Wearable Remote Monitoring of Heart Rate and Respiratory Rate for Heart Failure
NCT ID: NCT04455828
Last Updated: 2023-01-26
Study Results
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Basic Information
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WITHDRAWN
OBSERVATIONAL
2021-03-01
2022-04-14
Brief Summary
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Detailed Description
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The WHOOP device, a wearable strap similar to a Fitbit, allows for real-time HR monitoring and can determine RR using respiratory sinus arrhythmia \[www.whoop.com/experience; Berryhill, 2020\]. It is one of the few devices on the market that can accurately track heart rate as well as respiratory rate in real-time (during activity) and is equipped with a multidirectional accelerometer for activity tracking. The WHOOP device was recently externally validated against polysomnography and continuous electroencephalogram (EEG) for sleep tracking, and continuous electrocardiogram (ECG) for HR and HRV (with less than 5% error) \[Berryhill, 2020\]. HRV, which represents the balance of the sympathetic and parasympathetic nervous systems, is a known predictor of cardiac events. It is especially useful in HF, which is a chronically elevated catecholamine state leading to depressed HRV and is tied to NYHA HF class, an indicator of severity of disease \[Bullinga, 2005; Tsuji, 1996\].
Data so far regarding the efficacy of remote physiologic monitoring using cardiac implantable electronic devices (CIEDs), although promising in theory, has not yet proved sensitive in the detection of HF exacerbation. The aim of the CLEPSYDRA study was to use data extracted from implanted cardiac resynchronization therapy with defibrillation (CRT-D) devices in HF patients to predict heart failure events; although the main variables used in the novel algorithm, minute ventilation and patient activity, would intuitively seem to be predictors of poor outcome/HF exacerbation, the sensitivity of the algorithm to predict an event was only 34% \[6\]. It would appear that this combination of variables is not sufficient to predict adverse HF events. However, the HOME-CARE (HOME Monitoring in CArdiac REsynchronization Therapy) study showed more promising results, as their enhanced predictor, utilizing seven diagnostic variables from implanted CRT-Ds, boasted a sensitivity of 65.4% \[Sack, 2011\]. While the data from these studies is helpful, no study has been able to adequately identify and assess accurate predictors of HF class.
Current efficacious management strategies are limited to hemodynamic or multisensor monitoring systems. However, these are only available in implanted cardioverter-defibrillator (ICD) or cardiac resynchronization therapy-defibrillator (CRT-D) devices. These are not implanted in every HF patient \[Al-Zaiti, 2019\]. Non-invasive monitoring that provides similar data, such as wearable device monitoring, would expand the cohort of patients that would benefit from remote monitoring and would avoid the risks of having implanted hardware. Furthermore, better prediction of HF severity could help guide follow-up care and predict HF events \[Boehmer, 2015; Boehmer 2017\]. This would lead to more efficient management, less hospital readmission, and improve outcomes for HF patients overall \[Dickinson, 2018\].
The investigators propose a feasibility study in HF patients to better assess HF disease state, which can aid in management and improve outcomes. Subjects will wear the WHOOP device, which measures both activity and HR parameters and can derive RR using respiratory sinus arrhythmia, for 90 days. During this period, their HR and RR will be recorded at rest, during activity and post-activity recovery phases. This combined measure of HR/RR has been shown to predict NYHA HF class, an indicator of severity of disease, in implantable devices with multi-sensor monitoring capabilities; thus, it represents a useful management strategy in HF patients \[Bullinga, 2005; Tsuji, 1996\]. A continuous external monitoring device worn on the wrist, such as the WHOOP device, would provide valuable physiologic data for a cohort of HF patients who were previously unable to be monitored in this fashion. Secondary analysis of this study will investigate the use of intra- and post-activity HR and RR as predictors of hospitalization rates, a common problem in HF patients that correlate with worse mortality outcomes.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Hospitalized Heart Failure subjects
Subjects hospitalized for heart failure exacerbation will be enrolled, prior to discharge from hospital, to wear the WHOOP device for 90 days.
