An updated concept of the polarization index enables the integration of three intensity zones in one number and identifies polarized training in an extended definition range

Introduction Polarized endurance training is an important and frequently discussed training intensity distribution (TID). The polarized TID is described as the largest fraction of training time or sessions spent with low-intensity exercise in intensity zone (z)1, followed by a considerable fraction of high intensity exercise (z3), and a relatively small fraction of threshold intensity (z2), so that z1 > z3 > z2. The polarization index concept (PI; Figure 1, equation 1) provides a clear, numerical cut-off for this rather vague, verbal description of a polarized TID by relating the fractions of z1, z2, and z3 and defining a given TID as polarized if the result is > 2.00 (Treff et al., 2019). The concept has successfully been applied in more than 36 peer-reviewed articles. Since the publication of the high-performance sports centered PI, a variety of issues related to transforming context conditions appeared, revealing some limitations of the original PI: (i) The increasing use of automatic database entries can result in TIDs with intensity zones < 1%. These can cause rounding problems. (ii) TIDs are attracting increasing attention in the public health sector (Festa et al., 2023). Here, due to lower total volume and session frequencies, TIDs may occur that are virtually never reported in high-performance sports as, e.g., z2 < z1 < z3. These TIDs can result in negative log10-arguments of equation 1. It has been proposed to limit the calculation of the PI to TIDs z1 > z3 >z2 (Arjona et al., 2023). This would, however, limit the PI’s applicability. Here, we propose an update to adapt the PI without limiting its definition space. Methods Methods: Given that z1, z2, z3 ∈ [0, 1] and z1 + z2 + z3 = 1, where z1, z2 denote the measured fractions rounded to two decimals and z3 = 1 - z1 - z2, new PI is calculated according to equation 2 (Figure 1). Results A TID (given as z1 - z2 - z3) partially consisting of fractions below 0.01 (0.991 - 0.001 - 0.008) will be quantized to (0.99 - 0.00 - 0.01) and the respective PI results in 0, thereby indicating a non-polarized TID in contrast to 2.90 with equation 1 (Figure 1). Thus, the false-positive indication of a polarized TID is avoided. Applying equation 2 (Figure 1), the PI of the TID (0.15 - 0.05 - 0.80) will result in -2.38 (inversely polarized) instead of 2.38 (polarized) with equation 1 (Figure 1). Discussion & Conclusion Particularly the exclusive consideration of intensity zones with at least 1% of the total proportion avoids negative arguments in the log10 algorithm and the indication of a polarized TID if the fraction of z3 is not higher than 0.01. Furthermore, equation 2 (Figure 1) allows for the identification of inversely polarized TIDs while avoiding false positive indication of a polarized TID when z2 < z1 < z3. For polarized distributions (z1 > z3 > z2) with z2 = 0, subtracting the 0.01 fraction from z1 accounts for the underestimation of the z3 percentages especially in heart rate-based quantifications. Thus, our proposal allows a broad application of the PI concept in health and performance-focused databases to accurately detect polarized TIDs. References Arjona, O. A. M., Arjona, J. M., Blasco Lafarga, C., & Cordellat, A. (2023). The polarization-index: A simple calculation to distinguish polarized from non-polarized training intensity distributions [Commentary]. Frontiers in Physiology, 14, Article 1179769. https://doi.org/10.3389/fphys.2023.1179769 Festa, R. R., Jofré-Saldía, E., Candia, A. A., Monsalves-Álvarez, M., Flores-Opazo, M., Peñailillo, L., Marzuca-Nassr, G. N., Aguilar-Farias, N., Fritz-Silva, N., Cancino-Lopez, J., Soto-Sánchez, J., Sepulveda, C., Huerta Ojeda, Á., Núñez-Espinosa, C., Valladares-Ide, D., & Jannas-Vela, S. (2023). Next steps to advance general physical activity recommendations towards physical exercise prescription: A narrative review. BMJ Open Sport & Exercise Medicine, 9(4), Article e001749. https://doi.org/10.1136/bmjsem-2023-001749 Treff, G., Winkert, K., Sareban, M., Steinacker, J. M., & Sperlich, B. (2019). The Polarization-Index: A aimple calculation to distinguish polarized from non-polarized training intensity distributions. Frontiers in Physiology, 10, Article 707. https://doi.org/10.3389/fphys.2019.00707