Design of the SHARE study: A randomized controlled trial evaluating the regulation of cues treatment for adults with overweight or obesity and high food responsiveness.

Design of the SHARE Study: A Randomized Controlled Trial Evaluating the Regulation of Cues Treatment for Adults with Overweight or Obesity and High Food Responsiveness - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Contemp Clin Trials . Author manuscript; available in PMC: 2026 Jun 1. Published in final edited form as: Contemp Clin Trials. 2025 Jun 1;155:107970. doi: 10.1016/j.cct.2025.107970 Design of the SHARE Study: A Randomized Controlled Trial Evaluating the Regulation of Cues Treatment for Adults with Overweight or Obesity and High Food Responsiveness Kerri N Boutelle a, b, c, * , Dawn M Eichen a , Saori Obayashi a , Ellen K Pasquale c, d , David R Strong b , Allison S Tietz a , Kristie L Reed a , Carol B Peterson e Author information Article notes Copyright and License information a Department of Pediatrics, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093 USA b Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093 USA c Department of Psychiatry, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093 USA d San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, 6363 Alvarado Court, Suite 102, San Diego CA, 92120 USA e Department of Psychiatry and Behavioral Sciences, University of Minnesota, F282/2A West 2450 Riverside Ave, Minneapolis, MN 55454 USA * Corresponding Author: Kerri Boutelle: 9500 Gilman Drive, MC 0874, La Jolla, CA, 92093 USA, kboutelle@health.ucsd.edu Issue date 2025 Aug. PMC Copyright notice PMCID: PMC12248186  NIHMSID: NIHMS2091938  PMID: 40460908 The publisher's version of this article is available at Contemp Clin Trials Abstract Obesity is a heterogeneous disease influenced by individual behavioral factors, environment, genes, and neural processes. Behavioral weight loss (BWL), the current gold-standard treatment for overweight and obesity (OW/OB), does not produce sustained weight loss for all individuals. Appetitive traits, such as food responsiveness (FR), are risk factors that could account for differences in how individuals interact with today’s food environment and increase susceptibility for overeating and weight gain. Research shows that individuals high in FR have attenuated weight loss in BWL programs. We developed the Regulation of Cues (ROC) program to reduce overeating through improving sensitivity to appetitive cues and decreasing FR. In this study, we combined ROC with BWL recommendations (ROC+BWL), a treatment approach that may address the unique needs of this phenotype. The current study is a 3-arm randomized controlled trial comparing the ROC+BWL program to BWL and an active comparator on body mass index. Two hundred ninety-three adults with high FR and OW/OB were randomized to 1 of 3 treatment arms and will complete 6 months of treatment and assessment visits over 18 months: baseline, during treatment, post-treatment (6 months), 6-month follow-up (12 months) and 12-month follow-up (18 months). This study could provide important evidence regarding the ROC+BWL program among individuals with high FR and OW/OB and may inform future precision medicine approaches for OW/OB. Clinical trials # NCT05004883 Keywords: weight loss, obesity, regulation of cues, appetitive traits, food responsiveness, satiety responsiveness, behavioral weight loss 1. Introduction Obesity is a heterogeneous, complex disease influenced by individual behavioral factors, environment, genetics, and neural processes. 1 - 5 The current behavioral treatment for overweight and obesity (OW/OB) is behavioral weight loss (BWL), which includes recommendations on energy intake and physical activity and behavioral change skills. 6 - 8 Although BWL provides weight loss for some adults, 9 many BWL treatment programs produce modest results, 10 , 11 and most individuals are unable to maintain weight loss after treatment ends. 12 Genetically determined appetitive traits are risk factors that account for some of the differences in how individuals interact with today’s food environment. According to the behavioral susceptibility theory (BST), food responsiveness (FR; responsiveness to signals to start eating), and satiety responsiveness (SR; responsiveness to signals to stop eating), are two appetitive traits that influence eating behaviors and lead to weight gain. 13 - 16 Our research suggests that both children and adults with high FR have attenuated weight loss in BWL programs, suggesting that this could be a unique phenotype of individuals. 17 , 18 We developed the Regulation of Cues (ROC) program, which is based on the BST and targets both SR and FR. ROC reduces overeating through appetite awareness training to increase SR and cue-exposure treatment for food to decrease FR by extinguishing automatic associations between food cues and eating and ultimately increasing control over cravings. Our research shows that ROC facilitates decreases in weight and binge eating in adults. 17 , 19 In a randomized trial for individuals with OW/OB comparing ROC, ROC+BWL, BWL and active comparator (AC), we found that high FR moderated treatment outcomes. 17 Thus, this study is the next step and will test the effects of ROC+BWL as a targeted treatment approach for adults with high FR and OW/OB. We hypothesize that the ROC+BWL program can lead to more durable outcomes for this unique phenotype. 2. Study objectives The SHARE (Solutions for Hunger and Regulating Eating) study is a parallel group, randomized controlled trial in which 293 adults with high FR and OW/OB were randomized to one of 3 treatments: ROC+BWL, BWL, or an active comparator (AC). All treatments are 6 months and outcomes are assessed at post-treatment and 6- and 12-month follow-up (projected completion Spring 2026). The primary aim of the study is to evaluate whether ROC+BWL leads to differential changes in body mass index (BMI) compared to BWL and AC over treatment and follow-up. Secondary aims include evaluating whether BWL leads to differential changes in BMI compared to AC over treatment and follow-up and evaluating the effects of all 3 treatment groups on key mechanisms, including eating behaviors and cognitions (i.e., FR, SR, inhibition, restriction, and energy intake), feasibility and acceptability. Exploratory aims include evaluating moderators and mediators of the treatments on BMI over time. 3. Study design 3.1. Participants Participants are adults (N = 293; mean age = 43.5 (sd = 12.2) years; 88% female, mean BMI = 33.7 (sd = 5.0); 47.4% White, 29.7% Hispanic, 22.9% Other). Inclusion criteria were: aged 18–65 years; 25 kg/m 2 ≤ BMI ≤ 45 kg/m 2 ; ≥ 5th grade reading level in English; willing and able to participate in assessment visits and treatment sessions in-person at the study site; willing to maintain contact with the study team for 18 months; able to access a smartphone (for an ecological momentary assessment ancillary study); and high levels of self-reported FR (≥ 23 on the Food Cue Responsivity Scale (FCRS)). 