Exercise Intensity Modulates the Validity of Non-Linear Heart Rate Time Series Analysis Window Length: Implications for DFAa1 Monitoring

Abstract Purpose Detrended fluctuation analysis alpha-1 (DFAa1) has emerged as a promising non-invasive biomarker for exercise intensity assessment. However, the standard 2-min analysis window lacks temporal resolution necessary for real-time training applications. This study systematically investigated the validity of shortened DFAa1 windows (30s and 1min) versus the 2-min reference across different intensities. Methods Physically active males completed three continuous cycling protocols: low-intensity training at the first lactate threshold (LOW, n=19), moderate-intensity training at the second lactate threshold (MOD, n=19), and a 30-min self-paced time trial (TT 30 , n=18). DFAa1 was calculated using 30-s, 1-min, and 2-min moving windows, advancing in 1s increments. Validity was assessed using intraclass correlation coefficients (ICC), Bland-Altman analysis, and standard error of measurement (SEM). Results During LOW, both shortened windows showed poor agreement with the 2-min reference (30s: ICC=0.02, mean bias of –0.05; 1min: ICC=0.37, –0.02). During MOD, the 30-s window remained unreliable (ICC=0.32, –0.01), while the 1-min window achieved moderate reliability (ICC=0.63, 0.00). During TT 30 , both shortened windows substantially improved performance (30s: ICC=0.78, –0.02; 1min: ICC=0.95, –0.01), with the 1-min window achieving excellent reliability. Conclusion DFAa1 analysis window validity is intensity-dependent, with shortened windows showing progressively improved agreement as exercise intensity and heart rate increases. While the 2-min window remains essential for low-intensity monitoring, 1-min or 30-s windows provide appropriate validity during high-intensity exercise, enabling more-responsive real-time feedback. These results support adaptive windowing strategies that dynamically adjust window length based on exercise intensity and the number of included data points, to optimize the analytical validity-temporal responsiveness trade-off.