Findings from a Longitudinal Study of Internet Gambling Behavior

1
Findings from a Longitudinal Study of Internet
Gambling Behavior
Actual Internet Gambling

• 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

2
Objectives

• Briefly review the knowledge base about Internet
  gambling
• Examine the findings from two studies of Internet
  sports and casino gambling behavior
• Examine the findings from two studies of attempts
  to intervene with Internet gamblers who might be
  experiencing problems

3
The Division on Addictions Receives Support from

• 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

4

• bwin Interactive Entertainment, AG provided
  primary support for this study.
• Drs. Howard Shaffer, Richard LaBrie, and Debi
  LaPlante contributed to this presentation.

5
Jean Rostand (French biologist, writer)
Nothing leads the scientist so astray as a
premature truth. Pensées dun Biologiste (1939
repr. in The Substance of Man, A Biologists
Thoughts, ch. 7, 1962).
6
Brief History of Internet Gambling Research
7
Concerns about the Internet
8
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 friends 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_sufferin
g_from_faceboo.html
9
Internet Disorders Not Otherwise Specified

• Youtube Addiction Disorder (YAD)
• Google Search Addiction Disorder (GSAD)
• Widget Addiction Disorder (WAD)
• Twitter Addiction Disorder (TAD)
• Blackberry Addiction Disorder (BAD)

10
Speculation about Internet Gambling

• Internet gambling is prolific and growing
• Growth increases exposure
• Increased accessibility makes internet gambling
  more addictive than other types of gambling
• No standardized product safety regulations to
  protect vulnerable populations

11
State of Knowledge Internet Gambling

• Very little peer-reviewed and published empirical
  research
• Theoretical propositions and opinion papers
  represent most of the professional discussion
  surrounding this topic
• The available empirical findings are from studies
  that use variations of retrospective self-report
  methodology

12
Methods Procedures

• Used PubMed PsycINFO databases to identify the
  gambling literature that included
• Internet and gambling
• Three inclusion criteria for studies
• 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
• 30 publications met these criteria

13

• 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

14
(No Transcript)
15
Internet Gambling Publications
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"...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.
17
Solutions

• Approaches need to go beyond retrospective
  self-report and include objective measures, such
  as actual Internet gambling behavior
• 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

18
Internet Gambling Risk and Resource?

• Internet Gambling provides unique opportunities
  for the study of gambling behavior and problems.
• Unlike land-based gambling, the very technology
  that makes Internet gambling a potential risk
  allows for the study of actual real-time gambling
  behavior.

19
bwin / Division on Addictions Research
Collaborative
20
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.
• The principal goal of this project is to
  empirically examine Internet gambling.
• A second goal is to provide Bwins 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.

21
Assessing the Playing Field Internet Sports
Gambling
22
Present Study

• Epidemiological description of characteristics of
  40,499 sequentially subscribed Internet sports
  gamblers
• Epidemiological description of the gambling
  behavior of these Internet gamblers over the
  course of 8 months
• Epidemiological description of the gambling
  behavior of empirically determined groups of the
  heavily involved bettors

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Participants
42,647 internet gamblers
925 did not bet w/ own money w/in month of study
end
41,722 bet w/ own money w/in month of study end
40,499 sports bettors
1,223 non-sports bettors
15,705 fixed-odds only
780 live-action only
24,014 fixed-odds and live-action
39,719 fixed-odds bettors
24,794 live-action bettors
24
Measures

• Demographics
• Age
• Gender
• Country of residence
• Types of bets
• Fixed-odds
• Live-action
• Actual betting records (daily aggregate)
• Bets
• Value of bets
• Winnings

25
Types of Bets

• 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
• 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

26
Betting Behavior (derived from daily aggregate
records)

• Duration
• of days from first to last eligible bet
• Frequency
• of days within duration interval that included
  a bet
• of bets
• Sum of daily aggregates
• Bets per day
• of bets / days on which a bet was placed

