Impact of wagering inducements on the gambling behaviors of on-line gamblers: A longitudinal study based on gambling tracking data

Marianne Balem, Bastien Perrot, Jean-Benoit Hardouin, Elsa Thiabaud, Anaïs Saillard, Marie Grall-Bronnec, Gaëlle Challet-Bouju, Marianne Balem, Bastien Perrot, Jean-Benoit Hardouin, Elsa Thiabaud, Anaïs Saillard, Marie Grall-Bronnec, Gaëlle Challet-Bouju

Abstract

Aims: To estimate whether the use of wagering inducements has a significant impact on the gambling behaviors of on-line gamblers and describe this temporal relation under naturalistic conditions.

Design: This longitudinal observational study is part of the second stage of the Screening for Excessive Gambling Behaviors on the Internet (EDEIN) research program.

Setting: Gambling tracking data from the French national on-line gambling authority (poker, horse race betting and sports betting) and from the French national lottery operator (lotteries and scratch games).

Participants: A total of 9306 gamblers who played poker, horse race or sports betting and 5682 gamblers who played lotteries and scratch games completed an on-line survey. The gender ratio was largely male (between 87.1% and 92.9% for poker, horse race betting and sports betting, and equal to 65.1% for lotteries). Median age ranged from 35 (sports betting) to 53 (horse race betting and lotteries).

Measurements: The survey used the Problem Gambling Severity Index (PGSI) to determine the status of the gamblers (at-risk or not). Gambling tracking data included weekly gambling intensity (wagers, deposits), gambling frequency (number of gambling days), proxies of at-risk gambling behaviors (chasing and breadth of involvement) and use of wagering inducements.

Findings: The use of wagering inducements was associated with an increase of gambling intensity [β between -0.06 (-0.08; -0.05) and 0.57 (0.54; 0.60)], gambling frequency [β between 0.12 (0.10; 0.18) and 0.29 (0.28; 0.31)] and at-risk gambling behaviors [odds ratio between 1.32 (1.16; 1.50) and 4.82 (4.61; 5.05)] at the same week of their use. This effect was stronger for at-risk gambling behaviors and at-risk gamblers.

Conclusions: Wagering inducements may represent a risk factor for developing or exacerbating gambling problems.

Trial registration: ClinicalTrials.gov NCT02415296.

Keywords: cross-correlations; gambling disorder; gambling tracking data; generalized linear mixed models; on-line gambling; wagering inducements.

Conflict of interest statement

M.B., B.P. and J.B.‐H. declare that they have no competing interests in relation to this work.

E.T., A.S., M.G.‐B. and G.C.‐B. declare that the University Hospital of Nantes received funding from the gambling industry [Française des Jeux (FDJ) and Pari Mutuel Urbain (PMU)] in the form of a philanthropic sponsorship (donations that do not assign the purpose of use). Scientific independence with respect to these gambling industries is guaranteed, and this funding did not influence the present work.

© 2021 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.

Figures

FIGURE 1
FIGURE 1
Flow‐chart of the gamblers included in cross‐correlations and generalized linear mixed‐models (GLMMs) analyses
FIGURE 2
FIGURE 2
Results of the cross‐correlations for the five indicators of gambling behavior

