Background: The presence of discrete but fluctuating precipitants, in combination with the dynamic nature of gambling episodes, calls for the development of tailored interventions delivered in real time, such as just-in-time adaptive interventions (JITAIs). JITAIs leverage mobile and wireless technologies to address dynamically changing individual needs by providing the type and amount of support required at the right time and only when needed. They have the added benefit of reaching underserved populations by providing accessible, convenient, and low-burden support. Despite these benefits, few JITAIs targeting gambling behavior are available.
Objective: This study aims to redress this gap in service provision by developing and evaluating a theoretically informed and evidence-based JITAI for people who want to reduce their gambling. Delivered via a smartphone app, GamblingLess: In-The-Moment provides tailored cognitive-behavioral and third-wave interventions targeting cognitive processes explicated by the relapse prevention model (cravings, self-efficacy, and positive outcome expectancies). It aims to reduce gambling symptom severity (distal outcome) through short-term reductions in the likelihood of gambling episodes (primary proximal outcome) by improving craving intensity, self-efficacy, or expectancies (secondary proximal outcomes). The primary aim is to explore the degree to which the delivery of a tailored intervention at a time of cognitive vulnerability reduces the probability of a subsequent gambling episode.
Methods: GamblingLess: In-The-Moment interventions are delivered to gamblers who are in a state of receptivity (available for treatment) and report a state of cognitive vulnerability via ecological momentary assessments 3 times a day. The JITAI will tailor the type, timing, and amount of support for individual needs. Using a microrandomized trial, a form of sequential factorial design, each eligible participant will be randomized to a tailored intervention condition or no intervention control condition at each ecological momentary assessment across a 28-day period. The microrandomized trial will be supplemented by a 6-month within-group follow-up evaluation to explore long-term effects on primary (gambling symptom severity) and secondary (gambling behavior, craving severity, self-efficacy, and expectancies) outcomes and an acceptability evaluation via postintervention surveys, app use and engagement indices, and semistructured interviews. In all, 200 participants will be recruited from Australia and New Zealand.
Results: The project was funded in June 2019, with approval from the Deakin University Human Research Ethics Committee (2020-304). Stakeholder user testing revealed high acceptability scores. The trial began on March 29, 2022, and 84 participants have been recruited (as of June 24, 2022). Results are expected to be published mid-2024.
Conclusions: GamblingLess: In-The-Moment forms part of a suite of theoretically informed and evidence-based web-based and mobile gambling interventions. This trial will provide important empirical data that can be used to facilitate the JITAI’s optimization to make it a more effective, efficient, and scalable tailored intervention.
Trial Registration: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12622000490774; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380757&isClinicalTrial=False
International Registered Report Identifier (IRRID): PRR1-10.2196/38958
Gambling disorder (formerly pathological gambling) has been reclassified in the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) as an addiction and related disorder alongside alcohol and substance use disorders . Consistent with public health frameworks that conceptualize gambling problems across a continuum of risk [ ], many jurisdictions, including Australia and New Zealand, use the term problem gambling to refer to gambling that results in adverse consequences for gamblers, families, and communities [ ]. Internationally, estimates of past-year problem gambling have ranged from 0.1% to 5.8% over the past decade [ ]. Specifically, Australian and New Zealand national estimates suggest that past-year problem gambling affects 0.4% to 0.7% of adults, with a further 2% to 11% displaying moderate-risk gambling and 3.0% to 7.7% displaying low-risk gambling [ - ]. Despite relatively low prevalence estimates, problem gambling is associated with a high burden of harm [ ], which can include financial strain and loss, relationship breakdown, emotional and psychological distress, health decline, cultural upset, reduced work or study performance, and social deviance [ ]. Problem gambling is also highly comorbid with a range of mental health issues, including mood, anxiety, alcohol and substance use, and personality disorders [ - ].
The Relapse Prevention Model
The relapse prevention model , a prominent and influential social-cognitive theory originally developed to explain relapse in substance use disorders, classifies factors or situations that can precipitate or contribute to relapse. Generally, these factors can be immediate determinants (high-risk situations, coping skills, outcome expectancies, and the abstinence violation effect) or covert antecedents that indirectly influence relapse (lifestyle imbalances, rationalizations, denial, apparently irrelevant decisions, and urges or cravings). A basic assumption of this model is that lapses are immediately preceded by a high-risk situation, broadly defined as any context that confers vulnerability to engaging in the target behavior, such as negative emotional states, interpersonal conflict, social pressure, testing of personal control, and nonspecific cravings. The model posits that positive outcome expectancies become particularly salient in high-risk situations, whereby the immediate positive effects of addictive behavior may be anticipated, and the possible delayed negative consequences of addictive behavior are ignored or discounted [ ]. It also highlights that effective behavioral and cognitive coping in response to high-risk situations enhances self-efficacy, thereby reducing the probability of relapse [ , ].
The relapse prevention model has been reconceptualized  to emphasize the multidimensional, complex, nonlinear, and dynamic interaction among various precipitants that act jointly and interactively within high-risk situations to determine the likelihood of relapse. This model also incorporates the interaction among background factors (eg, years of dependence, family history, social support, and comorbid psychopathology), physiological states (eg, physical withdrawal), cognitive processes (eg, self-efficacy, outcome expectancies, craving, motivation, and abstinence violation effect), affective states, and coping skills. However, responding to a high-risk situation is related to both distal and proximal risk factors operating within both tonic processes and phasic responses. Tonic processes are distal risks or stable background factors that determine the set point or initial threshold for relapse. These processes, which indicate chronic vulnerability to relapse, often accumulate and lead to the instigation of a high-risk situation, providing the foundation for the possibility of relapse. In contrast, phasic responses are situational cognitive, affective, or physical states that can fluctuate across time and contexts and serve to activate lapses. Momentary coping responses can also serve as phasic events that determine whether a high-risk situation culminates in a lapse. The model predicts feedback loops, whereby lapse episodes can have reciprocal effects on the same factors (cognitive processes, affective states, and coping behavior) that contribute to the lapse. There is considerable empirical support for relapse prevention models across addictions [ , ].
In this model, cognitive processes that are relatively stable over time, such as outcome expectancies and global self-efficacy, are conceptualized as tonic processes, whereas cognitive processes that fluctuate over contexts and time, such as urges or cravings, as well as transient changes in outcome expectancies and self-efficacy, are conceptualized as phasic responses. Because it emphasizes the importance of nonlinear relationships and the timing or sequencing of events, the model does not articulate the temporal relationships between each of these cognitive processes. For example, a momentary reduction in self-efficacy in a high-risk situation could have a disproportionate influence on other cognitive processes, such as outcome expectancies . There is emerging evidence of the role that cognitive processes play in gambling behavior and relapse as tonic processes; however, there is less evidence in relation to the role they play as phasic responses.
Craving is a central phenomenon in addiction science. Despite the abundance of theoretical models, there is little consensus about its definition, etiology, and maintenance, and the terms craving and urge are often used interchangeably [, ]. In the relapse prevention model, cravings are defined as the subjective desire to experience an appetitive target and urges are described as relatively sudden behavioral intentions or impulses to seek out and engage in an appetitive target [ , ]. This conceptualization is consistent with the integrative elaborated intrusion theory of desire [ , ], in which craving is defined as intense subjective desires for an appetitive target and urges are defined as specific desires for positive or negative reinforcement from an appetitive target [ ]. Recent empirical studies attempting to delineate between gambling cravings and urges suggest that gambling craving is a higher-order and multifaceted construct, which is characterized by mental imagery, desire thoughts, and physiological sensations and triggered by various stimuli, including positive affect, negative affect, external cues, mental imagery, and desire thoughts [ ]. In contrast, urges are a more narrowly defined construct comprising 2 core dimensions: intent and desire to gamble (due to expectations of positive reinforcement) and relief (due to expectations of negative reinforcement) [ ].
Despite this conceptual confusion, the emerging cross-sectional literature highlights the important role that craving plays in the maintenance, exacerbation, and relapse of gambling problems. Specifically, findings suggest that gambling cravings are positively associated with problem gambling severity [, ] and gambling relapse [ ], negatively associated with abstinence [ , ], and are among the most frequent precipitants of relapse [ ]. These findings are supported by qualitative research in which gambling cravings have been identified as a key construct associated with an increased risk of gambling relapse [ , ]. There is growing evidence that gambling cravings are relevant and useful intervention targets and potential mechanisms of change in both cognitive behavioral and mindfulness-based gambling interventions. Craving has predicted outcomes following cognitive behavioral treatment [ ], and interventions that include craving management components have demonstrated efficacy in reducing cravings [ , , - ]. These studies typically targeted cravings using cognitive behavioral techniques, such as self-monitoring, psychoeducation, development of alternative responses, behavioral exposure exercises, and relapse prevention strategies, as well as mindfulness-based strategies such as urge surfing and guided breathing or body scan meditations.
Self-efficacy, an important construct within social-cognitive theory, refers to feelings of confidence and capability to perform a behavior in a specific situational context to produce a desired outcome . Addiction science has predominantly conceptualized self-efficacy in terms of perceived confidence to resist engaging in addictive behaviors in high-risk situations, but self-efficacy measures frame such resistance slightly differently, including confidence in controlling addictive behavior [ ], resisting the urge to engage in addictive behavior [ ], avoiding addictive behavior [ ], refusing to engage in addictive behavior [ ], or abstaining from addictive behavior [ ]. Regardless of how resistance is framed, cross-sectional studies have consistently found that self-efficacy is negatively associated with both gambling behavior and problem gambling severity [ , , , , - ] and accurately discriminates between nonproblem and problem gambling samples [ , ]. Qualitative research supports these findings, suggesting that self-efficacy is a key construct in preventing relapse, which in turn increases motivation and commitment to maintain abstinence over time; however, the protective effect of self-efficacy weakens once relapse has occurred [ , ]. Similarly, there is some evidence that self-efficacy plays a protective role in preventing cravings from transitioning to gambling behavior but not when cravings are intense [ ]. These findings highlight the potential of self-efficacy as an important intervention target and mechanism of change in treatment. Furthermore, self-efficacy has been demonstrated as an important predictor of treatment outcomes for gambling across several studies [ - ], and there is a small but growing body of literature reporting improvements in self-efficacy following interventions incorporating relapse prevention, cognitive behavioral, and motivational interviewing strategies [ , , , , - ].
Positive Outcome Expectancies
Positive outcome expectancies are typically described as higher expectations or anticipation of the positive effects of future experience [, , ]. Theoretical conceptualizations suggest that outcome expectancies are associations among mental representations in long-term memory that are automatically activated under specific circumstances [ ]. There is now growing cross-sectional evidence that global positive outcome expectancies [ - ] and specific positive outcome expectancies, such as financial, excitement, escape, ego enhancement, and social expectancies [ - ], are positively associated with problem gambling severity and related harm. Although few studies have explored the degree to which these expectancies change during treatment or are predictive of treatment outcomes, one study has found clinically and statistically significant reductions in global positive outcome expectancies from pre- to postresidential gambling treatment [ ].
Ecological Momentary Assessment of Cravings, Self-efficacy, and Positive Outcome Expectancies
These predominantly cross-sectional studies, which are subject to recall bias, treat cravings, self-efficacy, and gambling outcome expectancies as stable and enduring traits rather than transient or phasic states [- ]. However, the reformulated relapse prevention model posits that transient changes in these cognitive processes can constitute phasic responses that interact with tonic processes and determine the likelihood of relapse [ ]. Ecological momentary assessment (EMA), an event-level longitudinal methodology, overcomes the limitations of cross-sectional research by repeatedly measuring symptoms, emotions, behavior, and thoughts in real time and in natural environments [ ]. Although there is now substantial EMA evidence that momentary cognitive processes (cravings, self-efficacy, and positive outcome expectancies) predict the occurrence of tobacco, alcohol, and substance use [ - ], few EMA studies have explored the associations between these processes and gambling behavior [ - ]. In the available studies, momentary cravings and self-efficacy, but not positive outcome expectancies, have predicted the likelihood of a subsequent gambling episode [ - ]. Moreover, all of these momentary cognitive processes constitute situational determinants of gambling behavior when they interact with other factors implicated in the relapse prevention model, such as high-risk positive reinforcement situations, self-efficacy, coping motives, cravings, positive emotional states, and coping styles [ - ].
