This study aimed to analyze the placebo effect associated with caffeine ingestion on running performance. Methods: Thirteen recreationally trained males in long-distance running (age: 38.5 ± 11.9 years, 11.0 ± 8.8 years of running experience) performed a 6 min time trial test in each experimental condition (caffeine-informed/placebo ingested (placebo) and non-ingested (control)) separated by 7 days. The total distance covered was measured, and partial times of each 400 m were used to analyze the pacing strategy. Heart rate and kinematic variables were recorded for each split. In addition, the rate of perceived exertion and prevalence of caffeine side effects was measured using questionnaires. Results: Placebo ingestion improved running performance in the 6 min time trial test (1668 ± 139 m placebo vs. 1642 ± 158 m control, t = 2.49; p = 0.03; moderate ES = 0.69), while pacing, heart rate, RPE, and kinematic variables were similar between conditions (p > 0.05 in all cases). Minor side effects were reported. Conclusions: Beliefs of caffeine ingestion can improve running performance at speeds around maximal aerobic speed in recreationally trained runners without affecting pacing strategy so this “nutritional” strategy could be useful in competition scenarios. As a practical application, recreationally trained runners could improve ≈5 s in 1500 m or mile competitions.

Objective: This study evaluated the effects of advanced footwear technology (AFT) spikes on running performance measures, spatiotemporal variables, and perceptive parameters on different surfaces (track and grass). Methods: Twenty-seven male trained runners were recruited for this study. In Experiment 1, participants performed 12 × 200 m at a self-perceived 3000 m running pace with a recovery of 5 min. Performance (time in each repetition), spatiotemporal, and perceptive parameters were measured. In Experiment 2, participants performed 8 × 5 min at 4.44 m/s while energy cost of running (W/kg), spatiotemporal, and perceptive parameters were measured. In both experiments the surface was randomized and mirror order between spike conditions (Polyether Block Amide (PEBA) and PEBA + Plate) was used. Results: Experiment 1: Runners were faster on the track (p = 0.002) and using PEBA + Plate spike (p = 0.049). Experiment 2: Running on grass increased energy cost (p = 0.03) and heart rate (p < 0.001) regardless of the spike used, while PEBA + Plate spike reduced respiratory exchange ratio (RER) (p = 0.041). Step frequency was different across surfaces (p < 0.001) and spikes (p = 0.002), with increased performance and comfort perceived with PEBA + Plate spikes (p < 0.001; p = 0.049). Conclusions: Running on the track surface with PEBA + Plate spikes enhanced auto-perceived 3000 m running performance, showed lower RER, and improved auto-perceptive comfort and performance. Running on grass surfaces increased energy cost and heart rate without differences between spike conditions.

Background: Deceptive caffeine ingestion has shown inconsistent effects as an ergogenic aid for short-term exercises. Objective: Therefore, the aim of this study was to evaluate the potential placebo effect of deceptive caffeine ingestion on short-term performance during countermovement and repeated-jump tests, as well as bench press throw and bench press-to-failure assessments, and to document any associated side effects. Methods: A repeated, randomized, and counterbalanced design was implemented to compare the effects of ingesting a placebo claimed to be caffeine with a control condition in which no substance was consumed. Twenty-five physically active young adults (17 men and 8 women) completed a countermovement jump (CMJ) test, a 15 s repeated-jump test, bench press throws at 25%, 50%, and 75% of 1RM, and a bench press-to-failure test at 75% of 1RM and also completed a questionnaire regarding potential side effects. Results: Performance was similar between the placebo and control in the CMJ (38.1 ± 6.8 vs. 37.5 ± 6.8 cm; p = 0.225; d = 0.25) and in the 15 s repeated-jump test (p > 0.05; trivial–small effects). In the bench press throw, propulsive mean velocity did not differ at 25% 1RM (p = 0.296; d = 0.23) or 50% 1RM (p = 0.626; d = 0.10). However, deceptive caffeine ingestion increased propulsive mean velocity at 75% 1RM (0.500 ± 0.131 vs. 0.480 ± 0.131 m/s; p = 0.024; d = 0.48) and increased repetitions to failure at the same load (11.9 ± 3.7 vs. 11.0 ± 3.1; p = 0.047; d = 0.42). Mean velocity during the first 3 repetitions tended to be higher with the placebo (p = 0.064; d = 0.39), while final repetitions were similar (p = 0.469; d = 0.15). The most common side effects were increased activeness (34.8%) and nervousness (17.4%). Conclusions: In summary, deceptive caffeine ingestion had minimal impact on jump and ballistic bench press performance in physically active individuals. However, repetitions to failure were improved by ≈1 repetition (+8.2%). These findings suggest that the placebo effect of caffeine is unlikely to serve as a reliable strategy for enhancing short-term exercise performance.

