Momentary Associations Across Specific Affective States and Dysregulated Eating Experiences Among Children and Adolescents With Loss of Control Eating Symptoms.

Objective: Dysregulated eating is common among youth and is associated with trait-level negative affect and emotion regulation difficulties. Despite the transient nature of affect, momentary associations among affect and eating behavior are unclear, which limits development of more impactful treatment tools, such as "just-in-time" intervention approaches (JITAI). The current study (N = 62) drew from two ecological momentary assessment (EMA) studies involving children and adolescents who endorsed loss of control (LOC) eating symptoms during a two-week assessment period.

Supervised machine learning to predict smoking lapses from Ecological Momentary Assessments and sensor data: Implications for just-in-time adaptive intervention development.

Specific moments of lapse among smokers attempting to quit often lead to full relapse, which highlights a need for interventions that target lapses before they might occur, such as just-in-time adaptive interventions (JITAIs). To inform the decision points and tailoring variables of a lapse prevention JITAI, we trained and tested supervised machine learning algorithms that use Ecological Momentary Assessments (EMAs) and wearable sensor data of potential lapse triggers and lapse incidence. We aimed to identify a best-performing and feasible algorithm to take forwards in a JITAI.

The Fully Understanding Eating and Lifestyle Behaviors (FUEL) trial: Protocol for a cohort study harnessing digital health tools to phenotype dietary non-adherence behaviors during lifestyle intervention.

Objective: Lifestyle intervention can produce clinically significant weight loss and reduced disease risk/severity for many individuals with overweight/obesity. Dietary lapses, instances of non-adherence to the recommended dietary goal(s) in lifestyle intervention, are associated with less weight loss and higher energy intake. There are distinct "types" of dietary lapse (e.

An explanation for the accuracy of sensor-based measures of energy intake: Amount of food consumed matters more than dietary composition.

Understanding and intervening on eating behavior often necessitates measurement of energy intake (EI); however, commonly utilized and widely accepted methods vary in accuracy and place significant burden on users (e.g., food diaries), or are costly to implement (e.

Detecting Eating Episodes From Wrist Motion Using Daily Pattern Analysis.

This paper presents new methods to detect eating from wrist motion. Our main novelty is that we analyze a full day of wrist motion data as a single sample so that the detection of eating occurrences can benefit from diurnal context. We develop a two-stage framework to facilitate a feasible full-day analysis.

State-level working memory and dysregulated eating in children and adolescents: An exploratory ecological momentary assessment study.

Background: Children with loss of control (LOC) eating and overweight/obesity have relative deficiencies in trait-level working memory (WM), which may limit adaptive responding to intra- and extra-personal cues related to eating. Understanding of how WM performance relates to eating behavior in real-time is currently limited.

Methods: We studied 32 youth (ages 10-17 years) with LOC eating and overweight/obesity (LOC-OW; n = 9), overweight/obesity only (OW; n = 16), and non-overweight status (NW; n = 7).

A randomized trial examining the effect of yoga on dietary lapses and lapse triggers following behavioral weight loss treatment.

Background: Dietary lapses can hinder weight loss and yoga can improve self-regulation, which may protect against lapses. This study examined the effect of yoga on dietary lapses, potential lapse triggers (e.g.

Association of weight and shape concern with weight change and weight-related behaviors in behavioral weight loss treatment.

Weight and shape concern (WSC) is a facet of negative body image that is common among individuals with overweight/obesity seeking behavioral weight loss treatment (BWL), but remains understudied. This secondary analysis evaluates associations between WSC, weight change, and weight-related behaviors among individuals in a 24-week BWL. Adults (n = 32) with body mass index 25-50 kg/m completed a baseline WSC questionnaire, measured weight at 12 and 24 weeks, measured physical activity via accelerometer, and completed 24-hour dietary recalls.

Classification of Lapses in Smokers Attempting to Stop: A Supervised Machine Learning Approach Using Data From a Popular Smoking Cessation Smartphone App.

Introduction: Smoking lapses after the quit date often lead to full relapse. To inform the development of real time, tailored lapse prevention support, we used observational data from a popular smoking cessation app to develop supervised machine learning algorithms to distinguish lapse from non-lapse reports.

Aims And Methods: We used data from app users with ≥20 unprompted data entries, which included information about craving severity, mood, activity, social context, and lapse incidence.

Applying a Social Determinants of Health Framework to Guide Digital Innovations That Reduce Disparities in Chronic Disease.

Chronic diseases are among the top causes of global death, disability, and health care expenditure. Digital health interventions (e.g.

Dietary lapses are associated with meaningful elevations in daily caloric intake and added sugar consumption during a lifestyle modification intervention.

