ECSS Paris 2023: OP-MH03
INTRODUCTION: Advanced ageing is naturally associated with a decline in physical fitness. However, master athletes who stay physically active may slow down this decline. Yet, there are some differences in physical functioning between master and young athletes. The study aimed to assess physiological function and muscle morphology in lifelong master endurance runners against their young trained counterparts METHODS: In a cross-sectional design, groups of 8 young best endurance runners (YER: age: 27.38 ± 3.07 yrs, BW: 72.21 ± 7.06 kg) and 10 best master endurance runners (MER: age 70.22 ± 3.87 yrs, BW: 71.44 ± 9.99 kg;) who underwent assessments of body composition, aerobic capacity, muscle strength and muscle morphology. The inclusion criteria were the following: 1) above 300 mins per week of endurance running, 2) regular participation of YER (at least 3 years) and MER (at least 25 years) in running competitions (in 10 km, half, and full marathon), 3) to have their personal best time on 10 km run in last season under 35 mins in YER and under 55 mins in MER. To compare the two groups, body composition (weight, muscle mass, body fat, BMI) was measured using bioimpedance, lower limb muscle strength was tested with knee extension on a dynamometer, and aerobic capacity was measured on a cycle ergometer (heart rate, maximum oxygen consumption, power output). Muscle biopsies were taken from the Vastus Lateralis to examine Immunohistochemically analysed muscle structure and myonuclear domain determination. RESULTS: In body composition, significant differences were observed in body fat (YER: 10.61 ± 3.35 %; MER: 19.07 ± 6.36 %, p ≤ .005) and muscle (p ≤ .005) in favour of young endurance runners, but not in BMI. According to maximal and relative voluntary contraction, in both, young endurance runners were significantly stronger, as well as in comparing dominant and nondominant legs separately. Testing aerobic capacity, young endurance runners also proved their VO2max is significantly higher compared to their master counterparts (YER: 64.60 ± 5.51 ml/kg.min-1; MER: 42.88 ± 5.88 ml/kg.min-1, p < .000). The significant difference was also observed in HRmax (YER: 174.75 ± 5.55 bpm, MER: 15.00 ± 14.76 bpm, p ≤ .001) and maximal (YER: 374.50 ± 49.13W, MER: 228.89 ± 33.33W, P < .001) as well as relative power-output (P < .001). The differences were also observed on the muscle morphological and myonuclear domain levels CONCLUSION: Finally, our findings demonstrate that lifelong Master endurance runners display superior aerobic capacity, body composition, and muscle fibre morphology, yet there is still a deterioration due to natural ageing in the parameters compared to young endurance runners. Therefore, regular lifelong endurance activity can offer significant protection against declining physiological health and function as individuals age. The study was funded by the INTERREG V-A Slovakia—Austria (acronym CAA, ITMS2014 + 305041X157) and by the Slovak Research and Development Agency (Grant no. APVV-21-0164)
Read CV Ľudmila OreskáECSS Paris 2023: OP-MH03
INTRODUCTION: Background: An important training goal for older adults (OA) is improving and retaining Functional Movement (FM), defined as fundamental movement patterns that require a balance of mobility and stability (including neuromuscular/motor control) used in the performance of basic locomotor, manipulative, and stabilizing movements [1]. While FM has been positively associated with physical activity and increases in muscular power, it is not clear whether high intensity interval training (HIIT) or moderate intensity continuous training (MICT) is more effective for preserving and promoting FM. Objective: To determine the differences in adaptation to HIIT and MICT on FM, the relationship between FM and neuromuscular mechanical power development, the relative efficiency of HIIT vs MICT, and the association between FM and health related quality of life (HRQoL) in adults aged 50-70 years. METHODS: A mixed sex sample of 31 healthy, physically active participants was recruited for the free-living intervention study. Testing included FM screening (FMS), a graded exercise test (GXT) to 90% of maximum heart rate, and counter movement jump (CMJ) trials recorded with integrated kinematic and kinetic data. The participants were randomized by FMS score into HIIT and MICT intervention groups. Training protocols were executed 3 x week-1 for six weeks with exercise session location, duration, intensity and