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Pacing Behaviour Development and Acquisition: A Systematic Review

Sports Medicine - Open volume 8, Article number: 143 (2022) Cite this article

Abstract

Background

The goal-directed decision-making process of effort distribution (i.e. pacing) allows individuals to efficiently use energy resources as well as to manage the impact of fatigue on performance during exercise. Given the shared characteristics between pacing behaviour and other skilled behaviour, it was hypothesized that pacing behaviour would adhere to the same processes associated with skill acquisition and development.

Methods

PubMed, Web of Science and PsycINFO databases between January 1995 and January 2022 were searched for articles relating to the pacing behaviour of individuals (1) younger than 18 years of age, or (2) repeatedly performing the same exercise task, or (3) with different levels of experience.

Results

The search resulted in 64 articles reporting on the effect of age (n = 33), repeated task exposure (n = 29) or differing levels of experience (n = 13) on pacing behaviour. Empirical evidence identifies the development of pacing behaviour starts during childhood (~ 10 years old) and continues throughout adolescence. This development is characterized by an increasingly better fit to the task demands, encompassing the task characteristics (e.g. duration) and environment factors (e.g. opponents). Gaining task experience leads to an increased capability to attain a predetermined pace and results in pacing behaviour that better fits task demands.

Conclusions

Similar to skilled behaviour, physical maturation and cognitive development likely drive the development of pacing behaviour. Pacing behaviour follows established processes of skill acquisition, as repeated task execution improves the match between stimuli (e.g. task demands and afferent signals) and actions (i.e. continuing, increasing or decreasing the exerted effort) with the resulting exercise task performance. Furthermore, with increased task experience attentional capacity is freed for secondary tasks (e.g. incorporating opponents) and the goal selection is changed from achieving task completion to optimizing task performance. As the development and acquisition of pacing resemble that of other skills, established concepts in the literature (e.g. intervention-induced variability and augmented feedback) could enrich pacing research and be the basis for practical applications in physical education, healthcare, and sports.

Key Points

  • Pacing behaviour develops during childhood and adolescence, as individuals gain the capability to appreciate that the distribution of effort leads to increased exercise task performance.

  • Gaining experience allows an exerciser to refine the match between their performance capabilities and the task demands, resulting in pacing behaviour that better fits the task demands (i.e. task characteristics and environmental factors) facilitating increased exercise task performance.

  • Future research should investigate the exciting idea of applying lessons from the skill acquisition and development literature to aid individuals’ pacing behaviour and as a result enhance their exercise performance.

Background

Humans are unable to sustain high-intensity physical work indefinitely and thus exercise performance has limitations, of which the causes are diverse depending on the specific activity [1]. Sustained physical work over a defined performance duration has been shown to result in either an involuntary decline in motor skill execution or requires an increasing effort to maintain performance level [2]. These phenomena are interlinked with changes in sensations that regulate the physiological integrity of the exerciser, such as localized pain, nausea and heat stress, which collectively represent the concept of fatigue [2]. To deal with these phenomena in a sports setting, the exerciser needs to manage the balance of exertion required to successfully complete the task’s goal, with an optimal distribution of energy resources adapted to the duration of the event [1, 3]. This balances the power losses needed to overcome velocity-related frictional forces, and power production [4] while avoiding premature deterioration of motor skill performance due to overwhelming or catastrophic fatigue [5]. Achieving this balance is of particular relevance in technical sports such as speed skating [6]. In order to perform this feat, exercisers engage in a process of decision-making regarding how and when to exert effort to successfully complete physical tasks [7, 8]. At a fundamental level, the continuous decision to be made by the exerciser is whether to increase, decrease or continue exerting the same level the effort [9]. This decision is influenced by factors such as the exercise task characteristics (e.g. exercise duration [10] and biomechanical traits [6]) and the environment (e.g. presence of opponents [11] or temperature [12]), in combination with afferent signals from the musculoskeletal system [1]. This goal-directed decision-making process regulating the distribution of effort over a predetermined exercise task has been defined as pacing [7, 13]. Given that humans are not entirely rational decision-makers [14], factors like motivation [15], mood [16], and self-efficacy [17] impact the decision-making and the subsequent task performance. It is important to state that the self-regulatory elements of the pacing process are thought to be cyclical; the experience that is gained with each iteration of the task is used to recalibrate the informed decision-making for the next task execution [18].

Pacing fits the description of a skill, as it is task-specific, goal-directed behaviour that is improved with increased training and experience [19, 20]. Skills are often investigated at a behavioural level; the study of skilled behaviour is concerned with quantification of the extent to which a given behaviour achieved the goal that was intended or instructed [19]. When considering pacing in this context, the outcome of the goal-directed decision-making process regarding the distribution of effort could therefore be defined as ‘pacing behaviour’. Quantifying pacing behaviour has generally been achieved by plotting an outcome measure for effort (e.g. power output) over time [21, 22].

The view of pacing as a skill reflects that it goes through development and has to be acquired [23, 24]. Skill development encompasses the effect of age, specifically in maturing children and adolescents, on skilled behaviour [19]. Any real-world exercise task necessarily entails both cognitive and motor components, which undergo drastic development during childhood and adolescence [25]. To illustrate, on average, the adolescent growth spurt starts at approximately 9 years of age in girls and about 11 years of age in boys, with a peak height velocity at an average age of 12 and 14 years old, for girls and boys, respectively [26, 27]. Physical attributes which play a key role during exercise, such as total lung capacity, alveolar surface, stroke volume and cardiac output of the heart, and muscle mass develop accordingly [28, 29]. Additionally, the period between 10 and 24 years old is distinguished by a reorganization of the neural circuitry of the higher brain centres [30, 31]. The higher brain centre to develop most during this period is the prefrontal cortex, the area of the brain associated with abstract thinking, planning and decision-making [31, 32]. Neurological evidence suggests that the prefrontal cortex is essential to pacing as it is said to facilitate the integration of afferent feedback into top-down control of motor unit recruitment [33]. As pacing encompasses a complex psychophysiological process [1, 34], it seems more than likely that it develops throughout childhood and adolescence [1, 18, 35]. Developing the skill to adequately pace an exercise task is crucial in an individual's development as it provides a feeling of competence, motivating children and adolescents to engage more in exercise, with all the associated health benefits in later life [1]. Vice versa, inadequate development of pacing behaviour could negatively impact exercise performance while also affecting individuals’ long term exercise practices, health and well-being [35, 36]. A repeated inaccuracy in the distribution of effort during repeated exercise tasks over a longer period of time could lead to task overexertion, which could result in overtraining, injuries, burn-out and disincentivization to exercise, eventually causing drop-out from exercise and physical activity [1, 36]. At an acute level, a sub-optimal development of pacing behaviour could also yield problems for populations who experience difficulty self-regulating effort [37], such as people with an intellectual impairment [38]. A better understanding of the pathway and underpinning mechanisms of pacing behaviour development would therefore be a valuable tool to aid children’s development [18, 35].

Skill acquisition is known to be the relatively permanent change in behaviour as a result of prior experience [19, 39]. It is thought that skill acquisition goes through phases [19, 25, 40]. Initially, learners focus mainly on associating stimuli and actions in order to achieve the task goal. As acquisition continues, the relationship between variations in behaviour and task performance is used as a recalibration of the skill: good strategies are maintained, and inappropriate ones are discarded. The late stage of skill acquisition is often evidenced by the level of automatization; the learner performs the task using less of their conscious attention, leaving cognitive capacity for the execution of secondary tasks. When categorizing pacing as a skill, it is logical to assume that similar processes underlie the process of learning how to pace an exercise task. This allows for the application of lessons from the skill acquisition literature in the field of pacing. Studying pacing in a skill acquisition framework could therefore not only provide valuable information to the ongoing discussion regarding the debated workings of the pacing process [15, 41] but also provide practical information to coaches and healthcare professionals who aim to correct or fine-tune an individual’s distribution of effort by means of practice, to improve physical activity performance in both sports or healthcare settings [21, 42].

