Am I Top of the Pops? Does Feedback of Live GPS Between Sets of Hurling-Specific Small-Sided Games Improve Subsequent Running and Physiological Performance?

Abstract

The investigation aimed to determine if live feedback of team- and player-specific global positioning system (GPS) running performance data between bouts of hurling small-sided games (SSGs) altered the physical and physiological responses during subsequent bouts of SSGs during a 6-week hurling pre-season period. Twenty-four (n = 24) hurling players (age 25.5 ± 3.2 years; height 177.9 ± 3.2 cm; body mass 83.5 ± 4.5 kg) received either feedback or no feedback during hurling-specific SSGs across a 6-week pre-season period. Teams were assigned to two specific groups, a) GPS live feedback or b) no GPS live feedback (control) for each session, with feedback provided during the SSG rest interval. Running performance (10-Hz, STATSports, Apex, Northern Ireland), heart rate (Polar T31 coded, Polar Electro, Finland), and rating of perceived exertion (RPE) were measured. Data was analyzed using linear mixed-effect models with the effect size (Cohen’s d) used to determine the size of the effect between feedback and non-feedback conditions. Trivial-o-small differences at all time points were observed in heart rate and RPE measures during SSGs, respectively. Trivial-to-moderate effects were observed between feedback and non-feedback conditions for total distance (p = 0.04; ES = 0.25; small) high-speed running (p = 0.043; ES = 0.59; moderate), maximal speed (p = 0.345; ES = 0.11; trivial) and accelerations (p = 0.03; ES = 0.55; moderate). The current data suggests that coaches and applied practitioners may use live GPS feedback to alter the running and physiological performance within hurling-specific SSGs during a pre-season period.

1. Introduction

Hurling is one of the national sports of Ireland [1]. It is a fast-paced, intermittent team sport played with a stick (camán) and a ball (sliothar). The game requires complex skills (catching, high fielding, evasion, striking on the run) that must be executed under extreme pressure from opposition players and at very high speeds given the rapid movement of the ball, requiring exceptional hand–eye coordination [2,3]. A hurling match is played between two teams consisting of fourteen outfield players, plus a goalkeeper, on a pitch measuring approximately 140 m by 88 m, representing a relative player area of 422 m² [4]. The aim of match-play is to outscore the opposing team by striking the ball over the H-shaped goals (1 point) or into the goals (3 points) [1,2,3,4]. At the elite level, hurling matches are 70 min long plus additional time, while sub-elite matches are 60 min plus additional time [4]. During a match, players cover an average total distance of 7506 ± 1364 m, including 1169 ± 260 m of high-speed running (HSR; ≥17 km·h⁻¹) and 350 ± 93 m of sprinting (≥22 km·h⁻¹) [4]. This intermittent, acyclical pattern of play, with HSR, rapid acceleration and deceleration, changes of direction, jumping, and body contacts, places significant demands on players’ aerobic and anaerobic energy systems [1,2,3,4,5,6,7].

To prepare players for the physiological and physical requirements of hurling, training regimens often include a combination of aerobic and anaerobic conditioning methodologies, with players typically training 2–3 times per week with the addition of athletic development sessions and match-play competition [4]. Within these sessions, the coaches aim to balance training volume, intensity, and density to ensure appropriate balance between stress and recovery to promote physical, physiological, tactical, and technical adaptations across the duration of a season [4]. One training method that is utilized extensively within team sports to prepare athletes for the demands of competitions is small-sided games (SSGs). SSGs allow for the concurrent development of aerobic and anaerobic fitness characteristics [6,7,8,9,10,11,12]. For hurling coaches, the challenge is to improve athletic and physiological performance without compromising the technical and tactical aspects of training; hence, SSGs are a common methodology for the development of athletic and technical capacities within hurling cohorts [4,5,6,7].

