Publication
Challenging the Standards of Mental Care: An Analysis of Self-Rating with respect to Sensor based State Detection
Agnes Grünerbl; Gernot Bahle; Paul Lukowicz
In: UbiComp '24: Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computi. International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp-2024), October 5-9, Melbourne, Australia, ACM, 2024.
Abstract
Despite all the developments in AI and Ubiquitous computing, man- agement of mental health and mental disorders mainly relies on human assessments, like daily self-reporting and standard psy- chological questionnaires. Self-reporting however, in addition to compliance issues, comes with the drawback of being subjective and thus often inaccurate. In a depressive episode, it is hard to recall the manic phase of the last weeks. Thus, mainly experienced and self-aware patients can use self-reports effectively. Even though, in the last 15 years, sensor-based objective algorithms to monitor mental disorders showed promising results [3 ],[1], such systems are not established in psychiatric care. We believe it would help psychi- atrists and patients greatly to consider using objective sensor-based support systems if it would be possible to visualize the drawbacks of self-reporting and the abilities of sensor-based analysis. Our work provides a direct comparison of the performance of sensor-based analysis, self-reporting, and psychological diagnosis. It is based on a real-life data set collected with psychiatric patients. It consists of smartphone sensor data, respective daily self-reporting ques- tionnaires, and a ground truth of standardized psychiatric scale tests. As a highlight of this work, in the evaluation, we can provide evidence that observations of deferred self-perception of patients concerning their mental states, as doctors reported to us, can be measured in the sensor data.