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

Abstract

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 effects -- whether 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 arising from the use of an open, non-profit online chess platform. 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.

A Manifesto for More Productive Psychological Games Research

“…One fundamental example of this concerns playtime. Evidence is rapidly accumulating that selfreport measures of digital technology use are often inaccurate (Parry et al 2021), and that this is true of games in particular (Kahn et al 2014;Mok and Anderson 2021;Johannes et al 2021b). The degree of inaccuracy may even be related to well-being, creating a possible confound for certain research questions (Sewall et al 2020).…”

A Rule Mining and Bayesian Network Analysis to Explore the Link Between Depression and Digital Behavioral Markers of Games App Usage

“…As subjective data does not reflect the actual habit [18], we retrieve participants' actual app usage data through our developed Android app (Figure 1(a)). We used the Java class UsageStatsManager [9] to retrieve the foreground and background events' data, and to store it, we used the Google Firebase database.…”

“…Some of these studies found a positive association of depression with problematic online gaming [24] and addiction to video gaming [5]. However, as subjective data does not present the actual app usage behavior [18], the findings of these studies may not unveil the exact relation. Researchers used objectively measured data for purposes such as exploring the feasibility of gamification in having a positive impact on sleep-wake [12], and exploring patterns in Game playing behavior [8].…”

“…Researchers used objectively measured data for purposes such as exploring the feasibility of gamification in having a positive impact on sleep-wake [12], and exploring patterns in Game playing behavior [8]. In another recent study [18], researchers used both subjective and objective data of an online chess platform to explore the relationship with problematic effects (e.g., disrupted sleep). However, as far as we have seen, through objective app usage data, none of the previous studies explored the relation of Games with depression.…”

(Re)Politicizing Digital Well-Being: Beyond User Engagements

“…The Internet Addiction Questionnaire, for example, measures different dimensions of time spent on a platform, whether platform usage influences a user's life offline, or whether a user thinks about a platform while not using it [115]. Other studies adopt similar temporal, disconnection, and engagement-based measures of well-being and addiction [5,76], with more moralized discourses, such as the aforementioned time well spent movement, calling for personal reductions of certain negative types of user engagement.…”