Digital Health Program for Obesity

(Rural PREVENT Trial)

Recruiting at 1 trial location

Overseen By Maura Kepper, PhD

Age: 18 - 65

Sex: Any

Trial Phase: Academic

Sponsor: Washington University School of Medicine

Disqualifiers: Diabetes, Hypertension, Bipolar, others

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

Compensation: Varies

Number of Visits: 3

100 Participants Needed

Trial Summary

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

What data supports the effectiveness of the PREVENT Tool treatment for obesity?

The PREVENT Tool is designed to help healthcare teams deliver personalized behavior change recommendations and support for physical activity and diet, which are key factors in managing obesity. It uses a user-centered design process based on Self-Determination Theory to motivate behavior change, and initial feedback from healthcare teams suggests it is useful and well-organized, indicating potential effectiveness in promoting weight stabilization or loss. ^{1 2 3 4 5}

How is the PREVENT Tool treatment for obesity different from other treatments?

The PREVENT Tool is a digital health program that uses technology to provide personalized and accessible obesity prevention and treatment, making it unique compared to traditional in-person methods. It leverages computerized decision support and machine learning to tailor interventions, which can be more engaging and less stigmatizing, especially for youth. ^{3 5 6 7 8}

What is the purpose of this trial?

This project will conduct a pilot hybrid study that examines the implementation (Aims 1 \& 2) and preliminary effectiveness (Aim 3) of PREVENT, a digital health intervention, among patients with overweight/obesity (N=100) using a clinic-randomized design. The central hypothesis of the study is that PREVENT will be feasible and show improvements in health behavior counseling and the patient experience that will improve patients' motivation to change, and their CVH health behaviors and outcomes.

Research Team

Maura Kepper, PhD

Principal Investigator

Washington University School of Medicine

Eligibility Criteria

This trial is for individuals in rural areas with cardiovascular disease or obesity. Participants should be interested in using a digital health intervention to improve their health behaviors and outcomes.

Inclusion Criteria

Patients with low income (household income <200% FPL)

Patients at risk for poor CVH (body mass index greater than or equal to 30)

All providers and clinic staff (physicians, nurses, community health workers, clinic staff, clinic research associates) in the Missouri Highlands Healthcare Clinics are eligible to participate.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Baseline Assessment

Participants complete questionnaires at baseline to assess initial health behaviors and CVH risk

1 week

1 visit (in-person or virtual)

Intervention

Participants receive tailored health behavior counseling using the PREVENT tool, with follow-up support from Community Health Workers

6 months

Monthly follow-ups (virtual or in-person)

Follow-up

Participants are monitored for changes in health behaviors and CVH outcomes after the intervention

6 months

2 visits (in-person or virtual)

Treatment Details

Interventions

• PREVENT Tool

Trial Overview The PREVENT tool, a novel digital health intervention designed to support tailored health behavior counseling, is being tested against a wait-list control group in primary care clinics.

Participant Groups

Group I: Patients- PREVENT Tool Experimental Treatment 1 Intervention

* Complete questionnaires at baseline, within 48 hours of their routine clinic visit, and 6-months after the clinic visit. All surveys will be administered electronically or by mail * At the clinic visit, the provider will use the PREVENT tool to discuss CVH risk and deliver a tailored behavioral change plan inclusive of patient-centered community resources. Community Health Workers will follow up with patients monthly to support behavior change.

Group II: Providers Active Control 1 Intervention

All eligible providers will be sent questionnaires electronically to their email at baseline, following provider training and follow-up. Providers will be invited to attend a training session to educate them on the PREVENT tool at baseline.

Group III: Patients - Wait-List Control Active Control 1 Intervention

* Complete questionnaires at baseline, within 48 hours of their routine clinic visit, and 6-months after the clinic visit. All surveys will be administered electronically or by mail * A PREVENT action plan (behavior change prescription, community resources, and education) will be provided to the patient via email after the completion of the follow-up measurement.

Who Is Running the Clinical Trial?

