Actual Internet Gambling Findings from a Longitudinal Study of Internet Gambling Behavior Sarah E. Nelson, Ph. D. Division on Addictions Cambridge Health Alliance, Harvard Medical School Presented at the Alberta Gaming Research Institute 2009 Banff Conference on Internet Gambling
Objectives Briefly review the knowledge base about Internet gambling n Examine the findings from two studies of Internet sports and casino gambling behavior n Examine the findings from two studies of attempts to intervene with Internet gamblers who might be experiencing problems n
The Division on Addictions Receives Support from: n n n n n National Institutes of Health (NIDA, NIAAA) bwin Interactive Entertainment, AG National Center for Responsible Gaming University of Nevada at Las Vegas University of Michigan Robert Wood Johnson Foundation Port Authority of Kansas City St. Francis House Las Vegas Sands Corporation Massachusetts Council on Compulsive Gambling
n bwin Interactive Entertainment, AG provided primary support for this study. n Drs. Howard Shaffer, Richard La. Brie, and Debi La. Plante contributed to this presentation.
Jean Rostand (French biologist, writer) “Nothing leads the scientist so astray as a premature truth. ” Pensées d’un Biologiste (1939; repr. in The Substance of Man, “A Biologist’s Thoughts, ” ch. 7, 1962).
Brief History of Internet Gambling Research
Concerns about the Internet
Facebook Addiction Disorder (FAD) 1. The first thing is tolerance. This refers to the need for increasing amounts of time on Facebook to achieve satisfaction and/or significantly diminished effect with continued use of the same amount of time. They often have multiple Facebook windows opened at any one time. 3 is usually a sign and over 5 you're helpless. 2. After reduction of Facebook use or cessation, it causes distress or impairs social, personal or occupational functioning such as wondering why your Vista is so fast and improved etc. These include anxiety; obsessive thinking about what is written on your wall on Facebook etc. 3. Important social or recreational activities are greatly reduced and or migrated to Facebook. Instead of sending an email you post a message on your friend’s page about canceling a lunch appointment. You now stop answering your phone call from your Mom and insist she should contact you through Facebook chat. 4. This is getting serious if you start kissing your girlfriend's home page or a VRML virtual walk through a park is your idea of a date. 5. Your bookmark takes 20 minutes just to scroll from top to bottom or 8 of 10 people in your friend's list you have no idea of who they are. 6. When you meet people you start introducing yourself by following "see you in Facebook" or your dog has its own Facebook profile. You invite anyone you've met and any notifications, messages and invites reward you with an unpredictable high, much like gambling. http: //blog. futurelab. net/2008/05/are_you_suffering_from_faceboo. html
Internet Disorders n Youtube Addiction Disorder (YAD) Not Otherwise Specified… Google Search Addiction Disorder (GSAD) n Widget Addiction Disorder (WAD) n Twitter Addiction Disorder (TAD) n Blackberry Addiction Disorder (BAD) n
Speculation about Internet Gambling n Internet gambling is prolific and growing – Growth increases exposure Increased accessibility makes internet gambling more addictive than other types of gambling n No standardized product safety regulations to protect vulnerable populations n
State of Knowledge: Internet Gambling n Very little peer-reviewed and published empirical research n Theoretical propositions and opinion papers represent most of the professional discussion surrounding this topic n The available empirical findings are from studies that use variations of retrospective self-report methodology
Methods: Procedures n Used Pub. Med & Psyc. INFO databases to identify the gambling literature that included – n “Internet” and “gambling” – Published between 1903 & 2007 in peer-review journals Have the word “gambling” and “Internet” in one of four citation fields: title, keyword, abstract, and text Have some relevance to the field of gambling studies Three inclusion criteria for studies: – – n 30 publications met these criteria
n. We classified these 30 into three publication groups: – Commentaries - articles with no empirical data – Self-report surveys - articles with empirical data provided by participants – Actual Internet gambling - articles with data describing actual Internet Gambling
Internet Gambling Publications
". . . self-report appears to have all but crowded out all other forms of behavior. Behavioral science today. . . mostly involves asking people to report on their thoughts, feelings, memories, and attitudes. . Direct observation of meaningful behavior is apparently passe´" (p. 397). Baumeister, R. F. , Vohs, K. D. , & Funder, D. C. (2007). Psychology as the science of self-reports and finger movements: whatever happened to actual behavior? Psychological Science, 2(4), 396 -403.
