Differences in mobility patterns according to machine learning models in patients with bipolar disorder and patients with unipolar disorder

• Published in: Journal of affective disorders Vol. 306; pp. 246 - 253
• Main Authors: Faurholt-Jepsen, Maria, Busk, Jonas, Rohani, Darius Adam, Frost, Mads, Tønning, Morten Lindberg, Bardram, Jakob Eyvind, Kessing, Lars Vedel
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.06.2022
• Subjects: Affect; Bipolar disorder; Bipolar Disorder - diagnosis; Digital phenotyping; Humans; Machine Learning; Mobile sensing; Mobility; Mood; Self-Assessment; Smartphone; Unipolar disorder; Digital phenotyping; Mobility; Bipolar disorder; Mobile sensing; Mood; Unipolar disorder
• ISSN: 0165-0327; 1573-2517
• DOI: 10.1016/j.jad.2022.03.054

It is essential to differentiate bipolar disorder (BD) from unipolar disorder (UD) as the course of illness and treatment guidelines differ between the two disorders. Measurements of activity and mobility could assist in this discrimination. Aims: 1) To investigate differences in smartphone-based location data between BD and UD, and 2) to investigate the sensitivity, specificity, and AUC of combined location data in classifying BD and UD. Patients with BD and UD completed smartphone-based self-assessments of mood for six months, along with same-time passively collected smartphone data on location reflecting mobility patterns, routine and location entropy (chaos). A total of 65 patients with BD and 75 patients with UD were included. A total of 2594 (patients with BD) and 2088 (patients with UD) observations of smartphone-based location data were available. During a depressive state, compared with patients with UD, patients with BD had statistically significantly lower mobility (e.g., total duration of moves per day (eB 0.74, 95% CI 0.57; 0.97, p = 0.027)). In classification models during a depressive state, patients with BD versus patients with UD, there was a sensitivity of 0.70 (SD 0.07), a specificity of 0.77 (SD 0.07), and an AUC of 0.79 (SD 0.03). The relative low symptom severity in the present study may have contributed to the magnitude of the AUC. Mobility patterns derived from mobile location data is a promising digital diagnostic marker in discriminating between patients with BD and UD. •Information on activity and mobility could discriminate between bipolar disorder (BD) and unipolar disorder (UD).•Patients with BD and UD provided smartphone-based assessments of mood and continuous location data•Patients with BD had lower mobility compared with patients with UD•The AUC of location data was rather high in classifying patients with BD compared with patients with UD.•Alterations in location data may be a promising digital diagnostic marker in patients with BD and UD.