Mobile App Health Data Collection for Peripheral Arterial Disease
(ROAMM-EHR Trial)
Recruiting in Palo Alto (17 mi)
Age: 18+
Sex: Any
Travel: May Be Covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: University of Florida
Disqualifiers: Dementia, Hearing loss, Vision impairment, others
No Placebo Group
Trial Summary
What is the purpose of this trial?In recent years, mobile health (mHealth) apps have promised improved monitoring of health conditions to improve clinical outcomes. The objective of this study is to conduct a pilot randomized clinical trial (RCT) to evaluate the impact of using remotely collected patient generated health data (PGHD) from older patients undergoing bypass surgery due to chronic limb threatening ischemia. The hypothesis is that integrating PGHD with an EHR system will help providers manage post-surgical symptoms and thus improve post-operative mobility and quality of life health outcomes.
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 data supports the effectiveness of this treatment for Peripheral Arterial Disease?
Research shows that using mobile health apps for supervised exercise therapy can be an effective way to manage Peripheral Arterial Disease (PAD). These apps help patients follow exercise programs more easily and allow healthcare providers to monitor progress, which can improve health outcomes.
12345Is the mobile app for health data collection safe for humans?
The research does not provide specific safety data for the mobile app used in health data collection for peripheral arterial disease, but it suggests that using mobile health applications for delivering therapy is acceptable to patients and healthcare professionals.
14678How does the mobile app treatment for peripheral arterial disease differ from other treatments?
This treatment is unique because it uses a mobile app to deliver supervised exercise therapy (SET) remotely, making it more accessible and convenient for patients compared to traditional in-person sessions. The app allows for monitoring by healthcare professionals and includes features like exercise videos and walking distance tracking, which are tailored to the needs of patients with peripheral arterial disease.
1291011Eligibility Criteria
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
I have been diagnosed with a dementia related to aging, like Alzheimer's.
Non-English speaker
+3 more
Trial Timeline
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
Participant Groups
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.
2Treatment groups
Experimental Treatment
Active Control
Group I: ROAMM-EHRExperimental Treatment1 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 ComparisonActive Control1 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.
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
Institute of AgingGainesville, FL
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Who Is Running the Clinical Trial?
University of FloridaLead Sponsor
National Institute on Aging (NIA)Collaborator
References
Patient and Healthcare Professional Priorities for a Mobile Phone Application for Patients With Peripheral Arterial Disease. [2023]Introduction Supervised exercise therapy (SET) is the first-line treatment for the peripheral arterial disease (PAD), however, access and compliance are low. An alternative method of delivering this therapy is through mobile health applications, which can be more accessible and convenient for patients. The aim of this study is to evaluate patient, public and healthcare professional (HCP) priorities with regard to a dedicated mobile phone application to deliver remote SET. Methods Bespoke questionnaires were designed for patients and HCPs to assess app functionality and prioritisations for development. These were distributed through social media and the Norfolk and Norwich University Hospital. Results Functionality questionnaires were completed by 62 patients and 44 HCPs. Eighty-four per cent of patients wanted their therapy to be monitored by their vascular team with the majority (78%) interested in measuring walking distances. Most patients (76%) were interested in watching exercise videos. These views were shared by HCPs. A communication platform was prioritised for messaging and pictures by the patient (74% and 68% respectively), but not so by HCPs (40%). Documenting other forms of physical activity and the use of wearable technology was less valuable to patients but favoured by HCPs (50%). The ability to interact with other users was not prioritised by either group. Conclusion Delivery of a mobile phone application to deliver health programmes for SET in patients with PAD is an acceptable method for patients and HCPs. This data will enable the next stages of mobile phone application development to be appropriately prioritised, focusing on building exercise videos, a communication platform and further walking tests.
Supervised Exercise Therapy Using Mobile Health Technology in Patients With Peripheral Arterial Disease: Pilot Randomized Controlled Trial. [2021]Mobile health interventions are intended to support complex health care needs in chronic diseases digitally, but they are mainly targeted at general health improvement and neglect disease-specific requirements. Therefore, we designed TrackPAD, a smartphone app to support supervised exercise training in patients with peripheral arterial disease.
