Exercise Clinical Trial
— PEAKSOfficial title:
PEAKS: Validation of Mobile Technologies for Clinical Assessment, Monitoring and Intervention
Verified date | May 2019 |
Source | University of Florida |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
The overall objective of this project is to evaluate and validate the accuracy and usability of a deployed beta sensor-based system. This is a cross-sectional validation study of 100 apparently health community dwelling adults aged 60+ years. Participants will perform standardized lifestyle, exercise and sedentary type physical activities in a clinic laboratory and home setting.
Status | Completed |
Enrollment | 113 |
Est. completion date | May 11, 2018 |
Est. primary completion date | May 11, 2018 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | All |
Age group | 60 Years and older |
Eligibility |
Inclusion Criteria: - Age 60+ years old - Community dwelling adults without significant health issues that would impact safety of participation - Willingness to undergo all testing procedures - Weight stable for at least three months - Able to understand and speak English Exclusion Criteria: - Failure to provide informed consent - Use of walker (use of a cane is permitted) - Lower extremity amputation - Develops chest pain or severe shortness of breath during physical stress - Post-stroke syndrome causing ambulatory deficits (other stroke survivors permitted) - Needs assistance with basic activities of daily living: feeding, dressing, continence, bathing, toileting, and transferring from a bed to a chair or from a chair to walking - Lives in a nursing home; persons living in assisted or independent housing are not excluded - Heavy drinking as drinking 5 or more drinks on the same occasion on each of 5 or more days in the past 30 days. - For women who are child-bearing age (up to 62 years of age): pregnancy or breast-feeding - Participation in a structured weight loss program or fad diet in the last month - Weight reduction surgery in the past year - Known neuromuscular disorder that restricts activity (e.g. Rhabdomyolysis, Myasthenia Gravis, Ataxia, Apraxia, post-polio syndrome, mitochondrial myopathy, etc.) - Diagnosed neuropathy that causes pain that restricts activity - Symptomatic peripheral arterial disease that restricts activity - Unable to communicate because of severe hearing loss or speech disorder - Severe visual impairment, which would preclude completion of the assessments - Progressive, degenerative neurologic disease (e.g., Parkinson's Disease, Multiple Sclerosis, ALS) - Severe rheumatologic or orthopedic diseases that significantly restricts activity (e.g., awaiting joint replacement, active inflammatory disease) - Terminal illness, as determined by a physician - Severe pulmonary disease, requiring the use of supplemental oxygen or steroid therapy - Severe cardiac disease, including NYHA Class III or IV congestive heart failure, clinically significant aortic stenosis, recent history of cardiac arrest, use of a cardiac defibrillator, or uncontrolled angina - Other significant comorbid disease discovered during medical screening that would elicit safety concerns, (e.g. renal failure on hemodialysis, psychiatric disorder, chronic fatigue syndrome, etc.) - Pacemakers and implanted cardiac defibrillators |
Country | Name | City | State |
---|---|---|---|
United States | UF Institute on Aging Clinical and Translational Research Building | Gainesville | Florida |
Lead Sponsor | Collaborator |
---|---|
University of Florida | EveryFit, Inc, National Cancer Institute (NCI), National Institute on Aging (NIA) |
United States,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Metabolic equivalent (MET) value | MET value of physical activities defined as the oxygen uptake (VO2 = milliliter• min-1•kg-1) during a steady state rate expressed as a function 3.5 milliliter• min-1•kg-1. | within one month | |
Secondary | Tradeoff Accuracy in metabolic equivalent estimation and power consumption | We will manipulate the state machine that samples data from the device with different duty cycles based on contextual changes as measured by the accelerometer or classified by models that use the accelerometer data to classify behavior or categories of behavior. | within one month |
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