• Euros per bet
• Total wagered / of bets
• Total wagered
• Sum of daily aggregates
• Net loss
• Total wagered Total winnings
• Percent lost
• Net loss / Total wagered 100

27
Cohort Characteristics Gender and Age

• Mean age 31

• 91.6 male

28
Cohort Characteristics Country

• 85 countries

3.4
57.9
5.7
4.9
3.3
1.4
5.6
2.3
5.8
5.7
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Gambling Behavior Type of Game
30
Gambling Behavior Duration
M(SD), Median Fixed-Odds 118(89),
116 Live-Action 79(83), 40
31
Gambling Behavior Frequency
M(SD), Median Fixed-Odds 32(27),
23 Live-Action 42(37), 27
32
Gambling Behavior of Bets
M(SD), Median Fixed-Odds 135(496),
36 Live-Action 99(407), 15
33
Gambling Behavior Bets per Day
M(SD), Median Fixed-Odds 4.1(7.7),
2.5 Live-Action 4.3(5.0), 2.8
34
Gambling Behavior Euros per Bet
M(SD), Median Fixed-Odds 12(32),
4 Live-Action 11(25), 4
35
Gambling Behavior Total Wagered
M(SD), Median Fixed-Odds 729(3439), 148
Live-Action 1319(8592), 61
36
Gambling Behavior Net Loss
M(SD), Median Fixed-Odds 97(579), 33
Live-Action 85(571), 9
37
Gambling Behavior Percent Lost
M(SD), Median Fixed-Odds 32(62), 29
Live-Action 23(61), 18
38
Longitudinal Cohort
Median Fixed Odds Behavior Median Fixed Odds Behavior
Measure Total (39,719)
Duration 116 (of 244)
Frequency 23
Bets/day 2.5
Euros/bet 4
Total Wagered 148
Net Loss 33
Lost 29
39
Longitudinal Cohort
Median Live Action Behavior Median Live Action Behavior
Measure Total (24,794)
Duration 40 (of 244)
Frequency 27
Bets/day 2.8
Euros/bet 4
Total Wagered 61
Net Loss 9
Lost 18
40
Heavily Involved Bettors

• On 5 of 8 measures, 1 of the sample exhibited
  behavior that was discontinuously high
• e.g.

41
Heavily Involved Bettors

• 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

42
Fixed Odds Heavily Involved Bettors Overlap
43
Live Action Heavily Involved Bettors Overlap
44
Heavily Involved Bettors Fixed Odds
Top 1 Net Loss (n397) Top 1 Net Loss (n397) Top 1 Total Wagered (n397) Top 1 Total Wagered (n397) Top 1 of Bets (n397) Top 1 of Bets (n397)
Mean (SD) Median Mean (SD) Median 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
Lost 35 (22) 29 10 (16) 9 19 (17) 18
45
Heavily Involved Bettors Live Action
Top 1 Net Loss (n247) Top 1 Net Loss (n247) Top 1 Total Wagered (n247) Top 1 Total Wagered (n247) Top 1 of Bets (n247) Top 1 of Bets (n247)
Mean (SD) Median Mean (SD) Median 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
Lost 15 (12) 12 14 (7) 4 9 (7) 7
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Longitudinal Cohort
Median Behaviors Fixed Odds Total Sample and Most Involved Losers Median Behaviors Fixed Odds Total Sample and Most Involved Losers Median Behaviors Fixed Odds Total Sample and Most Involved Losers
Measure Total (39,719) Top BL (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
47
Sum of Stakes by Month (Total Sample)
48
Sum of Stakes By Day (Most involved)
49
Caveat

• We dont know how much disposable income these
  betters had available
• Therefore, it is not possible to calibrate the
  social harm these losses might have caused

50
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.
51
Inside the Virtual Casino Internet Casino
Gambling
52
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
• Most peoples play adapted, following the
  prototypical public health adaptation curves
• 1 of the sample did not adapt

53
Casino Play Hypotheses

• 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
• A small minority (i.e. 5 or less) will exhibit
  excessive gambling behavior.