Just-in-Time Adaptive Interventions
These findings, which support the relapse prevention model, suggest that cravings, self-efficacy, and positive outcome expectancies constitute phasic precipitants of gambling behavior, although this may only occur for positive outcome expectancies when they interact with tonic precipitants, such as problem gambling severity . The presence of these discrete but fluctuating precipitants, in combination with the complex and dynamic nature of gambling episodes or lapses, calls for the development of tailored interventions delivered in real time, such as just-in-time adaptive interventions (JITAIs). JITAIs are mobile health (mHealth) interventions that address dynamically changing individual needs by providing the type and amount of support required at the right time and only when needed [ - ]. They are push interventions, in which decisions about when and how support is provided are initiated by intervention protocols via computer algorithms rather than pull interventions initiated by individuals when they feel they require support [ , ]. mHealth interventions characterized by just-in-time (provision of the right type, timing, or amount of support) and adaptive (use of dynamic information from the individual to repeatedly select the type, timing, or amount of support) components have also been described as ecological momentary interventions, as long as they are dynamically and individually tailored [ ].
The overall aim of JITAIs is to prevent negative health outcomes and promote the adoption and maintenance of positive health outcomes [- ]. They are designed to provide support when individuals are in a state of vulnerability (a period of susceptibility to negative health outcomes) or a state of opportunity (a period of susceptibility to positive health behavior change), as well as a state of receptivity (able and willing to receive, process, and use the provided support) [ , ]. JITAIs identify how and when support should be offered by continuously monitoring dynamic internal states and ecological contexts in real time and in the natural environments of individuals using mobile and wireless technologies, including smartphone-embedded or wearable sensors and smartphone-delivered EMAs [ - , ].
Nahum-Shani et al [- ] have developed a comprehensive organizing scientific framework to guide the design of JITAIs. This framework describes the four key components that play an important role in JITAI design: (1) decision points (points in time at which intervention decisions are made), (2) intervention options (potential type, dose, timing, and delivery mode of support that can be delivered at any given decision point), (3) tailoring variables (data about the individual’s internal state or ecological context that is used to decide when and how to intervene), and (4) decision rules (a specification of which intervention option to offer, for whom, and when at each level of the tailoring variables). These components are guided primarily by the ultimate, long-term goal of the intervention (distal outcome) but also by the clearly defined near-time, short-term goals that the intervention is intended to achieve (proximal outcomes) [ ]. JITAIs have been effective in supporting behavior change across a range of health behaviors, including addictive disorders, such as smoking, binge drinking, heavy drinking, and alcohol use disorders [ , , , , ].
Similar to other mHealth interventions, JITAIs are characterized by high availability and accessibility, convenience, anonymity, portability, cost-effectiveness, and low burden, as well as the potential for real-world translation, scalability, and accurate data recording [, , , - ]. They also have the potential to reach underserved populations, including those who are unable or unwilling to participate in other interventions [ , , ]. This is particularly important for gambling populations, given evidence that only a small proportion of people with problem and moderate-risk gambling (1 in 5 and 1 in 25 in Australia, respectively) access specialist face-to-face gambling services [ ], despite an established evidence base indicating their efficacy [ - ]. These findings imply that face-to-face gambling treatment delivery does not provide sufficient access to evidence-based treatment [ ]. The barriers to accessing face-to-face gambling treatment, which are now well-documented [ - ], include personal factors (eg, denial, shame, stigma, embarrassment, and a desire to deal with one’s own problem), resource limitations (eg, a lack of available services and trained clinicians), geographic inaccessibility, low awareness of treatment options, treatment costs, time commitments, childcare requirements, and reluctance to engage in treatments with a prespecified goal of abstinence. JITAIs overcome many of these barriers by leveraging mobile and wireless technologies to provide immediate, cost-effective, and low-burden treatment in moments of need.
Despite these clear benefits, the development of JITAIs targeting gambling behavior has been slow. Two smartphone apps that send notifications in response to the detection of proximity or entry into gambling venues by passive assessments using geolocation sensors to collect automated data (GPS, accelerometer, gyroscope, and magnetometer) have been developed: a smartphone-based problem gambling evaluation and technology testing initiative (SPGeTTI)  and Don’t Go There [ ]. SPGeTTI also includes pull features that can be accessed on demand (self-monitoring gambling diary, relapse prevention tips, and help service contacts), whereas Don’t Go There allows an elected health professional to access the individual’s information. Despite low recruitment rates for a planned randomized controlled trial of SPGeTTI, focus group interviews revealed that gamblers reported high interest in the app. However, specific issues with SPGeTTI have been identified, such as excessive battery drainage. Don’t Go There is yet to be evaluated, with a usability study currently underway.
Two other gambling JITAIs that use active assessments via smartphone-delivered EMAs to collect data on internal states have been developed: Jeu-contrôle  and GamblingLess: Curb Your Urge [ , ]. Yet to be evaluated, Jeu-contrôle is a publicly available JITAI that uses EMAs to provide personalized feedback in relation to goal limits, with a view to supporting adherence to expenditure and time limits. In contrast, GamblingLess: Curb Your Urge is informed by the relapse prevention model and aims to reduce gambling cravings to prevent subsequent gambling episodes. This intervention, which was adapted from GamblingLess, an evidence-based web-based self-directed gambling program [ , , - ], tailors craving management activities to EMAs evaluating craving intensity and also provides these activities on demand. Key stakeholders rated the intervention content, helpfulness, acceptability, and usability highly and indicated that they would recommend the app to gamblers given its potential to increase gambling knowledge, attitudes, awareness, behavior change, intention to change, and help-seeking [ , ]. A pilot study of this JITAI [ ] revealed promising findings, with more than a 70% reduction in the average number of gambling episodes and craving occurrences during the intervention period and a 10% decrease in momentary craving intensity immediately after a recommended intervention. There were also significant medium-to-large reductions in gambling symptom severity, gambling frequency, gambling expenditure, cravings, and self-efficacy at the postintervention and 1-month follow-up evaluations. At the 1-month follow-up evaluation, nearly half of the participants (10/21, 48%) reported recovery or improvement in the severity of gambling symptoms.
This project aims to redress the gap in existing gambling service provision by evaluating a theoretically informed and evidence-based JITAI that builds on pilot data provided by the evaluation of GamblingLess: Curb Your Urge [, ]. GamblingLess: In-The-Moment is a smartphone-delivered JITAI for people who want to quit or gamble less. It uses EMAs to collect comprehensive and accurate data on the dynamic cognitive processes articulated by the relapse prevention model. The JITAI uses decision rules specifying that individuals who are in a state of receptivity (available for treatment) and report a state of cognitive vulnerability characterized by high craving intensity, low self-efficacy, or positive outcome expectancies (tailoring variables) in EMAs sent during 3 semirandom times a day (decision points) are delivered tailored cognitive behavioral and third-wave interventions targeting these cognitive processes (intervention options). The intervention aims to reduce gambling symptom severity in the long term (distal outcome), and reduce the likelihood of gambling episodes (primary proximal outcome) in the short term via improved craving intensity, self-efficacy, and positive outcome expectancies (secondary proximal outcomes). The JITAI is intended for use as a stand-alone or adjunctive treatment during periods of active gambling behavior or as a relapse prevention tool during recovery.
A microrandomized trial (MRT), a form of sequential factorial design in which every participant serves as their own control, will be used to inform the optimization of this JITAI [, ]. In this MRT, each participant will be randomized to a tailored intervention condition or no intervention control condition at each decision point across a 28-day period [ , ]. The primary aim of the MRT is to explore whether it is worthwhile to deliver a tailored intervention option at a time of cognitive vulnerability. Specifically, the aim is to explore whether, compared with the delivery of no intervention, the delivery of a tailored intervention reduces the probability of a subsequent gambling episode (primary proximal outcome) and improves craving intensity, self-efficacy, and positive outcome expectancies (secondary proximal outcomes). It is hypothesized that the delivery of a tailored intervention will be more effective than no intervention in reducing the probability of a gambling episode and improving craving intensity, self-efficacy, and positive outcome expectancies by the subsequent EMA. Should data allow, secondary exploratory research questions include the following:
- Which type of intervention option is most beneficial at a time of cognitive vulnerability? Is the delivery of one intervention option (targeting cravings, self-efficacy, or positive outcome expectancies) more likely to reduce the probability of a subsequent gambling episode than the other intervention options?
- Under what conditions is the delivery of an intervention option most beneficial? How do time-variant (EMA) factors (time of day, time of week, craving intensity, self-efficacy, positive outcome expectancies, psychological distress, impulsivity, subjective alcohol intoxication, readiness to change, gambling availability [financial and location], and social context) and time-invariant (preintervention survey) factors (gambling symptom severity, gambling frequency, gambling expenditure, gender, and age) influence the intervention effect on the probability of a subsequent gambling episode?
- How do the proximal effects of intervention options change over time as the treatment progresses? How does the effect of a tailored intervention on the probability of a subsequent gambling episode change over the course of the 28-day MRT?
This trial has been approved by the Deakin University Human Research Ethics Committee (2020-304) and registered with the Australian New Zealand Clinical Trials Registry (ACTRN12622000490774).
MRTs have significant advantages over randomized controlled trials as participants act as their own control group, providing a strong capacity for causal inferences and increased power to detect treatment effects . Moreover, they are designed to facilitate the optimization of JITAIs, which involves determining how a JITAI should be adjusted to make it more effective, efficient, and scalable [ , , ]. Participants will participate in a 28-day MRT, in which they will be prompted via push notifications on their smartphones to complete a time-based EMA 3 times daily (decision points). In this EMA, tailoring variables used to determine intervention eligibility include momentary craving intensity, self-efficacy, and positive outcome expectancies. Decision rules based on EMA item cut points will determine eligibility for a tailored intervention, which could consist of a craving, self-efficacy, or positive outcome expectancy intervention option. Participants will be randomly allocated to either a tailored intervention condition or no intervention control condition at each decision point across the 28-day trial period. To maintain the integrity of the MRT evaluation, the JITAI will be evaluated as an entirely push intervention during the 28-day MRT period [ , ]. This trial will provide important empirical data that can be used to facilitate the optimization of the JITAI to make it a more effective, efficient, and scalable intervention.
The MRT will be supplemented with (1) a within-group follow-up evaluation to explore the long-term outcomes of the intervention in relation to the primary (gambling symptom severity) and secondary (gambling frequency, gambling expenditure, cravings, self-efficacy, and positive outcome expectancies) outcomes from the preintervention evaluation to the postintervention and 6-month follow-up evaluations as well as the predictors of long-term treatment outcomes and (2) an evaluation of the acceptability of the JITAI using postintervention surveys, app use and engagement indices, and semistructured interviews. An overview of the trial design is shown in.
Participant Recruitment and Reimbursement
Participants will be recruited across Australia and New Zealand, using a range of strategies, such as web-based advertising (eg, Google Adwords), social media (eg, Facebook and Instagram), gambling-related websites (eg, GambleAware, Gambling Helpline, Gambling Help Online, and Australasian Gaming Council), and advertisements in public places (eg, universities, general practices, health services, mental health services, and alcohol and other drug services). Gambling counseling services and gambling venues may also be requested to assist with participant recruitment. The eligibility criteria will include (1) current Australian or New Zealand residence, (2) ≥18 years of age, (3) installation of the app from an internet-enabled smartphone, (4) willingness to receive notifications from the app, (5) fluency in the English language, and (6) seeking support for one’s own gambling. The target population for GamblingLess: In-The-Moment comprises people who want to quit or gamble less. Consistent with a pragmatic design, the intervention will be available to any interested gambler, regardless of the level of gambling symptom severity or whether they are seeking other forms of support or treatment . Moreover, consistent with a harm minimization approach, participants can select abstinence or nonabstinence treatment goals [ - ]. Participants in the trial will be compensated for their time in e-gift vouchers: US $0.70 for each EMA completed and a US $14 bonus if >75% of the EMAs are completed (to a maximum of US $70), US $35 for completion of the postintervention survey, US $50 for completion of the 6-month follow-up survey, and US $35 for the optional semistructured interview.
