Developments in Mobile Health Just-in-Time Adaptive Interventions for Addiction Science

Carpenter, Stephanie M.; Menictas, Marianne; Nahum‐Shani, Inbal; Wetter, David W.; Murphy, Susan A.

doi:10.1007/s40429-020-00322-y

Cited by 55 publications

(37 citation statements)

References 47 publications

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“…Moreover, they indicate a need for interventions designed to target momentary positive outcome expectancies that change across time and contexts. Just-In-Time Adaptive Interventions (JITAIs) are emerging mobile health (mHealth) intervention designs that address dynamically changing individual needs by providing the type and amount of support, at the right time, and only when needed [ 88 , 89 ]. JITAIs leverage mobile and wireless technologies, such as smartphone-embedded or wearable sensors and EMAs to continuously monitor dynamic internal states and ecological contexts in real time to identify when and how support should be offered [ 89 ].…”

Section: Discussionmentioning

confidence: 99%

“…Dynamic and individually tailored JITAIs that employ EMAs as their method of assessment have also been described as Ecological Momentary Interventions (EMIs) [ 90 ]. JITAIs and EMIs have been effective in the broader mental health and addiction fields [ 88 , 89 , 90 , 91 ]; and have recently been adopted in the gambling field [ 92 , 93 , 94 , 95 , 96 ]. For example, GamblingLess: Curb Your Urge [ 95 , 96 ] is informed by the relapse prevention model and aims to reduce gambling cravings to prevent subsequent gambling episodes.…”

Section: Discussionmentioning

confidence: 99%

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Ecological Momentary Assessment of the Relationship between Positive Outcome Expectancies and Gambling Behaviour

Dowling

Merkouris

Spence

2021

JCM

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Relapse prevention models suggest that positive outcome expectancies can constitute situational determinants of relapse episodes that interact with other factors to determine the likelihood of relapse. The primary aims were to examine reciprocal relationships between situational positive gambling outcome expectancies and gambling behaviour and moderators of these relationships. An online survey and a 28 day Ecological Momentary Assessment (EMA) were administered to 109 past-month gamblers (84% with gambling problems). EMA measures included outcome expectancies (enjoyment/arousal, self-enhancement, money), self-efficacy, craving, negative emotional state, interpersonal conflict, social pressure, positive emotional state, financial pressures, and gambling behaviour (episodes, expenditure). Pre-EMA measures included problem gambling severity, motives, psychological distress, coping strategies, and outcome expectancies. No reciprocal relationships between EMA outcome expectancies and gambling behaviour (episodes, expenditure) were identified. Moderations predicting gambling episodes revealed: (1) cravings and problem gambling exacerbated effects of enjoyment/arousal expectancies; (2) positive emotional state and positive reframing coping exacerbated effects of self-enhancement expectancies; and (3) instrumental social support buffered effects of money expectancies. Positive outcome expectancies therefore constitute situational determinants of gambling behaviour, but only when they interact with other factors. All pre-EMA expectancies predicted problem gambling severity (OR = 1.61–3.25). Real-time interventions addressing gambling outcome expectancies tailored to vulnerable gamblers are required.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Ecological Momentary Assessment of the Relationship between Positive Outcome Expectancies and Gambling Behaviour

Dowling

Merkouris

Spence

2021

JCM

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“…EMA compliance was measured as the rate at which participants completed the EMA during the baseline and intervention periods. EMI compliance was measured as the rate at which participants completed any intervention content within 60 minutes of receiving an EMI recommendation to use the intervention because they reported a current craving to gamble during an EMA [38,49,50]. Feasibility was also assessed via intervention use more generally.…”

Section: Feasibility Measuresmentioning

confidence: 99%

A Smartphone-Delivered Ecological Momentary Intervention for Problem Gambling (GamblingLess: Curb Your Urge): Single-Arm Acceptability and Feasibility Trial

