The longitudinal project followed the eight gymnasts on a single high-level team in China through an eight-week pre-competition mesocycle. Their mean age was 19 years, mean training experience was 11.75 years, and mean height and weight were 167.45 centimeters and 49.66 kilograms, respectively. The program called for training on six days each week, with morning and afternoon sessions of approximately four hours on most days. It included flexibility work, apparatus-specific technical preparation, jumps and balance sequences, routine repetitions, twice-weekly strength and conditioning, once-weekly psychological skills training and Sunday recovery. The full available squad was included, so no formal sample-size calculation was performed. All eight athletes completed the monitoring period without missing observations in the variables analyzed, but a single-team cohort cannot represent rhythmic gymnasts generally.

The researchers combined subjective and objective measures rather than relying on a single marker of training stress. Daily rating of perceived exertion, or RPE, was recorded twice on each monitored day, while the Chinese-language Athlete Training State Monitoring Scale assessed eight dimensions: emotional stress, general well-being, fatigue, self-regulation, physical recovery, psychological exhaustion, psychological fatigue and self-efficacy. Heart rate was monitored during Weeks 1, 3, 5, 7 and 8, blood samples were analyzed in Weeks 1 and 8, urine was sampled before morning training and after afternoon training, and body composition was measured after training in Weeks 1, 5 and 8. The design captured repeated responses but did not include a control group, so it could describe changes associated with preparation without establishing their causes.

Perceived exertion changed over the monitored weeks, whereas the eight psychological subscales did not show clear evidence of longitudinal change after statistical correction. Weekly mean values differed across weeks for both the first and second daily RPE ratings, with partial eta-squared values of 0.655 and 0.641, respectively, and nominal p values below 0.001. By contrast, all psychological week-effect p values were at least 0.1211. Heart-rate exposure also varied. Absolute time in the 50%–59%, 60%–69% and 70%–79% zones of age-predicted maximum heart rate changed across weeks, with nominal p values below 0.001, but after exposure was normalized to each athlete’s total valid monitoring time, only the 50%–59% zone retained a clear week effect. Thus, some apparent changes in minutes reflected differences in how much daytime activity was recorded rather than a systematic shift in relative intensity.

Several blood and urine measures changed, but their interpretation was mixed. LDL cholesterol and apolipoprotein B were higher at Week 8, with nominal p values of 0.028 and 0.032, while total cholesterol and lipoprotein(a) were lower, with nominal p values of 0.003 and 0.045. The investigators cautioned that this discordant lipid pattern should not be treated as evidence of a specific training-induced adaptation, particularly given inter-individual variability, biological variation and analytical variation. Mean corpuscular hemoglobin concentration remained below its reference range at both blood assessments, while mean corpuscular volume decreased despite remaining within its conventional reference interval. The study did not measure ferritin, transferrin saturation, dietary intake, endocrine markers or menstrual-cycle status, so it could not determine whether the low concentration reflected iron deficiency or low energy availability.

Urine protein showed evidence of variation across weeks and was higher in post-training samples overall, but the week-by-sampling-condition interaction was not significant. Urobilinogen changed over the study period, and its pre- versus post-training pattern differed by week, while urine pH, specific gravity and leukocytes showed no clear evidence of the tested effects. The pre-training and post-training samples represented the beginning and end of an entire training day, not measurements around one identical session. Hydration, diet, sampling timing and the semi-quantitative nature of some test-strip results could influence the findings, so the results did not establish a change in hydration status. Ketone results were retained for description only because the records could not reliably distinguish an unrecorded result from a confirmed negative.

The study also examined whether changes in irisin were associated with psychological measures. An athlete’s increase in irisin from Week 1 to Week 8 was strongly negatively associated with their mean emotional-stress score across the five monitoring assessments, with Pearson’s r equal to −0.859, a 95% confidence interval of −0.974 to −0.390 and a nominal p value of 0.006. The change was also positively associated with mean psychological exhaustion, although that estimate was less certain, with r equal to 0.704, a confidence interval extending slightly below zero and a nominal p value of 0.051. Correlations with mean RPE, general well-being, fatigue, self-efficacy, self-regulation, physical recovery and psychological fatigue did not provide clear evidence of association. These are exploratory associations among only eight athletes, not evidence that irisin caused any psychological response.

Sensitivity analyses showed why the irisin-stress result requires restraint. Mean emotional stress was positively associated with baseline irisin, although the confidence interval included zero, and negatively associated with Week 8 irisin. Baseline irisin was also inversely associated with the subsequent change, while the two athletes whose irisin decreased had the two highest starting concentrations. Mathematical coupling between baseline and change scores, regression to the mean, measurement variability and small-sample bias could therefore contribute to the pattern. Moreover, the main correlations linked the change in irisin with each athlete’s average psychological score, not with psychological change over time. The results do not demonstrate that irisin buffered emotional stress or that rising irisin reflected better psychological adaptation.

