Correlation of significant places with self-reported state of bipolar disorder patients

Abstract

Capabilities of smartphones can be utilised to monitor a range of aspects of users' behaviour. This has potential to affect a number of areas where users' behaviour is considered relevant information. Most notably, healthcare in general and mental health in particular are excellent candidates to utilise capabilities of smartphones, since mental disorders typically have a strong behaviour component. This is especially true for bipolar disorder, where mobility and activity of the patients is considered an indicator of a bipolar episode (depressive or manic). In this work we report on results of using capabilities of smartphones to monitor mobility of the patients, monitored over the period of 12 weeks. Through the continuous discovery of Wi-Fi access points we have inferred significant places (where the patient spent majority of the time) for each patient and investigate correlation of these places with patients' self-reported state. The results show that for majority of patients there exists negative correlation between time spent in clinic and their self-assessment score, while there is a positive correlation between self-assessment scores and time spent outside the home or clinic.

Mobile Network Data for Public Health: Opportunities and Challenges

“…Diagnosing affective disorders, the patients’ episodes and current state is mostly based on both self-reports of behavior and mood and on direct observations by psychiatrists or informal caregivers, which suffers from subjectivity and human errors. Therefore, reliable ground-truth when it comes to mental health is also a challenge as there are neither firm biomarkers nor imaging techniques that reliably diagnose mental conditions ( 84 ).…”

Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review

“…Nine studies were conducted among bipolar disorder patients [50,58,61,63,64,67,72,73,78]. Among those with bipolar disorder, physical activity was related to psychiatric assessment scores [50,58,61], but the association of voice features and patients’ psychiatric evaluation was incongruent [58,63].…”

“…A correlation between depressive and manic symptoms and phone usage was also detected [64,72]. Location recordings correlated with depressive symptoms and a weak negative association between staying in clinics and self-reported state was found [67,73]. Typing features (ie, interkey delay, backspace ratio, and autocorrect rate) were positively related to depression and mania [78].…”

Smartphone-based Mental State Estimation: A Survey from a Machine Learning Perspective

“…Time spent at significant location (home, clinic, office, school, etc.) [32], [39], [41], [42], [43], [47], [48], [49], [53], [55], [56], [58], [59], [60], [61], [62], [63], [64], [65], [66] LOC -3 Number of places visited [32], [37], [39], [41], [42], [48], [53], [55], [56], [57], [60], [61], [67] LOC -4 Transition time [32], [37], [41], [42], [43], [50], [61], [64], [66] LOC -5 Routine index [32], [37], [41], [42], [60] LOC-6…”

“…The stress and anxiety category includes studies that used PSS, GAD-7, SIAS, and STAI. The depression category includes studies that used PHQ-2, PHQ-8, PHQ-9, CES-D, HDRS-17, HAMD, QID, [21], [45], [48], [51], [52], [54], [62], [64], [65] [45], [51], [62], [65], [68], [69] [21], [48], [52], [54], [62], [65], [68], [69], [77] [69], [77] [45], [51], [62], [68] [45], [51] (100%) Depression 16 (32.0%) 10 (20.0%) 10 (20.0%) 2 (4.0%) 9 (18.0%) 3 (6.0%) 50 [32], [35], [39], [41], [42], [43], [44], [45], [47], [51], [53], [55], [61], [62], [63], [67] [39], [43], [45], [47], [51],…”