Advancing Peripheral Artery Disease Quality of Care and Outcomes Through Patient-Reported Health Status Assessment: A Scientific Statement From the American Heart Association. [2023]Peripheral artery disease (PAD) is chronic in nature, and individualized chronic disease management is a central focus of care. To accommodate this reality, tools to measure the impact and quality of the PAD care delivered are necessary. Patient-reported outcomes (PROs) and instruments to measure them, that is, PRO measures, have been well studied in the research and clinical trial context, but a shift toward integrating them into clinical practice has yet to take place. A framework to use PRO measures as indicators of the quality of PAD care delivered, that is, PRO performance measures (PRO-PMs), is provided in this scientific statement. Measurement goals to consider by PAD clinical phenotypes are provided, as well as an overview of potential benefits of adopting PRO-PMs in the clinical practice of PAD care, including reducing unwanted variability and promoting health equity. A central discussion with considerations for risk adjustment of PRO-PMs, individualized PAD care, and the need for patient engagement strategies is offered. Furthermore, necessary conditions in terms of required competencies and training to handle PRO-PM data are discussed because the interpretation and handling of these data come with great responsibility and consequences for designing care that adopts a broader framework of risk that goes beyond the inclusion of biomedical variables. To conclude, health system perspectives and an agenda to reach the next steps in the implementation of PRO-PMs in PAD care are offered.
International Consortium of Vascular Registries Consensus Recommendations for Peripheral Revascularisation Registry Data Collection. [2021]To achieve consensus on the minimum core data set for evaluation of peripheral arterial revascularisation outcomes and enable collaboration among international registries.
Leveraging the Electronic Health Record to Create an Automated Real-Time Prognostic Tool for Peripheral Arterial Disease. [2023]Background Automated individualized risk prediction tools linked to electronic health records ( EHR s) are not available for management of patients with peripheral arterial disease. The goal of this study was to create a prognostic tool for patients with peripheral arterial disease using data elements automatically extracted from an EHR to enable real-time and individualized risk prediction at the point of care. Methods and Results A previously validated phenotyping algorithm was deployed to an EHR linked to the Rochester Epidemiology Project to identify peripheral arterial disease cases from Olmsted County, MN, for the years 1998 to 2011. The study cohort was composed of 1676 patients: 593 patients died over 5-year follow-up. The c-statistic for survival in the overall data set was 0.76 (95% confidence interval [CI], 0.74-0.78), and the c-statistic across 10 cross-validation data sets was 0.75 (95% CI, 0.73-0.77). Stratification of cases demonstrated increasing mortality risk by subgroup (low: hazard ratio, 0.35 [95% CI, 0.21-0.58]; intermediate-high: hazard ratio, 2.98 [95% CI, 2.37-3.74]; high: hazard ratio, 8.44 [95% CI, 6.66-10.70], all P
Process of care and outcomes in patients with peripheral arterial disease. [2022]We investigated the association of process of care measures with adverse limb and systemic events in patients with peripheral arterial disease (PAD).
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. [2021]The Registry Assessment of Peripheral Devices (RAPID) convened a multidisciplinary group of stakeholders including clinicians, academicians, regulators and industry representatives to conduct an in-depth review of limitations associated with the data available to assess the paclitaxel mortality signal. Available studies were evaluated to identify strengths and limitations in the study design and data quality, which were translated to lessons learned to help guide the design, execution, and analyses of future studies. We suggest numerous actionable responses, such as the development and use of harmonized data points and outcomes in a consensus lean case report form. We advocate for reduction in missing data and efficient means for accrual of larger sample sizes in Peripheral arterial disease studies or use of supplemental datasets. Efforts to share lessons learned and working collaboratively to address such issues may improve future data in this device area and ultimately benefit patients. Condensed Abstract: Data sources evaluating paclitaxel-coated devices were evaluated to identify strengths and limitations in the study design and data quality, which were translated to lessons learned to help guide the design, execution, and analyses of future studies. We suggest numerous actionable responses, which we believe may improve future data in this device area and ultimately benefit patients.
