Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Matcham F

PROVIDER: S-EPMC8860359 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Publications

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 F Leightley Daniel D Siddi Sara S Lamers Femke F White Katie M KM Annas Peter P de Girolamo Giovanni G Difrancesco Sonia S Haro Josep Maria JM Horsfall Melany M Ivan Alina A Lavelle Grace G Li Qingqin Q Lombardini Federica F Mohr David C DC Narayan Vaibhav A VA Oetzmann Carolin C Penninx Brenda W J H BWJH Bruce Stuart S Nica Raluca R Simblett Sara K SK Wykes Til T Brasen Jens Christian JC Myin-Germeys Inez I Rintala Aki A Conde Pauline P Dobson Richard J B RJB Folarin Amos A AA Stewart Callum C Ranjan Yatharth Y Rashid Zulqarnain Z Cummins Nick N Manyakov Nikolay V NV Vairavan Srinivasan S Hotopf Matthew M

BMC psychiatry 20220221 1

<h4>Background</h4>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 adh ...[more]

PMID: 35189842

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Project description: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.

Project description:AimTo evaluate the longitudinal association of sedentary behavior, light and moderate-to-vigorous physical activity (MVPA) participation with depressive symptoms and whether their possible association changed depending on the pandemic phase.MethodsThis longitudinal study conducted secondary analysis from the Spanish cohort of the Remote Assessment of Disease and Relapse - Major Depressive Disorder (RADAR-MDD) study. Depressive symptoms were assessed by the Patient Health Questionnaire (PHQ-8). Sedentary behavior and physical activity were estimated via wrist-worn devices. Linear mixed models evaluated the longitudinal associations of sedentary behavior and physical activity (light and moderate-to-vigorous intensities) with depressive symptoms.ResultsIn total, 95 participants (67.5% women, 53.0 [±10.5] years of age on average) were monitored pre-COVID-19 and included in the analyses. Pre-COVID-19, 73.7% of participants presented depression, and, on average, participated in 13.2 (±1.08) hours/day of sedentary behavior, 2.42 (±0.90) hours/day of light physical activity and 23.6 (±19.80) minutes/day of MVPA. Considering all the observations (from November 2019 to October 2020), an additional hour/day of sedentary behavior was longitudinally associated with higher depressive symptoms [βstd = 0.06, 95% confidence interval (CI) 0.10 to 0.47], whereas an additional hour/day in light physical activity was associated with lower depressive symptoms (βstd = -0.06, 95% CI -0.59 to -0.15). Time in MVPA was not associated with depressive symptomatology. The association of sedentary behavior and light physical activity with depressive symptoms was significant only during pre-COVID-19 and COVID-19 relaxation periods, whereas during the strictest periods of the pandemic with regards to the restrictions (lockdown and de-escalation), the association was not observed.ConclusionSedentary behavior and light physical activity were longitudinally associated with depressive symptoms in participants with a history of MDD. The incorporation of light physical activity should be stimulated in adults with a history of MDD. Neither sedentary behavior nor light physical activity were associated with depressive symptoms during the most restrictive COVID-19 phases, whereas sedentary behavior (positively) and light physical activity (negatively) were associated with depressive symptoms in persons with MDD before and after the COVID-19 pandemic.

Project description:Depressive symptoms are related to abnormalities in the autonomic nervous system (ANS), and physiological signals that can be used to measure and evaluate such abnormalities have previously been used as indicators for diagnosing mental disorder, such as major depressive disorder (MDD). In this study, we investigate the feasibility of developing an objective measure of depressive symptoms that is based on examining physiological abnormalities in individuals when they are experiencing mental stress. To perform this, we recruited 30 patients with MDD and 31 healthy controls. Then, skin conductance (SC) was measured during five 5-min experimental phases, comprising baseline, mental stress, recovery from the stress, relaxation, and recovery from the relaxation, respectively. For each phase, the mean amplitude of the skin conductance level (MSCL), standard deviations of the SCL (SDSCL), slope of the SCL (SSCL), mean amplitude of the non-specific skin conductance responses (MSCR), number of non-specific skin conductance responses (NSCR), and power spectral density (PSD) were evaluated from the SC signals, producing 30 parameters overall (six features for each phase). These features were used as input data for a support vector machine (SVM) algorithm designed to distinguish MDD patients from healthy controls based on their physiological responses. Statistical tests showed that the main effect of task was significant in all SC features, and the main effect of group was significant in MSCL, SDSCL, SSCL, and PSD. In addition, the proposed algorithm achieved 70% accuracy, 70% sensitivity, 71% specificity, 70% positive predictive value, 71% negative predictive value in classifying MDD patients and healthy controls. These results demonstrated that it is possible to extract meaningful features that reflect changes in ANS responses to various stimuli. Using these features, detection of MDD was feasible, suggesting that SC analysis has great potential for future diagnostics and prediction of depression based on objective interpretation of depressive states.

Project description:Objective: To examine the dose-dependent relationship of different types of statins with the occurrence of major depressive disorder (MDD) and prescription of antidepressant medication. Methods: This cross-sectional study used medical claims data for the general Austrian population (n = 7,481,168) to identify all statin-treated patients. We analyzed all patients with MDD undergoing statin treatment and calculated the average defined daily dose for six different types of statins. In a sub-analysis conducted independently of inpatient care, we investigated all patients on antidepressant medication (statin-treated patients: n = 98,913; non-statin-treated patients: n = 789,683). Multivariate logistic regression analyses were conducted to calculate the risk of diagnosed MDD and prescription of antidepressant medication in patients treated with different types of statins and dosages compared to non-statin-treated patients. Results: In this study, there was an overrepresentation of MDD in statin-treated patients when compared to non-statin-treated patients (OR: 1.22, 95% CI: 1.20-1.25). However, there was a dose dependent relationship between statins and diagnosis of MDD. Compared to controls, the ORs of MDD were lower for low-dose statin-treated patients (simvastatin>0- < =10 mg:OR: 0.59, 95% CI: 0.54-0.64; atorvastatin>0- < =10 mg:OR:0.65, 95%CI: 0.59-0.70; rosuvastatin>0- < =10 mg:OR: 0.68, 95% CI: 0.53-0.85). In higher statin dosages there was an overrepresentation of MDD (simvastatin>40- < =60 mg:OR: 2.42, 95% CI: 2.18-2.70, >60-80 mg:OR: 5.27, 95% CI: 4.21-6.60; atorvastatin>40- < =60 mg:OR: 2.71, 95% CI: 1.98-3.72, >60- < =80 mg:OR: 3.73, 95% CI: 2.22-6.28; rosuvastatin>20- < =40 mg:OR: 2.09, 95% CI: 1.31-3.34). The results were confirmed in a sex-specific analysis and in a cohort of patients taking antidepressants, prescribed independently of inpatient care. Conclusions: This study shows that it is important to carefully re-investigate the relationship between statins and MDD. High-dose statin treatment was related to an overrepresentation, low-dose statin treatment to an underrepresentation of MDD.

Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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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