The relation between pacing behaviour and various physiological [1, 13], biomechanical [43], psychological [44] and more recently neurological [45] variables has been extensively studied to gain a deeper understanding of the symbiotic relation between pacing behaviour and exercise task performance. However, the development of pacing behaviour during childhood and adolescence and the acquisition of the skill through experience have received limited attention, despite holding the promise of a wealth of theoretical knowledge and practical applications. This review, therefore, aims to investigate the development of pacing behaviour during childhood and adolescence as well as the acquisition of the skill through experience. To achieve this aim, the existing literature will be systematically analysed for the effect of age (up until 18 years old) and gathering experience on pacing behaviour. Recognizing the similarities between pacing behaviour and skilled behaviour, it is hypothesized that pacing behaviour would adhere to the same characteristics associated with skill acquisition and development. If this is indeed the case, lessons learned for skill acquisition and development could be used to enrich the field of pacing research with future research goals and form practical guidelines to improve exercise performance.

Methods

The current systematic review will be restricted to pacing behaviour in a sports and exercise setting, including only articles investigating a healthy population (for more information on pacing behaviour in a healthcare setting we recommend the review of Abonie et al. [42]). Although the study of pacing behaviour has a valuable role in healthcare and rehabilitation settings such as when reacquiring skills after neurological injury [37, 42], the majority of literature investigating pacing behaviour is set in a sports science setting where competition and maximal effort trials are common. The sports laboratory environment is well suited as a basis for experimental research, as it facilitates a standardized approach to a physical performance task in a controlled setting, measured by validated and accurate equipment [46]. PubMed, Web of Science and PsycINFO databases were searched for literature pertaining to the development and acquisition of pacing behaviour. The following search strategy was used:

  1. (1)

    Sport [Mesh]

    AND

  2. (2)

    Pacing OR Pacing behaviour OR Pacing strategy OR Race analysis

    AND

  3. (3)

    Develop OR Learn OR Experience OR Novice OR Age OR Children OR Adolescence OR Junior OR Youth OR Boy OR Girl.

Included articles had to be written in English, published between January 1995 and January 2022 and peer-reviewed. The option for human participants was selected for PubMed and PsycINFO; in Web of Science, (AND Human*) was added to line 1 of the search strategy. Following the literature on skill learning and development [19, 20], the included articles had to report on one or a combination of the following topics: the pacing behaviour of individuals younger than 18 years of age or the pacing behaviour of individuals repeatedly performing the same (or a very similar) exercise task or the effect of a period of practice on pacing behaviour (e.g. through a training program) or the comparison of pacing behaviour between groups with different levels of experience (i.e. novices vs. experts). To provide an extensive overview of the available literature, no selection was made regarding the type of exercise task (e.g. endurance, team-sport, resistance). Included articles had to quantify pacing behaviour by expressing a measure of effort (e.g. energy store depletion, power output, velocity) over a subset of the full exercise task (e.g. percentage of task completed). The initial search resulted in 505 articles (248 PubMed, 189 Web of Science, 68 PsycINFO). After eliminating duplicates, 447 articles remained. Screening the titles and abstracts, followed by screening the full text, led to exclusion of 388 articles, leaving 59 included articles (Fig. 1). To these included articles, the authors added five articles, which did not occur in the literature search, but instead were found through the reading of the introduction and discussion sections of included articles (specifically marked in Table 1). These five articles met the inclusion criteria and were deemed to yield valuable information regarding the aim of the current study.

Fig. 1
figure 1

Flow diagram of the literature selection process with included articles (n) after each stage

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Table 1 Overview of the included articles (n = 64), categorized by sport and distance
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Results

The articles included in this evaluation comprise a broad selection of exercise tasks and research designs (Table 1). The majority of the articles investigated tasks related to endurance sports, encompassing cycling (n = 19, distance: 1500 m–20 km), running (n = 16, distance: 400 m–42.2 km), rowing (n = 6, distance: 1–2 km), swimming (n = 7, distance: 50–1500 m), speed skating (n = 4, distance: 1500 m) cross-country skiing (n = 3, distance: 4.3–90 km) or another endurance sport (n = 4). Team sports were investigated in two articles. The other studies investigated individuals performing an exercise circuit (n = 2), a repeated jumping (n = 1), sprinting (n = 1) or resistance (n = 1) task. In total, 41 studies investigated pacing behaviour in a controlled laboratory or field setting, whereas 21 articles investigated the pacing behaviour of athletes during competition. Two studies combined both study designs.

The Development of Pacing Behaviour in Individuals Under 18 Years of Age

A total of 33 included articles reported on the pacing behaviour of individuals under the age of 18 years, distributed between the age ranges of 5–10 (n = 1), 10–14 (n = 4) and 14–18 (n = 28) years old (Table 1). Six studies compared the pacing behaviour of children and adolescents of differing ages, four of which used a cross-sectional design [72, 86, 91, 95] and two used a longitudinal design [93, 96].

When cross-sectionally comparing the pacing behaviour of schoolchildren, performing a 3–4 min running task, the groups with an age averaging 5.6 and 8.7 years old exhibited an all-out pacing behaviour, characterized by a fast start and a gradual decline in speed until the end of the task [72]. Conversely, groups of older children (averaging 11.8 and 14.0 years old) exhibited a parabolic distribution of effort, with a fast start and an end-spurt finish but a relatively slower middle section. This parabolic distribution has also been reported by two other articles studying the pacing behaviour of children between 10 and 13 years old, performing similar exercise tasks [73, 74]. Four included studies compared the pacing behaviour of adolescents and adults, performing middle-distance tasks of running [33, 77], swimming [91], and cross-country skiing [97], reporting that adolescents exhibited a parabolic distribution of effort, whereas adults exhibited a more even pace. Indeed, when long-track speed skaters competing in a 1500-m race were longitudinally measured at 15, 17 and 19 years of age, the skaters exhibited a development of pacing behaviour towards that of adult skaters, characterized by a relatively slower start and faster middle section [93].

Parallel to development in the distribution of effort itself, the influence of environmental factors on pacing behaviour seems to develop with age. The presence of other exercisers was reported to have a detrimental effect on exercise performance in younger children (10.3 ± 0.7 years old) [74], no effect in adolescents (15.8 ± 1.0 years old) [49] and resulted in an improvement in performance in adults [50]. An alteration of pacing behaviour was reported to be the cause of the change in exercise performance [50, 74]. Further corroboration was provided by two studies investigating short-track speed skating, a head-to-head type of competition featuring a highly interactive environment [95, 96]. Throughout adolescence, short-track speed skaters seemed to develop both their positioning during the race as well as their capability to preserve energy during the initial phase of the race [95, 96]. These adaptations indicate an improved integration of environmental factors in the athletes’ pacing behaviour and are linked to a higher velocity during the critical final laps resulting in improved race performance [110].

The Effect of Experience on Pacing Behaviour

The effect of prior experience on pacing behaviour has been investigated in thirteen included articles (Table 1) by means of comparing the pacing behaviour of adult exercisers of differing levels of experience performing a predetermined exercise task. More experienced exercisers are not only better able to exercise at a prescribed pace [75], but also exhibit a pacing behaviour more suited to the needs of the specific exercise task. Generally, this is expressed as an all-out behaviour during short tasks [71] (< 120 s), or a more even pacing behaviour during longer exercise tasks [82,83,84] (> 120 s). Experienced individuals are also able to successfully incorporate environmental factors (e.g. terrain) into their pacing behaviour, aiding their performance [99, 102]. Novice exercisers seem to prefer information regarding task completion (distance) and mainly report dissociative, outward monitoring thoughts [57, 58]. Experienced exercisers, on the other hand, prefer information concerning task performance (speed) and primarily report associative, task-focused thoughts (concerning power and cadence) [57, 58].

