<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/NCT04434716</url>
  </required_header>
  <id_info>
    <org_study_id>2019-5684</org_study_id>
    <nct_id>NCT04434716</nct_id>
  </id_info>
  <brief_title>Monitoring and Self-management of Sleep Fatigue and Dyspnea</brief_title>
  <official_title>Monitoring and Self-management of Sleep Fatigue and Dyspnea to Improve Heart Failure Outcomes in African-Americans</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>University of Massachusetts, Amherst</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>University of Massachusetts, Amherst</source>
  <oversight_info>
    <has_dmc>Yes</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>
      African Americans have the highest risk for developing heart failure. When African Americans&#xD;
      are diagnosed with heart failure (AAHF) it is usually more advanced HF compared to other&#xD;
      races. African-Americans have the highest rate of hospitalization for HF compared to any&#xD;
      other ethnic groups. Thus, life style modification, awareness of signs and symptoms of HF by&#xD;
      continuous, rather than intermittent monitoring, is essential in beginning to develop HF&#xD;
      interventions that can provide early detection. Early interventions would lead to reduced&#xD;
      re-hospitalization, prevent hospital readmission and reduce the mortality rate associated&#xD;
      with HF.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Symptoms of heart failure due to circulatory fluid overload: Signs of circulatory fluid&#xD;
      overload are theleading to cardiac decompensation or worsening heart failure are: orthopnea,&#xD;
      dyspnea, fatigue, weight gain, abdominal swelling, fluid retention, extended jugular vein,&#xD;
      leg edema, crackles, and ascites. Identifying early signs of CFO in HF would provide patients&#xD;
      more time to respond and self-manage symptoms at home.&#xD;
&#xD;
      Currently most HF patients are monitored intermittently for changes in symptoms&#xD;
&#xD;
      . According to the American Heart Association establishing self- monitoring practices is the&#xD;
      best method for improving health behaviors and health outcomes in individuals.&#xD;
&#xD;
      Fatigue and sleep in HF and gaps in symptom self-management: Fatigue in heart failure&#xD;
      patients was previously measured using a self-reported questionnaire and concluded that&#xD;
      identifying fatigue early could result in initiation of treatment to prevent HF&#xD;
      decompensation. A study by also concluded that severe HF symptoms are associated with higher&#xD;
      levels of fatigue in HF patients. found that increases in fatigue in cardiovascular patients&#xD;
      resulted in poorer self-care and poorer cardiovascular outcomes, but fatigue was not an&#xD;
      indication of disease severity. . Similarly another study concluded that there is a&#xD;
      relationship between sleep, fatigue and functional performance in HF patients. However,&#xD;
      sleep, fatigue and HF symptoms were only intermittently, rather than continuously, monitored&#xD;
      in these studies to assess its impact on HF patient outcomes.&#xD;
&#xD;
      The wrist-worn wearable device, Readiband (Fatigue Science)has a 93 accuracy rate in&#xD;
      measuring sleep. The Readiband and the biomathematical fatigue model SAFTE (Sleep, Activity,&#xD;
      Fatigue, and Task Effectiveness)have being successfully used to measure sleep and fatigue in&#xD;
      multiple areas of research The Readiband has a one month battery life and has the ability to&#xD;
      sync to mobile phones, or iPads via a Sync app. It allows for Minute-by-minute actigraphy&#xD;
      values and sleep/wake classification. The Readiband has the ability to track, high recurring&#xD;
      wake episodes, frequency of daytime sleep episodes, high sleep latency, wake after sleep&#xD;
      onset and total sleep quantity. The Readiband has been used successfully to measure fatigue&#xD;
      in athletes and law enforcement officers In the following studies the Readiband was use to&#xD;
      assess the correlation between sleep and fatigue: risk for accidents in medical residents&#xD;
      risk for making medical errors, and to predict football player's risk for injury Each study&#xD;
      has shown some level of statistical significance of the relationship between sleep and&#xD;
      fatigue. This study is adding another component of assessing if sleep and fatigue correlates&#xD;
