Optimizing the Implementation of a Lifestyle Dementia Prevention Intervention for Older Patients in an Academic Healthcare System.

Background: Interventions that promote healthy lifestyles are critical for the prevention of Alzheimer's disease and Alzheimer's disease related dementias (AD/ADRD). However, knowledge of the best practices for implementing AD/ADRD prevention in healthcare settings remains limited. Objective: We aimed to qualitatively identify barriers and facilitators to implementing a clinical trial of a novel lifestyle intervention (My Healthy Brain) in our medical center for older patients with subjective cognitive decline who are at-risk for AD/ADRD. Methods: We conducted focus groups with 26 healthcare professionals (e.g., physicians, psychology, nursing) from 5 clinics that treat older patients (e.g., memory care, psychiatry). Our qualitative analysis integrated two implementation frameworks to systematically capture barriers and facilitators to AD/ADRD prevention (Consolidated Framework for Implementation Science Research) that impact implementation outcomes of acceptability, appropriateness, and feasibility (Proctor's framework). Results: We found widespread support for an RCT of My Healthy Brain and AD/ADRD prevention. Participants identified barriers related to patients (stigma, technological skills), providers (dismissiveness of "worried well," doubting capacity for behavior change), clinics (limited time and resources), and the larger healthcare system (underemphasis on prevention). Implementation strategies guided by Expert Recommendations for Implementing Change (ERIC) included: developing tailored materials, training staff, obtaining buy-in from leadership, addressing stigmatized language and practices, identifying "champions," and integrating with workflows and resources. Conclusions: The results will inform our recruitment, enrollment, and retention procedures to implement the first randomized clinical trial of My Healthy Brain. Our study provides a blueprint for addressing multi-level barriers to the implementation of AD/ADRD prevention for older patients in medical settings.

Smartband-based smoking detection and real-time brief mindfulness intervention: findings from a feasibility clinical trial.

BACKGROUND: Smartbands can be used to detect cigarette smoking and deliver real time smoking interventions. Brief mindfulness interventions have been found to reduce smoking. OBJECTIVE: This single arm feasibility trial used a smartband to detect smoking and deliver brief mindfulness exercises. METHODS: Daily smokers who were motivated to reduce their smoking wore a smartband for 60 days. For 21 days, the smartband monitored, detected and notified the user of smoking in real time. After 21 days, a 'mindful smoking' exercise was triggered by detected smoking. After 28 days, a 'RAIN' (recognize, allow, investigate, nonidentify) exercise was delivered to predicted smoking. Participants received mindfulness exercises by text message and online mindfulness training. Feasibility measures included treatment fidelity, adherence and acceptability. RESULTS: Participants (N=155) were 54% female, 76% white non-Hispanic, and treatment starters (n=115) were analyzed. Treatment fidelity cutoffs were met, including for detecting smoking and delivering mindfulness exercises. Adherence was mixed, including moderate smartband use and low completion of mindfulness exercises. Acceptability was mixed, including high helpfulness ratings and mixed user experiences data. Retention of treatment starters was high (81.9%). CONCLUSIONS: Findings demonstrate the feasibility of using a smartband to track smoking and deliver quit smoking interventions contingent on smoking.

Parameter Space and Potential for Biomarker Development in 25 Years of fMRI Drug Cue Reactivity: A Systematic Review.

