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Despite the athlete monitoring cycle becoming increasingly popular within sport, very little evidence exists with regards to the relationships present between its measures or its relationship with illness incidence in youth athletes. The aim of this thesis was to evaluate the true predictive ability of an integrated athlete monitoring cycle model, incorporating measures of the training dose (training load), training recovery (sleep) and training response (wellness questionnaires (DWB and PRS), countermovement jumps and salivary IgA (s-IgA)), with regards to illness incidence in youth athletes. Study 1 outlined the reliability and usefulness of DWB (poor/marginal), PRS (poor/marginal) and CMJ (good/useful). Despite study 1's findings, study 2 showed that CMJ was not suitable for use as a training response measure in youth athletes. Studies 3 and 4 supported the use of the sleep quality subscale as a training recovery measure rather than within the DWB training response measure (which was reduced to the four item DWBno-sleep). The overall DWBno-sleep score, fatigue, stress and mood were statistically related to the training recovery, whereas only muscle soreness was related to the training dose. Statistically, PRS was related to both the training dose and recovery. Despite the presence of these statistical relationships, only the effect of training load, including match exposure, on PRS was practically interpretable. Unfortunately, technical issues prevented the true predictive ability of an integrated athlete monitoring cycle model with regards to illness incidence being tested. However, study 5 showed that s-IgA measures could not accurately predict illness in youth athletes. Furthermore, analysis of the longitudinal trends of s-IgA, DWBno-sleep and PRS showed that the subjective fatigue/wellness measures were more responsive to qualitative events than objective measures of immune function. Overall, the results of this thesis provide support for the use of the integrated athlete monitoring cycle in youth athletes, particularly when subjective training response measures are included. However, future research needs to consider the true predictive ability of the proposed integrated athlete monitoring cycle model with regards to illness incidence.

In the sport of weightlifting, the ability to produce force known as strength is of great importance. Coaches can implement strategies to help their weightlifters increase their maximal strength levels such as performing full lifts like the snatch and clean & jerk or performing derivatives of those movements such as a clean pull from the knee or a mid-thigh pull. Performing weightlifting derivatives helps strengthen key technical positions in the full lifts, aiding their success. Having muscular weakness in these positions may be detrimental to the success of a lift. Therefore, it is in the best interest of the weightlifting coach to assess maximal strength adaptations of their weightlifters to determine how they are adapting to the training program or to determine if there are any aspects of the training that need to be addressed. A common form of athlete strength monitoring is the isometric mid-thigh pull (IMTP). This test is relatively safe and non-fatiguing and is favored by weightlifting coaches as it mimics the start of the 2nd pull (power position) which is a vital phase in Olympic lifts. However, there are other positions and phases during Olympic lifts, but these are not commonly used as strength assessment tools, nor are they well researched as a paucity of literature exists on isometric force-time characteristics of clean positions other than the IMTP. To address this gap in the literature, the aim of this dissertation was to compare isometric force-time characteristics of weightlifters at three key positions in the clean and weightlifting performances at USAW sanctioned meets. Seventeen male and female weightlifters participated in isometric strength assessments at three different positions. These positions consisted of the IMTP, an isometric pull from the start of the transition (IPST), and an isometric pull from the start position (IPSP). These isometric assessments were performed on force plates in an isometric mid-thigh pull rack. The force-time characteristics obtained from the isometric testing were then compared to individual weightlifting competition performance through a Pearson Correlation Coefficient analysis. The results indicated that statistically significant Very Large and Nearly Perfect correlations exist across all three isometric positions in isometric peak force (IPF), rate of force production (RFD), and impulse (IMP) in the combined group. These results suggest that all three isometric positions can be used to predict weightlifting performance. This study demonstrates the importance of strength characteristics in weightlifters, specifically IPF and RFD. These should be monitored at the three key isometric positions and a strength profile for each individual athlete may be built to better understand the progress a weightlifter makes throughout the training process.

