LINK-HF2 - Remote Monitoring Analytics in Heart Failure (LINK-HF2)

April 7, 2026 updated by: VA Office of Research and Development

Continuous Wearable Monitoring Analytics to Improve Outcomes in Heart Failure - LINK-HF2 Multicenter Implementation Study

Heart failure (HF) is a type of heart disease that leads to need of admissions to the hospital during worsening of symptoms. These admissions are expensive and very inconvenient for patients. The investigators have previously shown that monitoring of patients with a using a small wearable sensor combined with a mathematical model can detect worsening of HF before the patient needs medical care.

In this study the investigators will test whether the remote monitoring and prediction of HF worsening can be used to find out when patients are at risk, change their treatment and avoid a hospitalization.

The study will enroll 240 Veterans with HF and randomly assign half of them to monitoring and communication of the information on HF worsening to their medical teams. The investigators hope to find our how to best use this approach in routine care of HF. The investigators also plan to determine if this approach will indeed led to less admissions to the hospital among these patients, shorter hospital stays and better quality of life.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

Heart failure (HF) represents a major health burden, with 80% of the HF health care costs attributable to hospitalizations. In a pilot multicenter study funded by the VA Center for Innovation, the investigators demonstrated that multivariate physiological telemetry using a small wearable sensor has a high compliance rate and provides accurate early detection of impending readmission for HF. In this study the investigators will implement non-invasive remote monitoring within the VA system and perform a feasibility evaluation of the intervention and its programmatic effectiveness after implementation. Our hypothesis is that the implementation of this program will be feasible and acceptable to clinicians working in VA HF clinics. The investigators also hypothesize that algorithmic response to an alert generated by the predictive algorithm using a continuous stream of remote monitoring data will be feasible and provide the basis for further testing of this approach to decrease the risk of hospitalization for HF and improve other key clinical outcomes. The specific aims of our study are:

Aim 1. Implement remote monitoring into the clinical workflow of HF care. Aim 1a. Design implementation strategies for non-invasive remote monitoring and algorithmic response to clinical alerts generated by the predictive analytics platform. In HF programs at five VA medical centers, eligible patients will be enrolled at the time of hospital discharge for HF exacerbation and receive a wearable monitor and a smart phone with cellular service. Data continuously uploaded to a secure server will be analyzed by the predictive analytics algorithm and a clinical alert will be generated when physiological derangements correlated with impending HF exacerbation are identified. A clinical response algorithm will provide instructions for management response to the alert, to include medication changes and/or urgent/non-urgent outpatient assessment. The intervention will include electronic health record integration. The investigators will design implementation processes for this program using the integrated Promoting Action on Research Implementation in Health Services (i-PARiHS) framework, adapted for the VA QUERI. The investigators will design 3 phases of implementation: 1) implementation intervention planning through workflow analysis, technology assessments, and recipient/stakeholder interviews; 2) formative evaluation of pilot implementation at two vanguard sites to test initial acceptability, reliability, and equipment performance; and 3) implementation fidelity monitoring by assessing consistency, safety and satisfaction.

Aim 1b. Evaluate implementation outcomes, including clinician and patient perceptions and adoption of the use of ambulatory remote monitoring data. The investigators will use both quantitative and qualitative research methods to examine the eight core dimensions of implementation outcomes. Focus groups and semi-structured interviews will be done to assess clinician and patient perceptions of acceptability and feasibility. Adoption behaviors will be tracked including alert response rates and appropriateness of decisions. Fidelity of implementation will be monitored by assessing compliance with all aspects of the study protocol. Penetration and sustainability will be evaluated by assessing variation in implementation outcomes across the five study sites as well as participant perceptions from the qualitative work at the end of the study.

Aim 2. Conduct a feasibility study of non-invasive remote monitoring in chronic HF.

Aim 2a. Define key characteristics that will inform design of a pivotal trial of non-invasive remote monitoring aimed at reducing rehospitalization and improving quality of life in HF. The investigators will enroll 240 patients hospitalized for HF exacerbation. At enrollment, subjects will undergo 1:1 randomization to intervention or control arm. While all study subjects will use the monitoring device for 90 days after discharge, in the intervention arm, clinicians will be notified of clinical alerts and will follow the response algorithm to modify HF treatment and/or recommend urgent clinic visit/emergency room visit. In the control arm, information from the sensor will be collected, but clinical alerts will not be generated or communicated to providers. The main study outcomes will include the proportion of randomized patients who meet the algorithm's criteria for at least one alert, the proportion of time the remote monitor is in use and functioning properly, HF hospitalization rate, length of hospital stay, and health-related quality of life. Implementation factors identified in Aim 1 will help clarify the results of this aim.

