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Monitoring and Self-management of Sleep Fatigue and Dyspnea

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

Status
UNKNOWN
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT04434716
Enrollment
20
Registered
2020-06-17
Start date
2019-08-15
Completion date
2021-04-30
Last updated
2020-06-17

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Heart Failure Patients

Keywords

Heart failure, African Americans, Wrist worn device, Mixed- method

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.

Interventions

DEVICEFeasibility 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?

Sponsors

University of Massachusetts, Amherst
Lead SponsorOTHER

Study design

Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DEVICE_FEASIBILITY
Masking
NONE

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.

Eligibility

Sex/Gender
ALL
Age
30 Years to 85 Years
Healthy volunteers
No

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

*

Design outcomes

Primary

MeasureTime frameDescription
Measure if the Readiband is able to measure Sleep and Fatigue42 daysSpecific 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.

Secondary

MeasureTime frameDescription
Correlation between data from the Readiband and the PROMIS scales42 daysSpecific 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.

Countries

United States

Contacts

Primary ContactHeather Hamilton, PhD RN
hamilton@umass.edu413-545-7174
Backup ContactIan Cooke, PhD
icooke@umass.edu4135455087

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026