Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): a multi-centre prospective cohort study protocol

BackgroundThere 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.MethodsRADAR-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).DiscussionThis 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.

[1]  P. Harris,et al.  Research electronic data capture (REDCap) - A metadata-driven methodology and workflow process for providing translational research informatics support , 2009, J. Biomed. Informatics.

[2]  Thomas J. Carmody,et al.  The Inventory of Depressive Symptomatology (IDS): Clinician (IDS‐C) and Self‐Report (IDS‐SR) ratings of depressive symptoms , 2000 .

[3]  G. Papakostas.  Cognitive symptoms in patients with major depressive disorder and their implications for clinical practice. , 2014, The Journal of clinical psychiatry.

[4]  S. Hollon,et al.  Mood reactivity rather than cognitive reactivity is predictive of depressive relapse: a randomized study with 5.5-year follow-up. , 2013, Journal of consulting and clinical psychology.

[5]  B. Yersin,et al.  Reliability and validity of the Alcohol Use Disorders Identification Test (AUDIT) imbedded within a general health risk screening questionnaire: results of a survey in 332 primary care patients. , 2000, Alcoholism, clinical and experimental research.

[6]  Julia Dmitrieva,et al.  Item-wording and the dimensionality of the Rosenberg Self-Esteem Scale: do they matter? , 2003 .

[7]  A. Beekman,et al.  Reconsidering the prognosis of major depressive disorder across diagnostic boundaries: full recovery is the exception rather than the rule , 2017, BMC Medicine.

[8]  James E. Hunton,et al.  Capturing affect via the experience sampling method: Prospects for accounting information systems researchers , 2011, Int. J. Account. Inf. Syst..

[9]  James R. Lewis.  Psychometric Evaluation of the PSSUQ Using Data from Five Years of Usability Studies , 2002 .

[10]  P. Cuijpers,et al.  Increased mortality in depressive disorders: A review , 2004, Current psychiatry reports.

[11]  U. Hegerl,et al.  Smartphone-Based Monitoring of Objective and Subjective Data in Affective Disorders: Where Are We and Where Are We Going? Systematic Review , 2017, Journal of medical Internet research.

[12]  J. M. Garzón-Rey,et al.  Remote Assessment of Disease and Relapse – Central Nervous System- RADAR-CNS , 2017 .

[13]  Dimitra Vergyri,et al.  Using Prosodic and Spectral Features in Detecting Depression in Elderly Males , 2011, INTERSPEECH.

[14]  B. Lebowitz,et al.  Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. , 2006, The American journal of psychiatry.

[15]  I. Žeželj,et al.  Memory Bias in Depression: Effects of Self-Reference and Age , 2017 .

[16]  D. Boomsma,et al.  Validity of LIDAS (LIfetime Depression Assessment Self-report): a self-report online assessment of lifetime major depressive disorder , 2016, Psychological Medicine.

[17]  Robert L. Spitzer,et al.  Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7. Copyright: American Medical Association. , 2006 .

[18]  R. Drake,et al.  Treating co-morbid chronic medical conditions and anxiety/depression , 2011, Epidemiology and Psychiatric Sciences.

[19]  Eric J. Topol,et al.  The emerging field of mobile health , 2015, Science Translational Medicine.

[20]  Scott C Roesch,et al.  Modeling trait and state variation using multilevel factor analysis with PANAS daily diary data. , 2011, Journal of research in personality.

[21]  Wynne W. Chin,et al.  A Fast Form Approach to Measuring Technology Acceptance and Other Constructs , 2008, MIS Q..

[22]  J. Os,et al.  Experience sampling research in psychopathology: opening the black box of daily life , 2009, Psychological Medicine.

[23]  A. Tylee,et al.  Quality of Life in Depressed Patients in UK Primary Care: The FINDER Study , 2013, Neurology and Therapy.

[24]  R. Kessler,et al.  The World Health Organization Composite International Diagnostic Interview short‐form (CIDI‐SF) , 1998 .

[25]  Sona Dimidjian,et al.  Relapse prevention in major depressive disorder: Mindfulness-based cognitive therapy versus an active control condition. , 2015, Journal of consulting and clinical psychology.

[26]  Mark C. Field,et al.  Technology-Enabled Remote Monitoring and Self-Management — Vision for Patient Empowerment Following Cardiac and Vascular Surgery: User Testing and Randomized Controlled Trial Protocol , 2016, JMIR research protocols.

