Mobile App Health Data Collection for Peripheral Arterial Disease

(ROAMM-EHR Trial)

Overseen By HOBI department

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: University of Florida

Disqualifiers: Dementia, Hearing loss, Vision impairment, others

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

Compensation: Varies

Number of Visits: Unknown

50 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests how a mobile health app on a smartwatch can assist doctors in managing recovery after surgery for individuals with chronic limb-threatening ischemia, a condition that reduces blood flow to the limbs. The app collects health data and sends it to doctors to determine if this improves patients' mobility and quality of life post-surgery. Participants will have their data either shared with their doctors (actionable remotely generated health data) or not (non-actionable remotely generated health data) to compare outcomes. The trial seeks individuals with chronic limb-threatening ischemia who are scheduled for bypass surgery and do not have severe dementia or other major health issues that could interfere with the study. As an unphased trial, this study offers participants the chance to contribute to innovative research that could enhance post-surgical care.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It is best to discuss this with the study team or your doctor.

What prior data suggests that this mobile app health data collection is safe for patients with peripheral arterial disease?

Research has shown that using health data from smart devices is generally safe for patients. In studies, walking programs for people with peripheral artery disease (PAD) that incorporate smart devices have demonstrated benefits such as improved walking ability and enhanced overall well-being. No major safety issues have been reported when using these devices to collect health data.

For both types of data—those that require action and those that don't—studies have not identified any major negative effects. Patients track their symptoms with smartwatches, aiding healthcare providers in making informed decisions. This method focuses on gathering information and does not involve new medications or invasive procedures, which typically carry more risks.

Overall, using smart devices to collect health data appears to be well-tolerated by patients without causing harmful effects. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about this trial because it explores how remotely generated health data from smartwatches could transform the management of Peripheral Arterial Disease (PAD). Unlike traditional methods that rely heavily on in-person monitoring and decisions based on periodic check-ups, this approach provides real-time data directly to healthcare providers. This could lead to more personalized and timely medical decisions, potentially improving patient outcomes. Additionally, by comparing actionable data visible to doctors with non-actionable data, the study aims to understand the impact of real-time information on treatment strategies.

What evidence suggests that using remotely generated health data is effective for managing post-surgical symptoms in patients with peripheral arterial disease?

Research has shown that mobile health (mHealth) apps can assist people with peripheral arterial disease (PAD). Studies have found that these apps can ease walking and reduce pain during walking. One study discovered that exercise programs using mHealth apps increased both the time people could walk without pain and the farthest distance they could walk. Another study demonstrated that wearable devices, combined with home exercises, improved overall walking ability. In this trial, participants in the ROAMM-EHR arm will have their health data shared with doctors, potentially aiding in symptom management and improving life quality after surgery. Meanwhile, participants in the Active Comparison arm will use the smartwatch app without sharing their data with their medical team. ^{1 2 6 7 8}

Who Is on the Research Team?

Todd Manini, PhD

Principal Investigator

University of Florida

Are You a Good Fit for This Trial?

This trial is for individuals aged 60 or older with chronic limb-threatening ischemia who are undergoing bypass surgery. It's not suitable for those at high risk of post-surgical amputation, non-English speakers, dementia patients, those with severe hearing loss or vision impairment that affects assessments and safety, or other serious diseases as judged by the study physician.

Inclusion Criteria

I am undergoing surgery to improve blood flow in my limbs due to severe blockage.

I am 60 years old or older.

Exclusion Criteria

Other significant co-morbid disease that in the opinion of the investigators and study physician would impair the ability to participate in the study or be a safety concern

Non-English speaker

I have been diagnosed with a dementia related to aging, like Alzheimer's.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Pre-Surgery Monitoring

Participants wear a smartwatch equipped with the ROAMM app before their surgery to collect baseline data

1-2 weeks

Daily monitoring via smartwatch

Post-Surgery Monitoring

Participants continue to wear the smartwatch for approximately a month after surgery to report symptoms and activity levels

4 weeks

Daily monitoring via smartwatch

Follow-up

Participants are monitored for safety and effectiveness after treatment, including assessments of 6-min walk distance and quality of life

4 weeks

What Are the Treatments Tested in This Trial?

Interventions

• Actionable remotely generated health data
• Non-Actionable remotely generated health data

Trial Overview The ROAMM-EHR Study tests if patient health data collected remotely can help manage symptoms after bypass surgery for critical limb ischemia. Participants will either receive actionable health data alerts integrated with their EHR system or non-actionable information to compare outcomes.

How Is the Trial Designed?