SSGs have emerged as an alternative training methodology within team sports like hurling, serving as a major component of the training process [9,10,11,12]. These games provide a multifaceted approach to training, allowing for the simultaneous development of technical, tactical, and physical capacities [12]. During SSG training, players typically complete multiple bouts of high-intensity intermittent exercise, with each bout lasting 2–5 min [5,6,7,12,13,14]. This training approach is often viewed as more time-efficient as it incorporates sport-specific technical and tactical elements while also eliciting physiological adaptations at maximal oxygen uptake (VO_2max) [2]. However, SSGs are susceptible to pacing strategies employed by players, leading to a more variable dose–response compared to traditional training methods [2,14]. One proposed strategy to mitigate the effects of pacing in SSGs is the provision of augmented feedback. Feedback on performance metrics, such as total distance, HSR, maximal speed, and sprint distances, have been shown to promote acute performance enhancements, targeted physiological adaptations, and the mitigation of fatigue effects in team-sport athletes [13,14,15,16]. Moreover, positive coaching encouragement and an external focus on the impact of fatigue can improve perceptions of running performance and intensity [12,17].

The advent of GPS technology has enabled more accurate and “live” monitoring of running performance during training and match-play [18,19,20]. This real-time feedback could be leveraged as a tool to provide athletes with information about their locomotor performance relative to match demands [14]. However, the effects of such augmented feedback have not been investigated within a hurling-specific context. Therefore, the present research aims to examine the impact of augmented feedback on running, physiological, and psychophysiological metrics during a pre-season training period in a sub-elite hurling cohort. This investigation will contribute to the understanding of how targeted feedback can be used to optimize the training process and mitigate the challenges associated with pacing during SSG-based training in team sports. It was hypothesized that the feedback of live GPS will result in an autoregulatory effect on running, physiological and psychophysiological measures of performance within hurling-specific SSGs.

2. Materials and Methods

2.1. Experimental Approach to the Problem

To investigate the impact of GPS feedback on the autoregulation of running and physiological performance measures within hurling-specific SSGs, a reverse counterbalanced experimental design was implemented across a pre-season training period with a cohort of sub-elite hurling players. All participants completed 12 sessions of hurling-specific SSGs over the study period (see Figure 1). Each session consisted of two bouts of SSGs where players were assigned to teams that they remained in for the duration of the observational period. These bouts of SSGs were separated by a 15 min passive-skill station. The SSG bouts comprised 5 × 4 min periods of hurling-specific gameplay (Figure 2). Prior to the study period, players were familiarized with the SSG protocols through several additional training sessions (n = 10) to ensure they understood the aims and objectives. All training sessions were conducted on the same pitch, at the same time of day (6 pm to 8 pm), to minimize the potential confounding effects of circadian variations on the measured variables [21]. Temperatures during training ranged from 3 to 10 °C. Each session began with a standardized 15 min warm-up, including both technical and dynamic movement components. During the rest periods between SSG bouts, players were allowed to hydrate ad libitum, with water and carbohydrate drinks placed centrally within the training area. Following each SSG bout, participants received either feedback on their GPS-derived performance metrics or no feedback (i.e., control) during the designated wash-up period (coach and player review period post completion of SSGs). The feedback condition was implemented in a reverse counterbalanced design, with teams receiving feedback every other session [14]. Crucially, the same opposition teams were used, and the SSG rules remained consistent across all sessions. This experimental approach allowed for the systematic examination of the impact of augmented GPS feedback on the autoregulation of running and physiological performance within a hurling-specific SSG training context. The reverse counterbalanced design helps to minimize potential confounding factors and ensure the internal validity of the findings.

2.2. Participants

Twenty-four (n = 24) hurling players (age 25.5 ± 3.2 years; height 177.9 ± 3.2 cm; body mass 83.5 ± 4.5 kg) took part in this study. Players were part of the same team, a division 1 team with a minimum playing experience of 3 years (range of 3–8 years of playing experience). The players completed SSG-specific training during all pitch-based sessions throughout the investigation period. After ethical approval, participants attended an information event where they were briefed about the purpose, benefits, and procedures of this study. Written informed consent and medical declaration were obtained from participants in line with the Declaration of Helsinki and the procedures set by the local institutions’ research ethics committee (Technological University Dublin, Tallaght).