adherence collected from GPS enabled heart rate monitors. Exercise volume and estimates of energy expenditure were calculated from HR data directly. RESULTS: Both HIIT and MICT groups increased FM scores by 9.3 ± 12.3% and 10.5 ± 12.9% respectively. The effect size of HIIT vs MICT on FM was trivial and insignificant (d = 0.09 95% CI [-0.69, 0.88] p = 0.82). The CMJ outcome measures were net vertical impulse (NVI), maximum knee angle (MKA), knee joint angular velocity (JAV), pelvic vertical displacement (PVD), pelvic vertical velocity (PVV), and jump height performance. Within-group measures that reached statistical significance/near significance were PVD: HIIT d = 0.31, 95% CI [-0.01, 0.66] p = 0.06; MICT d = 0.32, 95% CI [0.08, 0.58] p = 0.01 and Jump Performance: HIIT d= 0.21, 95% CI [0.08, 0.37] p = 0.003; MICT d = 0.25, 95% CI [0.06, 0.47] p = 0.01. All other within and between group measures were trivial to small and did not achieve statistical significance. Training effects of HIIT and MICT on the physical summary and mental summary of the HRQoL scores was trivial to small and did not reach statistical significance. CONCLUSION: Both HIIT and MICT can improve FM and produce small but statistically significant change in neuromuscular mechanical power generation. HIIT can produce equivalent adaptations to MICT in 28.5% less time, 24.7% lower energy expenditure, and 21.2% lower exercise volume. References: 1. Cook G, Burton L, Hoogenboom BJ, Voight M. Functional movement screening: the use of fundamental movements as an assessment of function. Int J Sport Phys Ther. 2014.
Read CV Guy SternECSS Paris 2023: OP-MH03
INTRODUCTION: Age-related performance decline is a well-established phenomenon. However, experienced runners may effectively counteract the impact of aging on their performance. Thus, the objective of this study was to compare runners of different ages, considering their accumulated experience, and examine whether age and years of experience predict their absolute VO2max, weight-adjusted VO2max, and lower limb lean mass-adjusted VO2max differently. METHODS: Seventy-eight runners aged 20 to 70 were assessed (average age 45.35 ± 11.51 years; running experience 11.00 ± 8.95 years; height 1.77 ± 0.05 m; weight 74.84 ± 6.17 kg). Body composition was evaluated using dual-emission X-ray absorptiometry. VO2max was measured as the absolute value (l·min−1) on a treadmill, and then total body mass-adjusted VO2max (ml·kg−1·min−1) and lower limb lean mass-adjusted VO2max (ml·kgLM−1·min−1) were calculated. Hierarchical multiple regression models were fitted to examine the relationships first between age and subsequently adding years of experience to the model. All assumptions for multiple regression analyses were met. RESULTS: For absolute VO2max, the model including only age (β = -0.66; t = -7.77; p < 0.001) was statistically significant [F(1, 76) = 60.41; p < 0.001; R² = 0.44], while adding years of experience (β = -0.01; t = -0.15; p = 0.884) to age (β = -0.66; t = -6.35; p < 0.001) did not improve the model [F(2, 75) = 29.83; p < 0.001; R² = 0.443]. The same pattern was observed for VO2max adjusted for body mass, where the model including only age (β = -0.64; t = -7.21; p < 0.001) was statistically significant [F(1, 76) = 51.93; p < 0.001; R² = 0.406], and adding years (β = -0.01; t = -0.03; p = 0.973) to age (β = -0.63; t = -5.95; p < 0.001) of experience did not enhance the model [F(2, 75) = 25.62; p < 0.001; R² = 0.406]. Similarly, for VO2max adjusted for lean mass of lower limbs, the model including only age (β = -0.45; t = -4.41; p < 0.001) was statistically significant [F(1, 76) = 19.44; p < 0.001; R² = 0.204]. However, adding years of experience (β = 0.04; t = 0.36; p = 0.723) to age (β = -0.48; t = -3.85; p < 0.001) also did not improve the model [F(2, 75) = 9.67; p < 0.001; R² = 0.205]. CONCLUSION: This study revealed a consistent and significant association between age and absolute VO2max, weight-adjusted VO2max, and lower limb lean mass-adjusted VO2max. Surprisingly, the addition of years of running experience does not show a more assertive predictive relationship in the regression model. Despite the cross-sectional designs limitations, these findings offer valuable insights into agings effects on physiological determinants of aerobic performance. They can contribute to developing effective training programs for runners, especially master athletes, a growing category of practitioners in recent decades.
Read CV Aldo SeffrinECSS Paris 2023: OP-MH03