Online Gambling

Question 1

Public health model of harm: game, gambler, environment

Answer 1

• person-product interaction
• Gambler/person: individual factors that put you at risk for developing problem gambling (ex. Impulsivities, dopamine, genetics)
• Game/product: features of the game that act on the gambler (ex. Speed of play, near-misses, losses disguised as wins)
• Gambling environment: the legal status and online availability of gambling -> influences both game and gambler

Question 2

emergence of online gambling

Answer 2

• BC was first Canadian province to introduce online gambling in 2004
• PlayNow.com is the only legal, regulated site for BC residents
• Europe, Australia, etc. have hundreds of gambling websites
• People tend to think online gambling is the most harmful form

Question 3

online gambling: the concerns

Answer 3

• 24/7 accessibility
• Geographical distance (brings the casino to your living room)
• • Mobile access via smartphones (on the bus…)
• Playing on credit card (‘the plastic trap’: people part with their money more readily compared to using cash)
• • Unlike in a casino, where you have to use cash to play slot machines
• Cannot control alcohol / drug use (playing while intoxicated)
• Social isolation / reduced stigma

Question 4

is online gambling more addictive?

Answer 4

• Basic study: find a group of people who gamble online and a group who gamble ‘offline’ (i.e. don’t gamble online). Measure PGSIs.
• • Online group show higher PGSI
• The problem: the online group tend to engage in more forms of gambling, incl. offline games
• In studies that control for number of different gambling forms, online engagement by itself is not reliably predictive of gambling problems

Question 5

“player tracking” potential

Answer 5

• Gambler behaviour is collected automatically and linked to one user – could we use this data science for player protection?
• The bwin studies: European data from 2005-2007, mostly sports betting
• • Averaging data over a day of play
• • Look at gambling frequency (e.g. days active), intensity (e.g. average bet size), variability (number of games) all linked to (likely) gambling problems
• LaPlante study:
• • Looked at 46,339 online sports bettors
• • Typical usage: short term increase with later adaptation/cessation
• • 1-5% of users show endless escalating behaviours towards gambling problems

Question 6

how do we know which online gamblers have problems?

Answer 6

• Potential solution - host a clinical questionnaire (PGSI) on website
• • Likely problems: low response rate, sample representativeness
• Other “Red flags” for likely problem gambling:
• • Self-exclusion from the website
• • Frequent deposits / withdrawals / changing account limits
• • Analyzing email correspondence with website operator