Distal and Proximal Outcomes
The distal outcome of GamblingLess: In-The-Moment is the severity of gambling symptoms. The primary proximal outcome is reduced probability of a subsequent gambling episode (measured at the subsequent EMA). This outcome will be measured in the EMA using the item, “Have you gambled since the last time you checked in?” with a binary response option (yes or no). The secondary proximal outcomes are improvements in subsequent craving intensity, self-efficacy, and positive outcome expectancies (measured at the subsequent EMA).
GamblingLess: In-The-Moment will use an EMA protocol () employing in-app time-based sampling (ie, semirandomly prompting individuals to input internal states and ecological contexts), which will incorporate event-based sampling (collecting data around specific and discrete gambling episodes). Each EMA, which will take approximately 1 minute to complete, will comprise 10 items measuring momentary internal states and ecological contexts (including the tailoring variables), most of which are recorded on varying 5-point response scales presented in a multiple-choice format. To ensure an accurate record of gambling events, participants will also be administered an event record ( ) in each EMA, in which they will record gambling episodes (primary proximal outcome) and associated expenditure using single items that have been used in previous EMA and ecological momentary intervention gambling research [ , , , ].
During the 28-day MRT, participants will be prompted via push notifications to complete an EMA delivered through the app 3 times daily at random times during the prespecified periods: morning (8:30 AM-11 AM), afternoon (1 PM-3:30 PM), and evening (5:30 PM-8 PM;). At each EMA notification, participants can auto-launch the EMA via the notification or app. Participants will be allowed 2 hours to complete an EMA to preserve the momentary nature of the intervention while accommodating the potential for possible unavailability (eg, driving and working) of the participant at the initial prompt time [ , ].
The tailoring variables for GamblingLess: In-The-Moment include momentary craving intensity, self-efficacy, and positive outcome expectancies measured during each EMA (). The craving item was adapted from the first item of the Gambling Symptom Assessment Scale [ ], the self-efficacy item was adapted from the Brief Situational Confidence Questionnaire-Gambling [ ], and the positive outcome expectancy item was adapted from the Gambling Outcome Expectancy Scale [ ]. The JITAI will tailor the type, timing, and amount of support to individual needs [ - , , ] ( ). The tailoring variable data received from each participant will be used to individualize treatment by repeatedly selecting the type of intervention content (craving, self-efficacy, or positive outcome expectancies). The flexible collection of tailoring variable data will allow for the timely individualization of intervention options at specific moments when individuals are especially in need of support but not when they do not need support or are unreceptive to support. Finally, the dosage or amount of support will be tailored to individual needs via an intervention loop, whereby participants who continue to require support after completing an intervention activity will be offered additional support.
Additional EMA Items
Additional single items measuring other momentary internal states and ecological contextual factors highlighted by the relapse prevention model [, ] will be included in each EMA to explore the conditions under which the JITAI is most effective: psychological distress (based on the distress thermometer: Psychosocial Distress Practice Guidelines) [ ], readiness to change (based on the gambling readiness ruler) [ ], subjective alcohol intoxication (based on the Subjective Effects of Alcohol Scale) [ ], impulsivity (based on an EMA item from the Momentary Impulsivity Scale) [ ], social context (based on an EMA item measuring social context for alcohol use) [ ], financial gambling availability (based on an EMA item assessing money for preferred products) [ ], and location gambling availability (based on an EMA item assessing cigarette availability [ ]) ( ).
Welcome and Reminder Protocol
The following contact protocol will be adopted to enhance EMA compliance during the 28-day MRT: (1) an automated welcome email to all participants following onboarding, (2) a reminder email to participants who fail to complete onboarding or fail to complete an EMA for more than 48 hours following onboarding, and (3) a reminder telephone call by clinically or qualitatively trained research fellows to participants who still fail to complete onboarding or an EMA in the subsequent 48-hour period (with a follow-up SMS message if no answer or second follow-up email if no valid phone number was provided). Participants who fail to complete an EMA following this protocol will receive no further contact but (as long as they complete onboarding and at least one EMA and have some engagement with app-intervention activities) will be contacted to request completion of the postintervention and 6-month follow-up surveys.
Tailored Intervention Condition
The intervention options in GamblingLess: In-The-Moment are informed by the relapse prevention model [, ] as well as data from the GamblingLess program of research [ , , - ]. The intervention options were designed to target the cognitive processes that mark a state of cognitive vulnerability (cravings, low self-efficacy, and positive outcome expectancies) (secondary proximal outcomes) for the occurrence of a gambling episode (primary proximal outcome). The resulting program comprises 53 activities across three tailored intervention options (modules): (1) Curbing Cravings, (2) Tackling Triggers, and (3) Exploring Expectancies. Consistent with more recent conceptualizations of coping strategies explicated by the relapse prevention model, these interventions include cognitive, behavioral, and third-wave (mindfulness and acceptance) strategies [ , , ]. Cognitive behavioral treatments have the most established evidence base in both face-to-face and mobile treatment of problem gambling [ - , , ], with mindfulness-based interventions receiving emerging empirical support [ , ]. The activities in each of these modules are displayed in .
The intervention activities were selected based on their appropriateness for repeated delivery, effectiveness in previous research, and previous acceptability feedback [, , , ]. The selected activities were developed for smartphone delivery with a focus on engagement, interactivity, user preferences, participant literacy, inclusiveness, and ease of use, with the aim of encouraging autonomy and creating an aesthetically pleasing design. Activities involved user interaction and gamified using multimedia delivery strategies comprising a combination of video activities, audio activities, personalized feedback, quizzes, open-ended items, and multiple-choice items. All video-based activities were publicly sourced videos from YouTube, with written permission obtained from each creator. To assist participants in selecting an activity appropriate to their current environment or social situation, all activities are labeled as text, video, interactive, audio, or text and image on each menu. Consistent with the pilot study, GamblingLess: Curb Your Urge [ , ], most activities take <5 minutes to complete.
Intervention Option 1: Curbing Cravings
The Curbing Cravings intervention option includes a bank of 10 craving management activities, including psychoeducation, distraction, breathing exercises, progressive muscle relaxation, mindfulness meditation, urge surfing, imagery, cognitive reframing, decisional balance, and planning [, ]. In the GamblingLess trial, participants reported the lowest self-efficacy for high-risk situations related to urges and temptations [ , ]. Despite the delineation between craving and urges described earlier, the term urges was used in all user-facing aspects of the intervention (ie, EMA items and activity content; the intervention title is not user-facing), given anecdotal evidence in the addiction field that this is the most understandable, accessible, and commonly used term in addiction science [ - ].
Intervention Option 2: Tackling Triggers
The Tackling Triggers intervention option contains 25 activities to improve self-efficacy across 5 types of high-risk situations: financial pressure, unpleasant emotions, social pressure to gamble, testing control over gambling, and conflict with others. Participants in the GamblingLess trial [, ] reported the lowest self-efficacy in these situations (with the exception of urges and temptations, which are targeted separately in the Curbing Cravings intervention option). Each situation type comprises a bank of 5 activities designed to increase a sense of competency and mastery by teaching participants to identify, anticipate, plan for, and effectively cope with these high-risk situations [ , ]. These include behavioral (eg, self-monitoring, goal-setting, behavioral substitution, progressive muscle relaxation, psychoeducation, assertiveness training, conflict resolution training, lapse management, and planning), cognitive (eg, cognitive reframing, imagery, and decisional balance), and acceptance or mindfulness (eg, cognitive defusion and mindfulness meditation) strategies [ , ].
Intervention Option 3: Exploring Expectancies
The Exploring Expectancies intervention option contains 18 activities to redress 3 types of positive outcome expectancies: excitement, escape, and money. These positive outcome expectancies have consistently displayed positive associations with problem gambling severity and gambling-related harm, particularly in Australian adult samples [, , ]. Consistent with the relapse prevention model [ ], the focus of intervention activities in this intervention was to explore the validity and reality of positive outcome expectancies by contrasting the possible immediate positive consequences with the delayed negative consequences of gambling [ ]. Meta-analytic evidence supports the efficacy of such expectancy challenge interventions for alcohol abuse prevention [ ]. Each type of expectancy comprises 6 activities, including behavioral (eg, self-monitoring, personalized normative feedback, psychoeducation, progressive muscle relaxation, and behavioral substitution), cognitive (eg, decisional balance, imagery, and expectancy challenging), and third-wave (eg, mindfulness meditation, and cognitive defusion) activities to redress these positive outcome expectancies.
No Intervention Control Condition
In the MRT, the no intervention control condition involves participants being delivered a brief encouraging message, after which their interaction with the app will end. This tailored message involves acknowledgment that participants have an urge, low self-efficacy in high-risk situations, or positive outcome expectancies, as well as encouragement to consider doing something to reduce their urge, avoid or cope with high-risk situations, or reduce their expectations. In this condition, the participants are not provided with any intervention activities.
The decision rules are illustrated in. At each decision point during the 28-day MRT, responses to EMA items assessing craving intensity (tailoring variable 1), self-efficacy (tailoring variable 2), and positive outcome expectancies (tailoring variable 3) are used to determine eligibility for the delivery of the tailored intervention, according to a set of predetermined decision rules (scoring >0 on each tailoring variable). At each decision point, participants who do not exceed the cut point for any of the tailoring variables are not eligible for any intervention. These participants will receive a brief encouraging message and their interaction with the app will end. In contrast, participants who exceed the cut point on one or more of the tailoring variables are eligible for a tailored intervention (ie, curbing craving, tackling triggers, or exploring expectancies).
The microrandomization procedure will then be applied, whereby eligible participants will be microrandomized to a tailored intervention condition or no intervention control condition (). Overall, the microrandomization procedure will involve eligible participants having a 75% chance of being microrandomized in the tailored intervention condition and a 25% chance of being microrandomized in the no intervention control condition. However, because the reformulated relapse prevention model does not presume that certain factors are more influential than others [ ], participants exceeding the cut point on more than one of these tailoring variables will be randomly allocated to a relevant intervention option (curbing cravings, tackling triggers, or exploring expectancies). Specifically, participants who are eligible for 2 intervention options will have a 37.5% chance of receiving either intervention option, and those who are eligible for 3 intervention options will have a 25% chance of receiving any of the intervention options. This microrandomization protocol is fully automated, which guarantees that the administration of treatments and assessment of outcomes are blinded.
Following completion of each EMA, participants who are microrandomized to the no intervention control condition will be sent an encouraging message, and their interaction with the app will end. Participants microrandomized to the tailored intervention condition will be sent to the relevant intervention dashboard, which comprises a menu of intervention activities from which they can select. Specifically, participants who are allocated to the craving intervention will be taken to the craving intervention dashboard and asked to select an intervention activity. In contrast, participants who are allocated to the self-efficacy and expectancies interventions will be asked to select a specific type of trigger or expectation, administered an EMA item specific to their selected trigger or expectation (), then taken to the relevant intervention dashboard, and asked to select an intervention activity.
Following the completion of an intervention activity, participants are asked to complete the specific EMA item associated with the intervention group to which they were allocated (postintervention EMA item;). Their response to the postintervention EMA item is then subjected to the same decision rules used for the time-based EMA. Participants who fail to reach the cut point on this postintervention EMA item will be presented with an encouraging message, and their interaction with the app will end. Participants who exceed the cut point (ie, score one or more) on the specific EMA item will be presented with a feedback message in which their response to their postintervention EMA item is compared with their time-based EMA response on the same item, encouraged to select another intervention activity, and returned to the relevant intervention dashboard. This intervention loop continues until the participant fails to exceed the cut point or closes the app ( ). At several locations within the app, as well as in the welcome email and trial plain language statements, participants are informed that they can stop the loop at any time by closing the app to ensure that they do not adjust their response to break the loop.
Provide Nothing Option
Importantly, a provide nothing option is provided for situations in which the participant is unreceptive, support is not required, or the provision of support may be unsafe, inconvenient, or unethical [- , ]. Specifically, support will not be offered if participants ignore the push notification prompting EMA completion or press the snooze function to indicate that they are currently unable to complete the EMA (which suggests that they are not in a state of receptivity).