2.3. Data Collection

Running and physiological performance data was collected during the SSGs using GPS systems (STATSports APEX, Newry, Northern Ireland, UK) and heart-rate monitors (Polar T31 coded, Polar Electro Oy, Kempele, Finland). The GPS devices had the following specifications: 30 × 80 mm, 548 g in mass, equipped with a 10 Hz sampling rate, a 100 Hz gyroscope, a 100 Hz triaxial accelerometer, and a 10 Hz magnetometer [19,20,22]. Prior to each training session, the GPS units were activated 20 min in advance to ensure satellite lock, with the number of connected satellites ranging between 18 and 21 throughout the intervention [19,20,22]. To reduce the effect of inter-unit variability, each player was assigned the same unit for the duration of this research investigation [20,22]. Furthermore, research has demonstrated that 10 Hz GPS devices provide valid and reliable assessments of team-sport movements [19,20,22]. The mean number of connected satellites and horizontal dilution of precision during data collection were 18.4 ± 0.5 and 0.57 ± 0.04, respectively. Any files with data exceeding 10 m·s⁻¹, fewer than 6 satellites, or a horizontal dilution of precision greater than 2 were removed from the analysis; this resulted in 2% of files being removed across the investigation period [18,19]. The raw velocity data from the GPS devices was exported at 0.10 s intervals using proprietary software (STATSports SONRA, STATSports, Firmware: 5.0.12, Newry, UK) and then further analyzed using a customized Microsoft Excel spreadsheet (Microsoft, Redmond, WA, USA). This allowed for the calculation of various running performance metrics, including total distance (m), relative distance (m·min⁻¹), high-speed running distance m; (HSR; ≥5.5 m·s⁻¹), relative high-speed running (m·min⁻¹), sprint distance (m; ≥7 m·s⁻¹), maximal speed (m·s⁻¹), accelerations (n; ≥3 m·s⁻²), and decelerations (n; ≥3 m·s⁻²). Physiological performance during the SSGs was assessed through heart-rate analysis, with heart rate recorded every 5 s using a telemetric device (Polar T31 Coded; Polar Electro Oy, Kempele, Finland). The heart-rate maximum (HRmax) of each player was determined by means of the Yo-Yo intermittent recovery test level 2 (Yo-YoIR2). as completed in previous hurling research on SSGs [2,5,6,7]. The heart rates for each SSG were recorded and expressed as a percentage (%) of individual maximum to provide an indication of the overall intensity of the SSG in relation to the mean and maximum HR obtained in the Yo-YoIR2 (HR_mean and %HR_max). The coefficient of variation in HR responses (%HR_max) during SSGs has been reported as 1.3–4.8% [5,6,7]. Additionally, after each SSG, players were asked to manually report their rate of perceived exertion (RPE) using the Borg CR-10 scale, which has been shown to be a reliable tool for assessing exertion levels in team-sport settings [23].

2.4. Live Data Feedback

Following each SSG bout, the participants received feedback on their running performance metrics during the 2 min wash-up period. This feedback was provided by the sports scientist in a slightly louder-than-conversational volume, with the designated feedback team of players gathered around the sports scientist of the team. Specifically, the players were given verbal feedback on the distance covered (m·min⁻¹), and this represented a percentage of their typical game outputs (% Game Speed) achieved during the preceding 4 min SSG bout at a team (e.g., “the team had an average of 120 m·min⁻¹ for TD and 12 m·min⁻¹ for HSR, this is 110% of total distance game speed and 115% of HSR game speed”) and at an individual level. Typical game outputs were representative of players’ average game outputs in meters per minute (m·min⁻¹) across league and championship competition. The distance metrics were conveyed in descending order for both total distance and HSR (≥5.5 m·s⁻¹). GPS feedback was delivered while the opposing team waited at the opposite end of the pitch and engaged in their own specific wash-up from a technical and tactical angle. The performance data was obtained in real-time using a live GPS antenna (STATSports Sonra, firmware: 4.1.12) positioned 10–15 m behind the SSG playing area, which is within the manufacturer’s recommended range of ~120 m. The validity of live data has been previously shown in the literature [19,22]. The receiver was positioned to face the players throughout the drill, ensuring that they were always 10–80 m from the device. The live data, sampled at 10 Hz, was then transferred to a handheld iPad (Apple iPad Pro; 13 Inch; Cupertino, CA, USA), allowing the sports scientist to effectively communicate the feedback to the players during the wash-up period.