Measurement limitations add another layer of uncertainty. The irisin concentrations were measured with a commercial enzyme-linked immunosorbent assay, and duplicate optical-density readings consistently differed by position, so a technical effect could not be excluded. The lower calibration range also showed discrepancies when reconstructed from the assay equation, and several early samples fell in that range, potentially influencing the estimated size of the Week 1-to-Week 8 increase. Bioelectrical impedance estimates of body fat and visceral fat may likewise be affected by hydration. External workload was documented broadly, but apparatus repetitions, jump counts, landing counts and direct performance outcomes were not systematically recorded. Maximum heart rate was estimated from age rather than measured in a maximal test, adding possible error to zone classification.

Taken together, the pilot study supports the value of examining several domains at once while emphasizing how little can be concluded from eight athletes over one preparation block. Irisin increased markedly, perceived exertion and selected physiological markers varied, and a strong association appeared between irisin change and average emotional stress, but the broader psychological scales did not show clear longitudinal change. The findings are best treated as hypothesis-generating: the team was observational, numerous outcomes were tested without an overall correction for multiplicity, and important influences such as nutrition, sleep, menstrual cycle, hydration and stress outside sport were not controlled. Larger studies with comparison groups, more repeated measurements, quantified workload, direct performance outcomes, nutritional and endocrine assessment and validated irisin methods will be needed to determine whether multidimensional monitoring can support individualized training and recovery decisions in elite rhythmic gymnastics.

Subject of Research: Longitudinal monitoring of physiological, psychological and irisin responses in elite rhythmic gymnasts during pre-competition training

Article Title: Exploratory longitudinal monitoring of physiological, hematological, psychological, and irisin responses during an 8-week pre-competition period in elite rhythmic gymnasts

Article References: Ye, M., Qian, Y., Ren, S., Kéringer, J., Lin, C., Wang, M., & Radák, Z. (2026). Exploratory longitudinal monitoring of physiological, hematological, psychological, and irisin responses during an 8-week pre-competition period in elite rhythmic gymnasts. Frontiers in Sports and Active Living, 8, Article 1925319. https://doi.org/10.3389/fspor.2026.1925319

Image Credits: AI Generated

DOI: 10.3389/fspor.2026.1925319

Keywords: rhythmic gymnastics, elite athletes, irisin, training-load monitoring, psychological stress, pre-competition training, heart-rate zones, blood biomarkers, physiological monitoring, Exploratory, longitudinal, monitoring

The research team, led by Ethan J. Grimmond of the University of New South Wales alongside colleagues from the club and the university’s School of Mathematics and Statistics, set out to answer a question that has long nagged sports scientists: do the neuromuscular assessments practitioners administer every day actually capture changes in muscle function that meaningfully alter subsequent physical output? The countermovement jump has long been the default measure of whole-body neuromuscular fatigue in team sports, but it has a known blind spot. Because jump performance relies on coordinated contribution from many muscle groups, fatigue isolated in one muscle can be masked by compensatory strategies elsewhere. Targeted isometric tests of individual muscle groups, by contrast, can expose fatigue that a global jump test would never detect.

The researchers focused on two assessments with particular relevance to Australian Rules football, a collision-heavy invasion sport with a high prevalence of hamstring and groin injuries. The first, labeled KNEEFLEX, measured isometric knee flexor strength using a NordBord dynamometer, with players in a kneeling plank position at 30 degrees of knee flexion, pulling their heels as hard as possible against the hook apparatus. This position was chosen deliberately because the lengthened hamstring and the hip and knee angles approximate the running gait during the foot-strike phase. The second, HIPADD, assessed long-lever isometric hip adductor strength on a ForceFrame, with players lying supine and squeezing their medial malleoli against force transducers. Before each test, players completed warm-up contractions at roughly 40, 60 and 80 percent of perceived maximum effort, followed by a set of three maximal three-to-five-second contractions, with the highest total force value retained for analysis.

Strength testing took place in the penultimate hour before each of three weekly on-field sessions across the 2023/2024 preseason, generating 746 individual player-session observations across 22 training days. On the training track, physical output was captured using Vector S7 global navigation satellite system devices with integrated triaxial accelerometers, each player wearing the same individually assigned unit throughout to minimize inter-device variability. The satellite units quantified running distances and intensities across absolute and relative velocity bands, while inertial movement analysis algorithms derived change-of-direction effort counts in both the mediolateral and anteroposterior planes at low, medium and high magnitude thresholds. The researchers then built linear mixed models that related each player’s percentage change in strength from his rolling seasonal average to subsequent training outputs, while statistically accounting for session, week, playing position and individual player identity.