Other App Features
Within-Group Follow-up Evaluation
A within-group evaluation of outcomes over a 6-month follow-up period will supplement the MRT to (1) evaluate within-group change over a longer period and (2) explore predictors of longer-term treatment outcomes (including app use over the 6-month follow-up period). Although the preintervention survey will be automated via the app before beginning the 28-day MRT period, participants will be prompted by email to complete the postintervention and follow-up surveys via Qualtrics (Qualtrics XM). Descriptive and covariate measures will include participant type (gambler, family member or friend, and clinician, researcher, or policy maker), sociodemographic characteristics, problem gambling activities, intended gambling behavior (measured using the Timeline Follow-Forward, a novel adaptation of the Timeline Follow-Back ), and other help-seeking (measured using the Help Seeking Questionnaire [ ]). The primary outcome will be gambling symptom severity (measured using the Gambling Symptom Assessment Scale [ ]), and secondary outcomes will include gambling frequency and expenditure (measured using a timeline follow-back at the preintervention evaluation, the EMA event record data at the posttreatment evaluation, and single items at the 6-month evaluation) and the cognitive processes targeted by the intervention: craving severity (measured using the Penn Gambling Craving Scale [ ]), self-efficacy (measured using the Brief Situational Confidence Questionnaire-Gambling [ ]), and positive outcome expectancies (measured using the Excitement, Escape, and Money subscales of the Gambling Outcome Expectancies Scale [ ]; ). Each evaluation will be completed in approximately 10 to 15 minutes. Ideally, follow-up evaluation surveys will also be conducted 12 and 24 months after the intervention, but this will be dependent on obtaining additional funding.
A follow-up protocol will be implemented to enhance the completion of the postintervention and 6-month follow-up surveys: (1) an email requesting survey completion, (2) a reminder email requesting survey completion within a week to participants who fail to complete the survey in the subsequent 1-week period, and (3) up to 2 reminder telephone calls by clinically or qualitatively trained research fellows to participants who fail to complete the survey in the subsequent 3-week period (with a follow-up SMS text message if no answer or a further follow-up email if no valid phone number was provided). An advance notice email will also be sent 1 week before the 6-month surveys are sent. At each time point, the option to complete the survey over the phone with a trained research fellow will be offered. Participants who fail to complete the surveys following this protocol will receive no further contact, but participants failing to complete the postintervention survey will be contacted to request the completion of the 6-month follow-up survey.
During the 6-month follow-up evaluation period, tailored intervention content will be available to participants on demand. Although Nahum-Shani et al  defines JITAI designs as a push intervention approach, participant-determined features may accommodate conditions in which participants know when and what type of support is required, promote autonomy by facilitating agency and control, reduce waste and disruption, generalize learned skills, and maintain therapeutic gains [ , - , , ]. During this period, participants will not receive push notifications to complete EMAs and the microrandomization protocol will not be applied (ie, all participants will be allocated to the tailored intervention condition, with the no intervention control condition turned off). However, they will be able to access tailored intervention content via participant-initiated EMAs (ie, they can complete an EMA at any time, which will direct them to tailored intervention content). This approach has been adopted to encourage participants to incorporate coping skills in their everyday lives when they recognize states of vulnerability or opportunity and are motivated to initiate access to support [ - , ]. The degree to which app use across the 6-month follow-up period influences longer-term treatment outcomes will be explored.
The acceptability of GamblingLess: In-The-Moment, operationalized as a multifaceted construct that reflects the extent to which participants consider the intervention to be appropriate based on the cognitive and emotional responses they have to the intervention , will be explored using multiple methodologies. Postintervention surveys will evaluate the subjective quality and perceived impact of the JITAI using the 4-item subjective quality and 6-item perceived impact subscales of the Mobile App Rating System [ ], as well as the perceived helpfulness of additional features (eg, in-person support, web-based discussion boards, motivational messages, feedback, and push and pull features), EMA frequency, and program duration. App use and engagement indices will be used across the 28-day MRT and 6-month follow-up period to explore download information, onboarding information, app use information (eg, EMA compliance, intervention eligibility, intervention compliance, participant retention, and intervention activities selected), and the use of specific app features (eg, intervention loop, Pick For Me, and Get More Support). Semistructured interviews will be conducted with a minimum of 10 participants from the MRT to explore the reasons for using the app, program duration, EMA frequency and timing, perceived helpfulness of the intervention activities and specific features, perceived helpfulness of additional features, impact on behavior change, the app’s look and feel, and areas for improvement. Given the funding source, participants from New South Wales will be prioritized and stratified according to gender and app use. These interviews, which will be conducted at the end of the 28-day MRT, will be conducted by clinically or qualitatively trained research fellows via video conferencing or telephone. Interviews will be audio recorded for transcription and data analysis purposes, and data will be analyzed using thematic analysis at a semantic level based on the guidelines by Braun and Clarke [ ].
To assess the research questions, the method of generalized estimating equations (GEE) will be used, with an appropriate link function for the outcome of interest (eg, identity and logit). Although the intention is to use an exchangeable working correlational structure for analyses, alternative correlational structures based on the observed within-person correlation pattern over the course of the study (eg, independent or autoregressive) will be considered. For all MRT analyses, the (lagged) outcome of interest (eg, gambling episode at Timet+1) will be regressed on to a variable denoting the intervention received (eg, intervention options 1, 2, and 3 or no intervention) at Timet, as well as covariates (including unbalanced time, time since prior assessment, and other forms of help-seeking). To assess the primary research question and secondary research question 1, the analyses will examine the effects of the tailored intervention versus no intervention control on the probability of a subsequent gambling episode, stratified analyses focusing on each intervention option separately versus no intervention control (on the specific outcome related to each intervention option; ie, craving intensity, self-efficacy, and positive outcome expectancies), and formal tests comparing the magnitude of each intervention with the no intervention condition on the probability of a subsequent gambling episode. Secondary research questions 2 and 3 will be examined by specifying the interaction terms between the intervention variable and the interaction variable of interest (eg, psychological distress and time). Consideration will be given to making appropriate adjustments in line with modern causal methods for assessing effect moderation .
To explore the long-term outcomes of the intervention (within-group follow-up evaluation), distal outcomes will be assessed using GEE by regressing the outcome of interest (eg, gambling symptom severity) on a variable denoting time (ie, preintervention, postintervention, 6-month follow-up variables) and covariates. The factors associated with longer-term treatment outcomes will also be explored using GEE by regressing the outcome of interest (eg, clinically significant change in gambling symptom severity) with selected preintervention, postintervention, and app use variables (eg, the number of participant-initiated EMAs completed in the 6-month follow-up period). Where appropriate, missingness will be addressed using multiple imputation, with appropriate accounting for the multilevel nature of the data (eg, see multilevel multiple imputation ).
Supplementary analyses will consider the clinical significance of any effect (ie, meaningful changes in the participant’s life) . Effect sizes will be calculated for all primary and secondary outcomes. These group-level examinations of effectiveness will also be supplemented by metrics of individual-level change for all primary and secondary outcomes. A reliable change index will be used to assess changes beyond those attributable to chance or measurement error [ ]. Clinically significant change, as outlined by Jacobson and Truax [ ], will subsequently be calculated using functional score ranges where possible (Gambling Symptom Assessment Scale score of ≤20) or a convention of at least a 25% change in scores in the positive direction [ ]. At the posttreatment and 6-month follow-up evaluations, each participant’s status will be defined as recovered (final score falls into the functional range and corresponds to a reliable change), improved (final score falls into the dysfunctional range and corresponds to a reliable change), unchanged (final score does not correspond to a reliable change), or deteriorated (final score corresponds to a relative change in the negative direction).
The project aims to recruit a sample of 200 participants. A sample size of 120 participants would provide >80% power to detect a small true intervention effect size for the primary binary outcome (ie, gambling episode) of relative risk=1.21 (at Cronbach α=.05; availability parameter=0.3; randomization P=.25; outcome without intervention P=.25) [, ]. In GamblingLess: Curb Your Urge, attrition was 39% when considering participants who completed the baseline measures and 19% when considering participants who downloaded the app after completing the baseline measures [ ]. As such, this project will provide sufficient power to detect true effects even under a conservative attrition rate of 40% from the original sample of 200 participants at baseline.
Development of GamblingLess: In-The-Moment
Development and evaluation of GamblingLess: In-The-Moment was funded in June 2019 by the New South Wales Government’s Responsible Gambling Fund. The development of GamblingLess: In-The-Moment involved a multidisciplinary team comprising behavior change expertise from clinical and social psychology, implementation science, biostatistics and research design, and technology developers, consistent with recommendations for technology development . GamblingLess: In-The-Moment is part of a suite of theoretically informed and evidence-based web-based and mobile gambling interventions (GamblingLess). The development of the treatment content was led by the NAD, a clinical psychologist, and the app was hosted on the Cogniss behavior change platform, which was created by 2and2, specialist developers of custom tech solutions for learning, health, and behavior change. Illustrative screenshots of JITAI are displayed in .
User Testing of GamblingLess: In-The-Moment
Following its development, GamblingLess: In-The-Moment was subjected to user testing with 13 gambling stakeholders from June 2021 to July 2021, including 5 gambling clinicians, 5 gambling researchers, and 3 gambling consumers, who had scores in the problem gambling range of the Problem Gambling Severity Index (mean 18.7, SD 10.6). These user-testing participants comprised 5 men and 8 women, who tested the app on both Android (4/13, 31%) and Apple (9/13, 69%) devices. They were reimbursed with a US $35 e-gift voucher to download the app, create an account, test that the app functions as intended over a 3-day period, and evaluate the acceptability of the app via a Qualtrics XM survey (). Participants completed the Mobile App Rating Scale [ ], which comprises 23 items across 4 subscales measuring the overall quality of the app, the subjective quality of the app, and the perceived impact of the app [ ].
Participants rated the user-testing version of GamblingLess: In-The-Moment over a minimum acceptability score of 3 for all Mobile App Rating Scale subscales, suggesting that the app can be recommended for reducing gambling symptom severity . All tailored interventions were rated highly in terms of ease of completion (>8 out of 10) and helpfulness (>7 out of 10). Users also rated specific features highly (out of 10), particularly the helpfulness of the Click to Call/Email (mean 8.9, SD 1.3), Pick for Me option (mean 8.6, SD 1.8), and Did You Know? messages (mean 8.5, SD 1.3). Qualitative data were also generally positive, with positive comments relating to comprehensive, accurate, and concise information; ease of use; strategy range; helpfulness, practicality, and interactivity; the customization of feedback based on participant responses; the use of accessible and respectful language; the look and feel of the app, including graphics, multimedia, and interactivity; and the Pick For Me function. Participants generally indicated that the app would be a great resource, both as a stand-alone and adjunct intervention. Although few content or flow issues were identified, participants commented on several technical issues, predominantly in relation to notification frequency, the functionality of the deep link from the notification to the EMA, the accuracy of the check-in duration windows, and loading times (particularly in relation to Timeline Follow-Back and Timeline Follow-Forward calendars). These issues were resolved before the app was released for evaluation.
GamblingLess: In-The-Moment is available for download during this trial on Apple (App Store) and Android (Google Play Store) devices. Following advertising on March 22, 2022, a total of 3 pilot participants were recruited to ensure that the app and evaluation protocols functioned as intended. Advertising for the trial began on March 29, 2022. As of June 24, 2022, a total of 84 participants had been recruited for the trial. The results are expected to be published mid- to late-2024.
This project aims to redress the gap in existing service provision by developing and evaluating a theoretically informed and evidence-based JITAI for gamblers who want to quit or gamble less. Consistent with JITAI development recommendations , GamblingLess: In-The-Moment was developed by a multidisciplinary team involving clinical and social psychology, implementation science, biostatistics and research design, and technology development. It uses decision rules specifying that individuals who are in a state of receptivity (available for treatment) and report a state of cognitive vulnerability characterized by high craving intensity, lowered self-efficacy, and positive outcome expectancies (tailoring variables) in time-based EMAs sent 3 semirandom times a day (decision points) are delivered tailored cognitive behavioral and third-wave interventions targeting these cognitive processes (intervention options). The JITAI will tailor the type, timing, and amount of support for individual needs.
The evaluation of the JITAI will involve a 28-day MRT, in which the JITAI will be evaluated as an entirely push intervention approach. Information from the MRT will be used to optimize GamblingLess: In-The-Moment to make it a more effective, efficient, and scalable intervention. This trial will provide important empirical data to identify more refined decision rules specifying which intervention options should be delivered as well as to explore when and for whom the intervention is effective [, ]. This may involve discarding less effective or more burdensome options, delivering the intervention during specific internal states and ecological contexts, or modifying the timing and cut points for each tailoring variable [ - ].