A total of seven articles reported on the acquisition of pacing behaviour through repeatedly exposing adult novices to the same exercise task. Two of these studies incorporated a training program between repeated bouts of exercise [65, 68]. All seven studies involved exercise tasks with a duration longer than 120 s (minimum: 189.4 s [48], maximum: 2708.35 s [62]), and all reported a change in pacing behaviour with repeated task exposure. Within three studies this was expressed by a decrease in effort during the first section of the task and an increase in the final section [48, 52, 62]. Four studies reported an increase in effort during the initial and middle sections and a decrease during the final section [57, 65, 68, 107]. Two studies reported that the adaptation of power output distribution during consecutive tasks was paralleled by the anaerobic energy expenditure and blood lactate levels, indicating a change in the management of energy reserves over the bout duration [48, 65]. Lastly, the manipulation of the effect of gaining experience on pacing behaviour by interventions such as withholding information on task duration [53], providing only a half familiarization [62], or withholding duration feedback or providing false feedback during the trial (5% improvement compared to actual performance) [61], lead to a maladaptation of pacing behaviour and a decrease in exercise performance.

Discussion

This review provides the first consolidated evidence that pacing behaviour in exercisers up to 18 years old develops with age. This demonstrates that pacing behaviour development starts in childhood and continues through adolescence. All included studies examining the effect of repeated task exposure on pacing behaviour in adults (n = 7) support the hypothesis that pacing behaviour is acquired through the gathering of exercise task experience, similarly to other skilled behaviour. Manipulation of the skill acquisition process by interfering with the gathering of task experience results in a maladaptation of pacing behaviour (n = 3). It is therefore apparent that pacing is similar to other skills, in so far that it has a developmental pathway and appears not to reach maturity until adulthood, by which time there is still capacity to further improve through task experience.

Pacing Behaviour Development

The characteristics of pacing behaviour among young children (< 9 years old) tend to manifest in inconsistent approaches to the task demands, encompassing both the task characteristics (e.g. workload) and the environment (e.g. other competitors). An example is the adoption of an all-out approach of maximal effort until fatigue in an exercise task lasting over 120 s, in which an even distribution is known to lead to better performance [111, 112]. However, with age, a development towards a parabolic distribution of effort is evident in tasks with similar demands. This parabolic pacing behaviour includes a conservation of effort at the start of the exercise, reflecting an increased involvement of goal-directed decision-making regarding effort distribution. The development of pacing behaviour continues during adolescence, with the manifestation of pacing behaviour which increasingly fits the task demands. As part of this development, exercisers are not only able to pace their efforts during an isolated time trial event, but also in complex situations in which environmental factors (e.g. opponents) need to be taken into consideration [95, 96].

Given that pacing behaviour is similar to other skilled behaviour, it is likely that the origin of the development of pacing behaviour can be traced to primary features of childhood and adolescence: physical maturation and cognitive development. Indeed, various physical maturity milestones, such as the growth spurt and the trajectory of muscle mass development [26,27,28,29], seem to match the roadmap of pacing behaviour development, as described above. Unfortunately, only four included studies reported on the pacing behaviour of children within the age-range of the growth spurt (10–14 years old) and none of these articles reported on the relationship between a measure for physical maturation and pacing behaviour [72,73,74, 86]. With regard to cognitive development, Micklewright et al. [72] reported the same development of pacing behaviour could be found based on children’s ages as based on children’s scores for cognition, as measured by Piaget’s stages of intellectual development. It could therefore be proposed that pacing behaviour development is linked not specifically to age, but rather to the rate of cognitive development. Comparing the stages of cognitive development proposed by Piaget to the roadmap of pacing behaviour development during childhood and adolescence strengthens this hypothesis. Piaget’s third stage (i.e. concrete operational stage) spans the ages 7–11. During this stage, children gradually gain the capability to concentrate on more than one aspect of a problem simultaneously and mentally represent actions or events based on previous experience [24]. These mental capabilities could provide children with the aptitude to recall and appreciate that making decisions regarding effort distribution before and during exercise (i.e. a conservation of effort during the opening phase of the exercise), could improve their overall task performance. However, children at this stage are limited to pondering situations that are real or based on their own experiences [24]. This could explain why the presence of opponents has a detrimental effect on the pacing behaviour of children [74], as the presence of opponents likely provides a stronger stimulant than the abstract notions of hypothetical future performance improvement, afforded by adopting a slower pace. Piaget’s fourth stage of cognitive development (i.e. formal operational stage) ranges from 11 to 20 years old [24]. During this stage, individuals gain the capability of considering ideas that are not based on reality, observable objects or experience-based thoughts [24]. Additionally, individuals acquire the aptitude to systematically generate and consider multiple possible solutions to a problem [24]. These mental capabilities provide a better grasp on the hypothetical future rewards from pacing one’s efforts and likely underpin the continuation of the development of pacing behaviour throughout adolescence. Furthermore, these cognitive capabilities facilitate the adaptable pacing behaviour needed in complex competitive environments and therefore enable the integration of environmental factors (e.g. opponents) into the development of pacing behaviour, which occurs during adolescence.

Acquisition of Skill Pacing

The current study is the first to investigate the available literature for the effect of experience on pacing behaviour. From the consolidated literature, it can be concluded that there is an evident effect of gathering experience on pacing behaviour across exercise types and durations. More experienced exercisers are not only better at adopting a prescribed pace but also exhibit a pacing behaviour that better suits the task demands and competitive environment. All included studies featuring repeated tasks revealed that with experience, novice exercisers adapt their pacing behaviour. Although the direction of change seemed to ostensibly differ between studies, collectively all studies reported a change towards a more even distribution of effort as experience increased (Fig. 2). Within skill learning literature the behaviour of novices is characterized by large errors and relatively large corrections for these errors [25]. As the learning process proceeds, individuals will learn to match the task stimuli and their actions with a resulting task performance [19, 25]. Similarly, the proposed explanation for the effect of experience on pacing behaviour is a reduction of the mismatch between the exerciser’s individualized performance capabilities and the task demands, encompassing both the task characteristics (e.g. workload) and the environment (e.g. terrain) [48, 53, 61, 62, 65, 105, 107]. This mismatch results in the exerciser exerting too much effort (i.e. overestimation of the exerciser’s performance capabilities or underestimation of the task demands) or not enough (i.e. underestimation of the exerciser’s performance capabilities or overestimation of the task demands). As the pacing process is continuous, repeating mismatches between stimuli and action can result in an undulating pace over the course of a task. Unnecessary accelerations and decelerations are detrimental to performance, as even minor fluctuations in velocity result in a greater overall energy cost [111]. However, as the task is repeated, exercisers learn to associate the stimuli (e.g. task demands and afferent signals) and actions (i.e. continuing, increasing or decreasing the exerted effort) with the resulting task performance. This knowledge results in more informed decision-making, reducing the occurrence of inefficient adoptions of overly aggressive or conservative pace. Within tasks longer than 120 s, this results in a more even distribution of effort, which is linked to increased task performance [111, 112].