The expanding opportunities to implement sport science frameworks in elite-level basketball environments coincide with the sport’s increasing global prominence. Concomitant to these opportunities is the continual growth of the sport technology market (e.g., wearables, force plates) and computational power (e.g., data management tools, coding capabilities), which yields solutions and challenges for both athletes and practitioners. Due to the rapid influx of new sport technologies in high performance environments, particularly American Collegiate Men’s Basketball, more formal and ecologically valid research on how to effectively utilize data derived from them, particularly over long periods of time (i.e., multiple seasons) is needed. To address these gaps in the research, the primary aim of this line of research was to retrospectively analyze longitudinal athlete monitoring data in NCAA Division-I men’s basketball athletes with the intent of generating practical and actionable insights for coaches to implement into future training plans. A novel component of this research was embedding a sport scientist within NCAA Division-I Men’s basketball for several consecutive years, which ultimately enabled this dissertation and the retrospective analysis of longitudinal data. To achieve the primary aim, there were three sub-studies that each address separate research questions and contribute uniquely to an overall athlete monitoring framework. The first sub-study aimed to simplify retrospective external workload data (from inertial measurement units) obtained during competitions via principal component analysis (PCA) and subsequent logistic regression. The PCA revealed two principal components that explained 81.42% of the total variance in external workload demands, while the multinomial logistic regression was able to accurately predict positional groups (Overall model, p < 0.0001; AUCCenters = 0.93, AUCGuards = 0.88, AUCForwards = 0.80) based on external load variables. Of note, maximal speeds, as well as deceleration and jumping volumes were the most sensitive to positional demands during competitions, indicating that powerful, eccentric neuromuscular capacities are a crucial component to basketball. With that in mind, the second sub-study sought to assess the utility of no arm-swing countermovement (CMJ) and loaded (20 kg) countermovement (LCMJ) jumps for monitoring potential alterations in deceleration capacities in response to the external workloads imposed on the athletes during training and competition. At varying timepoints and strength training periodization blocks across multiple seasons, at least two maximal CMJs and LCMJs were performed on dual force plates that sampled at 1,000 Hz. Linear mixed modeling (LMM) was utilized to predict jump heights while controlling for random effects of individual athletes and fixed effects of loading conditions (LOAD; 2 levels: CMJ, LCMJ), strength blocks (BLOCK; 3 levels: In-Season, Off-Season, Post-Season), interactions between LOAD and BLOCK, and reliable braking strategy metrics from the force plates. The model revealed a significant random effect for athletes (i.e., individualized athlete monitoring is warranted), as well as significant fixed effects for LOAD, Braking Net Impulse, and Avg. Braking Velocity. These findings suggested that LOAD during LCMJs elicited altered neuromuscular deceleration strategies (i.e., lower jump heights with slower and longer albeit more forceful decelerations during LCMJ) and that greater jump heights across the different LOAD and BLOCK conditions were obtained via slightly decreased braking net impulses that were mostly influenced by remarkably faster braking velocities. Lastly, the third sub-study aimed to integrate data from the IMU and force plate technologies leveraged in the first and second studies, respectively. More specifically, the final sub-study sought to establish relationships between the external workload demands of training and competitions across a basketball season and their outcomes on neuromuscular capacities and deceleration strategies. Several LMMs were constructed to predict jump heights and different deceleration strategy metrics from the volume and intensity demands of competitions and practices. Power output (i.e., force plate jump heights) was found to increase with increases in deceleration volumes and acceleration intensities but decrease with increases in deceleration intensities and acceleration volumes. This suggested that while performing considerable volumes of decelerations may help promote maintenance of capacities, it is important for practitioners to keep close watch of intensities of decelerations as to not overly fatigue the athletes such that power output (i.e., jump height) is not adversely affected. These findings elucidate the widespread contributions of high volumes of intense eccentric contractions performed during basketball training and competition, as well as the relevancy for monitoring deceleration strategies to enhance long-term power output development. Moreover, this line of research comprised the addition of a sport scientist to the daily operations of player development in American collegiate men’s basketball, which served to produce a general longitudinal framework for exploring research questions centered around individualized athlete monitoring in ecologically valid environments. The framework comprised three main elements, which include 1) Programmatic Buy-In, 2) Technology Evaluation and Data Management, and 3) Periodized Athlete Training and Recovery, all of which are underpinned by an iterative and routine program evaluation process (Evaluate, Assess, Enhance). Indeed, certain elements of technology evaluations, data management, and periodization planning will inevitably need to be considered simultaneous to establishing programmatic buy-in. Also, in some cases, outcomes and processes stemming from technology evaluation, data management, and periodization may help promote staff engagement. Then, at time points that are applicable to the scenario and environment, the sport scientist must work with other relevant stakeholders to evaluate, assess, and enhance the various subcomponents of the athlete monitoring framework, as well as the entire framework itself. This line of research may serve future sport scientists who aim to effectively immerse themselves into their respective team culture while concurrently striving to integrate various data sources, manage team-specific databases, individualize athlete monitoring, and promote longitudinal development.