Solutions Approaches need to go beyond retrospective self -report and include objective measures, such as actual Internet gambling behavior n Using actual behavior avoids the difficulties inherent in self-report (National Research Council, 1999) as well as the need to compress the information about actual behavior occurring during long intervals into a few summary descriptions elicited by survey questions n
Internet Gambling: Risk and Resource? n Internet Gambling provides unique opportunities for the study of gambling behavior and problems. n Unlike land-based gambling, the very technology that makes Internet gambling a potential risk allows for the study of actual real-time gambling behavior.
bwin / Division on Addictions Research Collaborative Responsible Gaming BWIN Corporate Social Responsibility Database Research Experimental Research
BWin / DOA Collaborative: Objectives To address the dearth of scientific information on Internet gambling, bwin and the DOA have entered into a seminal research collaboration relying substantially on data provided by bwin subscriber gaming activity. n The principal goal of this project is to empirically examine Internet gambling. n A second goal is to provide Bwin’s current corporate social responsibility department with evidence-based research, tools, and programs about problem gambling, so that they can effectively protect the health of the general public as well as the industry. n
Assessing the Playing Field: Internet Sports Gambling
Present Study n Epidemiological description of characteristics of 40, 499 sequentially subscribed Internet sports gamblers n Epidemiological description of the gambling behavior of these Internet gamblers over the course of 8 months n Epidemiological description of the gambling behavior of empirically determined groups of the heavily involved bettors
Participants 42, 647 internet gamblers 41, 722 bet w/ own money w/in month of study end 1, 223 non-sports bettors 15, 705 fixed-odds only 925 did not bet w/ own money w/in month of study end 40, 499 sports bettors 24, 014 fixed-odds and live-action 39, 719 fixed-odds bettors 780 live-action only 24, 794 live-action bettors
Measures n Demographics – Age – Gender – Country of residence n Types of bets – Fixed-odds – Live-action n Actual betting records (daily aggregate) – Bets – Value of bets – Winnings
Types of Bets n Fixed-Odds – bets made on the outcomes of sporting events or games in which the amount paid for a winning bet is set by the betting service – relatively slow-cycling betting propositions; the outcomes of a bet are generally not known for hours or even (in the case of cricket matches) days n Live-Action – bets made on propositions about outcomes within a sporting event (e. g. , which side will have the next corner kick or whether the next tennis game in a match will be won at love by the server) – More rapidly cycling betting propositions; provides many, relatively quick-paced, betting propositions posed in real-time during the progress of a sporting event
Betting Behavior (derived from daily aggregate records) n Duration n – # of days from first to last eligible bet n Frequency – % of days within duration interval that included a bet n n – Total wagered / # of bets n Bets per day – # of bets / days on which a bet was placed Total wagered – Sum of daily aggregates n Net loss – Total wagered – Total winnings # of bets – Sum of daily aggregates Euros per bet n Percent lost – [Net loss / Total wagered] * 100
Cohort Characteristics: Gender and Age n 91. 6% male n Mean age = 31
Cohort Characteristics: Country n 85 countries 3. 4% 57. 9% 4. 9% 1. 4% 5. 6% 5. 7% 3. 3% 2. 3% 5. 7% 5. 8%
Gambling Behavior: Type of Game
Gambling Behavior: Duration M(SD), Median: Fixed-Odds 118(89), 116; Live-Action 79(83), 40
Gambling Behavior: Frequency M(SD), Median: Fixed-Odds 32%(27), 23%; Live-Action 42%(37), 27%
Gambling Behavior: # of Bets M(SD), Median: Fixed-Odds 135(496), 36; Live-Action 99(407), 15
Gambling Behavior: Bets per Day M(SD), Median: Fixed-Odds 4. 1(7. 7), 2. 5; Live-Action 4. 3(5. 0), 2. 8
Gambling Behavior: Euros per Bet M(SD), Median: Fixed-Odds 12(32), 4; Live-Action 11(25), 4
Gambling Behavior: Total Wagered M(SD), Median: Fixed-Odds 729(3439), 148; Live-Action 1319(8592), 61
Gambling Behavior: Net Loss M(SD), Median: Fixed-Odds 97(579), 33; Live-Action 85(571), 9
