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
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
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

bwin Interactive Entertainment, AG provided
primary support for this study.

Drs. Howard Shaffer, Richard LaBrie, and
Debi LaPlante 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
Not Otherwise Specified…





Youtube Addiction Disorder (YAD)
Google Search Addiction Disorder (GSAD)
Widget Addiction Disorder (WAD)
Twitter Addiction Disorder (TAD)
Blackberry Addiction Disorder (BAD)
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

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

Methods: Procedures


Used PubMed & PsycINFO databases to
identify the gambling literature that included
–
“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:
–
–

30 publications met these criteria
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
0%
33%
67%
Commentaries
Self-Reports
Actual Behavior
Internet Gambling Publications
5
4
3
2
1
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
0
Commentaries
Self-Reports
Actual Behavior
"...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
 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

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.

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.
 The principal goal of this project is to empirically
examine Internet gambling.
 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.

Assessing the Playing Field:
Internet Sports Gambling
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

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

Demographics
– Age
– Gender
– Country of residence

Types of bets
– Fixed-odds
– Live-action

Actual betting records (daily aggregate)
– Bets
– Value of bets
– Winnings
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
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


– Total wagered / # of
bets

Bets per day
– # of bets / days on
which a bet was placed
Total wagered
– Sum of daily aggregates

Net loss
– Total wagered – Total
winnings
# of bets
– Sum of daily aggregates
Euros per bet

Percent lost
– [Net loss / Total
wagered] * 100
Cohort Characteristics:
Gender and Age

91.6% male
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
<21
21-30

31-40
41-50
Mean age = 31
51-60
61+
Cohort Characteristics:
Country

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
100%
90%
80%
70%
60%
50%
59%
40%
30%
39%
20%
10%
2%
0%
Fixed Odds Only
Fixed and Live
Live Action Only
Gambling Behavior:
Duration
50%
47%
Fixed Odds
Live Action
40%
30%
28%
26%
20%
13% 11%
10% 10%
10%
0%
7%
1-30
31-60
6%
61-90
6% 5%
5% 5%
5%
91-120
121-150
151-180
11%
6%
181-210
211-242
Days from First to Last Bet
M(SD), Median:
Fixed-Odds 118(89), 116;
Live-Action 79(83), 40
Gambling Behavior:
Frequency
30%
26%
20%
20%
24%
Fixed Odds
Live Action
24%
17%
15%
11%
10%
11%
7%
9%
6%
4%
7%
3%
4%
3%
3% 2%
1%1%
0%
0-10% 11-20% 21-30% 31-40% 41-50% 51-60% 61-70% 71-80% 81-90%
91100%
% of Days w/in Duration Including a Bet
M(SD), Median:
Fixed-Odds 32%(27), 23%; Live-Action 42%(37), 27%
Gambling Behavior:
# of Bets
60%
50%
Fixed Odds
Live Action
50%
41%
42%
40%
30%
23%
20%
13%
8%
10%
0%
5%
<10
10-100
101-200
3%
201-300
3%
2%
301-400
2%
1%
401-500
5%
4%
501+
# of Bets
M(SD), Median:
Fixed-Odds 135(496), 36;
Live-Action 99(407), 15
Gambling Behavior:
Bets per Day
35%
30%
Fixed Odds
Live Action
30%
25%
22%
22%
20%
17%
16%
15%
10%
12%
12%
10%
7%
8%
6%
5%6%
5%
0%
8%
1
1.-2
2.-3
3.-4
4.-5
5.-6
4%
3%
6.-7
3%
2%
2% 2% 1%
7.-8
8.-9
2%
9.-10
10.+
Bets per Gambling 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
35%
31% 30%
30%
29%
Fixed Odds
Live Action
26%
25%
22%
19%
20%
15%
10%
8% 7%
5%
0%
4% 4%
4%4%
2% 2% 2% 2% 1% 1%1%
1% 1%1% 1%1%
0-2
2.-5
5.-10 10.-15 15.-20 20.-25 25.-30 30.-35 35.-40 40.-45 45.-50
50.+
Euros per Bet
M(SD), Median:
Fixed-Odds 12(32), 4;
Live-Action 11(25), 4
Gambling Behavior:
Total Wagered
39%
40%
35%
35%
Fixed Odds
Live Action
30%
25%
20%
20%
17%
15%
10%
5%
0%
11%
9%
5%
6%
6% 6%
6% 3%
4% 3% 2% 2% 2% 1%
4% 6%
3%
2% 2% 1% 1% 1%
1%
1%
0
0
.+
00 200 300 400 500 600 700 800 900 000 000 00
0
-1
1
.
0
2
.
.
.
.
.
.
.
.
.0
-1
00
.-1
0
.
0
10 100 200 300 400 500 600 700 800 00.
1
0
0
9
10 200
Total Wagered
M(SD), Median: Fixed-Odds 729(3439), 148; Live-Action 1319(8592), 61
Gambling Behavior:
Net Loss
45%
45%
40%
Fixed Odds
Live Action
35%
30%
27%
25%
20%
23%
19%
16%
15%
11%
9%
10%
7%
5%
5%
0%
0
<
25
0
0
-5
.
25
3% 4%
2%
3%
7%
2%
1% 1% 2%1%
4%
3%2% 2%2%
5
+
00
25
50
75
00
00
00
-7
0.
0
1
1
1
2
5
1
.
0
1
.
.0.
5.
0.
5.
0.
50
10
0
0
2
5
7
0
75
0
1
1
1
1
2
5
Total Wagered - Total Winnings
M(SD), Median: Fixed-Odds 97(579), 33; Live-Action 85(571), 9
Gambling Behavior:
Percent Lost
25%
22%
20%
Fixed Odds
Live Action
17%
16%
15%
12%
10%
14%
13%
11%
10% 9%
7% 7%
5%
5%
0%
17%16%
5%
4% 4%
2% 3%2%
0
<
2% 1%
1% 0%
0%
0%
0%
0%
0%
0%
0%
0%
9%
0%
2
3
4
5
6
7
8
9
9
1
011
21
31
41
51
61
71
81
91
0%
0
1
(Net Loss / Total Wagered) * 100
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
 e.g.:

Fixed Odds
25000
Mean Total Wagered
20000
15000
10000
5000
0
0
5
10
15
20
25
30
35
40
45
50
55
Percentile
60
65
70
75
80
85
90
95
100
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
Fixed Odds Heavily Involved Bettors:
Overlap
100%
90%
80%
13%
13%
13%
8%
6%
6%
8%
36%
36%
70%
60%
50%
40%
73%
30%
20%
43%
45%
Top 1% Net Loss (NL)
Top 1% Total Wagered (TW)
10%
0%
NL Only
NoB & NL
TW Only
TW & NoB
NoB Only
All 3
Top 1% # of Bets (NoB)
NL & TW
Live Action Heavily Involved Bettors:
Overlap
100%
90%
27%
27%
27%
10%
11%
11%
80%
70%
60%
50%
10%
26%
26%
40%
30%
20%
52%
37%
36%
Top 1% Net Loss (NL)
Top 1% Total Wagered (TW)
10%
0%
NL Only
NoB & NL
TW Only
TW & NoB
NoB Only
All 3
Top 1% # of Bets (NoB)
NL & TW
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
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
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
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
15 (12)
12
14 (7)
4
9 (7)
7
% Lost
Longitudinal Cohort
Median Behaviors – Fixed Odds
Total Sample and Most Involved Losers
Measure
Duration
Frequency
Bets/day
Euros/bet
Total Wagered
Net Loss
% Lost
Total (39,719) Top B&L* (144)
116 (of 244)
219 (of 244)
23%
50%
2.5
7
4
42
148
21,807
33
3,914
29%
18%
Sum of Stakes by Month
(Total Sample)
10000000.00
9000000.00
8000000.00
Sum of Stakes (Full Sample)
7000000.00
6000000.00
5000000.00
4000000.00
3000000.00
2000000.00
1000000.00
0.00
1
2
3
4
5
6
7
8
9
10
11
12
Months
STAKE fixed odds
STAKE live action
13
14
15
16
17
18
Sum of Stakes By Day
(Most involved)
80000
70000
Sum of Stakes (1% FO-S)
60000
50000
40000
30000
20000
10000
0
1
4
7
10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88
Days
STAKE fixed odds
STAKE live action
Caveat
We don’t know how much disposable
income these betters had available
 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
 Most people’s play adapted, following the
prototypical public health adaptation curves
– 1% of the sample did not adapt

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.

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 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
• P<0.05
Gambling behavior of
internet casino gamblers
Casino vs Sports Gambling
Frequency of play for each game type
Cost of play for each game type
6
7
Live action
Fixed-odds
5
4
3
2
Casino games
6
Play per month
Cost per day (€)
7
5
Live action
Fixed-odds
Casino
4
3
2
1
1
0
0
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.

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.
Total stakes wagered on casino
games
Gambling behavior of extreme 5 and
95% subgroups of casino bettors
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 < 0.001).
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.

Responsible Gambling Efforts in
the Virtual World
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
Corporate Social Responsibility
Corporate Deposit Limits
 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
 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
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
Sample Description
160 (0.3%; 5 women) of the sample received at
least one notification (Exceeders)
 Exceeders received between 1 and 267
notifications (M=14 notifications)

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
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
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
 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
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
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
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
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
Results: Games Played
100%
90%
Percent Playing
80%
70%
60%
50%
40%
30%
20%
10%
0%
Fixed Odds Live Action
Casino
Supertoto Softgames
Rest of Sample
SLs
Lottery
Flash
Poker
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
Results: Self Limiter Pre-Post Behavior
(Fixed Odds & Live Action Combined; n=477)
Pre-limit
 % active days bet:
Post-limit
 % active days bet:
– 33.0 (SD: 29.5)*
– 29.5 (SD: 26.2)*

Bets per day:

– 7.1 (SD: 6.9)*

Net loss/stakes:
– 6.2 (SD: 7.1)*

– .23 (SD: .35)

Average bet size:
– €7.0 (SD: €12.0)*
Bets per day:
Net loss/stakes:
– .24 (SD: .48)

Average bet size:
– €8.3 (SD: €14.8)*
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
General Limitations

Limiting resources are
only helpful if people
can access them
easily
General Limitations

Interventions will only
work if the message
gets through to the
target
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

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

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), 463477.
Available Resources &
Links
 www.divisiononaddictions.org
 www.basisonline.org
 www.thetransparencyproject.org
 [email protected]

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.)
The Transparency Project website http://www.thetransparencyproject.org

Scientific information often is locked away with limited
accessibility

There is a need to facilitate greater access to privatelyfunded 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

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