Working…
COVID-19 is an emerging, rapidly evolving situation.
Get the latest public health information from CDC: https://www.coronavirus.gov.

Get the latest research information from NIH: https://www.nih.gov/coronavirus.
ClinicalTrials.gov
ClinicalTrials.gov Menu

Evaluating Efficacy of Digital Health Technology in the Treatment of Congestive Heart Failure

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04394754
Recruitment Status : Recruiting
First Posted : May 19, 2020
Last Update Posted : October 1, 2020
Sponsor:
Collaborators:
Boehringer Ingelheim
Medullan
Information provided by (Responsible Party):
Yale University

Brief Summary:
The purpose of this study is to determine the efficacy of three novel digital health technologies versus usual care in the management of congestive heart failure, as assessed by a primary outcome of improvement in quality of life, and a variety of secondary outcomes that include metrics measuring patient and provider satisfaction, clinical efficiency, and patient outcomes.

Condition or disease Intervention/treatment Phase
Congestive Heart Failure Device: BodyPort Other: Noom Other: Conversa Not Applicable

Detailed Description:

Heart failure is the most common cause of mortality and morbidity in the United States and in Western Europe. However, patient etiology and prognosis varies considerably, and guidance about how to best treat patients has relied on large clinical trials that only include snapshots of the syndrome (at the time the patient interaction with the healthcare system). It remains to be seen whether behavioral interventions can improve patient engagement, increase self-management of the conditions, and thus improve overall clinical outcomes.

Digital health technologies have a great potential to streamline and optimize the clinical management of heart failure. Such technologies can take the form of mobile applications or wearable devices that may provide both patients and providers with valuable real-time information about patient status and cardiovascular health, provide automated patient-tailored coaching and motivational tools, or a mix of both. Integration of these technologies into healthcare systems may allow for genuine engagement of the patient in their own care and management of their disease and/or enhance clinical decision making. To date, no prior study has comprehensively examined the ability of digital heath technologies to improve self-management of heart failure or subsequent clinical outcomes.

This study is an unblinded, 4-arm, parallel group randomized controlled trial to measure the efficacy of four digital health technologies in improving the management of care and quality of life of patients with congestive heart failure (CHF). Patients enrolled in one of Yale's Disease Management Clinics for management of their CHF will be eligible for this study and approached for consent. Enrolled subjects will be randomized to one of four groups: a control (usual care) arm, or to one of three intervention arms, each of which assesses one of three digital health technologies. These technologies are:

  • BodyPort: A data-driven smart scale that provides enhanced cardiac monitoring and risk assessment data.
  • Noom: A live, data-driven coaching application providing personalized diet and weight management.
  • Conversa: An automated conversational platform providing patient motivation and educational tools for CHF management.

Patients will be enrolled in the study for 6 months. The first three months will involve active clinic management, while the final three months will involve patient follow-up via monthly check-ins (via phone calls) from providers.

The primary outcome is the rate of improvement in quality of life after 90 days post-enrollment, as measured by the Kansas City Cardiomyopathy Questionnaire (KCCQ). A variety of prespecified secondary outcomes will be measured to determine effects on patient outcomes, quality of care, clinical efficiency, and provider and patient satisfaction with the product.

Layout table for study information
Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 200 participants
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Single (Outcomes Assessor)
Primary Purpose: Supportive Care
Official Title: Assessing the Efficacy of Digital Health Technology in the Management of Congestive Heart Failure: An Evaluation of Three Novel Digital Health Products
Actual Study Start Date : September 21, 2020
Estimated Primary Completion Date : June 1, 2021
Estimated Study Completion Date : September 1, 2021

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Heart Failure

Arm Intervention/treatment
No Intervention: Control
Patients will receive usual care and no digital health device.
Experimental: BodyPort
Patients will receive the BodyPort device.
Device: BodyPort
Patients will receive BodyPort, a smart scale that provides advanced cardiac monitoring.

Experimental: Noom
Patients will receive a subscription to the Noom platform.
Other: Noom
Patients will receive a subscription to use Noom, a personalized diet and weight management application.

Experimental: Conversa
Patients will receive a subscription to the Conversa platform.
Other: Conversa
patients will receive a subscription to use Conversa, a personalized automated coaching and motivational application.




Primary Outcome Measures :
  1. Improvement in quality of life [ Time Frame: Day 90 after enrollment ]
    Assessed by KCCQ survey. This survey is a 23 item instrument used to quantify physical function, symptoms (frequency, severity and recent change), social function, self-efficacy and knowledge, and quality of life. An overall summary scored is derived for each domain and transformed to a score range of 1-100. Higher scores reflect better health status and quality of life.


Secondary Outcome Measures :
  1. Improvement in quality of life [ Time Frame: 180 days post enrollment ]
    Assessed by KCCQ survey. This survey is a 23 item instrument used to quantify physical function, symptoms (frequency, severity and recent change), social function, self-efficacy and knowledge, and quality of life. An overall summary scored is derived for each domain and transformed to a score range of 1-100. Higher scores reflect better health status and quality of life.

  2. Number of hospital admissions since enrollment [ Time Frame: 90 days post enrollment ]
    Via medical record review

  3. Number of hospital admissions since enrollment [ Time Frame: 180 days post enrollment ]
    Via medical record review

  4. Prescribing of guideline-directed medical therapy [ Time Frame: 90 days post enrollment ]
    Documented prescription or change in dosing of any one of the following medication classes: beta blockers, ACEi/ARBs, and/or spironolactone; assessed via Surescripts data collection.