      with increase severity of HF symptoms.The SAFTE Fatigue Model (Sleep, Activity, Fatigue, and&#xD;
      Task Effectiveness)will interpret the data collected from the Readiband. The SAFTE Fatigue&#xD;
      Model and the Readiband has never been use to monitor the correlation between sleep, fatigue&#xD;
      and decompensation in HF symptoms. The data from the Readiband will be transmitted to the&#xD;
      SAFTE Fatigue model. The data will analyze the patient sleep wake pattern to detect patient's&#xD;
      level of fatigue and data will be provided with the patient.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Unknown status</overall_status>
  <last_known_status>Recruiting</last_known_status>
  <start_date type="Actual">August 15, 2019</start_date>
  <completion_date type="Anticipated">April 30, 2021</completion_date>
  <primary_completion_date type="Anticipated">April 30, 2021</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>N/A</allocation>
    <intervention_model>Single Group Assignment</intervention_model>
    <intervention_model_description>The overall specific aim of this study is to test whether daily monitoring of fatigue and sleep using a wrist-worn device can improve self-management and aid in the early detection of the signs and symptoms of CFO indicating increasing cardiac decompensation in heart failure. A mixed methods approach will be used to study the specific aims.</intervention_model_description>
    <primary_purpose>Device Feasibility</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Measure if the Readiband is able to measure Sleep and Fatigue</measure>
    <time_frame>42 days</time_frame>
    <description>Specific Aim #1: To evaluate the ability of HF patients to continuously wear a wrist-worn device (Readiband) for up to 42 days to monitor fatigue, activity and sleep.&#xD;
These data will be gathered via the Readiband which is a wrist-worn device. It is not an instrument or a scale. The wrist-worn wearable device, Readiband (Fatigue Science) has a 93% accuracy rate in measuring sleep The Readiband and the biomathematical fatigue model SAFTE (Sleep, Activity, Fatigue, and Task Effectiveness) have being successfully used to measure sleep and fatigue in multiple areas of research.</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Correlation between data from the Readiband and the PROMIS scales</measure>
    <time_frame>42 days</time_frame>
    <description>Specific Aim #2: To determine if HF patients can use and interpret the data obtained from a wrist-worn device on their level of fatigue, activity, sleep, and other symptoms to self-manage symptoms. This aim will be addressed via descriptive statistics that will present items completed by study participants that reflect the use and ability of study participants to interpret data the data obtained from a wrist-worn device on their level of fatigue and sleep(BRICS NINR PROMIS Fatigue Short Form6a scale and the BRICS NINR PROMIS SF v1.0-Sleep Disturbance 6a scale) Readiband will be worn for 42 days and data will be generated on a daily basis.</description>
  </secondary_outcome>
  <number_of_arms>1</number_of_arms>
  <enrollment type="Anticipated">20</enrollment>
  <condition>Heart Failure Patients</condition>
  <arm_group>
    <arm_group_label>Feasibility of Wearing a Readiband</arm_group_label>
    <arm_group_type>Other</arm_group_type>
    <description>Participants will wear the Fatigue Science Readiband for 42 consecutive day. On day one, every seventh day and at the end of the study each participant will complete the Dyspnea-Characteristic scale, BRICS NINR PROMIS Fatigue Short Form6a scale , Modified Pulmonary Functional Status, Dyspnea Questionnaire and the BRICS NINR PROMIS SF v1.0-Sleep Disturbance 6a scale.The Minnesota Living with Heart Failure Questionnaire and Self-Care of Heart Failure Index will be completed on day one and day 60. The purpose of this intervention is to assess the Feasibility of Wearing a Readiband.&#xD;
Semi-structured Interview will be conducted at the end of 42 days to assess patient comfort and challenges with wearing the Readiband.</description>
  </arm_group>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>Feasibility of wearing a Readiband to monitor Sleep and Fatigue</intervention_name>