WHOOP strap 3.0
WHOOP strap 3.0, a photodiode-based device that tracks heart rate, respiratory rate, sleep, and heart rate variability.
Non-hospitalized Heart Failure subjects
Subjects who have not been hospitalized in the past 1 year, but have a diagnosis of heart failure, will be enrolled during routine outpatient care to wear the WHOOP device for 90 days.
WHOOP strap 3.0
WHOOP strap 3.0, a photodiode-based device that tracks heart rate, respiratory rate, sleep, and heart rate variability.
Interventions
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WHOOP strap 3.0
WHOOP strap 3.0, a photodiode-based device that tracks heart rate, respiratory rate, sleep, and heart rate variability.
Eligibility Criteria
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Inclusion Criteria
2. Male or female over the age of 18 years
3. The patient is either hospitalized with a primary diagnosis of acute heart failure or was discharged with a primary diagnosis of acute heart failure within 2 weeks prior to enrollment; or carries a diagnosis of heart failure and is seen as an outpatient at Hershey Medical Center.
4. NYHA functional class II-IV at time of enrollment
5. Subject willing to wear the WHOOP for the 90-day study period.
6. Subject owns a phone for pairing with the WHOOP device (required for data storage and transfer)
Exclusion Criteria
2. Subjects with severe aortic stenosis.
3. Subjects who are hemodynamically unstable requiring support with intravenous vasoactive medications or mechanical circulatory support
4. Subjects with symptomatic ventricular arrhythmias within the past 6 months.
18 Years
99 Years
ALL
Yes
Sponsors
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Milton S. Hershey Medical Center
OTHER
Responsible Party
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Principal Investigators
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John Boehmer, MD
Role: PRINCIPAL_INVESTIGATOR
Milton S. Hershey Medical Center
Locations
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Penn State Hershey Medical Center
Hershey, Pennsylvania, United States
Countries
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References
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Lloyd-Jones D, Adams RJ, Brown TM, Carnethon M, Dai S, De Simone G, Ferguson TB, Ford E, Furie K, Gillespie C, Go A, Greenlund K, Haase N, Hailpern S, Ho PM, Howard V, Kissela B, Kittner S, Lackland D, Lisabeth L, Marelli A, McDermott MM, Meigs J, Mozaffarian D, Mussolino M, Nichol G, Roger VL, Rosamond W, Sacco R, Sorlie P, Stafford R, Thom T, Wasserthiel-Smoller S, Wong ND, Wylie-Rosett J; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Executive summary: heart disease and stroke statistics--2010 update: a report from the American Heart Association. Circulation. 2010 Feb 23;121(7):948-54. doi: 10.1161/CIRCULATIONAHA.109.192666. No abstract available.
Yancy CW, Jessup M, Bozkurt B, Butler J, Casey DE Jr, Colvin MM, Drazner MH, Filippatos GS, Fonarow GC, Givertz MM, Hollenberg SM, Lindenfeld J, Masoudi FA, McBride PE, Peterson PN, Stevenson LW, Westlake C. 2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. J Card Fail. 2017 Aug;23(8):628-651. doi: 10.1016/j.cardfail.2017.04.014. Epub 2017 Apr 28. No abstract available.
Ross JS, Chen J, Lin Z, Bueno H, Curtis JP, Keenan PS, Normand SL, Schreiner G, Spertus JA, Vidan MT, Wang Y, Wang Y, Krumholz HM. Recent national trends in readmission rates after heart failure hospitalization. Circ Heart Fail. 2010 Jan;3(1):97-103. doi: 10.1161/CIRCHEARTFAILURE.109.885210. Epub 2009 Nov 10.
Chaudhry SI, Wang Y, Concato J, Gill TM, Krumholz HM. Patterns of weight change preceding hospitalization for heart failure. Circulation. 2007 Oct 2;116(14):1549-54. doi: 10.1161/CIRCULATIONAHA.107.690768. Epub 2007 Sep 10.