[27]  J. Markowitz,et al.  The epidemiology of chronic major depressive disorder and dysthymic disorder: results from the National Epidemiologic Survey on Alcohol and Related Conditions. , 2010, The Journal of clinical psychiatry.

[28]  M. Hotopf,et al.  Barriers to and Facilitators of Engagement With mHealth Technology for Remote Measurement and Management of Depression: Qualitative Analysis , 2019, JMIR mHealth and uHealth.

[29]  B. Löwe,et al.  A brief measure for assessing generalized anxiety disorder: the GAD-7. , 2006, Archives of internal medicine.

[30]  J. Mundt,et al.  The Work and Social Adjustment Scale: a simple measure of impairment in functioning. , 2002, The British journal of psychiatry : the journal of mental science.

[31]  Mitchell D. Wilkes,et al.  Evaluation of Voice Acoustics as Predictors of Clinical Depression Scores. , 2017, Journal of voice : official journal of the Voice Foundation.

[32]  M. Knapp,et al.  Client Socio-Demographic and Service Receipt Inventory – European Version: development of an instrument for international research , 2000, British Journal of Psychiatry.

[33]  J. Concato,et al.  A simulation study of the number of events per variable in logistic regression analysis. , 1996, Journal of clinical epidemiology.

[34]  R. Baldessarini,et al.  Prevention of Relapse and Recurrence in Adults with Major Depressive Disorder: Systematic Review and Meta-Analyses of Controlled Trials , 2015, The international journal of neuropsychopharmacology.

[35]  D. Mohr,et al.  Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning. , 2017, Annual review of clinical psychology.

[36]  M. Hotopf,et al.  Barriers to and Facilitators of Engagement With mHealth Technology for Remote Measurement and Management of Depression: Qualitative Analysis (Preprint) , 2018 .

[37]  Meha Bhatt,et al.  Acceptability of the Fitbit in behavioural activation therapy for depression: a qualitative study , 2017, Evidence Based Journals.

[38]  T. Brugha,et al.  The List of Threatening Experiences: the reliability and validity of a brief life events questionnaire , 1990, Acta psychiatrica Scandinavica.

[39]  E. Berndt,et al.  The Clinical and Occupational Correlates of Work Productivity Loss Among Employed Patients With Depression , 2004, Journal of occupational and environmental medicine.

[40]  M. aan het Rot,et al.  Mood disorders in everyday life: a systematic review of experience sampling and ecological momentary assessment studies. , 2012, Clinical psychology review.

[41]  Elizabeth Broadbent,et al.  The brief illness perception questionnaire. , 2006, Journal of psychosomatic research.

[42]  T. Strine,et al.  The PHQ-8 as a measure of current depression in the general population. , 2009, Journal of affective disorders.

[43]  John Zimmerman,et al.  Detection of Behavior Change in People with Depression , 2014, AAAI Workshop: Modern Artificial Intelligence for Health Analytics.

[44]  Thomas F. Quatieri,et al.  Vocal and Facial Biomarkers of Depression based on Motor Incoordination and Timing , 2014, AVEC '14.

[45]  G. Dingle,et al.  Social group memberships protect against future depression, alleviate depression symptoms and prevent depression relapse. , 2013, Social science & medicine.

[46]  T. Wykes,et al.  Barriers to and Facilitators of Engagement With Remote Measurement Technology for Managing Health: Systematic Review and Content Analysis of Findings , 2018, Journal of medical Internet research.

[47]  Richard Dobson,et al.  RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices , 2019, JMIR mHealth and uHealth.

[48]  A. Goldstone,et al.  A validation study of Fitbit Charge 2™ compared with polysomnography in adults , 2018, Chronobiology international.

[49]  Diego Klabjan,et al.  Predictive Analytics Using Smartphone Sensors for Depressive Episodes , 2016, ArXiv.

[50]  P. Wilhelm,et al.  Assessing Mood in Daily Life Structural Validity, Sensitivity to Change, and Reliability of a Short-Scale to Measure Three Basic Dimensions of Mood , 2007 .

[51]  Lisa A. Marsch,et al.  Emerging mHealth and eHealth interventions for serious mental illness: a review of the literature , 2015, Journal of mental health.

[52]  L. Horwood,et al.  How accurate is recall of key symptoms of depression? A comparison of recall and longitudinal reports , 2004, Psychological Medicine.

[53]  S. Hollon,et al.  A lifetime approach to major depressive disorder: The contributions of psychological interventions in preventing relapse and recurrence. , 2015, Clinical psychology review.