Master athletes are typically defined as men and women older than 35 years who continue physical training throughout life and compete in organized events. The number of masters athletes involved in sport activities is continuously increasing, particularly in long-distance running events (Tanaka and Seals, 2008). For instance, statistics from United States indicate that marathon finishers aged over 40 years were 37,180 (26% of total finishers) in 1980, and 249,410 (49% of total finishers) in 2015 (Running USA Annual Marathon Report, 2015)¹

Master athletes are still capable of accomplishing outstanding performances (Trappe et al., 2013). However, exercise performance inevitably declines with aging despite regular training and participation in sporting competitions (Donato et al., 2003; Reaburn and Dascombe, 2008; Tanaka and Seals, 2008; Brisswalter and Nosaka, 2013). V˙O_2max seems to be the major determinant of this decrease whereas running economy and lactate threshold, when expressed relative to the percentage of V˙O_2max, appear well preserved with aging (Reaburn and Dascombe, 2008; Tanaka and Seals, 2008; Brisswalter and Nosaka, 2013).

In endurance master athletes, age-associated reduction in V˙O_2max is due to a decline in both maximal cardiac output and maximal arterio-venous oxygen difference and it is likely mediated by a reduction in the exercise training “stimulus” (i.e., exercise-training intensity, session duration, and weekly frequency). Indeed, master athletes have often less time for training than young athletes do and the increased job- and family-related responsibilities may impinge on the availability of time and energy for the intensive training required to remain competitive (Tanaka and Seals, 2008). In addition, training stimulus can be inadequate because few master athletes still have coaches or follow structured training programs. The increased prevalence of exercise training-associated injuries in this population probably also contributes to their reduced training intensity and volume (Reaburn and Dascombe, 2008).

In general, the main modalities of training used to improve endurance exercise performance are: (i) continuous training at low- to moderate-intensity (CMIT) characterized by high volumes of training (> 30 min per session) with intensities below the “anaerobic threshold” or between 60 and 80% of V˙O_2max (Kubukeli et al., 2002; Laursen and Jenkins, 2002; Seiler and Kjerland, 2006) and (ii) discontinuous high-intensity training (DHIT) characterized by repeated short bouts of exercise performed at an intensity corresponding to V˙O_2max or above, separated by brief periods of low-intensity work or inactivity which allow a partial but often not a full recovery of resting heart rate and V˙O₂ values (Kubukeli et al., 2002; Laursen and Jenkins, 2002; Gibala et al., 2006). Until a few years ago, it was widely believed that DHIT was a prerogative of elite athletes or recreational runners accustomed to sustain strenuous efforts and exercises of low/moderate intensity and high-volume were primarily prescribed to sedentary or moderately trained subjects, as this was considered safer and effective to improve aerobic metabolism (Holloszy and Coyle, 1984; Laursen, 2010). However, several studies have shown that in adult sedentary or moderately trained subjects, DHIT might be an efficient strategy to improve running performance (Franch et al., 1998; Billat et al., 1999; Helgerud et al., 2007; Laursen, 2010). Indeed, an improved maximal oxygen uptake (V˙O_2max) (Milanović et al., 2015) and a reduced energy expenditure at submaximal running speeds (Franch et al., 1998; Billat et al., 1999; Helgerud et al., 2007; Iaia et al., 2009) have been reported and it is widely accepted that these both factors, in addition to the ability to sustain a high percentage of V˙O_2max for an extended period of time (f V˙O₂), are the main physiological determinants of running performance (di Prampero, 1986; Capelli, 1999).

To the best of our knowledge, there is a lack of data on the effects of DHIT programs in middle-aged master runners with several years of training experience. Thus, the aim of this study was to compare the effects of CMIT and DHIT programs, characterized by the same volume, on running performance and the main physiological variables related to endurance performance in master runners. A group of runners, who maintained their training habits, was utilized as control. Our hypothesis was that DHIT, by affecting V˙O_2max and energy cost of running, would be as effective as CMIT in ameliorating running performance.

The main finding of this study was that both moderate-intensity continuous training and high-intensity discontinuous training improve running performance in middle-aged master runners.