Aim 2b. Identify costs associated with implementation and clinical use of non-invasive remote monitoring in HF. Correct classification of costs associated with implementation of non-invasive remote monitoring will set the stage for cost-effectiveness analyses in a future pivotal trial.

Recent advances in technology and in machine learning provide an opportunity for processing of new sources of real-time patient-level data to generate clinically actionable information. An important knowledge gap is how to best implement this technology-based approach into clinical practice. Our study addresses this critical question of clinical implementation, and will generate feasibility data for a design of a pivotal clinical trial of non-invasive remote monitoring with predictive analytics during the high-risk period after hospital discharge. This work has potential to result in changes to care of Veterans with HF and other chronic health conditions.

Study Type

Interventional

Enrollment (Actual)

176

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • United States
    • California
      • Palo Alto, California, United States, 94304-1207
        • VA Palo Alto Health Care System, Palo Alto, CA
    • Florida
      • Gainesville, Florida, United States, 32608-1135
        • North Florida/South Georgia Veterans Health System, Gainesville, FL
    • Utah
      • Salt Lake City, Utah, United States, 84148-0001
        • VA Salt Lake City Health Care System, Salt Lake City, UT
    • Virginia
      • Richmond, Virginia, United States, 23249-0001
        • Hunter Holmes McGuire VA Medical Center, Richmond, VA

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  • Subject must be 18 years old or older
  • New York Heart Association Functional Classification Class II-IV, documented in site's medical record system.
  • Subject able and willing to sign Informed Consent Document, and if participating in a patient interview, able to comprehend and agree with items listed in the VA Consent Cover Letter.
  • Subject willing and able to perform all study related procedures.

Exclusion Criteria:

  • Expected Left Ventricular Assist Device implantation or heart transplantation in the next 30 days.
  • Skin damage or significant arthritis, preventing wearing of device.
  • Uncontrolled seizures or other neurological disorders leading to excessive abnormal movements or tremors in the upper body.
  • Pregnant women or those who are currently nursing.
  • Visual/cognitive impairment that as judged by the investigator does not allow the subject to independently follow rules and procedures of the protocol.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Health Services Research
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Quadruple

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Active arm
Subjects will undergo remote monitoring, remote monitoring data will be analyzed on a predictive platform, alerts indicating HF worsening shared with treating team, and algorithmic response to alerts implements.
Other: Remote monitoring and predictive analytics
Subjects will undergo remote monitoring, remote monitoring data will be analyzed on a predictive platform, alerts indicating HF worsening shared with treating team, and algorithmic response to alerts implements.
Sham Comparator: Control
Subjects will wear a sensor, but data from the sensor will not generate alerts and will not be shared with the treating team.
Other: Sham comparator
Subjects will wear a sensor, but data from the sensor will not generate alerts and will not be shared with the treating team.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Heart Failure Hospitalization Rate
Time Frame: 90 days
90-day hospitalization rate in subjects in active arm vs control arm
90 days

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Kansas City Cardiomyopathy Questionaire Score
Time Frame: 90 days
Scale range 0-100. Higher result is better.
90 days

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

VA Office of Research and Development

Collaborators

VA Palo Alto Health Care System
Michael E. DeBakey VA Medical Center
Malcom Randall VA Medical Center
George E. Wahlen Department of Veterans Affairs Medical Center
Hunter Holmes McGuire VA Medical Center
VHA Innovation Ecosystem

Investigators

  • Principal Investigator: Josef Stehlik, MD MPH, VA Salt Lake City Health Care System, Salt Lake City, UT

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

April 19, 2021

Primary Completion (Actual)

August 31, 2024

Study Completion (Actual)

October 30, 2024

Study Registration Dates

First Submitted

August 4, 2020

First Submitted That Met QC Criteria

August 4, 2020

First Posted (Actual)

August 6, 2020

Study Record Updates

Last Update Posted (Actual)

April 28, 2026

Last Update Submitted That Met QC Criteria

April 7, 2026

Last Verified

April 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • IIR 19-238

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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