Hawker¹,

Merkouris²,

Youssef³

et al. 2021

J Med Internet Res

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Background Low uptake rates of traditional gambling treatments highlight the need for innovative treatment modalities. Smartphone apps can provide unprecedented access to real-time ecological momentary interventions (EMIs) delivered in people’s everyday lives. Objective This study aims to examine the acceptability, feasibility, and preliminary effectiveness of GamblingLess: Curb Your Urge, the first smartphone app–delivered EMI that aims to prevent gambling episodes by reducing craving intensity in people seeking help for gambling problems. Methods This study was a single-arm, 5-week acceptability and feasibility trial (1-week baseline and 4-week intervention periods) involving ecological momentary assessments (EMAs) delivered 3 times daily. The EMAs measured gambling episodes, cravings, and self-efficacy. Web-based evaluations at baseline, postintervention, and 1-month follow-up measured gambling outcomes (severity, cravings, frequency, expenditure, and self-efficacy) and the intervention’s perceived helpfulness, relevance, burden, satisfaction, and impact in relation to gambling cravings. Results A total of 36 participants, of whom 22/36 (61%) were male and 34/36 (94%) were problem gamblers, completed the baseline measures, with 61% (22/36) completing the postintervention evaluation and 58% (21/36) completing the follow-up evaluation. The intervention was considered acceptable, as participants perceived all intervention content to be above average in helpfulness and the EMA to be highly relevant but somewhat burdensome. Participants reported that they were satisfied with the intervention and that the intervention improved their knowledge, attitudes, awareness, behavior change, intention to change, and help-seeking behavior for gambling cravings. Regarding the intervention’s feasibility, compliance rates for the EMA (51%) and EMI (15%) were low; however, the intervention was used 166 times, including 59 uses within 60 minutes of EMA completion and 107 on-demand uses. Regarding the intervention’s preliminary effectiveness, descriptive EMA data showed that, compared with the baseline period, 71% and 72% reductions in the average number of gambling episodes and craving occurrences were reported in the intervention period, respectively. In addition, clustered paired-sample two-tailed t tests revealed a significant 5.4% reduction in real-time craving intensity (P=.01) immediately after intervention use, which increased to 10.5% (P=.01), where use was recommended based on craving occurrence. At the group level, significant medium-to-large reductions were observed in mean gambling symptom severity (P=.01 and .003), cravings (P=.03 and .02), frequency (P=.01 and .004), and expenditure (P=.04 and .003) at postintervention and follow-up; moreover, increased mean gambling self-efficacy and craving self-efficacy (P=.01 and .01) were observed at postintervention and increased gambling self-efficacy (P=.04) was observed at follow-up. At the individual level, over a quarter of participants (6/22, 27% to 10/21, 48%) could be categorized as recovered or improved regarding their gambling symptom severity and cravings. Conclusions The results support the acceptability, feasibility, and preliminary effectiveness of this app-delivered EMI for preventing gambling episodes through craving management in people with gambling problems, which has implications for extending the reach of evidence-based treatment to moments of vulnerability in people’s everyday lives.

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“…Zhao et al (2011) attempted to optimize the timing to initiate second-line therapy in the context of clinical trials with two-stage treatments using Q-learning, but only considered two options (i.e., immediately or delayed after induction therapy). Other work on optimizing intervention timing with a fixed number of treatment options include initiation of antiretroviral therapy in HIV (Robins et al, 2008), just-in-time adaptive interventions in mobile health (Nahum-Shani et al, 2018;Carpenter et al, 2020), and advantage doubly robust policy learning that optimizes when to treat (Nie et al, 2021). Guan et al (2019) developed a Bayesian nonparametric method that learns to recommend a regular recall time for patients with periodontal diseases.…”

Section: Why Not Use Existing Methods?mentioning

confidence: 99%

Personalized Dynamic Treatment Regimes in Continuous Time: A Bayesian Approach for Optimizing Clinical Decisions with Timing

et al. 2022

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Accurate models of clinical actions and their impacts on disease progression are critical for estimating personalized optimal dynamic treatment regimes (DTRs) in medical/health research, especially in managing chronic conditions. Traditional statistical methods for DTRs usually focus on estimating the optimal treatment or dosage at each given medical intervention, but overlook the important question of "when this intervention should happen." We fill this gap by developing a two-step Bayesian approach to optimize clinical decisions with timing. In the first step, we build a generative model for a sequence of medical interventions-which are discrete events in continuous time-with a marked temporal point process (MTPP) where the mark is the assigned treatment or dosage. Then this clinical action model is embedded into a Bayesian joint framework where the other components model clinical observations including longitudinal medical measurements and time-to-event data conditional on treatment histories. In the second step, we propose a policy gradient method to learn the personalized optimal clinical decision that maximizes the patient survival by interacting the MTPP with the model on clinical observations while accounting for uncertainties in clinical observations learned from the posterior inference of the Bayesian joint model in the first step. A signature application of the proposed approach is to schedule follow-up visitations and assign a dosage at each visitation for patients after kidney transplantation. We evaluate our approach with comparison to alternative methods on both simulated and real-world datasets. In our experiments, the personalized decisions made by the proposed method are clinically useful: they are interpretable and successfully help improve patient survival.

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Developments in Mobile Health Just-in-Time Adaptive Interventions for Addiction Science

Cited by 55 publications

References 47 publications

Ecological Momentary Assessment of the Relationship between Positive Outcome Expectancies and Gambling Behaviour

Ecological Momentary Assessment of the Relationship between Positive Outcome Expectancies and Gambling Behaviour

A Smartphone-Delivered Ecological Momentary Intervention for Problem Gambling (GamblingLess: Curb Your Urge): Single-Arm Acceptability and Feasibility Trial

Personalized Dynamic Treatment Regimes in Continuous Time: A Bayesian Approach for Optimizing Clinical Decisions with Timing

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