Evaluations of JITAIs, particularly those using MRTs, generally preclude evaluations of long-term outcomes. Therefore, MRT will be supplemented with a 6-month within-group follow-up evaluation to assess and predict long-term outcomes. During this 6-month period, participants will be able to access the intervention content on demand via participant-initiated EMAs when they recognize states of vulnerability or opportunity and are motivated to initiate access to support. Unlike many other JITAI evaluations , within-group follow-up evaluations will also facilitate the consideration of the clinical impact of GamblingLess: In-The-Moment in addition to the statistical significance of the findings.
Consistent with recommendations [, ], the development of GamblingLess: In-The-Moment used an iterative and user-centered approach to its design. The intervention has been subject to stakeholder user testing, which suggests that the JITAI is an acceptable gambling intervention, and subsequent consideration of user-testing feedback. The acceptability of the intervention will also be explored in the trial, using both qualitative and quantitative methods. This information can be used to evaluate participants’ perceptions related to the app’s subjective quality and perceived impact. It will also inform the future development of the app by providing information relating to individual intervention activities, the app’s specific features, and additional features that could be included in future iterations of the app. For example, it may be that participants indicate a preference for a more traditional pull approach, in which they initiate intervention access when they require support, or the addition of some participant-determined features or on-demand intervention content to this JITAI [ ]. The addition of such features may accommodate situations in which individuals recognize states of vulnerability, thereby maintaining therapeutic gains by encouraging coping skills in everyday life, enhancing generalization of learned skills, promoting autonomy by facilitating agency and control, and involving less disruption and burden [ , - , ]. Similarly, participants may indicate a preference for the addition of human support via the involvement of clinicians, guides, coaches, or peers [ , - ] or digital avatars in the form of personal coaches and assistants [ ], which may enhance motivation, engagement, and adherence to the requirements of the intervention [ ]. The acceptability evaluation will also offer the opportunity to explore participant preferences for program duration, as well as the frequency and timing of EMAs. For example, there is a risk that 3 EMAs each day are an obstacle to sustained engagement or that restricting the timing of the EMAs to daytime hours is not aligned with high-risk situations occurring outside these hours. This information is therefore particularly important to inform the limited evidence base regarding transitory changes in the presence of urges or cravings, self-efficacy in high-risk situations, or positive outcome expectancies.
This study will be one of the first to examine the effectiveness of real-time support for reducing gambling behavior and the first to achieve this by comprehensively addressing the cognitive processes underlying gambling lapses and relapses, as articulated by the highly influential relapse prevention model. Given that JITAI development and evaluation of gambling problems is an emerging area of research, this study can establish an evidence base for future research using optimized interventions. For example, future research could establish the efficacy of interventions when human support is added [, - ], with an emphasis on when and for whom human support adds value given the effectiveness and lower cost of unguided interventions [ , ]. As recommended by Nahum-Shani et al [ ], future research is required to explore how best to add participant-initiated or on-demand features to this JITAI to ensure that personal volition is balanced with planned, externally initiated support. Future iterations of GamblingLess: In-The-Moment could also combine EMA data with tailoring variables from passive assessments from sensors or other technologies to provide additional contextual information, lower user burden, and enhance user awareness of behavior [ , ]. Future research using cost evaluation analyses that weigh the relative costs and outcomes of GamblingLess: In-The-Moment with other interventions could inform health care resource allocation decisions [ ], and the efficacy of this intervention delivered as a transdiagnostic intervention to address the cognitive processes underlying all addictions could improve treatment efficacy, efficiency, and cost-effectiveness [ ]. Finally, there is scope for harnessing machine learning approaches to develop accurate models identifying response patterns that predict the risk of unplanned gambling in real time, ideally with respect to targeting factors contributing to the risk of gambling for each individual [ ].
Dissemination of Findings
The findings of this evaluation of GamblingLess: In-The-Moment will be disseminated in peer-reviewed journal articles, conference presentations, stakeholder forums, and professional development seminars. A summary of the findings of the trial will be available on the GamblingLess website when they become available .
This project is a collaboration between Deakin University and its partner organizations (University of Auckland, Turning Point, and 2and2 [app developers]), funded by the New South Wales Government’s Responsible Gambling Fund. The funding body played no role in the study design or writing of the manuscript.
The authors thank the research fellows, Mingjun Yang, Stephanie Dias, George Loram, and Natalia Booth, for assisting with project management, app testing, and content upload. The authors also thank 2and2 for their ongoing support with the technical development of the app and their input into the technical aspects of the manuscript, particularly Anna Goldfeder, Randy Olan, Colin Walker, Arun Ramalingam, Leon Young, and Toby Wong. They thank Rick Loos for his assistance with the Get More Support functionality and the user-testing component and the clinicians, researchers, and consumers who took part in the user testing. Finally, they thank the New South Wales Office of Responsible Gambling for its support, particularly John McInerney, Rhonda Blackett, and Natalie Wright.
The development of the GamblingLess: In-The-Moment intervention is led by NAD (Deakin University) and is part of a suite of theoretically informed and evidence-based web-based and mobile gambling interventions (GamblingLess). NAD, with support from SSM and SNR, conceived the project, developed the methodology, and acquired funding. NAD wrote the first draft of the manuscript, with statistical advice from GJY. All the authors contributed to the final draft of the manuscript.
Conflicts of Interest
The authors have no conflicts of interest to declare in relation to this article.
The 3-year declaration of interest statement of this research team is as follows: NAD, SSM, GJY, DIL, ACT, SNR Delaware, KLB, and ACT received funding from multiple sources, including government departments and the Victorian Responsible Gambling Foundation (through hypothecated taxes from gambling revenue), and the International Center for Responsible Gaming, a charitable organization that derives its funding from multiple stakeholders (with funding decisions the responsibility of a scientific advisory board). SSM is the recipient of the New South Wales Office of a Responsible Gambling Postdoctoral Fellowship. None of the authors have received research funding from the gambling, tobacco, alcohol industries, or any industry-sponsored organization. The authors have no conflicts of interest to declare in relation to this article.
GamblingLess: In-The-Moment ecological momentary assessments items.DOCX File , 16 KB
Overview of the GamblingLess: In-The-Moment intervention options.DOCX File , 14 KB
GamblingLess: In-The-Moment intervention eligibility.DOCX File , 14 KB
GamblingLess: In-The-Moment intervention eligibility.DOCX File , 21 KB
GamblingLess: In-The-Moment user-testing scores.DOCX File , 15 KB
- American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th edition. Arlington, VA, USA: American Psychiatric Association; 2013.
- Shaffer HJ, Korn DA. Gambling and related mental disorders: a public health analysis. Annu Rev Public Health 2002;23:171-212. [CrossRef] [Medline]
- Neal P, Delfabbro P, O'Neil M. Problem gambling and harm: toward a national definition. Department of Justice, State of Victoria. Melbourne, Australia: Office of Gaming and Racing; 2005. URL: https://www.semanticscholar.org/paper/Problem-gambling-and-harm%3A-Towards-a-national-Neal-Delfabbro/24dfce5370a6efe131b70dc97b7dd589114e7b6e [accessed 2006-01-01]
- Calado F, Griffiths MD. Problem gambling worldwide: an update and systematic review of empirical research (2000-2015). J Behav Addict 2016 Dec;5(4):592-613 [FREE Full text] [CrossRef] [Medline]
- Dowling NA, Youssef GJ, Jackson AC, Pennay DW, Francis KL, Pennay A, et al. National estimates of Australian gambling prevalence: findings from a dual-frame omnibus survey. Addiction 2016 Mar;111(3):420-435 [FREE Full text] [CrossRef] [Medline]
- Gainsbury SM, Russell A, Hing N, Wood R, Lubman DI, Blaszczynski A. The prevalence and determinants of problem gambling in Australia: assessing the impact of interactive gambling and new technologies. Psychol Addict Behav 2014 Sep;28(3):769-779. [CrossRef] [Medline]
- Abbott M. Gambling and gambling harm in New Zealand: a 28-year case study. Int J Ment Health Addiction 2017 May 1;15(6):1221-1241. [CrossRef]
- Browne M, Greer N, Rawat V, Rockloff M. A population-level metric for gambling-related harm. Int Gambl Stud 2017 Mar 31;17(2):163-175. [CrossRef]
- Langham E, Thorne H, Browne M, Donaldson P, Rose J, Rockloff M. Understanding gambling related harm: a proposed definition, conceptual framework, and taxonomy of harms. BMC Public Health 2016 Jan 27;16:80 [FREE Full text] [CrossRef] [Medline]
- Lorains FK, Cowlishaw S, Thomas SA. Prevalence of comorbid disorders in problem and pathological gambling: systematic review and meta-analysis of population surveys. Addiction 2011 Mar;106(3):490-498. [CrossRef] [Medline]
- Dowling NA, Cowlishaw S, Jackson AC, Merkouris SS, Francis KL, Christensen DR. Prevalence of psychiatric co-morbidity in treatment-seeking problem gamblers: a systematic review and meta-analysis. Aust N Z J Psychiatry 2015 Jun;49(6):519-539 [FREE Full text] [CrossRef] [Medline]
- Dowling NA, Cowlishaw S, Jackson AC, Merkouris SS, Francis KL, Christensen DR. The prevalence of comorbid personality disorders in treatment-seeking problem gamblers: a systematic review and meta-analysis. J Pers Disord 2015 Dec;29(6):735-754. [CrossRef] [Medline]
- Marlatt GA, Gordon JR. Relapse Prevention: Maintenance Strategies in the Treatment of Addictive Behaviors. New York, NY, USA: The Guilford Press; 1985.