Form a physiological point of view, it is widely accepted that running performance depends on maximal oxygen uptake (V˙O_2max), the ability to sustain a high percentage of V˙O_2max for an extended period of time (f V˙O₂), and the energy cost of running (Cr): v = f V˙O_2max · Cr⁻¹ (di Prampero, 1986; Capelli, 1999). After training, V˙O_2peak was unchanged in the master runners participating to the present study. At the same time, in the master runners participating to the present study we have observed a significant decrease of Cr following both CMIT and DHIT (4.4 and 4.9%, respectively). The magnitude of this improvement was close to the change in energy cost of running reported in young trained distance runners after a period of interval training (Franch et al., 1998; Billat et al., 1999; Helgerud et al., 2007). We are unaware of similar studies performed in middle-aged master endurance athletes. Of interest is the observation that the positive effect on Cr was achieved despite a significant reduction of the previous self-managed training volume. Our results are in line with the theoretical model of the performance determinants: v = f V˙O_2max·Cr⁻¹ (di Prampero, 1986; Capelli, 1999). After training V˙O_2peak, GET and calculated f V˙O_2max (91 and 90% at PRE and POST, respectively) were unchanged. However, the analysis of 5-km finish time with the theoretical model of performance by considering the Cr value in POST lead to a reduction of 3.1% in CMIT, the same as with Capelli's model 1999 and of 2.2% in DHIT, that was lower than the model prediction of 4.9%. Thus, other factors (i.e., biomechanical, neurophysiological, psychological etc.) counterbalanced the reduction of Cr and negatively influenced running performance of DHIT runners.

The energy cost of running, as is well known, is influenced by several metabolic, neuromuscular, and/or biomechanical factors (Saunders et al., 2004; Lacour and Bourdin, 2015). It has been suggested that training interventions able to reduce V˙E, HR, and Δ[La⁻]_b during exercise may be beneficial to Cr (Saunders et al., 2004). In our study, we observed after training, a significant reduction of V˙E and HR at the same running speed both in CMIT and DHIT. However, it is hard to think that these changes were responsible for the overall V˙O₂ reduction and consequently for the Cr decrease. Taking into account the ratio of 1.5–2.0 mL·min⁻¹ V˙O₂ per L·min⁻¹ of hyperpnea calculated by Aaron et al. (1992), a 2–4 L·min⁻¹ difference in V˙E should correspond to a reduction of about 3–8 mL O₂·min⁻¹ (0.04–0.11 mL O₂·min⁻¹ when expressed per kilogram body weight). Likewise, assuming a myocardial V˙O₂ of 0.4 mL O₂ per 100 g of left ventricular mass and per beat (Sheffield, 1988), the reduction of 4–5 b·min⁻¹ observed after training should induce a negligible effect on the overall energy balance. Finally, in both PRE and POST the Δ[La⁻]_b measured indicates that the metabolic energy produced via anaerobic glycolysis was negligible, as expected for an exercise of moderate intensity, and we can assume that lactate did not contribute significantly to total energy expenditure. Thus, the metabolic factors cannot completely explain the improved energy cost of running.

Beside physiological aspects, neuromuscular, and biomechanical factors may also be important determinants of Cr (Lacour and Bourdin, 2015). Previous studies have reported an improvement of neuromuscular characteristics following plyometric (Spurrs et al., 2003) and strength (Paavolainen et al., 1999) training in young runners. Similar results were observed also in master runners following concurrent strength and endurance training (Piacentini et al., 2013). Moreover, high-intensity training may lead to a high engagement of the neuromuscular system (Buchheit and Laursen, 2013) and improve muscular function, especially in older athletes that experience a decrease in muscle mass and neural function with aging. In our study, we did not directly evaluate neuromuscular function but the improvement in v_peak (with no change in V˙O_2peak) for DHIT group after training might be related, at least in part, to improvements in muscular function (Esfarjani and Laursen, 2007). Thus, the reduction of the energy cost of running observed may be due to an improved neuromuscular function, at least in the subjects enrolled in the DHIT group.

From a biomechanical point of view, several parameters can be optimized (e.g., stride frequency, spring mass model related stiffness) or minimized (e.g., mechanical work) in order to improve Cr (Dalleau et al., 1998; Lieberman et al., 2015; Zamparo et al., 2016). However, published data on masters athletes or aged runners seem to show an already optimized system. Cavagna et al. (2008) reported that the freely chosen stride frequency is higher in old runners compared with young, but it is closer to the natural frequency of the system, and with a lower total mechanical work. Pantoja et al. (2016) showed that both vertical and leg stiffness are similar in young and master runners even if Cr is higher in master. Biomechanical analysis was beyond the aims of this study and we did not measure any of the aforementioned parameters, thus we cannot say if any change occurred after training and if they could have affected Cr.