Importance: In the last 25 years, functional magnetic resonance imaging drug cue reactivity (FDCR) studies have characterized some core aspects in the neurobiology of drug addiction. However, no FDCR-derived biomarkers have been approved for treatment development or clinical adoption. Traversing this translational gap requires a systematic assessment of the FDCR literature evidence, its heterogeneity, and an evaluation of possible clinical uses of FDCR-derived biomarkers. Objective: To summarize the state of the field of FDCR, assess their potential for biomarker development, and outline a clear process for biomarker qualification to guide future research and validation efforts. Evidence Review: The PubMed and Medline databases were searched for every original FDCR investigation published from database inception until December 2022. Collected data covered study design, participant characteristics, FDCR task design, and whether each study provided evidence that might potentially help develop susceptibility, diagnostic, response, prognostic, predictive, or severity biomarkers for 1 or more addictive disorders. Findings: There were 415 FDCR studies published between 1998 and 2022. Most focused on nicotine (122 [29.6%]), alcohol (120 [29.2%]), or cocaine (46 [11.1%]), and most used visual cues (354 [85.3%]). Together, these studies recruited 19 311 participants, including 13 812 individuals with past or current substance use disorders. Most studies could potentially support biomarker development, including diagnostic (143 [32.7%]), treatment response (141 [32.3%]), severity (84 [19.2%]), prognostic (30 [6.9%]), predictive (25 [5.7%]), monitoring (12 [2.7%]), and susceptibility (2 [0.5%]) biomarkers. A total of 155 interventional studies used FDCR, mostly to investigate pharmacological (67 [43.2%]) or cognitive/behavioral (51 [32.9%]) interventions; 141 studies used FDCR as a response measure, of which 125 (88.7%) reported significant interventional FDCR alterations; and 25 studies used FDCR as an intervention outcome predictor, with 24 (96%) finding significant associations between FDCR markers and treatment outcomes. Conclusions and Relevance: Based on this systematic review and the proposed biomarker development framework, there is a pathway for the development and regulatory qualification of FDCR-based biomarkers of addiction and recovery. Further validation could support the use of FDCR-derived measures, potentially accelerating treatment development and improving diagnostic, prognostic, and predictive clinical judgments.

App-Delivered Mindfulness Training to Reduce Anxiety in People with HIV Who Smoke: A One-Armed Feasibility Trial.

INTRODUCTION: People with HIV (PWH) who smoke have reported that managing anxiety is a barrier to making a quit attempt and maintaining abstinence post-quit. This study examined the feasibility and acceptability of an app-based mindfulness intervention, Unwinding Anxiety, to reduce anxiety prior to a quit attempt in PWH who were not planning to quit in the next 30 days. METHODS: Sixteen PWH (mean age 51.5 [SD = 13.2]; mean cigarettes per day 11.4 [SD = 5.4]) were enrolled and followed for eight weeks. A smartphone-based app with 30 modules designed to reduce anxiety was introduced at baseline; participants were encouraged to complete one module daily for four weeks. Symptoms of anxiety and readiness to quit smoking were measured at baseline and weeks 4 and 8. The mean number of modules completed, session attendance, and number of study completers were examined. Generalized estimating equations (GEE) were used to examine changes in self-reported anxiety and readiness to quit at baseline, week 4, and week 8. A brief qualitative interview was conducted at week 4 to explore the acceptability of the app. RESULTS: Feasibility was high, with 93% of participants completing the study. The mean number of study sessions completed was 2.7 (SD = 0.59), and the mean number of modules completed was 16.0 (SD 16.8). Anxiety was high at baseline (M = 14.4, SD = 3.9), but lower at week 4 (b = -5.5; CI: [-9.4, -1.7]; p = 0.004) and week 8 (b = -5.1; CI: [-8.8, -1.3]; p = 0.008), and stable between weeks 4 and 8 (b = 0.48; CI: [-2.0, 3.0]; p = 0.706). Readiness to quit significantly increased from baseline M = 5.5 (SD = 1.6) to week 4 (b = 0.56; CI: [0.20, 0.91]; p = 0.002) but was not significantly different from baseline at week 8 (b = 0.34; CI: [-0.30, 1.0]; p = 0.30). Ad-hoc moderation analyses found that anxiety had a small significantly positive association with readiness to quit at baseline (main effect: b = 0.10; SE = 0.03; p < 0.001) and significantly attenuated the increase in readiness to quit observed at week 4 (anxiety by week 4 interaction: b = -0.08; SE = 0.03; p = 0.009). CONCLUSIONS: App-based mindfulness training appears to be feasible and acceptable for PWH who smoke and report baseline anxiety. At week 4, anxiety was reduced and readiness to quit was increased, perhaps a key time point for a smoking cessation attempt.

