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Findings from a Longitudinal Study of Internet Gambling Behavior

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Title: 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
16
"...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

23
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
29
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
46
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.

54
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)

55
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

56
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.

60
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

69
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
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