References

    1. Calado F, Griffiths MD. Problem gambling worldwide: an update and systematic review of empirical research (2000–2015). J Behav Addict. 2016;5:592–613.
    1. Gainsbury SM. Online gambling addiction: the relationship between internet gambling and disordered gambling. Curr Addict Rep. 2015;2:185–93.
    1. Griffiths M. Internet gambling: issues, concerns, and recommendations. Cyberpsychol Behav. 2003;6:557–68.
    1. McCormack A, Griffiths MD. A scoping study of the structural and situational characteristics of Internet gambling. Int J Cyber Behav Psychol Learning. 2013;3:29–49.
    1. Effertz T, Bischof A, Rumpf H‐J, Meyer C, John U. The effect of online gambling on gambling problems and resulting economic health costs in Germany. Eur J Health Econ. 2018;19:967–78.
    1. Griffiths M, Wardle H, Orford J, Sproston K, Erens B. Internet gambling, health, smoking and alcohol use: findings from the 2007 British Gambling Prevalence Survey. Int J Ment Health Addict. 2011;9:1–11.
    1. Costes JM, Eroukmanoff V, Richard JB, Tovar ML. Gambling in France in 2014 (Les jeux d'argent et de hasard en France en 2014). Les notes de l'Observatoire Des Jeux [internet]. 2015;6:1–9. Available at: (accessed 11 September 2019).
    1. Costes J‐M, Richard J‐B, Eroukmanoff V. Gambling problems in France in 2019 (Les problèmes liés aux jeux d'argent en France, en 2019). Les notes de l'Observatoire Des Jeux [internet]. 2020;12:1–7. Available at: (accessed 4 August 2020).
    1. Gainsbury S. Player account‐based gambling: potentials for behaviour‐based research methodologies. Int Gambl Stud [internet]. 2011;11:153–71. Available at: 10.1080/14459795.2011.571217 (accessed 16 July 2019).
    1. Deng X, Lesch T, Clark L. Applying data science to behavioral analysis of online gambling. Curr Addict Rep [internet]. 2019;6:159–64. Available at: 10.1007/s40429-019-00269-9 (accessed 11 September 2019).
    1. Nature (Editorial) Science has a gambling problem [internet]. 2018. Available at: (accessed 16 July 2019).
    1. Hing N, Sproston K, Brook K, Brading R. The structural features of sports and race betting inducements: issues for harm minimisation and consumer protection. J Gambl Stud. 2017;33:685–704.
    1. Wohl MJA. Loyalty programmes in the gambling industry: potentials for harm and possibilities for harm‐minimization. Int Gambl Stud [internet]. 2018;18:495–511. Available at: 10.1080/14459795.2018.1480649 (accessed 18 December 2019).
    1. Van Dyke N, Jenner D, Maddern C. The role of loyalty programs in gambling: final report of findings from audit of electronic gaming machine gambling venues, literature review, online discussion boards and longitudinal telephone survey [internet]. Melbourne, Victoria, Australia: Office of Liquor, Gaming and Racing Department of Justice; 2016. Available at: (accessed 11 March 2020).
    1. Thomas S, Lewis S, McLeod C, Haycock J. ‘They are working every angle’. A qualitative study of Australian adults’ attitudes towards, and interactions with, gambling industry marketing strategies. Faculty Soc Sci [internet]. 2012;111–27. Available at: (accessed 11th March 2020).
    1. Hing N, Browne M, Russell AMT, Greer N, Thomas A, Jenkinson R, et al. Where's the bonus in bonus bets? Assessing sports bettors’ comprehension of their true cost. J Gambl Stud. 2019;35:587–99.
    1. Deans EG, Thomas SL, Derevensky J, Daube M. The influence of marketing on the sports betting attitudes and consumption behaviours of young men: implications for harm reduction and prevention strategies. Harm Reduct J [internet]. 2017;14(5):1–12. 10.1186/s12954-017-0131-8 (accessed 11 March 2020).
    1. Hing N, Russell AMT, Li E, Vitartas P. Does the uptake of wagering inducements predict impulse betting on sport? J Behav Addict. 2018;7:146–57.
    1. Rockloff MJ, Browne M, Russell AMT, Hing N, Greer N. Sports betting incentives encourage gamblers to select the long odds: an experimental investigation using monetary rewards. J Behav Addict. 2019;8:268–76.
    1. Hing N, Cherney L, Blaszczynski A, Gainsbury SM, Lubman DI. Do advertising and promotions for online gambling increase gambling consumption? An exploratory study. Int Gambl Stud. 2014;14:394–409.
    1. Browne M, Hing N, Russell AMT, Thomas A, Jenkinson R. The impact of exposure to wagering advertisements and inducements on intended and actual betting expenditure: an ecological momentary assessment study. J Behav Addict. 2019;8:146–56.
    1. Challet‐Bouju G, Grall‐Bronnec M, Saillard A, Leboucher J, Donnio Y, Péré M, et al. Impact of wagering inducements on the gambling behaviors, cognitions, and emotions of online gamblers: a randomized controlled study. Front Psychiatry [internet]. 2020;11:1–12. Available at: 10.3389/fpsyt.2020.593789/full (accessed 18 December 2020).
    1. Challet‐Bouju G, Hardouin J‐B, Thiabaud E, Saillard A, Donnio Y, Grall‐Bronnec M, et al. Modeling early gambling behavior using indicators from online lottery gambling tracking data: longitudinal analysis. J Med Internet Res. 2020;22(8):e17675.