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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)

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Demographics

• Average age 30
• 93 male
• Spread out across 46 countries
• Only 1 gender difference
• Women place more bets per day than men
• Mwomen 141, SD 206
• Mmen 114, SD 191
• Plt0.05

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Gambling behavior of internet casino gamblers
57
Correlations among gambling behavior measures for
casino betting (n 4222)
Duration Frequency No. of bets Bets per day Euro per Bet Total wagered Net loss Percent lost
Duration -0.63 0.26 0.01 0.05 0.27 0.23 -0.07
Frequency -0.63 0.22 0.13 0.09 0.27 0.16 -0.18
No. of Bets 0.26 0.22 0.87 -0.24 0.66 0.49 -0.26
Bets per day 0.01 0.13 0.87 -0.41 0.41 0.33 -0.14
Euros per Bet 0.05 0.09 -0.24 -0.41 0.52 0.32 -0.27
Total wagered 0.27 0.27 0.66 0.41 0.52 0.70 -0.43
Net loss 0.23 0.16 0.49 0.33 0.32 0.70 0.20
Percent lost -0.07 -0.18 -0.26 -0.14 -0.27 -0.43 0.20
Wagering decreased as losses increased shows
rational decision making
This is high because the outcome of casino
gambling is a function of chance and the house
odds
Shows day-to-day betting consistency
58
Casino vs Sports Gambling
Frequency of play for each game type
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.
• 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.

59
Implications

• Few people play internet casino games
• 18 of bwin subscribers played, half of whom
  never played more than three days.
• The typical daily cost of casino gambling is
  considerably larger than the sports betting costs
  of this cohort.

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Total stakes wagered on casino games
61
Gambling behavior of extreme 5 and 95 subgroups
of casino bettors
62
Cost of Casino Gambling

• 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 lt 0.001).

63
Limitations

• Casino gambling might not have been so popular
  because bwin is primarily a sports betting
  service.
• Females are underrepresented, although their
  betting behavior did not differ much from that of
  males.

64
Responsible Gambling Efforts in the Virtual World
65
Unique Opportunities for Intervention

• Tracking software for early identification of
  people who are at-risk for developing problems
• Limit-setting
• Time
• Losses
• Deposits
• Pop-up messaging and email by request or by design

66
Corporate Social Responsibility

• Corporate Deposit Limits
• Self-limitation of Deposits

67
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
• 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

Broda, LaPlante, Nelson, LaBrie, Bosworth,
Shaffer, 2008
68
Expectations

• Users who receive a notification constitute a
  group of extremely engaged gamblers
• Excessively large betting, high loss or high
  frequency of gambling
• Receiving a notification acts as a warning sign
• Gambling behavior would attenuate after such
  notification

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Sample Description

• 160 (0.3 5 women) of the sample received at
  least one notification (Exceeders)
• Exceeders received between 1 and 267
  notifications (M14 notifications)

70
Gambling Behavior Before After Notification

• 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
71
Summary

• In general, the mere existence of deposit limits
  might serve as a harm reduction device
• Exceeding established limits can serve as an
  indicator for heavy betting behavior and large
  overall losses
• Notification systems for exceeding deposit limits
  do not completely curtail betting behavior, but
  are associated with changes in betting strategy
• Moving away from smaller more frequent bets to
  larger more infrequent bets

72
General Comment on Notification Systems

• Apparent need to re-think the use of notification
  systems as harm reduction devices for those
  at-risk for excessive patterns of betting
• Similar limitations for other such systems
• 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

73
Self-limitation of Deposits

• bwin Interactive Entertainment, AG allows
  subscribers to self-impose deposit limits that
  are lower than those defined by corporate policy
• Attempts to exceed self-imposed deposit limits
  are blocked by the company software system