Fig. 2
figure 2

Example of repeated exercise task exposure affecting the pacing behaviour of novice adult exercisers. Grey dotted: exercisers initially exerting not enough effort, grey striped: exercisers initially exerting too much effort, black bold solid: more even pacing behaviour. Arrows: change with increased exercise task experience. The horizontal solid line represents the mean power output/velocity. This example is based on the collective results of included studies that reported on the change in pacing behaviour resulting from repeatedly exposing adult novices to the same exercise task (> 120 s)

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Furthermore, within skill acquisition literature, it is stated that as individuals gain more experience with a task, less attention is needed for the same level of task performance, allowing for secondary tasks to be performed simultaneously [19, 113]. The possession of residual attention capacity could explain why more experienced exercisers are able to process and integrate environmental factors, such as the terrain and the behaviour of opponents. Additionally, the skill acquisition literature states that the main goal for novices is to achieve a crude level of success in a task [19, 40]. Indeed, as novices lack a reference point for the workload required for a specific exercise task, novice exercisers are thought to have the primary goal of finishing the exercise without lasting negative consequences [57, 65]. To reach this goal, novice exercisers mainly acquire information regarding task completion (e.g. elapsed distance or time) [57] and concentrate on dissociating themselves from the afferent signals of fatigue (e.g. pain and discomfort) by means of outward thought [58]. Although this might help novice exercisers complete the exercise task, it also hinders their capability to match their afferent signals to the task demands [114]. Alternatively, experienced exercises have the knowledge that they are able to finish the task (without lasting negative consequences), which allows them to set the goal of realizing the best possible task performance. Experienced exercisers therefore direct their thoughts towards, and gather information about, factors relating to their task performance (e.g. power, cadence and speed) [57, 58].

A point should be made, that as each individual’s performance capabilities differ and the task demands remain constant, optimal pacing behaviour will slightly differ between individuals [115]. This variation between individuals is likely the cause of the variation of pacing behaviour between athletes at the elite level [116, 117]. The acquisition process, as described above, results in a better match of an individual's performance capabilities to the task demands, facilitating a more appropriate pacing behaviour and improving task performance. It is through repeating and optimizing this acquisition processes that the match between individual performance capacities and task demand is perfected, and an individual's pacing behaviour is optimized.

Practical Applications

It is assumed that pacing is evident in almost all non-reflex physical activity and that it is fundamental to the successful completion of physical tasks [1]. Given this, adequate pacing behaviour development could provide a feeling of competency, allowing for more task enjoyment and inhibition of drop-out from physical activity, with all the associated health benefits [1, 118]. Although in the current study, skill development and acquisition have been treated as separate processes, it is evident that gathering experience is a key factor in skill development [24]. From this stems the notion that children and adolescents should be provided with the opportunity to practice exercise tasks to optimally facilitate skill development [24]. Four out of five studies included in this review that investigated children and adolescents during repeated tasks, reported a change in pacing behaviour and/or an improvement exercise task performance [49, 70, 74, 87]. When asked to estimate the completion time of a 2-km cycling time trial before starting the trial, novice adolescent exercisers reported a significant (p < 0.05) difference between the expected (453.00 ± 249.18 s) and actual (240.50 ± 27.37 s) finish time during the first trial [49]. However, the gap between the expected finish time and the actual finishing time decreased by 66.2% after the first trial, indicating a better matching of performance capabilities and task demands, as task experience increased. These findings emphasize the importance of providing children and adolescents with the opportunity to gather exercise experience in order to develop their pacing behaviour over time. However, it is important to acknowledge that even with ample practice, variation in physical maturity and cognitive development will constitute some children to be able to adequately distribute their efforts at a relatively young age, whereas others might not exhibit this behaviour until they are much older. Coaches and parents are therefore advised to monitor the level of pacing behaviour development and gradually incorporate increasingly challenging pacing exercises during the course of childhood and adolescence, in order to support the development of pacing behaviour.

To facilitate and optimize the pacing skill acquisition, lessons from the skill acquisition literature suggest that exercisers should start with establishing a stable behaviour [119, 120]. It is suggested that in novice adults a relatively stable pacing behaviour occurs after three or four sessions of repeated task exposure [62, 65, 121]. However, this number could increase as variation in task demands increases (e.g. a highly interactive competition environment). After establishing a stable pacing behaviour, intervention-induced variability could provide exercisers with opportunities to test variants of their established pacing behaviour, enlarging their familiarity with the association between incoming stimulants, decisions made and the resulting task performance [25, 122]. This could lead to the discovery of a more fitting pacing behaviour for the specific exercise task. In addition, variable practice could lead to a greater generalization and flexibility of the exercisers’ pacing behaviour, allowing them to respond better to novel situations (e.g. the behaviour of competitors) [119]. Lastly, the provision of augmented feedback could also be used to adapt the difficulty of the task in order to provide an adequate challenge and optimize learning [120]. When practising the same task, novice exercisers might be helped by providing frequent and immediate feedback, whereas experienced exercisers might be challenged by the decrease, delay, or removal of feedback.

Future Directions

Although the match between milestones in pacing behaviour development and the changes in physical maturation and cognitive development form a logical framework, more (longitudinal) studies are needed to deepen the knowledge of the link between age, physical maturation, cognitive development and pacing behaviour development. Considering the relevant links between cognitive development and pacing behaviour development, a next step in research could be to dive deeper into which specific sections of cognitive functioning would underlie the development of pacing behaviour. Elferink-Gemser and Hettinga [18] previously proposed a model in which the pacing process mirrors the self-regulatory process, and suggested that improvement in meta-cognitive functions could be underpinning the development of pacing behaviour within childhood and adolescence [18]. Indeed, meta-cognition has been shown to be under development during childhood and adolescence [123, 124], positively related to exercise performance [125, 126] and can be measured by validated instruments [124, 127]. Future experimental research could therefore be done to find whether the development of meta-cognitive functioning indeed is part of the underlying mechanism of pacing behaviour development. It is evident that experience plays a key part in pacing behaviour development. Unfortunately, this relationship is often oversimplified in the literature, as researchers assume a strictly causal relationship between age and experience. By uncoupling experience and age, future research could further unravel the intricate role of experience within pacing behaviour development. Furthermore, it has been proposed previously that acquiring the skill to pace an exercise task is facilitated by the acquisition of other skills, including accurately perceiving time [128], inhibiting distracting stimuli [59], as well as planning and evaluating [18, 129]. Investigating the relationship between these other skills and pacing could provide a better understanding of what it takes to acquire this complex psychophysiological skill. Lastly, in the current review, the acquisition of pacing is most notably analysed by observing the effect of providing or manipulating experience on pacing behaviour. However, within the literature, definitions of skill acquisition commonly include the notion that learning has a relatively permanent effect on behaviour [19, 25]. To test this, experimental designs to test learning include retention tests. Within the current review, no studies were found that measured the retention of the skill after a period without practice. To further explore the effect of experience on pacing behaviour, future research designs should consider including retention tests.

Conclusion

The current review aimed to investigate the development of pacing behaviour during childhood and adolescence as well as the acquisition of the skill through experience. This was achieved by assembling and analysing the (sport) scientific literature discussing the effect of age (up to 18 years old) and gathered experience on pacing behaviour. The findings of this study demonstrated the first consolidated evidence that children display an initial development of decision-making regarding effort distribution from around 10 years old, a development that continues in adolescence. Based on shared milestones, a case can be made that pacing behaviour development is underpinned by an interconnected relation of physical maturation, cognitive development and gathered experience. The skill to adequately pace exercise tasks could provide children with an increased sense of competence and enjoyment in physical activity and exercise, emphasizing the importance of monitoring and practising pacing exercise tasks during childhood and adolescence. Task repetition results in an adaptation of pacing behaviour towards the task demands, including task characteristics (e.g. workload) and the environment (e.g. terrain). These changes can be explained by knowledge from the skill acquisition literature: pacing behaviour is acquired because with experience (1) the match between stimuli, actions and task results improves, (2) attentional capacity is freed for secondary tasks, (3) the task goal switches from task completion to improved task performance. The resemblance between the development and acquisition of pacing to the same processes in other skills invites the practical application of established concepts in skill acquisition and development literature (e.g. intervention-induced variability and augmented feedback) to the field of pacing research. This integration provides the field with exciting future research questions as well as practical applications in physical education, healthcare, and sports.

Availability of Data and Materials

All data generated or analysed during this study are included in this published article.

References

  1. Edwards A, Polman R. Pacing in sport and exercise: a psychophysiological perspective. New York: Nova Science Publishers; 2012.

