Predicting mental health using smart-phone usage and sensor data

Abstract

The prevalence of mental health problems is rising in the college-going population. To predict the mental health of students using smartphone usage and sensor data is an intriguing research problem. In this study, we aim to engineer feature variables related to daily-living behavior using smartphone usage and sensor data. Further, to develop models using these feature variables to predict if anybody is having a mental health issue or not. Independent-samples t-test has been used to compare the variation in means between the healthy group and group with mental illness. Correlation analysis is used to see the strength of the relationship between the independent and dependent variables. The classification model has been developed to predict mental health, (baseline: n = 45). The difference in means of various feature variables among the two groups is statistically significant (p ≤ 0.05). Many variables are strongly correlated with various mental health predictors. The area under curve of the prediction model for predicting stress is 82.6% and that for the depression is 74%. Our results are quite encouraging and point towards the novel application of smartphone-based data sensing in tracking or predicting mental health issues. The study has some implications for practice such as developing a smartphone-based automated system for predicting mental health that could be a useful tool for professionals in predicting mental health, especially in academic institutions.