Table 2.

Summary of mental health studies, ordered by condition then year of publication.

Condition	Author (Year) Location	Study Purpose	Principal Findings	Sensors Used	Sample Size & Type	Study Length (Days)
Bipolar disorder	Osmani et al. (2013) [53] Austria	Correlate physical activity with symptoms of bipolar disorder	Significant correlations between activity levels and bipolar states for some individual patients but not for others.	• Accelerometer	5 Patients with bipolar disorder	90
Bipolar disorder	Grünerbl et al. (2014) [45] Austria	Detect state and state change for people with bipolar disorder	Detection of state change with 96% precision and 94% recall; recognition of state with 80% accuracy.	• Accelerometer • GPS	12 Patients with bipolar disorder	84
Bipolar disorder	Grünerbl et al. (2015) [44] Austria	Detect state and state change for people with bipolar disorder	Detection of state change with 97% precision and 97% recall; recognition of state with a 76% accuracy.	• Accelerometer • Call logs • GPS • Microphone	10 Patients with bipolar disorder	84
Bipolar disorder	Abdullah et al. (2016) [29] USA	Predict scores on the social rhythm metric (SRM) scale among people with bipolar disorder using generalized and personalized models	Prediction of states with 85% precision and 86% recall. Social rhythm metric score inferred with 0.92 root-mean-square error for personalized models and 1.40 for the generalized model.	• Accelerometer • Call logs • Light sensor • SMS patterns	7 Patients with bipolar disorder	28
Bipolar disorder	Beiwinkel et al. (2016) [34] Germany	Detect features to be used for the monitoring of bipolar disorder	Significant correlations between subset smartphone sensor data on one hand and depressive and manic symptoms on the other, but none above clinical thresholds.	• Accelerometer • Antenna • Call logs • Device activity • GPS • SMS patterns	13 Patients with bipolar disorder	356
Depression	Burns et al. (2011) [38] USA	Reduce depressive symptoms among people with major depressive disorder	Prediction of depression from sensor data no better than chance.	• Accelerometer • Bluetooth • GPS • Light sensor	8 People with major depressive disorder	56
Depression	Canzian et al. (2015) [39] UK	Predict depressive symptoms from location data	• Prediction of depression with > 75% sensitivity and specificity, using a support vector machine classifier on a personalized model with a time span of 8 days or more. • Prediction of depression with > 60% sensitivity and specificity, using a support vector machine classifier on a generalized model with a time span of 8 days or more.	• Antenna • GPS	28 General sample	71*
Depression	Saeb et al. (2015) [57] USA	Predict depressive symptoms from location and phone usage data	Prediction of depression with 86% accuracy for the best feature, using a logistic regression classifier.	• Device activity • GPS	28 General sample	14
Depression	Saeb et al. (2016) [56] USA	Correlate location data with depression symptoms	• Significant negative correlations between GPS features (location variance, entropy, circadian movement) and depression. • Relation between GPS features and depression more evident on weekends, when participants are not constrained by work or school schedule.	• GPS	48 University students	70
Depression	Wahle et al. (2016) [61] Switzerland	Predict depression from smartphone data, using an application delivering context- sensitive cognitive behavioral therapy-based micro-interventions	• Prediction of depression with 61% accuracy using a support vector machine classifier. • Prediction of depression with 59% accuracy using a random forest classifier.	• Accelerometer • Calendar • Call logs • Device activity • GPS • SMS patterns	36 General sample	> 14
Schizophrenia	Ben-Zeev et al. (2016) [36] USA	Examine the feasibility and acceptance of passive sensing among people with schizophrenia	People with schizophrenia open to sensing, a third expressed concern about privacy, two-thirds expressed interest in receiving feedback.	• Accelerometer • Bluetooth • GP S • Light sensor • Microphone	20 Inpatients and outpatients with schizophrenia	10.15*
Schizophrenia	Difrancesco et al. (2016) [41] UK	Detect out-of-home activities among people with schizophrenia in order to infer social functioning	Detection of out-of-home activity with precision between 72% and 95%, and recall between 69% and 77% for the best method.	• GPS	5 Patients with schizophrenia	5
Schizophrenia	Wang et al. (2016) [62] USA	Correlate smartphone data with schizophrenia	Significant correlation between ground truth and predicted mental health status scores, using random forest regression.	• Accelerometer • Application usage • Call logs • Device activity • GPS • Light sensor • Microphone • SMS patterns	21 Patients with schizophrenia	133.76*
General mental health	Ma et al. (2014) [49] China	Predict mood from smartphone data	Prediction of mood with an accuracy of 70% for the model comprised of sensor and social features, using Markov-Chain Monte Carlo methods	• Accelerometer • Activity • Call logs • SMS patterns	15 University students and non-students	30
General mental health	Wang et al. (2014) [63] USA	Correlate smartphone data with depressive symptoms among college students	• Significant negative correlation between sleep and depression. • Significant negative correlations between conversation frequency and duration and depression.	• Accelerometer • Application usage • Bluetooth • Call logs • GPS • Light sensor • Microphone • SMS patterns	48 University students	70
General mental health	Ben-Zeev et al. (2015) [35] USA	Evaluate the prediction of daily stress levels, mental health status from smartphone data	• Sleep duration and mobility associated with daily stress levels. • Speech duration, geospatial activity, sleep duration, kinesthetic activity associated with mental health status.	• Accelerometer • Device activity • GPS • Light sensor • Microphone	47 University students	70
General mental health	Asselbergs et al. (2016) [32] Netherlands	Predict mood from smartphone data	Prediction of 55% to 76% of mood scores using personalized linear regression.	• Accelerometer • Application usage • Call logs • Device activity • SMS patterns	27 University students	35.5*
General mental health	Huang et al. (2016) [46] USA	Correlate places visited by university students with their social anxiety	Significant negative correlation between time spent at religious locations and reported social anxiety	• GPS	16 University students	10

GPS: Global Positioning System; SMS: Short Message Service;

^*average duration of subject participation; precision refers to positive predictive value; recall refers to sensitivity, or hit rate. Patients: participants receiving professional care.