    Google Scholar 

  2. Enoka RM, Duchateau J. Translating fatigue to human performance. Med Sci Sports Exerc. 2016;48(11):2228.

    Article  Google Scholar 

  3. Noakes TD, Gibson ASC, Lambert EV. From catastrophe to complexity: a novel model of integrative central neural regulation of effort and fatigue during exercise in humans: summary and conclusions. Br J Sports Med. 2005;39(2):120–4.

    Article  CAS  Google Scholar 

  4. Hettinga FJ, De Koning JJ, Schmidt LJ, Wind NA, MacIntosh BR, Foster C. Optimal pacing strategy: from theoretical modelling to reality in 1500-m speed skating. Br J Sports Med. 2011;45(1):30–5.

    Article  CAS  Google Scholar 

  5. Hettinga FJ, De Koning JJ, Hulleman M, Foster C. Relative importance of pacing strategy and mean power output in 1500-m self-paced cycling. Br J Sports Med. 2012;46(1):30–5.

    Article  CAS  Google Scholar 

  6. Stoter IK, MacIntosh BR, Fletcher JR, Pootz S, Zijdewind I, Hettinga FJ. Pacing strategy, muscle fatigue, and technique in 1500-m speed-skating and cycling time trials. Int J Sports Physiol Perform. 2016;11:337–43.

    Article  Google Scholar 

  7. Smits BLM, Pepping G-J, Hettinga FJ. Pacing and decision making in sport and exercise: the roles of perception and action in the regulation of exercise intensity. Sports Med. 2014;44:763–75.

    Article  Google Scholar 

  8. Renfree A, Martin L, Micklewright D, Gibson ASC. Application of decision-making theory to the regulation of muscular work rate during self-paced competitive endurance activity. Sports Med. 2014;44(2):147–58.

    Article  Google Scholar 

  9. Hettinga FJ, Konings MJ, Pepping G-J. The science of racing against opponents: affordance competition and the regulation of exercise intensity in head-to-head competition. Front Physiol. 2017;8:118.

    Article  Google Scholar 

  10. Foster C, Hettinga F, Lampen J, Dodge C, Bobbert M, Porcari JP. Effect of competitive distance on energy expenditure during simulated competition. Int J Sports Med. 2004;25:198–204.

    Article  CAS  Google Scholar 

  11. Konings MJ, Hettinga FJ. Pacing decision making in sport and the effects of interpersonal competition: a critical review. Sports Med. 2018;48(8):1829–43.

    Article  Google Scholar 

  12. Tucker R, Rauch L, Harley YX, Noakes TD. Impaired exercise performance in the heat is associated with an anticipatory reduction in skeletal muscle recruitment. Pflugers Arch. 2004;448(4):422–30.

    Article  CAS  Google Scholar 

  13. Edwards AM, Polman RCJ. Pacing and awareness: brain regulation of physical activity. Sports Med. 2013;43:1057–64.

    Article  CAS  Google Scholar 

  14. Tversky A, Kahneman D. Judgment under uncertainty: Heuristics and biases. Science. 1974;185(4157):1124–31.

    Article  CAS  Google Scholar 

  15. Marcora SM. Do we really need a central governor to explain brain regulation of exercise performance? Eur J Appl Physiol. 2008;104(5):929.

    Article  Google Scholar 

  16. Beedie CJ, Terry PC, Lane AM. The profile of mood states and athletic performance: two meta-analyses. J Appl Sport Psychol. 2000;12(1):49–68.

    Article  Google Scholar 

  17. Moritz SE, Feltz DL, Fahrbach KR, Mack DE. The relation of self-efficacy measures to sport performance: a meta-analytic review. Res Q Exerc Sport. 2000;71(3):280–94.

    Article  CAS  Google Scholar 

  18. Elferink-Gemser MT, Hettinga FJ. Pacing and Self-Regulation: Important Skills for Talent Development in Endurance Sports. Int J Sports Physiol Perform. 2017;12:831–5.

    Article  Google Scholar 

  19. Schmidt RA, Lee TD, Winstein C, Wulf G, Zelaznik HN. Motor control and learning: a behavioral emphasis. Human kinetics; 2018.

  20. McMorris T. Acquisition and performance of sports skills. Hoboken: Wiley; 2014.

    Google Scholar 

  21. Ulmer HV. Concept of an extracellular regulation of muscular metabolic rate during heavy exercise in humans by psychophysiological feedback. Experientia. 1996;52(5):416–20.

    Article  CAS  Google Scholar 

  22. Foster C, Schrager M, Snyder AC, Thompson NN. Pacing strategy and athletic performance. Sports Med. 1994;17(2):77–85.

    Article  CAS  Google Scholar 

  23. Hodges NJ, Williams AM. Skill acquisition in sport: research, theory and practice. 2012.

  24. Payne VG, Isaacs LD. Human motor development: a lifespan approach. London: Routledge; 2017.

    Book  Google Scholar 

  25. Krakauer JW, Hadjiosif AM, Xu J, Wong AL, Haith AM. Motor learning. Compr Physiol. 2019;9(2):613–63.

    Article  Google Scholar 

  26. Buckler JM, Wild J. Longitudinal study of height and weight at adolescence. Arch Dis Child. 1987;62(12):1224.

    Article  CAS  Google Scholar 

  27. Roche AF, Sun SS. Human growth: assessment and interpretation. Cambridge: Cambridge University Press; 2005.

    Google Scholar 

  28. Armstrong N, British Association of S, Exercise Sciences ib. Paediatric exercise physiology. Edinburgh, New York: Churchill Livingstone; 2007.

  29. Malina RM, Bouchard C, Bar-Or O. Growth, maturation, and physical activity. Human kinetics; 2004.

  30. Spreen O, Risser AH, Edgell D. Developmental neuropsychology. Oxford: Oxford University Press; 1995.

    Google Scholar 

  31. Arain M, Haque M, Johal L, Mathur P, Nel W, Rais A, et al. Maturation of the adolescent brain. Neuropsychiatr Dis Treat. 2013;9:449.

    Google Scholar 

  32. Casey B, Jones RM, Hare TA. The adolescent brain. Ann N Y Acad Sci. 2008;1124(1):111–26.

    Article  CAS  Google Scholar 

  33. Blasco-Lafarga C, Montoya-Vieco A, Martínez-Navarro I, Mateo-March M, Gallach JE. Six hundred meter-run and broken 800’s contribution to pacing improvement in eight hundred meter–athletics: role of expertise and training implications. J Strength Cond Res. 2013;27(9):2405–13.

    Article  Google Scholar 

  34. Renfree A, Casado A. Athletic races represent complex systems, and pacing behavior should be viewed as an emergent phenomenon. Front Physiol. 2018;9:1432.

    Article  Google Scholar 

  35. Menting SGP, Hendry DT, Schiphof-Godart L, Elferink-Gemser MT, Hettinga FJ. Optimal development of youth athletes toward elite athletic performance: how to coach their motivation, plan exercise training, and pace the race. Front Sports Active Living. 2019;1:14.

    Article  Google Scholar 

  36. Schiphof-Godart L, Hettinga FJ. Passion and pacing in endurance performance. Front Physiol. 2017;8:83.

    Article  Google Scholar 

  37. Thiel C, Pfeifer K, Sudeck G. Pacing and perceived exertion in endurance performance in exercise therapy and health sports. German J Exerc Sport Res. 2018;48(1):136–44.

    Article  Google Scholar 

  38. Van Biesen D, Hettinga FJ, McCulloch K, Vanlandewijck YC. Pacing ability in elite runners with intellectual impairment. Med Sci Sports Exerc. 2017;49(3):588–94.

