Clinical trial NCT04434716: Monitoring and Self-management of Sleep Fatigue and Dyspnea
Official title: Monitoring and Self-management of Sleep Fatigue and Dyspnea to Improve Heart Failure Outcomes in African-Americans
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Heart Failure Patients
Keywords: Heart failure
African Americans
Wrist worn device
Mixed- method
Lead sponsor: University of Massachusetts, Amherst
Sponsor class: OTHER
First posted: 2020-06-17
Enrollment: 20
Sex: ALL
Minimum age (years): 30
Maximum age (years): 85
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
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

Brief 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.

Detailed 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.

Eligibility criteria: 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.

Primary outcomes:
measure: Measure if the Readiband is able to measure Sleep and Fatigue
timeFrame: 42 days
Secondary outcomes:
measure: Correlation between data from the Readiband and the PROMIS scales
timeFrame: 42 days
Other outcomes:
Locations:
city: Amherst
country: United States
facility: University of Massachusetts Amherst
state: Massachusetts
Study officials:
affiliation: University of Massachusetts, Amherst
name: Heather M Hamilton, PhD, RN
role: PRINCIPAL_INVESTIGATOR
References:
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