<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT04455828</url>
  </required_header>
  <id_info>
    <org_study_id>STUDY15557</org_study_id>
    <nct_id>NCT04455828</nct_id>
  </id_info>
  <brief_title>Wearable Remote Monitoring of Heart Rate and Respiratory Rate for Heart Failure</brief_title>
  <official_title>Wearable Remote Monitoring of Heart Rate and Respiratory Rate for Heart Failure</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Milton S. Hershey Medical Center</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>Milton S. Hershey Medical Center</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      The primary objective of this study is to study in heart failure (HF) patients to better&#xD;
      assess HF disease state, which can aid in management and improve outcomes. Primary aims of&#xD;
      the study include: (1) Measure HR and RR at rest and during daily activity using the WHOOP&#xD;
      device. (2) Correlate HR and RR response to activity to New York Heart Association (NYHA)&#xD;
      class and 90-day HF hospitalization rate. (3) Identify additional predictors of NYHA class&#xD;
      and HF hospitalization rate for algorithm development to use the WHOOP device as a clinical&#xD;
      tool for HF management.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Heart Failure (HF) is a challenging condition to manage, with hospital readmission for HF&#xD;
      exacerbation having negative impacts on patient outcomes and financial burden to both patient&#xD;
      and health system [Lloyd-Jones, 2010; Yancy, 2017; Ross, 2009; Chaudhry, 2007]. An intuitive&#xD;
      need for more sensitive predictors of HF exacerbations has led researchers to explore remote&#xD;
      monitoring as a possible answer. Consumer-owned sensors have become more accurate in their&#xD;
      recording of vital signs, and thus could hold potential for remote monitoring [Dickinson,&#xD;
      2018]. The combined measure of heart rate (HR) and respiratory rate (RR) has been shown to&#xD;
      predict New York Heart Association (NYHA) HF class, an indicator of severity of heart&#xD;
      disease, in implantable cardiac devices with multi-sensor monitoring capabilities [Auricchio,&#xD;
      2014; Prasun, 2019; Boehmer, 2015; Boehmer, 2017]. Heart rate variability (HRV), a measure of&#xD;
      sympathetic autonomic function, has also shown potential in prediction of adverse cardiac&#xD;
      events [Al-Zaiti, 2019; Shaffer, 2017; Bullinga, 2005; Tsuji, 1996].&#xD;
&#xD;
      The WHOOP device, a wearable strap similar to a Fitbit, allows for real-time HR monitoring&#xD;
      and can determine RR using respiratory sinus arrhythmia [www.whoop.com/experience; Berryhill,&#xD;
      2020]. It is one of the few devices on the market that can accurately track heart rate as&#xD;
      well as respiratory rate in real-time (during activity) and is equipped with a&#xD;
      multidirectional accelerometer for activity tracking. The WHOOP device was recently&#xD;
      externally validated against polysomnography and continuous electroencephalogram (EEG) for&#xD;
      sleep tracking, and continuous electrocardiogram (ECG) for HR and HRV (with less than 5%&#xD;
      error) [Berryhill, 2020]. HRV, which represents the balance of the sympathetic and&#xD;
      parasympathetic nervous systems, is a known predictor of cardiac events. It is especially&#xD;
      useful in HF, which is a chronically elevated catecholamine state leading to depressed HRV&#xD;
      and is tied to NYHA HF class, an indicator of severity of disease [Bullinga, 2005; Tsuji,&#xD;
      1996].&#xD;
&#xD;
      Data so far regarding the efficacy of remote physiologic monitoring using cardiac implantable&#xD;
      electronic devices (CIEDs), although promising in theory, has not yet proved sensitive in the&#xD;
      detection of HF exacerbation. The aim of the CLEPSYDRA study was to use data extracted from&#xD;
      implanted cardiac resynchronization therapy with defibrillation (CRT-D) devices in HF&#xD;
      patients to predict heart failure events; although the main variables used in the novel&#xD;
      algorithm, minute ventilation and patient activity, would intuitively seem to be predictors&#xD;
      of poor outcome/HF exacerbation, the sensitivity of the algorithm to predict an event was&#xD;
      only 34% [6]. It would appear that this combination of variables is not sufficient to predict&#xD;
      adverse HF events. However, the HOME-CARE (HOME Monitoring in CArdiac REsynchronization&#xD;
      Therapy) study showed more promising results, as their enhanced predictor, utilizing seven&#xD;
      diagnostic variables from implanted CRT-Ds, boasted a sensitivity of 65.4% [Sack, 2011].&#xD;
      While the data from these studies is helpful, no study has been able to adequately identify&#xD;
      and assess accurate predictors of HF class.&#xD;
&#xD;
      Current efficacious management strategies are limited to hemodynamic or multisensor&#xD;
