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Precision medicine models for personalizing achieving sustained behavior change are largely outside of current clinical practice. Yet, changing self-regulatory behaviors is fundamental to the self-management of complex lifestyle-related chronic conditions such as depression and obesity - two top contributors to the global burden of disease and disability. To optimize treatments and address these burdens, behavior change and self-regulation must be better understood in relation to their neurobiological underpinnings. Here, we present the conceptual framework and protocol for a novel study, "Engaging self-regulation targets to understand the mechanisms of behavior change and improve mood and weight outcomes (ENGAGE)". The ENGAGE study integrates neuroscience with behavioral science to better understand the self-regulation related mechanisms of behavior change for improving mood and weight outcomes among adults with comorbid depression and obesity. We collect assays of three self-regulation targets (emotion, cognition, and self-reflection) in multiple settings: neuroimaging and behavioral lab-based measures, virtual reality, and passive smartphone sampling. By connecting human neuroscience and behavioral science in this manner within the ENGAGE study, we develop a prototype for elucidating the underlying self-regulation mechanisms of behavior change outcomes and their application in optimizing intervention strategies for multiple chronic diseases.

Human behavior emerges from planning over elaborate decompositions of tasks into goals, subgoals, and low-level actions. How are these decompositions created and used? Here, we propose and evaluate a normative framework for task decomposition based on the simple idea that people decompose tasks to reduce the overall cost of planning while maintaining task performance. Analyzing 11,117 distinct graph-structured planning tasks, we find that our framework justifies several existing heuristics for task decomposition and makes predictions that can be distinguished from two alternative normative accounts. We report a behavioral study of task decomposition (N = 806) that uses 30 randomly sampled graphs, a larger and more diverse set than that of any previous behavioral study on this topic. We find that human responses are more consistent with our framework for task decomposition than alternative normative accounts and are most consistent with a heuristic—betweenness centrality—that is justified by our approach. Taken together, our results suggest the computational cost of planning is a key principle guiding the intelligent structuring of goal-directed behavior.

Introduction To address the need for non-pharmacologic, scalable approaches for managing attention-deficit and hyperactivity disorder (ADHD) in young people, we report the results of a study of an application developed for a wearable device (Apple Watch) that was designed to track movement and provide visual and haptic feedback for ADHD. Methods Six-week, open label pilot study with structured rating scales ADHD and semi-structured qualitative interview. Apple Watch software application given to users that uses actigraphy and graphic interface as well as haptic feedback to provide feedback to users about level of movement during periods of intentional focus. Linear mixed models to estimate trajectories. Results Thirty-two participants entered the study. This application was associated with improvement in ADHD symptoms over the 6 weeks of the study. We observed an ADHD-Rating Scale change of β = −1.2 units/week (95% CI = −0.56 to −1.88, F = 13.4, P = .0004). Conclusions These positive clinical outcomes highlight the promise of such wearable applications for ADHD and the need to pursue their further development.

Background: Depression exerts a staggering toll that is worsened with co-occurring chronic conditions such as obesity. It is imperative to develop more effective interventions for depression and to identify objective and biological plausible neural mechanisms to understand intervention outcomes. The current study uses functional neuroimaging to determine whether a behavioural intervention changes the negative affect circuit and whether these changes relate to subsequent improvements in both symptom and problem-solving outcomes in depressed patients with co-occurring obesity. Methods: This study ('ENGAGE') was a pre-planned element of the randomized controlled trial, 'RAINBOW' (Clini-calTrials.gov NCT02246413). 108 depressed patients with obesity were randomized to receive an integrated collaborative care intervention (I-CARE) or usual care. Participants underwent functional neuroimaging using an established facial emotion task at baseline and two months (coinciding with the first two months of intervention focused on problem-solving therapy ('PST')). Amygdala, insula and anterior cingulate cortex activation was extracted using pre-planned definitions and standardized methods. The primary health and behavioural outcomes were depression symptom severity and problem-solving ability respectively, assessed at baseline, the main 6-month outcome point and at 12-month follow up. Mediation analyses used an intent-to-treat approach. Findings: PST, relative to usual care, reduced amygdala activation engaged by threat stimuli at two months. This reduction mediated subsequent improvements in depression severity in an intervention-dependent manner. PST did not change insula activation at two months but did temper the strength of the relationship between insula activation and improvements in problem-solving ability. Interpretation: The negative affect circuit may be an important neural target and potential mediator of PST in patients with comorbid obesity.