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. 2023 Nov 8:25:e40190.
doi: 10.2196/40190.

The Relationship Between Lockdowns and Video Game Playtime: Multilevel Time-Series Analysis Using Massive-Scale Data Telemetry

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The Relationship Between Lockdowns and Video Game Playtime: Multilevel Time-Series Analysis Using Massive-Scale Data Telemetry

David Zendle et al. J Med Internet Res. .

Abstract

Background: COVID-19 led governments worldwide to enact a variety of containment and closure policies. Substantial attention has been directed toward the idea that these public health measures may have unanticipated negative side effects. One proposed effect relates to video games. There is a nascent evidence base suggesting that individuals played video games for longer and in a more disordered manner during lockdowns and school closures specifically. These increases are commonly framed as a potential health concern in relation to disordered gaming. However, the evidence base regarding changes in gaming during the COVID-19 pandemic is based on self-report and, thus, is susceptible to bias. Therefore, it is unclear what the true consequences of lockdowns were for gaming behavior worldwide.

Objective: The primary objective of this study was to estimate whether any specific lockdown policy led to meaningful increases in the amount of time individuals spent playing video games.

Methods: Rather than relying on self-report, we used >251 billion hours of raw gameplay telemetry data from 184 separate countries to assess the behavioral correlates of COVID-19-related policy decisions. A multilevel model estimated the impact of varying enforcement levels of 8 containment and closure policies on the amount of time that individual users spent in-game. Similar models estimated the impact of policy on overall playtime and the number of users within a country.

Results: No lockdown policy can explain substantial variance in playtime per gamer. School closures were uniquely associated with meaningful increases in total playtime within a country (r2=0.048). However, this was associated with increases in the number of unique individuals playing games (r2=0.057) rather than increases in playtime per gamer (r2<0.001).

Conclusions: Previous work using self-report data has suggested that important increases in heavy gaming may occur during pandemics because of containment and closure ("lockdown") procedures. This study contrasts with the previous evidence base and finds no evidence of such a relationship. It suggests that significant further work is needed before increases in disordered or heavy gaming are considered when planning public health policies for pandemic preparedness.

Keywords: COVID-19; big data; disordered; disordered gaming; gaming; lockdown; lockdown policy; pandemic; playing; playtime; policy; public health; side effects; time; video games.

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Conflict of interest statement

Conflicts of Interest: Data for this study were provided by Unity Technologies. Unity Technologies played no role in the design of the study, its reporting, or its execution. No funds were disbursed to the research team for the work incorporated into this manuscript. This includes data access fees for the serverless data warehouse from which the data under analysis were accessed, which were paid by the lead author out of a research stipend he receives from his host institution. Access to the data used in this study was contingent on a data-sharing agreement between Unity Technologies and DZ’s host institution. Legal approval was sought from Unity for the sharing of the data in this paper before its submission for peer review. DZ has never received any form of funding from the games industry. He has worked as a paid consultant for governments seeking to understand the effects of video games and gambling. He has worked as an expert witness in cases relating to the video game industry but has never represented the games industry legally or been formally affiliated with any games industry body in any way. DZ has been involved in brokering data-sharing agreements with industry stakeholders in the past. He acknowledges that such data-sharing agreements constitute a conflict of interest as important as financial awards and wishes to highlight that he has used such data brokerage in ways that are likely to give him indirect financial advantage: he has used them as evidence for excellence in promotion applications; he has used them as evidence in grant applications. No such applications have been funded at the time of writing this manuscript. DZ is a member of the Advisory Board for Safer Gambling, a statutory body whose remit is to provide independent advice to the UK Gambling Commission. DZ is the recipient of an Academic Forum for the Study of Gambling Major Exploratory Grant that is derived from “regulatory settlements applied for socially responsible purposes” received by the UK Gambling Commission and administered by Gambling Research Exchange Ontario (GREO). He has no further conflicts to declare. CF has received funding from the Tides Foundation on the recommendation of the Unity Charitable Fund (grant TF2201-105180) for a separate project. AD has previously worked in a paid capacity within the video games industry as a game analytics consultant. He has worked on multiple industry-focused projects with a focus on knowledge transfer. He has received funding from the Tides Foundation on the recommendation of the Unity Charitable Fund (grant TF2201-105180) for a separate project.

Figures

Figure 1
Figure 1
Autocorrelation function (ACF) plots. The top row represents the ACF of residuals in uncorrected models (left to right: total playtime, playtime per user, and number of users). The bottom row represents the ACF of corrected models (left to right: total playtime, playtime per user, and number of users). The dotted lines represent significance bounds.
Figure 2
Figure 2
Q-Q plots of model residuals. The top row represents overall residuals (from left to right: total playtime, number of users, and playtime per user). The bottom row represents the residuals associated with the random slopes.
Figure 3
Figure 3
Total global daily playtime from January 1, 2020, to December 5, 2021. The solid blue line represents a weekly simple moving average. The dotted line represents the World Health Organization declaration of the pandemic on March 11, 2020.
Figure 4
Figure 4
All associations between containment and closure policies and outcomes for the 3 models. The error bars represent the 95% CIs of the effect size associated with each policy decision. The letters G and T in a policy’s annotation refer to whether that policy’s geographical scope was general or _targeted. The numbers (1-4) represent the enforcement level of that policy (see the Methods section for more details).

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