Washington University School of Medicine

Lead Sponsor

Trials

2,027

Recruited

2,353,000+

Missouri Highlands Health Care

Collaborator

Trials

1

Recruited

100+

Findings from Research

A 12-month randomized controlled trial will test a digital weight gain prevention intervention for patients with obesity, focusing on tailored behavior change and remote coaching, compared to usual care.

The primary goal is to prevent weight gain (defined as ≤3% change in baseline weight) in a diverse group of medically vulnerable patients, highlighting the potential of digital health solutions in primary care settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The Balance protocol: a pragmatic weight gain prevention randomized controlled trial for medically vulnerable patients within primary care. Berger, MB., Steinberg, DM., Askew, S., et al.[2021]

Digital health interventions using Computerized Decision Support (CDS) and Machine Learning (ML) have shown promise in the prevention and treatment of childhood obesity, with all identified studies reporting statistically significant outcomes.

CDS tools, particularly those utilizing Electronic Health Records and BMI alerts, can aid in self-management of obesity, while ML algorithms like decision trees and artificial neural networks are effective for predicting obesity risk, highlighting the potential for smart interventions in childhood obesity care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computerized decision support and machine learning applications for the prevention and treatment of childhood obesity: A systematic review of the literature. Triantafyllidis, A., Polychronidou, E., Alexiadis, A., et al.[2021]

A systematic review of 55 randomized controlled trials involving 3406 records found that technology-based interventions for treating pediatric obesity resulted in a small but significant weight loss effect (effect size d = -0.13), although many studies did not show significant differences compared to control groups.

In contrast, prevention interventions using technology did not demonstrate a significant impact on weight outcomes, indicating that more research is needed to assess their effectiveness compared to traditional methods.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Harnessing technological solutions for childhood obesity prevention and treatment: a systematic review and meta-analysis of current applications. Fowler, LA., Grammer, AC., Staiano, AE., et al.[2023]

The internet-based obesity prevention program significantly reduced the percentage of overweight and obese children in the intervention group (39.6%) compared to the control group (56.6%) after four months, indicating its efficacy in promoting healthier weight status.

Children in the intervention group experienced a smaller increase in BMI (0.40 kg/m²) compared to the control group (1.24 kg/m²), demonstrating that the program effectively helped maintain healthier body weight among participants.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Internet Based Obesity Prevention Program for Thai School Children- A Randomized Control Trial. Rerksuppaphol, L., Rerksuppaphol, S.[2020]

References

1. Switzerland pubmed.ncbi.nlm.nih.gov

Development of a Health Information Technology Tool for Behavior Change to Address Obesity and Prevent Chronic Disease Among Adolescents: Designing for Dissemination and Sustainment Using the ORBIT Model. [2023]

2. England pubmed.ncbi.nlm.nih.gov

The Balance protocol: a pragmatic weight gain prevention randomized controlled trial for medically vulnerable patients within primary care. [2021]

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

Computerized decision support and machine learning applications for the prevention and treatment of childhood obesity: A systematic review of the literature. [2021]

4. Netherlands pubmed.ncbi.nlm.nih.gov

Engagement with a nationally-implemented digital behaviour change intervention: Usage patterns over the 9-month duration of the National Health Service Digital Diabetes Prevention Programme. [2023]

5. England pubmed.ncbi.nlm.nih.gov

Harnessing technological solutions for childhood obesity prevention and treatment: a systematic review and meta-analysis of current applications. [2023]

6. India pubmed.ncbi.nlm.nih.gov

Internet Based Obesity Prevention Program for Thai School Children- A Randomized Control Trial. [2020]

7. Canada pubmed.ncbi.nlm.nih.gov

Technology-Based Obesity Prevention Interventions Among Hispanic Adolescents in the United States: Scoping Review. [2022]

8. Switzerland pubmed.ncbi.nlm.nih.gov

Development and implementation of a smartphone application to promote physical activity and reduce screen-time in adolescent boys. [2022]