The Relationship Between the Somatosensory System and Growth and Maturation in Pathway Triathletes;a Pilot Study

Abstract

Introduction

Growth and maturation during adolescence can affect the somatosensory system, potentially impacting coordination and proprioception. Optimal proprioception is important for performance and preventing lower limb injury. This study investigates the relationship between the somatosensory function, biological maturation and growth tempo in youth pathway triathletes to enable coaches and healthcare professionals to optimise training programs for long term injury prevention and enhance subsequent performance through key stages of development.

Methods

Fifty three pathway triathletes (aged 12-18 years) were assessed over two separate time points during the triathlon season. At each time point data was collected on growth (height and mass) and maturation (bone age and maturity offset), and somatosensory function using the Active movement extent discrimination assessment (AMEDA) assessment.

Results

AMEDA scores improved with increasing maturity offset in both males and females. Females had reduced symmetry in earlier adolescence and a decline in somatosensory function mid-adolescence compared to male triathletes. There was a weak negative association observed between faster growth tempo and lower somatosensory performance.

Conclusion

This study supports the need for youth triathletes training programs to account for athletes sex, stage and tempo of biological maturation. Incorporating lower limb focused proprioception training starting pre PHV may support injury prevention and enhance long term performance outcomes as triathletes progress through the athletic pathway.

Keywords

proprioception injury prevention endurance sport maturity offset youth athletes

Introduction

Triathlon is a multi-discipline sport that involves swimming, cycling, and running performed in succession, with athletes competing across varying distances (Vleck et al., 2014). In short-course triathlon, for youth Australian pathway athletes (ages 12–18) race distances vary from 300–750m for the swim, 10–20 km for the bike, and 2.5–5 km for the run, to the Olympic or standard distances for elite athletes, which include a 1500m swim, 40 km bike, and 10 km run (AusTriathlon, 2024). Short-course triathlons, from youth to elite levels, are typically draft-legal during the cycling leg, meaning athletes can ride closely behind another athlete’s wheel (Cuba-Dorado et al., 2022). Transitions between the swim-to-bike and bike-to-run segments are also important components of the race. Proficiency in these transitions can significantly influence race outcomes, as the drafting strategy and the ability to make the front pack during the cycling leg provide an opportunity for break aways and can have a substantial impact on performance during the subsequent run leg, either through time gaps or energy conservation (Cejuela et al., 2008; Cuba-Dorado et al., 2022; Ofoghi et al., 2016).

Consequently, triathlon requires a high volume of training load across three distinct disciplines, along with specific skills such as race starts, open water swimming, bike handling and efficient transitioning. The development of these skills, as well as the biomechanics involved in each individual sport, enhances physical fitness and technical proficiency (Bentley et al., 2002). However, the intensity and variety of training also increase the risk of health problems for athletes (Bergeron et al., 2015; Soligard et al., 2016). A recent systematic review examining health problems in short-course triathletes found that injury prevalence ranged from 2-15%. The most commonly reported injuries were lower-limb and overuse injuries, with more than 50% of studies indicating that injuries were more likely to occur during training rather than during competition (Guevara et al., 2024). Crunkhorn et al. (2024) also reported ankle (42%), foot (33%) and lower leg injuries (32%) as the most common site of injury in an elite cohort of Australian triathletes.

Youth athletes, still undergoing growth and maturation, may be more vulnerable to these injuries when subjected to the intense physical demands of training and competition in a multi-sport event like triathlon (Bergeron et al., 2024). During competition it has been highlighted that junior elite athletes and those aged 12-19 years, faced a higher risk of injury compared to athletes in all other age groups (Gosling et al., 2010). The growth spurt during adolescence, commonly referred to as peak height velocity (PHV), represents the period of fastest upward growth in stature (Mirwald et al., 2002). This rapid physical development can lead to alterations in the somatosensory system, which in turn may affect coordination, balance and proprioception (Quatman-Yates et al., 2012). Optimal balance control relies on good proprioception and is beneficial for performance in elite sporting environments and in preventing lower limb injuries (J. Han, Anson, et al., 2015). Han, Anson, et al. (2015) examined proprioceptive ability in elite athletes, focusing on the ankle, shoulder, and spine. Their study suggests that proprioceptive accuracy can influence the level of competition an athlete has reached (Han, Anson, et al., 2015). This highlights the potential impact of somatosensory processing, especially in relation to athletic performance.