Introduction: Elite athletes undergo vigorous training in the effort to initiate physiological responses that over time accumulate to cause positive adaptation, which result in improvements in physical performance. The training stimuli elicit responses from the endocrine and neuromuscular systems that play a part in adaptations and states of recovery. Approaches have been used to monitor the status of the athletes including observing changes in biochemical markers or neuromuscular performance. The preseason is thought to be very strenuous on the players in that they are in the midst of academic stressors as well as the physically taxing nature of the sport in parallel with energy system development and resistance training in preparation to build the resiliency for the duration of the season. Monitoring biochemical markers in conjunction with neuromuscular performance may provide useful information for the evaluation of recovery and strain of the athlete. Coupling the measurements of testosterone (T) and cortisol (C) for hormonal analyses and a sport specific performance test may enhance capacities as a performance practitioner to adjust training sessions in the attempt to enable optimal recovery and positive adaptations. Purpose: The primary purpose of this study was to evaluate changes in biochemical markers, specifically Testosterone and Cortisol, and the Testosterone:Cortisol (T:C) ratio, as well as neuromuscular performance over the course of the preseason. Methods: This retrospective study included 15 male NCAA Division I basketball players that participated in sport specific training sessions in the preseason training phase. Participants provided written informed consent for their data to be included in the analyses, de-identified data was given to the research team by the athlete performance staff for analysis. Participants provided a salivary sample on a day of no activity that served as the baseline assessment sample. Prior to practice, participants provided a salivary sample used to assess T, C, and T:C ratio as well as performed 3 CMJs on a dual cell force platform to evaluate neuromuscular performance. Following practice, participants provided another salivary sample and performed 3 more CMJs. Participants returned at the end of the preseason training phase four weeks later where they repeated the activities prior to practice and at the conclusion of practice. The eTL of each practice was captured through IMU devices and iTL was assessed through HR monitors and RPE scales. Statistical Analyses: Data normality was confirmed through the Shapiro-Wilk test statistic. The data was also tested for sphericity through Mauchly’s test to confirm variance in differences were similar between groups. If the data indicated the assumption of sphericity is violated through Mauchly’s Test of Sphericity, then the Greenhouse-Geisser correction was used to combat the violation. A paired t-test was used to evaluate the within subject differences in eTL parameters as well as iTL parameters across time. A two-way (Week [Week 1, Week 4] × Time [Pre-Practice, Post-Practice]) repeated measures (RM) analysis of variance (ANOVA) was used to examine Week and Time main effects and the interaction between week and time in CMJ variables. Additionally, the two-way RM ANOVA was used the evaluate the salivary endocrine measures. Statistical significance was set at p ≤ 0.05. If a significant interaction was determined, a Bonferroni post hoc analysis was performed to isolate simple main effects. When comparing two groups, Cohen’s d (d) effect sizes were utilized and interpreted as trivial (0 – 0.19), small (0.20 – 0.49), medium (0.50 – 0.79), and large (≥ 0.80). The intra- and inter-assay reliability of the hormone assay duplicates were evaluated using the coefficient of variation (CV%). Results: While not practically significant, the only significant difference seen in eTL was Duration, otherwise there were no significant differences observed in eTL or iTL between weeks. There were significant differences in CMJ variables, including EccMF, ConcPF, and Body Mass. For EccMF and Body Mass, the average of the Pre values from Week 1 and Week 4 were significantly lower than the average of the Post values from Week 1 and Week 4. In addition, ConcPF displayed a significant increase from Pre to Post in Week 1 but not in Week 4. However, when the CMJ performances were made relative to Body Mass, no significant differences were exhibited. No significant differences emerged in comparison across the Baseline Assessment and Pre values to examine the influence of an anticipatory effect. Although, T and C both displayed an increase in concentration of the average of the Pre values from Week 1 and Week 4 to the Post values from Week 1 and Week 4. Furthermore, C presented a significant increase from Pre to Post in Week 1, however, this was not apparent in Week 4. Moreover, the absolute change in C levels from Pre to Post in Week 1 was significantly higher than the absolute change produced in Week 4. The T:C ratio showed a significant increase from Week 1 Post to Week 4 Post. Also, the T:C ratio absolute and relative change experienced from Pre to Post in Week 1 was significantly lower than the absolute and relative change from Pre to Post in Week 4. Conclusion: These findings suggest neuromuscular performance remains unaltered over a single training exposure as well as throughout the preseason training phase in relation to body mass. Additionally, the cortisol response following four weeks of the preseason training phase showed to be blunted with the testosterone response unchanged resulting in a more anabolic state while examining the T:C ratio. Therefore, monitoring of physiological status may be of interest to gain insight of whether or not an individual is approaching concerning levels.