2.5. Hurling-Specific Small-Sided Games

The specific layout and format of the hurling-specific SSGs used in this study is presented in Figure 2. The objective of the SSGs was for each team to maintain possession and score by placing the ball in a designated end-zone area at the opposite end of the pitch. Once a score was achieved, the scoring team retained possession and aimed to work the ball back to the opposite end-zone. Teams were composed of 4 players, with the team selections based on player positions to best replicate the man-marking nature of competitive hurling match-play [1,2,4,5,6,7]. The SSGs were performed in a randomized, continuous manner within each training session to mitigate potential ordering effects on the physical and physiological data collected [12,13,14]. During the SSGs, full competition rules were applied, and players were supervised and motivated by coaches to maintain a high level of running performance [17,18]. Additionally, multiple replacement balls were readily available to promptly replace any balls hit out of play, minimizing disruptions to the flow of the games {5}. The specific data feedback (see Figure 1) was provided to the teams during the rest periods (wash-up period) between the SSG bouts. The current approach allowed for the systematic examination of the impact of augmented feedback on the autoregulation of running and physiological performance within the hurling-specific training context by replicating the key tactical and positional demands of competitive hurling while also controlling potential confounding factors [2].

2.6. Statistical Analysis

Data is presented as mean ± standard deviation (SD). All analyses were conducted using linear mixed-effect models implemented in SPSS (Version 24.0; IBM Corp., New York, NY, USA). A mixed-model approach was selected to account for the repeated-measure design and the non-independence of observations within participants. For comparisons between feedback and no-feedback conditions, feedback condition (feedback vs. no-feedback) was specified as a fixed effect, and participant identity was included as a random effect. Session number (treated as a continuous variable) was included as a fixed effect to account for time-related trends. Random intercepts for participants were included to model within-subject dependence. Random slopes for session number were tested but did not significantly improve model fit and resulted in convergence issues; therefore, the final models retained random intercepts only. Serial dependence across the intervention period was addressed by explicitly modeling session number and accounting for participant-level clustering within the mixed-effect framework. Residual diagnostics were conducted to verify model assumptions, including inspection for potential residual autocorrelation. Effect sizes were calculated using Cohen’s d and interpreted according to conventional thresholds (<0.20 = trivial; 0.20–0.59 = small; 0.60–1.19 = moderate; ≥1.20 = large). Uncertainty was expressed as 95% confidence intervals. The smallest worthwhile change (SWC) was defined as 0.2 of the between-condition effect size. Statistical significance was accepted at p < 0.05.

3. Results

The data for the differences in physical, physiological and psychophysiological outputs within hurling-specific SSGs is shown in

Table 1. Physical, physiological, and psychophysiological performance during hurling-specific SSGs, with data feedback and no data feedback.

	Data Feedback	No Data Feedback	Mean Difference	Effect Size (95% CI)	Inference
External Load Variables
Total Distance (m)	484 ± 158	443 ± 189	41.34	0.25 (0.11–0.54)	Small
Distance (m·min−1)	121 ± 39	111 ± 47	10.18	0.25 (0.08–0.54)	Small
High-Speed Running (m)	60 ± 12	48 ± 18	12.32	0.60 (0.23–0.67)	Moderate
HSR (m·min−1)	15 ± 3	12 ± 5	3.08	0.60 (0.34–0.83)	Moderate
High-Speed Efforts (n)	12 ± 3	10 ± 4	2.15	0.56 (0.23–0.78)	Small
Sprint Distance (m)	15 ± 8	14 ± 7	1.67	0.13 (0.03–0.24)	Trivial
Maximal Speed (m·s−1)	7.6 ± 0.6	7.5 ± 0.6	0.198	0.11 (0.05–0.33)	Trivial
Accelerations (n)	35 ± 10	30 ± 8	5.59	0.55 (0.23–0.78)	Small
Decelerations (n)	48 ± 14	42 ± 12	2.31	0.46 (0.32–0.72)	Small
Internal Load Variables
RPE (AU)	8.5 ± 1.4	8.2 ± 2.3	0.343	−0.15 (−0.21–0.00)	Trivial
HRmean (BPM)	185 ± 23	179 ± 32	4.878	−0.21 (−0.31–0.01)	Small
Exercise Intensity (% HRmax)	94 ± 6	92 ± 4	1.987	−0.39 (−0.43–0.01)	Small

Data is presented as mean ± SD, mean difference between conditions, Cohen’s D effect size with 95% confidence intervals and the inference for the specific size of difference between conditions. RPE = Rate of Perceived Exertion; HR_mean = Mean Heart Rate; % HR_max = Maximal Percentage of Heart-Rate Maximum; HSR = High-Speed Running.