Performance and usability testing of an automated tool for detection of peripheral artery disease using electronic health records. [2023]Peripheral artery disease (PAD) is a common cardiovascular disorder that is frequently underdiagnosed, which can lead to poorer outcomes due to lower rates of medical optimization. We aimed to develop an automated tool to identify undiagnosed PAD and evaluate physician acceptance of a dashboard representation of risk assessment. Data were derived from electronic health records (EHR). We developed and compared traditional risk score models to novel machine learning models. For usability testing, primary and specialty care physicians were recruited and interviewed until thematic saturation. Data from 3168 patients with PAD and 16,863 controls were utilized. Results showed a deep learning model that utilized time engineered features outperformed random forest and traditional logistic regression models (average AUCs 0.96, 0.91 and 0.81, respectively), P
Automated Detection of Exercise Sessions in Patients With Peripheral Artery Disease: EVIDENCE FOR AN EXERCISE DOSE RESPONSE TO TRAINING. [2022]Monitoring home exercise using accelerometry in patients with peripheral artery disease (PAD) may provide a tool to improve adherence and titration of the exercise prescription. However, methods for unbiased analysis of accelerometer data are lacking. The aim of the current post hoc analysis was to develop an automated method to analyze accelerometry output collected during home-based exercise.
Recruiting older patients with peripheral arterial disease: evaluating challenges and strategies. [2022]Peripheral arterial disease (PAD) is a group of syndromes characterized by chronic and progressive atherosclerosis with a high burden of physical disability and cardiovascular morbidity and mortality. Recruiting patients for clinical research is therefore challenging. In this article, we describe and evaluate our methods for recruiting participants for a cross-sectional feasibility study of PAD, nutritional status, and body composition. We used convenience and purposive sampling approaches to identify potential participants. Between May 2012 and April 2013, 1,446 patients were identified, and 165 patients (11.4%) responded to recruitment requests. The final enrollment was 64 participants (64/1,446; 4.4%), and four subjects (6.3%) subsequently withdrew from the study. Recruiting PAD patients presents a variety of challenges, due largely to the burdens of living with coexistent illnesses, and patients' reluctance or inability to travel for research. In this article, we delineate suggestions for improving the efficacy of recruitment methods in future PAD studies.
Clinical validation of smartphone-based activity tracking in peripheral artery disease patients. [2021]Peripheral artery disease (PAD) is a vascular disease that leads to reduced blood flow to the limbs, often causing claudication symptoms that impair patients' ability to walk. The distance walked during a 6-min walk test (6MWT) correlates well with patient claudication symptoms, so we developed the VascTrac iPhone app as a platform for monitoring PAD using a digital 6MWT. In this study, we evaluate the accuracy of the built-in iPhone distance and step-counting algorithms during 6MWTs. One hundred and fourteen (114) participants with PAD performed a supervised 6MWT using the VascTrac app while simultaneously wearing an ActiGraph GT9X Activity Monitor. Steps and distance-walked during the 6MWT were manually measured and used to assess the bias in the iPhone CMPedometer algorithms. The iPhone CMPedometer step algorithm underestimated steps with a bias of -7.2% ± 13.8% (mean ± SD) and had a mean percent difference with the Actigraph (Actigraph-iPhone) of 5.7% ± 20.5%. The iPhone CMPedometer distance algorithm overestimated distance with a bias of 43% ± 42% due to overestimation in stride length. Our correction factor improved distance estimation to 8% ± 32%. The Ankle-Brachial Index (ABI) correlated poorly with steps (R = 0.365) and distance (R = 0.413). Thus, in PAD patients, the iPhone's built-in distance algorithm is unable to accurately measure distance, suggesting that custom algorithms are necessary for using iPhones as a platform for monitoring distance walked in PAD patients. Although the iPhone accurately measured steps, more research is necessary to establish step counting as a clinically meaningful metric for PAD.