Cluster-based psychological phenotyping and differences in anxiety treatment outcomes.

The identification of markers of mental health illness treatment response and susceptibility using personalized medicine has been elusive. In the context of psychological treatment for anxiety, we conducted two studies to identify psychological phenotypes with distinct characteristics related to: psychological intervention modalities (mindfulness training/awareness), mechanism of action (worry), and clinical outcome (generalized anxiety disorder scale scores). We also examined whether phenotype membership interacted with treatment response (Study 1) and mental health illness diagnosis (Studies 1-2). Interoceptive awareness, emotional reactivity, worry, and anxiety were assessed at baseline in treatment-seeking individuals (Study 1, n = 63) and from the general population (Study 2, n = 14,010). In Study 1, participants were randomly assigned to an app-delivered mindfulness program for anxiety for two months or treatment as usual. Changes in anxiety were assessed 1 and 2 months post-treatment initiation. In studies 1-2, three phenotypes were identified: 'severely anxious with body/emotional awareness' (cluster 1), 'body/emotionally unaware' (cluster 2), and 'non-reactive and aware' (cluster 3). Study 1's results revealed a significant treatment response relative to controls (ps < 0.001) for clusters 1 and 3, but not for cluster 2. Chi-square analyses revealed that phenotypes exhibited significantly different proportions of participants with mental health diagnoses (studies 1-2). These results suggest that psychological phenotyping can bring the application of personalized medicine into clinical settings.Registry name and URL: Developing a novel digital therapeutic for the treatment of generalized anxiety disorder https://clinicaltrials.gov/ct2/show/NCT03683472?term=judson+brewer&draw=1&rank=1 .Trial registration: Registered at clinicaltrials.gov (NCT03683472) on 25/09/2018.

Mindfulness-Based Stress Reduction and Mindfulness-Based Cognitive Therapy for Depression: A Systematic Review of Cognitive Outcomes.

Objectives: Summarize existing literature on cognitive outcomes of MBSR and MBCT for individuals with depression. Methods: Following PRISMA (2021) guidance, we conducted a systematic review. We searched databases for studies published from 2000 to 2020 which examined cognitive outcomes of MBSR and MBCT in individuals with at least mild depressive symptoms. The search result in 10 studies (11 articles) meeting inclusion criteria. Results: We identified five single armed trials and five randomized controlled trials. Results indicated that three studies did not show any improvements on cognitive outcomes, and seven studies showed at least one improvement in cognitive outcomes. Conclusions: Overall, the review highlighted several inconsistencies in the literature including inconsistent use of terminology, disparate samples, and inconsistent use of methodology. These inconsistencies may help to explain the mixed results of MBSR and MBCT on cognitive outcomes. Recommendations include a more streamlined approach to studying cognitive outcomes in depressed individuals in the context of MBSR and MBCT.

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

Mindfulness training (MT) has been shown to influence smoking behavior, yet the involvement of reinforcement learning processes as underlying mechanisms remains unclear. This naturalistic, single-arm study aimed to examine slope trajectories of smoking behavior across uses of our app-based MT craving tool for smoking cessation, and whether this relationship would be mediated by the attenuating impact of MT on expected reward values of smoking. Our craving tool embedded in our MT app-based smoking cessation program was used by 108 participants upon the experience of cigarette cravings in real-world contexts. Each use of the tool involved mindful awareness to the experience of cigarette craving, a decision as to whether the participant wanted to smoke or ride out their craving with a mindfulness exercise, and paying mindful attention to the choice behavior and its outcome (contentment levels felt from engaging in the behavior). Expected reward values were computed using contentment levels experienced from the choice behavior as the reward signal in a Rescorla−Wagner reinforcement learning model. Multi-level mediation analysis revealed a significant decreasing trajectory of smoking frequency across MT craving tool uses and that this relationship was mediated by the negative relationship between MT and expected reward values (all ps < 0.001). After controlling for the mediator, the predictive relationship between MT and smoking was no longer significant (p < 0.001 before and p = 0.357 after controlling for the mediator). Results indicate that the use of our app-based MT craving tool is associated with negative slope trajectories of smoking behavior across uses, mediated by reward learning mechanisms. This single-arm naturalistic study provides preliminary support for further RCT studies examining the involvement of reward learning mechanisms underlying app-based mindfulness training for smoking cessation.