Group I: ROAMM-EHR Experimental Treatment 1 Intervention

Patients will wear a smartwatch equipped with a smartwatch app before their surgery and for approximately a month after the surgery. Patients will be asked to wear the watch every day when awake. When wearing the smartwatch, patients will be asked questions about their symptoms following surgery and data will be sent to providers electronically and displayed in their EHR portal. Healthcare Providers will use this information along with patient medical history to decide on the next course of action. The study does not provide specific instructions about how to care for the patient. Those decisions are made by the provider team and their clinical judgement.

Group II: Active Comparison Active Control 1 Intervention

The active comparison patients will wear and respond to questions on the smartwatch. However, the information will not be viewable by their doctor and medical team. All standard of care procedures will remain.

Who Is Running the Clinical Trial?

University of Florida

Lead Sponsor

Trials

1,428

Recruited

987,000+

National Institute on Aging (NIA)

Collaborator

Trials

1,841

Recruited

28,150,000+

Published Research Related to This Trial

In a pilot study involving 39 patients with peripheral arterial disease, the TrackPAD app significantly improved the 6-minute walking distance by an average of 83 meters over 3 months, while the control group experienced a decrease in walking distance.

The app also enhanced patients' quality of life related to their condition, particularly in symptom perception and physical functioning, indicating its potential as a supportive tool for managing chronic diseases.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Supervised Exercise Therapy Using Mobile Health Technology in Patients With Peripheral Arterial Disease: Pilot Randomized Controlled Trial. Paldán, K., Steinmetz, M., Simanovski, J., et al.[2021]

An automated algorithm developed to analyze accelerometer data effectively detected home-based exercise sessions in patients with peripheral artery disease (PAD), achieving a sensitivity of 85% and specificity of 90%.

The exercise sessions identified by the algorithm were correlated with improvements in peak walking time after 6 months of training, indicating that monitored home exercise can enhance performance in PAD patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Automated Detection of Exercise Sessions in Patients With Peripheral Artery Disease: EVIDENCE FOR AN EXERCISE DOSE RESPONSE TO TRAINING. Mays, RJ., Wesselman, CW., White, R., et al.[2022]

A consensus was reached among 25 international vascular experts on a minimum core data set for evaluating outcomes of peripheral arterial revascularization, which includes 79 recommended items covering patient characteristics, comorbidities, procedures, and complications.

This standardized data set aims to enhance collaboration among global registries, improve data quality, and facilitate the development of real-world evidence in vascular surgery, addressing limitations of single-country studies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

International Consortium of Vascular Registries Consensus Recommendations for Peripheral Revascularisation Registry Data Collection. Behrendt, CA., Bertges, D., Eldrup, N., et al.[2021]

A study involving 62 patients and 44 healthcare professionals found that 84% of patients preferred their supervised exercise therapy (SET) to be monitored by their vascular team, indicating a strong desire for professional oversight in their treatment.

Patients showed significant interest in features like exercise videos (76%) and communication tools for interaction with their healthcare team, while healthcare professionals valued the documentation of physical activity and wearable technology, highlighting differing priorities between the two groups.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Patient and Healthcare Professional Priorities for a Mobile Phone Application for Patients With Peripheral Arterial Disease. Sivagangan, P., Harwood, AE., Stather, PW.[2023]

The integration of Patient-Reported Outcomes (PROs) into clinical practice for managing Peripheral Artery Disease (PAD) is essential for improving care quality, as it allows for individualized management and better assessment of treatment impact.

The proposed framework for using PRO measures as performance indicators (PRO-PMs) aims to reduce variability in care and promote health equity, highlighting the need for proper training and competencies to effectively handle and interpret PRO-PM data.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Advancing Peripheral Artery Disease Quality of Care and Outcomes Through Patient-Reported Health Status Assessment: A Scientific Statement From the American Heart Association. Smolderen, KG., Alabi, O., Collins, TC., et al.[2023]

A new automated risk prediction tool for patients with peripheral arterial disease was developed using electronic health records, analyzing data from 1,676 patients over a 5-year follow-up period, which showed a strong ability to predict survival with a c-statistic of 0.76.

The tool stratifies patients into different risk categories, revealing significantly increased mortality risks for higher-risk groups, which can help healthcare providers make more informed decisions at the point of care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Leveraging the Electronic Health Record to Create an Automated Real-Time Prognostic Tool for Peripheral Arterial Disease. Arruda-Olson, AM., Afzal, N., Priya Mallipeddi, V., et al.[2023]

The VascTrac iPhone app, designed to monitor walking distance in patients with peripheral artery disease (PAD), showed that the iPhone's built-in distance algorithm significantly overestimated walking distance by 43%, indicating a need for custom algorithms for accurate monitoring.

