Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors

Abstract Background: While preliminary evidence suggests that sensors may be employed to detect presence of low mood it is still unclear whether they can be leveraged for measuring depression symptoms severity. This study evaluates the feasibility and performance of assessing depressive symptoms severity by using behavioral and physiological features obtained from wristband and smartphone sensors. Method: Participants were thirty-one individuals with Major Depressive Disorder (MDD). The protocol included eight weeks of behavioral and physiological monitoring through smartphone and wristband sensors and six in-person clinical interviews during which depression was assessed with the Hamilton Depression Rating Scale (HDRS). Results: Participants wore the right and left wrist sensors 92% and 94% of the time respectively. Three machine-learning models estimating depressive symptom severity were developed - one combining features from smartphone and wearables sensors, one including only features from the smartphones, and one including features from wrist sensors - and evaluated in two different scenarios. Correlations between the models’ estimate of HDRS scores and clinician-rated HDRS ranged from moderate to high (0.46 [CI:0.42,0.74] to 0.7 [CI:0.66,0.74]) and had moderate accuracy with Mean Absolute Error ranging between 3.88±0.18 and 4.74+1.24. The time split scenario of the model including only features from the smartphones performed the best. The ten most predictive features in the combined model were related to mobile phone engagement, activity level, skin conductance, and heart rate variability. Conclusion: Monitoring of MDD patients through smartphones and wrist sensors following a clinician-rated HDRS assessment is feasible and may provide an estimate of changes in depressive symptoms severity. Future studies should further examine the best features to estimate depressive symptoms and strategies to further enhance accuracy.