20 Upper limits of age and BMI were placed due to specific considerations for weight loss treatment among geriatric participants and individuals with more severe OB. Of note, while the initial enrollment plan specified a final sample of 300 participants, some individuals were excluded post-randomization if they never came to treatment or if a study exclusion criteria was met but not revealed until after randomization (e.g., cancer diagnosis), resulting in a final sample of 293. Participants were excluded with the following criteria: history of a major medical condition such as diabetes or recent history of coronary heart disease; symptoms of angina, stroke, osteoarthritis, osteoporosis, orthopedic problems that would limit activity during the following 18 months; or any other serious medical condition that would make physical activity unsafe. Additional exclusion criteria included: bulimia or anorexia nervosa; significant cognitive impairment; psychotic disorder; acute suicidal ideation; moderate or severe substance use disorder; unstable psychiatric illness (e.g., psychiatric hospitalization in the past year); any medical, psychological problems, or medications that could make adherence to the study protocol difficult or dangerous; or any changes in psychotropic medication or other medication that could have impacted weight during the previous 3 months. Additionally, participants were excluded who were pregnant, lactating, or planning to become pregnant in the next 18 months; participating in another organized weight control program or using medication for weight loss; who have had bariatric surgery; or those planning to have surgery in the next 18 months. 3.2. Recruitment and retention Participants were recruited from the San Diego Metropolitan area using social media advertisements, flyers, ResearchMatch, email listservs, physician referrals, clinicaltrials.gov , and electronic medical records. Participants who responded to recruitment efforts completed an online screen and a phone screen to determine eligibility and then attended a remote study orientation via Zoom to learn more about the study. Those who remained interested consented to the study and were scheduled for their first assessment. Recruitment began in August 2021 and was completed in August 2024. Participants were recruited in cohorts to enable sufficient allocations to one of 3 groups. Allocations were block-randomized (block sizes ranged from 3-6) using sex (male, female) and BMI (low: 25-31.0; medium 31.1 - 37.0; or high >37) with blockrand 21 software by the data analyst who was blind to participant identifying information. To maximize participant retention, interventionists offered make-up sessions during treatment when participants were unable to attend. The study also collected contact information for up to 3 close friends or relatives to assist with contacting unresponsive participants. The study was approved by the Institutional Review Board at UC San Diego (201362) and written consent was obtained from all participants. The trial was pre-registered on clinicaltrials.gov ( NCT05004883 ). 3.3. Assessment and outcome measures All measures along with corresponding timepoints are listed in Table 1 . Assessments began in September 2021 and the final assessment is projected to be completed in Spring 2026. Participants complete assessments at 4 primary timepoints: baseline, post-treatment (month 6), 6-month follow-up (month 12) and 12-month follow-up (month 18). Assessments include anthropometry, self-report questionnaires, behavioral and psychophysiological tasks, and structured clinical interviews. Participants provided weight measurements weekly during treatment and at 3-month follow up (month 9) and 9-month follow up (month 15). Mediators of treatment effects are measured bi-monthly (every other month) during treatment. Data collection is conducted by trained staff and supervised by licensed clinical psychologists. Table 1. Measures and assessment timepoints Instrument Timepoint 1 (BSL) Trt Visits 2 (PTX) 3 (6M) 4 (12M) Anthropometry Height and weight * X X X X X Demographics Age, gender, ethnicity, income, education X Screening Eating Disorder Examination Version 17.0 (overeating and compensatory behaviors only) X X X X The MINI-International Neuropsychiatric Interview Version 7.0 X Medical history questions X X X X Food Cue Responsivity Scale X X X X X Food responsiveness Food Responsiveness scale of the Adult Eating Behavior Questionnaire X X X X X Reward-Based Eating Drive Scale-13 X X X X X Power of Food Scale X X X X X Laboratory Craving Assessment with Heart Rate Variability X X X X Attention Bias to food cues X X X X Eye tracking X X X X Satiety responsiveness Satiety Responsiveness scale of the Adult Eating Behavior Questionnaire X X X X X Intuitive Eating Scale-2 X X X X X Water load task X X X X Inhibition to food cues Stop Signal Task with food pictures X X X X Food Stroop X X X X Eating behaviors, intake, cognitions Food Craving Questionnaire-Trait Reduced X X X X X Eating in the Absence of Hunger Questionnaire for Adults X X X X Three Factor Eating Questionnaire-Revised 18 item X X X X Acceptability Treatment acceptability and liking questions X Open in a new tab * Height taken at baseline only. Weight also assessed at 3-month and 9-month follow-up. Abbreviations: BSL = baseline; PTX = post-treatment, 6M = 6-month follow-up; 12M = 12-month follow-up 3.4. Measures 3.4.1. Demographics and screening. Participants report age, gender, ethnicity, marital status, income, and education. Eating Disorder Examination (EDE Version 17.0). 