Despite previous research investigating proprioception, neuromuscular control and sensory processing in elite athletes across sports such as swimming, soccer volleyball and basketball, as well as non -athletic adolescents changes across maturation (Duzgun et al., 2011; Han, Anson, et al., 2015; Seyedi et al., 2023; Yilmaz et al., 2024), there remains a gap in the literature regarding how somatosensory function develops during maturation in pathway youth triathletes aged 12-18 years.

Understanding the relationship between the somatosensory system during growth and maturation in youth triathletes can enable coaches and healthcare professionals to more effectively tailor training programs. This approach could optimise skill development and reduce the risk of fatigue and injury, to support the athlete’s long-term health and subsequent performance during key stages of development. Therefore, the aim of this study was to examine the relationship between somatosensory function, biological maturation, and growth tempo in youth female and male pathway triathletes aged 12-18years.

Methods

Participants and Study Design

Fifty-three state pathway triathletes (30 males, 23 females) volunteered to participate in this study during the 2,023–2,024 triathlon season. These athletes were recruited from state pathway development programs and regularly travelled nationally for both training camps and competitions. Participants were eligible for inclusion if they were aged between 12 and 19 years and were currently enrolled in a state pathway triathlon program. Participants were distributed across three age categories (<14 years, 14–16 years, and 16–19 years), with descriptive age characteristics of mean ± standard deviation (range) presented in Table 1. No additional exclusion criteria were applied beyond inability to complete the testing procedures or absence during the testing period.

Table 1.

Growth, Maturation and AMEDA Score (AUC = Area Under the Curve)

	Female			Male
	<14 years	14-16 years	16-19 years	<14 years	14-16 years	16-19 years
Age (years)	13.1 ± 0.7 (11.8, 13.8)	14.9 ± 0.7 (14.0, 15.9)	17.1 ± 0.9 (16.2, 18.8)	13.1 ± 0.7 (11.9, 13.9)	15.1 ± 0.7 (14.0, 16.0)	16.9 ± 0.7 (16.4, 18.0)
Bone age (years)	14.6 ± 1.0 (13.1, 15.6)	16.7 ± 0.9 (15.6, 18.3)	18.6 ± 1.0 (17.5, 20.3)	13.3 ± 1.2 (11.8, 16.2)	16.6 ± 1.3 (13.4, 18.2)	18.4 ± 0.9 (17.1, 19.5)
Maturity offset (years)	1.4 ± 0.5 (0.6, 2.0)	2.7 ± 0.6 (1.7, 3.6)	4.0 ± 0.5 (3.4, 4.8)	−0.1 ± 0.8 (−1.1, 1.9)	1.8 ± 1.0 (−0.1, 3.4)	3.2 ± 0.9 (2.3, 4.6)
Mass (kg)	49.0 ± 5.5 (42.6, 56.5)	56.4 ± 8.1 (47.5, 73.3)	60.5 ± 5.6 (51.5, 69.2)	45.8 ± 9.2 (35.5, 69.2)	58.9 ± 9.3 (36.4, 72.4)	66.0 ± 7.0 (57.2, 74.3)
Height (cm)	162.3 ± 3.6 (157.9, 167.1)	166.8 ± 5.7 (157.7, 175.3)	167.2 ± 4.9 (160.8, 176.9)	158.9 ± 10.1 (145.0, 183.5)	173.9 ± 8.7 (154.5, 185.3)	178.1 ± 5.4 (170.2, 183.4)
Height tempo (cm/month)	0.7 ± 0.0 (0.7, 0.7)	0.2 ± 0.2 (0.0, 0.6)	0.1 ± 0.1 (0.1, 0.2)	0.4 ± 0 (0.4, 0.4)	0.4 ± 0.4 (0.0, 1.1)	0.3 ± 0.3 (0.0, 0.6)
AMEDA Left AUC	65.9 ± 3.1 (61.0, 70.0)	63.1 ± 8.3 (43.5, 72.0)	66.7 ± 8.4 (48.0, 76.0)	62.8 ± 5.8 (53.5, 69.0)	65.3 ± 7.1 (51.5, 74.0)	68.6 ± 2.6 (65.5, 72.5)
AMEDA right AUC	59.1 ± 3.5 (55.0, 63.5)	62.7 ± 7.6 (50.5, 73.0)	64.4 ± 8.0 (49.0, 74.0)	60.6 ± 4.6 (55.5, 69.0)	64.6 ± 8.3 (50.0, 81.0)	66.7 ± 2.3 (64.0, 70.0)
AMEDA Min AUC	58.8 ± 3.3 (55.0, 63.5)	60.5 ± 9.8 (38.0, 71.0)	63.1 ± 7.9 (48.0, 74.0)	60.4 ± 5.2 (51.5, 68.0)	63.0 ± 7.5 (49.0, 73.0)	65.4 ± 3.5 (60.5, 70.0)
AMEDA Max AUC	66.2 ± 3.3 (61.0, 70.0)	65.4 ± 6.0 (54.0, 73.0)	67.8 ± 8.0 (49.0, 76.0)	62.8 ± 5.3 (56.0, 69.0)	67.0 ± 7.5 (52.5, 81.0)	69.9 ± 2.0 (67.0, 72.5)
AMEDA symmetry (%)	88.1 ± 4.0 (84.0, 95.2)	91.1 ± 9.6 (67.0, 99.0)	93.1 ± 6.7 (81.9, 98.0)	94.1 ± 4.2 (87.9, 99.0)	93.6 ± 4.7 (84.0, 98.6)	93.2 ± 5.1 (85.0, 98.0)

