Identification of possible depressive profile through mobile sensor data in university students - Saud, Conrado Santos; Motti, Vivian; Neris, Luciano; Bleicher, Tais; Neris, Vania Paula de Almeida (Anais do Simpósio Brasileiro sobre Fatores Humanos em Sistemas Computacionais (IHC), 2024)
The number of individuals with depression is gradually increasing worldwide. In the university population, the prevalence of depression is even higher than in the general population. Health and computing researchers have sought to create computational solutions that can support diagnosis and create interventions for people with a possible depressive profile, analyzing their behavior through the collection of digital phenotyping data, such as heart rate and sleep quality, coming from sensors present in mobile and wearable devices, such as smartphones and smartwatches. Research in this area is multidisciplinary, and Human-Computer Interaction (HCI) researchers have a major role in supporting the design of this technology. In this sense, the goal of this paper is mapping, among others, how final users and health professionals have been involved in this type of research. We performed a Systematic Mapping focusing on the university environment. We extracted data about the methodological approach adopted, sensor data collected, and human involvement. The results show which data is being collected and correlated with depression indicators, the absence of multidisciplinary teams, and the lack of adoption of protocols to manage mental health emergencies. We also discussed possibilities for human-in-the-loop approaches and some design implications for HCI teams.