Behavioral Interventions for Weight Loss

Overseen By Sharon O'Toole, M.A.T.

Age: 18+

Sex: Any

Trial Phase: Phase 2

Sponsor: The Miriam Hospital

Must not be taking: Weight loss medication

Disqualifiers: Heart condition, Pregnancy, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Prior Safety Data This treatment has passed at least one previous human trial

Compensation: Varies

Number of Visits: Unknown

Duration: 24 weeks

180 Participants Needed

Trial Summary

Will I have to stop taking my current medications?

The trial does not specify if you need to stop taking your current medications, but you cannot participate if you are currently taking weight loss medication.

What data supports the effectiveness of the treatment Control Systems Engineering for Weight Loss Maintenance?

Research shows that control systems engineering methods, like those used in the Healthy Mom Zone study, can effectively tailor weight management interventions to individual needs, suggesting potential benefits for weight loss maintenance. Additionally, personalized interventions using control systems engineering have been shown to improve physical activity, which is a key component of weight management. ^{1 2 3 4 5}

Is the behavioral intervention for weight loss safe for humans?

The research does not provide specific safety data for the behavioral interventions for weight loss, but these types of programs generally focus on lifestyle changes like diet and exercise, which are typically safe for most people. However, it's always best to consult with a healthcare provider before starting any new weight loss program. ^{6 7 8 9 10}

How is the Control Systems Engineering for Weight Loss Maintenance treatment different from other weight loss treatments?

The Control Systems Engineering for Weight Loss Maintenance treatment is unique because it likely incorporates principles from control systems engineering to help maintain weight loss, focusing on long-term behavioral changes and possibly using digital tools to support these changes, unlike traditional methods that primarily focus on diet and exercise alone. ^{11 12 13 14 15}

What is the purpose of this trial?

This project capitalizes on principles of control systems engineering to build a dynamical model that predicts weight change during weight loss maintenance using behavioral, psychosocial, and environmental indicators evaluated in a system identification experiment. A 6-month behavioral obesity treatment will be administered to produce weight loss. Participants losing at least 3% of initial body weight will be followed for an additional 12 months via daily smartphone surveys that incorporates passive sensing to objectively monitor key behaviors. Survey data pertaining to behavioral, psychosocial, and environmental indicators will be used to develop a controller algorithm that can predict when an individual is entering a heightened period of risk for regain and why risk is elevated. Interventions targeting key risk indicators will be randomly administered during the system ID experiment. Survey and passive sensing data documenting the effects of the interventions will likewise drive development of the controller algorithm, allowing it to determine which interventions are most likely to counter risk of regain.

Eligibility Criteria

This trial is for individuals who are obese and have successfully lost at least 3% of their initial body weight. Participants should be willing to use a smartphone daily for surveys and passive sensing over a period of 12 months.

Inclusion Criteria

Owns a smartphone

Body mass index (BMI) between 25 and 50 kg/m-squared

I can walk 2 city blocks without needing to stop.

Exclusion Criteria

Has been pregnant within the 6 months prior to enrolling

Plans to become pregnant within 18 months of enrolling

I have had heart issues, chest pain, or fainting in the last year.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Treatment

A 6-month behavioral obesity treatment is administered to produce weight loss

24 weeks

System Identification Experiment

Participants who lose at least 3% of initial body weight are followed for 12 months with daily smartphone surveys and passive sensing to monitor behaviors

52 weeks

Daily smartphone surveys

Follow-up

Participants are monitored for safety and effectiveness after treatment

4 weeks

Treatment Details

Interventions

• Control Systems Engineering for Weight Loss Maintenance

Trial Overview The study tests interventions aimed at maintaining weight loss by focusing on physical activity, sleep, stress, emotion regulation, motivation, self-efficacy, and eating habits. It uses control systems engineering to predict when someone might regain weight.

Participant Groups

Group I: System Identification Experimental Treatment 4 Interventions

All participants who lose at least 3% of their initial body weight will be asked to participate in a system identification experiment every day for 52 weeks.

Who Is Running the Clinical Trial?

The Miriam Hospital

Lead Sponsor

Trials

252

Recruited

39,200+

National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

Collaborator

Trials

2,513

Recruited

4,366,000+

Findings from Research

The Healthy Mom Zone (HMZ) intervention uses a Hybrid Model Predictive Control (HMPC) system to create personalized weight management strategies for pregnant women with overweight or obesity, aiming to address excessive gestational weight gain.

Simulations show that the HMPC framework can optimize complex interventions more effectively than traditional methods, allowing for tailored dosages and strategies based on individual needs, which could lead to better health outcomes for participants.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Optimizing Behavioral Interventions to Regulate Gestational Weight Gain With Sequential Decision Policies Using Hybrid Model Predictive Control. Guo, P., Rivera, DE., Dong, Y., et al.[2023]

Obesity results from a complex imbalance between energy intake and expenditure, regulated by three interconnected control systems: homeostatic, hedonic, and cognitive.

