The Interaction Between Gambling Activities and Modes of Access: A Comparison of Internet-Only, Land-Based-Only, and Mixed-Mode Gamblers (Gainsbury et al., 2015)

Region & Target Population

• ‍Region: Australia‍
• Target population: Adult gamblers aged 18+ who had gambled in the past 12 months

Study Design

• ‍Cross-sectional online survey‍
• Designed to overcome a common limitation in prior work by explicitly separating:
  
  • Internet-only gamblers (IGs)
  • Land-based-only gamblers (LBGs)
  • Mixed-mode gamblers (MMGs)
• Comparative subgroup analysis using:
  
  • ANOVAs
  • Chi-square tests with Bonferroni-adjusted post-hoc comparisons‍

Sample Characteristics (with data-collection years)

• Total sample: N = 4,594 past-year gamblers‍
• Data collection: 2012
• Mode subgroups:
  
  • Internet-only gamblers (IGs): n = 608
  • ‍Mixed-mode gamblers (MMGs): n = 2,570‍
  • Land-based-only gamblers (LBGs): n = 1,41677‍
• Age:‍
• MMGs were significantly younger (mean = 40) than both IGs and LBGs
• Younger age bands (including 18–29) were over-represented among MMGs‍
• Gender:‍
• IGs and MMGs approx. 86% male
• LBGs had a substantially higher proportion of women‍

Gambling-Mode Structure & Comparison Logic

The study conceptualizes gambling risk as an interaction between product type and mode of access, rather than a simple online vs offline dichotomy.

Three analytically distinct groups:

1. ‍Internet-only gamblers (IGs)
2. Land-based-only gamblers (LBGs)
3. Mixed-mode gamblers (MMGs)

The central logic is that mode matters most when combined with breadth of gambling activities, and that mixed-mode access enables greater product exposure, frequency, and escalation.‍

Measures Used‍

• ‍Gambling participation: Engagement in 10 commercial gambling forms, including:
  
  • Lotteries & scratch tickets
  • Sports betting
  • Horse/dog race betting
  • EGMs
  • Casino table games
  • Poker
  • Keno and bingo‍
• Gambling frequency: Weekly vs less frequent use by product‍
• Problem gambling severity:
  
  • Problem Gambling Severity Index (PGSI): Categories: non-problem, low-risk, moderate-risk, problem gambling‍
• Problem attribution: Participants with PGSI ≥3 identified which gambling form contributed most to problems‍
• Demographics: Age, gender, education, employment, household type

Research Questions

1. Do gamblers differ meaningfully in gambling behavior and harm based on mode of access (online, land-based, mixed)?
2. Which gambling forms are most strongly associated with problems within each mode subgroup?
3. Does multi-product involvement explain elevated problem gambling risk beyond mode alone?

Key Findings

Overall involvement

• Mixed-mode gamblers (MMGs) showed the highest involvement:
  
  • Played the greatest number of gambling activities
  • Reported the highest average PGSI scores
  • Two-thirds reported some level of gambling-related harm‍

Problem gambling prevalence

• Problem gambling rates:‍
  
  • IGs at the lowest risk
  • LBGs and MMGs showed similar proportions of problem gamblers, but for different reasons
• Mean PGSI scores were highest among MMGs, followed by LBGs, then IGs‍

Product-specific harm by mode

• Land-based gamblers (LBGs):‍
  
  • EGMs were overwhelmingly the primary harm driver
  • >50% of problem LBGs attributed harm to EGMs
  • Weekly EGM play was substantially more common in this group‍
• Internet-only gamblers (IGs):‍
  
  • Harm concentrated in sports betting and race wagering
  • These products accounted for approx. 50% of reported problems‍
• Mixed-mode gamblers (MMGs):‍
  
  • Harm attributed to both EGMs and sports/race betting
  • Reflects cumulative exposure across environments

‍Age and emerging adulthood‍

• MMGs were the youngest, least settled group:
  
  • More likely to live in group households
  • Less likely to be married
  • More likely to rely on mobile-only access
• This profile aligns closely with emerging adulthood, suggesting heightened vulnerability through mobility, disposable income, and digital access

Study Conclusion

The authors conclude that gambling harm cannot be understood by examining gambling mode or gambling product in isolation. Instead, risk emerges from the interaction between mode of access and breadth of gambling involvement. Mixed-mode gamblers—who are disproportionately younger—represent the highest-risk subgroup because they combine online convenience with land-based exposure, enabling engagement with a wide array of high-risk products. Electronic gaming machines dominate harm among land-based gamblers, while sports and race betting drive harm among online gamblers. For mixed-mode gamblers, harm accumulates across both domains, producing elevated problem severity even when no single product is uniquely responsible. This finding directly challenges simplistic narratives that portray online gambling as inherently more harmful than land-based gambling. For emerging adults, the study is particularly important because it shows how digital access expands gambling careers rather than replacing land-based play. The authors argue that effective harm-reduction strategies must therefore move beyond “online vs offline” debates and instead address multi-product exposure, environmental saturation, and ease of switching between modes, especially among younger gamblers navigating highly accessible gambling ecosystems.