Abstract
Smartphones have become an indispensable part of our daily life. Their improved sensing and computing capabilities bring new opportunities for human behavior monitoring and analysis. Most work so far has been focused on detecting correlation rather than causation among features extracted from smartphone data. However, pure correlation analysis does not offer sufficient understanding of human behavior. Moreover, causation analysis could allow scientists to identify factors that have a causal effect on health and well-being issues, such as obesity, stress, depression and so on and suggest actions to deal with them. Finally, detecting causal relationships in this kind of observational data is challenging since, in general, subjects cannot be randomly exposed to an event. In this article, we discuss the design, implementation and evaluation of a generic quasi-experimental framework for conducting causation studies on human behavior from smartphone data. We demonstrate the effectiveness of our approach by investigating the causal impact of several factors such as exercise, social interactions and work on stress level. Our results indicate that exercising and spending time outside home and working environment have a positive effect on participants stress level while reduced working hours only slightly impact stress.
Highlights
Nowadays, people generate vast amounts of data through the devices they interact with during their daily activities, leaving a rich variety of digital traces
We decided to conduct additional studies separately for these sub-populations because neuroticism and extroversion are strongly correlated with stress level according to Table
We have studied the causal effects of several factors, such as working, exercising and socializing, on stress level of students using data captured by means of smartphone sensors
Summary
People generate vast amounts of data through the devices they interact with during their daily activities, leaving a rich variety of digital traces. User location can be monitored and activities (e.g., running, walking, standing, traveling on public transit, etc.) can be inferred from raw accelerometer data captured by our smartphones [ , ]. Even more complex information such as our emotional state or our stress level can be inferred either by processing voice signals captured by means of smartphone’s microphones [ , ] or by combining information, extracted from several sensors, which correlates with our mood [ – ]. We keep track of our daily schedule by using digital calendars and we use social media to share our experiences, opinions and emotions with our friends. Wearable devices that are Tsapeli and Musolesi EPJ Data Science (2015) 4:24 able to monitor physical indicators with a very high level of accuracy are increasingly popular
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