- Larimer ME, Palmer RS, Marlatt GA. Relapse prevention. An overview of Marlatt's cognitive-behavioral model. Alcohol Res Health 1999;23(2):151-160 [FREE Full text] [Medline]
- Witkiewitz K, Marlatt GA. Relapse prevention for alcohol and drug problems: that was Zen, this is Tao. Am Psychol 2004;59(4):224-235. [CrossRef] [Medline]
- Menon J, Kandasamy A. Relapse prevention. Indian J Psychiatry 2018 Feb;60(Suppl 4):S473-S478 [FREE Full text] [CrossRef] [Medline]
- Canale N, Cornil A, Giroux I, Bouchard S, Billieux J. Probing gambling urge as a state construct: evidence from a sample of community gamblers. Psychol Addict Behav 2019 Mar;33(2):154-161. [CrossRef] [Medline]
- Skinner MD, Aubin HJ. Craving's place in addiction theory: contributions of the major models. Neurosci Biobehav Rev 2010 Mar;34(4):606-623. [CrossRef] [Medline]
- Kavanagh DJ, Andrade J, May J. Imaginary relish and exquisite torture: the elaborated intrusion theory of desire. Psychol Rev 2005 Apr;112(2):446-467. [CrossRef] [Medline]
- May J, Andrade J, Panabokke N, Kavanagh D. Images of desire: cognitive models of craving. Memory 2004 Jul;12(4):447-461. [CrossRef] [Medline]
- Cornil A, Lopez-Fernandez O, Devos G, de Timary P, Goudriaan AE, Billieux J. Exploring gambling craving through the elaborated intrusion theory of desire: a mixed methods approach. Int Gambl Stud 2017 Sep 13;18(1):1-21. [CrossRef]
- Iliceto P, Fino E, Schiavella M, Oei TP. Psychometric properties of the Italian versions of the Gambling Urge Scale (GUS) and the Gambling Refusal Self-Efficacy Questionnaire (GRSEQ). Int J Ment Health Addiction 2019 Dec 10;19(1):207-226. [CrossRef]
- Oei TP, Goh Z. Interactions between risk and protective factors on problem gambling in Asia. J Gambl Stud 2015 Jun 7;31(2):557-572. [CrossRef] [Medline]
- Oei TP, Gordon LM. Psychosocial factors related to gambling abstinence and relapse in members of gamblers anonymous. J Gambl Stud 2008 Mar;24(1):91-105. [CrossRef] [Medline]
- Tavares H, Zilberman ML, Hodgins DC, el-Guebaly N. Comparison of craving between pathological gamblers and alcoholics. Alcohol Clin Exp Res 2005 Aug;29(8):1427-1431. [CrossRef] [Medline]
- Hodgins DC, Currie S, el-Guebaly N, Peden N. Brief motivational treatment for problem gambling: a 24-month follow-up. Psychol Addict Behav 2004 Sep;18(3):293-296. [CrossRef] [Medline]
- Oakes J, Pols R, Battersby M, Lawn S, Pulvirenti M, Smith D. A focus group study of predictors of relapse in electronic gaming machine problem gambling, part 1: factors that 'push' towards relapse. J Gambl Stud 2012 Sep;28(3):451-464. [CrossRef] [Medline]
- Oakes J, Pols R, Battersby M, Lawn S, Pulvirenti M, Smith D. A focus group study of predictors of relapse in electronic gaming machine problem gambling, part 2: factors that 'pull' the gambler away from relapse. J Gambl Stud 2012 Sep;28(3):465-479. [CrossRef] [Medline]
- Smith DP, Battersby MW, Pols RG, Harvey PW, Oakes JE, Baigent MF. Predictors of relapse in problem gambling: a prospective cohort study. J Gambl Stud 2015 Mar;31(1):299-313. [CrossRef] [Medline]
- McIntosh CC, Crino RD, O'Neill K. Treating problem gambling samples with cognitive behavioural therapy and mindfulness-based interventions: a clinical trial. J Gambl Stud 2016 Dec;32(4):1305-1325. [CrossRef] [Medline]
- Myrseth H, Brunborg GS, Eidem M, Pallesen S. Description and pre-post evaluation of a telephone and Internet based treatment programme for pathological gambling in Norway: a pilot study. Int Gambl Stud 2013 Aug;13(2):205-220. [CrossRef]
- Smith DP, Battersby MW, Harvey PW, Pols RG, Ladouceur R. Cognitive versus exposure therapy for problem gambling: randomised controlled trial. Behav Res Ther 2015 Jun;69:100-110. [CrossRef] [Medline]
- Smith D, Fairweather-Schmidt AK, Pols R, Harvey P, Battersby M. Exploring patterns of change processes over distinct in-treatment phases of cognitive and exposure therapies for electronic gaming machine problem gamblers. Behav Change 2018 Aug 06;35(4):228-243. [CrossRef]
- Smith D, Harvey P, Battersby M, Pols R, Oakes J, Baigent M. Treatment outcomes and predictors of drop out for problem gamblers in South Australia: a cohort study. Aust N Z J Psychiatry 2010 Oct;44(10):911-920. [CrossRef] [Medline]
- Grant JE, Donahue CB, Odlaug BL, Kim SW, Miller MJ, Petry NM. Imaginal desensitisation plus motivational interviewing for pathological gambling: randomised controlled trial. Br J Psychiatry 2009 Sep;195(3):266-267 [FREE Full text] [CrossRef] [Medline]
- Toneatto T, Pillai S, Courtice EL. Mindfulness-enhanced cognitive behavior therapy for problem gambling: a controlled pilot study. Int J Ment Health Addiction 2014 Feb 15;12(2):197-205. [CrossRef]
- McConaghy N, Armstrong MS, Blaszczynski A, Allcock C. Controlled comparison of aversive therapy and imaginal desensitization in compulsive gambling. Br J Psychiatry 1983 Apr;142:366-372. [CrossRef] [Medline]
- McConaghy N, Armstrong MS, Blaszczynski A, Allcock C. Behavior completion versus stimulus control in compulsive gambling. Implications for behavioral assessment. Behav Modif 1988 Jul;12(3):371-384. [CrossRef] [Medline]
- Casey LM, Oei TP, Raylu N, Horrigan K, Day J, Ireland M, et al. Internet-based delivery of cognitive behaviour therapy compared to monitoring, feedback and support for problem gambling: a randomised controlled trial. J Gambl Stud 2017 Sep;33(3):993-1010. [CrossRef] [Medline]
- Castrén S, Pankakoski M, Tamminen M, Lipsanen J, Ladouceur R, Lahti T. Internet-based CBT intervention for gamblers in Finland: experiences from the field. Scand J Psychol 2013 Jun;54(3):230-235 [FREE Full text] [CrossRef] [Medline]
- Dowling N, Merkouris S, Rodda S, Smith D, Lavis T, Lubman D, et al. Development and evaluation of an online gambling self-directed program: effective integration into existing services. Gambling Research Exchange (GREO). Melbourne, Australia: Victorian Responsible Gambling Foundation; 2018. URL: https://responsiblegambling.vic.gov.au/resources/publications/development-and-evaluation-of-an-online-gambling-self-directed-program-effective-integration-into-existing-services-348/ [accessed 2018-04-01]
- Shead NW, Champod AS, MacDonald A. Effect of a brief meditation intervention on gambling cravings and rates of delay discounting. Int J Ment Health Addiction 2019 Sep 10;18(5):1247-1263. [CrossRef]
- Dowling NA, Merkouris SS, Rodda SN, Smith D, Aarsman S, Lavis T, et al. Gamblingless: a randomised trial comparing guided and unguided Internet-based gambling interventions. J Clin Med 2021 May 21;10(11):2224 [FREE Full text] [CrossRef] [Medline]
- Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev 1977 Mar;84(2):191-215. [CrossRef] [Medline]
- May RK, Whelan JP, Steenbergh TA, Meyers AW. The gambling self-efficacy questionnaire: an initial psychometric evaluation. J Gambl Stud 2003;19(4):339-357. [CrossRef] [Medline]
- Breslin FC, Sobell LC, Sobell MB, Agrawal S. A comparison of a brief and long version of the Situational Confidence Questionnaire. Behav Res Ther 2000 Dec;38(12):1211-1220. [CrossRef] [Medline]
- Barbaranelli C, Ghezzi V, Fida R, Vecchione M. Psychometric characteristics of a new scale for measuring self-efficacy in the regulation of gambling behavior. Front Psychol 2017 Jun 20;8:1025 [FREE Full text] [CrossRef] [Medline]
- Casey LM, Oei TP, Melville KM, Bourke E, Newcombe PA. Measuring self-efficacy in gambling: the Gambling Refusal Self-Efficacy Questionnaire. J Gambl Stud 2008 Jun;24(2):229-246. [CrossRef] [Medline]
- Hodgins D, Peden N, Makarchuk K. Self-efficacy in pathological gambling treatment outcome: development of a gambling abstinence self-efficacy scale (GASS). Int Gambl Stud 2004 Nov;4(2):99-108. [CrossRef]
- Lai MH, Wu AM, Tong KK. Validation of the Gambling Refusal Self-Efficacy Questionnaire for Chinese undergraduate students. J Gambl Stud 2015 Mar;31(1):243-256. [Medline]
- Wu AM, Tang CS. Problem gambling of Chinese college students: application of the theory of planned behavior. J Gambl Stud 2012 Jun;28(2):315-324. [CrossRef] [Medline]
- Winfree WR, Ginley MK, Whelan JP, Meyers AW. Psychometric evaluation of the gambling self-efficacy questionnaire with treatment-seeking pathological gamblers. Psychol Addict Behav 2014 Dec;28(4):1305-1310. [CrossRef] [Medline]
- Tang CS, Oei TP. Gambling cognition and subjective well-being as mediators between perceived stress and problem gambling: a cross-cultural study on White and Chinese problem gamblers. Psychol Addict Behav 2011 Sep;25(3):511-520. [CrossRef] [Medline]
- Hodgins DC, Ching LE, McEwen J. Strength of commitment language in motivational interviewing and gambling outcomes. Psychol Addict Behav 2009 Mar;23(1):122-130. [CrossRef] [Medline]
- Gomes K, Pascual-Leone A. A resource model of change: client factors that influence problem gambling treatment outcomes. J Gambl Stud 2015 Dec;31(4):1651-1669. [CrossRef] [Medline]
- Merkouris SS, Thomas SA, Browning CJ, Dowling NA. Predictors of outcomes of psychological treatments for disordered gambling: a systematic review. Clin Psychol Rev 2016 Aug;48:7-31 [FREE Full text] [CrossRef] [Medline]
- Ladouceur R, Sylvain C, Boutin C, Lachance S, Doucet C, Leblond J. Group therapy for pathological gamblers: a cognitive approach. Behav Res Ther 2003 May;41(5):587-596. [CrossRef] [Medline]
- Ladouceur R, Sylvain C, Boutin C, Lachance S, Doucet C, Leblond J, et al. Cognitive treatment of pathological gambling. J Nerv Ment Dis 2001 Nov;189(11):774-780. [CrossRef] [Medline]
- Marceaux JC, Melville CL. Twelve-step facilitated versus mapping-enhanced cognitive-behavioral therapy for pathological gambling: a controlled study. J Gambl Stud 2011 Mar;27(1):171-190. [CrossRef] [Medline]
- Sylvain C, Ladouceur R, Boisvert JM. Cognitive and behavioral treatment of pathological gambling: a controlled study. J Consult Clin Psychol 1997 Oct;65(5):727-732. [CrossRef] [Medline]
- Boudreault C, Giroux I, Jacques C, Goulet A, Simoneau H, Ladouceur R. Efficacy of a self-help treatment for at-risk and pathological gamblers. J Gambl Stud 2018 Jun;34(2):561-580. [CrossRef] [Medline]
- Hodgins DC, Currie SR, Currie G, Fick GH. Randomized trial of brief motivational treatments for pathological gamblers: more is not necessarily better. J Consult Clin Psychol 2009 Oct;77(5):950-960. [CrossRef] [Medline]
- Moss AC, Albery IP. A dual-process model of the alcohol-behavior link for social drinking. Psychol Bull 2009 Jul;135(4):516-530. [CrossRef] [Medline]
- Del Prete F, Steward T, Navas JF, Fernández-Aranda F, Jiménez-Murcia S, Oei TP, et al. The role of affect-driven impulsivity in gambling cognitions: a convenience-sample study with a Spanish version of the Gambling-Related Cognitions Scale. J Behav Addict 2017 Mar 01;6(1):51-63 [FREE Full text] [CrossRef] [Medline]
- Grall-Bronnec M, Bouju G, Sébille-Rivain V, Gorwood P, Boutin C, Vénisse JL, et al. A French adaptation of the Gambling-Related Cognitions Scale (GRCS): a useful tool for assessment of irrational thoughts among gamblers. J Gambl Stud 2012 Oct 01;27:1-21. [CrossRef]
- Oei TP, Lin J, Raylu N. Validation of the Chinese version of the Gambling Related Cognitions Scale (GRCS-C). J Gambl Stud 2007 Sep;23(3):309-322. [CrossRef] [Medline]
- Oei TP, Raylu N. Familial influence on offspring gambling: a cognitive mechanism for transmission of gambling behavior in families. Psychol Med 2004 Oct;34(7):1279-1288. [CrossRef] [Medline]
- Taylor RN, Parker JD, Keefer KV, Kloosterman PH, Summerfeldt LJ. Are gambling related cognitions in adolescence multidimensional?: factor structure of the Gambling Related Cognitions Scale. J Gambl Stud 2014 Jun;30(2):453-465. [CrossRef] [Medline]
- Flack M, Morris M. Problem gambling: one for the Money…? J Gambl Stud 2015 Dec;31(4):1561-1578. [CrossRef] [Medline]