Participant recruitment was based on convenience sampling from the finite population of eligible state pathway triathletes accessible during the study period, with all eligible and willing athletes invited to participate. Given the relatively small and specialised athlete cohort, a formal a priori power calculation was not performed. Instead, Bayesian statistical methods, specifically a Bayesian multivariate beta regression, were employed to model the relationship between maturity offset and AMEDA Max, AMEDA Min, and AMEDA symmetry. This approach is well suited to smaller sample sizes and allows quantification of uncertainty in parameter estimates within this sporting context.

Ethical approval was granted by Human Research Ethics committee and for all athletes under 18 years of age, informed consent was provided from parents or guardians and assent was obtained from the athletes. Athletes aged 18 years, provided their own informed consent. This study employed a prospective observational study design for 10 weeks during the months of September 2023 to January 2024. The reporting of the study’s results was guided by The Strengthening the Reporting of Observational studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies (von Elm et al., 2007).

Data Collection

Growth and Maturation Assessments

Growth and anthropometric measures were collected at two time points: the preseason training camp (Time point 1; September, mid-morning) and mid-season at a race venue (Time point 2; January, mid-morning). The order of assessment at each time point was standardised, with growth-related measures (stature and body mass) completed first, followed by anthropometric measurements required for maturation estimation. All measurements were conducted by the chief investigator (AF), who is certified by the International Society for the Advancement of Kinanthropometry (ISAK) (Silva & Vieira, 2020), ensuring standardised measurement protocols. All measurements were taken twice in succession, with the mean of the two trials used for analysis. This procedure was applied consistently across both time points. Growth tempo was calculated as the average monthly change in stature between Time point 1 and Time point 2 (Table 1).

Body mass (kg) was measured using a calibrated A&D UC-321 series scale and stretch stature (cm) and sitting stature (cm) were measured using a Seca 213 portable stadiometer. Anthropometric variables required for bone age estimation (arm girth, triceps skinfold, humerus diameter, and femur diameter) were collected using Harpenden skinfold callipers and a Holtain bone anthropometer. All instruments were standardised and checked prior to each testing session in accordance with manufacturer guidelines, including zeroing of the scale and callipers, symmetrical calliper arms, and verification of stadiometer using a spirit level for vertical alignment.

