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.
Willing and able to comply with protocol procedures and available for the duration of the study.
Willing to sign and date informed consent document for study participation.
Participant is undergoing the Cardiopulmonary Exercise Test (CPET) as Standard of Care
Participant is pregnant, lactating or ≤30 days post-partum.
Participant has limited or no intrinsic sinus node function (i.e. chronic atrial pacing).
Barostim (™) or similar noncardiac electrical pulse generating device in situ.
Any history of allergy to adhesive
Participant is undergoing the Cardiopulmonary Exercise Test (CPET) as Standard of Care
Case-control
Prospective
Not listed
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).
