Evan Forman - US grants

DESCRIPTION (provided by applicant): The United States is in the midst of an obesity epidemic and innovative approaches to address this problem are urgently needed. Although traditional behavioral treatment is the most empirically validated approach for weight loss, it typically results in poor maintenance of weight control behaviors, and within three to five years most participants in these programs will have regained the weight they originally lost. Standard behavioral weight loss treatment might be improved by incorporating components that (a) bolster participants'commitment to behavior change, (b) build distress tolerance skills and (c) promote mindful awareness of eating behaviors and goals. Such components are well- represented within newer models of behavior therapies that incorporate principles and technologies of mindfulness, experiential awareness, values for changing behavior, acceptance of distressing internal experiences, and willingness to tolerate distress in the service of valued behavior change. The primary goal of the proposed project is to determine if an innovative behavioral weight loss program that incorporates new developments from the field of behavior therapy produces superior weight control than standard behavioral treatment. A sample of 128 adults with a BMI of 27 to 40 kg/m2 will be recruited via media advertisements in the greater Philadelphia community to participate in this study. Participants will be randomly assigned to one of two conditions: standard behavioral treatment (SBT) or acceptance-based behavioral treatment (ABBT). Each treatment will be delivered in a group format on a weekly, and then bi-weekly, basis. Treatment will last 40 weeks: weeks 1-20 focus on weight loss, while weeks 21-40 partially shift focus to weight loss maintenance. Assessments will be completed at baseline, 10 weeks, 20 weeks (i.e., end of weight loss treatment), 40 weeks (i.e., end of weight loss maintenance treatment), and at 6-month follow-up. PUBLIC HEALTH RELEVANCE: Obesity is reaching epidemic proportions. Standard behavioral treatment for obesity effectively induces weight loss, but long-term maintenance of weight loss remains challenging. The primary aim of the proposed study is to develop and conduct a preliminary evaluation of an innovative, acceptance-based behavioral weight loss treatment that is designed to promote long-term adherence to healthy eating and physical activity behaviors and thereby improve weight loss maintenance.

DESCRIPTION (provided by applicant): Obesity has become so prevalent it is now considered a national and global epidemic. The limitations of pharmacologic, surgical and environmental interventions point to the critical need for behavioral programs. However, standard behavioral treatments are only minimally effective in the long term, with most participants eventually gaining back their lost weight. Weight regain can be traced to participants' difficulty following recommended diet and physical activity prescriptions, which appears tied to immutable biological drives and internal (e.g., sadness, anxiety, cravings) and external (e.g., presence of delicious foods and labor- saving devices) cues. In our lab we have found that the ability to resist these cues is bolstered by interventions that increase distress tolerance (e.g., tolerance of negative effect, food cravings), present-moment awareness of internal states and how these may be affecting behavior (i.e., metacognitive awareness), and clarity about one's personal values. In particular, we have shown that acceptance-based behavioral interventions, which incorporate these strategies, have promise for weight control. Our NIH-funded pilot study demonstrated an especially strong effect of acceptance-based strategies on weight loss for participants who reported higher levels of depression, psychological responsivity to food, and internally and externally-cued eating. The primary goal of the proposed project is to evaluate the longer-term efficacy of ABT in relation to gold standard behavioral treatment for obesity. A secondary goal is to test hypothesized mechanisms of action of the two treatments, both during active intervention and during the post-treatment weight loss maintenance phase. Building on our recent work using lab-based behavioral measures and repeatedly-administered, ecological momentary assessment (EMA) to better understand the challenges of adopting and maintaining healthful behavior choices, we will use both these measurement strategies to most accurately capture causal pathways to in-the-moment eating and physical activity lapses. EMA offers considerable advantages over retrospective self-reports which are subject to inaccuracies and biases. We also aim to evaluate moderation hypotheses stating that the superiority of ABT will be especially pronounced for those with greater mood disturbance, sensitivity to the food environment, and internal and external disinhibited eating. Our aims work towards longer-range goals of using evidence to maximize the most effective components of interventions, matching patients to treatment type, and developing real-time interventions aimed at correcting problematic eating and physical activity decisions as they occur naturalistically. Accordingly, we will randomly assign 200 overweight participants to 24 sessions of ABT or SBT, delivered over one year. All participants will be followed until 36 months post-baseline. EMA and lab-based behavioral assessments will allow more valid measurement of moderating and mediating pathways including the relationship between internal states and their impact on behavior, and how these associations are affected by treatment. PUBLIC HEALTH RELEVANCE: Obesity is a leading cause of death in the U.S, and efforts to treat it are a national health priority. The primary goal of the proposed project is to evaluate th efficacy of an innovative weight loss intervention. This intervention will help participants learn skills for changing their diet and exercise behaviors in sustainable ways.

