BackgroundResearch on the identification and treatment of problem gambling has been characterized by a wide range of outcome measures and instruments. However, a single instrument measuring gambling behavior, severity, and specific deleterious effects is lacking.ObjectiveThis protocol describes the development of the Gambling Disorder Identification Test (G-DIT), which is a 9- to 12-item multiple-choice scale with three domains: gambling consumption, symptom severity, and negative consequences. The scale is analogous to the widely used Alcohol Use Disorders Identification Test (AUDIT) and the Drug Use Disorders Identification Test (DUDIT).MethodsThe G-DIT is developed in four steps: (1) identification of items eligible for the G-DIT from a pool of existing gambling measures; (2) presentation of items proposed for evaluation by invited expert researchers through an online Delphi process and subsequent consensus meetings; (3) pilot testing of a draft of the 9- to 12-item version in a small group of participants with problem gambling behavior (n=12); and (4) evaluation of the psychometric properties of the final G-DIT measure in relation to the existing instruments and self-reported criteria of the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5), among individuals with problem gambling and nonproblematic recreational gambling behaviors (n=600). This protocol article summarizes step 1 and describes steps 2 and 3 in detail.ResultsAs of October 2018, steps 1-3 are complete, and step 4 is underway.ConclusionsImplementation of this online Delphi study early in the psychometric development process will contribute to the face and construct validity of the G-DIT. We believe the G-DIT will be useful as a standard outcome measure in the field of problem gambling research and serve as a problem-identification tool in clinical settings.International Registered Report Identifier (IRRID)RR1-10.2196/12006

Objectives: Diverse instruments are used to measure problem gambling and Gambling Disorder intervention outcomes. The 2004 Banff consensus agreement proposed necessary features for reporting gambling treatment efficacy. To address the challenge of including these features in a single instrument, a process was initiated to develop the Gambling Disorder Identification Test (GDIT), as an instrument analogous to the Alcohol Use Disorders Identification Test and the Drug Use Disorders Identification Test.Methods: Gambling experts from 10 countries participated in an international tworound Delphi (n = 61; n = 30), rating 30 items proposed for inclusion in the GDIT.Gambling researchers and clinicians from several countries participated in three consensus meetings (n = 10; n = 4; n = 3). User feedback was obtained from individuals with experience of problem gambling (n = 12) and from treatment-seekers with Gambling Disorder (n = 8).Results: Ten items fulfilled Delphi consensus criteria for inclusion in the GDIT (M ≥ 7 on a scale of 1-9 in the second round). Item-related issues were addressed, and four more items were added to conform to the Banff agreement recommendations, yielding a final draft version of the GDIT with 14 items in three domains: gambling behavior, gambling symptoms and negative consequences. Conclusions:This study established preliminary construct and face validity for the GDIT.

This study aimed to investigate changes in gambling behaviors during the first and second waves of the COVID-19 pandemic in Sweden. Participants who had gambled within the past year were recruited from social media and the Swedish National Helpline (n = 325, mean age 39.8 years, 64.8% males, 31.3% with problem gambling) and completed an online survey measuring gambling behaviors, consequences of the pandemic in general and worries related to the pandemic. A sub-sample (n = 139) completed a follow-up survey, during the second wave. The results showed no significant associations between COVID-19 consequences (financial or increased isolation) and increased monthly gambling behavior. No major migrations were observed between game types. However, gambling on a high-risk game (OR = 7.44, p < 0.001) and worrying about mental health due to the pandemic (OR = 2.85, p < 0.001) were significantly associated with past year gambling problems and increased monthly gambling problems from the first to the second wave. More longitudinal research is needed in vulnerable populations, to fully understand the long-term consequences of the pandemic.