Targeting Anxiety to Improve Sleep Disturbance: A Randomized Clinical Trial of App-Based Mindfulness Training.

OBJECTIVE: Sleep disturbance is experienced by nearly 20% of Americans and is highly comorbid with anxiety. Sleep disturbances may predict the development of anxiety disorders. Mindfulness training (MT) has shown efficacy for anxiety yet remains limited by in-person-based delivery. Digitally delivered MT may target habitual worry processes, yet its effects on sleep have not been studied. This study tested if app-based MT for anxiety could reduce worry and improve sleep and examined the underlying mechanisms. METHODS: Individuals reporting worry interfering with sleep were randomized to treatment as usual (TAU; n = 40) or TAU + app-based MT (n = 40). Treatment-related changes in worry-related sleep disturbances (WRSDs), worry, nonreactivity, and anxiety were evaluated via self-report questionnaires at 1 and 2 months after treatment initiation. Fitbit devices were used to record total sleep time and estimate sleep efficiency. At 2 months, TAU received access to app-based MT, and both groups were reassessed at 4 months. RESULTS: In a modified intent-to-treat analysis, WRSD scores decreased by 27% in TAU + MT (n = 36) and 6% in TAU (n = 35) at 2 months (median [IQR] change = 11 [4.3] versus 15 [5.0], p = .001). These WRSD reductions were mediated by decreased worry, particularly improved nonreactivity (p values < .001). At 4 months, TAU reported a significant 29% reduction after beginning app-based MT at 2 months and TAU + MT maintained its gains. No significant between-group differences in average estimated total sleep time or sleep efficiency were found after 2 months of using the app. CONCLUSIONS: Few mindfulness-related apps have been evaluated for clinical efficacy and/or mechanism. Results from this study demonstrate a mechanistic link between MT and increased emotional nonreactivity, decreased worry, and reduction in reported sleep disturbances, suggesting that app-based MT may be a viable option to help individuals who report that worry interferes with their sleep.Trial Registration: ClinicalTrials.gov identifier: NCT03684057.

Mindfulness-Based Programs: Why, When, and How to Adapt?

This paper provides a framework for understanding why, when and how to adapt mindfulness-based programs (MBPs) to specific populations and contexts, based on research that developed and adapted multiple MBPs. In doing so, we hope to support teachers, researchers and innovators who are considering adapting an MBP to ensure that changes made are necessary, acceptable, effective, cost-effective, and implementable. Specific questions for reflection are provided such as (1) Why is an adaptation needed? (2) Does the theoretical premise underpinning mainstream MBPs extend to the population you are considering? (3) Do the benefits of the proposed adaptation outweigh the time and costs involved to all in research and implementation? (4) Is there already an evidenced-based approach to address this issue in the population or context? Fundamental knowledge that is important for the adaptation team to have includes the following: (1) essential ingredients of MBPs, (2) etiology of the target health outcome, (3) existing interventions that work for the health outcome, population, and context, (4) delivery systems and settings, and (5) culture, values, and communication patterns of the target population. A series of steps to follow for adaptations is provided, as are case examples. Adapting MBPs happens not only by researchers, but also by MBP teachers and developers, who endeavor to best serve the populations and contexts they work within. We hope that these recommendations for best practice provide a practical framework for skilfully understanding why, when, and how to adapt MBPs; and that this careful approach to adaptation maximizes MBP safety and efficacy.

Mindfulness-based interventions among people of color: A systematic review and meta-analysis.