While the iPhone accurately counted steps, with a bias of -7.2%, further research is needed to determine if step counting can be a clinically meaningful measure for assessing walking ability in PAD patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Clinical validation of smartphone-based activity tracking in peripheral artery disease patients. Ata, R., Gandhi, N., Rasmussen, H., et al.[2021]

In a study of 796 patients with peripheral arterial disease (PAD), maintaining good glucose control was found to significantly reduce the risk of adverse limb events and death, with a hazard ratio of 0.74, indicating a protective effect.

African American patients were found to be at a higher risk (2.8 times) for adverse limb events compared to White or Hispanic patients, but this increased risk was mitigated when their glucose levels were well-controlled.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Process of care and outcomes in patients with peripheral arterial disease. Collins, TC., Beyth, RJ., Nelson, DB., et al.[2022]

A multidisciplinary group reviewed the limitations of existing studies on paclitaxel-coated devices, highlighting the need for improved study design and data quality to better assess safety and efficacy.

The group recommends actionable steps, such as creating standardized data points and reducing missing data, to enhance future research in peripheral arterial disease, ultimately aiming to improve patient outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Important considerations for trials for peripheral arterial disease: Lessons learned from the paclitaxel mortality signal: A report on behalf of the registry assessment for peripheral interventional Devices (RAPID) Paclitaxel Pathways Program. Lottes, AE., Whatley, EM., Royce, SM., et al.[2021]

A machine learning model developed from electronic health records (EHR) demonstrated high accuracy in identifying undiagnosed peripheral artery disease (PAD), achieving an average AUC of 0.96, significantly outperforming traditional models.

Physicians showed a strong acceptance of an automated PAD risk assessment tool integrated into EHR systems, with 75% expressing interest in its use, highlighting the potential for improved patient outcomes through better diagnosis and management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Performance and usability testing of an automated tool for detection of peripheral artery disease using electronic health records. Ghanzouri, I., Amal, S., Ho, V., et al.[2023]

Out of 1,446 identified patients with peripheral arterial disease (PAD), only 64 participants (4.4%) were successfully enrolled in a feasibility study, highlighting significant challenges in recruiting this population.

The study identified that patients with PAD often face difficulties due to coexisting health issues and travel limitations, suggesting that future recruitment strategies need to be tailored to address these barriers for better participation rates.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Recruiting older patients with peripheral arterial disease: evaluating challenges and strategies. Brostow, DP., Hirsch, AT., Kurzer, MS.[2022]

Citations

1. clinicaltrials.gov clinicaltrials.gov/study/NCT06033924

Telehealth Delivered Home-based Walking for Vets With ... Walking is beneficial for adults with peripheral arterial disease. Benefits include the ability to walk for longer periods and general well being (quality ...

2. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC7920758/

Effectiveness of Mobile Health–Based Exercise Interventions ... mHealth-based exercise interventions for patients with PAD were beneficial for improving pain-free walking, maximal walking, and 6MWT distance.

3. clinicaltrials.gov clinicaltrials.gov/ct2/show/NCT04113057

Home-based Walking Program With Smart Devices | ... Intermittent claudication (IC), the most common manifestation of peripheral artery disease (PAD), represents not only a significant detriment in quality of life ...

4. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC5933394/

Effect of a Home-Based Exercise Intervention of Wearable ... A home-based exercise intervention of wearable technology and telephone coaching on walking performance in peripheral artery disease.

5. researchgate.net researchgate.net/publication/349326887_Effectiveness_of_Mobile_Health-Based_Exercise_Interventions_for_Patients_with_Peripheral_Artery_Disease_Systematic_Review_and_Meta-Analysis

(PDF) Effectiveness of Mobile Health–Based Exercise ... Effectiveness of Mobile Health–Based Exercise Interventions for Patients with Peripheral Artery Disease: Systematic Review and Meta-Analysis.

6. ctv.veeva.com ctv.veeva.com/study/the-roamm-ehr-study

The ROAMM-EHR Study - ClinicalTrials.Veeva The objective of this study is to conduct a pilot randomized clinical trial (RCT) to evaluate the impact of using remotely collected patient ...

7. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC8285054/

Leveraging machine learning and artificial intelligence to ... Peripheral artery disease (PAD) is an atherosclerotic disorder, that when present, portends poor patient outcomes. Low diagnosis rates perpetuate poor ...

8. ahajournals.org ahajournals.org/doi/10.1161/HCG.0000000000000095

Data Interoperability for Ambulatory Monitoring of ... This scientific statement aims to address the best practices, gaps, and challenges pertaining to data interoperability in this area.