? DESCRIPTION (provided by applicant): Dietary choices, and in particular, excess calorie intake leading to obesity, are strong, but reversible risk factors for cancer. For example, foods high in solid fats and added sugars (SoFaS) are low-nutrient, high calorie foods that increase the risk of cancer by promoting weight gain. As such, the reduction of SoFaS is consistent with American Institute for Cancer Research and the American Cancer Society dietary recommendations. Behavioral interventions to alter diet have limited long-term efficacy, most likely because eating decisions are governed by automatic neurocognitive processes that are not addressed in conventional interventions. In particular, the ability to refrain from consuming unhealthy, but widely available, palatable foods, is increasingly understood to depend on inhibitory control, i.e., the ability to cut off action tendencies that are put in motion by innate drives towards rewarding behaviors. Recent work by our team and others have demonstrated that computer-based inhibitory control trainings result in short-term, specific changes in behavior, such as reducing intake of salty snack food, chocolate, and alcoholic beverages. An automatized, home computer-based inhibitory control training offers the potential of an inexpensive and highly disseminable method of lowering cancer risk across wide swaths of the population. As such, we aim to evaluate the feasibility, acceptability, mechanism of action, effectiveness and persistence of a home computer-based inhibitory control training. In particular, we hypothesize that a high-repetition training in inhibitory control will result in increased adherence to a low-SoFaS diet, and that effects will be mediated through improved inhibitory control. We further hypothesize the training will be most effective for those starting of with impaired inhibitory control, as well as those with strongest desire for palatable foods and those with strongest explicit health goals. Lastly, we aim to examine the impact of inhibitory control training on secondary outcomes, including on overall caloric intake, and on short-term weight loss. To achieve these aims, the proposed study will recruit 150 overweight and obese individuals who currently eat high-SoFaS diets, and who wish to improve their diets. Participants will be assigned a reduced-SoFaS diet for 12 weeks. After a baseline period, participants will be randomized to receive 6 weeks of either inhibitory control training or a sham training. The 6-week intervention will consist of 15 minutes per day of home computer- based inhibitory control training, and will be followed by a 2-week booster and then 2-week follow-up period. Dietary adherence will be measured via a customized smartphone app that will prompt repeated recording of targeted food consumption (i.e., ecological momentary assessment; EMA) and via automated 24-hour food recall. Neurocognitive variables will be assessed pre and post-training in order to test trainings' mechanism of action, and moderation will be assessed through baseline trait measures of explicit health goals, implicit attitudes towards appetitive stimuli, boy mass index, and responsivity to food cues.

The past decade has witnessed a surge of intelligent systems capable of providing personalized user experiences in many aspects of modern life. Personalized adaptive systems such as personalized search, social media filtering, and e-commerce recommendation systems have demonstrated potentials to improve productivity and enjoyment. However, by catering to the immediate preferences on individuals and de-emphasizing collective needs, these systems also contributed to emergent social issues such as intellectual isolation. This project seeks to understand how to reduce the current blind spots in personalized adaptive systems and will directly address two key challenges in personalized adaptive systems: how to balance 1) short- and long-term needs/preferences and 2) needs of multiple individuals in a group.