    <description>On day one of the study participants will complete a demographic survey. On day one, every seventh day and at the end of the study each participant will complete all the scales; The Minnesota Living with Heart Failure Questionnaire (MLHFQ) and Self-Care of Heart Failure Index will be completed on day one and day 60. At the end of the intervention an Interview will be conducted to assess participants experiences using the Readiband: On a scale of 0-10, how would you rate your digital literacy? 2) Why that number? 3) Tell me about a day using the readiband? 4) Were there any challenges to wearing the band, forgetting to wear it, level of comfort wearing the band? Anything else etc..? 5) How did the digital tools enhance your health? 6) Did the use of digital tools cause you to take a proactive approach rather than a reactive approach to your health? 7) As I use the readiband in a next study, what suggestions do you have for me?</description>
    <arm_group_label>Feasibility of Wearing a Readiband</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria&#xD;
&#xD;
          -  Age 30-85years.&#xD;
&#xD;
          -  Diagnosis of heart failure based on patient's medical record.&#xD;
&#xD;
          -  Meets the criteria for New York Heart Failure (NYHF) classification for stage I-III&#xD;
             heart failure.&#xD;
&#xD;
          -  Meets the criteria for ACA/AHA HF classification Stage A and B (Patient with clinical&#xD;
             HF).&#xD;
&#xD;
        Exclusion Criteria&#xD;
&#xD;
          -  Exclusion criteria include patients with a diagnosis of dementia, patients on the&#xD;
             heart transplant&#xD;
&#xD;
          -  list and stage IV HF.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>30 Years</minimum_age>
    <maximum_age>85 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Heather M Hamilton, PhD, RN</last_name>
    <role>Principal Investigator</role>
    <affiliation>University of Massachusetts, Amherst</affiliation>
  </overall_official>
  <overall_contact>
    <last_name>Heather Hamilton, PhD RN</last_name>
    <phone>413-545-7174</phone>
    <email>hamilton@umass.edu</email>
  </overall_contact>
  <overall_contact_backup>
    <last_name>Ian Cooke, PhD</last_name>
    <phone>4135455087</phone>
    <email>icooke@umass.edu</email>
  </overall_contact_backup>
  <location>
    <facility>
      <name>University of Massachusetts Amherst</name>
      <address>
        <city>Amherst</city>
        <state>Massachusetts</state>
        <zip>01003</zip>
        <country>United States</country>
      </address>
    </facility>
    <status>Recruiting</status>
    <contact>
      <last_name>Heather Hamilton, PhD RN</last_name>
      <phone>413-545-7174</phone>
      <email>hamilton@umass.edu</email>
    </contact>
    <investigator>
      <last_name>Heather Hamilton, PhD RN</last_name>
      <role>Principal Investigator</role>
    </investigator>
  </location>
  <location_countries>
    <country>United States</country>
  </location_countries>
  <reference>
    <citation>Benjamin, E.J., Muntner. P., Alonso, A., Bittencourt, M.S., Callaway, C.W., Carson, A.P., Chamberlain, A.M., Chang, A.R., Cheng, S., Das, S.R., Delling, F.N., Djousse, L., Elkind, M.S.V., Ferguson, J.F., Fornage, M., Jordan, L.C., Khan, S.S., Kissela, B.M., Knutson, K.L.,Kwan, T.W., Lackland, D.T., Lewis, T.T., Lichtman, J.H., Longenecker, C.T., Loop, M.S., Lutsey, P.L., Martin, S.S., Matsushita, K., Moran, A.E., Mussolino, M.E., O'Flaherty, M., Pandey, A., Perak, A.M., Rosamond, W.D., Roth, G.A., Sampson, U.K.A., Satou, G.M., Schroeder, E.B., Shah, S.H., Spartano, N.L., Stokes, A., Tirschwell, D.L., Tsao, C.W., Turakhia, M.P., VanWagner, L.B., Wilkins, J.T., Wong, S.S., Virani, S.S. (2019); on behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics-2019 update: a report from the American Heart Association [published online ahead of print January 31, 2019]. Circulation. doi: 10.1161/CIR.0000000000000659.</citation>
  </reference>
  <reference>
    <citation>Bibbins-Domingo K, Pletcher MJ, Lin F, Vittinghoff E, Gardin JM, Arynchyn A, Lewis CE, Williams OD, Hulley SB. Racial differences in incident heart failure among young adults. N Engl J Med. 2009 Mar 19;360(12):1179-90. doi: 10.1056/NEJMoa0807265.</citation>
    <PMID>19297571</PMID>
  </reference>
  <reference>
    <citation>Bui AL, Fonarow GC. Home monitoring for heart failure management. J Am Coll Cardiol. 2012 Jan 10;59(2):97-104. doi: 10.1016/j.jacc.2011.09.044.</citation>
    <PMID>22222071</PMID>
  </reference>
  <reference>
    <citation>Fatigue Science(2018)Retrieved from https://www.fatiguescience.com</citation>
