Open AccessMoving beyond threshold‐based dichotomous classification to improve the accuracy in classifying non‐responders
Author(s) -
Bonafiglia Jacob T.,
Nelms Matthew W.,
Preobrazenski Nicholas,
LeBlanc Camille,
Robins Lauren,
Lu Simo,
Lithopoulos Alexander,
Walsh Jeremy J.,
Gurd Brendon J.
Publication year - 2018
Publication title -
physiological reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.918
H-Index - 39
ISSN - 2051-817X
DOI - 10.14814/phy2.13928
Subject(s) - confidence interval , statistics , medicine , computer science , machine learning , artificial intelligence , mathematics
Abstract We examined maximal oxygen consumption responses following exercise training to demonstrate the limitations associated with threshold‐based dichotomous classification of responders and non‐responders and proposed alternative methods for classification. Specifically, we: 1) calculated individual probabilities of response, and 2) classified individuals using response confidence intervals (CI) and reference points of zero and a smallest worthwhile change of 0.5 METs. Our findings support the use of individual probabilities and individual CIs to improve the accuracy in non‐response classification.