      monitoring systems. However, these are only available in implanted cardioverter-defibrillator&#xD;
      (ICD) or cardiac resynchronization therapy-defibrillator (CRT-D) devices. These are not&#xD;
      implanted in every HF patient [Al-Zaiti, 2019]. Non-invasive monitoring that provides similar&#xD;
      data, such as wearable device monitoring, would expand the cohort of patients that would&#xD;
      benefit from remote monitoring and would avoid the risks of having implanted hardware.&#xD;
      Furthermore, better prediction of HF severity could help guide follow-up care and predict HF&#xD;
      events [Boehmer, 2015; Boehmer 2017]. This would lead to more efficient management, less&#xD;
      hospital readmission, and improve outcomes for HF patients overall [Dickinson, 2018].&#xD;
&#xD;
      The investigators propose a feasibility study in HF patients to better assess HF disease&#xD;
      state, which can aid in management and improve outcomes. Subjects will wear the WHOOP device,&#xD;
      which measures both activity and HR parameters and can derive RR using respiratory sinus&#xD;
      arrhythmia, for 90 days. During this period, their HR and RR will be recorded at rest, during&#xD;
      activity and post-activity recovery phases. This combined measure of HR/RR has been shown to&#xD;
      predict NYHA HF class, an indicator of severity of disease, in implantable devices with&#xD;
      multi-sensor monitoring capabilities; thus, it represents a useful management strategy in HF&#xD;
      patients [Bullinga, 2005; Tsuji, 1996]. A continuous external monitoring device worn on the&#xD;
      wrist, such as the WHOOP device, would provide valuable physiologic data for a cohort of HF&#xD;
      patients who were previously unable to be monitored in this fashion. Secondary analysis of&#xD;
      this study will investigate the use of intra- and post-activity HR and RR as predictors of&#xD;
      hospitalization rates, a common problem in HF patients that correlate with worse mortality&#xD;
      outcomes.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Withdrawn</overall_status>
  <why_stopped>
    IRB Approval lapsed&#xD;
  </why_stopped>
  <start_date type="Actual">March 1, 2021</start_date>
  <completion_date type="Actual">April 14, 2022</completion_date>
  <primary_completion_date type="Actual">April 14, 2022</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Cohort</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Daily average heart rate (HR)</measure>
    <time_frame>90 days</time_frame>
    <description>Daily average heart rate (HR) measured by the WHOOP device. This will be continuously measured for 90 days.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Daily average respiratory rate (RR)</measure>
    <time_frame>90 days</time_frame>
    <description>Daily average respiratory rate (RR) measured by the WHOOP device. This will be continuously measured for 90 days</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Heart Failure (HF) hospitalizations</measure>
    <time_frame>90 days</time_frame>
    <description>Hospital admission for the primary diagnosis for acute, or acute on chronic heart failure) will be assessed via Electronic Medical Record (EMR) and a 90-day patient follow up visit.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Average HR isolated during any activity</measure>
    <time_frame>90 days</time_frame>
    <description>Average HR isolated during any activity lasting greater than 15 minutes (causing at least a 20% increase in heart rate over baseline), for the duration of the activity, measured by the WHOOP device. This will be recorded according to HR gating measures (20% over baseline HR) for the entire 90-day follow up period.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Max HR isolated during any activity</measure>
    <time_frame>90 days</time_frame>
    <description>Max HR isolated during any activity lasting greater than 15 minutes (causing at least a 20% increase in heart rate over baseline), for the duration of the activity, measured by the WHOOP device. This will be recorded according to HR gating measures (20% over baseline HR) for the entire 90-day follow up period.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Average RR isolated during any activity</measure>
    <time_frame>90 days</time_frame>
    <description>Average RR isolated during any activity lasting greater than 15 minutes (causing at least a 20% increase in heart rate over baseline), for the duration of the activity, measured by the WHOOP device. This will be recorded according to HR gating measures (20% over baseline HR) for the entire 90-day follow up period.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>Max RR isolated during any activity</measure>
    <time_frame>90 days</time_frame>
