The Complementary Nature of Perceived and Actual Time Spent Online in Measuring Digital Well-being

As online platforms become ubiquitous, there is growing concern that their use can potentially lead to negative outcomes in users' personal lives, such as disrupted sleep and impacted social relationships. A central question in the literature studying these problematic effects is whether they are associated with the amount of time users spend on online platforms. This is often addressed by either analyzing self-reported measures of time spent online, which are generally inaccurate, or using objective metrics derived from server logs or tracking software. Nonetheless, how the two types of time measures comparatively relate to problematic effectswhether they complement or are redundant with each other in predicting problematicity-remains unknown. Additionally, transparent research into this question is hindered by the literature's focus on closed platforms with inaccessible data, as well as selective analytical decisions that may lead to reproducibility issues.

In this work, we investigate how both self-reported and data-derived metrics of time spent relate to potentially problematic effects a rising f rom t he u se o f a n o pen, n on-profit on line ch ess pl atform. These effects include disruptions to sleep, relationships, school and work performance, and self-control. To this end, we distributed a gamified survey to players and linked their responses with publicly-available game logs. We find problematic effects to be associated with both self-reported and data-derived usage measures to similar degrees. However, analytical models incorporating both self-reported and actual time explain problematic effects significantly more effectively than models with either type of measure alone. Furthermore, these results persist across thousands of possible analytical decisions when using a robust and transparent statistical framework. This suggests that the two methods of measuring time spent measure contain distinct, complementary information about problematic usage outcomes and should be used in conjunction with each other.

CCS Concepts: • Human-centered computing → Empirical studies in HCI; Empirical studies in collaborative and social computing; • Applied computing → Law, social and behavioral sciences.