American football is one of the most popular sports in the United States. However, in comparison to other mainstream sports such as soccer and rugby, there is limited literature using scientific principles and theory to examine the most appropriate ways to monitor the sport. This serves as a barrier to American football practitioners in their development and implementation of evidence-based sport preparation programs. Therefore, the primary aim of this line of research (i.e., dissertation) is to illustrate the efficacy of commonly used athlete monitoring tools within the sport of American collegiate football, while proposing a systematic framework to guide the development of an athlete monitoring program. This aim was achieved through a series of studies with the following objectives: 1) to quantify the physical demands of American collegiate football practice by creating physiological movement profiles through the use of integrated microtechnology metrics and heart rate indices, 2) to determine the positional differences in the physical practice demands of American collegiate football athletes, 3) to examine which integrated microtechnology metrics might be used to most efficiently monitor the training load of American collegiate football athletes, 4) to demonstrate the suitability of using the countermovement jump (CMJ) to assess training adaptations in American collegiate football athletes through examining weekly changes in CMJ performance over the course of two 4-week periodized training blocks (8 weeks total), and 5) to examine the effect of acute fatigue on CMJ performance in American football athletes. The first study from this line of research quantified the physical demands of American collegiate football by position groups and found significant differences in both running based and non-running based training load metrics. In addition, the first study utilized a principal component analysis to determine 5 'principal' components that explain approximately 81% of the variance within the data. The second study utilized a univariate analysis and found significant changes in CMJ performance due the effect of time with significant improvements in CMJ 'strategy' variables over the training period. Finally, the third study used effects sizes to illustrate a larger magnitude of change in CMJ 'strategy' variables than CMJ 'output' variables due to effect of acute fatigue. Results from studies 2 and 3 suggest the importance of monitoring CMJ strategy variables when monitoring training adaptations and fatigue in American collegiate football athletes. This line of research provides practitioners with a systematic framework through which they can develop and implement evidence-based sport preparation programs within their own organizational context. In addition, this line of research provides practitioners with recommendations for which metrics to monitor when tracking training load in American collegiate football using integrated microtechnology. Finally, this line of research demonstrates how to assess training adaptations and fatigue using the CMJ within the sport of American collegiate football, while providing an empirical base through which the selection of CMJ variables can take place. Collectively, this line of research uses scientific principles and theory to extend the current literature in American collegiate football, while providing practitioners with a guide to athlete monitoring within the sport.

Interlimb asymmetry refers to the performance of an extremity in relation to the other. This imbalance between limbs has often been considered a risk factor for injury, as well as a dynamic performance inhibitor. However, limited evidence exists on the prevalence of acute development of asymmetry from external load prescription. PURPOSE: The primary purpose of the present study was to evaluate functional performance differences in asymmetry pre-, immediately post-, and 24h post-basketball specific training. The secondary purpose was to evaluate the significance of sex differences in asymmetry pre-, immediately post-, and 24h post-basketball specific training. METHODS: A convenience sample of 20 (males = 12; females = 8) NCAA Division I collegiate basketball players were enrolled in the present study. The 1st visit consisted of a consent and familiarization, the 2nd and 4th consisted of pre-post countermovement jump (CMJ) testing, along with randomized cross-over designed basketball practice load prescription, while the 3rd and 5th visit consisted of solely a warmup and (CMJ) test. The 2nd and 4th visits consisted of a randomized training load, hereafter referred to as the “High” or “Low” conditions. RESULTS: Only Eccentric Mean Force displayed significance (p = 0.004) between training loads, across time points for Change Scores. There were no significant sex differences between condition and time, however small effect sizes were identified for Asymmetry Scores with Concentric Impulse, Concentric Mean Force, Concentric Peak Force, Eccentric Peak Force, Force at Zero Velocity, Force at Peak Power, and Takeoff Peak Force Pre to Post- and Post to 24hPost- for females (d = 0.20-0.49), while males exhibited trivial effects (d < 0.20). Additionally, we saw large effects within males and females in Change Scores (d > 0.80), with Concentric Impulse, Concentric Mean Force, Eccentric Mean Force, Force at Peak Power, and Takeoff Peak Force Pre to Post- in males, while females revealed small (d = 0.20-0.49) to moderate (d = 0.50-0.79) effects. CONCLUSIONS: It appears that Eccentric Mean Force may be a valuable metric to detect acute changes in asymmetry during the CMJ following offseason basketball specific training. Additionally important to note, practitioners may note sex differences in asymmetry change following offseason specific basketball training. These findings may also be exacerbated in different training phases throughout the year, in conjunction with increased intensities and training loads.

