In this study, the hypothesis being explored is that VO2Max and other CPET parameters can be accurately estimated from biosignals (namely, motion from accelerometers and cardiopulmonary variables from EKG) collected during activities of daily living using wearable biosensors worn by study participants. This study will aim to collect development and validation data for a machine learning algorithm and to evaluate the performance of the algorithm. A total of 1000 participants will be enrolled including: (Normal) 100 participants, self-reported healthy male and female participants aged 18 to 80 and (Standard of Care) 900 participants.
Study Type
OBSERVATIONAL
Enrollment
1,000
Mayo Clinic Arizona
Scottsdale, Arizona, United States
RECRUITINGVA Palo Alto Healthcare System
Palo Alto, California, United States
RECRUITINGUniversity of California San Francisco
San Francisco, California, United States
RECRUITINGThe Lundquist Institute
Torrance, California, United States
RECRUITINGNemours Cardiac Center
Wilmington, Delaware, United States
RECRUITINGMemorial Healthcare System, Office of Human Research
Hollywood, Florida, United States
RECRUITINGMayo Clinic Florida
Jacksonville, Florida, United States
RECRUITINGNew Generation of Medical Research
Naples, Florida, United States
RECRUITINGphysIQ
Chicago, Illinois, United States
COMPLETEDUniversity of Illinois Hospital & Health Sciences System
Chicago, Illinois, United States
RECRUITING...and 6 more locations
Collect development and validation data for a VO2Max (eVO2Max) machine learning algorithm and to evaluate the performance of the algorithm.
Develop an Estimated VO2Max (eVO2Max) algorithm which will estimate a participant's VO2Max value using data collected from physiological wearable biosensors (ECG \& Activity).
Time frame: March 2025
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