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Background Performance validity and test-retest reliability of ReVeRe.D, an iPad-administered cognitive test battery in major depressive disorder (MDD) were analyzed. Methods Participants aged 18-59 years had DSM-5 diagnosis of MDD with adequate visual and hearing acuity. All had responded to oral antidepressant treatment for a major depressive episode within the most recent 24-months and were stable with no greater than mild depressive symptoms as evidenced by Montgomery Asberg Depression Rating Scale total score 17. Participants were randomly assigned to 1 of 2 test sequences (AABB or BBAA; A=ReVeRe.D; B=examiner-administered tests) in a crossover design. Results 244 randomized participants (AABB: n=123; BBAA: n=121) had mean age of 38.3 years; 54.9% had a college, baccalaureate, or higher education. At first administration, Pearson correlation coefficients (PCC) for 6/10 pairs of corresponding ReVeRe.D vs examiner-administered tests exceeded the pre-specified acceptance criterion (PCC=0.53) for the primary analysis; 8 test score pairs had PCC exceeding 0.40. At second administration, PCC for 9/10 test scores pairs exceeded PCC=0.53. Together, the series of PCCs supports the concurrent validity for ReVeRe.D. Test-retest reliability for ReVeRe.D test scores was generally moderate to high. Limitations The study included stable participants with MDD who had responded to oral antidepressant treatment, with most in at least partial remission. The sample was limited to English-speaking participants, and skewed towards white, college-educated women. Further studies in acutely ill MDD patients who represent a broader demographic, are warranted. Conclusions iPad-administered ReVeRe.D is a valid and reliable computerized test battery for assessment of cognitive performance in MDD.

We assessed the prevalence and impact of COVID-19 among multiple sclerosis (MS) patients across Europe by leveraging participant data collected as part of the ongoing EU IMI2 RADAR-CNS major programme aimed at finding new ways of monitoring neurological disorders using wearable devices and smartphone technology. In the present study, 399 patients of RADAR-MS have been included (mean age 43.9 years, 60.7% females) with 87/399 patients (21.8%) reporting major symptoms suggestive of COVID-19. A trend for an increased risk of COVID-19 symptoms under alemtuzumab and cladribine treatments in comparison to injectables was observed. Remote monitoring technologies may support health authorities in monitoring and containing the ongoing pandemic. Electronic supplementary material The online version of this article (10.1007/s10072-020-04519-x) contains supplementary material, which is available to authorized users.

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. ispartof: JOURNAL OF AFFECTIVE DISORDERS vol:310 pages:106-115 ispartof: location:Netherlands status: published

The use of digital technologies may help to diagnose Alzheimer’s Disease (AD) at the pre-symptomatic stage. However, before implementation into clinical practice, digital measures (DMs) need to be evaluated for their diagnostic benefit compared to established questionnaire-based assessments, such as the Mini-Mental State Examination (MMSE) for cognition and Functional Activity Questionnaire (FAQ) for daily functioning. Moreover, the quantitative and qualitative relationship of DMs to these well understood scores needs to be clarified to aid interpretation. In this work we analyzed data from 148 subjects, 58 cognitively normal and 90 at different stages of the disease, which had performed a smartphone based virtual reality game to assess cognitive function. In addition, we used clinical data from Alzheimer’s Disease Neuroimaging Initiative (ADNI). We employed an Artificial Intelligence (AI) based approach to elucidate the relationship of DMs to questionnaire-based cognition and functional activity scores. In addition, we used Machine Learning (ML) and statistical methods to assess the diagnostic benefit of DMs compared to questionnaire-based scores. We found non-trivial relationships between DMs, MMSE, and FAQ which can be visualized as a complex network. DMs, in particular those reflecting scores of individual tasks in the virtual reality game, showed a better ability to discriminate between different stages of the disease than questionnaire-based methods. Our results indicate that DMs have the potential to act as a crucial measure in the early diagnosis and staging of AD.

To improve understanding of treatment-resistant depression (TRD) in a large population of individuals with depression, a self-reported antidepressant efficacy survey was designed and administered to 23andMe research participants. Participants with a current depressive episode or with a depressive episode within the last 5 years were queried for the effect of pharmacotherapy during the episode. TRD was defined as non-response to at least two antidepressants taken for at least 5-6 weeks. Non-TRD (NTRD) was defined as responsive to either the first or second medication taken for at least 3-4 weeks. Participants who could not be classified as TRD or NTRD were excluded from the analysis. Approximately 56,000 participants completed the survey, among which approximately 33,000 took medication for a depressive episode. The 3409 participants with self-reported TRD tended to have younger age of onset, and a more persistent course prior to initiation of treatment (e.g., a longer prior average episode duration and residual symptoms between episodes) than the 18,511 participants classified as NTRD. This survey identified depression characteristics, comorbidities, trigger events, and early childhood trauma that distinguish TRD from NTRD.

