[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128562-en":3,"doc-seo-128562-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128562,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Predicting life engagement and happiness from gaming motives and primary emotional traits before and during the COVID pandemic - a machine learning approach","The present study investigated whether life engagement and happiness can be predicted from gaming motives and primary emotional traits. Two machine learning algorithms, a random forest model and a one-dimensional convolutional neural network, were trained on data collected before the COVID-19 pandemic. The trained models were then evaluated on a pandemic dataset to test their generalization. The best results concerned happiness, with ρ = 0.758 and explained variance R2 = 0.575, supporting feasibility of using pre-pandemic derived models.","BIROn-Birkbeck Institutional Research Online  \nDagum, N. and Pontes, Halley and Montag, C. (2024) Predicting life engagement and happiness from gaming motives and primary emotional traits before and during the COVID pandemic: a machine learning approach. Discover Psychology 4 (78), ISSN 2731-4537 .  \nDownloaded from: [https://eprints.bbk.ac.uk/id/eprint/53758/](https://eprints.bbk.ac.uk/id/eprint/53758/)  \nUsage Guidelines:  \nPlease refer to usage guidelines at [https://eprints.bbk.ac.uk/policies.html](https://eprints.bbk.ac.uk/policies.html) or alternatively  \ncontact [lib-eprints@bbk.ac.uk](lib-eprints@bbk.ac.uk).  \nResearch  \nPredicting life engagement and happiness from gaming motivesand primary emotional traits before and during the COVID pandemic:  \na machine learning approach  \nNolan Dagum1 · Halley M. Pontes2 · Christian Montag3  \nReceived: 14 December 2023 / Accepted: 7 June 2024  \n© The Author(s) 2024 OPEN  \nAbstract  \nThe present study investigated whether life engagement and happiness can be predicted from gaming motives and primary emotional traits. Two machine learning algorithms (random forest model and one-dimensional convolutional neural network) were applied using a dataset from before the COVID-19 pandemic as the training dataset. The algorithms derived were then applied to test if they would be useful in predicting life engagement and happiness from gaming motives and primary emotional systems on a dataset collected during the pandemic. The best prediction values were observed for happiness with ρ = 0.758 with explained variance of R2 = 0.575 when applying the best performing algorithm derived from the pre-COVID dataset to the COVID dataset. Hence, this shows that the derived algorithm based on the pre-pandemic data set, successfully predicted happiness (and life engagement) from the same set of variables during the pandemic. Overall, this study shows the feasibility of applying machine learning algorithms to predict life engagement and happiness from gaming motives and primary emotional systems.  \nKeywords Gaming motives · Primary emotional systems · Happiness · Life engagement · Machine learning · Personality  \n1 Introduction  \nVideo gaming represents a multi-billion dollar industry with billions of people worldwide spending varying amounts of time playing video games [1]. Playing video games can have both positive and negative consequences, depending on several factors including, but not limited to gaming motives and the time spent playing games [2]. While video games can be a fun recreational activity with some studies even suggesting positive training effects (but see heterogeneous findings [3, 4]), excessive gaming can also lead to Gaming Disorder (GD) with adverse emotional effects and decline in academic performance [5] .  \nThe motivations for gaming and the potential for GD are relevant to understanding the impact of gaming on wellbeing. Two gaming motive frameworks have been proposed. Yee [6] put forth the three overarching motivational factors of achievement, social, and immersion motives that had a total of ten sub-components. Demetrovics et al. [7]  \nSupplementary Information The online version contains supplementary material available at [https://doi.org/10.1007/s44202-024-00191-w](https://doi.org/10.1007/s44202-024-00191-w).  \n* Christian Montag, [christian.montag@uni-ulm.de |](christian.montag@uni-ulm.de |1Independent Academic)[1](christian.montag@uni-ulm.de |1Independent Academic)[Independent Academic](christian.montag@uni-ulm.de |1Independent Academic), Los Altos, CA, USA. 2School of Psychological Sciences, Birkbeck, University of London, London, UK. 3Department of Molecular Psychology, Ulm University, Helmholtzstr. 8/1, 89081 Ulm, Germany.  \nDiscover Psychology  \n(2024) 4:78  \n| [https://doi.org/10.1007/s44202-024-00191-w](https://doi.org/10.1007/s44202-024-00191-w)  \nfurther developed the Motives for Online Gaming Questionnaire (MOGQ), which included seven gaming-related motives (i.e., s","cbCaip8r6zSIqcKp","https://ap.wps.com/l/cbCaip8r6zSIqcKp","pdf",3039138,1,17,"English","en",105,"# Abstract\n# Introduction\n## Gaming motives and wellbeing\n## Personaliy traits and primary emotional traits","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To determine whether life engagement and happiness can be predicted from gaming motives and primary emotional traits, both before and during the COVID-19 pandemic.\"},{\"question\":\"Which machine learning methods were used?\",\"answer\":\"A random forest model and a one-dimensional convolutional neural network were applied, with models trained on pre-COVID data and tested on COVID-era data.\"},{\"question\":\"What were the best prediction outcomes?\",\"answer\":\"The strongest results were for happiness, yielding ρ = 0.758 and an explained variance of R2 = 0.575 when using the best-performing pre-COVID derived algorithm on the COVID dataset.\"}]","Predicting life engagement and happiness from gaming motives and primary emotional traits before and during the COVID pandemic - 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