Digital Health Program for Obesity

(Rural PREVENT Trial)

Recruiting at 2 trial locations

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

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests a digital health tool called PREVENT, designed to help people with obesity improve heart health and motivation for healthier living. Participants will either receive the tool or be on a wait-list, and the study will compare their experiences. Those who may qualify include low-income individuals with a body mass index (BMI) of 30 or more who currently receive care from Missouri Highlands Healthcare. The goal is to evaluate the tool's effectiveness in real-world healthcare settings and its ability to guide healthier choices. As an unphased trial, this study offers participants the chance to contribute to innovative research that could lead to improved health tools for broader use.

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 prior data suggests that the PREVENT Tool is safe for patients with overweight/obesity?

Research has shown that the PREVENT tool is designed to help people change their habits and improve their health. Healthcare providers use this digital tool to assist patients in setting health goals and finding helpful resources. So far, no specific information about negative effects from using the PREVENT tool has emerged.

As a digital health tool, it is generally expected to be safe. These tools typically focus on education and habit change, without involving physical procedures. The research does not mention any physical or medical risks. Therefore, the tool is likely safe and easy to use. However, discussing any concerns with a healthcare provider is always advisable. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the PREVENT tool for managing obesity because it offers a personalized approach to behavior change, unlike the standard treatments like diet plans, exercise programs, or medication. The PREVENT tool is unique in how it integrates patient-centered community resources and involves ongoing support from Community Health Workers, which is not commonly part of standard care. This digital health program emphasizes cardiovascular health risk assessment and delivers a tailored action plan during clinic visits, aiming to empower patients with the tools and support they need for sustainable lifestyle changes.

What evidence suggests that the PREVENT tool is effective for obesity?

This trial will evaluate the effectiveness of the PREVENT tool, a digital health program for obesity. Studies have shown that digital health tools, such as the PREVENT tool, can aid in weight loss. On average, users of these tools lose an additional 2–2.5 kg compared to non-users over 12–18 months. These tools provide personalized plans and community support to promote healthier habits. Although some studies show success, participation may decrease over time in real-world settings. Overall, digital tools have effectively helped people lose weight and adopt healthy lifestyle changes. ^{6 7 8 9 10}

Who Is on the Research Team?

Maura Kepper, PhD

Principal Investigator

Washington University School of Medicine

Are You a Good Fit for This Trial?

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 for a Trial Participant

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)

What Are the Treatments Tested in This Trial?

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.

How Is the Trial Designed?

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+

Published Research Related to This Trial

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]

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]

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]

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]

Citations

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Evidence-Based Digital Tools for Weight Loss Maintenance

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sciencedirect.com

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Real-world effectiveness studies demonstrate lower retention rates (40-60% at 12 months) compared to randomized trial settings (60-75% at 12 ...

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This systematic review aimed to map IT-supported interventions designed to prevent obesity in adolescents, promoting healthy nutrition and physical activity.

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The effectiveness and usability of online, group-based ...

A meta-analysis on four studies indicated that online, group-based interventions had a statistically significant impact on weight loss.

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The American Heart Association PREVENT™ Online Calculator

Utilize the PREVENT Calculator to assess cardiovascular risk and guide preventive care. Access personalized insights and evidence-based recommendations.

7.

pubmed.ncbi.nlm.nih.gov

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PREVENT is a digital health tool designed for use by providers to engage patients in behavior change education and goal setting and provides resources to ...

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frontiersin.org

frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2021.648777/full

Development of a Health Information Technology Tool for ...

The PREVENT tool was developed to meet these needs. It facilitates prevention discussions, delivers tailored, evidence-based recommendations for physical ...

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researchgate.net/figure/PREVENT-tool-pathway-of-development-and-hypothesized-impact_fig2_349959891

PREVENT tool pathway of development and hypothesized ...

Predictive modeling using machine learning (ML) offers a powerful approach to identify individuals at risk of obesity and inform early intervention strategies.

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The Impact of Behavior Change Counseling Delivered via a ...

PREVENT is a patient-centered digital health tool designed to improve clinical care as a first-line approach to obesity prevention and ...