INTRODUCTION: Elite athletes undergo aggressive training regimens containing strategically induced fatigue in an attempt to maximize adaptation and performance improvements to meet the individual demands of the various sport. Athlete monitoring strategies are often used to track training loads and subsequent changes in performance. Inertial measurement units are often utilized to quantify the external training loads (eTL), also known as the biomechanical or locomotive stress, during indoor team sport activities, while the countermovement jump (CMJ) is employed to evaluate acute neuromuscular fatigue and performance. Little data exist examining the dose response of eTL and subsequent change in neuromuscular fatigue and performance, especially in basketball players. Additionally, although the CMJ is a commonly used field measure, exploring specific alterations in the CMJ force-time characteristics rather than gross output measures has been proposed to provide more insight with regards to changes in neuromuscular fatigue and performance compared to only considering gross output parameters, however this is yet to be explored among basketball players. PURPOSE: Therefore, the purpose of this dissertation was to examine the acute neuromuscular function and endocrine responses to High versus Low eTL basketball practices in a cohort of collegiate basketball players. A secondary aim sought to examine if neuromuscular alteration were primary central or peripheral in origin in response to a sport-specific basketball training session. METHODS: This dissertation was divided into two parts. Part I included 16 NCAA Division I basketball players (Male = 12; Female = 4) that performed a High and Low eTL practice, in a cross-over study design, where practice intensity measured using IMUs and intensity was characterized by PlayerLoad/minute (PL/min). Prior to each practice, participants provided a salivary sample used to assess testosterone, cortisol, and Testosterone:Cortisol ratio, as well as performed 3 CMJs on a dual cell force platform. At the conclusion of each practice, participants provided another salivary sample and perform 3 more CMJs. Participants returned 24-hours following practice to provide another salivary sample and perform 3 more CMJs. Perceived fatigue and muscle soreness were assessed using subject recovery questionnaires prior to practice and 24-Hours following practice. Part II include 15 NCAA Division I basketball players (Male = 9; Female = 6) underwent neuromuscular performance assessments before, immediately-after, and 24-hours following a team practice. The eTL of each practice was captured using an IMU. Maximal voluntary contraction (MVC) and twitch responses to electrical neuromuscular stimulation were assessed during the isometric knee extensor contraction and at rest to measure central (voluntary activation) and peripheral (twitch torque) fatigue, as well as responses in twitch torque at rest were used examine the prevalence of low frequency fatigue. In addition, participants performed 3 CMJs at each time point to characterize neuromuscular fatigue and performance. STATISTICAL ANALYSIS: Data normality was confirmed using descriptive and graphical information supplemented by the Shapiro-Wilk test statistic. In Part I, a 2-way (Sex [male, female]) × Condition [high load, low load)] repeated measures (RM) analysis of variance (ANOVA), evaluate differences in eTL. Data from the recovery questionnaire exhibited a non-normal distribution, therefore the equivalent nonparametric test was utilized. Friedman's non-parametric test was used to test for significant differences in the median rank scores across the different conditions and time points. A 3-way (Sex [male, female] x Condition [high load, low load) x Time [pre-, immediately post, 24 hours-]) RM ANOVA was used to assess sex, condition, and time main effects, as well as the interaction between Sex, Condition, and Time for each CMJ variable. Additionally, a 2-way (Condition × Time) RM ANOVA was also used to evaluate Sex × Condition, Sex × Time, and Condition × Time interactions, with significant interactions examined using a post-hoc pairwise comparison with a Bonferroni correction to isolate simple effects. In Part II, an independent T-Test was used to evaluate differences in Training Loads during practice between sexes. A 2-way (Sex [male, female] × Time [Pre, 24 hours-post exercise]) was utilized to evaluate difference in Recovery questionnaire parameters, with