Specifically, this project investigates how to increase and sustain physical activity using personalized adaptive systems for health. Two thirds of the adult population in the U.S. are affected by overweight and obesity, with sedentary behavior as a primary cause. In addition to address a public health issue, the technology developed in this project will advance theories in human behavior science. The empirical data generated from the planned system can shed light on the dynamic nature of people's social comparison process and reactions. To address the aforementioned challenges, the team will investigate novel participant modeling algorithms, specifically designed to model dynamic participant characteristics. Additionally, the research will contribute to the literature of experience management by developing algorithms that exploit those participant models to adapt interactive experiences to groups of users rather than individuals. The approach is innovative in bootstrapping design theory, algorithmic innovation, and health behavior science in a synergistic way to make scientific advancement.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Project Summary/Abstract Behavioral weight loss treatment (BT), which teaches cognitive and behavioral skills, is the gold standard and first line of treatment for obesity. Outcomes, while clinically significant, are considered suboptimal in that many participants fail to reach and/or maintain the 5 and 10% benchmarks associated with key health benefits. Over the past 30 years, many trials have tested cognitive and behavioral innovations on standard BT skills, but thus far virtually none has produced significantly improved weight losses. Mindfulness and acceptance-based behavioral treatments (MABTs) for obesity is an exception in that rigorous trials have demonstrated considerably greater weight losses for this approach when directly compared to gold standard BT. Yet, MABTs? outcomes have varied, as have their composition. The ability to continue improving and successfully disseminating behavioral treatments for obesity depends on the field increasing its understanding of which MABT components are most efficacious. Consistent with a Phase I of a Multiphasic Optimization Strategy (MOST) approach, we reviewed theoretical accounts of MABTs and identified three key MABT components: (1) Mindful Awareness, (2) Mindful Acceptance, and (3) Values Clarity. Consistent with the second phase of a MOST, this study will utilize a full 2x2x2 factorial design in which 288 overweight/obese participants are assigned to one of eight behavioral weight loss treatments, i.e., representing each permutation of MABT components being included or excluded from the treatment. Due to the ability of a factorial design to pool conditions to examine each main effect, analyses have the same power as a two-arm design. The primary aim of the current study is to evaluate the independent efficacy of mindful awareness, mindful acceptance and values clarity components of MABT on weight loss (at post-treatment and at 6, 12 and 24 months follow-up) over and above standard BT. Secondary aims are to: (1) To evaluate the independent efficacy of mindful awareness, mindful acceptance and values clarity components of MABT on waist circumference, calorie intake, physical activity and quality of life; (2) To confirm that each treatment component impacts the variable which it targets; (3) To test the hypotheses that the efficacy of the treatment components is moderated by susceptibility to internal and external food cues. The exploratory aim is to quantify the component interaction effects, which may be synergistic, fully additive, or partially additive. This study investigates an innovative and especially promising behavioral approach, and will be one of the first to utilize a full factorial design for obesity intervention optimization. Furthermore, it will be the first-ever study to conduct a component analysis to discern independent efficacies of MABT components, and the first to examine interactions of behavioral weight loss components with each other and with baseline characteristics of participants. Results will position the field for future work, i.e., the evaluation of an optimized treatment that can be expected to have superior efficacy and disseminability.

