• Mark Turnbull

Results of six weeks of a polarised training-intensity

Six weeks of a polarised training-intensity distribution leads to greater physiological and performance adaptations than a threshold model in trained cyclists

Craig M. Neal, Angus M. Hunter, Lorraine Brennan, Aifric O'Sullivan, D. Lee Hamilton, Giuseppe DeVito, and Stuart D. R. Galloway


This study was undertaken to investigate physiological adaptation with two endurance-training periods differing in intensity distribution. In a randomized crossover fashion, separated by 4 wk of detraining, 12 male cyclists completed two 6-wk training periods: 1) a polarised model [6.4 (±1.4 SD) h/wk; 80%, 0%, and 20% of training time in low-, moderate-, and high-intensity zones, respectively]; and 2) a threshold model [7.5 (±2.0 SD) h/wk; 57%, 43%, and 0% training-intensity distribution]. Before and after each training period, following 2 days of diet and exercise control, fasted skeletal muscle biopsies were obtained for mitochondrial enzyme activity and monocarboxylate transporter (MCT) 1 and 4 expression, and morning first-void urine samples were collected for NMR spectroscopy-based metabolomics analysis. Endurance performance (40-km time trial), incremental exercise, peak power output (PPO), and high-intensity exercise capacity (95% maximal work rate to exhaustion) were also assessed. Endurance performance, PPOs, lactate threshold (LT), MCT4, and high-intensity exercise capacity all increased over both training periods. Improvements were greater following polarised rather than threshold for PPO [mean (±SE) change of 8 (±2)% vs. 3 (±1)%, P < 0.05], LT [9 (±3)% vs. 2 (±4)%, P < 0.05], and high-intensity exercise capacity [85 (±14)% vs. 37 (±14)%, P < 0.05]. No changes in mitochondrial enzyme activities or MCT1 were observed following training. A significant multilevel, partial least squares-discriminant analysis model was obtained for the threshold model but not the polarised model in the metabolomics analysis. A polarised training distribution results in greater systemic adaptation over 6 wk in already well-trained cyclists. Markers of muscle metabolic adaptation are largely unchanged, but metabolomics markers suggest different cellular metabolic stress that requires further investigation.

Understanding the optimal exercise training-intensity distribution to maximize adaptation and performance is important for athletes who try to gain a competitive advantage. In addition, a greater understanding of the interactions among exercise-intensity distribution, physiological stress, and adaptation could be important for achieving the optimal health benefits from physical activity in the general population. Exercise-intensity distribution is determined from the percentage of time spent exercising at low [zone 1, typically <65% of peak power output (PPO), less than the lactate threshold (LT), <2 mM]; moderate [zone 2, ∼65–80% of PPO, between LT and lactate turn point (LTP)]; and high (zone 3, typically >80% of PPO, >LTP, >4 mM) intensities (8, 29, 46). It has been suggested that two distinct exercise training-intensity distribution models are adopted by endurance athletes (46). First, a polarised training model (POL) that consists of a high percentage of exercise time at low exercise intensity (∼75–80%) accompanied by little time at moderate intensity (∼5–10%) with the remainder spent at high intensity (∼15–20%). In contrast, the second model is a threshold training distribution (THR), in which moderate exercise intensity is the focus (typically 40–50% of training time) with relatively little or no high-intensity work and the balance of training time spent at low intensity.

It has been suggested by Seiler (47) and Laursen (32) that adopting a polarised intensity distribution may optimize adaptation to exercise while providing an acceptable level of training stress. Several studies have investigated adaptation to training at different intensities, with positive effects on LT and performance observed when a high proportion of training is conducted at low intensities (12, 13, 26). These studies suggest that the proportion of time in zone 1 is a key aspect that drives endurance adaptations and performance outcomes. However, other studies (33, 57, 58) have observed increased PPO and mean power sustainable during a 40-km time trial (40-km TT) when high-intensity interval work (zone 3 training) is incorporated into the schedules of already well-trained cyclists; i.e., when the cyclists adopted a more polarised training-intensity distribution. In addition, the change of intensity distribution toward a more polarised model has been shown to improve maximal oxygen consumption, running economy, and running performance in a case study of an international 1,500-m runner (27). Indeed, the powerful stimulus afforded by short-term, high-intensity interval work for promoting metabolic and performance adaptations has also been demonstrated in studies on trained-cyclist (51), healthy-active (52), and sedentary (23) men and women. These studies have shown significant increases in skeletal muscle oxidative capacity and mitochondrial function following only a few high-intensity interval exercise sessions, as well as improvements in markers of endurance performance. Thus the combination of a high proportion of time in zone 1 along with zone 3 interval work is likely to be a strong combination for optimal adaptations to training in endurance athletes, but to date, no study has directly compared the adaptations induced by POL vs. THR in already well-trained athletes.