    Article  Google Scholar 

  39. Kerr R. Psychomotor learning. Philadelphia: Saunders College Publication; 1982.

    Google Scholar 

  40. Fitts PM. Perceptual-motor skill learning. In: Categories of human learning. Elsevier; 1964. p. 243–85.

  41. Micklewright D, Kegerreis S, Raglin J, Hettinga F. Will the conscious–subconscious pacing quagmire help elucidate the mechanisms of self-paced exercise? New opportunities in dual process theory and process tracing methods. Sports Med. 2017;47(7):1231–9.

    Article  Google Scholar 

  42. Abonie US, Sandercock GRH, Heesterbeek M, Hettinga FJ. Effects of activity pacing in patients with chronic conditions associated with fatigue complaints: a meta-analysis. Disabil Rehabil. 2020;42(5):613–22.

    Article  Google Scholar 

  43. De Koning JJ, Foster C, Lucia A, Bobbert MF, Hettinga FJ, Porcari JP. Using modeling to understand how athletes in different disciplines solve the same problem: swimming versus running versus speed skating. Int J Sports Physiol Perform. 2011;6:276–80.

    Article  Google Scholar 

  44. Hyland-Monks R, Cronin L, McNaughton L, Marchant D. The role of executive function in the self-regulation of endurance performance: a critical review. Prog Brain Res. 2018;240:353–70.

    Article  Google Scholar 

  45. Wingfield G, Marino F, Skein M. The influence of knowledge of performance endpoint on pacing strategies, perception of effort, and neural activity during 30-km cycling time trials. Physiol Rep. 2018;6(21):e13892.

    Article  Google Scholar 

  46. Hettinga FJ. The race has begun! But how to learn how to race and pace? Champaign: Human Kinetics; 2018.

    Book  Google Scholar 

  47. Foster C, De Koning JJ, Hettinga F, Lampen J, La Clair KL, Dodge C, et al. Pattern of energy expenditure during simulated competition. Med Sci Sports Exerc. 2003;35(5):826–31.

    Article  Google Scholar 

  48. Corbett J, Barwood MJ, Parkhouse K. Effect of task familiarisation on distribution of energy during a 2000 m cycling time trial. Br J Sports Med. 2009;43(10):770–4.

    Article  CAS  Google Scholar 

  49. Menting SGP, Elferink-Gemser MT, Edwards AM, Hettinga FJ. Effects of experience and opponents on the pacing behaviour and 2-km cycling performance of novice youths. Res Q Exerc Sport. 2019;90(4):609–18.

    Article  Google Scholar 

  50. Konings MJ, Schoenmakers PPJM, Walker AJ, Hettinga FJ. The behavior of an opponent alters pacing decisions in 4-km cycling time trials. Physiol Behav. 2016;158:1–5.

    Article  CAS  Google Scholar 

  51. Ansley L, Schabort E, Gibson ASC, Lambert MI, Noakes TD. Regulation of pacing strategies during successive 4-km time trials. Med Sci Sports Exerc. 2004;36(10):1819–25.

    Article  Google Scholar 

  52. Williams CA, Bailey S, Mauger AR. External exercise information provides no immediate additional performance benefit to untrained individuals in time trial cycling. Br J Sports Med. 2012;46(1):49–53.

    Article  CAS  Google Scholar 

  53. Mauger AR, Jones AM, Williams CA. Influence of feedback and prior experience on pacing during a 4-km cycle time trial. Med Sci Sports Exerc. 2009;41(2):451–8.

    Article  Google Scholar 

  54. Mauger AR, Jones AM, Williams CA. Influence of exercise variation on the retention of a pacing strategy. Eur J Appl Physiol. 2010;108(5):1015–23.

    Article  Google Scholar 

  55. Jones HS, Williams EL, Marchant DC, Sparks SA, Bridge CA, Midgley AW, et al. Deception has no acute or residual effect on cycling time trial performance but negatively effects perceptual responses. J Sci Med Sport. 2016;19(9):771–6.

    Article  Google Scholar 

  56. Jeukendrup AE, Hopkins S, Aragón-Vargas LF, Hulston C. No effect of carbohydrate feeding on 16 km cycling time trial performance. Eur J Appl Physiol. 2008;104(5):831–7.

    Article  CAS  Google Scholar 

  57. Boya M, Foulsham T, Hettinga F, Parry D, Williams EL, Massey H, et al. Information acquisition differences between experienced and novice time trial cyclists. Med Sci Sports Exerc. 2017;49(9):1884–98.

    Article  Google Scholar 

  58. Whitehead AE, Jones HS, Williams EL, Rowley C, Quayle L, Marchant D, et al. Investigating the relationship between cognitions, pacing strategies and performance in 16.1 km cycling time trials using a think aloud protocol. Psychol Sport Exerc. 2018;34:95–109.

    Article  Google Scholar 

  59. Martin K, Staiano W, Menaspà P, Hennessey T, Marcora S, Keegan R, et al. Superior inhibitory control and resistance to mental fatigue in professional road cyclists. PLOS ONE. 2016;11(7):e159907.

    Article  Google Scholar 

  60. Marquet L-A, Hausswirth C, Molle O, Hawley JA, Burke LM, Tiollier E, et al. Periodization of carbohydrate intake: short-term effect on performance. Nutrients. 2016;8(12):755.

    Article  Google Scholar 

  61. Micklewright D, Papadopoulou E, Swart J, Noakes T. Previous experience influences pacing during 20 km time trial cycling. Br J Sports Med. 2010;44(13):952–60.

    Article  CAS  Google Scholar 

  62. Hibbert AW, Billaut F, Varley MC, Polman RC. Familiarization protocol influences reproducibility of 20-km cycling time-trial performance in novice participants. Front Physiol. 2017;8:488.

    Article  Google Scholar 

  63. Schmit C, Duffield R, Hausswirth C, Coutts AJ, Le Meur Y. Pacing adjustments associated with familiarization: heat versus temperate environments. Int J Sports Physiol Perform. 2016;11(7):855–60.

    Article  Google Scholar 

  64. Micklewright D, Parry D, Robinson T, Deacon G, Renfree A, St Clair Gibson A, et al. Risk perception influences athletic pacing strategy. Med Sci Sports Exerc. 2015;47(5):1026–37.

    Article  Google Scholar 

  65. Foster C, Hendrickson KJ, Peyer K, Reiner B, Lucia A, Battista RA, et al. Pattern of developing the performance template. Br J Sports Med. 2009;43:765–9.

    Article  CAS  Google Scholar 

  66. Cerasola D, Cataldo A, Bellafiore M, Traina M, Palma A, Bianco A, et al. Race profiles of rowers during the 2014 youth olympic games. J Strength Cond Res. 2018;32(7):2055–60.

    Article  Google Scholar 

  67. Filipas L, Mottola F, Tagliabue G, La Torre A. The effect of mentally demanding cognitive tasks on rowing performance in young athletes. Psychol Sport Exerc. 2018;39:52–62.

    Article  Google Scholar 

  68. Kennedy MD, Bell G. Development of race profiles for the performance of a simulated 2000-m rowing race. Can J Appl Physiol. 2003;28(4):536–46.

    Article  Google Scholar 

  69. Dimakopoulou E, Zacharogiannis E, Chairopoulou C, Kaloupsis S, Platanou T. The effect of pacing strategy on physiological, kinetic and performance variables during simulated rowing ergometer. J Sports Med Phys Fitness. 2018;58(7–8):1006–13.

    Google Scholar 

  70. Schabort E, Hawley J, Hopkins W, Blum H. High reliability of performance of well-trained rowers on a rowing ergometer. J Sports Sci. 1999;17(8):627–32.

    Article  CAS  Google Scholar 

  71. Hanon C, Gajer B. Velocity and stride parameters of world-class 400-meter athletes compared with less experienced runners. J Strength Cond Res. 2009;23(2):524–31.

    Article  Google Scholar 

  72. Micklewright D, Angus C, Suddaby J, St ACG, Sandercock G, Chinnasamy C. Pacing strategy in schoolchildren differs with age and cognitive development. Med Sci Sports Exerc. 2012;44(2):362–9.

