In a collaborative project with Rosalind Picard, ScD from MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health, Paola Pedrelli, PhD and colleagues from the Depression Clinical and Research Program have been working to develop machine-learning algorithms that could help diagnose and monitor symptoms in patients with major depressive disorder.
In a pilot study, Pedrelli and Picard used wearable devices and smartphones to gather data from 31 study participants with major depressive disorder (MDD), including information on skin conductance and temperature, heart rate, physical activity, sleep patterns, social activity, and personal assessments of mood. These readings were compared to clinicians’ ratings of depressive symptom severity assessed using the 17-item Hamilton Depression Rating Scale (HDRS-17).
In this pilot study, machine-learning models were developed to correlate behavioral and psychological data with clinician-rated depressive symptom severity. Correlations between the best models’ estimate of depression severity and clinician-rated HDRS scores were high (0.7, CI: 0.66, 0.74) and had moderate accuracy. The most predictive features were related to mobile phone engagement, activity level, skin conductance, and heart rate variability.
A new study from the same group will further assess and refine the ability of this method of monitoring to follow patients with major depression. This sort of passive, real-time symptom monitoring has the potential to improve our understanding of the physiological and behavioral markers of depression severity and relapse. In patients starting treatment, this information may facilitate the early detection of response or non-response to treatment, allowing clinicians to adjust and make changes to medications more quickly, ideally decreasing time to remission. In addition, this monitoring may facilitate early detection of depression relapse and allow expeditious delivery of effective treatments to patients.
More information on this study can be found HERE.
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Curtiss JE, Mischoulon D, Fisher LB, Cusin C, Fedor S, Picard RW, Pedrelli P. Rising early warning signals in affect associated with future changes in depression: a dynamical systems approach. Psychol Med. 2021 Dec 23:1-9. doi: 10.1017/S0033291721005183.
Pedrelli P, Fedor S, Ghandeharioun A, Howe E, Ionescu DF, Bhathena D, Fisher LB, Cusin C, Nyer M, Yeung A, Sangermano L, Mischoulon D, Alpert JE, Picard RW. Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors. Front Psychiatry. 2020 Dec 18; 11:584711. doi: 10.3389/fpsyt.2020.584711.
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