22 - 24 The EDE is a structured clinical interview that assesses binge eating and disordered attitudes and behaviors related to eating, body-shape and weight. Only the binge eating/overeating and compensatory behavior sections are administered. At baseline, the EDE assessed for eating disorders that warranted study exclusion. The EDE is also administered at subsequent assessments to evaluate eating disorder symptoms over treatment and follow-up. The MINI-International Neuropsychiatric Interview Version 7.0. 25 , 26 The MINI is a structured clinical interview that assesses psychiatric conditions that warrant study exclusion. Medical History Questions. Participants reported their medical and psychiatric history and current medication use at baseline. Participants report any changes in medical status and medications throughout treatment and at follow-up. Food Cue Responsivity Scale (FCRS). 20 , 27 The FCRS is a 6-item questionnaire assessing food responsiveness. Participant scores ≥ 23 on the FCRS were required for study eligibility. 1 3.5.2. Anthropometry. Height is measured using a Seca 222 wall-mounted stadiometer in triplicate to the nearest 0.1 cm. Body weight in kilograms is measured in duplicate on a calibrated Tanita Digital Scale (Model WB-380S) and recorded to the nearest 0.1 kg. Participants remove their shoes for height and weight measurements. Height and weight are converted to BMI (kg/m 2 ). Participants are provided Bluetooth scales (Withings) to take at-home weight measurements weekly during the intervention and at 3-month and 9-month follow-up timepoints. 3.5.3. Food responsiveness Food Responsiveness (FR) scale of Adult Eating Behavior Questionnaire (AEBQ). 28 , 29 The 4-item FR subscale of the AEBQ measures an individual’s reactivity to food cues in the environment. Reward-Based Eating Drive Scale-13 (RED-13). 30 The RED-13 is a 13-item questionnaire that assesses reward-based eating, including lack of satiety, preoccupation with food, and lack of control over eating. Power of Food Scale (PFS). 31 , 32 The PFS is a 15-item questionnaire that assesses an individual's drive to consume highly palatable foods. Heart Rate Variability (HRV). Participants complete a laboratory-based assessment protocol to assess changes in psychophysiological responses (HRV) to food cues. 33 Participants sit quietly in an assessment room for 6 minutes to establish a baseline, and then are exposed to a preferred palatable food. Over the course of 6 minutes participants are prompted to hold and smell the food at alternating 30-second intervals and rate their cravings on a 0 - 6 scale. Finally, the food is removed, and participants sit quietly for another 6 minutes. Attention Bias to Food Cues. This dot-probe task measures reaction time to food and neutral cues. Two stimuli (food and neutral [e.g., furniture]) are presented top and bottom on a computer screen followed by a probe appearing in the location of one of the stimuli. Participants are instructed to respond as quickly as possible by pressing a corresponding key on the computer’s keyboard to indicate the location of the probe. The task was adapted from an attention bias task for anxiety disorders using food and neutral pictures. 34 , 35 Eye Tracking. Eye tracking is measured during the dot-probe task. Three attention bias scores are derived from the eye movement data for the food and neutral stimuli: gaze direction bias scores, initial gaze duration bias scores, and gaze dwell time bias scores. 3.5.4. Satiety responsiveness Satiety Responsiveness (SR) scale of the AEBQ. 28 , 29 The 4-item SR subscale of the AEBQ measures an individual’s ability to respond to internal satiety signals. Intuitive Eating Scale-2 (IES-2). 36 The Intuitive Eating Scale-2 is a 23-item questionnaire that measures a participant's tendency to eat in response to physical hunger and their body's needs. Water Load Task (WLT). 37 , 38 The WLT assesses gastric sensitivity. Participants drink room temperature, non-carbonated water ad libitum from an unmarked flask containing 1500 mL of water hidden from their view until reaching the first signs of fullness (step 1). Participants then drink water from a second hidden flask until they feel the signs of maximum fullness (step 2). The volume of the water consumed in both steps is recorded and total volume is calculated. Participant’s gastric interoception is calculated as percentage of the volume measured in step 1 to the total volume (sum of steps 1 and 2). 3.5.5. Inhibition to food cues Stop Signal Task (SST) with Food Stimuli. 39 - 41 The SST measures response inhibition to food and nonfood stimuli. The “go” condition is a choice reaction time task in which participants respond to stimuli with a key press. A visual stop signal is presented on 25% of trials (“stop” trials) in which participants are instructed to withhold responding. Higher SST reaction times indicate decreased inhibitory control. Food Stroop Task. 42 - 44 The Food Stroop Task measures information processing biases utilizing cognitive interference. The Food Stroop Task measures differences in reaction times to naming the color of food-related versus neutral words. Slower naming of food words indicates an interference with the primary color-naming task. 3.5.6. Eating behavior, dietary intake, and eating cognitions Food Craving Questionnaire-Trait-Reduced (FCQ-T-r). 45 , 46 The FCQ-T-r is a 15-item questionnaire that assesses food cravings and evaluates food craving traits. Eating in the Absence of Hunger-Adult (EAH-A). 47 , 48 The EAH-A is a 14-item questionnaire for adults that assesses the frequency of initiating and continuing to eat in the absence of hunger in response to internal and external experiences. Three Factor Eating Questionnaire (TFEQ-R18). 49 The TFEQ-R18 is an 18-item questionnaire that assesses three cognitive and behavioral domains of eating: cognitive restraint, disinhibition, and hunger. 3.5.7. Treatment Feasibility and Acceptability Feasibility is assessed by attendance and retention. Acceptability is assessed using a self-report survey created by the study team which includes liking and perceived usefulness of the intervention. 