Objective In the United States, people of color (POC) are disproportionately affected by various sources of stress and prevalent mental and physical health issues that may benefit from Mindfulness-based Interventions (MBIs). However, effects of MBIs for POC are unclear. This meta-analysis examines the efficacy of MBIs through randomized controlled trials (RCTs) that included predominately POC (≥75% of the sample). Method: Random effects models were used to synthesize effect sizes. A total of 24 RCT samples were analyzed. Results: Samples were on average 94.4% POC and predominantly from low-income backgrounds (total N = 2,156). At post-treatment, MBIs yielded small but statistically superior outcomes to active controls (Hedges' g = 0.11) and inactive controls (g = 0.26). Compared to active controls, MBIs' effects on well-being were smaller than their effects on other outcome types. Compared to inactive controls, MBIs that focused on non-clinical populations and had higher proportion of POC had larger effect sizes. Attrition rates of MBIs did not differ from other active conditions in outpatient settings. Conclusion: Findings provide modest, preliminary empirical support for MBIs among POC. We discuss main findings, limitations, and implications for future MBI research for health promotion among POC.

Clinical Efficacy and Psychological Mechanisms of an App-Based Digital Therapeutic for Generalized Anxiety Disorder: Randomized Controlled Trial.

BACKGROUND: Current treatments for generalized anxiety disorder (GAD) often yield suboptimal outcomes, partly because of insufficient targeting of underlying psychological mechanisms (eg, avoidance reinforcement learning). Mindfulness training (MT) has shown efficacy for anxiety; yet, widespread adoption has been limited, partly because of the difficulty in scaling in-person-based delivery. Digital therapeutics are emerging as potentially viable treatments; however, very few have been empirically validated. OBJECTIVE: The aim of this study is to test the efficacy and mechanism of an app-delivered MT that was designed to target a potential mechanism of anxiety (reinforcement learning), based on which previous studies have shown concern regarding feedback and the perpetuation of anxiety through negative reinforcement. METHODS: Individuals with GAD were recruited using social media advertisements and randomized during an in-person visit to receive treatment as usual (n=33) or treatment as usual+app-delivered MT (Unwinding Anxiety; n=32). The latter was composed of 30 modules to be completed over a 2-month period. Associated changes in outcomes were assessed using self-report questionnaires 1 and 2 months after treatment initiation. RESULTS: We randomized 65 participants in this study, and a modified intent-to-treat approach was used for analysis. The median number of modules completed by the MT group was 25.5 (IQR 17) out of 30; 46% (13/28) of the participants completed the program. In addition, the MT group demonstrated a significant reduction in anxiety (GAD-7) compared with the control group at 2 months (67% vs 14%; median change in GAD-7: -8.5 [IQR 6.5] vs -1.0 [IQR 5.0]; P<.001; 95% CI 6-10). Increases in mindfulness at 1 month (nonreactivity subscale) mediated decreases in worry at 2 months (Penn State Worry Questionnaire; P=.02) and decreases in worry at 1 month mediated reductions in anxiety at 2 months (P=.03). CONCLUSIONS: To our knowledge, this is the first report on the efficacy and mechanism of an app-delivered MT for GAD. These findings demonstrate the clinical efficacy of MT as a digital therapeutic for individuals with anxiety (number needed to treat=1.6). These results also link recent advances in our mechanistic understanding of anxiety with treatment development, showing that app-delivered MT targets key reinforcement learning pathways, resulting in tangible, clinically meaningful reductions in worry and anxiety. Evidence-based, mechanistically targeted digital therapeutics have the potential to improve health at a population level at a low cost. TRIAL REGISTRATION: ClinicalTrials.gov NCT03683472; https://clinicaltrials.gov/ct2/show/NCT03683472.

