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BACKGROUND: Over the last decade, the provision of online gambling has intensified with increased access, enhanced betting markets, a broader product range, and prolific marketing. However, little research has explored how this intensification is influencing contemporary gambling experiences. This study focused on two research questions: 1) What changes in online gambling have online gamblers observed over the past decade? 2) How have these changes influenced the online gambling experiences and behaviours reported by treatment-seeking and non-treatment-seeking gamblers? METHODS: Two samples of Australian adults were interviewed: 1) 19 people who had been gambling online for at least a decade and with no history of treatment-seeking for online gambling, and 2) 10 people who had recently sought professional help for an online gambling problem. Telephone interviews were semi-structured, with questions that encouraged participants to consider how their online gambling, including any harmful gambling, had been influenced by changes in operator practices and online gambling environments. Data were analysed using thematic analysis. RESULTS: Both treatment- and non-treatment-seekers noted the increased speed and ease of online gambling, which now enables instant access from anywhere at any time and increased their gambling opportunities. Both groups highlighted the continued proliferation of advertising and inducements for online gambling, particularly during televised sports and racing events, in social media, and through targeted push marketing. Many treatment- and non-treatment-seekers were aware of the vast range of recently introduced bet types, particularly multi-bets. Treatment-seekers disproportionately reported negative effects from these changes, and described how and why they fostered their increased gambling, impulsive gambling, persistence and loss-chasing. They reported limited uptake and effectiveness of current harm minimisation tools. CONCLUSIONS: Counter t

Abstract Background and aimsProblem gambling severity and gambling-related harm are closely coupled, but conceptually distinct, constructs. The primary aim was to compare low-risk gambling limits when gambling-related harm was defined using the negative consequence items of the Problem Gambling Severity Index (PGSI-Harm) and the Short Gambling Harms Scale items (SGHS-Harm). A secondary aim was compare low-risk limits derived using a definition of harm in which at least two harms across different domains (e.g. financial and relationship) were endorsed with a definition of harm in which at least two harms from any domain were endorsed.MethodsData were collected from dual-frame computer-assisted telephone interviews of 5,000 respondents in the fourth Social and Economic Impact Study (SEIS) of Gambling in Tasmania. Receiver operating characteristic (ROC) curve analyse were conducted to identify low-risk gambling limits.ResultsPGSI-Harm and SGHS-Harm definitions produced similar overall limits: 30–37 times per year; AUD$510–$544 per year; expenditure comprising no more than 10.2–10.3% of gross personal income; 400–454 minutes per year; and 2 types of gambling activities per year. Acceptable limits (AUC ≥0.70) were identified for horse/dog racing, keno, and sports/other betting using the PGSI definition; and electronic gaming machines, keno, and bingo using the SGHS definition. The requirement that gamblers endorse two or more harms across different domains had a relatively negligible effect.Discussion and conclusionsAlthough replications using alternative measures of harm are required, previous PGSI-based limits appear to be robust th

BACKGROUND AND AIMS: Problem gambling severity and gambling-related harm are closely coupled, but conceptually distinct, constructs. The primary aim was to compare low-risk gambling limits when gambling-related harm was defined using the negative consequence items of the Problem Gambling Severity Index (PGSI-Harm) and the Short Gambling Harms Scale items (SGHS-Harm). A secondary aim was compare low-risk limits derived using a definition of harm in which at least two harms across different domains (e.g. financial and relationship) were endorsed with a definition of harm in which at least two harms from any domain were endorsed. METHODS: Data were collected from dual-frame computer-assisted telephone interviews of 5,000 respondents in the fourth Social and Economic Impact Study (SEIS) of Gambling in Tasmania. Receiver operating characteristic (ROC) curve analyse were conducted to identify low-risk gambling limits. RESULTS: PGSI-Harm and SGHS-Harm definitions produced similar overall limits: 30-37 times per year; AUD$510-$544 per year; expenditure comprising no more than 10.2-10.3% of gross personal income; 400-454 minutes per year; and 2 types of gambling activities per year. Acceptable limits (AUC ≥0.70) were identified for horse/dog racing, keno, and sports/other betting using the PGSI definition; and electronic gaming machines, keno, and bingo using the SGHS definition. The requirement that gamblers endorse two or more harms across different domains had a relatively negligible effect. DISCUSSION AND CONCLUSIONS: Although replications using alternative measures of harm are required, previous PGSI-based limits appear to be robust thresholds that have considerable potential utility in the prevention of gambling-related harm.

BACKGROUND AND AIMS: In the present research, we experimentally investigated whether the experience of winning (i.e., inflated payout rates) in a social casino game influenced social casino gamers' subsequent decision to gamble for money. Furthermore, we assessed whether facets of dispositional impulsivity - negative and positive urgency in particular - also influenced participants' subsequent gambling. METHODS: Social casino gamers who were also current gamblers (N = 318) were asked to play a social casino game to assess their perceptions of the game in exchange for $3. Unbeknownst to them, players were randomly assigned to one of three experimental conditions: winning (n = 110), break-even (n = 103), or losing (n = 105). After playing, participants were offered a chance to gamble their $3 renumeration in an online roulette game. RESULTS: A total of 280 participants (88.1%) elected to gamble, but no between-condition variation in the decision to gamble emerged. Furthermore, there were no differences in gambling on the online roulette between condition. However, higher levels of both negative and positive urgency increased the likelihood of gambling. Finally, impulsivity did not moderate the rel

BACKGROUND: A better understanding of how occupational indicators (e.g. job type, doing shift-work, hours worked, physical demand) influence sitting time will aid in the design of more effective health behaviour interventions. The aim of the study was to examine the associations between several occupational indicators and total, occupational and leisure-time sitting. METHODS: Cross-sectional self-report data was collected in November 2011 from 1194 participants through a telephone interview in regional Queensland, Australia (response rate was 51.9%). The Workforce Sitting Questionnaire was used to measure sitting time. Multiple logistic regression was applied to examine associations between sitting time and occupational indicators. RESULTS: Of all participants 77.9% were employed full-time, 72.7% had white-collar jobs, 35.7% were engaged in shift-work, 39.5% had physically demanding jobs, and 53.2% had high total sitting time (>8 hours a day). Those in physically demanding and blue-collar occupations were less likely to report high total (physically demanding: OR = 0.41,95% CI = 0.29-0.58; blue-collar: OR = 0.55,95% CI = 0.37-0.82) and occupational (physically demanding: OR = 0.26,95% CI = 0.14-0.24; blue-collar: OR = 0.32,95% CI = 0.21-0.49) sitting time compared to those in physically undemanding and white-collar occupations respectively. Working more than 8 hours per day was inversely associated with high leisure-time sitting (OR = 0.44,95% CI = 0.29-0.68). No evidence for 'compensation' effects, where lower occupational sitting is compensated with higher leisure-time sitting, was found. CONCLUSIONS: Behaviour change interventions are needed to reduce sitting time as a means to prevent chronic disease. Workplace initiatives to reduce sitting time may be particularly important among individuals employed in white-collar and physical undemanding occupations, although other intervention strategies targeting leisure-time sitting are also required.