The Dr. Jud Podcast

Evaluating the Quality of Smartphone Apps for Overeating, Stress, and Craving-Related Eating Using the Mobile Application Rating Scale

In this episode, Dr. Jud dives into the realm of mobile health apps targeting overeating, stress-related eating, and cravings, based on the paper Evaluating the Quality of Smartphone Apps for Overeating, Stress, and Craving-Related Eating Using the Mobile Application Rating Scale by Isabelle Moseley, Alexandra Roy, Alana Deluty, and Judson Brewer. The study reveals a significant variation in app quality and underscores the importance of evidence-based design for health outcomes. Dr. Jud discusses how the lack of regulation and clinical validation for many apps leaves consumers vulnerable, while apps like Eat Right Now, backed by clinical research, show promising results in addressing maladaptive eating behaviors through mindfulness. Learn about the future of digital therapeutics and how to choose apps that can genuinely impact behavior change.

Reference:
Moseley, I., Roy, A., Deluty, A., & Brewer, J. A. Evaluating the Quality of Smartphone Apps for Overeating, Stress, and Craving-Related Eating Using the Mobile Application Rating Scale. Current Addiction Reports, 2020. https://doi.org/10.1007/s40429-020-00319-7

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Meditation-Induced Changes in High-Frequency Heart Rate Variability Predict Smoking Outcomes

Dr. Jud explores a pivotal study linking mindfulness meditation and heart rate variability (HRV) to smoking cessation success. This episode delves into how acute changes in high-frequency HRV during meditation correlate with reduced cigarette consumption over time. By examining the physiological underpinnings of self-regulation and addiction, Dr. Jud highlights how HRV may serve as a biomarker to tailor interventions. Tune in to uncover how meditation can reshape the body's stress response and support lasting behavioral change.

Reference:
Libby DJ, Worhunsky PD, Pilver CE, Brewer JA. Meditation-induced changes in high-frequency heart rate variability predict smoking outcomes. Frontiers in Human Neuroscience. 2012;6:54. DOI: 10.3389/fnhum.2012.00054

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Smartband-Based Smoking Detection and Real-Time Brief Mindfulness Intervention: Findings from a Feasibility Clinical Trial

In this episode, Dr. Jud examines the groundbreaking results from a feasibility trial exploring the use of smartbands for smoking cessation. This innovative study showcases how wearable technology can detect smoking events in real time and deliver brief mindfulness interventions to address cravings. Dr. Jud discusses the study's design, key findings, and their implications for the future of digital health interventions. Learn how the integration of real-time behavioral data with mindfulness-based techniques holds the potential to transform smoking cessation efforts globally.

Reference:
Horvath M, Pittman B, O’Malley SS, Grutman A, Khan N, Gueorguieva R, Brewer JA, Garrison KA. Smartband-Based Smoking Detection and Real-Time Brief Mindfulness Intervention: Findings from a Feasibility Clinical Trial. Annals of Medicine. 2024;56(1):2352803. DOI: 10.1080/07853890.2024.2352803

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Smartband-Based Automatic Smoking Detection and Real-Time Mindfulness Intervention: Protocol for a Feasibility Trial

Join Dr. Jud in this episode as he dives into the innovative intersection of wearable technology, mindfulness, and smoking cessation. This episode explores a cutting-edge study protocol designed to evaluate the feasibility of using a smartband and smartphone app for real-time smoking detection and intervention. Learn how mindfulness exercises like "RAIN" are being tailored to moments of craving, leveraging data-driven insights to support smokers on their journey to quit. Dr. Jud discusses the potential implications of this technology for public health and addiction treatment, emphasizing how behavioral science and technology can converge to create impactful solutions.

Reference:
Horvath M, Grutman A, O'Malley SS, Gueorguieva R, Khan N, Brewer JA, Garrison KA. Smartband-Based Automatic Smoking Detection and Real-Time Mindfulness Intervention: Protocol for a Feasibility Trial. JMIR Research Protocols. 2021;10(10):e32521. DOI: 10.2196/32521

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Brain Mechanisms of Change in Addiction Treatment: Models, Methods, and Emerging Findings

In this episode, Dr. Jud explores groundbreaking insights into the brain mechanisms underlying addiction treatment. Drawing on models, methods, and findings presented at the 2015 Science of Change meeting, this research bridges neuroscience and psychotherapy to identify brain processes driving behavior change. Topics include the role of neuroimaging in understanding addiction treatments like CBT, mindfulness, and motivational interviewing, as well as how these insights pave the way for personalized, neuroscience-informed interventions.

Reference: Chung, T., Noronha, A., Carroll, K. M., Potenza, M. N., Hutchison, K., Calhoun, V. D., ... & Brewer, J. A. (2016). Brain Mechanisms of Change in Addiction Treatment: Models, Methods, and Emerging Findings. Current Addiction Reports, 3(4), 332-342. DOI: 10.1007/s40429-016-0113-z

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Mindfulness-Based Treatments for Co-Occurring Depression and Substance Use Disorders: What Can We Learn from the Brain?