. The within-bout data in each SSG is shown in Figure 3 and differences between conditions are presented in

Table 2. Between bout and condition differences following data feedback or no data feedback during hurling-specific SSGs.

	Bout 2	Bout 3	Bout 4	Bout 5
External Load Variables
Total Distance (m)	0.30 (0.19–0.35); small	0.22 (0.08–0.27); small	0.19 (0.07–0.24); trivial	0.16 (0.11–0.21); trivial
High-Speed Running (m)	0.71 (0.33–0.81); moderate	0.54 (0.13–0.61); moderate	0.66 (0.41–0.77); moderate	0.70 (0.28–0.70); moderate
High-Speed Efforts (n)	0.59 (0.33–0.78); small	0.57 (0.32–0.78); small	0.12 (0.08–0.21); trivial	0.09 (0.01–0.14); trivial
Sprint Distance (m)	0.20 (0.13–0.25); trivial	0.11 (0.02–0.21); trivial	0.09 (−0.17–0.14); trivial	0.19 (0.13–0.25); trivial
Maximal Speed (m·s−1)	0.17 (0.08–0.28); trivial	0.09 (0.03–0.18); trivial	−0.09 (−0.11–0.28); unclear	0.07 (0.08–0.28); trivial
Accelerations (n)	0.55 (0.23–0.78); small	0.15 (0.03–0.28); trivial	0.45 (0.33–0.88); small	0.55 (0.26–0.59); small
Decelerations (n)	0.44 (0.30–0.66); small	0.21 (0.09–0.24); small	−0.18 (−0.29–0.11); unclear	0.51 (0.33–0.59); small
Internal Load Variables
RPE (AU)	−0.15 (−0.21–0.00); trivial	−0.00 (−0.12–0.09); unclear	0.01 (−0.21–0.01); unclear	−0.24 (−0.34–0.01); trivial
HRmean (BPM)	−0.21 (−0.31–0.01); trivial	−0.03 (−0.39–0.02); unclear	0.24 (0.13–0.29); small	0.16 (0.09–0.32); trivial
HRmax (%)	−0.39 (−0.43–0.01); trivial	−0.04 (−0.43–0.03); unclear	0.20 (0.15–0.43); small	0.09 (−0.03–0.29); trivial

Data presented as Cohen’s d within 95%, confidence intervals and the inference associated with the between condition Cohen’s d difference. RPE = Rate of Perceived Exertion; HR_mean = Mean Heart Rate; % HR_max = Maximal Percentage of Heart-Rate Maximum.

. Bout 1 of the SSGs was not included in the analysis within Table 2 as feedback has not been provided. Finally, Figure 4 presents the duration-specific running performance between conditions across running performance within hurling-specific SSGs.

4. Discussion

The aim of the current investigation was to explore the potential impact that feedback of running performance data during the recovery period of hurling-specific SSGs could have on subsequent running, physiological and psychophysiological performance measures. The data showed that feedback of running performance data resulted in trivial-to-small differences at all time points across heart rate and RPE measures during hurling-specific SSGs. Additionally, we observed trivial-to-moderate effects between feedback and non-feedback conditions within SSGs for total distance (ES = 0.25; small), high-speed running (ES = 0.60; moderate), maximal speed (ES = 0.11; trivial) and accelerations (ES = 0.55; small). Overall, the data appears to show that the use of GPS live feedback could potentially improve subsequent running performance and physiological outputs within hurling-based SSGs. However, we observed unclear-to-moderate differences when within-bout analysis was considered across all running, physiological, and psychophysiological responses, with moderate effects shown between feedback and non-feedback conditions within bouts of SSGs for total distance (m·min⁻¹), HSR (m·min⁻¹), RPE (AU), and high-speed efforts (n). The current data suggests that there is potential for live GPS feedback to alter running and psychophysiological performance within SSG bouts. These findings contrast with previous findings within collision-based sports such as rugby union [14]. The current data therefore adds to this growing space by showing that the process of live data feedback during wash-up or recovery phases of SSGs may be utilized by performance staff to autoregulate the running, physiological and psychophysiological performance within stick-and-ball-based running sports such as hurling.