  </reference>
  <reference>
    <citation>Health Measures (2019).http://www.healthmeasures.net/explore-measurement-systems/promis Heart failure Society of America.(2018)Patient Application. Retrieved from</citation>
  </reference>
  <reference>
    <citation>Heart Failure Society of America (20190 HEART FAILURE HEALTH STORYLINES. Retrieved from hfsa.org/patient/patient-tools/patient</citation>
  </reference>
  <reference>
    <citation>Chen LH, Li CY, Shieh SM, Yin WH, Chiou AF. Predictors of fatigue in patients with heart failure. J Clin Nurs. 2010 Jun;19(11-12):1588-96. doi: 10.1111/j.1365-2702.2010.03218.x.</citation>
    <PMID>20579199</PMID>
  </reference>
  <reference>
    <citation>Riegel B, Dickson VV, Lee CS, Daus M, Hill J, Irani E, Lee S, Wald JW, Moelter ST, Rathman L, Streur M, Baah FO, Ruppert L, Schwartz DR, Bove A. A mixed methods study of symptom perception in patients with chronic heart failure. Heart Lung. 2018 Mar-Apr;47(2):107-114. doi: 10.1016/j.hrtlng.2017.11.002. Epub 2018 Jan 3.</citation>
    <PMID>29304990</PMID>
  </reference>
  <reference>
    <citation>U.S. Food and Drugs Administration (2018). Human Factors and Medical Devices. Retrieved from https://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/Huma</citation>
  </reference>
  <verification_date>June 2020</verification_date>
  <study_first_submitted>March 11, 2020</study_first_submitted>
  <study_first_submitted_qc>June 12, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 17, 2020</study_first_posted>
  <last_update_submitted>June 12, 2020</last_update_submitted>
  <last_update_submitted_qc>June 12, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">June 17, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Heart failure</keyword>
  <keyword>African Americans</keyword>
  <keyword>Wrist worn device</keyword>
  <keyword>Mixed- method</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Dyspnea</mesh_term>
    <mesh_term>Heart Failure</mesh_term>
  </condition_browse>
  <!-- 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/NCT04434716
org study id: 2019-5684
nct id: NCT04434716
lead sponsor: 
has dmc: Yes
is fda regulated drug: No
is fda regulated device: No
African Americans have the highest risk for developing heart failure. When African Americans
      are diagnosed with heart failure (AAHF) it is usually more advanced HF compared to other
      races. African-Americans have the highest rate of hospitalization for HF compared to any
      other ethnic groups. Thus, life style modification, awareness of signs and symptoms of HF by
      continuous, rather than intermittent monitoring, is essential in beginning to develop HF
      interventions that can provide early detection. Early interventions would lead to reduced
      re-hospitalization, prevent hospital readmission and reduce the mortality rate associated
      with HF.
Symptoms of heart failure due to circulatory fluid overload: Signs of circulatory fluid
      overload are theleading to cardiac decompensation or worsening heart failure are: orthopnea,
      dyspnea, fatigue, weight gain, abdominal swelling, fluid retention, extended jugular vein,
      leg edema, crackles, and ascites. Identifying early signs of CFO in HF would provide patients
      more time to respond and self-manage symptoms at home.

      Currently most HF patients are monitored intermittently for changes in symptoms

      . According to the American Heart Association establishing self- monitoring practices is the
      best method for improving health behaviors and health outcomes in individuals.

      Fatigue and sleep in HF and gaps in symptom self-management: Fatigue in heart failure
      patients was previously measured using a self-reported questionnaire and concluded that
      identifying fatigue early could result in initiation of treatment to prevent HF
      decompensation. A study by also concluded that severe HF symptoms are associated with higher
      levels of fatigue in HF patients. found that increases in fatigue in cardiovascular patients
      resulted in poorer self-care and poorer cardiovascular outcomes, but fatigue was not an
      indication of disease severity. . Similarly another study concluded that there is a
      relationship between sleep, fatigue and functional performance in HF patients. However,
      sleep, fatigue and HF symptoms were only intermittently, rather than continuously, monitored
      in these studies to assess its impact on HF patient outcomes.