    <description>Max RR isolated during any activity lasting greater than 15 minutes (causing at least a 20% increase in heart rate over baseline), for the duration of the activity, measured by the WHOOP device. This will be recorded according to HR gating measures (20% over baseline HR) for the entire 90-day follow up period.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>New York Heart Association (NYHA) functional HF class</measure>
    <time_frame>90 days</time_frame>
    <description>New York Heart Association (NYHA) functional HF class will be measured at baseline and, if applicable, will be assessed during readmission for heart failure. NYHA Functional Classification places patients in one of four categories based on how much they are limited during physical activity; Class I being best and Class IV being the worst functional class. Class I has no limitation to physical activity, Class II has slight limitation to physical activity, Class III has marked limitation of physical activity, and Class IV is unable to carry out any physical activity without severe discomfort. This will also be repeated at 90-day follow up. Higher NYHA class is expected to be associated with increases in average HR and RR and HR and RR increases with activity, and increased HF hospitalization rate.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>6-minute walk test</measure>
    <time_frame>90 days</time_frame>
    <description>6-minute walk test is used to assess exercise tolerance and hypoxia with ambulation. Decreased exercise tolerance and increased hypoxia with walking are expected to be associated with worse NYHA HF class, increases in average HR and RR and HR and RR increases with activity, and increased HF hospitalization rate.</description>
  </secondary_outcome>
  <secondary_outcome>
    <measure>NT-proBNP (N-terminal-pro hormone B-type natriuretic peptide)</measure>
    <time_frame>90 days</time_frame>
    <description>NT-proBNP is used to assess atrial stretch, a surrogate for acute, chronic, or acute on chronic HF. Increased NT-proBNP (compared to baseline) is expected to correlate with worse NYHA HF class, increases in average HR and RR and HR and RR increases with activity, and increased HF hospitalization rate.</description>
  </secondary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Actual">0</enrollment>
  <condition>Heart Failure</condition>
  <condition>Heart Failure With Reduced Ejection Fraction</condition>
  <condition>Heart Failure With Preserved Ejection Fraction</condition>
  <arm_group>
    <arm_group_label>Hospitalized Heart Failure subjects</arm_group_label>
    <description>Subjects hospitalized for heart failure exacerbation will be enrolled, prior to discharge from hospital, to wear the WHOOP device for 90 days.</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Non-hospitalized Heart Failure subjects</arm_group_label>
    <description>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.</description>
  </arm_group>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>WHOOP strap 3.0</intervention_name>
    <description>WHOOP strap 3.0, a photodiode-based device that tracks heart rate, respiratory rate, sleep, and heart rate variability.</description>
    <arm_group_label>Hospitalized Heart Failure subjects</arm_group_label>
    <arm_group_label>Non-hospitalized Heart Failure subjects</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        Patients, 18 and older, admitted to the hospital with a primary diagnosis of acute heart&#xD;
        failure or any stable patient who presents for routine outpatient care, but has a diagnosis&#xD;
        of Heart Failure, and is NYHA class II-IV at time of enrollment.&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          1. Subject has provided informed consent&#xD;
&#xD;
          2. Male or female over the age of 18 years&#xD;
&#xD;
          3. The patient is either hospitalized with a primary diagnosis of acute heart failure or&#xD;
             was discharged with a primary diagnosis of acute heart failure within 2 weeks prior to&#xD;
             enrollment; or carries a diagnosis of heart failure and is seen as an outpatient at&#xD;
             Hershey Medical Center.&#xD;
&#xD;
          4. NYHA functional class II-IV at time of enrollment&#xD;
&#xD;
          5. Subject willing to wear the WHOOP for the 90-day study period.&#xD;
&#xD;
          6. Subject owns a phone for pairing with the WHOOP device (required for data storage and&#xD;
             transfer)&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          1. Subjects who are limited by angina.&#xD;
&#xD;
          2. Subjects with severe aortic stenosis.&#xD;
&#xD;
          3. Subjects who are hemodynamically unstable requiring support with intravenous&#xD;
             vasoactive medications or mechanical circulatory support&#xD;
&#xD;
          4. Subjects with symptomatic ventricular arrhythmias within the past 6 months.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>99 Years</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>John Boehmer, MD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Milton S. Hershey Medical Center</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Penn State Hershey Medical Center</name>