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

BACKGROUND: Smoking is the leading cause of preventable death in the United States. Smoking cessation interventions delivered by smartphone apps are a promising tool for helping smokers quit. However, currently available smartphone apps for smoking cessation have not exploited their unique potential advantages to aid quitting. Notably, few to no available apps use wearable technologies, most apps require users to self-report their smoking, and few to no apps deliver treatment automatically contingent upon smoking. OBJECTIVE: This pilot trial tests the feasibility of using a smartband and smartphone to monitor and detect smoking and deliver brief mindfulness interventions in real time to reduce smoking. METHODS: Daily smokers (N=100, ≥5 cigarettes per day) wear a smartband for 60 days to monitor and detect smoking, notify them about their smoking events in real time, and deliver real-time brief mindfulness exercises triggered by detected smoking events or targeted at predicted smoking events. Smokers set a quit date at 30 days. A three-step intervention to reduce smoking is tested. First, participants wear a smartband to monitor and detect smoking, and notify them of smoking events in real time to bring awareness to smoking and triggers for 21 days. Next, a "mindful smoking" exercise is triggered by detected smoking events to bring a clear recognition of the actual effects of smoking for 7 days. Finally, after their quit date, a "RAIN" (recognize, allow, investigate, nonidentification) exercise is delivered to predicted smoking events (based on the initial 3 weeks of tracking smoking data) to help smokers learn to work mindfully with cravings rather than smoke for 30 days. The primary outcomes are feasibility measures of treatment fidelity, adherence, and acceptability. The secondary outcomes are smoking rates at end of treatment. RESULTS: Recruitment for this trial started in May 2021 and will continue until November 2021 or until enrollment is completed. Data monitoring and management are ongoing for enrolled participants. The final 60-day end of treatment data is anticipated in January 2022. We expect that all trial results will be available in April 2022. CONCLUSIONS: Findings will provide data and information on the feasibility of using a smartband and smartphone to monitor and detect smoking and deliver real-time brief mindfulness interventions, and whether the intervention warrants additional testing for smoking cessation. TRIAL REGISTRATION: ClinicalTrials.gov NCT03995225; https://clinicaltrials.gov/ct2/show/NCT03995225. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/32521.

Can Approaching Anxiety Like a Habit Lead to Novel Treatments?

Anxiety disorders make up the most prevalent class of mental illnesses. Given the growing prevalence of anxiety in the United States and beyond, there is an urgent clinical need to develop nonpharmacologic treatments that effectively treat and reduce its core symptoms (eg, worry). A leading theory posits that although worrying may be unpleasant, the immediate emotions that are avoided by concentrating on worry are often perceived as more aversive (eg, fear, anger, grief). From a mechanistic perspective, worry is thought to be learned and reinforced in a similar manner to other types of positively and negatively reinforced behaviors: habits. Mindfulness training, a practice that brings awareness to cognitive, affective, and physiological experiences, when delivered in-person via programs such as mindfulness-based stress reduction (MBSR) and mindfulness-based cognitive therapy (MBCT), has demonstrated effectiveness in reducing anxiety, but is difficult to scale in this manner. In this review, we explore novel approaches to using mindfulness training to specifically target the theoretical mechanisms underlying the perpetuation of anxiety (eg, worry as a habit), and the emergence of mobile health platforms (eg, digital therapeutics) as potential vehicles for remote delivery of treatment.

Awareness, affect, and craving during smoking cessation: An experience sampling study.

OBJECTIVE: Mindfulness has received attention in smoking cessation research, yet the mechanisms by which mindfulness may promote smoking cessation are not well understood. Mindfulness training may help individuals increase awareness and respond skillfully to processes that contribute to smoking, such as affective states and craving. This study used experience sampling (ES) to test how awareness was related to craving, positive and negative affect and smoking, in the moment, among smokers in treatment for smoking cessation. METHOD: Participants (N = 228) were part of a clinical trial evaluating Craving to Quit, a smartphone app for mindfulness training for smoking cessation, compared to an app delivering only ES. All participants were asked to complete 22 days of ES, with up to 6 ES surveys per day, measuring awareness, craving, positive and negative affect and smoking. Data were analyzed using multilevel linear modeling. RESULTS: Both at the within and between-person level, higher awareness was associated with higher positive affect, lower craving and lower negative affect. Lower within-person craving was associated with lower smoking. Within-person awareness, positive and negative affect were not significantly associated with smoking. At the between-person level, higher awareness and higher positive affect, and lower negative affect and lower craving were associated with lower smoking. CONCLUSIONS: Awareness of current experience was related to key psychological variables linked to behavior change in smoking cessation, namely positive and negative affect and craving, among smokers trying to quit. Future studies should test whether learning to increase awareness, such as through mindfulness training, may benefit smokers in treatment. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Negative Mood and Food Craving Strength Among Women with Overweight: Implications for Targeting Mechanisms Using a Mindful Eating Intervention.