      The wrist-worn wearable device, Readiband (Fatigue Science)has a 93 accuracy rate in
      measuring sleep. The Readiband and the biomathematical fatigue model SAFTE (Sleep, Activity,
      Fatigue, and Task Effectiveness)have being successfully used to measure sleep and fatigue in
      multiple areas of research The Readiband has a one month battery life and has the ability to
      sync to mobile phones, or iPads via a Sync app. It allows for Minute-by-minute actigraphy
      values and sleep/wake classification. The Readiband has the ability to track, high recurring
      wake episodes, frequency of daytime sleep episodes, high sleep latency, wake after sleep
      onset and total sleep quantity. The Readiband has been used successfully to measure fatigue
      in athletes and law enforcement officers In the following studies the Readiband was use to
      assess the correlation between sleep and fatigue: risk for accidents in medical residents
      risk for making medical errors, and to predict football player's risk for injury Each study
      has shown some level of statistical significance of the relationship between sleep and
      fatigue. This study is adding another component of assessing if sleep and fatigue correlates
      with increase severity of HF symptoms.The SAFTE Fatigue Model (Sleep, Activity, Fatigue, and
      Task Effectiveness)will interpret the data collected from the Readiband. The SAFTE Fatigue
      Model and the Readiband has never been use to monitor the correlation between sleep, fatigue
      and decompensation in HF symptoms. The data from the Readiband will be transmitted to the
      SAFTE Fatigue model. The data will analyze the patient sleep wake pattern to detect patient's
      level of fatigue and data will be provided with the patient.
allocation: N/A
intervention model: Single Group Assignment
intervention model description: The overall specific aim of this study is to test whether daily monitoring of fatigue and sleep using a wrist-worn device can improve self-management and aid in the early detection of the signs and symptoms of CFO indicating increasing cardiac decompensation in heart failure. A mixed methods approach will be used to study the specific aims.
primary purpose: Device Feasibility
masking: None (Open Label)
measure: Measure if the Readiband is able to measure Sleep and Fatigue
time frame: 42 days
description: Specific Aim #1: To evaluate the ability of HF patients to continuously wear a wrist-worn device (Readiband) for up to 42 days to monitor fatigue, activity and sleep.
These data will be gathered via the Readiband which is a wrist-worn device. It is not an instrument or a scale. The wrist-worn wearable device, Readiband (Fatigue Science) has a 93% accuracy rate in measuring sleep The Readiband and the biomathematical fatigue model SAFTE (Sleep, Activity, Fatigue, and Task Effectiveness) have being successfully used to measure sleep and fatigue in multiple areas of research.
measure: Correlation between data from the Readiband and the PROMIS scales
time frame: 42 days
description: Specific Aim #2: To determine if HF patients can use and interpret the data obtained from a wrist-worn device on their level of fatigue, activity, sleep, and other symptoms to self-manage symptoms. This aim will be addressed via descriptive statistics that will present items completed by study participants that reflect the use and ability of study participants to interpret data the data obtained from a wrist-worn device on their level of fatigue and sleep(BRICS NINR PROMIS Fatigue Short Form6a scale and the BRICS NINR PROMIS SF v1.0-Sleep Disturbance 6a scale) Readiband will be worn for 42 days and data will be generated on a daily basis.
arm group label: Feasibility of Wearing a Readiband
arm group type: Other
description: Participants will wear the Fatigue Science Readiband for 42 consecutive day. On day one, every seventh day and at the end of the study each participant will complete the Dyspnea-Characteristic scale, BRICS NINR PROMIS Fatigue Short Form6a scale , Modified Pulmonary Functional Status, Dyspnea Questionnaire and the BRICS NINR PROMIS SF v1.0-Sleep Disturbance 6a scale.The Minnesota Living with Heart Failure Questionnaire and Self-Care of Heart Failure Index will be completed on day one and day 60. The purpose of this intervention is to assess the Feasibility of Wearing a Readiband.
