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

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. KEYWORDS: M-health; Major depressive disorder; Observational cohort; Outcome measurement; Passive sensing; Prospective study; Remote measurement technolog

The association between persistent cognitive difficulties and depression and functional outcomes in people with Major Depressive Disorder

Background: Cognitive symptoms are common during and following episodes of depression. Little is known about the persistence of self-reported and performance-based cognition with depression and functional outcomes. Methods: This is a secondary analysis of a prospective naturalistic observational clinical cohort study of individuals with recurrent Major Depressive Disorder (MDD; N=623). Participants completed app-based self-reported and performance-based cognitive function assessments alongside validated measures of depression, functional disability, and self-esteem every three months. Participants were followed-up for a maximum of 2-years. Multilevel hierarchically nested modelling was employed to explore between- and within-participant variation over time to identify whether persistent cognitive difficulties are related to levels of depression and functional impairment during follow-up. Results: 508 individuals (81.5%) provided data (mean age: 46.6, SD: 15.6; 76.2% female). Increasing persistence of self-reported cognitive difficulty was associated with higher levels of depression and functional impairment throughout the follow-up. In comparison to low persistence of objective cognitive difficulty (75% of timepoints) reported significantly higher levels of depression (B=5.17, SE=2.21, p=0.019) and functional impairment (B=4.82, SE=1.79, p=0.002) over time. Examination of the individual cognitive modules shows that persistently impaired executive function is associated with worse functioning, and poor processing speed is particularly important for worsened depressive symptoms. Conclusions: We replicated previous findings of greater persistence of cognitive difficulty with increasing severity of depression and further demonstrate that these cognitive difficulties are associated with pervasive functional disability. Difficulties with cognition may be an indicator and target for further treatment input

Investigating a psychological model of mental conditions and coping during the COVID-19 pandemic driven by participatory methods

BACKGROUND: There is evidence of increased mental health problems during the early stages of the COVID-19 pandemic. We aimed to identify the factors that put certain groups of people at greater risk of mental health problems. METHODS: We took a participatory approach, involving people with lived experience of mental health problems and/or carers, to generate a set of risk factors and potential moderators of the effects of COVID on mental health. An online cross-sectional survey was completed by 1464 United Kingdom residents between 24th April and 27th June 2020. The survey had questions on whether respondents were existing mental health service users and or carers, level of depression (PHQ9) and anxiety (GAD7), demographics, threat and coping appraisals, perceived resilience (BRS), and specific coping behaviours (validated as part of this study). The relationship between responses and coping strategies was measured using tetrachoric correlations. Structural equation modelling was used to test the model. RESULTS: A model significantly fit our data (rel χ(2) = 2.05, RMSEA = 0.029 95%, CI (0.016, 0.042), CFI = 0.99, TLI = 0.98, SRMR = 0.014). Age and coping appraisal predicted anxiety and depression. Whereas, threat appraisal and ethnicity only predicted anxiety, and resilience only predicted depression. Additionally, specific coping behaviours predicted anxiety and depression, with overlap on distraction. CONCLUSIONS: Some, but not all, risk factors significantly predict anxiety and depression. While there is a relationship between anxiety and depression, different factors may put people at greater risk of one or the other during the pandemic

The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity

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

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

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

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

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

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

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