Another finding of this study is that CMIT and DHIT group significantly improved speed at GET by 3.7 and 4.8% for CMIT and DHIT, respectively. It is known that a rightward shift of the GET to a higher running speed is a clear sign of successful endurance training programs. This adaptation allows a higher absolute exercise intensity to be sustained without the accumulation of blood lactate that can be translated in a higher average running speed during competitions (Jones and Carter, 2000). Indeed, participants in both CMIT and DHIT group were able to run the 5-km time trial at higher speed after training (~2.5% faster). Nevertheless, we did not find any change in GET when expressed as a percentage V˙O_2peak. This may be due to an already high pre-training GET value (overall mean ~87% of V˙O_2peak) that is a common feature of endurance master athletes (Kusy et al., 2012), which signified a limited potential for improvements with training.

Previous studies have consistently reported that both CMIT and DHIT induce large improvements in the V˙O₂max of untrained/moderately trained healthy adults aged 18–45 years (for a review see Milanović et al., 2015). In our master runners, however, we did not find any significant change in V˙O_2peak following the two training programs. When considering this discrepancy, two factors should be taken into account: (1) participants were master runners with several years of training experience; (2) both CMIT and DHIT induced a significant reduction of the previous self-managed training volume that could negatively affected V˙O_2peak. Thus, our data should be observed with a different point of view: a significant reduction in training volume did not change V˙O_2peak after both CMIT and DHIT whereas the 5-km running performance improved after both training protocols. These findings are interesting because they indicate that when master athletes have reached a good maximal aerobic capacity, other factors could be important in order to obtain further improvements in running performance. This seems to be supported by the stepwise regression analysis performed before the training intervention. Our data showed that all variables (physiological and mechanical) measured were significantly correlated with 5-km performance (Table 3). However, the v_peak and speed at GET had the higher correlation coefficient (r = −0.856 and r = −0.831, respectively). This result supports previous studies, in which v_peak and the fractional utilization of the V˙O_2peak (expressed as lactate or ventilatory threshold) were highly correlated with performance in 5-km in young (Lacour et al., 1990) and middle-aged runners (Takeshima and Tanaka, 1995). Moreover, the stepwise regression analysis revealed that only v_peak, GET, and Cr predicted the 5-km performance (adjusted r² = 0.855). In the present study, DHIT improved Cr, speed at GET and v_peak whereas CMIT only influenced Cr and speed at GET. These findings are in accordance with other studies showing that v_peak, lactate or ventilatory threshold and energy cost may be better predictors of endurance performance than V˙O_2peak in a homogeneous group of trained endurance athletes (Conley and Krahenbuhl, 1980; Allen et al., 1985; Stratton et al., 2009).

From a practical point of view, it should be highlighted that a sizeable reduction of self-managed training volume replaced by a controlled training program significantly improved performance. Before the intervention the whole group of runners reported to undertake a combination of both high-intensity (~4–10% of total) and high-volume (~ 90% of total) training sessions for a training volume of about 50 km·wk⁻¹. However, subjects did not follow any individualized training program and training intensity, as well as training volume was based only on the participants' experience. Previous studies have emphasized that the reduction in the exercise training stimulus with advancing age may have a critical role in the decline of peak performance (Tanaka et al., 1997). Our findings suggest that the quality of training should also be taken into account to improve running performance.

Finally, the master athlete is more likely to experience sports-related injuries because the tissues that make up the tendons, ligaments, cartilage, and muscle break down more easily and heal with greater difficulty (Maharam et al., 1999). Although it was not the aim of this study, we observed a low incidence (one runner in DHIT and one runner in CMIT) of muscular or orthopedic injuries after training intervention. These results indicate that a controlled DHIT program can be safely carried out not only in young athletes (Gibala et al., 2006) but also in master runners as outlined by the observation that, in the same cohort of athletes, DHIT does not cause higher level of exercise-induced oxidative stress than CMIT (Vezzoli et al., 2014).

In conclusion, this study indicates that both well-controlled CMIT and DHIT training program may significantly reduce the amount of weekly training volume (with a consistent reduction of time) and significantly improve running performance in middle-aged master runners, mainly lowering the energy cost of running. v_peak, fractional utilization of V˙O_2peak, and energy cost appear to be the best predictors of 5-km performance in middle-aged master runners suggesting that training sessions should focus on the improvement of these parameters.

LP, SP, AL, FS, and MM: participated in the study design; LP, SP, and MM: conducted the experiments; MM: was responsible for medical expertise and screening of the athletes; LP, SP, AV, and GP: performed the data analysis; LP, SP, AV, AL, FS, GP, and MM: wrote or contributed to the writing of the manuscript.