Fengqing Zhang

The independent and interacting effects of hedonic hunger and executive function on binge eating

Poor executive function (EF; pre-frontal cognitive control processes governing goal-directed behavior) and elevated hedonic hunger (i.e., preoccupation with palatable foods in the absence of physiological hunger) are theoretical risk and maintenance factors for binge eating (BE) distinct from general obesity. Recent theoretical models posit that dysregulated behavior such as BE may result from a combination of elevated appetitive drive (e.g., hedonic hunger) and decreased EF (e.g., inhibitory control and delayed discounting). The present study sought to test this model in distinguishing BE from general obesity by examining the independent and interactive associations of EF and hedonic hunger with BE group status (i.e., odds of categorization in BE group versus non-BE group). Treatment-seeking overweight and obese women with BE (n = 31) and without BE (OW group; n = 43) were assessed on measures of hedonic hunger and EF (inhibitory control and delay discounting). Elevated hedonic hunger increased the likelihood of categorization in the BE group, regardless of EF. When hedonic hunger was low, poor EF increased the likelihood of categorization in the BE group. Results indicate that the interplay of increased appetitive drives and decreased cognitive function may distinguish BE from overweight/obesity. Future longitudinal investigations of the combinatory effect of hedonic hunger and EF in increasing risk for developing BE are warranted, and may inform future treatment development to target these factors.

Efficacy of environmental and acceptance-based enhancements to behavioral weight loss treatment: The ENACT trial

This study was designed to compare weight loss through a traditional behavioral treatment (BT) approach that integrated skills for managing the obesogenic food environment (BT + E) with an approach that integrated environmental and acceptance‐based skills (BT + EA). Moderators were examined as an exploratory aim.

Return of the JITAI: Applying a Just-in-Time Adaptive Intervention Framework to the Development of m-Health Solutions for Addictive Behaviors

PurposeLapses are strong indicators of later relapse among individuals with addictive disorders, and thus are an important intervention target. However, lapse behavior has proven resistant to change due to the complex interplay of lapse triggers that are present in everyday life. It could be possible to prevent lapses before they occur by using m-Health solutions to deliver interventions in real-time.MethodJust-in-time adaptive intervention (JITAI) is an intervention design framework that could be delivered via mobile app to facilitate in-the-moment monitoring of triggers for lapsing, and deliver personalized coping strategies to the user to prevent lapses from occurring. An organized framework is key for successful development of a JITAI.ResultsNahum-Shani and colleagues (2014) set forth six core elements of a JITAI and guidelines for designing each: distal outcomes, proximal outcomes, tailoring variables, decision points, decision rules, and intervention options. The primary aim of this paper is to illustrate the use of this framework as it pertains to developing a JITAI that targets lapse behavior among individuals following a weight control diet.ConclusionWe will detail our approach to various decision points during the development phases, report on preliminary findings where applicable, identify problems that arose during development, and provide recommendations for researchers who are currently undertaking their own JITAI development efforts. Issues such as missing data, the rarity of lapses, advantages/disadvantages of machine learning, and user engagement are discussed.

Evaluation of meal replacements and a home food environment intervention for long-term weight loss: a randomized controlled trial

Background Lifestyle change treatments for weight loss produce medically meaningful weight reductions, but lost weight is usually regained. Meal replacements (MRs) represent one avenue for improving long-term weight loss. Another, nutrition-focused approach involves having participants make specific changes in the energy density, composition, and structure of the foods in their personal food environments. Objective Three conditions were compared: behavior therapy (BT), BT plus MRs (BT+MR), and a nutrition-focused treatment aimed at modifying the home food environment (HFE). Design Overweight and obese individuals (n = 262) were randomly assigned to 1 of the 3 conditions. Treatment occurred in weekly groups for 6 mo and in biweekly groups for 6 mo. Assessments were conducted at baseline and at 6, 12, 18, 24, and 36 mo. Multilevel models were used to estimate weight-change trajectories for each participant and to examine the treatment group effect on long-term weight loss. Results A multilevel analysis indicated that all 3 groups showed significant weight loss over 12 mo that was gradually regained to the 36-mo follow-up. Mean ± SD percentages of baseline weight loss at 12 mo for BT, BT+MR, and HFE were 9.41% ± 7.92%, 10.37% ± 7.77%, and 10.97% ± 7.79%, respectively. Comparable percentages at 36 mo were 4.21% ± 8.64%, 3.06% ± 6.93%, and 4.49% ± 7.83%. Those in the HFE condition lost more weight than those receiving BT through the 36-mo assessment (P < 0.01), as reflected in 2 treatment × time interactions. Further analyses showed that HFE produced the largest increases in cognitive restraint and that this increase largely mediated the HFE groups improved weight loss. Conclusion The nutrition-focused intervention studied here produced modestly greater long-term weight loss than BT, an effect that was largely explainable by an unexpected boost in cognitive restraint in this condition. This study was registered at clinicaltrials.gov as NCT01065974.

Small weight gains during obesity treatment: normative or cause for concern?

The objectives of the study are to characterize the frequency and size of small weight gains during behavioural weight loss treatment and to evaluate the relationship between small weight gains and weight loss outcomes.

Application of Machine Learning to Predict Dietary Lapses During Weight Loss

Background: Individuals who adhere to dietary guidelines provided during weight loss interventions tend to be more successful with weight control. Any deviation from dietary guidelines can be referred to as a “lapse.” There is a growing body of research showing that lapses are predictable using a variety of physiological, environmental, and psychological indicators. With recent technological advancements, it may be possible to assess these triggers and predict dietary lapses in real time. The current study sought to use machine learning techniques to predict lapses and evaluate the utility of combining both group- and individual-level data to enhance lapse prediction. Methods: The current study trained and tested a machine learning algorithm capable of predicting dietary lapses from a behavioral weight loss program among adults with overweight/obesity (n = 12). Participants were asked to follow a weight control diet for 6 weeks and complete ecological momentary assessment (EMA; repeated brief surveys delivered via smartphone) regarding dietary lapses and relevant triggers. Results: WEKA decision trees were used to predict lapses with an accuracy of 0.72 for the group of participants. However, generalization of the group algorithm to each individual was poor, and as such, group- and individual-level data were combined to improve prediction. The findings suggest that 4 weeks of individual data collection is recommended to attain optimal model performance. Conclusions: The predictive algorithm could be utilized to provide in-the-moment interventions to prevent dietary lapses and therefore enhance weight losses. Furthermore, methods in the current study could be translated to other types of health behavior lapses.