The full protocol for RADAR-MDD has been reported elsewhere (22). In short, RADAR-MDD is a multi-centre, prospective observational cohort study. The study aimed to examine whether data collected via multiparametric RMT can be used to reliably track illness course and predict relapse in MDD. The study sought to recruit 600 individuals with a recent history of MDD and follow them up for a maximum of 24 months. The study involved no randomisation, intervention, or allocation of people into groups. The study has three recruitment sites: King’s College London (KCL; UK); Amsterdam University Medical Centre, location VUmc (Amsterdam, The Netherlands); and Centro de Investigación Biomédica en Red (CIBER; Barcelona, Spain).
To be eligible for participation in RADAR-MDD, individuals must: 1) have met DSM-5 diagnostic criteria for non-psychotic MDD within the past 2 years; 2) have recurrent MDD (having had a lifetime history of at least 2 episodes); 3) be able and willing to complete self-reported assessments via smartphone; 4) be able to give informed consent; 5) be fluent in English, Dutch, Spanish or Catalan; 5) have an existing Android smartphone, or willingness to swap to Android as their only phone; 6) be aged 18 or over.
Exclusion criteria were: 1) having a lifetime history of bipolar disorder, schizophrenia, MDD with psychotic features, or schizoaffective disorder; 2) dementia; 3) history of moderate or severe drug or alcohol use in the 6 months prior to enrolment; 4) a major medical diagnosis which might impact an individual’s ability to participate in normal daily activities for more than two weeks; 5) pregnancy (although once enrolled, becoming pregnant did not result in withdrawal).
Eligible participants were identified via several recruitment channels, including through existing research cohorts who have consented to be contacted for future research opportunities, through primary and secondary mental health services, or through advertisements for the study placed on mental health charity websites, circulars or Twitter notices(22). Participants in Amsterdam were partially recruited through Hersenonderzoek.nl (https://hersenonderzoek.nl/). All participants provided written consent and provided detailed baseline assessments including sociodemographic, social environment, medical history, medical comorbidities and technology use questionnaires, alongside depression history measured via the Lifetime Depression Assessment – Self-Report (LIDAS; (24)).
Remote Data Collection
Data collection started in London (UK) in November 2017 in a pilot phase of app development, with additional assessments being added to the protocol throughout the first 18 months of the study period to allow small-scale functionality testing and quality control before international large-scale data collection commenced. Data collection started in Barcelona and Amsterdam in September 2018 and February 2019, respectively. A detailed description of the recruitment procedures across sites and schedule of observations has previously been published (22). The data collected used RADAR-base, an open source platform designed to leverage data from wearables and mobile technologies (25). RADAR-base provides both passive and active data collection via two apps – the RADAR active and passive monitoring apps.
The passive RMT (pRMT) app unobtrusively collected information about phone usage throughout participation, requiring no input from the participant. It collected data on ambient noise, ambient light, location, app usage, Bluetooth connectivity, phone usage, and battery life. Whilst some measurements were added later, some data sources were also removed from the protocol throughout follow-up (summarised in additional file 1), due to either technical changes or feasibility. For example, the original protocol aimed to collect data about SMS and call logs, however changes to Google’s Play Store permissions prevented access to this data as of January 2019. Data pertaining to SMS and call logs have not been reported in the current paper due to data collection from this sensor ceasing whilst one site had only recruited 30 individuals and another site had not started recruitment at all.
Participants were additionally required to wear a Fitbit Charge 2/3 device for the duration of participation, providing information about individuals’ sleep and physical activity levels. Participants could keep the Fitbit at the end of the time in the study.
The RADAR-base active RMT (aRMT) app administered validated measurements of depression and self-esteem every 2-weeks via the 8-item Patient Health Questionnaire (PHQ8; (26)) and Rosenberg Self-Esteem Scale (RSES; (27)). Items on the PHQ8 can be totalled and used as a continuous score with higher scores indicating increased depression severity, and scores totalling ≥10 indicating those with significant symptoms (26). The RSES requires reversing of 5 of the 10 items, which then can be totalled to create a total score with higher scores representing increased self-esteem (27).
The aRMT app also delivered a speech task every 2-weeks, requesting participants to record a pre-determined text from the “North Wind and the Sun”, which is phonetically balanced across all three languages (28) and answer a question relating to plans for the upcoming week. Finally, the aRMT app included an ESM protocol (22), requiring participants to complete brief questions relating to mood, stress, sociability, activity and sleep, multiple times per day for 6 days at scheduled times throughout the course of follow-up.