post-hoc pairwise comparison using Bonferroni corrections used when a significant difference was detected. A 2-way (Sex [male, female] × Time [Pre, immediately post, 24 hours-post exercise]) RM ANOVA was used to examine Sex and Time main effects and the interaction between sex and time for each variable: CMJ variables, MVC, voluntary activation, twitch characteristics and Low frequency fatigue. If a significant Sex × Time interaction was verified, the statistical model was decomposed by examining the simple effects with separate one-way repeated measures ANOVAs with Bonferroni correction factors for each group and time point. For both Part I and Part II, statistical significance was set at p  0.05. When comparing three or more groups, partial eta-squared (ηp2) effect sizes were calculated and interpreted as small (0.0099), medium (0.0588) and large (0.1379). When comparing between two groups, Cohen’s d (d) effect sizes were utilized and interpreted as trivial (0–0.19), small (0.20–0.49), medium (0.50–0.79), and large (≥0.80). RESULTS: In Part I, there were significant differences in eTL during the High compared to the Low condition, including PlayerLoad per Minute and PlayerLoad (p < 0.05), while there were no practical differences in duration. The high condition also exhibited significantly greater iTL response (p < 0.05). However, there were no differences in perceived responses between condition or across time (p < 0.05). No significant differences emerged for any CMJ variable between condition or across time (p < 0.05), however, 6 of 7 CMJ Tradition Variables, 4 of 6 CMJ Concentric Alternative Variables, 3 of 5 CMJ Eccentric Alternative Variables, and 3 of 7 CMJ Phase Duration Alternative Variables did display a small effect (d = 0.20-0.49) during the High condition from Pre to Post-practice, which was none of these effects were observed during the Low condition. Additionally, the majority of these variables that showed an effect following practice during the High condition, revealed trivial to no effect at the 24-hour assessment following practice, signifying a resolved back to baseline. There were significant sex differences in endocrine responses to eTL (p < 0.05). There were no differences in endocrine responses between the high or low eTL conditions (p > 0.0.5) 4) In men, there were significant increases in testosterone from Pre to Post-Practice that returned to baseline at 24-hour following practice (p < 0.05) and cortisol appeared to increase from pre to post practice, but also return to baseline 24-hours following practice. In addition, testosterone:cortisol ratio appeared unaffected by condition and across time (p < 0.05). In testosterone, cortisol and T:C ratio appeared unaffected between conditions and unchanged across time (p < 0.05). In Part II, eTL appeared longer in duration and higher in volume (PlayerLoad), but lower in intensity (Pl/min) compared to those experienced in Part I. There were no significant differences in CMJ variables across time (p < 0.05), with changes exhibiting trivial to no effect. Men experienced a significant decrease in Rate of Torque Development from Pre to Post-practice (p < 0.05), which returned to baseline at 24-hours following practice, while women experienced no changes across time. Low frequency fatigue appeared to emerge immediately following practice but resolved back to baseline at 24H-post practice. There were significant Sex differences in CMJ variables, MVC, Interpolated Twitch Torque, EMG, and twitch characteristics (p > 0.05), but no differences in percent voluntary activation. CONCLUSIONS: These findings suggest High eTL practice evoke a small acute effect on CMJ variables immediately following practice, which resolve to baseline by 24-hours, whereas Low eTL seems to induce no alteration in CMJ performance. In men, practices stimulated acute increase in testosterone and cortisol, while no changes occurred in T:C ratio. Varying eTL did not influence these acute responses in testosterone and cortisol. In women, testosterone, cortisol, and T:C ratio remained unchanged by both High and Low eTLs. Sport-specific practice appears to increase MVC and central components of neuromuscular function (voluntary activation), especially 24-hours following the exposure. Only small signs of mild peripheral fatigue occur following practice, which included low frequency fatigue appear immediately after practice, but resolved by 24-hours following the practice exposure.