      <address>
        <city>Hershey</city>
        <state>Pennsylvania</state>
        <zip>17033</zip>
        <country>United States</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <link>
    <url>http://www.whoop.com/experience/</url>
    <description>WHOOP measures heart rate, respiratory rate, Heart Rate Variability (HRV), Resting Heart Rate (RHR), and sleep duration (broken down into sleep stages)..</description>
  </link>
  <reference>
    <citation>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. Erratum In: Circulation. 2010 Mar 30;121(12):e259.</citation>
    <PMID>20177011</PMID>
  </reference>
  <reference>
    <citation>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.</citation>
    <PMID>28461259</PMID>
  </reference>
  <reference>
    <citation>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.</citation>
    <PMID>19903931</PMID>
  </reference>
  <reference>
    <citation>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.</citation>
    <PMID>17846286</PMID>
  </reference>
  <reference>
    <citation>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.</citation>
    <PMID>30308242</PMID>
  </reference>
  <reference>
    <citation>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.</citation>
    <PMID>24639140</PMID>
  </reference>
  <reference>
    <citation>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.</citation>
    <PMID>28254128</PMID>
  </reference>
  <reference>
    <citation>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.</citation>
    <PMID>30476644</PMID>
  </reference>
  <reference>
    <citation>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.</citation>
    <PMID>29034226</PMID>
  </reference>
  <reference>
    <citation>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.</citation>
    <PMID>16360965</PMID>
  </reference>
  <reference>
    <citation>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.</citation>
    <PMID>8941112</PMID>
  </reference>
  <reference>
    <citation>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.</citation>
    <PMID>32043961</PMID>
  </reference>
  <reference>
    <citation>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.</citation>
    <PMID>21852311</PMID>
  </reference>
  <verification_date>January 2023</verification_date>
  <study_first_submitted>June 29, 2020</study_first_submitted>
  <study_first_submitted_qc>June 29, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">July 2, 2020</study_first_posted>
  <last_update_submitted>January 25, 2023</last_update_submitted>
  <last_update_submitted_qc>January 25, 2023</last_update_submitted_qc>
  <last_update_posted type="Actual">January 26, 2023</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Heart Failure</keyword>
  <keyword>Remote Monitoring</keyword>
  <keyword>WHOOP strap 3.0</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Heart Failure</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04455828
org study id: STUDY15557
nct id: NCT04455828
lead sponsor: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
The primary objective of this study is to study in heart failure (HF) patients to better
      assess HF disease state, which can aid in management and improve outcomes. Primary aims of
      the study include: (1) Measure HR and RR at rest and during daily activity using the WHOOP
      device. (2) Correlate HR and RR response to activity to New York Heart Association (NYHA)
      class and 90-day HF hospitalization rate. (3) Identify additional predictors of NYHA class
      and HF hospitalization rate for algorithm development to use the WHOOP device as a clinical
      tool for HF management.
Heart Failure (HF) is a challenging condition to manage, with hospital readmission for HF
      exacerbation having negative impacts on patient outcomes and financial burden to both patient
      and health system [Lloyd-Jones, 2010; Yancy, 2017; Ross, 2009; Chaudhry, 2007]. An intuitive
      need for more sensitive predictors of HF exacerbations has led researchers to explore remote
      monitoring as a possible answer. Consumer-owned sensors have become more accurate in their
      recording of vital signs, and thus could hold potential for remote monitoring [Dickinson,
      2018]. The combined measure of heart rate (HR) and respiratory rate (RR) has been shown to
      predict New York Heart Association (NYHA) HF class, an indicator of severity of heart
      disease, in implantable cardiac devices with multi-sensor monitoring capabilities [Auricchio,
      2014; Prasun, 2019; Boehmer, 2015; Boehmer, 2017]. Heart rate variability (HRV), a measure of
      sympathetic autonomic function, has also shown potential in prediction of adverse cardiac
      events [Al-Zaiti, 2019; Shaffer, 2017; Bullinga, 2005; Tsuji, 1996].

