Validating a New Machine-Learned Accelerometer Algorithm Using Doubly Labeled Water
University of Wisconsin, Milwaukee
Summary
The purpose of this study is to validate previously developed physical function-clustered specific machine-learned accelerometer algorithms to estimate total daily energy expenditure (TDEE) in individuals with general movement and functional limitations.
Description
Current algorithms for examining accelerometer data were developed primarily using data from individuals without movement limitations or impairments. As such, the current available analytic algorithms are inadequate for use with individuals with limitations and impairments to estimate total daily energy expenditure (TDEE). The creation of a new algorithm that can accurately assess TDEE in individuals with movement limitations will be beneficial for future research examining physical activity interventions targeted to these individuals. This study will serve to validate a new algorithm that was…
Eligibility
- Age range
- 18+ years
- Sex
- All
- Healthy volunteers
- Yes
Inclusion Criteria: * must be 18+ years of age * be able to ambulate on own, unassisted, on a regular basis * speak and read English * must have access to a working smart phone and a computer with internet access Exclusion Criteria: * wheelchair reliant * assistive walking device reliant (cannot walk for at least 50 feet without an assistive device) * diagnosed uncontrolled hypertension (above 160/100 mgHg) * diagnosed cognitive impairment or inability to follow study procedures such as Alzheimer's disease or dementia * cannot take metabolic altering medications * cannot be pregnant * canno…
Interventions
- OtherDoubly-Labeled Water
All eligible participants will receive a dose of doubly-labeled water.
Location
- University of Wisconsin-MilwaukeeMilwaukee, Wisconsin