OBJECTIVES: When experiencing negative mood, people often eat to improve their mood. A learned association between mood and eating may cultivate frequent food cravings, detracting from health goals. Training in mindful eating may target this cycle of emotion-craving-eating by teaching individuals to manage urges when experiencing negative mood. We examined the impact of a mobile mindful eating intervention on the link between negative mood and food cravings among overweight women. METHODS: In a single-arm trial, participants (n = 64, M age = 46.1 years, M BMI = 31.5 kg/m2) completed ecological momentary assessments of negative mood and food cravings 3 times/day for 3 days pre- and post-intervention, as well as 1-month post-intervention. Using multilevel linear regression, we compared associations between negative mood and food craving strength at pre- vs. post-intervention (model 1) and post-intervention vs. 1-month follow-up (model 2). RESULTS: In model 1, negative mood interacted with time point (ß = - .20, SE = .09, p = .02, 95% CI [- .38, - .03]) to predict craving strength, indicating that the within-person association between negative mood and craving strength was significantly weaker at post-intervention (ß = 0.18) relative to pre-intervention (ß = 0.38). In model 2, negative mood did not interact with time point to predict craving strength (ß = .13, SE = .09, p = .10, 95% CI - .03, .31]); the association did not significantly differ between post-intervention and 1-month follow-up. CONCLUSIONS: Training in mindful eating weakened the mood-craving association from pre- to post-intervention. The weakened association remained at follow-up. Our findings highlight the mood-craving link as a target-worthy mechanism of mindful eating that should be assessed in clinical trials. TRIAL REGISTRATION: NCT02694731. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12671-021-01760-z.

Awareness drives changes in reward value which predict eating behavior change: Probing reinforcement learning using experience sampling from mobile mindfulness training for maladaptive eating.

BACKGROUND AND AIMS: Maladaptive eating habits are a major cause of obesity and weight-related illness. The development of empirically-based approaches, such as mindfulness training (MT) that target accurate mechanisms of action to address these behaviors is therefore critical. Two studies were conducted to examine the impact of MT on maladaptive eating and determine the involvement of reinforcement learning mechanisms underlying these effects. METHODS: In Study1, maladaptive eating behaviors were assessed using self-report questionnaires at baseline and 8 weeks after an app-based MT intervention (n = 46). A novel mindful eating craving tool was embedded in our intervention to assess: eating behaviors (intake frequency/magnitude), and reward (contentment ratings) experienced after eating. Using a well-established reinforcement learning (Rescorla-Wagner) model, expected reward values (EV) were estimated as a function of contentment levels reported after eating. In Study2 (n = 1,119), craving tool assessments were examined in an independent sample using the app in a real-world naturalistic context. RESULTS: Study 1's results revealed a significant decrease in EV and eating behaviors across craving tool uses. In addition, changes in reward values predicted decreases in eating behaviors. Finally, Study 1's results revealed significant pre-post intervention reductions in self-reported eating behaviors. In Study2, we observed a significant decrease in EV, but not in eating behaviors, across craving tool uses. Study 2 also revealed a predictive relationship between EV and eating behaviors. DISCUSSION AND CONCLUSIONS: These results support the implementation of MT to prevent and treat maladaptive eating behaviors, which target reinforcement learning processes as mechanisms of action.

Mindfulness and Behavior Change.