The use of augmented live GPS feedback was shown to provide meaningful increases in locomotor performance within SSGs throughout the investigation period. Most notably, increases in total distance and HSR outputs were observed after augmented feedback. Players were shown to cover more absolute and relative distances when compared to the non-feedback conditions (Table 1). Additionally, this finding was not limited to volume-based measures but also mechanical-based efforts such as accelerations, decelerations and high-speed efforts (Figure 3). Our findings show that the real-time feedback of locomotor performance data during recovery periods can modulate autoregulation within team-sport athletes. This appears to suggest that feedback of data can positively influence player pacing strategies through performance salience and influencing locomotor intensity regulation within subsequent SSG bouts. These changes could be related to increasing task engagement and awareness of previous performances relative to typical-match running percentages. By explicitly contextualizing running outputs as a percentage of typical game values, the feedback may have encouraged players to self-adjust their work rate to meet or exceed expected performance thresholds, thereby reducing conservative pacing strategies often observed in constrained training environments [24]. Within this investigation, the feedback structure included verbal communication, public team comparison, and descending ranking of performance metrics. Therefore, the increases in locomotor output may not be explained solely by pacing. Indeed, social comparison and competitive arousal effects likely contributed, particularly given the public dissemination of players’ performance data during “wash-up periods”. Therefore, the behavioral response may reflect an interaction between informational regulation and psychosocial drivers of effort. From a practical perspective, our findings suggest the need for performance staff to understand in detail the team and positional game demands of players, particularly in terms of meters and efforts per minute, to appropriately contextualize augmented feedback and to deliberately manipulate training intensity beyond match-play demands when required in a safe and periodized manner. When underpinned by accurate game-demand profiling, the strategic use of augmented feedback between SSG bouts may therefore serve as an effective tool to drive locomotor outputs above typical competitive demands, supporting targeted overload and performance adaptation within an ergonomic training model [14,25].

While this data contrasts with the previous findings of Weakley and colleagues [14], the comparison between these two investigations may not be warranted given the difference in sporting dynamics between hurling and rugby union. At a broader level, the findings align with previous SSG literature, showing that the manipulation of specific task constraints can alter the physical and physiological responses of within-bout performance across the concurrent methodology of training [10,11,12]. Indeed, previous literature analyzing SSGs has shown that coach encouragement [17], game format [5,11], player numbers [5,6,7,14], pitch size [5,10,17], goal type [5], knowledge of bout duration [12,17] and rule modifications [5,6] can increase the running intensity of players; this highlights the sensitivity of players’ locomotor performance to external stimuli beyond the structure of the SSG drill itself. Taken together, these findings show that live GPS feedback may function as an additional informational constraint that can be layered into SSG design, one that can increase the external locomotor outputs of players without the requirement to change the dynamics of the game itself. This aligns with strength, conditioning and skill-acquisition literature that argues that augmented information can shape behavior and performance outcomes [13,15,24].

Bout-to-bout analysis showed that the effects of feedback were evident within the early bouts, with some reduction in effects of feedback in later bouts of hurling-specific SSGs. This finding is consistent with previous literature showing that athletes may adjust their effort within SSGs based on the knowledge of previous performances and anticipated task demands within subsequent bouts of SSGs [12,14]. It is possible that the initial feedback of previous running performance data heightened task engagement, competitive intent and performance salience within players, which promoted an elevation in locomotor performance and effort in subsequent bouts of SSGs. However, as the SSG sequence progressed, the influence of feedback may have been attenuated due to accumulated neuromuscular and metabolic fatigue. This, in turn, would have reduced the players’ capacity to sustain the elevated intensities despite continued augmented live GPS feedback between bouts. This may suggest that the efficacy of live GPS feedback is not uniform across a training session and may be subject to diminishing returns [26]. This may suggest that augmented live feedback may be context-dependent and temporally constrained within SSG formats. Accordingly, practitioners should, like feedback within a resistance training construct, consider the strategic timing and frequency of augmented feedback across a season to allow for maximal training effects and quality to be observed [16,24].