Abstract Men in the United States have an exceptionally high prevalence of overweight and obesity, i.e., 71.3%, and 42% of men are currently attempting weight loss. However, men are dramatically underrepresented in weight loss programs. Men find conventional weight loss program (i.e., group-based, education and counseling- orientated, dietary/calorie-focused) unappealing because they involve receiving counseling, focus on replacing ?masculine? foods (e.g., meat) with ?feminine? ones (e.g., salad), and provide minimal personalization or autonomy. As such, attempts have been made to increase recruitment and appeal through targeted recruitment and adaptations to standard weight loss programs. However, these efforts have been disappointing. Mobile applications (mHealth apps) have attractive features, but have low male enrollment and poor efficacy as conventionally delivered. A gamified mHealth program offers the possibility of engaging men and enhancing efficacy given that (1) video gaming is highly appealing to men; (2) gamification features (e.g., digital rewards for attaining ?streaks? and milestones, competition) are known enhance enjoyment and motivation and facilitate desired behaviors; and (3) ?neurotraining? video games featuring repetitive action mechanics, adaptive difficulty, and feedback can train inhibitory control, a basic brain capacity to inhibit intrinsically-generated approach responses that is strongly linked to body mass and the consumption of high- calorie foods. Inhibitory control training (ICT) games have been successful at reducing consumption of targeted foods/beverages and improving short-term weight loss. For instance, in our preliminary work we demonstrated that a weight loss workshop plus a short, daily ICT produced greater weight loss for individuals with higher- than-average implicit preferences for high-sugar foods, compared to a robust attention control (i.e., the workshop plus a sham training), and we found that adding gamification elements (e.g., story, music, levels) to a rudimentary game produced additional 8-week weight loss for men (4.1% vs 2.5%). This project extends previous work by evaluating the independent effects of gamification and ICT on long-term engagement and outcomes. As such, 243 men with BMI ? 25 will be recruited, with 15 participating in usability testing and 228 assigned to a 12-month mHealth weight loss program that prescribes digital self-monitoring and dietary and physical activity targets. Utilizing an efficient 2 x 2 factorial design, participants will be randomized to receive either a standard or fully-gamified program, comprised of a behavior change program featuring team-based competition, and digital reinforcers for attainment of streaks and milestones, and also randomized to receive either sham or active inhibitory control neurotraining. Aims include evaluating the efficacy of gamification and ICTon weight loss, diet and physical activity at 12 months, as well as evaluating hypothesized mediators (engagement and inhibitory control) and moderators (baseline frequency of video game play and implicit preferences for ICT-targeted foods).

Abstract Seventy percent of American adults are overweight or obese, presenting an unprecedented challenge to the nation?s health systems. Effective behavioral programs exist, but these programs are intensive, long-term and require highly-trained clinicians, making them prohibitively expensive and thus limiting disseminability. Approaches to decreasing costs include replacing highly-trained clinicians with paraprofessionals, reducing contact frequency, and/or automating intervention. However, although these alternative interventions result in considerably lower average weight losses, variability of weight loss is high. Specifically, and consistent with a Supportive Accountability Model, a substantial minority of participants in high-intensity interventions receive no benefit, while a subset of those receiving low-intensity interventions achieve clinically significant weight loss. An ideal weight loss treatment system would enhance outcomes and reduce costs by matching each participant to the intervention he/she needs, thus adapting to participants? needs and conserving resources where they are not needed. Stepped care represents one such system, but has had mixed success and suffers from a number of shortcomings. The innovative artificial intelligence (AI) strategy of reinforcement learning (RL) provides rapidly and repeatedly-varying features of intervention, continuously learning which features provide optimal responses for which participants. Our team recently completed a pilot of an AI weight loss system in which overweight adults received a brief in-person weight loss intervention and then were randomly assigned to receive 3 months of non-optimized interventions (i.e., 12-minute phone calls) or an optimized combination of phone calls, text exchanges, and automated messages, selected based on each participants? response to each intervention as determined by weight and behavioral data. As hypothesized, we achieved equivalent weight losses at a fraction of the time cost. The proposed study would recruit 320 overweight adults, provide 1 month of group-based behavioral weight loss treatment and then randomize participants to either continue to receive group-based behavioral weight loss in a remote format for 11 months (BWL-S) or to reinforcement learning-based treatment (BWL-AI). In line with our Supportive Accountability model, BWL-AI would vary modality, intensity and counselor skill based on continuously-monitored participant digital data. The proposed study--the first of its kind--would expand on our pilot in several ways including sample size, duration, and features of intervention selected by the AI system. Aims of this project are to test the hypotheses that weight loss outcomes in BWL-AI will be equivalent to or better than BWL-S, and that the cost per participant and per kg of lost weight will be less in BWL-AI than in BWL-S. Other include characterizing the AI system (in terms of interventions selected), assessing feasibility and acceptability of the refined AI system, evaluating psychological and demographic predictors of AI intervention selection and investigating differences between responders and non-responders in how the AI system allocates resources.