Dickinson MG, Allen LA, Albert NA, DiSalvo T, Ewald GA, Vest AR, Whellan DJ, Zile MR, Givertz MM. Remote Monitoring of Patients With Heart Failure: A White Paper From the Heart Failure Society of America Scientific Statements Committee. J Card Fail. 2018 Oct;24(10):682-694. doi: 10.1016/j.cardfail.2018.08.011. Epub 2018 Oct 9.
Auricchio A, Gold MR, Brugada J, Nolker G, Arunasalam S, Leclercq C, Defaye P, Calo L, Baumann O, Leyva F. Long-term effectiveness of the combined minute ventilation and patient activity sensors as predictor of heart failure events in patients treated with cardiac resynchronization therapy: Results of the Clinical Evaluation of the Physiological Diagnosis Function in the PARADYM CRT device Trial (CLEPSYDRA) study. Eur J Heart Fail. 2014 Jun;16(6):663-70. doi: 10.1002/ejhf.79. Epub 2014 Mar 17.
Boehmer JP, Hariharan R, Devecchi FG, Smith AL, Molon G, Capucci A, An Q, Averina V, Stolen CM, Thakur PH, Thompson JA, Wariar R, Zhang Y, Singh JP. A Multisensor Algorithm Predicts Heart Failure Events in Patients With Implanted Devices: Results From the MultiSENSE Study. JACC Heart Fail. 2017 Mar;5(3):216-225. doi: 10.1016/j.jchf.2016.12.011.
Al-Zaiti SS, Pietrasik G, Carey MG, Alhamaydeh M, Canty JM, Fallavollita JA. The role of heart rate variability, heart rate turbulence, and deceleration capacity in predicting cause-specific mortality in chronic heart failure. J Electrocardiol. 2019 Jan-Feb;52:70-74. doi: 10.1016/j.jelectrocard.2018.11.006. Epub 2018 Nov 6.
Shaffer F, Ginsberg JP. An Overview of Heart Rate Variability Metrics and Norms. Front Public Health. 2017 Sep 28;5:258. doi: 10.3389/fpubh.2017.00258. eCollection 2017.
Bullinga JR, Alharethi R, Schram MS, Bristow MR, Gilbert EM. Changes in heart rate variability are correlated to hemodynamic improvement with chronic CARVEDILOL therapy in heart failure. J Card Fail. 2005 Dec;11(9):693-9. doi: 10.1016/j.cardfail.2005.06.435.
Tsuji H, Larson MG, Venditti FJ Jr, Manders ES, Evans JC, Feldman CL, Levy D. Impact of reduced heart rate variability on risk for cardiac events. The Framingham Heart Study. Circulation. 1996 Dec 1;94(11):2850-5. doi: 10.1161/01.cir.94.11.2850.
Berryhill S, Morton CJ, Dean A, Berryhill A, Provencio-Dean N, Patel SI, Estep L, Combs D, Mashaqi S, Gerald LB, Krishnan JA, Parthasarathy S. Effect of wearables on sleep in healthy individuals: a randomized crossover trial and validation study. J Clin Sleep Med. 2020 May 15;16(5):775-783. doi: 10.5664/jcsm.8356. Epub 2020 Feb 11.
Sack S, Wende CM, Nagele H, Katz A, Bauer WR, Barr CS, Malinowski K, Schwacke H, Leyva F, Proff J, Berdyshev S, Paul V. Potential value of automated daily screening of cardiac resynchronization therapy defibrillator diagnostics for prediction of major cardiovascular events: results from Home-CARE (Home Monitoring in Cardiac Resynchronization Therapy) study. Eur J Heart Fail. 2011 Sep;13(9):1019-27. doi: 10.1093/eurjhf/hfr089.
Related Links
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WHOOP measures heart rate, respiratory rate, Heart Rate Variability (HRV), Resting Heart Rate (RHR), and sleep duration (broken down into sleep stages)..
Other Identifiers
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STUDY15557
Identifier Type: -
Identifier Source: org_study_id
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