Semi-structured Interview will be conducted at the end of 42 days to assess patient comfort and challenges with wearing the Readiband.
intervention type: Device
intervention name: Feasibility of wearing a Readiband to monitor Sleep and Fatigue
description: On day one of the study participants will complete a demographic survey. On day one, every seventh day and at the end of the study each participant will complete all the scales; The Minnesota Living with Heart Failure Questionnaire (MLHFQ) and Self-Care of Heart Failure Index will be completed on day one and day 60. At the end of the intervention an Interview will be conducted to assess participants experiences using the Readiband: On a scale of 0-10, how would you rate your digital literacy? 2) Why that number? 3) Tell me about a day using the readiband? 4) Were there any challenges to wearing the band, forgetting to wear it, level of comfort wearing the band? Anything else etc..? 5) How did the digital tools enhance your health? 6) Did the use of digital tools cause you to take a proactive approach rather than a reactive approach to your health? 7) As I use the readiband in a next study, what suggestions do you have for me?
arm group label: Feasibility of Wearing a Readiband
criteria: 
gender: All
minimum age: 30 Years
maximum age: 85 Years
healthy volunteers: No
last name: Heather M Hamilton, PhD, RN
role: Principal Investigator
affiliation: University of Massachusetts, Amherst
last name: Heather Hamilton, PhD RN
phone: 413-545-7174
email: hamilton@umass.edu
last name: Ian Cooke, PhD
phone: 4135455087
email: icooke@umass.edu
facility: 
status: Recruiting
contact: 
investigator: 
country: United States
citation: Benjamin, E.J., Muntner. P., Alonso, A., Bittencourt, M.S., Callaway, C.W., Carson, A.P., Chamberlain, A.M., Chang, A.R., Cheng, S., Das, S.R., Delling, F.N., Djousse, L., Elkind, M.S.V., Ferguson, J.F., Fornage, M., Jordan, L.C., Khan, S.S., Kissela, B.M., Knutson, K.L.,Kwan, T.W., Lackland, D.T., Lewis, T.T., Lichtman, J.H., Longenecker, C.T., Loop, M.S., Lutsey, P.L., Martin, S.S., Matsushita, K., Moran, A.E., Mussolino, M.E., O'Flaherty, M., Pandey, A., Perak, A.M., Rosamond, W.D., Roth, G.A., Sampson, U.K.A., Satou, G.M., Schroeder, E.B., Shah, S.H., Spartano, N.L., Stokes, A., Tirschwell, D.L., Tsao, C.W., Turakhia, M.P., VanWagner, L.B., Wilkins, J.T., Wong, S.S., Virani, S.S. (2019); on behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics-2019 update: a report from the American Heart Association [published online ahead of print January 31, 2019]. Circulation. doi: 10.1161/CIR.0000000000000659.
citation: Bibbins-Domingo K, Pletcher MJ, Lin F, Vittinghoff E, Gardin JM, Arynchyn A, Lewis CE, Williams OD, Hulley SB. Racial differences in incident heart failure among young adults. N Engl J Med. 2009 Mar 19;360(12):1179-90. doi: 10.1056/NEJMoa0807265.
PMID: 19297571
citation: Bui AL, Fonarow GC. Home monitoring for heart failure management. J Am Coll Cardiol. 2012 Jan 10;59(2):97-104. doi: 10.1016/j.jacc.2011.09.044.
PMID: 22222071
citation: Fatigue Science(2018)Retrieved from https://www.fatiguescience.com
citation: Health Measures (2019).http://www.healthmeasures.net/explore-measurement-systems/promis Heart failure Society of America.(2018)Patient Application. Retrieved from
citation: Heart Failure Society of America (20190 HEART FAILURE HEALTH STORYLINES. Retrieved from hfsa.org/patient/patient-tools/patient
citation: Chen LH, Li CY, Shieh SM, Yin WH, Chiou AF. Predictors of fatigue in patients with heart failure. J Clin Nurs. 2010 Jun;19(11-12):1588-96. doi: 10.1111/j.1365-2702.2010.03218.x.
PMID: 20579199
citation: Riegel B, Dickson VV, Lee CS, Daus M, Hill J, Irani E, Lee S, Wald JW, Moelter ST, Rathman L, Streur M, Baah FO, Ruppert L, Schwartz DR, Bove A. A mixed methods study of symptom perception in patients with chronic heart failure. Heart Lung. 2018 Mar-Apr;47(2):107-114. doi: 10.1016/j.hrtlng.2017.11.002. Epub 2018 Jan 3.
PMID: 29304990
citation: U.S. Food and Drugs Administration (2018). Human Factors and Medical Devices. Retrieved from https://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/Huma
responsible party type: Sponsor
mesh term: Dyspnea
mesh term: Heart Failure