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Articles

Behaviour change strategies for problem gambling: an analysis of online posts

ORCID Icon ORCID Icon ORCID Icon, ORCID Icon, ORCID Icon, , ORCID Icon & ORCID Icon
Pages 420-438 | Received 08 Jul 2017, Accepted 22 Jan 2018, Published online: 08 Feb 2018
 

ABSTRACT

Reducing or quitting problematic gambling often requires implementing a variety of behaviour change strategies, but there is limited evidence regarding the breadth of specific strategies that gamblers use to control or limit gambling behaviours. This study aimed to identify the range of change strategies reported by gamblers in a naturalistic setting (i.e. two online forums for problem gambling). A total of 2937 change strategies were extracted from online posts (N = 1370). Content analysis identified 27 discrete change strategies that were pre-decisional (i.e. barriers – behavioural and psychological, decisional balance, realization – behaviour and cognitions, set reasons to change, seek knowledge and information, self-assessment), pre-actional (i.e. action planning, commitment, goal setting), actional (i.e. alternative activity, behavioural substitution, avoidance – abstinence, environment and financial, consumption control, maintain readiness, reinforcement, urge management, cognitive restructuring, seek inspiration, self-monitoring and spiritual) and multi-phased (i.e. external support, social support and well-being). This study suggests the breadth and depth of change strategies are far more complex than previously reported. Future research with a broader population needs to determine which change strategies are most effective for those experiencing different levels of gambling problems.

Acknowledgements

We gratefully acknowledge the financial assistance for this research project provided by Gambling Research Australia. We also acknowledge all members of the broader research team including John Cunningham, Elaine Nuske, Orson Rapose, Ravi Iyer and Tomas Cartmill. We also acknowledge the work of three research assistants who extracted and coded the data: Brenna Knaebe (BK), Natalia Booth (NB) and Michael Vacaru (MV).

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