    1. Perrot B, Hardouin J‐B, Grall‐Bronnec M, Challet‐Bouju G. Typology of online lotteries and scratch games gamblers’ behaviours: a multilevel latent class cluster analysis applied to player account‐based gambling data. Int J Methods Psychiatr Res. 2018;27(4):e1746.
    1. Challet‐Bouju G, Perrot B, Balem M. Impact of wagering inducements on the gambling behaviors of online gamblers: a study based on gambling tracking data—hypotheses [nternet]. Open Science Framework; 2020. Available at: (accessed 23 November 2020).
    1. Perrot B, Hardouin J‐B, Costes J‐M, Caillon J, Grall‐Bronnec M, Challet‐Bouju G. Study protocol for a transversal study to develop a screening model for excessive gambling behaviours on a representative sample of users of French authorised gambling websites. BMJ Open. 2017;7(5):e014600.
    1. Ferris J, Wynne H. The Canadian Problem Gambling Index: Final Report [L'indice canadien du jeu excessif: Rapport final] [Internet]. Centre canadien de lutte contre l'alcoolisme et les toxicomanies; 2001, p. 1–72. Available at: (accessed 11 September 2019).
    1. Currie SR, Hodgins DC, Casey DM. Validity of the problem gambling severity index interpretive categories. J Gambl Stud. 2013;29:311–27.
    1. Challet‐Bouju G, Perrot B, Balem M. Impact of wagering inducements on the gambling behaviors of online gamblers: a study based on gambling tracking data—analysis plan [internet]. Open Science Framework; 2020. Available at: (accessed 23 November 2020).
    1. Bowie C, Prothero D. Finding causes of seasonal diseases using time series analysis. Int J Epidemiol. 1981;10:87–92.
    1. Hussain S, Parker S, Edwards K, Finch J, Jeanjean A, Leigh R, et al. Effects of indoor particulate matter exposure on daily asthma control. Ann Allergy Asthma Immunol. 2019;123:375–380.e3.
    1. Lambert D. Zero‐Inflated Poisson regression, with an application to defects in manufacturing. technometrics [internet]. 1992;34:1–14. Available at: (accessed 15 December 2020).
    1. Hall DB. Zero‐inflated Poisson and binomial regression with random effects: a case study. Biometrics. 2000;56:1030–9.
    1. Giles D. Notes on the Zero‐Inflated Poisson Regression Model [internet]. University of Victoria; 2010. Available at: (accessed 15 December 2020).
    1. Olsen MK, Schafer JL. A two‐part random‐effects model for semicontinuous longitudinal data. J Am Stat Assoc [internet]. 2001;96:730–45. Available at: (accessed 15 December 2020).
    1. Farewell V, Long DL, Tom B, Yiu S, Su L Two‐part and related regression models for longitudinal data. 2017. Available at: (accessed 15 December 2020).
    1. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc Ser B (Methodological) [internet]. 1995;57:289–300. Available at: (accessed 26 May 2021).
    1. Challet‐Bouju G, Perrot B, Balem M. Impact of wagering inducements on the gambling behaviors of online gamblers: a study based on gambling tracking data—analysis code [internet]. Open Science Framework; 2021. Available at: (accessed 28 May 2021).
    1. Costes JM, Eroukmanoff V. Internet gambling in France in 2017 (Les pratiques de jeux d'argent sur Internet en France en 2017). Les notes de l'Observatoire des jeux [internet]. 2018;9:1–8. Available at: (accessed 11 September 2019).
    1. McCormack A, Shorter GW, Griffiths MD. An empirical study of gender differences in online Gambling. J Gambl Stud. 2012;1–18.
    1. Ciccarelli M, Cosenza M, D'Olimpio F, Griffiths MD, Nigro G. An experimental investigation of the role of delay discounting and craving in gambling chasing behavior. Add Behav [internet]. 2019;93:250–6. Available at: (accessed 6 August 2019).
    1. Breen RB, Zuckerman M. ‘Chasing’ in gambling behavior: personality and cognitive determinants. Personal Individ Differ. 1999;27:1097–111.
    1. Lesieur HR. The compulsive gambler's spiral of options and involvement. Psychiatry. 1979;42:79–87.
    1. Toce‐Gerstein M, Gerstein DR, Volberg RA. A hierarchy of gambling disorders in the community. Addiction. 2003;98:1661–72.
    1. Baggio S, Dupuis M, Berchtold A, Spilka S, Simon O, Studer J. Is gambling involvement a confounding variable for the relationship between internet gambling and gambling problem severity? Comput Human Behav. 2017;71:148–52.
    1. Wiehler A, Peters J. Reward‐based decision making in pathological gambling: the roles of risk and delay. Neurosci Res. 2015;90:3–14.
    1. Hopley AAB, Dempsey K, Nicki R. Texas Hold'em online poker: a further examination. Int J Ment Health Addict. 2012;10:563–72.
    1. Braverman J, Tom MA, Shaffer HJ. Accuracy of self‐reported versus actual online gambling wins and losses. Psychol Assess. 2014;26:865–77.
    1. Baumeister RF, Vohs KD, Funder DC. Psychology as the science of self‐reports and finger movements: whatever happened to actual behavior? Perspect Psychol Sci. 2007;2:396–403.
    1. Garber MC, Nau DP, Erickson SR, Aikens JE, Lawrence JB. The concordance of self‐report with other measures of medication adherence: a summary of the literature. Med Care. 2004;42:649–52.
    1. Rundle‐Thiele S. Bridging the gap between claimed and actual behaviour. Qualit Market Res. 2009;12:295–306. Available at: 10.1108/13522750910963818 (accessed 25th May 2020).

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