      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.
observational model: Cohort
time perspective: Prospective
measure: Daily average heart rate (HR)
time frame: 90 days
description: Daily average heart rate (HR) measured by the WHOOP device. This will be continuously measured for 90 days.
measure: Daily average respiratory rate (RR)
time frame: 90 days
description: Daily average respiratory rate (RR) measured by the WHOOP device. This will be continuously measured for 90 days
measure: Heart Failure (HF) hospitalizations
time frame: 90 days
description: Hospital admission for the primary diagnosis for acute, or acute on chronic heart failure) will be assessed via Electronic Medical Record (EMR) and a 90-day patient follow up visit.
measure: Average HR isolated during any activity
time frame: 90 days
description: Average HR isolated during any activity lasting greater than 15 minutes (causing at least a 20% increase in heart rate over baseline), for the duration of the activity, measured by the WHOOP device. This will be recorded according to HR gating measures (20% over baseline HR) for the entire 90-day follow up period.
measure: Max HR isolated during any activity
time frame: 90 days
description: Max HR isolated during any activity lasting greater than 15 minutes (causing at least a 20% increase in heart rate over baseline), for the duration of the activity, measured by the WHOOP device. This will be recorded according to HR gating measures (20% over baseline HR) for the entire 90-day follow up period.
measure: Average RR isolated during any activity
time frame: 90 days
description: Average RR isolated during any activity lasting greater than 15 minutes (causing at least a 20% increase in heart rate over baseline), for the duration of the activity, measured by the WHOOP device. This will be recorded according to HR gating measures (20% over baseline HR) for the entire 90-day follow up period.
measure: Max RR isolated during any activity
time frame: 90 days
description: Max RR isolated during any activity lasting greater than 15 minutes (causing at least a 20% increase in heart rate over baseline), for the duration of the activity, measured by the WHOOP device. This will be recorded according to HR gating measures (20% over baseline HR) for the entire 90-day follow up period.
measure: New York Heart Association (NYHA) functional HF class
time frame: 90 days
description: New York Heart Association (NYHA) functional HF class will be measured at baseline and, if applicable, will be assessed during readmission for heart failure. NYHA Functional Classification places patients in one of four categories based on how much they are limited during physical activity; Class I being best and Class IV being the worst functional class. Class I has no limitation to physical activity, Class II has slight limitation to physical activity, Class III has marked limitation of physical activity, and Class IV is unable to carry out any physical activity without severe discomfort. This will also be repeated at 90-day follow up. Higher NYHA class is expected to be associated with increases in average HR and RR and HR and RR increases with activity, and increased HF hospitalization rate.
measure: 6-minute walk test
time frame: 90 days
description: 6-minute walk test is used to assess exercise tolerance and hypoxia with ambulation. Decreased exercise tolerance and increased hypoxia with walking are expected to be associated with worse NYHA HF class, increases in average HR and RR and HR and RR increases with activity, and increased HF hospitalization rate.
measure: NT-proBNP (N-terminal-pro hormone B-type natriuretic peptide)
time frame: 90 days
description: NT-proBNP is used to assess atrial stretch, a surrogate for acute, chronic, or acute on chronic HF. Increased NT-proBNP (compared to baseline) is expected to correlate with worse NYHA HF class, increases in average HR and RR and HR and RR increases with activity, and increased HF hospitalization rate.
arm group label: Hospitalized Heart Failure subjects
description: Subjects hospitalized for heart failure exacerbation will be enrolled, prior to discharge from hospital, to wear the WHOOP device for 90 days.
arm group label: Non-hospitalized Heart Failure subjects
description: 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.
intervention type: Device
intervention name: WHOOP strap 3.0
description: WHOOP strap 3.0, a photodiode-based device that tracks heart rate, respiratory rate, sleep, and heart rate variability.
arm group label: Hospitalized Heart Failure subjects
arm group label: Non-hospitalized Heart Failure subjects
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: 99 Years
healthy volunteers: Accepts Healthy Volunteers
last name: John Boehmer, MD
role: Principal Investigator
affiliation: Milton S. Hershey Medical Center
facility: 
country: United States
url: http://www.whoop.com/experience/
description: WHOOP measures heart rate, respiratory rate, Heart Rate Variability (HRV), Resting Heart Rate (RHR), and sleep duration (broken down into sleep stages)..
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responsible party type: Sponsor
mesh term: Heart Failure
sharing ipd: No