The headline result concerned the knee flexors. Changes in pretraining knee flexor strength from baseline were significantly associated with subsequent running intensity measured in meters per minute at velocities exceeding 70 percent of each player’s relative maximum velocity. Notably, this was the only running variable that showed a significant association: neither absolute high-speed running thresholds nor very high-speed or sprint distances, nor acceleration and deceleration counts, tracked with pretraining hamstring strength. The authors had hypothesized that the strongest associations would appear at the very highest intensities, given that hamstring activation increases in proportion to running velocity, but the data told a different story. Average running intensity at velocities above 25.2 kilometers per hour was a mere 0.79 meters per minute, and above 80 percent relative maximum velocity just 0.46 meters per minute, so scores clustered near zero left little variance for fatigue to explain at those extremes.

The second major finding concerned the hip adductors, and it came with a number attached. Declines in pretraining hip adduction strength were significantly associated with subsequent change-of-direction outputs at low and medium magnitude thresholds, but not at high magnitudes. The summated variables told an even stronger story, with the greatest association observed for combined low-to-medium efforts per minute. Translated into practical terms, decreases of 10, 15 and 20 percent in pretraining hip adductor strength corresponded to estimated reductions of roughly 2.49, 3.76 and 5.04 percent in change-of-direction efforts per minute, respectively. Such strength decrements were not rare curiosities: the club recorded drops below minus 15 percent on 38 occasions and below minus 20 percent on 20 occasions across the preseason, representing 5.2 and 2.8 percent of all hip adductor measurements.

The absence of associations at the highest thresholds carried its own important message about measurement technology. The manufacturer’s high-magnitude change-of-direction threshold of greater than 3.5 meters per second registered only 0.39 efforts per minute on average, a scarcity the authors argue likely reflects collision-based positional changes rather than the agile, preplanned and reactive cuts that define football movement. Elite soccer studies using lower high-intensity thresholds have found better agreement with video-coded explosive changes of direction, and tackle quantification research in Australian football shows collision-driven movements occur at far greater magnitudes than agility cuts. If the high threshold primarily captures collisions, then the low and medium bands are where agility genuinely lives, and those were precisely the bands most sensitive to adductor fatigue.

The findings also fuel a broader debate about absolute versus relative speed zones in team sport monitoring. Only the relative running threshold, expressed as a percentage of each player’s own maximum velocity, proved sensitive to neuromuscular state, while fixed absolute thresholds did not. This aligns with a growing body of criticism that arbitrary absolute cutoffs overestimate high-velocity running for faster players and underestimate it for slower ones, potentially misinforming decisions about readiness and load. For practitioners, the implication is that individualized velocity bands, paired with localized strength screening, provide a more physiologically faithful picture of the stress each athlete is actually absorbing.

The authors are careful to frame the study as observational, and they acknowledge its limitations. The data came from a single club during a single preseason, adherence to maximal effort in testing cannot be fully guaranteed despite athlete familiarization, and the use of a rolling seasonal average as the baseline means early-season scores may have been skewed by familiarization effects. Yet the practical implications are tangible. A strength drop exceeding 15 percent could serve as a triage flag, prompting medical staff to review a player before training or match play, and the estimated output reductions allow coaches to anticipate whether planned session demands can be tolerated or should be scaled to facilitate recovery. Rather than replacing the countermovement jump, the knee flexor and hip adductor tests appear to measure largely independent qualities, strengthening the case for a diverse neuromuscular battery. As accumulated neuromuscular fatigue is linked to elevated injury risk and degraded performance, a 90-second squeeze and a heel pull may become indispensable tools for deciding, before the first whistle of the day, exactly how hard a footballer’s body is ready to work.

Subject of Research: Pretraining lower-limb isometric strength changes and subsequent physical training outputs in elite Australian Rules football players

Article Title: Examining the Relationship Between Changes in Lower-Limb Isometric Strength and Subsequent Physical Training Outputs in Elite Australian Rules Football Players

Article References: Grimmond, E. J., Stindl, T. J., Engel, A. C., Maros, T. M., Lehane, S., & Borges, N. R. (2026). Examining the Relationship Between Changes in Lower-Limb Isometric Strength and Subsequent Physical Training Outputs in Elite Australian Rules Football Players. Sports Medicine – Open, 12(1), Article 132. https://doi.org/10.1186/s40798-026-01105-x

Image Credits: AI Generated

DOI: 10.1186/s40798-026-01105-x

Keywords: neuromuscular fatigue, isometric strength, Australian Rules football, player readiness, knee flexor strength, hip adductor strength, change of direction, GNSS tracking, training load monitoring, relative maximum velocity, dynamometry, preseason training