Control of Eating Behavior Using a Novel Feedback System
May 8th, 2018
Subjects eat food from a plate placed on a scale connected to a computer that records the weight loss of the plate during the meal. Feedback on the computer screen allows the subject to adapt her/his eating behavior to reference curves thus normalizing body weight.
The overall goal of this medical device is to allow under and overweight patients to eat normally, restoring body weight and improving physical and mental health and to manipulate the rate of eating in healthy subjects for experimental purposes. This method can help us answer key questions concerning the management of body weight problems such as eating disorders and obesity. The main advantage of this method is that it allows the objective measurement of eating behavior and also to change eating behavior for experimental and clinical purposes.
The implications of this technique extend toward therapy of anorexia, bulimia, and obesity because the patients do not know how to eat or how to feel full normally. We first got the idea to develop this method when an anorexic patient said she didn't know how to eat or how to feel full and she wanted to use the device to learn how to eat. Begin by downloading the application onto the smartphone device.
Then turn on the scale by pressing the red button on the bottom. Open the application and select a meal by pressing either control to eat without visual feedback or pressing training to eat with visual feedback. Next, connect the scale to the smartphone device by selecting one of the scales paired with the device from the list and pressing connect at the bottom.
Place a plate on the scale. Press done in the upper right of the screen to continue. Escort the subject into the testing room and seat them in front of the plate of food and scale.
Point out the smartphone screen and ask them to adapt their rate of eating to the dashed blue reference curve. Inform them that the blue curve will change as more food is eaten off the plate. When the rating scales appear on the screen, instruct the subject to rate their feeling of fullness.
Press save to continue eating. Advise the subject to slow down if they are eating too quickly. When there's no food left on the plate, press no to confirm they are finished eating.
Finally, note the meal duration on the screen and press done to exit. In a group of five patients who were treated to remission from anorexia nervosa, results indicated an accelerating rate of eating in the first test without visual feedback and intake of a large meal three days later with reference curve providing visual feedback. Device recordings are automatically converted to the cumulative curve of food intake, a quadratic model of eating behavior.
Further, there was a doubling of the rate of eating at the time of remission in comparison with the two recordings in the early part of treatment in one of the patients. The time of remission was variable across the five patients. Patients with a normal body mass index at the time of remission consumed more food than they did at admission and that the duration of the meal although not reduced was less variable.
Lastly, while three patients ate at an accelerating rate at admission, all but one ate at a decelerated rate when in remission. The initial rate of eating had increased at the time of remission. This device is easy to use and has been used by hundreds of eating disorder patients and overweight patients practicing eating at home.
This application has also been used in experiments on healthy subjects to examine the effect of dieting and in naturalistic settings such as school lunches. Patients with eating disorders like using the device. They feel safe and they trust it and they can use it wherever they choose to eat, at home, eating with friends at the school cafeteria, or at restaurants.
This method allows you to control how much you eat by providing support.
Overview
This study introduces a medical device designed to help patients with eating disorders and weight management by providing real-time feedback on eating behavior. The device aims to normalize body weight and improve overall health by allowing users to adapt their eating habits based on visual cues.
Key Study Components
Area of Science
- Eating behavior
- Weight management
- Clinical applications for eating disorders
Background
- The device was inspired by an anorexic patient who expressed difficulty in understanding how to eat and feel full.
- It provides objective measurements of eating behavior.
- The method has implications for treating anorexia, bulimia, and obesity.
- Patients can use the device in various settings, enhancing its accessibility.
Purpose of Study
- To restore normal eating patterns in underweight and overweight patients.
- To manipulate eating rates in healthy subjects for experimental purposes.
- To explore the management of body weight problems.
Methods Used
- Subjects use a smartphone application connected to a scale.
- Participants adapt their eating rate based on visual feedback from the device.
- Data on food intake and fullness ratings are recorded during meals.
- The device tracks cumulative food intake and eating behavior patterns.
Main Results
- Patients showed an increased rate of eating and improved food intake over time.
- Results indicated variability in the time of remission among patients.
- Patients with normal BMI at remission consumed more food than at admission.
- Most patients transitioned from an accelerating to a decelerated eating rate upon remission.
Conclusions
- The device is user-friendly and has been effective for many patients.