Cognitive function was measured every 6-weeks via an additional THINC-it app®, which was integrated into the RADAR-base platform. The app has been validated to identify cognitive dysfunction within the context of depressive disorder (29). The app contains the 5-item Perceived Deficits Questionnaire (PDQ-5; (30)), alongside computerised versions of the Choice Reaction Time Identification Task (“Code Breaker”), One-Back Test (“Spotter”), Digit Symbol Substitution Test (“Symbol Check”) and Trail Making Test-Part B (“Trails”) tasks to assess processing speed, working memory, concentration and attention (29).
Primary and Secondary Outcome Assessments
All primary and secondary outcome measurements were collected via automatic surveys sent every 3 months via the Research Electronic Data Capture (REDCap) software (31). A full description of the outcome assessment schedule is provided in our published protocol paper (22).
Depressive state was measured using the Inventory of Depressive Symptomatology – Self Report (IDS-SR; (32)) to capture changes in symptom severity, and the World Health Organisation’s Composite Diagnostic Interview – Short Form (CIDI-SF; (33)) to identify people meeting DSM-5 criteria for MDD at each timepoint. These two measurements were used to identify different operationalisations of depression across follow-up, summarised in supplementary additional file 2. Briefly, participants were categorised as being “symptomatic” (scoring ≥26 on the IDS-SR and meeting CIDI-SF criteria for MDD), having “some symptoms” (scoring ≤25 on the IDS-SR and meeting CIDI-SF criteria for MDD; or >21 on the IDS-SR and not meeting CIDI-SF criteria for MDD) or having “no symptoms” (scoring ≤21 on the IDS-SR and not meeting CIDI-SF criteria for MDD).
As described previously (22), the primary outcome of interest in RADAR-MDD is depressive relapse, defined here as switching from a state of “no symptoms” to “symptomatic” over a period of 6-months. Secondary depression outcomes are: remission (switching from a state of “symptomatic” to “no symptoms” over a period of 6-months); and change in the severity of depressive symptoms (measured via the continuous IDS-SR).
Anxiety was measured via the 7-item Generalised Anxiety Disorder questionnaire (GAD7; (34)), used as a continuous indicator of anxiety symptom severity (a total of 21, with higher scores indicating increased anxiety severity) and a total score ≥10 indicating significant symptoms. This threshold has previously been shown to have good levels of sensitivity and specificity (35).
Functional ability was measured using the Work and Social Adjustment Scale (WSAS; (36), using a continuous score from 0-40 to describe the level of impairment, with scores of 0-10, 11-20 and >20 to indicate no, some and significant impairment respectively (36).
The Alcohol Use Disorders Identification Test (AUDIT; (37)) was used to measure alcohol use across timepoints. A total score out of 40 describes the level of alcohol use; scores of 0-7 indicate low risk alcohol consumption; 8-15 indicate hazardous alcohol consumption; 16-19 indicate harmful alcohol consumption; and scores >20 indicate likely alcohol dependence. (38)
The Brief Illness Perceptions Questionnaire (BIPQ; (39)) measured emotional and cognitive representations of illness, capturing perceptions relating to illness identity, causes, control, consequences, timeline, concern, understanding and emotional response. Total scores for each domain can be used individually, or totalled, with higher scores representing a more threatening view of their illness.
Health Service Use
Access to health services, as well as changes in treatment, and care received was measured via a modified Client Service Receipt Inventory (CSRI; (40)), adapted to be suitable for online delivery and participant self-report.
Any significant life events which may have happened between outcome assessments were measured via the List of Threatening Experiences Questionnaire (LTE-Q; (41)). Changes in employment status were recorded regularly as part of the CSRI (40).
Self-reported adherence to depression medication was measured with the 5-item Medication Adherence Report Scale (MARS-5; (42)).
Baseline characteristics of the sample were described using means and standard deviations or numbers and percentages as appropriate. To examine whether depressed mood is associated with the availability of data across all modes of data collection, participants were divided using scores on the IDS-SR and CIDI-SF (see additional file 2 for operationalisation) into those who are symptomatic at baseline and those who are not (those with no symptoms and some symptoms are pooled together due to the low number of people with no symptoms at baseline (N=4)). Chi-squared tests examined between group-differences in categorial data, and linear regressions in continuous data.
The number and percentage of people who have provided any data via the aRMT and pRMT apps and the wearable device throughout the course of follow-up have been summarised, then divided into quartiles to examine the numbers of people who have provided 0-25% of expected data, 26-50%, 51-75% and >75% of data throughout follow-up. Fitbit wear time estimates were calculated based on the presence of a single heart rate value, greater than zero, per 15-minute window.
P-values comparing the amount of data available between people with symptoms of depression at baseline and those without symptoms of depression at baseline were created using Chi-Squared tests. T-tests compared the number of ESM questions completed in total across all follow-up timepoints between those with and without depression symptoms at baseline. Data were analysed using STATA v16.0.