Public health · 2025
Problem gambling in the crypto era: A study of gambling motivations and cognitive distortions amongst cryptocurrency traders.
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Xu T, Grubbs J B, Kraus S W
Аннотация
OBJECTIVES: The rise of cryptocurrency trading has sparked global interest and raised concerns about its potential links to problematic gambling behaviours. This study examined the prevalence of problematic gambling amongst cryptocurrency traders and identified psychological predictors, focusing on gambling motivations and cognitive distortions.
STUDY DESIGN: Cross-sectional survey study using YouGov Opinion Polling's sample-matching methodology.
METHODS: A sample of 700 cryptocurrency traders was drawn from a larger behavioural addiction project (N = 4363). Participants completed the Problem Gambling Severity Index (PGSI), Gambling Motives Questionnaire-Financial (GMQ-F), and Gambling Related Cognitions Scale (GRCS). Analyses included chi-square tests, one-way ANOVAs with Tukey's post-hoc tests, and multinomial logistic regression.
RESULTS: Problematic gambling was identified in 33.7 % of traders, with 33.9 % classified as at-risk gambling and 32.4 % as non-problematic gambling. Enhancement motivation (OR = 1.60, 95 % CI [1.10, 2.34]) and interpretative bias (OR = 1.38, 95 % CI [1.06, 1.81]) positively predicted at-risk gambling, whereas social motivation showed protective effects (OR = 0.61, 95 % CI [0.41, 0.91]). Coping motivation strongly predicted problematic gambling (OR = 4.47, 95 % CI [2.28, 8.78]), as did inability to stop gambling (OR = 3.18, 95 % CI [2.22, 4.54]). Age was negatively associated with problematic gambling (OR = 0.94, 95 % CI [0.91, 0.97]).
CONCLUSIONS: Findings reveal high rates of problematic gambling amongst cryptocurrency traders, with distinct motivational and cognitive predictors at different risk levels. Results suggest the need for targeted educational programmes and intervention strategies tailored to address specific risk factors.
STUDY DESIGN: Cross-sectional survey study using YouGov Opinion Polling's sample-matching methodology.
METHODS: A sample of 700 cryptocurrency traders was drawn from a larger behavioural addiction project (N = 4363). Participants completed the Problem Gambling Severity Index (PGSI), Gambling Motives Questionnaire-Financial (GMQ-F), and Gambling Related Cognitions Scale (GRCS). Analyses included chi-square tests, one-way ANOVAs with Tukey's post-hoc tests, and multinomial logistic regression.
RESULTS: Problematic gambling was identified in 33.7 % of traders, with 33.9 % classified as at-risk gambling and 32.4 % as non-problematic gambling. Enhancement motivation (OR = 1.60, 95 % CI [1.10, 2.34]) and interpretative bias (OR = 1.38, 95 % CI [1.06, 1.81]) positively predicted at-risk gambling, whereas social motivation showed protective effects (OR = 0.61, 95 % CI [0.41, 0.91]). Coping motivation strongly predicted problematic gambling (OR = 4.47, 95 % CI [2.28, 8.78]), as did inability to stop gambling (OR = 3.18, 95 % CI [2.22, 4.54]). Age was negatively associated with problematic gambling (OR = 0.94, 95 % CI [0.91, 0.97]).
CONCLUSIONS: Findings reveal high rates of problematic gambling amongst cryptocurrency traders, with distinct motivational and cognitive predictors at different risk levels. Results suggest the need for targeted educational programmes and intervention strategies tailored to address specific risk factors.