ScreenLife Capture: An open-source and user-friendly framework for collecting screenome data from Android smartphones

Abstract

As our interactions with each other become increasingly digitally mediated, there is growing interest in the study of people’s digital experiences. To better understand digital experiences, some researchers have proposed the use of screenomes. This involves the collection of sequential high-frequency screenshots to provide detailed objective records of individuals’ interaction with screen devices over time. Despite its usefulness, there remains no readily available tool which researchers can use to run their own screenome studies. To fill this gap, we introduce ScreenLife Capture, a user-friendly and open-source software to collect screenomes from smartphones. Using this tool, researchers can set up smartphone screenome studies even with limited programming knowledge and resources. We piloted the tool in an exploratory mixed-method study of 20 college students, collecting over 740,000 screenshots over a two-week period. We found that smartphone use is highly heterogeneous, characterized by threads of experiences. Using in-depth interviews, we also explored the impact that constant background surveillance of smartphone use had on participants. Participants generally had slight psychological discomfort which fades after a few days, would suspend screen recording for activity perceived to be extremely private, and recounted slight changes in behavior. Implications for future research is discussed.

Beyond Active-Passive: Towards the Next Stage of Social Media and Mental Health Research

“…Recently, an open source solution to conduct multi-platform "screenomics" analyses of users' mobile SMU has been successfully piloted (Yee et al, 2022), although more work is needed to apply this at scale and answer message-centered research questions, not least because of the immense diversity of user-generated and mass-produced content on SM. For now, methodological alternatives to studying message effects can employ static vignettes of SM posts (e.g., Meier et al, 2020), which is highly common in the body image literature (Vandenbosch et al, 2022), or use dynamic mock-up SM environments (for a review of potential methods, see Parry et al, 2022).…”

Is it Time to Abandon the Media Multitasking Index?

“…"picture in picture" mode). Evidence from studies using data collected through high-frequency screenshots of media usage (i.e., "screenomes") indicates that individuals rapidly switch between applications/programs/content categories on their smartphones and laptops, with sessions typically lasting mere seconds before they switch away (Brinberg et al, 2023;Reeves et al, 2019;Yee et al, 2022). Echoing findings produced in studies using smartphone tracking methods (e.g., Siebers et al, 2023), this research highlights the highly fragmented and interleaved nature of media use and shows, in particular, that media use typically involves frequent switches between media tasks in rapid succession with each application/program used for a very brief period before the user switches to another (see Brinberg et al, 2023).…”