Open Play: A Longitudinal Dataset of Multi-Platform Video Game Digital Trace Data and Psychological Measures, 2022-2025

Przybylski, Andrew and Ballou, Nick and Foldes, Tamas (2026). Open Play: A Longitudinal Dataset of Multi-Platform Video Game Digital Trace Data and Psychological Measures, 2022-2025. [Data Collection]. Colchester, Essex: UK Data Service. 10.5255/UKDA-SN-858275

Despite their popularity, science has a limited understanding of video games and their effects on our mental health. This limited understanding is a direct result of several limitations of the method of previous research: low data quality, unrepresentative samples, and a lack of transparency. Together, such limitations have resulted in research that lacks a principled approach to the study of video games: describing the behaviour (i.e., gaming), predicting the behaviour, testing what the behaviour itself predicts. With this proposal, we develop such a principled research programme that addresses these limitations. Specifically, we aim to generate an authoritative data set to provide us with an accurate understanding of video game play and mental health. We will combine a large, nationally representative survey of active video game players' mental health, personality, and motivations with objective data of behaviour in video games. We will obtain these behavioural data from industry partners that we have established working relations with (Nintendo, Sony, Microsoft).

This combination of survey data with behavioural data requires a new, first-of-its kind collaboration between academics and a video games industry that has been historically reluctant to share data. We will recruit a large sample of 7,000 active adult players representative of the UK population. These participants will complete a half-hour survey on mental health, well-being, motivations, personality, and demographic factors. To link participant survey data with their video game play behaviours, the survey will direct participants to a website where they select all game services they use and consent to donate their play data to the study. The systems of our industry partners then record the consent and an anonymous identifier, which is then recorded with the survey data. This workflow allows our team to query the data bases of our partners for those anonymous identifiers for behavioural video game data.

The resulting data set represents a unique resource and, more importantly, a unique opportunity to finally obtain an accurate understanding of video game behaviour. The games industry records what people do in their games (e.g., how long someone played a game, how good they were in the game). Combining these data with a representative sample allows us to conduct each step of a principled research programme: describing video game play, predicting it, and testing whether video game play predicts mental health. Together, these steps form a much needed foundation for a science of video game pay and mental health. Not only will we be able to tell what games people play when and for how long; we can also examine what types of players there are (e.g., subgroups with certain motivations and personalities). Moreover, we can test what characteristics allow us an accurate prediction of what genres a player engages with, how long they play for, etc. Last, we will finally know what types of play are related to mental health.

Meeting these objectives will finally allow a comprehensive understanding of video games. This understanding is sorely needed: Many welfare agencies and government bodies need evidence of the kind we produce to potentially regulate gaming. At the same time, both academics and public stakeholders have been asking for industry to share their data with independent researchers. With our project, we will set the standard for such collaborations: ethical, transparent, and independent. Being the first to collaborate with the games industry on such a scale, we aim to lead by example. Not only will we produce the first authoritative data set on video games and mental health; we will share these data set publicly. This data set is a valuable resource for scientists all around the world. Further, communicating our findings to policymakers will finally provide them with a firm evidence base and an example of how to engage with industry partners.

Data description (abstract)

A major limitation to understanding digital technology use, and its potential psychological consequences, is the lack of sufficiently detailed, multidimensional, and accurate data. We present a dataset of 2.0K individuals’ video game play telemetry data from Nintendo Switch, Steam, and Xbox, paired with psychological measures across multiple dimensions of mental health, motivations, well-being, and cognitive ability. The data were collected under a preregistered design that included 12 weeks of survey data (thirty daily surveys, six biweekly surveys, three biweekly cognitive tests), and digital trace data for 43 months. Cleaned data include 1.5M hours of video game play across 10,475 titles, 23K responses to 14 survey instruments, and 2.9K attention ability measures to facilitate examining longitudinal associations between play behaviors and psychological functioning. Data and codebook are available under a CC0 (with supplemental reidentification clause) license at https://doi.org/10.5281/zenodo.17536656.

Data creators:
Creator Name Affiliation ORCID (as URL)
Przybylski Andrew
Ballou Nick https://orcid.org/0000-0003-4126-0696
Foldes Tamas https://orcid.org/0000-0002-0623-9149
Contributors:
Name Affiliation ORCID (as URL)
Foldes Tamas https://orcid.org/0000-0002-0623-9149
Sponsors: ESRC
Grant reference: ES/W012626/1
Topic classification: Media, communication and language
Science and technology
Psychology
Keywords: DIGITAL GAMES, DIGITAL TECHNOLOGY, MENTAL HEALTH
Project title: Understanding Video Game Play and Mental Health
Grant holders: Andrew Przybylski, Vuorre Matti, Magnusson Kristoffer, Johannes Niklas
Project dates:
FromTo
1 March 202331 December 2025
Date published: 27 Jan 2026 12:32
Last modified: 27 Jan 2026 12:32

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