Willingness to Participate in Smartphone-Based Mobile Data Collection Studies

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Abstract

Today, digital and mobile forms of data collection are increasingly being used to capture information on the real lives of older adults. One method used is smartphone-based mobile data collection, which uses a standard smartphone to collect information related to people’s daily lives. Even though smartphones are useful in measuring the daily variance of behaviors and the situational context in which these behaviors take place, little is known about openness to participate in mobile data collection studies among the general population. By utilizing representative data from Switzerland, this paper presents data on adults’ openness to participate in those studies and their willingness to share self-recorded smartphone data with researchers. Analyses were based on a cross-sectional survey involving 1,394 participants aged 18 years and older (age range: 18–93 years; mean age: 48 years). The survey was conducted at the end of 2020. Both univariate and multivariate analyses were conducted. The results indicate that 24.8% are very open to participate, while 31.1% are willing to share their self-recorded smartphone data with researchers in mobile data collection studies. Nevertheless, the bivariate analyses show that those in the younger age group (18–64 years) are more open to participate than those in the older age group (aged 65 years and older). Multivariate analyses indicate that aside from age, interest in science is a predictor of openness to participate. While the results reveal that only 25% are open to participate, this initial evaluation of openness to participate in mobile data collection studies among younger and older adults should nevertheless enrich discussions on the acceptance of wearables as data collection tools in future research.

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APA

Seifert, A. (2022). Willingness to Participate in Smartphone-Based Mobile Data Collection Studies. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13330 LNCS, pp. 237–247). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-05581-2_18

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