Prism: A Data-Driven Platform for Monitoring Mental Health

Abstract

Neuropsychiatric disorders are the leading cause of disability worldwide and there is no gold standard currently available for the measurement of mental health. This issue is exacerbated by the fact that the information physicians use to diagnose these disorders is episodic and often subjective. Current methods to monitor mental health involve the use of subjective DSM-5 guidelines, and advances in EEG and video monitoring technologies have not been widely adopted due to invasiveness and inconvenience. Wearable technologies have surfaced as a ubiquitous and unobtrusive method for providing continuous, quantitative data about a patient. Here, we introduce PRISM — Passive, Real-time Information for Sensing Mental Health. This platform integrates motion, light and heart rate data from a smart watch application with user interactions and text insights from a web application. We have demonstrated a proof of concept by collecting preliminary data through a pilot study of 13 subjects. We have engineered appropriate features and applied both unsupervised and supervised learning to develop models that can recapitulate user-reported ratings of their emotional state. This demonstrates that the data has the potential to be useful for evaluating mental health. This platform will allow us to leverage continuous streams of passive data for early and accurate diagnosis as well as constant monitoring of patients suffering from mental disorders.

Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review

“…The final list was critically investigated by all authors, which led to the exclusion of 16 papers due to outcome measures that did not represent mood assessment (eg, happiness scales [29-31], Quality of Life [32], or Satisfaction With Life Scale [33], as these do not reflect abnormal depressed mood) or wearables that were not consumer based (eg, a Holter monitor [34] or multisensory clothing [35-37]).…”

“…Several studies only reported correlation strengths or did not include correlation results between the objective features and the outcome assessment [9,14,31,38]. For these studies, we contacted the corresponding author via email and acquired the relevant data in all cases.…”

Correlations between objective behavioral features collected from mobile and wearable devices, and depressive mood symptoms in affective disorders: A systematic review (Preprint)

“…The size of each pie chart in Figure 2 reflects the total number of SFs across all studies, and each pie chart is divided in proportions of reported statistically significant (green) and statistically non-significant (red) correlation results. In cases of missing statistical evaluation (e.g., [33,41]) of the correlation value, we treated the directionality values as statistically non-significant, biasing the pie charts towards false negatives. A complete overview of the SFs and their correlation or prediction of mood is included in Table S3.…”

“…The resulting list, together with review papers from phase 1 were then used in a cited reference search by two authors (DAR, JEB) to produce the final list. The final list was critically investigated by all authors which led to the exclusion of 16 papers due to outcome measures that did not represent mood assessment (e.g., happiness scales [31][32][33], Quality of Life [34] , or Satisfaction With Life Scale [35]), or wearables that was not deemed consumer-based, e.g., an Holter monitor [36], or multisensory clothing [37][38][39]. Several studies did not include correlation results between the objective features and the outcome assessment (e.g., [9,14,33,40]).…”

“…However, a limitation is that some of them are questionable in the assessment of mood and depression. For instance, studies assessing 'happiness', 'well-being' and 'quality of life', have been excluded in this review [31][32][33][34][35], even though it has been shown that happiness scores correlated moderately with depression, measured with BDI (r = -.57, p < .001) [75].…”