Problem Gambling in the Fitness World-A General Population Web Survey.
Anders HåkanssonArtin EntezarjouGöran KenttäFernando Fernández-ArandaSusana Jiménez-MurciaBjörn GunnarssonPublished in: International journal of environmental research and public health (2020)
The world of sports has a complex association to problem gambling, and the sparse research examining problem gambling in athletes has suggested an increased prevalence and particularly high male predominance. The present study aimed to study frequency and correlates of problem gambling in populations with moderate to high involvement in fitness or physical exercise. This is a self-selective online survey focusing on addictive behaviors in physical exercise distributed by 'fitness influencers' on social media and other online fitness forums to their followers. Respondents were included if they reported exercise at least thrice weekly, were above 15 years of age, and provided informed consent (N = 3088). Problem gambling, measured with the Lie/Bet, was studied in association with demographic data, substance use, and mental health variables. The occurrence of lifetime problem gambling was 8 percent (12 percent in men, one percent in women). In logistic regression, problem gambling was associated with male gender, younger age, risky alcohol drinking, obsessive-compulsive disorder, and less frequent exercise habits. In conclusion, in this self-recruited population with moderate to high fitness involvement, problem gambling was moderately elevated. As shown previously in elite athletes, the male predominance was larger than in the general population. The findings strengthen the link between problem gambling and the world of sports.
Keyphrases
- social media
- physical activity
- body composition
- mental health
- obsessive compulsive disorder
- high intensity
- health information
- resistance training
- machine learning
- metabolic syndrome
- healthcare
- type diabetes
- risk factors
- cross sectional
- pregnant women
- polycystic ovary syndrome
- electronic health record
- deep learning
- big data
- insulin resistance