Purpose : This study aimed to evaluate the reliability and reproducibility of running economy measurements across multiple days and within the same day using 2 models of advanced footwear technology. Methods : Thirteen trained male runners (age: 27.6 [8.2] y; body mass: 64.4 [6.1] kg; height: 1.74 [0.06] m) completed 4 treadmill sessions separated by 7 days. Two advanced footwear technology models were tested in a randomized crossover design. Each session included two 5-minute trials per shoe condition at 13 km·h ⁻¹ . Reliability and reproducibility of running economy measures were evaluated using intraclass correlation coefficients (ICCs), SEM, coefficient of variation, and smallest worthwhile change. Results : Intrasession ICCs and coefficient of variations for oxygen cost and metabolic cost were excellent for both shoe conditions (.917 and .945; .921 and .948; ∼1%). Intersession reliability was good (oxygen cost ICC = .819; metabolic cost ICC = .849), though coefficient of variations increased (∼2%). No significant differences in metabolic variables were observed between footwear models ( P ≥ .05), and high variability was observed between individuals. Day-to-day variability affected oxygen cost and metabolic cost ( P < .05), independent of shoe condition. Conclusion : Running economy measurements are highly reliable within sessions but demonstrate moderate variability across days. Despite mechanical differences in footwear conditions, no differences were observed between the 2 models. These findings highlight the importance of multitrial, multiday, and individual testing when evaluating footwear conditions.

Background: This study evaluated the reliability and reproducibility of biomechanical variables from a foot-mounted inertial measurement unit (IMU; Stryd^®) and a trunk-mounted triaxial accelerometer (TTA; RunEASI^®) during treadmill running. Methods: Thirteen trained male runners (age: 27.6 ± 8.2 years; body mass: 64.4 ± 6.1 kg; height: 174.2 ± 6.4 cm) completed four treadmill sessions separated by seven days, each with four 5-min trials at 13 km·h⁻¹. Reliability and reproducibility were assessed using intraclass correlation coefficients (ICCs), standard error of measurement (SEM), smallest worthwhile change (SWC) and coefficient of variation (CV). Results: Both devices showed excellent intra-session reliability (ICC ≥ 0.90). Inter-session reproducibility was high for the IMU in running power, step frequency, and ground contact time (ICC = 0.906–0.992; CV = 0.31–1.50%); the TTA showed excellent reproducibility for the spatiotemporal variables—step frequency, ground contact time, and flight ratio (ICC = 0.931–0.956), whereas impact-related variables showed only good reproducibility (ICC = 0.823–0.871; CV up to 7.76%). Mean step frequency values were similar between devices, but trial-to-trial agreement was limited (r = 0.447; R² = 0.20; Bland–Altman bias = 0.70 steps·min⁻¹, LoA: −15.08 to 16.47), and ground contact time showed systematic discrepancies, with the TTA underestimating values (bias = 16.86 ms, LoA: −45.64 to 11.92). Conclusions: Both sensors provide consistent measurements within their own systems; however, differences in sensing location produce different absolute values, so they should not be used interchangeably and interpretation must remain device-specific.