Purpose The aim was to develop and validate an individualised internal training exposure method by deriving weighting factors for each heart rate (HR) from detrended fluctuation analysis of heart rate variability (DFA-α1) during a graded exercise test. Methods Thirty-seven participants (17 females; 32.72 ± 9.26 years; maximal oxygen uptake, <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="421_2025_5841_Article_IEq1.gif" Format="GIF" Height="18" Rendition="HTML" Resolution="72" Type="Linedraw" Width="14" /> </InlineMediaObject> <EquationSource Format="TEX">$\dot{V}$</EquationSource> <EquationSource Format="MATHML"><math> <mover accent="true"> <mi>V</mi> <mo>˙</mo> </mover> </math></EquationSource> </InlineEquation>O2max = 48.32 ± 7.95 mL kg−1 min−1) completed a step- and a ramp incremental test to measure blood lactate (BLa), DFA-α1, and cardiorespiratory fitness (CRF) variables, i.e. speed at lactate, ventilatory thresholds (LTs/VTs), and <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="421_2025_5841_Article_IEq1.gif" Format="GIF" Height="18" Rendition="HTML" Resolution="72" Type="Linedraw" Width="14" /> </InlineMediaObject> <EquationSource Format="TEX">$\dot{V}$</EquationSource> <EquationSource Format="MATHML"><math> <mover accent="true"> <mi>V</mi> <mo>˙</mo> </mover> </math></EquationSource> </InlineEquation>O2max. Exponential fitting of the fractional elevation of HR (ΔHR) with BLa (individualised training impulse; iTRIMP) or DFA-α1 (αTRIMP) generated individualised coefficients for both methods. The TRIMP weightings were interpolated values of BLa or DFA-α1 derived at each ΔHR through coefficients to represent individualised physiological intensity. Principal component regression evaluated the relationship between combined CRF variables and the TRIMP coefficients or weightings. Results Large inter-individual variation was observed at the same physiological thresholds (ΔHR at LT1/VT1 = 0.51–0.83 and LT2/VT2 = 0.63–0.96), underscoring the need for TRIMP methods to weight ΔHR and account for different exposure at similar intensity. CRF had a moderate relationship with coefficients for iTRIMP and αTRIMP methods (R2average = 0.52–0.67), but a moderate to strong relationship with their weightings at a fixed ΔHR (R2average = 0.67–0.78). Conclusion αTRIMP is a valid and practically accessible method for quantifying internal training exposure using ECG-based HR monitors, which individualises physiological intensity through DFA-α1-derived weightings among individuals of varied fitness exercising at same percentages of HR.

Internal training exposure: development and construct validation of an individualised method using heart rate variability

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2025年6月11日

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Samrat Sheoran,
Antonis Stavropoulos-Kalinoglou,
Josh Darrall-Jones,
Dan Weaving

摘要

Purpose

The aim was to develop and validate an individualised internal training exposure method by deriving weighting factors for each heart rate (HR) from detrended fluctuation analysis of heart rate variability (DFA-α1) during a graded exercise test.

Methods

Thirty-seven participants (17 females; 32.72 ± 9.26 years; maximal oxygen uptake, $\dot{V}$ $\dot{V}$ O_2max = 48.32 ± 7.95 mL kg⁻¹ min⁻¹) completed a step- and a ramp incremental test to measure blood lactate (BLa), DFA-α1, and cardiorespiratory fitness (CRF) variables, i.e. speed at lactate, ventilatory thresholds (LTs/VTs), and $\dot{V}$ $\dot{V}$ O_2max. Exponential fitting of the fractional elevation of HR (ΔHR) with BLa (individualised training impulse; iTRIMP) or DFA-α1 (αTRIMP) generated individualised coefficients for both methods. The TRIMP weightings were interpolated values of BLa or DFA-α1 derived at each ΔHR through coefficients to represent individualised physiological intensity. Principal component regression evaluated the relationship between combined CRF variables and the TRIMP coefficients or weightings.

Results

Large inter-individual variation was observed at the same physiological thresholds (ΔHR at LT₁/VT₁ = 0.51–0.83 and LT₂/VT₂ = 0.63–0.96), underscoring the need for TRIMP methods to weight ΔHR and account for different exposure at similar intensity. CRF had a moderate relationship with coefficients for iTRIMP and αTRIMP methods (R²_average = 0.52–0.67), but a moderate to strong relationship with their weightings at a fixed ΔHR (R²_average = 0.67–0.78).

Conclusion

αTRIMP is a valid and practically accessible method for quantifying internal training exposure using ECG-based HR monitors, which individualises physiological intensity through DFA-α1-derived weightings among individuals of varied fitness exercising at same percentages of HR.