Computerized Cognitive Behavioral Therapy to Treat Emotional Distress After Stroke: A Feasibility Randomized Controlled Trial

$\textbf{Background:}$ Depression and anxiety are common complications following stroke. Symptoms could be treatable with psychological therapy, but there is little research on its efficacy. $\textbf{Objectives:}$ The aim of this study was to investigate (1) the acceptability and feasibility of computerized cognitive behavioral therapy (cCBT) to treat symptoms of depression and anxiety and (2) a trial design for comparing the efficacy of cCBT compared with an active comparator. $\textbf{Methods:}$ Of the total 134 people screened for symptoms of depression and anxiety following stroke, 28 were cluster randomized in blocks with an allocation ratio 2:1 to cCBT (n=19) or an active comparator of computerized cognitive remediation therapy (cCRT, n=9). Qualitative and quantitative feedback was sought on the acceptability and feasibility of both interventions, alongside measuring levels of depression, anxiety, and activities of daily living before, immediately after, and 3 months post treatment. $\textbf{Results:}$ Both cCBT and cCRT groups were rated as near equally useful (mean = 6.4 vs 6.5, $d$=0.05), while cCBT was somewhat less relevant (mean = 5.5 vs 6.5, $d$=0.45) but somewhat easier to use (mean = 7.0 vs 6.3, $d$=0.31). Participants tolerated randomization and dropout rates were comparable with similar trials, with only 3 participants discontinuing due to potential adverse effects; however, dropout was higher from the cCBT arm (7/19, 37% vs 1/9, 11% for cCRT). The trial design required small alterations and highlighted that future-related studies should control for participants receiving antidepressant medication, which significantly differed between groups ($P$=.05). Descriptive statistics of the proposed outcome measures and qualitative feedback about the cCBT intervention are reported. $\textbf{Conclusions:}$ A pragmatic approach is required to deliver computerized interventions to accommodate individual needs. We report a preliminary investigation to inform the development of a full randomized controlled trial for testing the efficacy of computerized interventions for people with long-term neurological conditions such as stroke and conclude that this is a potentially promising way of improving accessibility of psychological support.National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) East of England (EoE) at Cambridgeshire and Peterborough NHS Foundation Trus

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.status: publishe

Patient preferences for key drivers and facilitators of adoption of mHealth technology to manage depression: A discrete choice experiment

BackgroundIn time, we may be able to detect the early onset of symptoms of depression and even predict relapse using behavioural data gathered through mobile technologies. However, barriers to adoption exist and understanding the importance of these factors to users is vital to ensure maximum adoption.MethodIn a discrete choice experiment, people with a history of depression (N = 171) were asked to select their preferred technology from a series of vignettes containing four characteristics: privacy, clinical support, established benefit and device accuracy (i.e., ability to detect symptoms), with different levels. Mixed logit models were used to establish what was most likely to affect adoption. Sub-group analyses explored effects of age, gender, education, technology acceptance and familiarity, and nationality.ResultsHigher level of privacy, greater clinical support, increased perceived benefit and better device accuracy were important. Accuracy was the most important, with only modest compromises willing to be made to increase other factors such as privacy. Established benefit was the least valued of the attributes with participants happy with technology that had possible but unknown benefits. Preferences were moderated by technology acceptance, age, nationality, and educational background.ConclusionFor people with a history of depression, adoption of technology may be driven by the desire for accurate detection of symptoms. However, people with lower technology acceptance and educational attainment, those who were younger, and specific nationalities may be willing to compromise on some accuracy for more privacy and clinical support. These preferences should help shape design of mHealth tools.</p

Predictors of engagement with remote sensing technologies for symptom measurement in Major Depressive Disorder

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