Gambling Behavior: Percent Lost M(SD), Median: Fixed-Odds 32(62), 29; Live-Action 23(61), 18
Heavily Involved Bettors On 5 of 8 measures, 1% of the sample exhibited behavior that was discontinuously high n e. g. : n
Heavily Involved Bettors n We examined the betting behavior of: – – – individuals who fell in the top 1% on total wagered individuals who fell in the top 1% on net loss individuals who fell in the top 1% on # of bets
Fixed Odds Heavily Involved Bettors: Overlap
Live Action Heavily Involved Bettors: Overlap
Heavily Involved Bettors: Fixed Odds Top 1% Net Loss (n=397) Top 1% Total Wagered (n=397) Top 1% # of Bets (n=397) Mean (SD) Median Duration 189 (57) 215 194 (53) 217 204 (43) 220 Frequency 45% (22) 42% 51% (21) 48% 57% (21) 57% # of Bets 1545 (3241) 423 1438 (3151) 423 3497 (3153) 2371 Bets/Day 18. 0 (51. 0) 5. 4 13. 0 (27. 2) 4. 7 37. 3 (51. 2) 26. 4 Euros/Bet 55 (94) 23 77 (96) 44 3 (5) 1 Total Wagered 15037 (15709) 10259 22891 (23879) 16784 8421 (12898) 4144 Net Loss 3491 (2617) 2645 1838 (4547) 1544 1261 (2232) 740 35 (22) 29 10 (16) 9 19 (17) 18 % Lost
Heavily Involved Bettors: Live Action Top 1% Net Loss (n=247) Top 1% Total Wagered (n=247) Top 1% # of Bets (n=247) Mean (SD) Median Duration 189 (53) 213 188 (50) 209 206 (34) 217 Frequency 50% (23) 49% 57% (21) 56% 64% (18) 65% # of Bets 1767 (2678) 973 1700 (2315) 1034 2938 (2451) 2150 Bets/Day 16. 1 (16. 5) 11. 3 14. 6 (13. 9) 10. 7 23. 0 (15. 7) 18. 5 Euros/Bet 59 (63) 34 81 (79) 53 15 (26) 6 Total Wagered 47954 (56687) 29144 64740 (53046) 44111 36115 (54215) 15743 Net Loss 4189 (3062) 3052 2642 (4270) 1973 2159 (3115) 1111 15 (12) 12 14 (7) 4 9 (7) 7 % Lost
Longitudinal Cohort Median Behaviors – Fixed Odds Total Sample and Most Involved Losers Measure Total (39, 719) Top B&L* (144) Duration 116 (of 244) 219 (of 244) Frequency 23% 50% Bets/day 2. 5 7 Euros/bet 4 42 Total Wagered 148 21, 807 Net Loss 33 3, 914 % Lost 29% 18%
Sum of Stakes by Month (Total Sample)
Sum of Stakes By Day (Most involved)
Caveat n We don’t know how much disposable income these betters had available n Therefore, it is not possible to calibrate the social harm these losses might have caused
Conclusion Despite the caveat about discretionary funds, the results do suggest problem gambling is not as common among Internet sports bettors as the speculations and the consequent conventional wisdom suggested.
Inside the Virtual Casino: Internet Casino Gambling
Sports Betters Revisited Most people play moderately – 1% of the sample played differently from the rest, making a median of 4. 7 bets every other day n Most people’s play adapted, following the prototypical public health adaptation curves – 1% of the sample did not adapt n
Casino Play Hypotheses n Individuals betting in virtual casinos will exhibit riskier behaviors than observed among Internet sports bettors and poker players. – Example: more excessive loss patterns or time spent gambling Moderate and consistent gambling among the majority of the population n A small minority (i. e. 5% or less) will exhibit excessive gambling behavior. n
Internet Casino Gamblers Ever played Casino Games (n = 8, 472) 20% of Longitudinal Sample – Excluded (n = 4, 250) § Gambled 3 or fewer times (4, 225) § Gambled with promotional funds (10) § Gambling began less than one month before the end to the study (15) – Final sample (n = 4, 222)
Demographics Average = 30 • 93% male • Spread out across 46 countries • Only 1 gender difference: – Women place more bets per day than men • Mwomen = 141, SD = 206 n • Mmen = 114, SD = 191 • P<0. 05
Gambling behavior of internet casino gamblers
Casino vs Sports Gambling Frequency of play for each game type Play per month Cost per day (€) Cost of play for each game type Even though casino spending was higher than spending on other types of games, the cohort of casino bettors played less frequently than the sports bettors. n The observation that casino game bettors incur larger losses at each gambling session compared to sports bettors is consistent with our hypothesis that casino-type games offer an additional risk for players. n
Implications n Few people play internet casino games – 18% of bwin subscribers played, half of whom never played more than three days. n The typical daily cost of casino gambling is considerably larger than the sports betting costs of this cohort.