4. Intervention Individuals were randomly assigned to 1 of 3 conditions, ROC+BWL, BWL and AC, by gender and BMI. All 3 group treatments included twenty 90-minute group sessions over 6 months. Groups met weekly for the first 16 weeks and biweekly (every other week) for the final 2 months. All treatments included a mix of didactic teaching, discussion, and experiential activities. Key differences between the treatment arms are outlined in Table 2 and are described below. Due to the COVID-19 pandemic, the first cohort of participants ( n = 47) completed treatment remotely via password-protected Zoom meetings. The subsequent cohorts participated in in-person treatment. Table 2. ROC+BWL, BWL and AC topics during 6-month intervention period ROC+BWL BWL AC Dietary prescription Restricted calories to 1200-1400/day or more based on weight. Participants learned to control physiological and psychological responding to food, as well as included information on the USDA’s MyPlate, energy and nutrient density, and the glycemic index to choose foods to stay full longer. Restricted calories to 1200-1400/day or more based on weight using a low-fat, low-calorie diet. Sessions included information on energy and nutrient density, healthy eating recommendations from the USDA’s MyPlate guidelines, learning about food labels, food shopping, cooking and portion control. Healthy eating using the USDA’s MyPlate guidelines. Participants learned about food labels, shopping, cooking and portion control. Self-monitoring Food intake, hunger and fullness, caloric intake, cravings, and out-of-session exposure practice. Food and caloric intake None Experiential learning Participants brought meals and/or palatable foods to each session. Hunger and satiety were monitored in-session during meals and participants learned to practice mindful eating. Exposures to highly craved foods were conducted in-session while cravings were monitored. Occasional in-session activities including: meal planning, review menus and choose healthy choices for dining out, breaking behavior chains. Mindfulness activities Physical activity prescription Physical activity was prescribed to decrease cravings and help regulate responding to food cues, as well as to increase energy expenditure to assist in weight loss. Recommended physical activity goal of 250 minutes/week. Physical activity was prescribed to increase energy expenditure to assist in weight loss. Recommended physical activity goal of 250 minutes/week. Physical activity was recommended for health and stress management. Recommended physical activity goal of 250 minutes/week. Stimulus control Tolerance and master of physiological and psychological arousal to food cues, removing palatable energy dense foods from the home, reducing cues in the home paired with eating, and planning ahead for high-risk eating situations. Recommended removing palatable energy dense foods from the home, planning ahead for eating in restaurants and parties, and avoiding or planning ahead for high-risk eating situations. None Goal setting SMART goal setting focused on self-monitoring (food intake, hunger, calories, cravings, physical activity) and adherence to diet and physical activity recommendations. SMART goal setting focused on self-monitoring (food intake, calories, physical activity), and adherence to diet and physical activity recommendations. None Coping skills Methods for managing psychological and physiological arousal. Discussed each week regarding mastery and tolerance of physiological /psychological arousal. Discussed in terms of how to cope to reduce barriers to diet and physical activity adherence. Social support, self-compassion, self-care Problem solving Reducing barriers to diet and physical activity adherence, identifying contextual cues and triggers for cravings and overeating, problem solving for high-risk situations, and using behavioral chains to break unhealthy habits. Reducing barriers to diet and physical activity adherence, problem solving for high-risk situations, and using behavioral chains to break unhealthy habits. None Cognitive restructuring Thoughts as contexts for overeating; identifying and restructuring thoughts that cause overeating or giving in to cravings. None None Other health issues Some stress management tips. Some stress management tips. Greater emphasis on sleep and stress management. Open in a new tab 4.1. Regulation of Cues with Behavioral Weight Loss (ROC+BWL) ROC+BWL targets increases in SR via appetite awareness training and decreases in FR via cue-exposure treatment for food. In addition to psychoeducation, coping skills, and experiential learning exercises, ROC+BWL incorporates dietary and energy intake recommendations from BWL. The first phase of ROC+BWL focuses on managing overeating by training SR and reducing energy intake. Each session includes psychoeducation surrounding environmental, biological, and psychological influences that lead to overeating. Participants bring dinner to group and eat while monitoring their hunger and satiety levels throughout and after the meal (sessions 2-19). Conditions are manipulated to simulate eating and monitoring hunger under different conditions ( e.g., hungry, full, bored). Coping skills are presented to use during high-risk circumstances, and to help reduce intake ( e.g., deep breathing, relaxation, distraction). The second phase of ROC+BWL focuses on managing FR through toleration of cravings and inhibitory learning. Psychoeducation is provided about classical conditioning and learning theory, and surrounding cue identification, prediction error, and habituation. During visits 10–19, participants learn to assess and rate their cravings (defined as urges to eat when not physically hungry). Participants create a craving hierarchy and bring their own highly craved foods to the group. Using their highly craved foods, they complete 2 exposures at most sessions (starting at session 10) when physically sated, one of which is an imaginal exposure exercise. During the 5-minute exposure, participants rate their cravings and confidence in resisting the food at 30-second intervals on a scale of 1-10 (1 = “not at all” and 10 = “highest possible”) while looking at the food, holding the food, smelling the food, and after taking 2 small bites of the food. The participants then dispose of the remaining food. Participants rate their expectations prior to the exposure, and afterward, process their experience and outcomes to highlight prediction error. Participants are encouraged to practice out-of-session exposures to increase generalizability of skills. Core tenets of BWL ( e.g., self-monitoring, energy density, caloric reduction, stimulus control, problem solving) are integrated with ROC. Information on more