In this episode, Dr. Jud discusses the intersection of mindfulness, neuroscience, and treatment for individuals struggling with co-occurring depression and substance use disorders (SUDs). This seminal paper explores the shared neurobiological mechanisms underpinning these conditions and how mindfulness training (MT) addresses them. Discover the evidence behind mindfulness as a dual-diagnosis treatment, its impact on stress and rumination, and the potential for broader applications in mental health.

Reference: Brewer, J. A., Bowen, S., Smith, J. T., Marlatt, G. A., & Potenza, M. N. (2010). Mindfulness-based treatments for co-occurring depression and substance use disorders: What can we learn from the brain? Addiction, 105(10), 1698–1706. DOI: 10.1111/j.1360-0443.2009.02890.x

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HIV Patients’ Perceptions of a Potential Multi-Component Mindfulness-Based Smoking Cessation Smartphone Application Intervention
In this episode, Dr. Jud explores a pivotal study on adapting a mindfulness-based smoking cessation app for people living with HIV (PLWH). This research highlights how a multi-component intervention—combining mindfulness training, contingency management, and adherence tracking for antiretroviral therapy—addresses the unique challenges faced by PLWH smokers. Discover the app's potential to reduce smoking-related health disparities, its innovative features, and participant insights on feasibility, acceptability, and improvement opportunities.
Reference: Asfar, T., Alcaide, M. L., Jones, D. L., McClure, L. A., Brewer, J., Lee, D. J., & Carrico, A. (2022). HIV patients’ perceptions of a potential multi-component mindfulness-based smoking cessation smartphone application intervention. PLOS ONE, 17(8), e0271946. DOI: 10.1371/journal.pone.0271946.

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App-Based Mindfulness Training Predicts Reductions in Smoking Behavior by Engaging Reinforcement Learning Mechanisms: A Preliminary Naturalistic Single-Arm Study

In this episode, Dr. Jud Brewer and colleagues explore how mindfulness-based smoking cessation tools can target the brain's reinforcement learning mechanisms to disrupt addictive behaviors. The study highlights the use of the "Craving to Quit" app, which combines mindful awareness practices with real-time feedback on cravings and their outcomes. By recalibrating the reward value of smoking through mindfulness, the app achieved significant reductions in smoking frequency among participants. Discover how this research advances our understanding of habit loops and offers scalable, innovative solutions for smoking cessation.

Full Reference:
Taylor, V. A., Smith, R., & Brewer, J. A. (2022). App-based mindfulness training predicts reductions in smoking behavior by engaging reinforcement learning mechanisms: A preliminary naturalistic single-arm study. Sensors, 22(14), 5131. https://doi.org/10.3390/s22145131

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Can We Agree on What Skilled Mindfulness-Based Teaching Looks Like? Lessons from Studying the MBI:TAC

In this episode, Dr. Jud Brewer and Dr. Rebecca Crane explore the Mindfulness-Based Interventions: Teaching Assessment Criteria (MBI:TAC), a widely used tool for assessing the competence of mindfulness-based program (MBP) teachers. As mindfulness-based interventions (MBIs) gain popularity in clinical and workplace settings, ensuring teaching integrity and program fidelity becomes increasingly important. This study tested a training model to help mindfulness teachers reliably assess MBP teaching quality. The findings show that international MBP teachers can align their understanding of teaching competence, paving the way for standardized mindfulness education. Tune in to learn how mindfulness training is evolving to maintain high standards in research and practice.

Full Reference:
Crane, R. S., Hecht, F. M., Brewer, J. A., Griffith, G. M., Hartogensis, W., Koerbel, L., Moran, P., Sansom, S., Yiangou, A., & Kuyken, W. (2020). Can we agree on what skilled mindfulness-based teaching looks like? Lessons from studying the MBI:TAC. Global Advances in Health and Medicine, 0(1), 1–11. https://doi.org/10.1177/2164956120964733

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Baseline Craving Strength as a Prognostic Marker of Benefit from Smartphone App-Based Mindfulness Training for Smoking Cessation
In this episode, Dr. Jud explores cutting-edge research into how baseline craving strength influences the effectiveness of smartphone app-based mindfulness training for smoking cessation. Discover how mindfulness-based programs help smokers reduce their reliance on cigarettes by disrupting the association between cravings and smoking. We discuss the role of craving strength as a potential marker for personalized treatment, key findings from the study, and implications for future smoking cessation strategies.
Reference: Roos, C. R., Brewer, J. A., O'Malley, S. S., & Garrison, K. A. (2019). Baseline Craving Strength as a Prognostic Marker of Benefit from Smartphone App-Based Mindfulness Training for Smoking Cessation. Mindfulness, 10, 2165–2171. DOI: 10.1007/s12671-019-01188-6.

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