Bone age, as an indicator of skeletal maturation, was estimated at both time points using a validated prediction equation incorporating arm girth (cm), triceps skinfold (mm), humerus diameter (cm), and femur diameter (cm), as outlined in the bone age predication model for sports (Cabral et al., 2013).

E s t i m a t e d b o n e a g e = - 11.620 + 7.004 (S t a t u r e) + 1.226 (D s e x) + 0.749 (a g e) - 0.068 (T r) + 0.214 (A C P) - 0.588 (H D) + 0.388 (F D)

Equation (1). Prediction model equation to estimate bone age using measures of arm girth, triceps skinfolds, humerus diameter, femur diameter, age, and sex, as described in Cabral et al., (2013). Male sex: Dsex = 0; female sex: Dsex = 1, Stature = (standing height in m). Age (years), Tr = tricipital skinfold (mm), ACP = arm corrected perimeter (arm perimeter-tricipital skinfold, cm), HD = humeral diameter (cm), FD = femoral diameter (cm).

Somatic maturation was assessed at both time points using the Mirwald et al. (2002) maturity offset method via a validated spreadsheet calculator (Towlson et al., 2021), incorporating chronological age, standing height, sitting height, body mass, and sex to estimate years from peak height velocity (PHV). Data entry and processing were performed by AF (Figure 1).

Figure 1.

Overview of Data collection Timeline and measures. Bone Age (BA). Active movement extent discrimination (AMEDA.)

Test–retest reliability for ISAK-standard anthropometric measures has been shown to be acceptable in trained ISAK-accredited anthropometrists, with technical error of measurement (TEM) values within acceptable limits for stature, skinfolds, and limb breadths (Esparza-Ros et al., 2019).

Somatosensory Assessments

Active movement extent discrimination assessment (AMEDA) was used for assessing the acuity of the sensorimotor system at the lower limb. This is a valid and reliable test for ankle proprioception and previous studies have reported good test-retest reliability in healthy adult and athletic populations for the AMEDA protocol, with an intra-class correlation coefficient (ICC) of 0.80 (Han et al., 2015; Han, Waddington, et al., 2015; McGrath et al., 2025; Witchalls et al., 2014). The AMEDA test evaluates the athlete’s ability to discriminate small variations in the extent of ankle range of movement and the ankle movements are actively controlled by the athlete (J. Han, Anson, et al., 2015). The AMEDA test required the athletes to stand on a platform barefoot, with the right or left foot being tested independently while the non-test foot is resting on a platform of equal height. The athlete was familiarised with the assessment, whereby five angles of ankle joint inversion depth are given in succession, starting with the smallest joint change (position 1) to the largest joint change (position 5). Athletes were allowed three familiarisation movements to each stop before beginning the actual test. Throughout the test, athletes were instructed to maintain a forward gaze and avoid looking at their feet. In the main phase of the test, 50 randomly selected joint positions (ranging from 1 to 5) were delivered, and the athlete was required to invert their ankle by rotating the platform, return to a neutral position, and vocally identify the position they believe was delivered by the AMEDA device. The chief investigator AF recorded the response, and the test continued until all 50 positions were completed (Dowse et al., 2021; J. Han, Anson, et al., 2015; Han et al., 2016). Area Under the Curve (AUC) is a metric derived from the receiver operating characteristic (ROC) curve used to quantify the athletes ability to distinguish between five extents of ankle movement (1 through 5) and has been demonstrated as an effective method for analysing AMEDA responses with differing sensorimotor performance characteristics across life stages (Yang et al., 2019). An AUC of 1.0 indicates perfect discrimination between positions and AUC of 0.5 indicates a performance equivalent to random chance. This score is converted to a percentage by averaging the AUC values across four comparisons (position 1 vs 2, 2 vs 3, 3 vs 4, 4 vs 5) (McGrath et al., 2025).