Although the benefits of exercise on Major Depressive Disorder (MDD) are well established, longitudinal studies of objectively measured activity in clinical populations are needed to establish specific guidelines for exercise by persons with moderate-to-severe depression. This study examines the association between objectively assessed daily step count and depressive symptoms over a 24-week follow- up period in outpatients receiving treatment for moderate-to-severe depression.Participants were US Veterans with MDD enrolled in the Precision Medicine in Mental Health Care study (PRIME Care), a pragmatic, multi-site, randomized, controlled trial that examines the utility of genetic testing in the context of pharmacotherapy for MDD. Participants were a subset (N = 66) enrolled in actigraphy (using GT9X ActiGraph) monitoring component of the trial. Daily steps were examined as a predictor of depressive symptoms over 4-, 8-, 12-, 18-, and 24-weeks.On average, participants took 3,460 (±1,768) steps per day. In generalized linear mixed models, an increase in 1,000 steps per day was associated with a 0.6-point decrease in depressive symptom severity at the subsequent follow-up assessment.Activity monitoring was observational and causal inferences cannot be made between daily steps and subsequent depressive symptom severity. Results may not generalize to non-treatment-seeking populations.Study findings provide an initial metric for persons with clinically significant MDD, of whom most do not get sufficient daily activity. The findings can inform future trials aimed at determining how much daily activity is needed to improve symptoms in individuals with MDD.

Introduction The objective of this study is to evaluate the reliability and validity of the ReVeReTM word list recall test (RWLRT), which uses speech recognition, when administered remotely and unsupervised. Methods Prospective cohort study. Participants included 249 cognitively intact community dwelling older adults. Measures included clinician administered neuropsychological assessments at baseline and unsupervised remotely administered tests of cognition from six time-points over sixmonths. Results The RWLRT showed acceptable validity. Reliability coefficients varied across time points, with poor reliability between times 1 and 2 and fair-to-good reliability across the remaining five testing sessions. Practice effects were observed with repeated administration as expected. Discussion Unsupervised computerized tests of cognition, particularly word list learning and memory tests that use speech recognition, have significant potential for large scale early detection and long-term tracking of cognitive decline due to AD.

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.

The use of remote measurement technologies (RMTs) across mobile health (mHealth) studies is becoming increasingly popular, given their potential for high-frequency symptom monitoring outside of routine clinical appointments. However, many RMT studies fail to report on engagement and recruitment statistics, with the few who do citing a wide range of recruitment rates. There is a need for the standardisation of best practices for successful recruitment into RMT research, critical for both research validity and reproducibility. The current paper aims to create a framework for successful recruitment into RMT studies, reflecting on the experience of RADAR-MDD, a large-scale, multi-site prospective cohort study utilising RMT to explore the clinical course of people with major depressive disorder across the UK, Netherlands, and Spain. More specifically, the paper assesses four key strategies for successful recruitment, alongside a review of the common barriers to participation and how to avoid them. Finally, the strategies and barriers outlined are combined into a single model of recruitment, that can be used as a framework to inform future study design and evaluation. Such a model will be applicable to a variety of stakeholders using RMT in healthcare research and practice.

Background Sleep problems tend to vary according to the course of the disorder in individuals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings. Objective The main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8). Methods Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z score was used to evaluate the significance of the coefficient of each feature. Results We tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly (P Conclusions We demonstrated that several derived sleep features extracted from consumer wearable devices show potential for the remote measurement of sleep as biomarkers of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant.

Most smartphones and wearables are nowadays equipped with location sensing (using Global Positioning System and mobile network information) that enable continuous location tracking of their users. Several studies have reported that the amount of time an individual experiencing symptoms of Major Depressive Disorder (MDD) spends at home a day (i.e., home stay), as well as various mobility related metrics, are associated with symptom severity in MDD. Due to the use of small and homogeneous cohorts of participants, it is uncertain whether the findings reported in those studies generalize to a broader population of individuals with the MDD symptoms. In the present study, we examined the relationship between overall severity of the depressive symptoms, as assessed by the eight-item Patient Health Questionnaire (PHQ-8), and median daily home stay over the two weeks preceding the completion of a questionnaire in individuals with MDD. We used questionnaire and geolocation data of 164 participants collected in the observational Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) study. Participant age and severity of the MDD symptoms were found to be significantly related to home stay, with older and more severely affected individuals spending more time at home. The association between home stay and symptom severity appeared to be stronger on weekdays than on weekends. Furthermore, we found a significant modulation of home stay by occupational status, with employment reducing home stay. Our findings suggest that home stay is associated with symptom severity in MDD and demonstrate the importance of accounting for confounding factors in future MDD studies.

