Monitoring and Self-management of Sleep Fatigue and Dyspnea

Heart Failure Patients Clinical Trial

Official title:

Monitoring and Self-management of Sleep Fatigue and Dyspnea to Improve Heart Failure Outcomes in African-Americans

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04434716
• Other study ID #: 2019-5684
• Status: Recruiting
• Phase: N/A
• Start date: August 15, 2019
• Est. completion date: April 30, 2021

Study information

• Verified date: June 2020
• Source: University of Massachusetts, Amherst
• Contact: Heather Hamilton, PhD RN
• Phone: 413-545-7174
• Email: hamilton[@]umass.edu
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

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.

Description:

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 20
• Est. completion date: April 30, 2021
• Est. primary completion date: April 30, 2021
• Accepts healthy volunteers: No
• Gender: All
• Age group: 30 Years to 85 Years

Eligibility

Inclusion Criteria

• Age 30-85years.

• Diagnosis of heart failure based on patient's medical record.

• Meets the criteria for New York Heart Failure (NYHF) classification for stage I-III heart failure.

• Meets the criteria for ACA/AHA HF classification Stage A and B (Patient with clinical HF).

Exclusion Criteria

• Exclusion criteria include patients with a diagnosis of dementia, patients on the heart transplant

• list and stage IV HF.

Related Conditions & MeSH terms

• Dyspnea
• Fatigue
• Heart Failure
• Heart Failure Patients

Intervention

Device:
• Feasibility of wearing a Readiband to monitor Sleep and Fatigue
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?

Locations

Country	Name	City	State
United States	University of Massachusetts Amherst	Amherst	Massachusetts

Sponsors (1)

Lead Sponsor	Collaborator
University of Massachusetts, Amherst

Country where clinical trial is conducted

United States, 

References & Publications (9)

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.

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. — View 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. Review. — View 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. — View Citation

Fatigue Science(2018)Retrieved from https://www.fatiguescience.com

Health Measures (2019).http://www.healthmeasures.net/explore-measurement-systems/promis Heart failure Society of America.(2018)Patient Application. Retrieved from

Heart Failure Society of America (20190 HEART FAILURE HEALTH STORYLINES. Retrieved from hfsa.org/patient/patient-tools/patient

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. — View Citation

U.S. Food and Drugs Administration (2018). Human Factors and Medical Devices. Retrieved from https://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/Huma

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Measure if the Readiband is able to measure Sleep and Fatigue	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.	42 days
Secondary	Correlation between data from the Readiband and the PROMIS scales	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.	42 days