Statistical Methods

A Bayesian multivariate beta regression was used to model the relationship between maturity offset and AMEDA Max, AMEDA Min and AMEDA Symmetry. For the maturity offset model, an interaction term was included between maturity offset and sex, and a random intercept was included for each participant. To model the relationship between height tempo and any change in AMEDA score (Max, Min, Symmetry) from Time point 1 to Time point 2, a multivariate robust linear model was used. A robust model was chosen to minimise the influence of outliers in the residuals on the parameter estimates. AMEDA score at Time point 1 was included as a covariate. Results are reported as the median (B) and 95% high-density credible intervals [lower, upper] for the posterior distribution of model coefficients, and probability of direction (PD). All analyses were conducted using R (version 4.4.2) in RStudio (version 2,024.12.1 + 563).

Results

Descriptive statistics for all measures can be found in Table 1.

For females, the AMEDA Max (B = 1.5 [−1.0, 3.9], PD = 0.90), AMEDA Min (B = 2.3, [−0.5, 5.1], PD = 0.95) and AMEDA Symmetry (B = 1.4 [−0.8, 4.1], PD = 0.90) scores were higher among participants with a higher maturity offset than a lower maturity offset, (Figures 2 and 3). For males, the AMEDA Max (B = 1.1 [−0.6, 2.9], PD = 0.89) and AMEDA Min (but with less certainty) (B = 0.9 [−1.1, 2.8], PD = 0.79) were higher among participants with a higher maturity offset. There was weak evidence of a negative relationship between maturity offset and AMEDA Symmetry among male participants (B = −0.5 [−1.9, 0.8], PD = 0.76).

Figure 2.

Relationship between Maturity Offset and AMEDA Max and Min. Points show observed values with participant observations joined by a grey line. Coloured lines and ribbons show the median and 95% high-density credible interval for the posterior distribution of the marginal mean

Figure 3.

Relationship between Maturity Offset and AMEDA Symmetry. Points show observed values and lines and ribbons show the median and 95% high-density credible interval for the posterior distribution of the marginal mean

There was weak evidence of a negative relationship between height tempo (cm per month) and change in AMEDA Max (B = −1.5 [−7.8, 4.5], PD = 0.68), AMEDA Min (B = −2.0 [−10.0, 5.6], PD = 0.69), and AMEDA Symmetry (B = −0.6 [−9.7, 7.9], PD = 0.57).

Discussion

The aim of this study was to examine the relationship between somatosensory function in pathway triathletes to support potential tailoring of training prescription and injury prevention programs and performance optimisation. This study’s findings suggest that sex and biological maturity influence somatosensory function, as measured by the AMEDA protocol, in youth pathway triathletes and appropriate methods of training prescription accounting for these factors could be beneficial.

Female AMEDA scores were lower on average compared with males, however they exhibited better somatosensory performance and left-right leg proprioceptive symmetry scores the further they were from PHV (greater maturity offset, Figure 2). Males also demonstrated improvements in performance the further from PHV, but less than females (although starting at higher values) and in some cases a decline in symmetry (Figure 3). These differences in somatosensory function between the sexes during maturation may be due to differences in the timing of adolescent changes in hormones, flexibility and muscle-tendon unit growth during early to mid-adolescence. Females mature earlier than males and also experience increased flexibility compared to males during early adolescence which may affect neuromuscular control earlier and explain the lower AMEDA scores closer to PHV (Patel et al., 2021; Roemmich & Rogol, 1995).

A weak inverse relationship was observed between growth tempo and AMEDA Max scores, with a decrease of 1.5 units for every 1 cm per month increase in height. While the evidence is relatively weak, it suggests that ‘rapid’ linear growth may transiently impact somatosensory performance. Growth rates of >0.6 cm per month have previously been seen in the literature as an increase in injury risk in youth athletes (Kemper et al., 2015; Monasterio et al., 2024; Wik et al., 2020). In youth triathletes, this decline in proprioceptive function, during periods of accelerated growth may play a role in this injury risk. However, larger sample sizes and further longitudinal designs with more data collection points would be needed to further explore the relationship between growth tempo and somatosensory performance and injury risk.