To better understand and address obesity, future research should focus on integrating these systems into dynamic models, as current approaches are often fragmented and lack comprehensive interaction analysis.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Dynamic interplay among homeostatic, hedonic, and cognitive feedback circuits regulating body weight. Hall, KD., Hammond, RA., Rahmandad, H.[2022]

A study involving 10 adults over 16 weeks showed that personalized text message interventions can effectively influence physical activity and sedentary behavior, with responses varying based on the type of message and individual differences.

The use of control systems engineering to model these responses suggests a promising approach for creating tailored interventions that optimize physical activity behaviors in real-time.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Personalized models of physical activity responses to text message micro-interventions: A proof-of-concept application of control systems engineering methods. Conroy, DE., Hojjatinia, S., Lagoa, CM., et al.[2022]

A new personalized and dynamic approach to obesity management was developed through a five-year integrative coaching program, involving collaboration among various health professionals and experts in systems thinking.

The program utilized a systems health model to identify key factors influencing obesity and incorporated participant subtyping based on traditional Chinese medicine, allowing for tailored interventions that adapt to individual needs and goals.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Developing a Personalized Integrative Obesity-Coaching Program: A Systems Health Perspective. Brink, SM., Wortelboer, HM., Emmelot, CH., et al.[2022]

The article explores the application of control systems engineering techniques to improve physical activity interventions, suggesting that these methods can refine our understanding of how to encourage physical activity.

The authors propose that personalized, adaptive interventions based on individual behavior patterns will be more effective in increasing physical activity levels compared to traditional, one-size-fits-all approaches.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Engineering Person-Specific Behavioral Interventions to Promote Physical Activity. Conroy, DE., Lagoa, CM., Hekler, E., et al.[2022]

Conventional weight loss interventions often lead to relapse over time, suggesting that they may not provide sufficient practice in new skills across various community contexts for sustainable weight loss.

Using computer simulation models, the study estimates a potential 12.9% decrease in child percent overweight over 30 months with a novel socio-environmental treatment approach, indicating promise for future large-scale trials.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The use of biosimulation in the design of a novel multilevel weight loss maintenance program for overweight children. Wilfley, DE., Van Buren, DJ., Theim, KR., et al.[2021]

The BestFIT study is designed to evaluate the effectiveness of two adaptive interventions for individuals struggling with weight loss: using portion-controlled meals to reduce self-regulation demands and switching to acceptance-based treatment to enhance self-regulation skills.

This research aims to determine the optimal timing for these interventions, potentially leading to a more personalized approach to weight loss that could improve outcomes for those who do not respond well to standard programs.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The BestFIT trial: A SMART approach to developing individualized weight loss treatments. Sherwood, NE., Butryn, ML., Forman, EM., et al.[2022]

In a study of 36 participants who completed a 10-week behavioral weight control program, most individuals regained a significant amount of weight, averaging 11.94 pounds, and were, on average, heavier than their starting weight after 5 years.

Low adherence to the treatment program after it ended was common, but those who consistently used more program techniques and for longer periods experienced less weight regain, highlighting the importance of ongoing commitment to weight management strategies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Do behavioral treatments of obesity last? A five-year follow-up investigation. Stalonas, PM., Perri, MG., Kerzner, AB.[2019]

The study aims to investigate dietary lapses in behavioral obesity treatment (BOT) using a multi-method approach, involving 40 participants over a 24-week period, to better understand how these lapses affect weight loss outcomes.

Preliminary data from pilot participants indicate that combining ecological momentary assessment (EMA) with continuous monitoring of eating behavior through wearable devices can provide valuable insights into dietary lapses, which may help develop more effective interventions for individuals struggling with weight management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Combining ecological momentary assessment, wrist-based eating detection, and dietary assessment to characterize dietary lapse: A multi-method study protocol. Goldstein, SP., Hoover, A., Evans, EW., et al.[2022]

Participants who identified negative emotional situations as the most challenging were significantly more likely to experience dietary lapses, with 87% of them lapsing during such situations.

Those who generated more coping strategies for high-risk scenarios at the start of the program tended to lose more weight, indicating that preparation and response strategies can enhance the effectiveness of behavioral weight control programs.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Responses to hypothetical high risk situations: do they predict weight loss in a behavioral treatment program or the context of dietary lapses? Drapkin, RG., Wing, RR., Shiffman, S.[2019]

In a study of 501 overweight and obese adults, regular attendance in lifestyle intervention sessions led to better self-monitoring of diet and exercise, which contributed to weight loss.

