The frequent use of the label ‘smartphone addiction’ to describe teenagers’ smartphone use illustrates that are concerns in both the public and scholarly domain that some teenagers are using their phones in ways that are unhealthy and that threaten their wellbeing. Smartphone usage among teenagers is also linked to positive outcomes, however. It thus appears that the link between smartphone use and wellbeing is complex: Both smartphone use and wellbeing are dynamic and multifaceted constructs. To further complicate matters, research also supports a bi-directional causality: Adolescent smartphone use may be both a predictor and outcome of wellbeing.

This project, in which researchers from TSH and TSB collaborate in setting up an ESM study, examines the link between adolescent smartphone use and wellbeing using a novel approach that acknowledges both bi-directional causality, and individual variability between adolescents, by exploring unique patterns in behavioral smartphone data, and associating them with dynamic indicators of wellbeing over a longer period of time. In the longer run, the project may illuminate for whom and why smartphone use may impact upon youths well-being.

Linking smartphone use to wellbeing

This project examines the directionality of the link between adolescent smartphone use and wellbeing by, first, observing how teenagers use their smartphone, and, second, exploring whether unique differences in usage patterns explain unique differences in the development of wellbeing over time, and vice versa. In terms of usage patterns, we look for differences in not only the frequency and duration of smartphone use, but also in aspects such as the timing, the fragmentation, and in the types and ordering of applications used. With respect to the development of wellbeing over time, we acknowledge that there may be individual variability in indicators of wellbeing that can only be captured via multiple momentary assessments.

Combining logging, experience sampling and machine learning

We combine two novel methodological approaches. First, we measure adolescent smartphone use by logging it. The smartphone log data gives us a precise, time-stamped overview of when how long adolescents activated their smartphone screen to access certain mobile applications. Using machine learning techniques, we can identify specific and unique patterns in teenagers’ smartphone use behavior. Second, we measure indicators of adolescent wellbeing using the Mobile Experience Sampling Method (ESM). The ESM data provide a dynamic assessment of teenagers’ momentary feelings and experiences. Via time series modelling, the identified smartphone use patterns can be used to predict the development of teenagers’ wellbeing over months and years, thereby revealing unique pathways of how smartphone use hampers or contributes to wellbeing, and vice versa.

Project team

George Aalbers is a PhD student at the Department of Cognitive Science and Artificial Intelligence (Tilburg University). He is fascinated by data-driven approaches to health-related social science. George is supervised by an interdisciplinary team: Dr. Mariek Vanden Abeele (Cognition and Communication), Dr. Drew Hendrickson (Cognitive Science and Artificial Intelligence), Dr. Loes Keijsers (Developmental Psychology), and Prof. Dr. Eric Postma (Cognitive Science and Artificial Intelligence).

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