Youth triathletes AMEDA scores were also compared descriptively against normative values (Supplemental file 1) based on chronological age groups. This normative data was derived from a mixed cohort (50% male, 50% female) of youth athletes across mixed sports. The results revealed further distinct patterns between sexes and age groups.

Among the under 14 group, female triathletes had a greater Left and Max AMEDA values compared to normative data but lower symmetry scores. This may be indicative of early unilateral dominance in motor control due to sport demands, although, triathlon is a symmetrical, bilateral sport therefore more likely to reflect asymmetrical neuromuscular development at this stage (Kalata et al., 2020). In contrast, male under 14 triathletes displayed AMEDA scores consistent with normative values or slightly above for Left, Right and Max and also had better symmetry scores than females (94.1% male vs 88.1% female).

In the 14-16 age group, female triathletes showed slightly lower AMEDA values across all measures compared to both normative data and male 14-16 age group. This, as previously discussed, may be due to pubertal hormonal changes and regression of sensorimotor mechanisms during growth in female triathletes at this time, and has been implicated in vulnerability for an increased injury risk (Quatman-Yates et al., 2012). Previous literature has described female susceptibility to injury risk (anterior cruciate ligament (ACL) injuries) due to increased ligament laxity during this period of mid-adolescence (Mancino et al., 2024). In male triathletes 14-16 years, scores were similar to normative data, or slightly above normative data and continued to 16-19 years. The females 16-19 years scores were lower than both the normative values and the male values in all measures of AMEDA, however left right symmetry had improved by this age approaching male and normative data (Table 1, Supplemental file 1).

In summary, both sexes had trends of improving AMEDA scores with maturity, males displayed a higher bilateral symmetry and more consistent neuromuscular control throughout the age groups, and less variability in AMEDA scores in the 16-19 age group than females. Given that ankle proprioception scores have been shown to be a predictor of level of competitive success and lower risk of injury (J Han, Anson, et al., 2015), and female triathletes fell below the normative values, they would likely benefit from targeted proprioception intervention programs to address lower limb asymmetry during early to mid-adolescence (J. Han, Anson, et al., 2015).

Practical Considerations

When designing skill acquisition and strength and conditioning programs for youth triathletes, it is important to consider maturity status, growth tempo and sex, as these factors may influence performance capacity, progression and possibly injury risk. Incorporating proprioceptive training earlier (pre PHV) and continuing through maturation, along with single limb balance exercises, may improve somatosensory symmetry and optimise competitive level attained and performance success (J Han, Anson, et al., 2015). Coaches can adjust training intensity and skill complexity accordingly, accommodating sex and symmetry differences, and that faster growth tempo may put athletes at an increased injury risk or may demonstrate reduced ability in tasks requiring coordination and higher cognitive loads, swim starts, transitions, mounting and dismounting bike, exiting water, pack riding, and turning around cones.

These data also provide a benchmark measure in youth triathlon to identify pathway triathlete’s that may be at increased risk of injury or delayed in proprioceptive development for their specific sport demands. Research suggests that proprioceptive development may be constrained by genetic factors and that there is a biological determined set point, as the years of training in sport do not necessarily correlate with proprioceptive ability (J Han, Anson, et al., 2015; J. Han, Anson, et al., 2015). Thus, enabling training prescription targeting specific neuromotor deficits, muscular imbalances, outside of ‘swim, bike, run’ training may increase the development of enhanced ankle proprioception (Yılmaz et al., 2024).

Limitations

Larger sample sizes and a longer data collection period for the longitudinal study design would allow for greater certainty in understanding the relationships between somatosensory development, growth and maturation in youth triathletes. Pragmatically, biological maturation was estimated using field-based anthropometric prediction equations (e.g., maturity offset and bone age models), which provide indirect estimates of maturation but are subject to inherent prediction error due to reliance on anthropometric variables rather than direct assessment of skeletal maturity (e.g., x-rays). Additionally, while all assessments were conducted using standardised procedures, external factors such as sleep, seasonal training load, nutrition, injury history and participation in other sports were not controlled for and may represent potential confounding variables influencing performance-related outcomes. These factors should be considered in future research to better isolate the relationships between maturation and somatosensory development in youth triathletes.