    Article  Google Scholar 

  73. Chinnasamy C, Gibson ASC, Micklewright D. Effect of spatial and temporal cues on athletic pacing in schoolchildren. Med Sci Sports Exerc. 2013;45(2):395–402.

    Article  Google Scholar 

  74. Lambrick D, Rowlands A, Rowland T, Eston R. Pacing strategies of inexperienced children during repeated 800 m individual time-trials and simulated competition. Pediatr Exerc Sci. 2013;25:198–211.

    Article  Google Scholar 

  75. Green JM, Sapp AL, Pritchett RC, Bishop PA. Pacing accuracy in collegiate and recreational runners. Eur J Appl Physiol. 2010;108(3):567–72.

    Article  Google Scholar 

  76. Watkins J, Platt S, Andersson E, McGawley K. Pacing strategies and metabolic responses during 4-minute running time trials. Int J Sports Physiol Perform. 2017;12(9):1143–50.

    Article  Google Scholar 

  77. Diaz V, Peinado AB, Vleck VE, Alvarez-Sanchez M, Benito PJ, Alves FB, et al. Longitudinal changes in response to a cycle-run field test of young male national “Talent identification” and senior elite triathlon squads. J Strength Cond Res. 2012;26(8):2209–19.

    Article  Google Scholar 

  78. Deaner RO, Lowen A. Males and females pace differently in high school cross-country races. J Strength Cond Res. 2016;30(11):2991–7.

    Article  Google Scholar 

  79. Stevens C, Hacene J, Sculley D, Taylor L, Callister R, Dascombe B. The reliability of running performance in a 5 km time trial on a non-motorized treadmill. Int J Sports Med. 2015;36(09):705–9.

    Article  CAS  Google Scholar 

  80. Couto PG, Bertuzzi R, de Souza CC, Lima HM, Kiss MAPDM, de Oliveira FR, et al. High carbohydrate diet induces faster final sprint and overall 10000-m times of young runners. Pediatr Exerc Sci. 2015;27(3):355–63.

    Article  Google Scholar 

  81. Knechtle B, Bamert J, Rosemann T, Nikolaidis PT. Exercise-associated hyponatremia during a self-paced marathon attempt in a 15-year-old male teenager. Medicina. 2019;55(3):63.

    Article  Google Scholar 

  82. Santos-Lozano A, Collado P, Foster C, Lucia A, Garatachea N. Influence of sex and level on marathon pacing strategy Insights from the New York City race. Int J Sports Med. 2014;35(11):933–8.

    Article  CAS  Google Scholar 

  83. Trubee NW, Vanderburgh PM, Diestelkamp WS, Jackson KJ. Effects of heat stress and sex on pacing in marathon runners. J Strength Cond Res. 2014;28(6):1673–8.

    Article  Google Scholar 

  84. Deaner RO, Carter RE, Joyner MJ, Hunter SK. Men are more likely than women to slow in the marathon. Med Sci Sports Exerc. 2015;47(3):607.

    Article  Google Scholar 

  85. Morais JE, Barbosa TM, Silva AJ, Veiga S, Marinho DA. Profiling of elite male junior 50 m freestyle sprinters: understanding the speed-time relationship. Scand J Med Sci Sports. 2022;32(1):60–8.

    Article  Google Scholar 

  86. Dormehl S, Osborough C. Effect of age, sex, and race distance on front crawl stroke parameters in subelite adolescent swimmers during competition. Pediatr Exerc Sci. 2015;27(3):334–44.

    Article  Google Scholar 

  87. Skorski S, Faude O, Rausch K, Meyer T. Reproducibility of pacing profiles in competitive swimmers. Int J Sports Med. 2013;34:152–7.

    CAS  Google Scholar 

  88. Skorski S, Faude O, Abbiss CR, Caviezel S, Wengert N, Meyer T. Influence of pacing manipulation on performance of juniors in simulated 400-m swim competition. Int J Sports Physiol Perform. 2014;9(5):817–24.

    Article  Google Scholar 

  89. Turner AP, Smith T, Coleman SG. Use of an audio-paced incremental swimming test in young national-level swimmers. Int J Sports Physiol Perform. 2008;3(1):68–79.

    Article  Google Scholar 

  90. Scruton A, Baker J, Roberts J, Basevitch I, Merzbach V, Gordon D. Pacing accuracy during an incremental step test in adolescent swimmers. Open Access J Sports Med. 2015;6:249.

    Google Scholar 

  91. McGibbon KE, Pyne DB, Heidenreich LE, Pla R. A novel method to characterize the pacing profile of elite male 1500-m freestyle swimmers. Int J Sports Physiol Perform. 2020;16(6):818–24.

    Article  Google Scholar 

  92. Barbosa LP, Sousa CV, Aguiar SD, Gadelha AB, Nikolaidis PT, Villiger E, et al. The beginning of success: Performance trends and cut-off values for junior and the U23 triathlon categories. J Exerc Sci Fit. 2022;20(1):16–22.

    Article  Google Scholar 

  93. Wiersma R, Stoter IK, Visscher C, Hettinga FJ, Elferink-Gemser MT. Development of 1500-m pacing behavior in junior speed skaters: a longitudinal study. Int J Sports Physiol Perform. 2017;12(9):1224–31.

    Article  Google Scholar 

  94. Stoter IK, Hettinga FJ, Otten E, Visscher C, Elferink-Gemser MT. Changes in technique throughout a 1500-m speed skating time-trial in junior elite athletes: Differences between sexes, performance levels and competitive seasons. PLOS ONE. 2020;15(8):e0237331.

    Article  CAS  Google Scholar 

  95. Menting SGP, Konings MJ, Elferink-Gemser MT, Hettinga FJ. Pacing behavior of elite youth athletes: analyzing 1500-m short-track speed skating. Int J Sports Physiol Perform. 2019;14:222–31.

    Article  Google Scholar 

  96. Menting SGP, Huijgen BC, Konings MJ, Hettinga FJ, Elferink-Gemser MT. Pacing behavior development of youth short-track speed skaters: a longitudinal study. Med Sci Sports Exerc. 2020;52(5):1099–108.

    Article  Google Scholar 

  97. Sollie O, Gløersen Ø, Gilgien M, Losnegard T. Differences in pacing pattern and sub-technique selection between young and adult competitive cross-country skiers. Scand J Med Sci Sports. 2021;31(3):553–63.

    Article  Google Scholar 

  98. Formenti D, Rossi A, Calogiuri G, Thomassen TO, Scurati R, Weydahl A. Exercise intensity and pacing strategy of cross-country skiers during a 10 km skating simulated race. Res Sports Med. 2015;23(2):126–39.

    Article  Google Scholar 

  99. Carlsson M, Assarsson H, Carlsson T. The influence of sex, age, and race experience on pacing profiles during the 90 km Vasaloppet ski race. Open Access J Sports Med. 2016;7:11.

    Article  Google Scholar 

  100. Alves D, Cruz R, Lima-Silva A, Domingos P, Bertuzzi R, Osiecki R, et al. Are experienced and high-level race walking athletes able to match pre-programmed with executed pacing? Braz J Med Biol Res. 2019;52(6).

  101. Sealey RM, Spinks WL, Leicht AS, Sinclair WH. Identification and reliability of pacing strategies in outrigger canoeing ergometry. J Sci Med Sport. 2010;13(2):241–6.

    Article  Google Scholar 

  102. Moss SL, Francis B, Calogiuri G, Highton J. Pacing during a cross-country mountain bike mass-participation event according to race performance, experience, age and sex. Eur J Sport Sci. 2019;19(6):793–801.

    Article  Google Scholar 

  103. Sampson JA, Fullagar HHK, Gabbett T. Knowledge of bout duration influences pacing strategies during small-sided games. J Sport Sci. 2015;33(1):85–98.