Objective: Lapses from the dietary prescription in lifestyle modification interventions for overweight/obesity are common and impact weight loss outcomes. While it is expected that lapses influence weight via increased consumption, there are no studies that have evaluated how dietary lapses affect dietary intake during treatment. This study examined the association between daily lapses and daily energy and macronutrient intake during a lifestyle modification intervention.

Combining passive eating monitoring and ecological momentary assessment to characterize dietary lapses from a lifestyle modification intervention.

Dietary lapses (i.e., specific instances of nonadherence to recommended dietary goals) contribute to suboptimal weight loss outcomes during lifestyle modification programs.

The Behavioral Intervention with Technology for E-Weight Loss Study (BITES): Incorporating Energy Balance Models and the Bite Counter into an Online Behavioral Weight Loss Program.

This study evaluated feasibility and acceptability of adding energy balance modeling displayed on weight graphs combined with a wrist-worn bite counting sensor against a traditional online behavioral weight loss program. Adults with a BMI of 27-45 kg/m (83.3% women) were randomized to receive a 12-week online behavioral weight loss program with 12 weeks of continued contact ( = 9; base program), the base program plus a graph of their actual and predicted weight change based on individualized physiological parameters ( = 7), or the base program, graph, and a Bite Counter device for monitoring and limiting eating ( = 8).

Preliminary evidence of contextual factors' influence on weight loss treatment outcomes: implications for future research.

Background/objectives: Behavioral health interventions, including behavioral obesity treatment, typically target psychosocial qualities of the individual (e.g., knowledge, self-efficacy) that are largely treated as persistent, over momentary contextual factors (e.

Optimizing a Just-in-Time Adaptive Intervention to Improve Dietary Adherence in Behavioral Obesity Treatment: Protocol for a Microrandomized Trial.

Background: Behavioral obesity treatment (BOT) is a gold standard approach to weight loss and reduces the risk of cardiovascular disease. However, frequent lapses from the recommended diet stymie weight loss and prevent individuals from actualizing the health benefits of BOT. There is a need for innovative treatment solutions to improve adherence to the prescribed diet in BOT.

Momentary predictors of dietary lapse from a mobile health weight loss intervention.

Identifying factors that influence risk of dietary lapses (i.e., instances of dietary non-adherence) is important because lapses contribute to suboptimal weight loss outcomes.

Comparing ecological momentary assessment to sensor-based approaches in predicting dietary lapse.

Ecological momentary assessment (EMA; brief self-report surveys) of dietary lapse risk factors (e.g., cravings) has shown promise in predicting and preventing dietary lapse (nonadherence to a dietary prescription), which can improve weight loss interventions.

Internalized weight bias is associated with perceived exertion and affect during exercise in a sample with higher body weight.

Objective: For individuals with overweight/obesity, internalized weight bias (IWB) is linked to low physical activity (PA). This study used a laboratory-based paradigm to test the hypothesis that IWB moderates the association between heart rate (HR) and perceived exertion and affect during PA.

Methods: Participants with overweight/obesity completed 30-min of supervised moderate-intensity treadmill walking (65%-75% of age-predicted maximal HR).

Identifying behavioral types of dietary lapse from a mobile weight loss program: Preliminary investigation from a secondary data analysis.

Success in behavioral weight loss (BWL) programs depends on adherence to the recommended diet to reduce caloric intake. Dietary lapses (i.e.

Examination of the relationship between lapses and weight loss in a smartphone-based just-in time adaptive intervention.

We developed a smartphone-based just-in-time adaptive intervention (JITAI), called OnTrack, that provides personalized intervention to prevent dietary lapses (i.e., nonadherence from the behavioral weight loss intervention diet).

Combining ecological momentary assessment, wrist-based eating detection, and dietary assessment to characterize dietary lapse: A multi-method study protocol.

Objectives: Behavioral obesity treatment (BOT) produces clinically significant weight loss and health benefits for many individuals with overweight/obesity. Yet, many individuals in BOT do not achieve clinically significant weight loss and/or experience weight regain. Lapses (i.

Evaluation of intervention components to maximize outcomes of behavioral obesity treatment delivered online: A factorial experiment following the multiphase optimization strategy framework.

Background: Behavioral lifestyle intervention (BLI) is recommended as a first-line treatment for obesity. While BLI has been adapted for online delivery to improve potential for dissemination while reducing costs and barriers to access, weight losses are typically inferior to gold standard treatment delivered in-person. It is therefore important to refine and optimize online BLI in order to improve the proportion of individuals who achieve a minimum clinically significant weight loss and mean weight loss.