It is well known that individuals who are unfit are at substantially greater risk for lifestyle-related diseases and premature death. Despite its high value in assessment of risk, fitness is not routinely measured in clinical practice. The likely reason for this is the costly and time consuming testing procedure that requires trained personnel and expensive equipment. Therefore, research has recently turned to non-exercise algorithms, which, without the need for expensive equipment or trained personnel, estimate fitness from available clinical information and information provided by the patient.
In a new study published in Mayo Clinic Proceedings, researchers from K.G.