Resolving intra-stride accelerations from training and game data routinely collected by athlete tracking devices is rarely attempted, even though these data can provide important insights into the physical condition of athletes. This thesis proposes a new method of extracting stride accelerations from athlete tracking data via a novel analysis tool, describes methods of analysing the results generated by the analysis tool and reports and the influence of instances of missed or modified training and game activity on those results. Accelerometer and GPS Data from twenty-two professional Australian Rules Footballers were examined from competitive games during an Australian Football League season. These data were processed with a novel analysis tool developed specifically for the purpose of identifying instances of high speed running in a straight line during games, extracting step waveforms in three axes from those sections and determining the variability of those waveforms via a within-section and between-section co-efficient of multiple determination (CMD) over the course of the game. The steps taken in the development of the analysis tool are described in the thesis. Numerous approaches to identifying matched sections of high speed running in a straight line were investigated, with the method resulting in the highest number of waveforms while still being mindful of theoretical considerations adopted. Similarly, numerous statistical approaches to identifying step waveform variability were investigated and the methods demonstrating the highest repeatability within the context of the number of waveforms available for analysis were adopted, and methods with a high possibility of providing limited value in an applied setting eliminated. Results exported from the analysis tool were analysed in a number of contexts. Season averages from raw CMD scores were calculated on steps taken on the left and right foot, and the magnitude of the difference between those scores within each subject was estimated through determining the 99% confidence interval for the mean raw CMD on each side and identifying where those confidence intervals for the left and right foot did not overlap. There was one subject whose 99% confidence intervals did not overlap in any analysis condition (within-section and between-section CMD across x, y and z axes), one subject where the 99% confidence intervals did not overlap in four of the six analysis conditions, ten subjects where there was an overlap in between one and three of the analysis conditions, and ten subjects where there were no analysis conditions in which there was an overlap. Raw co-efficient of multiple correlation scores were converted to z-scores within side and axis for each subject, and confidence intervals for z-scores collated by axis (combining steps from all subjects on right and left side) were determined via an empirical bootstrapping procedure. When combined with data on ii instances of missed or modified training in the week preceding or following a game, some significant results were identified. Instances of missed or modified training were divided into five categories; “load”, “groin”, “leg soft tissue”, “leg structural” and “other”. A lower within-section z-score (indicating more step waveform variability) was found when a training was modified due to “load” (p=0.02) and higher between-section z-scores (which indicates less step waveform variability) were found in the week preceding a training modification due to “leg structural” injuries encompassing injuries to a leg not encompassed by soft tissue injuries, such as an ankle ligament sprain (p=0.02). Subjects with no difference between sides in average within-section z-axis raw CMD scores or average between-section x-axis raw CMD scores were unlikely to require training modifications due to “load” (correctly predicted in 82% of cases) and “groin” (correctly predicted in 92% of cases) respectively. These procedures and results can immediately be integrated into athlete monitoring systems, though investigations into combining these procedures with more established parameters may enhance their ability to predict adverse events. In addition, results supported previous research into the association between movement variability and pathology, and further research into the mechanism behind the changes in step waveform variability utilising the procedures outlined in this study will aid in the development and testing of our theoretical hypothesis.

Athlete monitoring using movement sensors can provide important information for coaches and trainers. The main aim of this thesis is to design, implement and test an optical wireless communication link for swimming applications to give real time feedback to the swimmer so that adjustments can be made during swimming. As swimmers are located relatively close to the surface of the water, arm and leg movement can generate a significant number of air bubbles. Total internal reflection from the surface and attenuation due to bubbles is a major problem for communications both below and above the water. For the system the design challenges included: communication type, optical transmitter, bubbles effect, optical receiver, link budget and electronics requirements and optical wavelength. The solution was optical wireless communications in the green spectrum using frequency shift keying (FSK) and on-off keying (OOK). The effect of strong background light was solved using an optical filter on the receiver. This thesis describes a new system to provide a swimmer with real time feedback to adjust stroke rate during swimming. A wrist-mounted accelerometer with a communications link to a receiver located on the goggles allows visual signals to be given to the athlete. As the maximum distance between a swimmer’s wrist and goggles is approximately 1m the communications system must achieve this distance or more. The range varies because of the movement of the swimmer’s arms and head.