Total stakes wagered on casino games
Gambling behavior of extreme 5 and 95% subgroups of casino bettors
Cost of Casino Gambling n The top 5% of casino gamers lost a significantly smaller percent of their total wagers compared to the rest of the casino gamblers (t = 21. 0, ndf = 871, P < 0. 001).
Limitations n Casino gambling might not have been so popular because bwin is primarily a sports betting service. n Females are underrepresented, although their betting behavior did not differ much from that of males.
Responsible Gambling Efforts in the Virtual World
Unique Opportunities for Intervention n Tracking software for early identification of people who are at-risk for developing problems n Limit-setting – Time – Losses – Deposits n Pop-up messaging and email by request or by design
Corporate Social Responsibility n Corporate Deposit Limits n Self-limitation of Deposits
Deposit Limits bwin Interactive Entertainment, AG imposes corporate deposit limits on its subscribers and allows subscribers to set specific deposit limits, if they are lower than the corporate limits n Subscribers who try to deposit more than the allowed amount receive from bwin a notification message about the attempt to exceed the deposit limit and bwin rejects the attempted deposit n Broda, La. Plante, Nelson, La. Brie, Bosworth, & Shaffer, 2008
Expectations n Users who receive a notification constitute a group of extremely engaged gamblers – Excessively large betting, high loss or high frequency of gambling n Receiving a notification acts as a warning sign – Gambling behavior would attenuate after such notification
Sample Description 160 (0. 3%; 5 women) of the sample received at least one notification (Exceeders) n Exceeders received between 1 and 267 notifications (M=14 notifications) n
Gambling Behavior Before & After Notification n After receiving notification: – Exceeders did not reduce their number of active betting days – Exceeders patterns of losses did not change – Exceeders increased their average size of bet – Exceeders decreased the average number of bets per active betting day Exceeders made fewer, larger bets per active betting day after notification
Summary In general, the mere existence of deposit limits might serve as a harm reduction device n Exceeding established limits can serve as an indicator for heavy betting behavior and large overall losses n Notification systems for exceeding deposit limits do not completely curtail betting behavior, but are associated with changes in betting strategy n – Moving away from smaller more frequent bets to larger more infrequent bets
General Comment on Notification Systems Apparent need to re-think the use of notification systems as harm reduction devices for those atrisk for excessive patterns of betting n Similar limitations for other such systems: n – People who were given feedback that BAC exceeded legal limits have been subsequently observed to drive – Drivers who receive speed tickets are at increased risk of receiving subsequent speeding tickets – Smokers who receive biomedical feedback do not initiate appreciable changes toward quitting smoking
Self-limitation of Deposits n bwin Interactive Entertainment, AG allows subscribers to self-impose deposit limits that are lower than those defined by corporate policy n Attempts to exceed self-imposed deposit limits are blocked by the company software system Nelson, La. Plante, Peller, Schumann, La. Brie, & Shaffer, in press
Expectations n Participating in the self-limitation system could be an indicator of potential disordered gambling n Users who self-limit constitute a group of extremely engaged gamblers n Self-limitation will promote healthier gambling behavior – Decreased stakes, bets, and frequency of betting
Sample Description n 567 (1. 2%) of the sample participated in the self-limitation system (Limiters) – 7% of these individuals placed these limits before they made their first bet – 11% ceased betting completely after they self -imposed limits
Limiters versus Others: Pre-limit Comparisons n Limiters played a greater diversity of gambling games n Limiters bet on more days within their active betting period n Limiters placed more bets per day n Limiters wagered less money per bet n Limiters and others did not differ in terms of: – Total wagered, net loss, percent lost