peripheral BWL strategies ( e.g., cooking and baking tips, healthy dining out) are provided to participants as handouts but not discussed in-session due to limited time in incorporating both ROC and BWL (see Table 2 ). Participants are instructed to self-monitor their food intake, hunger levels, and physical activity daily with an application created by the study team, a calorie tracking app of their choice, or pen and paper. Starting in session 1, participants are taught to self-monitor their hunger and satiety levels on a 1-5 scale, with 1 = “starving” and 5 = “stuffed”, before, during, and after each meal or snack consumed. Starting in session 2, participants are also taught to self-monitor their energy intake and are provided a target calorie range starting at session 3 to achieve 1-2 lb. weight loss per week. Calorie tracking is presented as a secondary skill to hunger and satiety monitoring to aid in reducing overall intake and choosing more nutrient-dense foods. Halfway through the program (after session 10), participants are instructed to add self-monitoring of cravings, resisting highly craved foods, and practicing out-of-session exposures. Participants rate their craving and monitor contextual information surrounding their cravings and their confidence in resisting next time. 4.2. Behavioral Weight Loss (BWL) The BWL program includes recommendations about energy intake and physical activity and behavior change skills. 6 - 8 All participants are instructed to consume a balanced deficit diet following a provided calorie range and eating guidelines from the US Department of Agriculture’s MyPlate Guidelines. 50 Individualized calorie ranges are provided on a monthly basis based on body weight to achieve a 1-2 lb. weight loss per week. Participants are instructed to measure portion sizes, self-monitor food intake, and reduce caloric intake. Behavior change skills include stimulus control, self-monitoring, goal setting, managing high-risk situations, meal planning, problem solving, social support, motivation enhancement skills, relapse prevention skills, and skills for maintaining weight loss. Participants are instructed to self-monitor their food intake, caloric intake, and physical activity daily with an application created by the study team, a calorie tracking app of their choice, or pen and paper. 4.3. Active Comparator (AC) AC includes psychoeducation about nutrition, stress management and social support. Participants are provided information about reading food labels and different “fad” diets. Participants are provided psychoeducation about how stress leads to weight gain as well as mindfulness-based stress reduction, sleep hygiene, and time management. Assertiveness training along with conflict management skills are also included to build positive support networks. Participants complete a mindfulness exercise at each session and are encouraged to practice mindfulness at home. Participants in the AC group are not instructed to self-monitor. 4.4. Physical activity recommendations Participants in all 3 treatment arms receive the same goal of engaging in at least 250 minutes of moderate-to-vigorous physical activity per week. Participants are additionally encouraged to increase lifestyle activity (e.g., parking car further from the store, take stairs instead of elevator) in their weekly routine. In all 3 arms, physical activity is recommended to increase energy expenditure to assist in weight loss. In ROC+BWL, physical activity is also recommended to improve self-regulation and to tolerate both physiological and psychological urges to overeat. Participants in all arms are instructed to increase physical activity gradually to avoid injury; in ROC+BWL and BWL participants are encouraged to use shaping to build up their activity levels in duration and frequency over time. 4.5. Training and supervision A licensed clinical psychologist conducted weekly supervision to ensure adherence to assessment procedures, consensus among interviewers, and to determine whether individuals met inclusion criteria. Interventionists included registered dieticians, postdoctoral fellows, clinical psychologists, marriage and family therapists, and advanced graduate students in clinical psychology or marriage and family therapy. All interventionists completed training in their assigned treatment and attended weekly supervision with a licensed psychologist. All groups used standardized manuals and were audiotaped to prevent drift. 4. Statistical analyses 5.1. Sample size and power considerations We planned study recruitment to achieve a target sample of 300 participants to be allocated 1:1:1 for a 3-group parallel design to evaluate the efficacy of ROC+BWL to AC and BWL interventions. The primary outcomes of weight loss (%) and BMI assessments monthly during treatment and at 3-, 6-, 9- and 12-month follow-ups will be evaluated with a linear mixed-effects model (LME). The sample size was selected to ensure detection of expected improvements of ROC+BWL over AC and BWL using effect sizes reflecting a decrease in BMI d=−0.4 for ROC+BWL vs BWL and −0.7 for ROC+BWL vs AC, a moderate and clinically significant change. Empirical power estimates were assessed by generating multivariate random samples that were matched to the expected BMI for each condition using the same correlation structure of assessments over time as observed in a previous study. 17 The percentage of datasets with significant effects for the primary hypothesis comparing ROC+BWL to AC or BWL (i.e., p < .05) provided a simulation-based estimate of power for the primary hypothesis. With a median treatment effect of d=−0.40 (sd= 0.13) for ROC+BWL vs BWL and d = −0.35 (sd= 0.12) for BWL vs AC across 1000 data sets, the planned design would provide greater than 0.90 and 0.80 power for detecting the treatment differences with allowance for up to 20% lost to follow up. Empirical power analyses suggested this sample also will allow mediation analyses to sustain power >0.80 given expected medium effect of ROC+BWL on primary mediators and small-to-medium effects of primary mediators on changes in BMI. 51 Accrual of participants was maximized to enable sufficient group sizes which yielded a final sample of 293 participants. Re-estimation of empirical power with the obtained sample of 293 supported power of 0.88 and 0.80 with the a priori estimated effects and accounting for up to 20% lost to follow up. 5.2. Data analyses All analyses will include an intention-to-treat approach, and missing data will be evaluated prior to analyses. All models will include gender and baseline BMI as planned covariates given blocked allocation design factors along with age and race/ethnicity. 