- It provides a safe and trusted method for patients to practice eating.
- The application has potential for broader use in experimental and clinical settings.
Frequently Asked Questions
Executive Industry Relevance
This feedback-based eating behavior system provides objective, real-time measurement of food intake and eating rate, enabling mechanistic de-risking in behavioral interventions for weight-related disorders. By quantifying dynamic eating patterns and supporting adaptive behavior change through visual feedback, the method offers predictive value for target validation in neuropsychological and metabolic pathways governing satiety and intake control. Its translational continuity from experimental models to clinical application supports portfolio-relevant decision-making in appetite regulation and eating disorder therapeutics.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables interrogation of therapeutic hypotheses related to appetite regulation and satiety signaling through direct measurement of eating behavior dynamics.
- Operational Value: Supports biological de-risking by providing quantitative, objective readouts of food intake and meal duration for target engagement assessment.
- Predictive Value: Facilitates lead identification by allowing controlled manipulation of eating rate in healthy subjects to model disorder phenotypes and assess intervention effects.
Screening & Assay Development
- Assay Readiness: Generates standardized, reproducible quantitative outputs (cumulative intake, eating rate, meal duration) fitted to a quadratic model for high-throughput behavioral screening.
- Platform Utility: Enables screening readiness through automated data capture and analysis, supporting scalable evaluation of pharmacological or behavioral compounds on eating behavior.
- Quantitative Output: Delivers continuous, time-resolved measurements of dependent variables (food intake over time) essential for dose-response and temporal profiling in behavioral assays.
Translational & Preclinical Research
- Disease Relevance: Models human eating disorders (anorexia, bulimia, obesity) by capturing aberrant eating patterns and their normalization under intervention, supporting translational biomarker alignment.
- Preclinical Continuity: Bridges discovery to preclinical validation by enabling consistent measurement of eating behavior across healthy and diseased states, facilitating risk-adjusted advancement decisions.
- Mechanistic De-risking: Reduces ambiguity in target validation by isolating the effect of interventions on eating rate and intake, independent of confounding factors like self-report bias.
Pipeline & Workflow Integration
The method integrates into the discovery continuum from hypothesis testing in early discovery to lead identification and preclinical validation, offering a reusable platform for evaluating interventions targeting eating behavior and energy homeostasis.
- Discovery Biology: Supports hypothesis testing and pathway clarification by enabling precise manipulation and measurement of eating behavior as a phenotypic readout.
- Screening: Ensures assay readiness through automated, objective quantification of food intake and eating rate, enhancing reproducibility across experimental conditions.
- Analytics: Provides statistical modeling of eating behavior via quadratic fitting, enabling comparison of conditions and assessment of intervention-induced changes in intake dynamics.
- Translational Research: Connects to preclinical continuity by demonstrating restoration of normal eating patterns in remission, aligning with clinical outcomes in eating disorder and obesity models.
- Enterprise Reuse: Functions as a scalable, standardized capability for repeated use across studies, sites, and populations, reducing variability in behavioral phenotyping.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence in target validation by reducing mechanistic ambiguity through direct, objective measurement of eating behavior.
- Operational Value: Enhances standardization and reproducibility via automated data collection and algorithmic fitting, minimizing operator-dependent variability.
- Strategic Value: Improves go/no-go decisions by providing quantitative behavioral endpoints that reduce late-stage failure risk in appetite-modulating therapies.
- Portfolio Impact: Enables risk-adjusted prioritization of compounds based on their ability to normalize eating behavior, supporting capital efficiency in neuropsychiatric and metabolic drug development.
Implementation Considerations
- Requires expertise in behavioral neuroscience, eating disorder clinical assessment, and digital health tool deployment.
- Needs smartphone-compatible Bluetooth scale, custom application, and data analysis infrastructure for quadratic modeling of intake curves.
- Demands cross-team standardization in protocol execution (e.g., meal timing, food type, subject instructions) to ensure comparability across sites.
- Involves adaptation considerations for different populations (e.g., pediatric vs. adult, anorexia vs. obesity) in feedback threshold settings and reference curve parameters.
- Limited by potential reactivity in healthy subjects, where awareness of monitoring may alter natural eating behavior, though this can be leveraged in clinical settings for behavior shaping.