Results: Games Played
Gambling Behavior Before & After Self-Limitation Limiters behavior’ after imposing limits generally moved in the direction of fewer bets n For example, for fixed odds betting, limiters: n Active Betting Days Bets Per Day Amount Wagered
Results: Self Limiter Pre-Post Behavior (Fixed Odds & Live Action Combined; n=477) Pre-limit n % active days bet: Post-limit n % active days bet: – 33. 0 (SD: 29. 5)* – 29. 5 (SD: 26. 2)* n Bets per day: n – 7. 1 (SD: 6. 9)* n Net loss/stakes: – 6. 2 (SD: 7. 1)* n –. 23 (SD: . 35) n Average bet size: – € 7. 0 (SD: € 12. 0)* Bets per day: Net loss/stakes: –. 24 (SD: . 48) n Average bet size: – € 8. 3 (SD: € 14. 8)*
Summary n Limiters were more active bettors than others – Place more bets, bet on more days during active period, bet on greater diversity of products n If self-limitation is a sign of disordered gambling, involvement might be as important to indicating gambling-related problems as expenditures
General Limitations n Limiting resources are only helpful if people can access them easily
General Limitations n Interventions will only work if the message gets through to the target
General Limitations n Real behavior measures provide an unbiased assessment of actual Internet gambling, but cannot be used to determine rates of gambling-related problems n Healthy changes in gambling behavior for our sample do not preclude unhealthy changes in gambling behavior, or other behavior, on other websites or activities
Concluding Thoughts n The Internet provides some unique opportunities for harm reduction devices that might be executed with some success n Internet gambling is likely to continue to grow during the next decades, and empirical examination is necessary to the development of safe and effective responsible gaming intervention efforts
References n n n La. Brie, R. A. , La. Plante, D. A. , Nelson, S. E. , Schumann, A. , & Shaffer, H. J. (2007). Assessing the playing field: A prospective longitudinal study of Internet sports gambling behavior. Journal of Gambling Studies, 23, 347 -362. La. Brie R. A. , Kaplan, S. A. , La. Plante, D. A. , Nelson, S. E. , and Shaffer, H. J. (2008). Inside the virtual casino: A prospective longitudinal study of actual Internet casino gambling. European Journal of Public Health, 18(4), 410 -416. La. Plante, D. A. , Schumann, A. , La. Brie, R. A. , & Shaffer, H. J. (2008). Population trends in Internet sports gambling. Computers in Human Behavior, 24, 2399 -2414. Broda, A. , La. Plante, D. A. , Nelson, S. E. , La. Brie, R. A. , Bosworth, L. B. & Shaffer, H. J. (2008). Virtual harm reduction efforts for Internet gambling: Effects of deposit limits on actual Internet sports gambling behavior. Harm Reduction Journal, 5, 27. Nelson, S. E. , La. Plante, D. A. , Peller, A. J. , Schumann, A. , La. Brie, R. A. , & Shaffer, H. J. (2008). Real limits in the virtual world: Self-limiting behavior of Internet gamblers. Journal of Gambling Studies, 24(4), 463477.
Available Resources & Links n www. divisiononaddictions. org n www. basisonline. org n www. thetransparencyproject. org n snelson@hms. harvard. edu
n First ever public data repository for privately-funded datasets, such as industry -funded data n Addictive behavior datasets (e. g. , alcohol, drugs, gambling, excessive shopping, etc. )
The Transparency Project website http: //www. thetransparencyproject. org
n Scientific information often is locked away with limited accessibility n There is a need to facilitate greater access to privatelyfunded databases n A venue through which researchers can make public their private data is needed The Transparency Project Division on Addictions, The Cambridge Health Alliance a teaching affiliate of Harvard Medical School
n Promote transparency for privately-funded science and better access to scientific information n Collect and archive high quality addiction-related privately-funded data from around the world n Make data available to scientists to advance the available empirical evidence and knowledge base about addiction n Alleviate the burdens caused by addictive behaviors The Transparency Project Division on Addictions, The Cambridge Health Alliance a teaching affiliate of Harvard Medical School
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