5.2.1. Primary Objective 1 We will evaluate main effects of treatment allocation on change in BMI monthly during treatment and 3-, 6-, 9- and 12-month follow-ups for ROC+BWL compared to BWL and AC. LME models will include 2 dummy-coded indicators for planned treatment comparisons and an appropriate term for time that will be selected after evaluating non-linearity assumptions. 5.2.2. Secondary Objective 1 We will construct contrasts for the model of BMI over time to estimate the main effects of BWL compared to AC monthly during treatment and at 3-, 6-, 9- and 12-month follow-ups. Evaluation of additional contrasts will be adjusted for multiple comparisons using the Benjamini-Hochberg adjustment. 52 5.2.3. Secondary Objective 2 The extent to which treatment allocation affects FR, SR, food inhibition, restriction, caloric intake, and eating cognitions and behaviors over the course of the treatment and follow-up will also be evaluated using generalized LME using models with planned covariates mirroring primary outcomes. 5.2.4. Secondary Objective 3 Feasibility and acceptability will be evaluated across treatment conditions. Between-group differences in participant attendance, self-monitoring rates (excluding AC), measures of liking, and perceptions of effectiveness of each treatment will be evaluated with dummy-coded indices for each treatment condition in regression models along with individual characteristics that may suggest differential relationships with these outcomes. 5.2.5. Exploratory Objectives A series of analyses will explore whether variables including FR, SR, food inhibition, restriction, and food cognitions mediate the impact of ROC+BWL vs BWL and AC on BMI using a multiple mediator models. 53 , 54 Potential moderators will also be evaluated by adding a set of 2 interaction effects of dummy-coded treatment indicators for gender, age, education, and baseline BMI to the LME models assessing BMI and for primary objective 1 and secondary objective 2. Significance of moderators will be evaluated with an adjustment for multiple tests using Benjamini Hochberg procedures. 52 5.3. Missing data We will conduct exploratory analyses to determine if baseline characteristics predict any patterns of missingness. We will perform missing not at random (MNAR) sensitivity analyses using pattern mixture models. For infrequently observed patterns we will apply the Hedeker and Gibbons approach that uses a binary variable in the model to denote missing data at one or more timepoints. 55 Any significant (p<0.10) predictors of missingness will be included as covariates with multiple imputation methods 56 , 57 to pool final model estimates during primary outcome analyses and exploratory examination of moderators and mediators. 58 5. Discussion The SHARE study is an ongoing, fully powered, randomized control trial comparing ROC+BWL to BWL and AC for adults with high FR and OW/OB over 18 months. The ROC+BWL program uniquely targets FR, SR and includes energy reduction skills to reduce overeating, increase the ability to tolerate cravings, promote eating according to hunger and satiety cues, and increase physical activity to lead to weight loss. The SHARE study will provide important information about whether the ROC+BWL program leads to superior BMI reduction as compared to BWL and AC for adults with high FR and OW/OB. Additionally, this trial will also determine the effect of all 3 arms on FR, SR, inhibition, restriction, caloric intake, and eating cognitions over the course of the treatment and follow-up. Furthermore, to learn more about the effects of the 3 arms on adults with high FR and OW/OB, we will evaluate impacts of mediators and moderators of treatment effects on BMI change over time. As research suggests individuals with high FR and OW/OB have attenuated weight loss in BWL programs, the ROC+BWL program could contribute to precision medicine approaches for treating OW/OB. Acknowledgements We would like to acknowledge all the participants as well as the staff at CHEAR, without whom this study would not be possible. Funding This work was supported by the National Institutes of Health [R01DK122504] and the UC San Diego CTRI grant UL1TR001442. The National Institute of Health had no role in the study design, writing of the report and decision to submit the article for publication. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Footnotes 1 Of note, the first cohort was enrolled with high FR requirement ≥ 20 on the FCRS that was subsequently increased to require a score ≥ 23 in April 2022 due to FR scores averaging too low in the first cohort. References 1. Lowe MR. Self-regulation of energy intake in the prevention and treatment of obesity: Is it feasible? Obes Res. 2003;11(S10):44S–59S. doi: 10.1038/oby.2003.223 [ DOI ] [ PubMed ] [ Google Scholar ] 2. Kessler DA. The End of Overeating: Taking Control of the Insatiable American Appetite. Rodale; 2010. [ Google Scholar ] 3. Berthoud HR. Neural control of appetite: cross-talk between homeostatic and non-homeostatic systems. Appetite. 2004;43(3):315–317. doi: 10.1016/j.appet.2004.04.009 [ DOI ] [ PubMed ] [ Google Scholar ] 4. Ahima RS, Prabakaran D, Mantzoros C, et al. Role of leptin in the neuroendocrine response to fasting. Nature. 1996;382(6588):250–252. doi: 10.1038/382250a0 [ DOI ] [ PubMed ] [ Google Scholar ] 5. Yang W, Kelly T, He J. Genetic epidemiology of obesity. Epidemiol Rev. 2007;29(1):49–61. doi: 10.1093/epirev/mxm004 [ DOI ] [ PubMed ] [ Google Scholar ] 6. Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393–403. doi: 10.1056/NEJMoa012512 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 7. Look AHEAD Research Group, Pi-Sunyer X, Blackburn G, et al. Reduction in weight and cardiovascular disease risk factors in individuals with type 2 diabetes: one-year results of the look AHEAD trial. Diabetes Care. 2007;30(6):1374–1383. doi: 10.2337/dc07-0048 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 8. Wadden TA, Butryn ML, Wilson C. Lifestyle modification for the management of obesity. Gastroenterol. 