan V, BWJH P, Ranjan Y, Rashid Z, Rintala A, Siddi S, Simblett SK, Wykes T, Hotopf M. Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): a multi-centre prospective cohort study protocol. BMC Psychiatry 2019; 19:72. [PMID: 30777041 PMCID: PMC6379954 DOI: 10.1186/s12888-019-2049-z] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 02/01/2019] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND There is a growing body of literature highlighting the role that wearable and mobile remote measurement technology (RMT) can play in measuring symptoms of major depressive disorder (MDD). Outcomes assessment typically relies on self-report, which can be biased by dysfunctional perceptions and current symptom severity. Predictors of depressive relapse include disrupted sleep, reduced sociability, physical activity, changes in mood, prosody and cognitive function, which are all amenable to measurement via RMT. This study aims to: 1) determine the usability, feasibility and acceptability of RMT; 2) improve and refine clinical outcome measurement using RMT to identify current clinical state; 3) determine whether RMT can provide information predictive of depressive relapse and other critical outcomes. METHODS RADAR-MDD is a multi-site prospective cohort study, aiming to recruit 600 participants with a history of depressive disorder across three sites: London, Amsterdam and Barcelona. Participants will be asked to wear a wrist-worn activity tracker and download several apps onto their smartphones. These apps will be used to either collect data passively from existing smartphone sensors, or to deliver questionnaires, cognitive tasks, and speech assessments. The wearable device, smartphone sensors and questionnaires will collect data for up to 2-years about participants' sleep, physical activity, stress, mood, sociability, speech patterns, and cognitive function. The primary outcome of interest is MDD relapse, defined via the Inventory of Depressive Symptomatology- Self-Report questionnaire (IDS-SR) and the World Health Organisation's self-reported Composite International Diagnostic Interview (CIDI-SF). DISCUSSION This study aims to provide insight into the early predictors of major depressive relapse, measured unobtrusively via RMT. If found to be acceptable to patients and other key stakeholders and able to provide clinically useful information predictive of future deterioration, RMT has potential to change the way in which depression and other long-term conditions are measured and managed.
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Affiliation(s)
- F. Matcham
- King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - C. Barattieri di San Pietro
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Univeristy of Milan-Bicocca, Milan, Italy
| | - V. Bulgari
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - G. de Girolamo
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - R. Dobson
- King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | | | - A. A. Folarin
- King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - J. M. Haro
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - M. Kerz
- King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - F. Lamers
- Department of Psychiatry and Amsterdam Public Health Research Institute, VU University Medical Centre, Amsterdam, The Netherlands
| | - Q. Li
- Janssen Research and Development, LLC, Titusville, NJ USA
| | | | - D. C. Mohr
- Center for Behavioral Intervention Technologies, Department of Preventive Medicine, Northwestern University, Chicago, IL USA
| | - I. Myin-Germeys
- Department for Neurosciences, Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium
| | - V. Narayan
- Janssen Research and Development, LLC, Titusville, NJ USA
| | - Penninx BWJH
- Department of Psychiatry and Amsterdam Public Health Research Institute, VU University Medical Centre, Amsterdam, The Netherlands
| | - Y. Ranjan
- King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - Z. Rashid
- King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - A. Rintala
- Department for Neurosciences, Center for Contextual Psychiatry, KU Leuven, Leuven, Belgium
| | - S. Siddi
- Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - S. K. Simblett
- King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - T. Wykes
- King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
| | - M. Hotopf
- King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, UK
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