  5. Prescribing of guideline-directed medical therapy [ Time Frame: 180 days post enrollment ]
    Documented prescription or change in dosing of any one of the following medication classes: beta blockers, ACEi/ARBs, and/or spironolactone; assessed via Surescripts data collection.

  6. Number of subjects using guideline-directed medical therapy [ Time Frame: 90 days post enrollment ]
    Number of patients having documented prescription of all of the following medication classes: beta blockers, ACEi/ARBs, and spironolactone; assessed via Surescripts data collection.

  7. Number of subjects using guideline-directed medical therapy [ Time Frame: 180 days post enrollment ]
    Number of patients having documented prescription of all of the following medication classes: beta blockers, ACEi/ARBs, and spironolactone; assessed via Surescripts data collection.

  8. Number of emergency department visits [ Time Frame: 90 days post enrollment ]
    Via medical record review

  9. Number of emergency department visits [ Time Frame: 180 days post enrollment ]
    Via medical record review

  10. AKI/AKD development [ Time Frame: 90 days post enrollment ]
    Via medical record review. AKI is defined as an increase in serum creatinine of .3 mg/dL over 48 hours or an increase of 50% over 7 days. AKD is defined as an increase in serum creatinine by 50% or a decrease in GFR by 35% or greater over three months.

  11. AKI/AKD development [ Time Frame: 180 days post enrollment ]
    Via medical record review. AKI is defined as an increase in serum creatinine of .3 mg/dL over 48 hours or an increase of 50% over 7 days. AKD is defined as an increase in serum creatinine by 50% or a decrease in GFR by 35% or greater over three months.

  12. Mortality rates [ Time Frame: 90 days post enrollment ]
    Via medical record review

  13. Mortality rates [ Time Frame: 180 days post enrollment ]
    Via medical record review

  14. Number of clinic no-shows [ Time Frame: 90 days post enrollment ]
    Via medical record review; defined by number of missed scheduled clinic visits

  15. Intravisit contact between patient and provider [ Time Frame: 90 days post enrollment ]
    Via medical record review of telephone logs

  16. Intravisit contact between patient and provider [ Time Frame: 180 days post enrollment ]
    Via medical record review of telephone logs

  17. Number of clinic visits [ Time Frame: 90 days post enrollment ]
    Via medical record review

  18. Number of clinic visits [ Time Frame: 180 days post enrollment ]
    Via medical record review

  19. Average time devoted by provider to patient care [ Time Frame: 90 days post enrollment ]
    Via medical record review of chart openings per patient

  20. Average time devoted by provider to patient care [ Time Frame: 180 days post enrollment ]
    Via medical record review of chart openings per patient

  21. Number of subjects who complete on boarding and baseline assessments [ Time Frame: Within one week of consent ]
    Collected from patient enrollment platform

  22. Number of subjects who complete digital health product set up [ Time Frame: 90 days post enrollment ]
    Information collected from device metrics to assess usability of the product

  23. Number of weekly interactions with the device [ Time Frame: 90 days post enrollment ]
    Information collected from device metrics to assess frequency of use

  24. Number of weekly interactions with the device [ Time Frame: 180 days post enrollment ]
    Information collected from device metrics to assess frequency of use

  25. Average number of daily interactions with the device [ Time Frame: 90 days post enrollment ]
    Information collected from device metric to assess frequency of use

  26. Average number of daily interactions with the device [ Time Frame: 180 days post enrollment ]
    Information collected from device metric to assess frequency of use



Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


Layout table for eligibility information
Ages Eligible for Study:   18 Years to 79 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

  • Adults greater than or equal to 18 and less than 80 years of age
  • Enrolled in one of nine Yale Disease Management Clinics
  • Diagnosed with congestive heart failure (preserved or reduced ejection fraction with or without diabetes)

Exclusion Criteria:

  • Class IV heart failure
  • Stage 4 or end stage renal disease (eGFR < 30)
  • Recipient of a heart transplant of ventricular assist device
  • Under hospice care
  • Dementia
  • Incarceration
  • Pregnancy
  • Homelessness
  • Inability to consent
  • Currently enrolled in (or completed within the past 30 days) a study of an investigation drug or device
  • Life expectancy of less than 6 months as determined by clinical judgement of primary treating physician
  • weight greater than 400 pounds
  • unable to stand straight up for 30 seconds without assistance, such as from a cane, walker, or wall.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04394754


Contacts
Layout table for location contacts
Contact: Francis P Wilson, MD MSCE 203-737-1704 francis.p.wilson@yale.edu

Locations
Layout table for location information
United States, Connecticut
Yale New Haven Hospital Recruiting
New Haven, Connecticut, United States, 06520
Contact: Francis P Wilson, MD MSCE       francis.p.wilson@yale.edu   
Principal Investigator: Francis P Wilson, MD MSCE         
Sponsors and Collaborators
Yale University
Boehringer Ingelheim
Medullan
Investigators
Layout table for investigator information
Principal Investigator: Francis P Wilson, MD MSCE Yale University
Layout table for additonal information
Responsible Party: Yale University
ClinicalTrials.gov Identifier: NCT04394754    
Other Study ID Numbers: 2000027325
First Posted: May 19, 2020    Key Record Dates
Last Update Posted: October 1, 2020
Last Verified: September 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Yes
Plan Description: Deidentified data underlying results will be made available upon publication in an appropriate database.
Supporting Materials: Study Protocol
Statistical Analysis Plan (SAP)
Time Frame: Upon publication; indefinitely

Layout table for additional information
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: Yes
Product Manufactured in and Exported from the U.S.: Yes
Keywords provided by Yale University:
Digital Health
Additional relevant MeSH terms:
Layout table for MeSH terms
Heart Failure
Heart Diseases
Cardiovascular Diseases