Digital Health Tool for Cardiovascular Health and Obesity

Recruiting at 1 trial location

Age: 18 - 65

Sex: Any

Trial Phase: Academic

Sponsor: Washington University School of Medicine

Disqualifiers: Urban residence, High income, others

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

Compensation: Varies

Number of Visits: 7

Duration: 6 months

60 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 treatment PREVENT in the clinical trial for cardiovascular health and obesity?

Research shows that digital health interventions can help improve heart health and reduce weight, especially in people recovering from heart problems. These tools can also support better diet and exercise habits, which are important for managing heart and weight issues. ^{1 2 3 4 5}

How does the digital health tool for cardiovascular health and obesity differ from other treatments?

This digital health tool is unique because it uses mobile health (mHealth) technology, such as smartphone apps and tracking devices, to monitor and manage cardiovascular health and obesity. Unlike traditional treatments, it offers real-time feedback and personalized interventions, making healthcare more accessible and potentially reducing socioeconomic disparities. ^{6 7 8 9 10}

What is the purpose of this trial?

The focus on this application is low-income, rural patients, since cardiovascular disease (CVD) prevalence is 40% higher among rural than urban residents. Health behavior counseling and follow-up care are required for patients with an elevated body mass index who have increased risk for CVD. Counseling is most effective when developed with, and tailored to, the patient and offered with resources that support healthy food intake and physical activity. Resource referral and follow-up is particularly important in rural low income residents who often have more severe social needs that impede healthy behaviors. The proposed research will leverage the candidate's digital health tool (PREVENT) for healthcare teams to use within the clinic visit. PREVENT visually displays patient-reported and electronic health record (EHR) data to facilitate counseling and deliver tailored physical activity and healthy food intake goals and resources. PREVENT may improve the quality of required care and promote cardiovascular health equity. This research will: 1) collaborate with rural and clinic partners to modify and integrate the PREVENT tool for low-income, rural patients with obesity (Aim 1); and 2) conduct a pilot pragmatic clinical trial of PREVENT to optimize feasibility, acceptability, appropriateness, and potential health equity impact.

Eligibility Criteria

This trial is for low-income adults aged 18-64 living in rural areas, who are patients at Missouri Highlands with a BMI of 30 or higher. They must understand and agree to sign an informed consent form.

Inclusion Criteria

Low-income (household income <200% poverty)

Receiving care from the Missouri Highlands

Ability to understand and willingness to sign an IRB approved written informed consent document (or that of legally authorized representative, if applicable)

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Baseline Assessment

Participants complete questionnaires at baseline, administered electronically or by mail

1 week

1 visit (virtual or mail)

Treatment

Participants receive the PREVENT intervention, including a tailored behavioral change plan and ongoing support for 6 months

6 months

Monthly follow-ups (virtual or mail)

Follow-up

Participants are monitored for changes in cardiovascular health and behavior outcomes

6 months

Follow-up measures immediately after clinic visit and at 6 months

Treatment Details

Interventions

• PREVENT

Trial Overview The PREVENT digital health tool is being tested. It helps healthcare teams provide personalized counseling on healthy eating and physical activity during clinic visits to improve cardiovascular health among rural patients.

Participant Groups

Group I: PREVENT Intervention Experimental Treatment 1 Intervention

Complete questionnaires at baseline (administered electronically or by mail). Follow-up measures will be administered immediately following the clinic visit, and monthly for 6-months after the clinic visit electronically and by mail At the clinic visit, the provider will use the PREVENT tool to discuss CVH risk. A community health worker (CHW) will deliver a tailored behavioral change plan inclusive of patient-centered community resources. The CHW will provide ongoing support with goals and social needs for 6-months.

Group II: Wait-List Control Active Control 1 Intervention

Complete questionnaires at baseline (administered electronically or by mail). Follow-up measures will be administered immediately following their clinic visit and at 6-months after the clinic visit electronically and 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+

Findings from Research

Mobile health (mHealth) is significantly advancing cardiovascular care through innovative strategies like SMS for smoking cessation and apps for monitoring health, showing promise in prevention and rehabilitation.

Despite the potential of mHealth, many apps lack regulation and evidence-based support, highlighting the need for robust systems to ensure safety and effectiveness in delivering healthcare services.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

mHealth in Cardiovascular Health Care. Chow, CK., Ariyarathna, N., Islam, SM., et al.[2022]

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]

References

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

Digital health interventions for the prevention of cardiovascular disease: a systematic review and meta-analysis. [2018]

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

Using digital interventions to improve the cardiometabolic health of populations: a meta-review of reporting quality. [2022]

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

Web-Based Nutrition and Physical Activity Education Intervention to Ameliorate Cardiometabolic Risks: A Single-Arm and Non-Randomized Feasibility Study. [2023]

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

Can Digital Health Solutions Fill in the Gap for Effective Guideline Implementation in Cardiovascular Disease Prevention: Hope or Hype? [2022]

5. Canada pubmed.ncbi.nlm.nih.gov

Dose-Response Effect of a Digital Health Intervention During Cardiac Rehabilitation: Subanalysis of Randomized Controlled Trial. [2020]

6. Australia pubmed.ncbi.nlm.nih.gov

mHealth in Cardiovascular Health Care. [2022]

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

Adapting Technological Interventions to Meet the Needs of Priority Populations. [2021]

8. 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]

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

Advanced and Accurate Mobile Health Tracking Devices Record New Cardiac Vital Signs. [2019]

10. 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]