Conclusion

The present study provides insight into the relationship between somatosensory function during growth and maturation in youth pathway triathletes. Proprioception acuity scores tended to improve as maturity increased in both males and females. Females showed less symmetry in early adolescence, and a drop in somatosensory function mid-adolescence compared with males and youth athlete norms. There was also a weak association found between growth tempo and reduced somatosensory performance. Thus, training programs for youth triathletes should account for sex, maturity status and tempo in their design. Early, pre PHV, and continued ankle proprioceptive training could be beneficial for injury prevention and higher competitive level success as pathway triathletes grow and mature.

Future research incorporating larger cohorts and longer-term follow-up periods spanning pre-PHV, during, and post-PHV development may further clarify the temporal relationship between maturation and somatosensory function. The use of more direct assessments of skeletal maturity (e.g., X-ray), alongside consideration of factors such as training load, injury history, nutrition, sleep, and participation in other sports, may further improve understanding of somatosensory development and assist coaches in optimising proprioceptive skill development, reducing injury risk, and individualising training programs to support long-term athletic performance in youth triathletes.

Supplemental Material

Supplemental Material - The Relationship Between the Somatosensory System and Growth and Maturation in Pathway Triathletes; a Pilot Study

Supplemental Material for The Relationship Between the Somatosensory System and Growth and Maturation in Pathway Triathletes; a Pilot Study by Alison S. Fitch, Jocelyn Mara, Gordon Waddington in Perceptual and Motor Skills.

Footnotes

Acknowledgements

We would like to thank the athletes and their parents/guardians for their participation in the study. Thank you to Coach Education and State Pathway lead Robyn Low-Hart and all AusTri coaches involved for their support throughout the study. We also acknowledge Stephen MacGabhann (NSWIS/UCRISE) for his assistance during data collection.

ORCID iDs

Alison S. Fitch

Gordon Waddington

Ethical Considerations

This study was approved by the University of Canberra Human Research Ethics Committee (Approval #202312112).

Consent to Participate

Informed consent to participate in this study was obtained in writing from all individual participants included in the study.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: AF was supported by a PhD Industry scholarship between New South Wales Institute of Sport and the University of Canberra.

Declaration of Conflicting Interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Professor Gordon Waddington is a founding shareholder in Prism Neuro Pty Ltd.

Data Availability Statement

The data have not been openly shared due to the involvement of under 18 year old participants.*

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Dr Alison Fitch is a Sports and Exercise Physiotherapist and Exercise Scientist with over 25 years of experience across elite sport, clinical practice, academic research, and leadership, including a PhD in Sports and Exercise Science from the University of Canberra. Her research centres on growth and maturation in adolescent athletes and its relationship to injury and illness risk, neuromuscular development, and performance health outcomes. She also has expertise in musculoskeletal rehabilitation, athlete monitoring, and interdisciplinary service delivery across sport, rehabilitation and private practice systems.

Dr Jocelyn Mara (PhD, MDS) is a data scientist with 13 years academic research experience in sport science and health, formerly at the University of Canberra, Australia. Dr Mara is the founder and lead data scientist of Grounded Data Science, a consultancy that supports researchers and organisations with their data science needs.

Professor Gordon Waddington is UC AIS Research Professor of Sport and Exercise Medicine in the UC Research Institute for Sport and Exercise and Professor of Physiotherapy in the Faculty of Health. He gained his PhD from the University of Sydney in 2000. He has published more than 250 research papers in peer reviewed journals and is currentlyConsulting Editor of the Journal of Science and Medicine in Sport and on the review boards of the Australian Journal of Physiotherapy, Experimental Brain Research, British Journal of Sports Medicine, Perceptual and Motor Skills, Physical Therapy in Sport and Physiotherapy and the Grants Review Panel of the Physiotherapy Research Foundation and is a NHMRC Project Grant reviewer. His major research interest is in the assessment of human somatosensory function and its relationship to movement performance and the impact of injury and rehabilitation.

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