British Medical Bulletin
Noninvasive assessment of the anaerobic threshold in endurance athletes: an evidence-based narrative review of methods, validity, and field feasibility
Par Nicola Maffulli, Johnny Padulo et Francesco Oliva.
Contenu en anglais
Résumé
Abstract Introduction or Background The anaerobic threshold (AnT) marks the transition between heavy and severe exercise intensity domains and guides training prescription and performance monitoring. Criterion methods (blood lactate and gas exchange) are accurate but invasive, costly, or impractical outside laboratories, prompting the search for noninvasive surrogates. This review evaluates the validity of these estimation procedures rather than the underlying physiology. Sources of Data MEDLINE (via PubMed) was searched from inception without language restrictions, supplemented by reference-list and citation searching. Eligible studies compared noninvasive AnT indices, including heart rate variability (HRV; including detrended fluctuation analysis [DFA] α1), cardiac repolarization instability, heart rate deflection/field slope-variation methods, critical power (CP)/ critical speed (CS), near-infrared spectroscopy (NIRS), the talk test, and machine-learning (ML) models, with criterion standards. Risk of bias was assessed using QUADAS-2. Eighty-eight reports were synthesized narratively; no meta-analysis was performed. Areas of Agreement DFA α1, NIRS breakpoints, and CP/CS show strong associations with laboratory criteria and increasing field applicability through wearables. Accuracy and feasibility trade-offs require method selection according to the threshold, sport, and required precision. Areas of Controversy Correlation indicates group-level association rather than individual agreement; Bland–Altman and reliability analyses are more informative for substitution. Criterion standards are not interchangeable, and heart rate deflection is not consistently identifiable. Evidence in female athletes remains limited. Growing Points Multimodal wearables integrating HRV, muscle oxygenation, and running power with ML may improve field-based threshold estimation. Areas for Developing Research Priorities include standardized reporting, transparent bias assessment, female-athlete validation, and broader systematic and meta-analytic synthesis.