Nelson, LaPlante, Peller, Schumann, LaBrie,
Shaffer, in press
74
Expectations

• Participating in the self-limitation system could
  be an indicator of potential disordered gambling
• Users who self-limit constitute a group of
  extremely engaged gamblers
• Self-limitation will promote healthier gambling
  behavior
• Decreased stakes, bets, and frequency of betting

75
Sample Description

• 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

76
Limiters versus Others Pre-limit Comparisons

• Limiters played a greater diversity of gambling
  games
• Limiters bet on more days within their active
  betting period
• Limiters placed more bets per day
• Limiters wagered less money per bet
• Limiters and others did not differ in terms of
• Total wagered, net loss, percent lost

77
Results Games Played
78
Gambling Behavior Before After Self-Limitation

• Limiters behavior after imposing limits
  generally moved in the direction of fewer bets
• For example, for fixed odds betting, limiters

Active Betting Days
Bets Per Day
Amount Wagered
79
Results Self Limiter Pre-Post Behavior (Fixed
Odds Live Action Combined n477)

• Pre-limit
• active days bet
• 33.0 (SD 29.5)
• Bets per day
• 7.1 (SD 6.9)
• Net loss/stakes
• .23 (SD .35)
• Average bet size
• 7.0 (SD 12.0)

• Post-limit
• active days bet
• 29.5 (SD 26.2)
• Bets per day
• 6.2 (SD 7.1)
• Net loss/stakes
• .24 (SD .48)
• Average bet size
• 8.3 (SD 14.8)

80
Summary

• Limiters were more active bettors than others
• Place more bets, bet on more days during active
  period, bet on greater diversity of products
• If self-limitation is a sign of disordered
  gambling, involvement might be as important to
  indicating gambling-related problems as
  expenditures

81
General Limitations

• Limiting resources are only helpful if people can
  access them easily

82
General Limitations

• Interventions will only work if the message gets
  through to the target

83
General Limitations

• Real behavior measures provide an unbiased
  assessment of actual Internet gambling, but
  cannot be used to determine rates of
  gambling-related problems
• Healthy changes in gambling behavior for our
  sample do not preclude unhealthy changes in
  gambling behavior, or other behavior, on other
  websites or activities

84
Concluding Thoughts

• The Internet provides some unique opportunities
  for harm reduction devices that might be executed
  with some success
• 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

85
References

• LaBrie, R. A., LaPlante, 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.
• LaBrie R.A., Kaplan, S.A., LaPlante, 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.
• LaPlante, D.A., Schumann, A., LaBrie, R.A.,
  Shaffer, H.J. (2008). Population trends in
  Internet sports gambling. Computers in Human
  Behavior, 24, 2399-2414.
• Broda, A., LaPlante, D. A., Nelson, S. E.,
  LaBrie, 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., LaPlante, D. A., Peller, A. J.,
  Schumann, A., LaBrie, R. A., Shaffer, H. J.
  (2008). Real limits in the virtual world
  Self-limiting behavior of Internet gamblers.
  Journal of Gambling Studies, 24(4), 463-477.

86
Available Resources Links

• www.divisiononaddictions.org
• www.basisonline.org
• www.thetransparencyproject.org
• snelson_at_hms.harvard.edu

87
The Transparency Project

• First ever public data repository for
  privately-funded datasets, such as
  industry-funded data
• Addictive behavior datasets (e.g., alcohol,
  drugs, gambling, excessive shopping, etc.)

88
The Transparency Project website
http//www.thetransparencyproject.org
89
Rationale

• Scientific information often is locked away with
  limited accessibility
• There is a need to facilitate greater access to
  privately-funded databases
• 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
90
Goals

• Promote transparency for privately-funded science
  and better access to scientific information
• Collect and archive high quality
  addiction-related privately-funded data from
  around the world
• Make data available to scientists to advance the
  available empirical evidence and knowledge base
  about addiction
• Alleviate the burdens caused by addictive
  behaviors

The Transparency Project Division on Addictions,
The Cambridge Health Alliance a teaching
affiliate of Harvard Medical School