Validating a New Machine-Learned Accelerometer Algorithm Using Doubly Labeled Water

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.

Inclusion Criteria:must be 18+ years of agebe able to ambulate on own, unassisted, on a regular basisspeak and read Englishmust have access to a working smart phone and a computer with internet accessExclusion Criteria:wheelchair reliantassistive 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 dementiacannot take metabolic altering medicationscannot be pregnantcannot be breastfeedingcannot use supplemental oxygencannot completed required study activities for any reasoncannot have a resting heart rate > 100 bpm or a resting blood pressure > 160 mgHg during Visit 1cannot weigh more than 450 lbs