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This study assessed, over a maximum period of three months, a sample of 60 Australian/permanent residents, players of massively multiplayer online (MMO) games, aged between 18 to 29 years about their IGD behaviors. IGD behaviors have been shown to peak among MMO gamers within this age range, and are related to individual, family and gaming associated risk and protective factors (Kuss & Griffiths, 2012).The present study used a battery of questionnaires to assess psychopathological behaviors (i.e depression, anxiety, Attention Deficit and Hyperactivity symptoms) and personality traits as individual factors, family relationships (family cohesion) as a family factor.  Additional measures were: the level that the gamer is absorbed by the game action (online flow), and the game context (presence) and the bond between the gamer and his figure of representation (avatar) in the game (user self-presence) as gaming patterns related to IGD behaviors. Physiological activity and sleep duration were additionally measured using a fitness tracker (wearable actograph-bracelet). Basic demographic (i.e. gender, age, relationship status) and internet use questions (i.e. daily time consumed online) were included. Participants were provided with a plain language information sheet (PLS) and informed of all information needed toconsent to the study. Participants were then invited to complete the survey (40 minutes approximately) and to wear the fitness tracker (three days per measurement). The data were collected either online (survey monkey/lime survey) or face to face across three time points, one month apart, and participants’ measures were matched through a re-identifiable code. The study looked at unveiling risk and protective (individual, family and gaming related) factors for IGD behaviors.</p><p>Procedure:</p><p>Inclusion criteria<br>18 to 29 years of age; players of massively multiplayer online (MMO) games, who were able and consent to: either a) attend three face to face assessment sessions onsite and carry a fitness tracker for 9 days (3 days per assessment point, one month apart, over three months) or: b) to complete an online assessment sessions without carrying a fitness tracker.<br>Exclusion criteria<br>Due to self-monitoring and the inclusion of the physical activity tracking, those meeting the following criteria were not able to participate: had a documented life threatening illnesses or brain injury, mobility issues or a current untreated severe mental illness.  These was done through a brief face to face interview prior to the setup of the fitbit</p><p>Individuals interested received the Plain Language Information Statement (PLIS). If a person chose to participate they were then be required to provide informed consent (signing the consent form).<br>For physiological measurements, participants were provided with a fitness tracker (fitbit) to wear for three days (per assessment point). Fitness tracker data was collected through a unique online account referring to the device. The account was paired with an anonymous email address created by the information technology department of the University (i.e. fitbit1@federation.edu.au) where the data could be accessed. Each email address was assigned (randomly) to a different participant by the researchers.<br><br>For all other measurements, participants were required to attend onsite for an approximately (40) minute data collection session per time-point, wherein they had to fill in a battery of 12 (paper & pencil) questionnaires. Besides demographic and internet use information (17 items) and the Internet gaming disorder - short form 9 (9 items) (Pontes & Griffiths, 2015) to assess IGD behaviors, these included:<br>a.            Assessing individual psychopathological factors associated to IGD:  Beck depression inventory - second edition (21 items)(Beck, Steer & Brown, 1996), Beck anxiety inventory (Beck & Steer, 1990), Hikikomori-social withdrawal scale (5 items) (Teo et al., 2015), Attention Deficit Hyperactivity self-report scale (18 items) (Kessler et al., 2005) and the ten item personality inventory (Gosling, Rentfrow & Swann, 2003)<br>b.            Assessing family factors associated to IGD, the family cohesion scale (7 items) (Olson, 2011),<br>c.            Assessing gaming patterns associated to IGD: presence questionnaire (10 items) (Faiola, Newlon, Pfaff, & Smyslova, 2013), flow questionnaire (5 items) (Chen Wigand & Nilan, 2000), self-presence questionnaire (Ratan & Hasler, 2010) and the Gaming-Contingent Self-Worth Scale (12 items) (GCSW; Beard & Wickham, 2016).<br>d.     \tin face to face collection the assessment process was repeated three times. Fitbit accounts were deleted at the end of the study.</p>"}}]},{"typeName":"subject","multiple":true,"typeClass":"controlledVocabulary","value":["Computer and Information Science","Medicine, Health and Life Sciences","Social Sciences"]},{"typeName":"keyword","multiple":true,"typeClass":"compound","value":[{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Internet Gaming Disorder"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Risk and Protective Factors"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Preliminary Data Set"}}]},{"typeName":"language","multiple":true,"typeClass":"controlledVocabulary","value":["English"]},{"typeName":"productionDate","multiple":false,"typeClass":"primitive","value":"2021-03-02"},{"typeName":"distributionDate","multiple":false,"typeClass":"primitive","value":"2021-03-02"},{"typeName":"dateOfDeposit","multiple":false,"typeClass":"primitive","value":"2021-03-02"}]},"dansRights":{"displayName":"Rights Metadata","name":"dansRights","fields":[{"typeName":"dansRightsHolder","multiple":true,"typeClass":"primitive","value":["PhD VASILEIOS Stavropoulos (Victoria University)","PhD Vasileios Stavropoulos (Victoria University)"]},{"typeName":"dansPersonalDataPresent","multiple":false,"typeClass":"controlledVocabulary","value":"Unknown"},{"typeName":"dansMetadataLanguage","multiple":true,"typeClass":"controlledVocabulary","value":["English"]}]},"dansRelationMetadata":{"displayName":"Relation Metadata","name":"dansRelationMetadata","fields":[{"typeName":"dansAudience","multiple":true,"typeClass":"primitive","value":["https://www.narcis.nl/classification/D51000","https://www.narcis.nl/classification/D16800","https://www.narcis.nl/classification/D23350"],"expandedvalue":[{"@id":"https://www.narcis.nl/classification/D51000","termName":[{"lang":"en","value":"Psychology"},{"lang":"nl","value":"Psychologie"}],"vocabularyUri":"https://www.narcis.nl/classification/"},{"@id":"https://www.narcis.nl/classification/D16800","termName":[{"lang":"en","value":"Computer simulation, virtual reality"},{"lang":"nl","value":"Simulatie, virtual reality"}],"vocabularyName":[{"lang":"nl","value":"Classificatiecodes van de wetenschapsportal Narcis (www.narcis.nl). 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