When internal loading measures were considered, only trivial-to-small differences were evident in heart rate and RPE between feedback and non-feedback conditions. The disassociation between internal and external loading measures is not unknown within sporting contexts and the findings here further support the need to monitor both internal and external constructs of loading given the observed disassociation. When trying to understand the potential reasons for this within the context of live augmented feedback between SSG bouts, it may reflect players’ ability to redistribute locomotor effort in response to augmented feedback rather than experiencing a true effect from a metabolic or cardiovascular perspective. Similar findings have been shown within previous SSG-based literature [2,8,10,11]. From a mechanistic perspective, the absence of changes in heart rate and RPE may be reflective of the natural intermittent and stochastic nature of SSGs, whereby the locomotor demands from accelerations, decelerations and high-speed efforts contribute disproportionately to external load without a meaningful increase in the internal response to these efforts. Such shorter-duration mechanical efforts can elevate external load measures while remaining insufficiently prolonged to alter the cardiovascular responses of players. This suggests the need to have a holistic approach when monitoring SSG and team-sport training given that the internal load response has been consistently shown to alter cardiovascular fitness and running efficiency within team-sport cohorts [9,26,27].

The novelty of the current investigation’s findings of examining live GPS feedback within a stick-and-ball running-based sport such as hurling through a reverse counterbalanced design represents a potential strength of the current investigation. It must be acknowledged that several specific limitations within the design and structure of this study are present and therefore some of the overall findings must be interpreted with a degree of caution. Firstly, the current study should be seen as a case-study exploration of the impact of real-time feedback on subsequent physical, physiological and psychophysiological performance. As such, the generalizability of the findings may be questioned. Additionally, this study was conducted over a short pre-season window; therefore, the inferences found may not be relevant when considering longer-term adaptations with respect to augmented live GPS feedback across a longer timeframe. Again, a limitation of the current work is the use of a sub-elite cohort for analysis; as such, these results may not be transferable to an elite-level context given the increased aerobic fitness levels and higher training exposure of these cohorts [4]. Furthermore, given the repeated feedback exposure, there may have been expectancy effects and learning effects that could not be accounted for within the study design. The repeated exposure to public performance feedback may have introduced expectancy or demand characteristics that could partially explain the observed increases in locomotor outputs. Future research should aim to expand on these findings within SSGs and aim to understand the long-term training adaptations across a full season that live feedback may produce within hurling cohorts. Additionally, given the pre-season nature of this study, additional focus on understanding if these effects remain true during the in-season period within Gaelic sports athletes is warranted. Furthermore, there may be a requirement to explore different feedback mechanisms such as individual versus team feedback and verbal versus visual feedback mechanisms and their potential impact on performance within subsequent bouts of SSGs. Future investigations should attempt to incorporate blinding procedures where feasible or include psychological profiling measures (e.g., competitiveness, motivation, self-efficacy) to better delineate mechanistic pathways. Therefore, there may also be a requirement to understand the psychological constructs of feedback across player motivation within SSG competitiveness and self-efficacy. It is also important to recognize that an SSG represents a concurrent method of training; therefore, there is a need to elucidate the impact of augmented feedback of live GPS on the technical and tactical outputs of players within future studies. Finally, although a reverse counterbalanced design was employed, players were not blinded to condition allocation.

5. Conclusions

The current study investigated if providing GPS-based feedback to players between recovery periods of hurling-specific SSGs could modulate changes in physical, physiological and psychophysiological performance within a hurling pre-season period. The data showed that the provision of augmented live GPS feedback during the wash-up/recovery periods of hurling-specific SSGs can meaningfully influence subsequent running performance, particularly HSR, and mechanical-based effort measures such as accelerations, decelerations and high-speed efforts; however, this was in the absence of meaningful changes in the internal load of players. Given that augmented feedback is commonly used to enhance performance outcomes within high-performance sports, with this completed within resistance training and pitch-based constructs, the results of the current study suggest that, within hurling cohorts, augmented feedback may serve as a practical tool for practitioners who aim to modulate external training load within SSGs during the pre-season period. It is, however, cautioned that given the attenuation of the effects across bouts of SSGs, the provision of feedback should be used in a periodized manner to maximize the potential performance effects. Practically, coaches and practitioners can use individual- and team-level match data to establish position-specific meters- and effort-per-minute data, and utilize augmented feedback during SSGs to deliberately prescribe, monitor, and overload these match-play requirements within pre-season training to ensure sufficient locomotor overload and preparation for competitive play.