Specifically, participants who attended more sessions reduced their fat intake and increased self-monitoring, both of which were linked to significant weight loss over a 6-month period.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Examining behavioral processes through which lifestyle interventions promote weight loss: results from PREMIER. Fitzpatrick, SL., Bandeen-Roche, K., Stevens, VJ., et al.[2022]

Obesity rates in adults and children have nearly doubled in the past 30 years, leading to serious health issues like cardiovascular disease and Type 2 diabetes, which are increasingly affecting younger populations.

The article discusses traditional behavioral weight loss strategies and suggests that incorporating insights from behavioral economics and cognitive processing could enhance the effectiveness of these interventions for weight loss and maintenance.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Behavioral interventions for obesity in children and adults: Evidence base, novel approaches, and translation into practice. Wilfley, DE., Hayes, JF., Balantekin, KN., et al.[2023]

Combining diet therapy, exercise, and behavioral therapy is essential for effective weight reduction and long-term maintenance in obese patients, as these methods help correct energy balance.

Specific behavioral strategies like self-monitoring and stress management are crucial for sustaining weight loss, with daily weight charting being particularly effective for reinforcing these changes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

[Behavioral therapy for obesity]. Yoshimatsu, H.[2011]

The study developed a digital behavior change intervention called eCHANGE, which integrates persuasive system design (PSD) principles and behavior change techniques (BCTs) to support long-term weight maintenance, based on input from 45 stakeholders including end users and health care providers.

Seventeen key design features were identified and grouped into four main components of the intervention, emphasizing personalization, self-monitoring, and social support, which are crucial for engaging users and promoting sustainable behavior change.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Combining Persuasive System Design Principles and Behavior Change Techniques in Digital Interventions Supporting Long-term Weight Loss Maintenance: Design and Development of eCHANGE. Asbjørnsen, RA., Hjelmesæth, J., Smedsrød, ML., et al.[2022]

Patients with obesity often have unrealistic expectations about weight loss, so it's important to help them accept moderate weight losses as successful outcomes.

Long-term behavioral change requires ongoing support and training for over a year, emphasizing flexible eating habits rather than strict control, and involving multiple behavioral dimensions to enhance success.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The therapeutic challenge: behavioral changes for long-term weight maintenance. Westenhoefer, J.[2014]

References

1. England pubmed.ncbi.nlm.nih.gov

Optimizing Behavioral Interventions to Regulate Gestational Weight Gain With Sequential Decision Policies Using Hybrid Model Predictive Control. [2023]

2. United States pubmed.ncbi.nlm.nih.gov

Dynamic interplay among homeostatic, hedonic, and cognitive feedback circuits regulating body weight. [2022]

3. Netherlands pubmed.ncbi.nlm.nih.gov

Personalized models of physical activity responses to text message micro-interventions: A proof-of-concept application of control systems engineering methods. [2022]

4. Switzerland pubmed.ncbi.nlm.nih.gov

Developing a Personalized Integrative Obesity-Coaching Program: A Systems Health Perspective. [2022]

5. United States pubmed.ncbi.nlm.nih.gov

Engineering Person-Specific Behavioral Interventions to Promote Physical Activity. [2022]

6. United States pubmed.ncbi.nlm.nih.gov

The use of biosimulation in the design of a novel multilevel weight loss maintenance program for overweight children. [2021]

7. United States pubmed.ncbi.nlm.nih.gov

The BestFIT trial: A SMART approach to developing individualized weight loss treatments. [2022]

8. England pubmed.ncbi.nlm.nih.gov

Do behavioral treatments of obesity last? A five-year follow-up investigation. [2019]

9. United States pubmed.ncbi.nlm.nih.gov

Combining ecological momentary assessment, wrist-based eating detection, and dietary assessment to characterize dietary lapse: A multi-method study protocol. [2022]

10. United States pubmed.ncbi.nlm.nih.gov

Responses to hypothetical high risk situations: do they predict weight loss in a behavioral treatment program or the context of dietary lapses? [2019]

11. United States pubmed.ncbi.nlm.nih.gov

Examining behavioral processes through which lifestyle interventions promote weight loss: results from PREMIER. [2022]

12. United States pubmed.ncbi.nlm.nih.gov

Behavioral interventions for obesity in children and adults: Evidence base, novel approaches, and translation into practice. [2023]

13. Japan pubmed.ncbi.nlm.nih.gov

[Behavioral therapy for obesity]. [2011]

14. Canada pubmed.ncbi.nlm.nih.gov

Combining Persuasive System Design Principles and Behavior Change Techniques in Digital Interventions Supporting Long-term Weight Loss Maintenance: Design and Development of eCHANGE. [2022]

15. England pubmed.ncbi.nlm.nih.gov

The therapeutic challenge: behavioral changes for long-term weight maintenance. [2014]