- Simmons JL, Whelan JP, Meyers AW, Wickwire EM. Gambling outcome expectancies and gambling behavior among African-American adolescents: gender as a moderating variable. J Gambl Stud 2016 Mar;32(1):205-215. [CrossRef] [Medline]
- Wickwire EM, Whelan JP, Meyers AW. Outcome expectancies and gambling behavior among urban adolescents. Psychol Addict Behav 2010 Mar;24(1):75-88. [CrossRef] [Medline]
- Gillespie MA, Derevensky JL, Gupta R. II. The utility of outcome expectancies in the prediction of adolescent gambling behaviour. J Gambl Issues 2007 Jan 01(19):69. [CrossRef]
- Ginley MK, Whelan JP, Relyea GE, Simmons JL, Meyers AW, Pearlson GD. College student beliefs about wagering: an evaluation of the adolescent gambling expectancies survey. J Gambl Stud 2015 Mar;31(1):161-171 [FREE Full text] [CrossRef] [Medline]
- St-Pierre RA, Temcheff CE, Gupta R, Derevensky J, Paskus TS. Predicting gambling problems from gambling outcome expectancies in college student-athletes. J Gambl Stud 2014 Mar;30(1):47-60. [CrossRef] [Medline]
- Browne M, Hing N, Rockloff M, Russell AM, Greer N, Nicoll F, et al. A multivariate evaluation of 25 proximal and distal risk-factors for gambling-related harm. J Clin Med 2019 Apr 13;8(4):509 [FREE Full text] [CrossRef] [Medline]
- Flack M, Stevens M. Gambling motivation: comparisons across gender and preferred activity. Int Gambl Stud 2019;19(1):69-84. [CrossRef]
- Walters GD, Contri D. Outcome expectancies for gambling: empirical modeling of a memory network in federal prison inmates. J Gambl Stud 1998;14(2):173-191. [CrossRef] [Medline]
- Flack M, Morris M. The temporal stability and predictive ability of the Gambling Outcome Expectancies Scale (GOES): a prospective study. J Gambl Stud 2016 Sep;32(3):923-933. [CrossRef] [Medline]
- Ledgerwood DM, Dyshniku F, McCarthy JE, Ostojic-Aitkens D, Forfitt J, Rumble SC. Gambling-related cognitive distortions in residential treatment for gambling disorder. J Gambl Stud 2020 Jun;36(2):669-683. [CrossRef] [Medline]
- Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol 2008;4:1-32. [CrossRef] [Medline]
- Shiffman S. Ecological momentary assessment (EMA) in studies of substance use. Psychol Assess 2009 Dec;21(4):486-497 [FREE Full text] [CrossRef] [Medline]
- Gwaltney CJ, Shiffman S, Sayette MA. Situational correlates of abstinence self-efficacy. J Abnorm Psychol 2005 Nov;114(4):649-660. [CrossRef] [Medline]
- Benitez B, Goldman MS. Using future-oriented expectancy associates to probe real-time variations in motivation to consume alcohol. Psychol Addict Behav 2019 Sep;33(6):540-551. [CrossRef] [Medline]
- Cambron C, Haslam AK, Baucom BR, Lam C, Vinci C, Cinciripini P, et al. Momentary precipitants connecting stress and smoking lapse during a quit attempt. Health Psychol 2019 Dec;38(12):1049-1058 [FREE Full text] [CrossRef] [Medline]
- Gwaltney CJ, Shiffman S, Balabanis MH, Paty JA. Dynamic self-efficacy and outcome expectancies: prediction of smoking lapse and relapse. J Abnorm Psychol 2005 Nov;114(4):661-675. [CrossRef] [Medline]
- Morgenstern J, Kuerbis A, Houser J, Muench FJ, Shao S, Treloar H. Within-person associations between daily motivation and self-efficacy and drinking among problem drinkers in treatment. Psychol Addict Behav 2016 Sep;30(6):630-638 [FREE Full text] [CrossRef] [Medline]
- Serre F, Fatseas M, Swendsen J, Auriacombe M. Ecological momentary assessment in the investigation of craving and substance use in daily life: a systematic review. Drug Alcohol Depend 2015 Mar 01;148:1-20. [CrossRef] [Medline]
- Shiffman S, Balabanis MH, Paty JA, Engberg J, Gwaltney CJ, Liu KS, et al. Dynamic effects of self-efficacy on smoking lapse and relapse. Health Psychol 2000 Jul;19(4):315-323. [CrossRef] [Medline]
- Van Zundert RM, Ferguson SG, Shiffman S, Engels RC. Dynamic effects of self-efficacy on smoking lapses and relapse among adolescents. Health Psychol 2010 May;29(3):246-254. [CrossRef] [Medline]
- Dowling NA, Merkouris SS, Spence K. Ecological momentary assessment of the relationship between positive outcome expectancies and gambling behaviour. J Clin Med 2021 Apr 15;10(8):1709 [FREE Full text] [CrossRef] [Medline]
- Gee P, Coventry KR, Birkenhead D. Mood state and gambling: using mobile telephones to track emotions. Br J Psychol 2005 Feb;96(Pt 1):53-66. [CrossRef] [Medline]
- Hawker CO, Merkouris SS, Youssef GJ, Dowling NA. Exploring the associations between gambling cravings, self-efficacy, and gambling episodes: an Ecological Momentary Assessment study. Addict Behav 2021 Jan;112:106574. [CrossRef] [Medline]
- Carpenter SM, Menictas M, Nahum-Shani I, Wetter DW, Murphy SA. Developments in mobile health just-in-time adaptive interventions for addiction science. Curr Addict Rep 2020 Sep;7(3):280-290 [FREE Full text] [CrossRef] [Medline]
- Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, et al. Just-in-Time Adaptive Interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med 2018 May 18;52(6):446-462 [FREE Full text] [CrossRef] [Medline]
- Nahum-Shani I, Hekler EB, Spruijt-Metz D. Building health behavior models to guide the development of just-in-time adaptive interventions: a pragmatic framework. Health Psychol 2015 Dec;34S:1209-1219 [FREE Full text] [CrossRef] [Medline]
- Nahum-Shani I, Smith SN, Tewari A, Witkiewitz K, Collins LM, Spring B, et al. Just-intime adaptive interventions (JITAIs): an organizing framework for ongoing health behavior support. (Technical Report No. 14-126). The Pennsylvania State University. University Park, PA, USA: The Methodology Center, Penn State; 2014. URL: https://www.semanticscholar.org/paper/Just-in-Time-Adaptive-Interventions-(JITAIs)%3A-An-Nahum-Shani-Smith/0ee1ec1c45bbdc854856db742aafd887f7f09a1f [accessed 2018-07-25]
- Walton A, Nahum-Shani I, Crosby L, Klasnja P, Murphy S. Optimizing digital integrated care via micro-randomized trials. Clin Pharmacol Ther 2018 Jul;104(1):53-58 [FREE Full text] [CrossRef] [Medline]
- Klasnja P, Hekler EB, Shiffman S, Boruvka A, Almirall D, Tewari A, et al. Microrandomized trials: an experimental design for developing just-in-time adaptive interventions. Health Psychol 2015 Dec;34S:1220-1228 [FREE Full text] [CrossRef] [Medline]
- Heron KE, Smyth JM. Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments. Br J Health Psychol 2010 Feb;15(Pt 1):1-39 [FREE Full text] [CrossRef] [Medline]
- Wang L, Miller LC. Just-in-the-Moment Adaptive Interventions (JITAI): a meta-analytical review. Health Commun 2020 Nov;35(12):1531-1544. [CrossRef] [Medline]
- Bakker D, Kazantzis N, Rickwood D, Rickard N. Mental health smartphone apps: review and evidence-based recommendations for future developments. JMIR Ment Health 2016 Mar 01;3(1):e7 [FREE Full text] [CrossRef] [Medline]
- Kim J, Marcusson-Clavertz D, Yoshiuchi K, Smyth JM. Potential benefits of integrating ecological momentary assessment data into mHealth care systems. Biopsychosoc Med 2019;13:19 [FREE Full text] [CrossRef] [Medline]
- Klasnja P, Pratt W. Healthcare in the pocket: mapping the space of mobile-phone health interventions. J Biomed Inform 2012 Feb;45(1):184-198 [FREE Full text] [CrossRef] [Medline]
- Bijker R, Booth N, Merkouris SS, Dowling NA, Rodda SN. Global prevalence of help-seeking for problem gambling: A systematic review and meta-analysis. Addiction 2022 Jul 13:15952. [CrossRef] [Medline]
- Cowlishaw S, Merkouris S, Dowling N, Anderson C, Jackson A, Thomas A. Psychological therapies for pathological and problem gambling. Cochrane Database Syst Rev 2012 Nov 14;11:CD008937. [CrossRef] [Medline]
- Petry NM, Ginley MK, Rash CJ. A systematic review of treatments for problem gambling. Psychol Addict Behav 2017 Dec;31(8):951-961 [FREE Full text] [CrossRef] [Medline]
- Thomas SA, Merkouris SS, Radermacher HL, Dowling NA, Misso ML, Anderson CJ, et al. Australian guideline for treatment of problem gambling: an abridged outline. Med J Aust 2011 Dec 19;195(11-12):664-665. [CrossRef] [Medline]
- Wilson GT, Zandberg LJ. Cognitive-behavioral guided self-help for eating disorders: effectiveness and scalability. Clin Psychol Rev 2012 Jun;32(4):343-357. [CrossRef] [Medline]
- Gainsbury S, Hing N, Suhonen N. Professional help-seeking for gambling problems: awareness, barriers and motivators for treatment. J Gambl Stud 2014 Jun;30(2):503-519. [CrossRef] [Medline]
- Suurvali H, Hodgins D, Toneatto T, Cunningham J. Treatment seeking among Ontario problem gamblers: results of a population survey. Psychiatr Serv 2008 Nov;59(11):1343-1346. [CrossRef] [Medline]
- Suurvali H, Cordingley J, Hodgins DC, Cunningham J. Barriers to seeking help for gambling problems: a review of the empirical literature. J Gambl Stud 2009 Sep;25(3):407-424. [CrossRef] [Medline]
- Humphrey G, Newcombe D, Whittaker R, Parag V, Bulleen C. SPGeTTI: a smartphone-based problem gambling evaluation and technology testing initiative final report. The University of Auckland. Auckland, New Zealand: National Institute for Health Innovation, Auckland UniServices Limited; 2019. URL: https://www.health.govt.nz/publication/spgetti-smartphone-based-problem-gambling-evaluation-and-technology-testing-initiative [accessed 2021-03-10]
- Coral R, Esposito F, Weinstock J. Don't go there: a zero-permission geofencing app to alleviate gambling disorders. In: Proceedings of the IEEE 17th Annual Consumer Communications & Networking Conference. 2020 Presented at: CCNC '20; January 10-13, 2020; Las Vegas, NV, USA p. 1-6 URL: https://ieeexplore.ieee.org/abstract/document/9045251 [CrossRef]
- Khazaal Y, Monney G, Richter F, Achab S. «Jeu-contrôle», rationnel d’une application de soutien aux limites de jeux. J Thér Comportement Cogn 2017 Sep;27(3):129-137. [CrossRef]
- Merkouris SS, Hawker CO, Rodda SN, Youssef GJ, Dowling NA. GamblingLess: Curb Your Urge: development and usability testing of a smartphone-delivered ecological momentary intervention for problem gambling. Int Gambl Stud 2020 Apr 10;20(3):515-538. [CrossRef]
- Hawker CO, Merkouris SS, Youssef GJ, Dowling NA. A smartphone-delivered ecological momentary intervention for problem gambling (GamblingLess: Curb Your Urge): single-arm acceptability and feasibility trial. J Med Internet Res 2021 Mar 26;23(3):e25786 [FREE Full text] [CrossRef] [Medline]
- Merkouris SS, Rodda SN, Austin D, Lubman DI, Harvey P, Battersby M, et al. GAMBLINGLESS: FOR LIFE study protocol: a pragmatic randomised trial of an online cognitive-behavioural programme for disordered gambling. BMJ Open 2017 Feb 23;7(2):e014226 [FREE Full text] [CrossRef] [Medline]
- Rodda SN, Merkouris S, Lavis T, Smith D, Lubman DI, Austin D, et al. The therapist experience of Internet delivered CBT for problem gambling: service integration considerations. Internet Interv 2019 Dec;18:100264 [FREE Full text] [CrossRef] [Medline]
- Humphrey G, Chu JT, Ruwhiu-Collins R, Erick-Peleti S, Dowling N, Merkouris S, et al. Adapting an evidence-based e-learning cognitive behavioral therapy program into a mobile app for people experiencing gambling-related problems: formative study. JMIR Form Res 2022 Mar 25;6(3):e32940 [FREE Full text] [CrossRef] [Medline]
- Humphrey G, Chu J, Dowling N, Rodda S, Merkouris S, Parag V, et al. Manaaki - a cognitive behavioral therapy mobile health app to support people experiencing gambling problems: a randomized control trial protocol. BMC Public Health 2020 Feb 06;20(1):191 [FREE Full text] [CrossRef] [Medline]
- Collins LM, Chakraborty B, Murphy SA, Strecher V. Comparison of a phased experimental approach and a single randomized clinical trial for developing multicomponent behavioral interventions. Clin Trials 2009 Feb;6(1):5-15 [FREE Full text] [CrossRef] [Medline]
- Collins LM, Murphy SA, Nair VN, Strecher VJ. A strategy for optimizing and evaluating behavioral interventions. Ann Behav Med 2005 Aug;30(1):65-73. [CrossRef] [Medline]
- Collins LM. Optimization of Behavioral, Biobehavioral, and Biomedical Interventions: The Multiphase Optimization Strategy (MOST). Cham, Switzerland: Springer; 2018.