An important aspect in adaptation to exercise is recovery and the ability to cope with the training stress. Seiler et al. (48) identified that recovery time from high-intensity training was not greater than from moderate-intensity training but that recovery time from low-intensity training was the shortest. Their data imply that recovery from a polarised training-intensity distribution would be better than recovery from a threshold intensity distribution. With new technologies, such as metabolomics that enable a more global overview of whole-body metabolic perturbations, the response to exercise-training stress, adaptation, and recovery can be studied in a more global manner.

Metabolomics technology has, in recent years, provided new insights in several fields of research, including toxicology, pharmacology, and human nutrition, and can aid identification of novel biomarkers (40). However, the application of metabolomics to exercise training has been under used in human exercise studies to date. There are only two cross-sectional human studies that have been published (11, 61), and both of these concluded that metabolomics is a promising tool for investigation of human responses to exercise. Therefore, the purpose of the present study was to compare the physiological adaptations and longitudinal metabolomics profile responses of well-trained male cyclists with training interventions that followed both a polarised and a threshold training-intensity distribution. We hypothesized that the polarised training-intensity distribution would lead to greater adaptive responses through a greater stimulus provided by the high-intensity interval exercise and the high proportion of training spent at low intensity. We also hypothesized that the metabolomics profile would provide new insights into understanding the training stresses induced by POL vs. THR in already well-trained humans.


Twelve well-trained male cyclists were recruited from two local cycling clubs. The mean (±SD) characteristics of the participants were: age 37 (±6) yr, body mass 76.8 (±6.6) kg, stature 178 (±6) cm, and PPO 4.7 (±0.5) W/kg. Participants had been training consistently for >4 yr and prior to entry into the study, trained 7–8 h/wk (range 5–10 h/wk), with four to five training sessions/wk for at least the previous 6 mo. Their training-intensity distribution prior to entering the study was estimated to be 53% zone 1, 38% zone 2, and 9% zone 3, with a training load [intensity zone × duration (min)] of 750 units. All participants were able to sustain a power output above 240 W for a 40-km TT time prior to entry into the study. Participants were all competitive road cyclists, but some also performed mountain biking within their training. Participants provided written, informed consent to take part in the study, which was approved by the University Ethics Committee in accordance with the Declaration of Helsinki.

Study design

A crossover, within-subject study design was used. All participants were in the study for a period of 29 wk (Fig. 1). This included prescreening and habituation trials in the first 2 wk before commencing a 4-wk controlled detraining period. Participants were then asked not to exercise and to record all of their food and fluid intake for 2 days prior to undertaking a baseline testing week. Following this, the participants entered the training intervention period. Participants (n = 6) were assigned to complete POL training first, and n = 6 were assigned to complete THR training first. Participants undertook 6 wk of training following either the POL training-intensity distribution (80% low intensity, 0% moderate intensity, 20% high intensity) or a THR training-intensity distribution (57% low intensity, 43% moderate intensity, 0% high intensity). This was followed by a post-training intervention testing week. Participants then completed a second, 4-wk controlled detraining period prior to undertaking the crossover arm of the study, in which they completed a pre training testing week, 6 wk of training following the alternate training-intensity distribution, and a post-training testing week. The two 6-wk training intervention periods were undertaken over the winter months November–December and January–March.

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In the habituation trials, participants undertook at least two 40-km TT test rides on their own bike mounted onto a CompuTrainer ergometer (RacerMate, Seattle, WA). To ensure that we recruited trained cyclists, only riders who completed the 40-km TT with a mean power output of ≥240 W were included in the study. During the 4-wk detraining periods, participants were instructed not to include any threshold/tempo rides, interval sessions, or races and to ride exclusively at low-intensity (zone 1). Participants completed only 4 h/wk (range 3–5 h/wk) of zone 1 training during this period. The time at which these detraining periods fell during the study made this possible, as they occurred during October and December–January. This strategy was used to ensure that no specific adaptations from training at moderate or high intensity would be gained in the 4 wk prior to each of the study intervention periods. To determine the effectiveness of the detraining period, we also examined whether PPO, 40-km TT time, mean power output, and high-intensity exercise capacity had all returned to baseline values before beginning the second training intervention.