2007;132(6):2226–2238. doi: 10.1053/j.gastro.2007.03.051 [ DOI ] [ PubMed ] [ Google Scholar ] 9. Wing RR, Hamman RF, Bray GA, et al. Achieving weight and activity goals among diabetes prevention program lifestyle participants. Obes Res. 2004;12(9):1426–1434. doi: 10.1038/oby.2004.179 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 10. Wadden TA, West DS, Neiberg RH, et al. One-year weight losses in the Look AHEAD Study: Factors associated with success. Obesity. 2009;17(4):713–722. doi: 10.1038/oby.2008.637 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 11. Franz MJ, Boucher JL, Rutten-Ramos S, VanWormer JJ. Lifestyle weight-loss intervention outcomes in overweight and obese adults with type 2 diabetes: A systematic review and meta-analysis of randomized clinical trials. J Acad Nutr Diet. 2015;115(9):1447–1463. doi: 10.1016/j.jand.2015.02.031 [ DOI ] [ PubMed ] [ Google Scholar ] 12. Group LAR, Chao AM, Wadden TA, et al. Weight change 2 years after termination of the intensive lifestyle intervention in the Look AHEAD Study. Obesity. 2020;28(5):893–901. doi: 10.1002/oby.22769 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 13. Carnell S, Wardle J. Appetite and adiposity in children: evidence for a behavioral susceptibility theory of obesity. Am J Clin Nutr. 2008;88(1):22–29. doi: 10.1093/ajcn/88.1.22 [ DOI ] [ PubMed ] [ Google Scholar ] 14. Llewellyn C, Wardle J. Behavioral susceptibility to obesity: Gene–environment interplay in the development of weight. Physiol Behav. 2015;152:494–501. doi: 10.1016/j.physbeh.2015.07.006 [ DOI ] [ PubMed ] [ Google Scholar ] 15. Carnell S, Benson L, Pryor K, Driggin E. Appetitive traits from infancy to adolescence: Using behavioral and neural measures to investigate obesity risk. Physiol Behav. 2013;121: 10.1016/j.physbeh.2013.02.015. doi: 10.1016/j.physbeh.2013.02.015 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 16. Llewellyn CH, Fildes A. Behavioural Susceptibility Theory: Professor Jane Wardle and the role of appetite in genetic risk of obesity. Curr Obes Rep. 2017;6(1):38–45. doi: 10.1007/s13679-017-0247-x [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 17. Boutelle KN, Eichen DM, Peterson CB, et al. Effect of a novel intervention targeting appetitive traits on body mass index among adults with overweight or obesity: A randomized clinical trial. JAMA Netw Open. 2022;5(5):e2212354. doi: 10.1001/jamanetworkopen.2022.12354 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Boutelle KN, Kang Sim DE, Manzano M, Rhee KE, Crow SJ, Strong DR. Role of appetitive phenotype trajectory groups on child body weight during a family-based treatment for children with overweight or obesity. Int J Obes. 2019;43(11):2302–2308. doi: 10.1038/s41366-019-0463-4 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 19. Boutelle KN, Knatz S, Carlson J, Bergmann K, Peterson CB. An open trial targeting food cue reactivity and satiety sensitivity in overweight and obese binge eaters. Cogn Behav Pract. 2017;24(3):363–373. doi: 10.1016/j.cbpra.2016.08.003 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 20. Kang Sim DE, Eichen DM, Strong DR, Manzano MA, Boutelle KN. Development and validation of the food cue responsivity scale. Physiol Behav. 2023;258:114028. doi: 10.1016/j.physbeh.2022.114028 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 21. Snow G, Blockrand: Randomization for block random clinical trials. 2020;R package version 1.5. https://CRAN.R-project.org/package=blockrand . [ Google Scholar ] 22. Cooper Z, Fairburn C. The eating disorder examination: A semi-structured interview for the assessment of the specific psychopathology of eating disorders. Int J Eat Disord. 1987;6(1):1–8. doi: 10.1002/1098-108X(198701)6:1<1::AID-EAT2260060102>3.0.CO;2-9 [ DOI ] [ Google Scholar ] 23. Fairburn C, Cooper Z, O’Connor M. Eating Disorder Examination Version 17. CREDO; 2014. [ Google Scholar ] 24. Berg KC, Peterson CB, Frazier P, Crow SJ. Psychometric evaluation of the eating disorder examination and eating disorder examination-questionnaire: a systematic review of the literature. Int J Eat Disord. 2012;45(3):428–438. doi: 10.1002/eat.20931 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. Lecrubier Y, Sheehan DV, Weiller E, et al. The Mini International Neuropsychiatric Interview (MINI). A short diagnostic structured interview: reliability and validity according to the CIDI. Eur Psychiatry. 1997;12(5):224–231. doi: 10.1016/S0924-9338(97)83296-8 [ DOI ] [ Google Scholar ] 26. Sheehan DV, Lecrubier Y, Sheehan KH, et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry. 1998;59 Suppl 20:22–33;quiz 34-57. [ PubMed ] [ Google Scholar ] 27. Pasquale EK, Strong DR, Eichen DM, Peterson CB, Kang-Sim DE, Boutelle KN. Validation of a laboratory craving assessment and evaluation of 4 different interventions on cravings among adults with overweight or obesity. Appetite. Published online June 20, 2024:107575. doi: 10.1016/j.appet.2024.107575 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 28. Hunot C, Fildes A, Croker H, Llewellyn CH, Wardle J, Beeken RJ. Appetitive traits and relationships with BMI in adults: Development of the Adult Eating Behaviour Questionnaire. Appetite. 2016;105:356–363. doi: 10.1016/j.appet.2016.05.024 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 29. Pasquale EK, Strong DR, Manzano MA, Eichen DM, Peterson CB, Boutelle KN. Exploring relationships among appetitive traits, negative affect, and binge eating in adults with overweight or obesity. Eat Behav. 2024;53:101871. doi: 10.1016/j.eatbeh.2024.101871 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 30. Epel ES, Tomiyama AJ, Mason AE, et al. The reward-based eating drive scale: a self-report index of reward-based eating. PloS One. 2014;9(6):e101350–e101350. doi: 10.1371/journal.pone.0101350 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 31. Lowe MR, Butryn ML, Didie ER, et al. The Power of Food Scale. A new measure of the psychological influence of the food environment. Appetite. 2009;53(1):114–118. doi: 10.1016/j.appet.2009.05.016 [ DOI ] [ PubMed ] [ Google Scholar ] 32. Espel-Huynh HM, Muratore AF, Lowe MR. A narrative review of the construct of hedonic hunger and its measurement by the Power of Food Scale. Obes Sci Pract. 2018;4(3):238–249. doi: 10.1002/osp4.161 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 33. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation. 