Do you have your smartphone with you? Behavioral barriers for measuring everyday activities with smartphone sensors. (February 2022)

• Record Type: Journal Article
• Authors: Keusch, Florian; Wenz, Alexander; Conrad, Frederick
• Is Part Of: Computers in human behavior. Volume 127(2022)
• Publication Date: 2022-02
• Subjects: Smartphones -- Passive measurement -- Sensor data -- Everyday activities -- Bias -- Usage behavior; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2021.107054
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: Smartphones have become central to our daily lives and are often present in the same contexts as their users. Researchers take advantage of this phenomenon by using data from smartphone sensors to infer everyday activities, such as mobility, physical activity, and sleep. For example, that a person is sleeping might be inferred from the fact that their phone is idle and that there is no sound and light around the phone. The success of inference from raw smartphone sensor data to activity outcomes depends, among other factors, on how smartphone owners use their device. Not having the smartphone in close proximity throughout the day, turning the device off, or sharing the device with others can constitute barriers that interfere with accurately measuring everyday activity with data from the phone's native sensors. Against this background, we surveyed two independent, large-scale samples of German smartphone owners (n1 = 3956; n2 = 2525) on how they use their smartphones, with a focus on three everyday activities: mobility, physical activity, and sleep. We find that both sociodemographic as well as smartphone-related characteristics are associated with how people use their smartphones, and that this affects the suitability of smartphone data for measuring everyday activities. Highlights: Turning smartphone off, leaving it at home, and not carrying it close to body are common behaviors among users. Smartphone usage behavior differs by sociodemographic and smartphone-related user characteristics. Bias in measures of everyday activities from smartphone sensors can be attributed to how people use their devices.