The ease of in-the-wild speech recording using smartphones has sparked considerable interest in the combined application of speech, remote measurement technology (RMT) and advanced analytics as a research and healthcare tool. For this to be realised, the acceptability of remote speech collection to the user must be established, in addition to feasibility from an analytical perspective. To understand the acceptance, facilitators, and barriers of smartphone-based speech recording, we invited 384 individuals with major depressive disorder (MDD) from the Remote Assessment of Disease and Relapse - Central Nervous System (RADAR-CNS) research programme in Spain and the UK to complete a survey on their experiences recording their speech. In this analysis, we demonstrate that study participants were more comfortable completing a scripted speech task than a free speech task. For both speech tasks, we found depression severity and country to be significant predictors of comfort. Not seeing smartphone notifications of the scheduled speech tasks, low mood and forgetfulness were the most commonly reported obstacles to providing speech recordings., Comment: Accepted to Interspeech 2021. Formatting changes + minor language edits

Background In the absence of a vaccine or effective treatment for COVID-19, countries have adopted nonpharmaceutical interventions (NPIs) such as social distancing and full lockdown. An objective and quantitative means of passively monitoring the impact and response of these interventions at a local level is needed. Objective We aim to explore the utility of the recently developed open-source mobile health platform Remote Assessment of Disease and Relapse (RADAR)–base as a toolbox to rapidly test the effect and response to NPIs intended to limit the spread of COVID-19. Methods We analyzed data extracted from smartphone and wearable devices, and managed by the RADAR-base from 1062 participants recruited in Italy, Spain, Denmark, the United Kingdom, and the Netherlands. We derived nine features on a daily basis including time spent at home, maximum distance travelled from home, the maximum number of Bluetooth-enabled nearby devices (as a proxy for physical distancing), step count, average heart rate, sleep duration, bedtime, phone unlock duration, and social app use duration. We performed Kruskal-Wallis tests followed by post hoc Dunn tests to assess differences in these features among baseline, prelockdown, and during lockdown periods. We also studied behavioral differences by age, gender, BMI, and educational background. Results We were able to quantify expected changes in time spent at home, distance travelled, and the number of nearby Bluetooth-enabled devices between prelockdown and during lockdown periods (P Conclusions RADAR-base, a freely deployable data collection platform leveraging data from wearables and mobile technologies, can be used to rapidly quantify and provide a holistic view of behavioral changes in response to public health interventions as a result of infectious outbreaks such as COVID-19. RADAR-base may be a viable approach to implementing an early warning system for passively assessing the local compliance to interventions in epidemics and pandemics, and could help countries ease out of lockdown.

Introduction Performance of “Revere”, a novel iPad‐administered word‐list recall (WLR) test, in quantifying deficits in verbal episodic memory, was evaluated versus examiner‐administered Rey Auditory Verbal Learning Test (RAVLT) in patients with mild cognitive impairment and cognitively normal participants. Methods Elderly patients with clinically diagnosed mild cognitive impairment (Montreal Cognitive Assessment score 24–27) and cognitively normal (Montreal Cognitive Assessment score ≥28) were administered RAVLT or Revere in a randomized crossover design. Results A total of 153/161 participants (Revere/RAVLT n = 75; RAVLT/Revere n = 78) were randomized; 148 (97%) completed study; 121 patients (mean [standard deviation] age: 70.4 [7.84] years) were included for analysis. Word‐list recall scores (8 trials) were comparable between Revere and RAVLT (Pearson's correlation coefficients: 0.12–0.70; least square mean difference [Revere‐RAVLT]: −0.84 [90% CI, −1.15; −0.54]). Model factor estimates indicated trial (P

BACKGROUND Despite the increasing use of remote measurement technologies (RMT) such as wearables or biosensors in health care programs, challenges associated with selecting and implementing these technologies persist. Many health care programs that use RMT rely on commercially available, “off-the-shelf” devices to collect patient data. However, validation of these devices is sparse, the technology landscape is constantly changing, relative benefits between device options are often unclear, and research on patient and health care provider preferences is often lacking. OBJECTIVE To address these common challenges, we propose a novel device selection framework extrapolated from human-centered design principles, which are commonly used in de novo digital health product design. We then present a case study in which we used the framework to identify, test, select, and implement off-the-shelf devices for the Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) consortium, a research program using RMT to study central nervous system disease progression. METHODS The RADAR-CNS device selection framework describes a human-centered approach to device selection for mobile health programs. The framework guides study designers through stakeholder engagement, technology landscaping, rapid proof of concept testing, and creative problem solving to develop device selection criteria and a robust implementation strategy. It also describes a method for considering compromises when tensions between stakeholder needs occur. RESULTS The framework successfully guided device selection for the RADAR-CNS study on relapse in multiple sclerosis. In the initial stage, we engaged a multidisciplinary team of patients, health care professionals, researchers, and technologists to identify our primary device-related goals. We desired regular home-based measurements of gait, balance, fatigue, heart rate, and sleep over the course of the study. However, devices and measurement methods had to be user friendly, secure, and able to produce high quality data. In the second stage, we iteratively refined our strategy and selected devices based on technological and regulatory constraints, user feedback, and research goals. At several points, we used this method to devise compromises that addressed conflicting stakeholder needs. We then implemented a feedback mechanism into the study to gather lessons about devices to improve future versions of the RADAR-CNS program. CONCLUSIONS The RADAR device selection framework provides a structured yet flexible approach to device selection for health care programs and can be used to systematically approach complex decisions that require teams to consider patient experiences alongside scientific priorities and logistical, technical, or regulatory constraints.