1996;93(5):1043–1065. [ PubMed ] [ Google Scholar ] 34. Amir N, Elias J, Klumpp H, Przeworski A. Attentional bias to threat in social phobia: facilitated processing of threat or difficulty disengaging attention from threat? Behav Res Ther. 2003;41(11):1325–1335. doi: 10.1016/S0005-7967(03)00039-1 [ DOI ] [ PubMed ] [ Google Scholar ] 35. Hendrikse JJ, Cachia RL, Kothe EJ, McPhie S, Skouteris H, Hayden MJ. Attentional biases for food cues in overweight and individuals with obesity: a systematic review of the literature. Obes Rev. 2015;16(5):424–432. doi: 10.1111/obr.12265 [ DOI ] [ PubMed ] [ Google Scholar ] 36. Tylka T, Kroon Van Diest A. The Intuitive Eating Scale–2: Item refinement and psychometric evaluation with college women and men. J Couns Psychol. 2013;60(1):137–153. doi: 10.1037/a0030893 [ DOI ] [ PubMed ] [ Google Scholar ] 37. van Dyck Z, Vögele C, Blechert J, Lutz APC, Schulz A, Herbert BM. The water load test as a measure of gastric interoception: Development of a two-stage protocol and application to a healthy female population. PLoS ONE. 2016;11(9):e0163574–e0163574. doi: 10.1371/journal.pone.0163574 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 38. Robinson E, Foote G, Smith J, Higgs S, Jones A. Interoception and obesity: a systematic review and meta-analysis of the relationship between interoception and BMI. Int J Obes 2005. 2021;45(12):2515–2526. doi: 10.1038/s41366-021-00950-y [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 39. Logan GD, Cowan WB. On the ability to inhibit thought and action: A theory of an act of control. Psychol Rev. 1984;91(3):295–327. doi: 10.1037/0033-295X.91.3.295 [ DOI ] [ PubMed ] [ Google Scholar ] 40. Logan GD. On the ability to inhibit thought and action: A users’ guide to the stop signal paradigm. In: Inhibitory Processes in Attention, Memory, and Language. Academic Press; 1994:189–239. [ Google Scholar ] 41. Verbruggen F, Logan GD, Stevens MA. STOP-IT: Windows executable software for the stop-signal paradigm. Behav Res Methods. 2008;40(2):479–483. doi: 10.3758/BRM.40.2.479 [ DOI ] [ PubMed ] [ Google Scholar ] 42. Stroop JR. Studies of interference in serial verbal reactions. J Exp Psychol. 1935;18(6):643–662. doi: 10.1037/h0054651 [ DOI ] [ Google Scholar ] 43. Ben-Tovim DI, Walker MK, Fok D, Yap E. An adaptation of the Stroop Test for measuring shape and food concerns in eating disorders: A quantitative measure of psychopathology? Int J Eat Disord. 1989;8(6):681–687. [ Google Scholar ] 44. Ben-Tovim DI, Walker MK. Further evidence for the stroop test as a quantitative measure of psychopathology in eating disorders. Int J Eat Disord. 1991;10(5):609–613. doi: 10.1002/1098-108X(199109)10:5<609::AID-EAT2260100513>3.0.CO;2-M [ DOI ] [ Google Scholar ] 45. Meule A, Hermann T, Kübler A. A short version of the Food Cravings Questionnaire—Trait: the FCQ-T-reduced. Front Psychol. 2014;5. doi: 10.3389/fpsyg.2014.00190 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 46. Hormes JM, Meule A. Psychometric properties of the English Food Cravings Questionnaire-Trait-reduced (FCQ-T-r). Eat Behav. 2016;20:34–38. doi: 10.1016/j.eatbeh.2015.11.011 [ DOI ] [ PubMed ] [ Google Scholar ] 47. Tanofsky-Kraff M, Ranzenhofer LM, Yanovski SZ, et al. Psychometric properties of a new questionnaire to assess eating in the absence of hunger in children and adolescents. Appetite. 2008;51(1):148–155. doi: 10.1016/j.appet.2008.01.001 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 48. Pasquale EK, Manzano MA, Strong DR, Eichen DM, Tanofsky-Kraff M, Boutelle KN. Psychometric properties of the Eating in the Absence of Hunger Questionnaire in treatment-seeking adults with overweight and obesity. Appetite. 2023;180:106376. doi: 10.1016/j.appet.2022.106376 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 49. Karlsson J, Persson LO, Sjöström L, Sullivan M. Psychometric properties and factor structure of the Three-Factor Eating Questionnaire (TFEQ) in obese men and women. Results from the Swedish Obese Subjects (SOS) study. Int J Obes Relat Metab Disord J Int Assoc Study Obes. 2000;24(12):1715–1725. doi: 10.1038/sj.ijo.0801442 [ DOI ] [ PubMed ] [ Google Scholar ] 50. U.S. Department of Agriculture. Learn how to eat healthy with MyPlate. Accessed 18 November 2024. http://www.myplate.gov/ 51. Fritz MS, MacKinnon DP. Required sample size to detect the mediated effect. Psychol Sci. 2007;18(3):233–239. doi: 10.1111/j.1467-9280.2007.01882.x [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 52. Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57(1):289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x [ DOI ] [ Google Scholar ] 53. Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008;40(3):879–891. doi: 10.3758/brm.40.3.879 [ DOI ] [ PubMed ] [ Google Scholar ] 54. VanderWeele TJ, Vansteelandt S. Mediation analysis with multiple mediators. Epidemiol Methods. 2014;2(1):95–115. doi: 10.1515/em-2012-0010 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 55. Hedeker D, Gibbons RD. Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychol Methods. 1997;2:64–78. doi: 10.1037/1082-989X.2.1.64 [ DOI ] [ Google Scholar ] 56. Barnard J, Meng XL. Applications of multiple imputation in medical studies: from AIDS to NHANES. Stat Methods Med Res. 1999;8(1):17–36. doi: 10.1177/096228029900800103 [ DOI ] [ PubMed ] [ Google Scholar ] 57. White IR, Royston P, Wood AM. Multiple imputation using chained equations: Issues and guidance for practice. Stat Med. 2011;30(4):377–399. doi: 10.1002/sim.4067 [ DOI ] [ PubMed ] [ Google Scholar ] 58. Gibbons RD, Hedeker D. Random effects probit and logistic regression models for three-level data. Biometrics. 1997;53(4):1527–1537. [ PubMed ] [ Google Scholar ] ACTIONS View on publisher site PDF (119.2 KB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

Related documents

• Interventions for Lifestyle Changes to Promote Weight Reduction, a Randomized Controlled Trial in Primary Health Care#314435
• The Efficacy of Individualized Dietary Advice in Improving Diet Quality and Cardiovascular Health#834417
• Avoid and Resist Strategies for Weight Management#828021
• Toward standardized personalized nutrition: a methodological scoping review and meta-analysis of randomized controlled trials in adults with overweight and obesity.#165625
• Pilot Test of Five Weight Neutral Interventions to Improve Health Among Adults of Higher Body Weight#849474