Feasibility of a Machine Learning-Based Smartphone Application in Detecting Depression and Anxiety in a Generally Senior Population

Abstract

BackgroundDepression and anxiety create a large health burden and increase the risk of premature mortality. Mental health screening is vital, but more sophisticated screening and monitoring methods are needed. The Ellipsis Health App addresses this need by using semantic information from recorded speech to screen for depression and anxiety.ObjectivesThe primary aim of this study is to determine the feasibility of collecting weekly voice samples for mental health screening. Additionally, we aim to demonstrate portability and improved performance of Ellipsis’ machine learning models for patients of various ages.MethodsStudy participants were current patients at Desert Oasis Healthcare, mean age 63 years (SD = 10.3). Two non-randomized cohorts participated: one with a documented history of depression within 24 months prior to the study (Group Positive), and the other without depression (Group Negative). Participants recorded 5-min voice samples weekly for 6 weeks via the Ellipsis Health App. They also completed PHQ-8 and GAD-7 questionnaires to assess for depression and anxiety, respectively.ResultsProtocol completion rate was 61% for both groups. Use beyond protocol was 27% for Group Positive and 9% for Group Negative. The Ellipsis Health App showed an AUC of 0.82 for the combined groups when compared to the PHQ-8 and GAD-7 with a threshold score of 10. Performance was high for senior participants as well as younger age ranges. Additionally, many participants spoke longer than the required 5 min.ConclusionThe Ellipsis Health App demonstrated feasibility in using voice recordings to screen for depression and anxiety among various age groups and the machine learning models using Transformer methodology maintain performance and improve over LSTM methodology when applied to the study population.

Validating the efficacy and value proposition of Mental Fitness Vocal Biomarkers in a psychiatric population: prospective cohort study

“…One recent smartphone app-based speech analysis approach using semantic information for depression and anxiety screening in a mostly older population and compared 5-min voice recordings with PHQ-8 and GAD-7 as reference as a validation study and showed good performance (63). The analysis approach differed from ours in not using acoustic features and providing only binary classifications, without feedback to participants, making it less suited as a personalized mental wellbeing tool.…”

A deep learning-based model for detecting depression in senior population

“…If the relevant diagnosis can be made in a private and convenient environment, it will greatly promote patients to receive early treatment. The examination process of electroencephalogram and neuroimaging is relatively complex and requires advanced equipment, domain experts to analyze the results, which makes a lot of limitations in the practical application of the scene ( 36 ). The advancements in automated screening using such data still have limitations on practical applications as the cost per diagnosis is higher limiting the access for general public.…”

Digital health technologies and major depressive disorder

“…Ellipsis Health is an ML-based mobile app that detects depression using speech analysis of smartphone audio, which can be easily recorded and transmitted, potentially allowing accessible screening and monitoring of depression. 114 Kintsugi is a passive vocal biomarker instrument that uses short clips of free-form speech for depression screening. 115,116 Kintsugi's technology can be integrated into enterprise call centers, telehealth platforms, and remote patient monitoring apps, potentially expanding access as well as standardizing the quality of screening in primary care settings.…”

“…Digital biomarkers hold promise in enabling objective detection of depression. Ellipsis Health is an ML-based mobile app that detects depression using speech analysis of smartphone audio, which can be easily recorded and transmitted, potentially allowing accessible screening and monitoring of depression 114 . Kintsugi is a passive vocal biomarker instrument that uses short clips of free-form speech for depression screening 115…”