Monitoring and Self-management of Sleep Fatigue and Dyspnea

University of Massachusetts, Amherst

Status: Unknown

Conditions: Heart Failure Patients

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

Study type: Interventional

Funder types: Other

Identifiers

NCT04434716

2019-5684

Details and patient eligibility

About

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.

Full 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.

Enrollment: 20 estimated patients

Sex: All

Ages: 30 to 85 years old

Volunteers: No Healthy Volunteers

Inclusion and exclusion 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.

Trial design

Primary purpose: Device Feasibility

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

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20 participants in 1 patient group

Feasibility of Wearing a Readiband

Other group

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.

Treatment:

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