- Treweek S, Zwarenstein M. Making trials matter: pragmatic and explanatory trials and the problem of applicability. Trials 2009 Jun 03;10:37 [FREE Full text] [CrossRef] [Medline]
- Dowling N, Smith D, Thomas T. A preliminary investigation of abstinence and controlled gambling as self-selected goals of treatment for female pathological gambling. J Gambl Stud 2009 Jun;25(2):201-214. [CrossRef] [Medline]
- Dowling N, Smith D. Treatment goal selection for female pathological gambling: a comparison of abstinence and controlled gambling. J Gambl Stud 2007 Sep;23(3):335-345. [CrossRef] [Medline]
- Ladouceur R. Controlled gambling for pathological gamblers. J Gambl Stud 2005;21(1):49-59. [CrossRef] [Medline]
- Goldstein SP, Evans BC, Flack D, Juarascio A, Manasse S, Zhang F, et al. Return of the JITAI: applying a just-in-time adaptive intervention framework to the development of m-health solutions for addictive behaviors. Int J Behav Med 2017 Oct;24(5):673-682 [FREE Full text] [CrossRef] [Medline]
- Kim SW, Grant JE, Potenza MN, Blanco C, Hollander E. The Gambling Symptom Assessment Scale (G-SAS): a reliability and validity study. Psychiatry Res 2009 Mar 31;166(1):76-84 [FREE Full text] [CrossRef] [Medline]
- Lostutter TW, Cronce JM. Gambling Behavioral Assessment: Improving Treatment Planning and Client Outcomes. National Council on Problem Gambling Annual Conference. Seattle, WA, USA: Center for the Study of Health & Risk Behaviors, Department of Psychiatry & Behavioral Sciences, University of Washington; 2013. URL: http://www.ncpgambling.org/files/Ty%20Lostutter%20Behavioral%20Assessment_07_18_13.pdf [accessed 2021-05-15]
- Riba MB, Donovan KA, Andersen B, Braun I, Breitbart WS, Brewer BW, et al. Distress management, version 3.2019, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw 2019 Oct 01;17(10):1229-1249 [FREE Full text] [CrossRef] [Medline]
- Rodda SN, Lubman DI, Iyer R, Gao CX, Dowling NA. Subtyping based on readiness and confidence: the identification of help-seeking profiles for gamblers accessing Web-based counselling. Addiction 2015 Mar;110(3):494-501. [CrossRef] [Medline]
- Morean ME, Corbin WR, Treat TA. The Subjective Effects of Alcohol Scale: development and psychometric evaluation of a novel assessment tool for measuring subjective response to alcohol. Psychol Assess 2013 Sep;25(3):780-795 [FREE Full text] [CrossRef] [Medline]
- Tomko RL, Solhan MB, Carpenter RW, Brown WC, Jahng S, Wood PK, et al. Measuring impulsivity in daily life: the momentary impulsivity scale. Psychol Assess 2014 Jun;26(2):339-349 [FREE Full text] [CrossRef] [Medline]
- O'Donnell R, Richardson B, Fuller-Tyszkiewicz M, Liknaitzky P, Arulkadacham L, Dvorak R, et al. Ecological momentary assessment of drinking in young adults: an investigation into social context, affect and motives. Addict Behav 2019 Nov;98:106019. [CrossRef] [Medline]
- Mead EL, Chen JC, Kirchner TR, Butler 3rd J, Feldman RH. An ecological momentary assessment of cigarette and cigar dual use among African American young adults. Nicotine Tob Res 2018 Aug 14;20(suppl_1):S12-S21 [FREE Full text] [CrossRef] [Medline]
- Hébert ET, Stevens EM, Frank SG, Kendzor DE, Wetter DW, Zvolensky MJ, et al. An ecological momentary intervention for smoking cessation: the associations of just-in-time, tailored messages with lapse risk factors. Addict Behav 2018 Mar;78:30-35 [FREE Full text] [CrossRef] [Medline]
- Marlatt GA, Witkiewitz K. Relapse prevention for alcohol and drug problems. In: Marlatt GA, Donovan DM, editors. Relapse Prevention: Maintenance Strategies in the Treatment of Addictive Behaviors. 2nd edition. New York, NY, USA: The Guilford Press; 2005.
- Gooding P, Tarrier N. A systematic review and meta-analysis of cognitive-behavioural interventions to reduce problem gambling: hedging our bets? Behav Res Ther 2009 Jul;47(7):592-607. [CrossRef] [Medline]
- Goslar M, Leibetseder M, Muench HM, Hofmann SG, Laireiter AR. Efficacy of face-to-face versus self-guided treatments for disordered gambling: a meta-analysis. J Behav Addict 2017 Jun 01;6(2):142-162 [FREE Full text] [CrossRef] [Medline]
- de Lisle SM, Dowling NA, Allen JS. Mindfulness and problem gambling: a review of the literature. J Gambl Stud 2012 Dec;28(4):719-739. [CrossRef] [Medline]
- Maynard BR, Wilson AN, Labuzienski E, Whiting SW. Mindfulness-based approaches in the treatment of disordered gambling: a systematic review and meta-analysis. Res Soc Work Pract 2018 Mar 1;28(3):348-362. [CrossRef]
- Kavanagh DJ, Statham DJ, Feeney GF, Young RM, May J, Andrade J, et al. Measurement of alcohol craving. Addict Behav 2013 Feb;38(2):1572-1584. [CrossRef] [Medline]
- Piasecki TM, McCarthy DE, Fiore MC, Baker TB. Alcohol consumption, smoking urge, and the reinforcing effects of cigarettes: an ecological study. Psychol Addict Behav 2008 Jun;22(2):230-239 [FREE Full text] [CrossRef] [Medline]
- Pickering D, Spoelma MJ, Dawczyk A, Gainsbury SM, Blaszczynski A. What does it mean to recover from a gambling disorder? Perspectives of gambling help service users. Addict Res Theory 2020;28(2):132-143. [CrossRef]
- Reesor L, Vaughan EM, Hernandez DC, Johnston CA. Addressing outcomes expectancies in behavior change. Am J Lifestyle Med 2017 Nov;11(6):430-432 [FREE Full text] [CrossRef] [Medline]
- Scott-Sheldon LA, Terry DL, Carey KB, Garey L, Carey MP. Efficacy of expectancy challenge interventions to reduce college student drinking: a meta-analytic review. Psychol Addict Behav 2012 Sep;26(3):393-405 [FREE Full text] [CrossRef] [Medline]
- Weinstock J, Whelan JP, Meyers AW. Behavioral assessment of gambling: an application of the timeline followback method. Psychol Assess 2004 Mar;16(1):72-80. [CrossRef] [Medline]
- Rodda SN, Dowling NA, Lubman DI. Gamblers seeking online help are active help-seekers: time to support autonomy and competence. Addict Behav 2018 Dec;87:272-275. [CrossRef] [Medline]
- Fukuoka Y, Lindgren T, Jong S. Qualitative exploration of the acceptability of a mobile phone and pedometer-based physical activity program in a diverse sample of sedentary women. Public Health Nurs 2012;29(3):232-240 [FREE Full text] [CrossRef] [Medline]
- Sekhon M, Cartwright M, Francis JJ. Acceptability of healthcare interventions: an overview of reviews and development of a theoretical framework. BMC Health Serv Res 2017 Jan 26;17(1):88 [FREE Full text] [CrossRef] [Medline]
- Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth 2015 Mar 11;3(1):e27 [FREE Full text] [CrossRef] [Medline]
- Braun V, Clark V. Thematic Analysis: A Practical Guide. Thousand Oaks, CA, USA: Sage Publications; 2021.
- Boruvka A, Almirall D, Witkiewitz K, Murphy SA. Assessing time-varying causal effect moderation in mobile health. J Am Stat Assoc 2018;113(523):1112-1121 [FREE Full text] [CrossRef] [Medline]
- Carpenter J, Kenward M. Multiple Imputation and its Application. Hoboken, NJ, USA: John Wiley & Sons; 2012.
- Jacobson NS, Truax P. Clinical significance: a statistical approach to defining meaningful change in psychotherapy research. J Consult Clin Psychol 1991 Feb;59(1):12-19. [CrossRef] [Medline]
- Christensen L, Mendoza JL. A method of assessing change in a single subject: an alteration of the RC index. Behav Ther 1986 Jun;17(3):305-308. [CrossRef]
- Maust D, Cristancho M, Gray L, Rushing S, Tjoa C, Thase ME. Psychiatric rating scales. Handb Clin Neurol 2012;106:227-237. [CrossRef] [Medline]
- Qian T, Walton AE, Collins LM, Klasnja P, Lanza ST, Nahum-Shani I, et al. The microrandomized trial for developing digital interventions: experimental design and data analysis considerations. Psychol Methods 2022 Jan 13. [CrossRef] [Medline]
- Qian T, Yoo H, Klasnja P, Almirall D, Murphy SA. Rejoinder: 'Estimating time-varying causal excursion effects in mobile health with binary outcomes'. Biometrika 2021 Sep;108(3):551-555. [CrossRef] [Medline]
- Masterson Creber RM, Maurer MS, Reading M, Hiraldo G, Hickey KT, Iribarren S. Review and analysis of existing mobile phone apps to support heart failure symptom monitoring and self-care management using the Mobile Application Rating Scale (MARS). JMIR Mhealth Uhealth 2016 Jun 14;4(2):e74 [FREE Full text] [CrossRef] [Medline]
- Yardley L, Spring BJ, Riper H, Morrison LG, Crane DH, Curtis K, et al. Understanding and promoting effective engagement with digital behavior change interventions. Am J Prev Med 2016 Nov;51(5):833-842. [CrossRef] [Medline]
- Mohr DC, Cuijpers P, Lehman K. Supportive accountability: a model for providing human support to enhance adherence to eHealth interventions. J Med Internet Res 2011 Mar 10;13(1):e30 [FREE Full text] [CrossRef] [Medline]
- Gustafson DH, McTavish FM, Chih MY, Atwood AK, Johnson RA, Boyle MG, et al. A smartphone application to support recovery from alcoholism: a randomized clinical trial. JAMA Psychiatry 2014 May;71(5):566-572 [FREE Full text] [CrossRef] [Medline]
- Fogg BJ, Eckles D. Mobile Persuasion: 20 Perspectives on the Future of Behavior Change. Stanford, CA, USA: Stanford Captology Media; 2007.
- Baumeister H, Reichler L, Munzinger M, Lin J. The impact of guidance on Internet-based mental health interventions — a systematic review. Internet Interv 2014 Oct;1(4):205-215. [CrossRef]
- Kim HS, Hodgins DC. Component model of addiction treatment: a pragmatic transdiagnostic treatment model of behavioral and substance addictions. Front Psychiatry 2018 Aug 31;9:406 [FREE Full text] [CrossRef] [Medline]
- GamblingLess. Deakin University. URL: https://gamblingless.org/ [accessed 2022-08-05]
|EMA: ecological momentary assessment|
|GEE: generalized estimating equations|
|JITAI: just-in-time adaptive intervention|
|mHealth: mobile health|
|MRT: microrandomized trial|
|SPGeTTI: smartphone-based problem gambling evaluation and technology testing initiative|
Edited by T Leung; submitted 23.04.22; peer-reviewed by J Thrul, P Chow; comments to author 16.05.22; revised version received 30.06.22; accepted 30.06.22; published 23.08.22Copyright
©Nicki A Dowling, Stephanie S Merkouris, George J Youssef, Dan I Lubman, Kathleen L Bagot, Chloe O Hawker, Hannah J Portogallo, Anna C Thomas, Simone N Rodda. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 23.08.2022.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.