Mood and Activity Measured Using Smartphones in Unipolar Depressive Disorder

Background: Smartphones comprise a promising tool for symptom monitoring in patients with unipolar depressive disorder (UD) collected as either patient-reportings or possibly as automatically generated smartphone data. However, only limited research has been conducted in clinical populations. We investigated the association between smartphone-collected monitoring data and validated psychiatric ratings and questionnaires in a well-characterized clinical sample of patients diagnosed with UD. Methods: Smartphone data, clinical ratings, and questionnaires from patients with UD were collected 6 months following discharge from psychiatric hospitalization as part of a randomized controlled study. Smartphone data were collected daily, and clinical ratings (i.e., Hamilton Depression Rating Scale 17-item) were conducted three times during the study. We investigated associations between (1) smartphone-based patient-reported mood and activity and clinical ratings and questionnaires; (2) automatically generated smartphone data resembling physical activity, social activity, and phone usage and clinical ratings; and (3) automatically generated smartphone data and same-day smartphone-based patient-reported mood and activity. Results: A total of 74 patients provided 11,368 days of smartphone data, 196 ratings, and 147 questionnaires. We found that: (1) patient-reported mood and activity were associated with clinical ratings and questionnaires (p < 0.001), so that higher symptom scores were associated with lower patient-reported mood and activity, (2) Out of 30 investigated associations on automatically generated data and clinical ratings of depression, only four showed statistical significance. Further, lower psychosocial functioning was associated with fewer daily steps (p = 0.036) and increased number of incoming (p = 0.032), outgoing (p = 0.015) and missed calls (p = 0.007), and longer phone calls (p = 0.012); (3) Out of 20 investigated associations between automatically generated data and daily patient-reported mood and activity, 12 showed statistical significance. For example, lower patient-reported activity was associated with fewer daily steps, shorter distance traveled, increased incoming and missed calls, and increased screen-time. Conclusion: Smartphone-based self-monitoring is feasible and associated with clinical ratings in UD. Some automatically generated data on behavior may reflect clinical features and psychosocial functioning, but these should be more clearly identified in future studies, potentially combining patient-reported and smartphone-generated data

Irritability in bipolar disorder and unipolar disorder measured daily using smartphone-based data:An exploratory post hoc study

Objective: To investigate (i) the proportions of time with irritability and (ii) the association between irritability and affective symptoms and functioning, stress, and quality of life in patients with bipolar disorder (BD) and unipolar depressive disorder (UD). Methods: A total of 316 patients with BD and 58 patients with UD provided self-reported once-a-day data on irritability and other affective symptoms using smartphones for a total of 64,129 days with observations. Questionnaires on perceived stress and quality of life and clinical evaluations of functioning were collected multiple times during the study. Results: During a depressive state, patients with UD spent a significantly higher proportion of time with presence of irritability (83.10%) as compared with patients with BD (70.27%) (p = 0.045). Irritability was associated with lower mood, activity level and sleep duration and with increased stress and anxiety level, in both patient groups (p-values&lt;0.024). In addition, in patients with UD, increased irritability was associated with decreased quality of life (p = 0.002). The results were not altered when adjusting for psychopharmacological treatments. Conclusions: Irritability is an important part of the symptomatology in affective disorders. Clinicians could have focus on symptoms of irritability in both patients with BD and UD during their course of illness. Future studies investigating treatment effects on irritability would be interesting

Using digital phenotyping to classify bipolar disorder and unipolar disorder – exploratory findings using machine learning models

The aims were to investigate 1) differences in smartphone-based data on phone usage between bipolar disorder (BD) and unipolar disorder (UD) and 2) by using machine learning models, the sensitivity, specificity, and AUC of the combined smartphone data in classifying BD and UD. Daily smartphone-based self-assessments of mood and same-time passively collected smartphone data on smartphone usage was available for six months. A total of 64 patients with BD and 74 patients with UD were included. Patients with BD during euthymic states compared with UD in euthymic states had a lower number of incoming phone calls/ day (B: -0.70, 95%CI: -1.37; -0.70, p = 0.040). Patients with BD during depressive states had a lower number of incoming and outgoing phone calls/ day as compared with patients with UD in depressive states. In classification by using machine learning models, 1) overall (regardless of the affective state), patients with BD were classified with an AUC of 0.84, which reduced to 0.48 when using a leave-one-patient-out crossvalidation (LOOCV) approach; similarly 2) during a depressive state, patients with BD were classified with an AUC of 0.86, which reduced to 0.42 with LOOCV; 3) during a euthymic state, patients with BD were classified with an AUC of 0.87, which reduced to 0.46 with LOOCV. While digital phenotyping shows promise in differentiating between patients with BD and UD, it highlights the challenge of generalizing to unseen individuals. It should serve as an complement to comprehensive clinical evaluation by clinicians