3 research outputs found
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Remote smartphone-based speech collection: acceptance and barriers in individuals with major depressive disorder
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
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Engagement with a remote symptom-tracking platform among participants with major depressive disorder (RADAR-Engage): a randomized controlled trial
Background:Multi-parametric remote measurement technologies (RMTs), comprising smartphones and wearable devices, have the potential to revolutionise understanding of the etiology and trajectory of major depressive disorder (MDD). Engagement with RMTs in MDD research is of the upmost importance for validity of predictive analytical methods and long-term use, and can be conceptualized as both objective engagement (data availability) or subjective engagement (system usability and experiential factors). In-app components provide an opportunity to promote engagement with RMTs while minimizing research team resources. Randomized controlled trials (RCTs) are the gold standard in quantifying the effect of in-app components on engagement with RMTs in those with MDD.Objective:This study aimed to evaluate whether a multi-parametric RMT system with a visual progress report tracking data completion, theoretically informed notifications, and access to research team contact details could promote objective and subjective engagement with remote symptom tracking over and above the system as usual.Methods:A 2-arm, parallel-group RCT (participant-blinded) with 1:1 randomization was conducted with 100 participants with MDD over 12 weeks. Participants in both arms employed the RADAR-base system, involving a smartphone app for weekly symptom assessments and a wearable Fitbit device for continuous passive tracking. Participants in the intervention arm (n=50) also had access to the additional in-app components. The primary outcome was objective engagement with the system, measured as the percentage of weekly questionnaires during follow-up. Secondary outcomes comprised measures of subjective engagement (system engagement, system usability, and emotional self-awareness).Results:Levels of completion of the PHQ-9 task were similar in the control (69.2%) and intervention (68.1%) arms (p-value for difference between arms = 0.83, 95% CI -9.32 to 11.65). Those in the intervention group reported slightly higher user engagement (1.93, 95% CI -1.91 to 5.78), emotional self-awareness (1.13, 95% CI -2.93 to 5.19) and system usability (2.29, 95% CI -5.93 to 10.52) scores at follow-up, however all confidence intervals were wide and included 0.Conclusions:The adapted system did not increase objective or subjective engagement with remote symptom tracking in our research cohort. This study provides important foundations for understanding engagement with RMTs for research, and the methodologies by which this work can be replicated in both community and clinical settings. Clinical Trial: This study was registered as a clinical trial (reference number NCT04972474; https://clinicaltrials.gov/ct2/show/NCT04972474), and published as a protocol (https://doi.org/10.2196/32653).</p
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Classifying depression symptom severity: assessment of speech representations in personalized and generalized machine learning models
There is an urgent need for new methods that improve the management and treatment of Major Depressive Disorder (MDD). Speech has long been regarded as a promising digital marker in this regard, with many works highlighting that speech changes associated with MDD can be captured through machine learning models. Typically, findings are based on cross-sectional data, with little work exploring the advantages of personalization in building more robust and reliable models. This work assesses the strengths of different combinations of speech representations and machine learning models, in personalized and generalized settings in a two-class depression severity classification
paradigm. Key results on a longitudinal dataset highlight the benefits of personalization. Our strongest performing
model set-up utilized self-supervised learning features and convolutional neural network (CNN) and long short-term memory (LSTM) back-end.
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