Predicting mental health using smart-phone usage and sensor data
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ORIGINAL RESEARCH
Predicting mental health using smart‑phone usage and sensor data Saurabh Singh Thakur1 · Ram Babu Roy1 Received: 16 May 2020 / Accepted: 10 October 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
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. Keywords eHealth · Mental health · mHealth · Predictive modeling · Pervasive computing · Smartphone
1 Introduction The rising prevalence of mental health disorders worldwide is becoming a challenging threat to society. Depression or other mental health (MH) disorders are distressing and disturbing. It is posing an enormous burden in terms of cost, morbidity, and mortality (Greenberg et al. 2015). Mental health disorders are the second leading cause of the years lost to disability (YLD) in the US (Murray et al. 2013). According to the world health organization (WHO) report published in 2017, more than 300 million people are suffering from depression (WHO 2017). This trend is not only present in the developed countries but the developing Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12652-020-02616-5) contains supplementary material, which is available to authorized users. * Ram Babu Roy [email protected] Saurabh Singh Thakur [email protected] 1
Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
nation are also witnessing the rise of prevalence of major depressive disorders. An increasing number of cases of depression, hypertension, stress, and suicide among adults has become a major mortality burden (Patel et al. 2012), (Radhakrish
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