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The study aimed to examine the effects that L-glutamine supplementation has on quadriceps muscle strength and soreness ratings following eccentric exercise. It was hypothesized that glutamine ingestion would quicken the recovery rate of peak force production and decrease muscle soreness ratings over a 72-hr recovery period. Sixteen healthy participants (8♀/8♂; 22 ± 4 years) volunteered in a double-blind, randomized, placebo-controlled crossover study. Supplement conditions consisted of isoenergetic placebo (maltodextrin, 0.6 g·kg^-1·day^-1) and L-glutamine (0.3 g·kg^-1·day^-1 + 0.3 g·kg^-1·day^-1 maltodextrin) ingestion once per day over 72 hr. Knee extensor peak torque at 0°, 30°, and 180° per second and muscle soreness were measured before, immediately following, 24, 48, and 72 hr posteccentric exercise. Eccentric exercise consisted of 8 sets (10 repetitions/set) of unilateral knee extension at 125% maximum concentric force with 2-min rest intervals. L-glutamine resulted in greater relative peak torque at 180°/sec both immediately after (71 ± 8% vs. 66 ± 9%), and 72 hr (91 ± 8% vs. 86 ± 7%) postexercise (all, p < .01). In men, L-glutamine produced greater (p < .01) peak torques at 30°/sec postexercise. Men also produced greater normalized peak torques at 30°/sec (Nm/kg) in the L-glutamine condition than women (all, p < .05). In the entire sample, L-glutamine resulted in lower soreness ratings at 24 (2.8 ± 1.2 vs. 3.4 ± 1.2), 48 (2.6 ± 1.4 vs. 3.9 ± 1.2), and 72 (1.7 ± 1.2 vs. 2.9 ± 1.3) hr postexercise (p < .01). The L-glutamine supplementation resulted in faster recovery of peak torque and diminished muscle soreness following eccentric exercise. The effect of L-glutamine on muscle force recovery may be greater in men than women.

The Physical Activity Vital Sign (PAVS) is a two-question assessment used to estimate habitual moderate to vigorous aerobic physical activity (MVPA). Previous studies have shown active adults cannot estimate the physical activity intensity properly. The initial purpose was to investigate the criterion validity of the PAVS for quantifying habitual MVPA in young adults meeting weekly MVPA guidelines (n = 140; 21 ± 3 years). A previously validated PiezoRx waist-worn accelerometer served as the criterion measure (wear time, 6.7 ± 0.6 days). All participants completed the PAVS once before wearing the PiezoRx. Standardized activity monitor validation procedures were followed. The PAVS (201 ± 142 min/week) underestimated (p < .001) MVPA compared to the PiezoRx (381 ± 155 min/week). To correct for this large error, the sample was divided into calibration model development (n = 70; 21 ± 3 years) and criterion validation (n = 70; 21 ± 3 years) groups. The PAVS score, age, gender, and body mass index outcomes from the development group were used to construct a multiple linear regression model-based calibrated PAVS (cPAVS) equation. In the validation group, the cPAVS was similar (p = .113; 352 ± 23 min/week) compared to accelerometry. Equivalence testing demonstrated the cPAVS, but not the PAVS, was equivalent to the PiezoRx. Despite achieving most statistical criteria, the PAVS and cPAVS still had high degrees of variability, preventing their use on an individual level. Alternative strategies are needed for the PAVS in an active young adult population. These results caution using the PAVS in active young adults and identify a case where obvious variabilities in accuracy conflict with statistically congruent results.

Stepping cadence is an important determinant of activity intensity, with faster stepping associated with the most health benefits. The Stryd monitor provides real-time feedback on stepping cadence. The limited existing literature has neither validated the Stryd across slow walking to fast running speeds nor strictly followed statistical guidelines for monitor validation studies. We assessed the criterion validity of the Stryd monitor to detect stepping cadence across multiple walking and jogging/running speeds. It was hypothesized that the Stryd monitor would be an accurate measure of stepping cadence across all measured speeds. Forty-six participants (23 ± 5 years, 26 females) wore the Stryd monitor on their shoelaces during a 10-stage progressive treadmill walking (Speeds 1–5) and jogging/running (Speeds 6–10) protocol (criterion: manually counted video-recorded cadence; total stages: 438). Standardized guidelines for physical activity monitor statistical analyses were followed. A two-way repeated-measure analysis of variance revealed the Stryd monitor recorded a slightly higher cadence (<1 steps/min difference, all p < .001) at 2 miles/hr (92.1 ± 6.2 steps/min vs. 91.5 ± 6.4 steps/min, p < .001), 2.5 miles/hr (101.3 ± 6.1 steps/min vs. 100.7 ± 6.4 steps/min), and 3.5 miles/hr (117.4 ± 5.9 steps/min vs. 117.0 ± 6.0 steps/min). However, equivalence testing demonstrated high equivalence of the Stryd and manually counted cadence (equivalence zone required: ≤± 2.6%) across all speeds. The Stryd activity monitor is a valid measure of stepping cadence across walking, jogging, and running speeds. By providing real-time cadence feedback, the Stryd monitor has strong potential to help guide the general public monitor their stepping intensity to promote more habitual activity at faster cadences.

Wearable activity monitors provide objective estimates of time in different physical activity intensities. Each continuous stepping period is described by its length and a corresponding single intensity (in metabolic equivalents of task [METs]), creating square wave–shaped signals. We argue that physiological responses do not resemble square waves, with the purpose of this technical report to challenge this idea and use experimental data as a proof of concept and direct potential solutions to better characterize activity intensity. Healthy adults (n = 43, 19♀; 23 ± 5 years) completed 6-min treadmill stages (five walking and five jogging/running) where oxygen consumption (3.5 ml O₂·kg⁻¹·min⁻¹ = 1 MET) was recorded throughout and following the cessation of stepping. The time to steady state was ∼1–1.5 min, and time back to baseline following exercise was ∼1–2 min, with faster stepping stages generally exhibiting longer durations. Instead of square waves, the duration intensity signal reflected a trapezoid shape for each stage. The METs per minute during the rise to steady state (upstroke slopes; average: 1.7–6.3 METs/min for slow walking to running) may be used to better characterize activity intensity for shorter activity bouts where steady state is not achieved (within ∼90 s). While treating each activity bout as a single intensity is a much simpler analytical procedure, characterizing each bout in a continuous manner may better reflect the true physiological responses to movement. The information provided herein may be used to improve the characterization of activity intensity, definition of bout breaks, and act as a starting point for researchers and software developers interested in using wearables to measure activity intensity.