Creation of a Digital Heart Failure Registry Using a Novel Mobile Health Platform: HUGO-HF

NCT ID: NCT03810638

Last Updated: 2020-08-04

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

85 participants

Study Classification

OBSERVATIONAL

Study Start Date

2019-01-15

Study Completion Date

2019-12-31

Brief Summary

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The investigators goal is to create a digital registry that allows for seamless integration of patient reported outcomes, electronic health record data, and pharmacy information into data collection. The investigators will accomplish this using a novel patient centered mobile health platform called Hugo that will allow them to engage patients in an entirely novel manner.

Detailed Description

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Heart failure is the most common cause of mortality and morbidity in the United States and in Western Europe. It is a complex and chronic illness, and patient journeys vary considerably. However, to date, guidance about how to best treat patients has relied on large clinical international trials that do not represent contemporary patients and only include snapshots of the syndrome-at times when the patients interact with the health care system. Additionally, patient participation in clinical research in the US is extremely low, approximated at 3% in cancer and far lower for disease states such as heart failure.

One of the key reasons for this is a lack of patient engagement and trust in the research enterprise, especially among the elderly and minorities, groups that are disproportionally impacted by heart failure. To address this, the investigators plan to test a novel patient-powered, smartphone-based mobile health platform (called Hugo) developed at Yale School of Medicine for real-world surveillance of patient reported outcomes in heart failure patients treated at 3 Major Academic Medical Centers. Participants will then be queried about specific symptoms and health conditions at enrollment and prespecified time points for 2 years. The subgroup of participants who own devices that track their activity data will have the option of syncing them to this mobile health platform to provide additional insights into their health and health outcomes. Additionally, participants will have the option to learn about opportunities to participate in heart failure clinical research. This digital registry will also allow seamless integration of patient reported outcomes, electronic health record (EHR) data, and pharmacy information into data collection.

Overall, the aim for this study is to create a digital registry using a novel patient centered mobile health platform of heart failure patients across large health care systems that allows investigators to engage patients in an entirely novel manner.

Conditions

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Heart Failure

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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Heart Failure Patients

All Heart Failure Patients seen at 3 major academic medical centers.

No interventions assigned to this group

Eligibility Criteria

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Inclusion Criteria

* Age\> 18 Years
* English Speaking
* Diagnosis of heart failure
* Participant is willing and able to read and sign consent and participate in study
* Participant has an email account

Exclusion Criteria

\- Unable to participate in registry
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Yale University

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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Yale New Haven Hospital

New Haven, Connecticut, United States

Site Status

Countries

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United States

References

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Ahmad T, Freeman JV, Asselbergs FW. Can advanced analytics fix modern medicine's problem of uncertainty, imprecision, and inaccuracy? Eur J Heart Fail. 2019 Jan;21(1):86-89. doi: 10.1002/ejhf.1370. Epub 2018 Dec 10. No abstract available.

Reference Type BACKGROUND
PMID: 30537243 (View on PubMed)

Krumholz HM. Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system. Health Aff (Millwood). 2014 Jul;33(7):1163-70. doi: 10.1377/hlthaff.2014.0053.

Reference Type RESULT
PMID: 25006142 (View on PubMed)

Bhavnani SP, Parakh K, Atreja A, Druz R, Graham GN, Hayek SS, Krumholz HM, Maddox TM, Majmudar MD, Rumsfeld JS, Shah BR. 2017 Roadmap for Innovation-ACC Health Policy Statement on Healthcare Transformation in the Era of Digital Health, Big Data, and Precision Health: A Report of the American College of Cardiology Task Force on Health Policy Statements and Systems of Care. J Am Coll Cardiol. 2017 Nov 28;70(21):2696-2718. doi: 10.1016/j.jacc.2017.10.018. No abstract available.

Reference Type RESULT
PMID: 29169478 (View on PubMed)

Related Links

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http://hugophr.com/

A Novel Digital Platform for Research

Other Identifiers

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2000024317

Identifier Type: -

Identifier Source: org_study_id

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