    Article  Google Scholar 

  104. Johnston RD, Gabbett TJ, Jenkins DG. The influence of physical fitness and playing standard on pacing strategies during a team-sport tournament. Int J Sports Physiol Perform. 2015;10(8):1001–8.

    Article  Google Scholar 

  105. Christie CJ, Elliot A, Pote L, Steenekamp T, Billaut F, Noakes TD. Effect of expertise on pacing strategies and sprint performance in batsmen. J Sci Med Sport. 2018;21(5):513–7.

    Article  Google Scholar 

  106. Moss SL, Twist C. Influence of different work and rest distributions on performance and fatigue during simulated team handball match play. J Strength Cond Res. 2015;29(10):2697–707.

    Article  Google Scholar 

  107. Burdon CA, Park J, Tagami K, Groeller H, Sampson JA. Effect of practice on performance and pacing strategies during an exercise circuit involving load carriage. J Strength Cond Res. 2018;32(3):700–7.

    Article  Google Scholar 

  108. Gross M, Hemund K, Vogt M. High intensity training and energy production during 90-second box jump in junior alpine skiers. J Strength Cond Res. 2014;28(6):1581–7.

    Article  Google Scholar 

  109. Reid JC, Greene RM, Herat N, Hodgson DD, Halperin I, Behm DG. Knowledge of repetition range does not affect maximal force production strategies of adolescent females. Pediatr Exerc Sci. 2017;29(1):109–15.

    Article  Google Scholar 

  110. Konings MJ, Noorbergen OS, Parry D, Hettinga FJ. Pacing behavior and tactical positioning in 1500-m short-track speed skating. Int J Sports Physiol Perform. 2016;11(1):122–9.

    Article  Google Scholar 

  111. Abbiss CR, Laursen PB. Describing and understanding pacing strategies during athletic competition. Sports Med. 2008;38:239–52.

    Article  Google Scholar 

  112. Foster C, Snyder A, Thompson NN, Green MA, Foley M, Schrager M. Effect of pacing strategy on cycle time trial performance. Med Sci Sports Exerc. 1993;25(3):383–8.

    Article  CAS  Google Scholar 

  113. Singer RN. Preperformance state, routines, and automaticity: What does it take to realize expertise in self-paced events? J Sport Exerc Psychol. 2002;24(4):359–75.

    Article  Google Scholar 

  114. Robinson DT, Cloak R, Lahart IM, Lane AM. Do I focus on the process of cycling or try to put my mind elsewhere? A comparison of concentration strategies for use in pacing by novice riders. Prog Brain Res. 2018;240:127–40.

    Article  Google Scholar 

  115. Azevedo RDA, Silva-Cavalcante MD, Cruz R, Couto P, Lima-Silva AE, Bertuzzi R. Distinct pacing profiles result in similar perceptual responses and neuromuscular fatigue development: why different “roads” finish at the same line? Eur J Sport Sci. 2021;22:1046–56.

    Article  Google Scholar 

  116. Hettinga FJ, Edwards AM, Hanley B. The science behind competition and winning in athletics: using world-level competition data to explore pacing and tactics. Front Sports Active Living. 2019; 11.

  117. Mytton GJ, Archer DT, Turner L, Skorski S, Renfree A, Thompson KG, et al. Increased variability of lap speeds: differentiating medalists and nonmedalists in middle-distance running and swimming events. Int J Sports Physiol Perform. 2015;10(3):369–73.

    Article  Google Scholar 

  118. Eime R, Harvey J, Charity M. Sport drop-out during adolescence: is it real, or an artefact of sampling behaviour? Int J Sport Policy Polit. 2019;11(4):715–26.

    Article  Google Scholar 

  119. Ranganathan R, Newell KM. Changing up the routine: intervention-induced variability in motor learning. Exerc Sport Sci Rev. 2013;41(1):64–70.

    Article  Google Scholar 

  120. Guadagnoli MA, Lee TD. Challenge point: a framework for conceptualizing the effects of various practice conditions in motor learning. J Mot Behav. 2004;36(2):212–24.

    Article  Google Scholar 

  121. Boyd L, Rogers T, Docherty D, Petersen S. Variability in performance on a work simulation test of physical fitness for firefighters. Appl Physiol Nutr Metab. 2015;40(4):364–70.

    Article  Google Scholar 

  122. Shea CH, Kohl RM. Specificity and variability of practice. Res Q Exerc Sport. 1990;61(2):169–77.

    Article  CAS  Google Scholar 

  123. Veenman MV, Spaans MA. Relation between intellectual and metacognitive skills: age and task differences. Learn Individ Differ. 2005;15(2):159–76.

    Article  Google Scholar 

  124. Diamond A. Executive functions. Annu Rev Psychol. 2013;64:135–68.

    Article  Google Scholar 

  125. Toering TT, Elferink-Gemser MT, Jordet G, Visscher C. Self-regulation and performance level of elite and non-elite youth soccer players. J Sports Sci. 2009;27(14):1509–17.

    Article  CAS  Google Scholar 

  126. Huijgen BCH, Leemhuis S, Kok NM, Verburgh L, Oosterlaan J, Elferink-Gemser MT, et al. Cognitive functions in elite and sub-elite youth soccer players aged 13 to 17 years. PLoS ONE. 2015;10(12):e0144580.

    Article  Google Scholar 

  127. Toering T, Elferink-Gemser MT, Jonker L, van Heuvelen MJ, Visscher C. Measuring self-regulation in a learning context: Reliability and validity of the Self-Regulation of Learning Self-Report Scale (SRL-SRS). Int J Sport Exerc Psychol. 2012;10(1):24–38.

    Article  Google Scholar 

  128. Edwards AM, McCormick A. Time perception, pacing and exercise intensity: maximal exercise distorts the perception of time. Physiol Behav. 2017;15(180):98–102.

    Article  Google Scholar 

  129. Alves DL, Cruz R, Bara C, Osiecki R, Lima JRP, De-Oliveira FR. Pre-planned vs executed real-time pacing strategies during 3-km race: role of rating perceived exertion. Res Q Exerc Sport. 2020;91(3):469–77.

    Article  Google Scholar 

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Acknowledgements

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Funding

The authors received no specific funding for this work.

Author information

Authors and Affiliations

  1. Department of Human Movement Sciences, University Medical Center Groningen, University of Groningen, Groningen, PO Box 196, 9700 AD, The Netherlands

    Stein Gerrit Paul Menting & Marije Titia Elferink-Gemser

  2. School of Psychology and Life Sciences, Canterbury Christ Church University, Canterbury, UK

    Andrew Mark Edwards

  3. Department of Sport, Exercise and Rehabilitation, Faculty of Health and Life Sciences, Northumbria University, Room 238, Northumberland Building, Newcastle Upon Tyne, NE1 8ST, UK

    Florentina Johanna Hettinga

Authors
  1. Stein Gerrit Paul Menting
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  2. Andrew Mark Edwards
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  3. Florentina Johanna Hettinga
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  4. Marije Titia Elferink-Gemser
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Contributions

The study conception and design were done in full collaboration with all authors. SM performed the literature search and wrote the first draft of the manuscript. SM, AE, FH and MEG critically revised the work. All authors read and approved the final manuscript.

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Correspondence to Florentina Johanna Hettinga.

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Stein Menting, Andrew Edwards, Florentina Hettinga and Marije Elferink-Gemser declare that they have no competing interests.

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Menting, S.G.P., Edwards, A.M., Hettinga, F.J. et al. Pacing Behaviour Development and Acquisition: A Systematic Review. Sports Med - Open 8, 143 (2022). https://doi.org/10.1186/s40798-022-00540-w

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Keywords

  • Pacing
  • Skill
  • Development
  • Junior
  • Acquisition
  • Experience
  • Sports
  • Exercise