A Novel iOS Data Donation Approach: Automatic Processing, Compliance, and Reactivity in a Longitudinal Study. (August 2023) - Baumgartner, Susanne E.; Sumter, Sindy R.; Petkevič, Vladislav; Wiradhany, Wisnu
For social scientists who are interested in understanding smartphone-usage patterns, as well as the causes and effects of smartphone use, it is crucial to get accurate estimates of smartphone and app usage. For this reason, we present a novel data donation approach based on screen video recordings of information provided by the iOS Battery Section. In this paper, we describe the Python script that can be used to process the data automatically. Moreover, we tested accuracy of the script, feasibility of the data collection method, as well as compliance and reactivity of the approach among a sample of 93 University students who participated in a 7-day mobile diary study. The findings of the study show that the approach works well, is very feasible and that the script resulted in accurate smartphone use data. Moreover, findings show that compliance rates were rather high, and no substantial signs of reactivity to the method occurred (i.e., participants did not change their smartphone behavior substantially while participating in this study). We therefore hope that the presented approach will encourage future researchers to use this or similar approaches to gain accurate smartphone use data in an ethical way.