Initiating and maintaining behavior change is key to the prevention and treatment of most preventable chronic medical and psychiatric illnesses. The cultivation of mindfulness, involving acceptance and nonjudgment of present-moment experience, often results in transformative health behavior change. Neural systems involved in motivation and learning have an important role to play. A theoretical model of mindfulness that integrates these mechanisms with the cognitive, emotional, and self-related processes commonly described, while applying an integrated model to health behavior change, is needed. This integrative review (1) defines mindfulness and describes the mindfulness-based intervention movement, (2) synthesizes the neuroscience of mindfulness and integrates motivation and learning mechanisms within a mindful self-regulation model for understanding the complex effects of mindfulness on behavior change, and (3) synthesizes current clinical research evaluating the effects of mindfulness-based interventions targeting health behaviors relevant to psychiatric care. The review provides insight into the limitations of current research and proposes potential mechanisms to be tested in future research and targeted in clinical practice to enhance the impact of mindfulness on behavior change.

Self-Regulation Without Force: Can Awareness Leverage Reward to Drive Behavior Change?

To reach longer-term goals and live aligned with their values, people typically must regulate their behavior. Effortful self-control is one way to achieve this and is usually framed as a forceful struggle between lower-level impulses and higher-level cognitive control processes. For example, people may restrain themselves from eating cake in order to lose weight. An alternative avenue of self-regulation draws on autonomous motivation: Individuals eat healthfully because it is values-congruent or intrinsically satisfying. Recent advances in the understanding of reward valuation on a neural level (e.g., ventromedial prefrontal cortex/orbitofrontal cortex) and emerging treatments on a clinical level (e.g., mindfulness training) suggest a possible mechanistic convergence between brain and behavior that is consistent with a shift from forced to unforced behavior change. Here we propose how an overlooked aspect of reinforcement learning can be leveraged using a simple yet critical feature of experience that is not reliant on willpower: Bringing awareness to one's subjective experience and behavior can produce a change in valuation of learned but unhealthy behaviors, leading to self-regulatory shifts that result in sustainable behavior change without force.

Can mindfulness mechanistically target worry to improve sleep disturbances? Theory and study protocol for app-based anxiety program.

OBJECTIVE: Anxiety is associated with sleep disturbance and insomnia. Mindfulness-based interventions, such as mindfulness-based stress reduction, have shown consistent anxiety reduction. Mindfulness training has been theorized to affect reinforcement learning, affecting habitual behaviors such as smoking and overeating, but a direct mechanistic link between the use of mindfulness training for anxiety reduction and improvement in sleep has not been studied. Moreover, the mechanisms by which mindfulness might affect worry and subsequent sleep disturbances have not been elucidated. This study protocol evaluates the impact an app-based mindfulness training program for anxiety might have on decreasing worry and improvement in sleep. METHOD: A randomized controlled study will be conducted in approximately 80 adults with worry that interferes with their sleep. Participants will be randomly allocated (1:1) to two groups: treatment-as-usual (TAU) or TAU + App-Based Mindfulness Training (Unwinding Anxiety app). The primary outcomes will be the non-reactivity subscale of the Five Facet Mindfulness Questionnaire and Patient-Reported Outcomes Measurement Information System sleep quality measures (Baer et al., 2008; Yu et al., 2011). Secondary outcomes will include the Penn State Worry Questionnaire, Generalized Anxiety Disorder-7, and Multidimensional Assessment of Interoceptive Awareness Scale (Mehling et al., 2012; Meyer, Miller, Metzger, & Borkovec, 1990; Spitzer, Kroenke, Williams, & Löwe, 2006). DISCUSSION: This study will be the first to test the mechanism of app-based mindfulness training on worry and sleep disturbance. Testing the mechanistic effects of mindfulness training using the science of behavior change framework will help move the field forward both in further elucidation of potential mechanisms of mindfulness (e.g., targeting reinforcement learning) and determining whether such a platform might be a viable method for delivering high-fidelity treatment at scale and for a low cost. (PsycInfo Database Record (c) 2020 APA, all rights reserved).