Pacing during endurance activity has been described as a decision making process involving how and when athletes invest their energy (1). As such, the pacing strategy and tactics adopted often depend on an individual’s personal goals and motivation and the likely demands of the activity (1–5). Research on pace-related decision making has burgeoned in recent years with a plethora of review and perspective articles dedicated to the topic (1–8). The field is not without contention, however, and rich debate surrounds the mechanisms underpinning endurance performance. Foremost among these is whether the brain controls muscular activity by conscious (4,9), subconscious (5,10), or alternative processes, such as regulation at different levels of awareness (2) or via the dual processes of intuitive and deliberative action (6).
Despite fundamental conceptual differences, most contemporary viewpoints agree on some psychological and contextual factors that determine endurance performance. Of these, perceived effort, the conscious sensation of how hard a task feels (11), and previous experience of endurance tasks of varying distances and duration are considered crucial to effective pace-related decision making (4,9,10,12). Others have suggested that affective state may also play a primary or mediatory role in pace-regulation (7,13). Central to the present study, there is also broad consensus that accurate knowledge of the distance/duration of a task is an important contextual factor influencing pace-related decision making (1,4,5,9,10,14). As such, studies manipulating knowledge of the exercise endpoint have demonstrated an impact on perceptual, attentional, and physiological responses during endurance performance. An early investigation involving two 20-min runs at 85% of maximal oxygen consumption (V˙O2max) (15), for example, found subjects reported a lower perception of effort when completing a trial they believed would last for 30 min in comparison with a known duration trial of 20 min. Later, Baden et al. (16) noted reduced effort perceptions when subjects expected to run for 20 min in comparison with a known 10-min trial. Attentional focus also tended toward greater associative thoughts (e.g., focusing on bodily sensations, movement technique, relaxing (17)) that correlated with effort perceptions in the known 10-min trial. Finally, Baden and colleagues (18) demonstrated how an unexpected increase in exercise duration altered responses during fixed-intensity 20-min running trials such that effort perceptions and oxygen consumption increased significantly more at the end of an expected 10-min run when the duration was unexpectedly extended for an additional 10 min. The authors suggested that the outcomes were influenced by increased negative affect and associative thoughts when expectations of the exercise duration were not met.
In contrast to knowledge of task distance/duration, little is known about the impact of anticipated task difficulty on regulatory processes during equidistant endurance tasks. The objective difficulty of an endurance activity is often judged according to the route profile (19). Major marathons, for example, are completed over relatively flat courses (e.g., Chicago, Berlin) or courses with notable changes in elevation, such as the 3.3% “Heartbreak Hill” incline in the latter stages of the Boston marathon (20). Similarly, cycling events take place on flat, hilly, or mountainous routes and other environmental factors, such as terrain (21) or wind speed (22), also impact on task difficulty and, consequently, endurance performance. Accordingly, how anticipated task difficulty influences pacing, psychological, and physiological responses in advance of increases in actual task difficulty is of primary interest in this study.
Research manipulating anticipated exercise duration also highlights the relationship between individuals’ associative focus of attention and perceptual responses during task performance (15,16,18). None of these studies identified the specific cognitions engaged by study subjects, however. This may be important because traditionally associative thoughts have contrasting effects on perceptual and performance outcomes during endurance activity. Recently, Brick and colleagues (17,23) recategorized associative thoughts as either active self-regulation (e.g., focus on relaxing, optimizing technique, use of motivational self-talk), internal sensory monitoring (e.g., breathing, effort sensations), or outward monitoring (e.g., route profile, distance indicators). Importantly, active self-regulatory thoughts were typically found to enhance endurance performance without necessarily elevating perceptions of effort (17). In contrast, an excessive focus on internal sensory monitoring may increase effort perceptions and/or reduce pace during self-paced endurance tasks (17,23,24). As such, insight into individuals’ specific thoughts may provide a more nuanced understanding of the effects of prior knowledge of contextual factors on endurance performance outcomes.
Accordingly, the purpose of this study was to investigate the effects of anticipated task difficulty on pacing, psychological, and physiological responses during 3000-m time trials. We also sought to determine subjects’ thought content using Brick and colleagues’ (17) attentional focus categorization. The main outcome of interest was pacing and it was hypothesized that initial running speed would be slower when subjects anticipated an increase in task difficulty later in a time trial. Given its importance to pace-regulation, we also hypothesized an elevated perception of effort during the initial stages of time trials expected to increase in difficulty.
Subjects, ethics, and informed consent
Baden and colleagues (16,18) reported moderate-to-large differences in psychological outcomes when manipulating anticipated task duration during fixed-intensity trials. Thus, an a priori analysis (G*Power version 220.127.116.11; repeated-measures [RM] ANOVA, within factors) with a moderate effect size (f = 0.25), a power of 0.80, an alpha level of 0.05, for three conditions, a modest correlation among RM (r = 0.50), and a nonsphericity correction (ɛ = 1) suggested a sample size of 28. Accordingly, 28 trained endurance runners (Table 1) of performance levels 2 (i.e., several years’ running experience, training approximately 2 h·d−1 at least 3 d·wk−1) to 3 (i.e., up to 10-yr experience, training frequently at least 5 h·wk−1) (25) were recruited. Volunteer subjects were given no incentives to participate and were healthy, injury free, and accustomed to treadmill running. The study was approved by the Ulster University School of Psychology research ethics committee, and written informed consent was obtained from all subjects before participation.
Study design and procedures
Subjects visited the laboratory on four occasions, each 3–7 d apart and at the same time of day (±1 h). In an RM crossover design, subjects were randomly assigned to order-counterbalanced conditions (see Time trials). Subjects were asked to avoid strenuous activity in the 24 h preceding all sessions, to avoid caffeine and food, and to maintain normal hydration in the 2 h before each trial. Body mass was recorded before time trials as a proxy for hydration status.
Session one involved measurement of V˙O2max and familiarization with the motorized treadmill and study procedures. Subjects ran on a motorized treadmill (h/p/cosmos quasar; h/p/cosmos Sports & Medical GmbH, Traunstein, Germany) set at a 1% gradient with continuous measurement of respiratory gas exchange (Quark C-PET; Cosmed Srl, Rome, Italy). Following a 5-min warm-up, subjects begin at a light intensity based on their ability. Stages lasted 3 min initially with 1 km·h−1 increments until the individual lactate turnpoint followed by 1 km·h−1 increments every 1 min until voluntary exhaustion. Heart rate was measured continuously (Polar RS400, Kempele, Finland). V˙O2max was determined as the highest value for a 10-breath rolling average. On completion, subjects were provided with an outline of sessions 2 to 4 but were naive to the study’s purpose and individual time trial protocols. Subjects were familiarized with all perceptual and affective scales (see Experimental sessions), exercise-anchored relative to V˙O2max test intensities (11). Subjects were also provided with a take-home sheet to familiarize with attentional focus categories.
Sessions 2 to 4 consisted of 3000-m running time trials on a motorized treadmill. Before each time trial, subjects were asked to complete the 3000 m as fast as possible and were provided either accurate or deceptive information about the objective time trial difficulty (see Time trials). Next, subjects rated their pretrial state motivation on a single-item scale (26) and their pretrial mood state using the 24-item Brunel Mood Scale (27). Motivation and mood state items required subjects to respond on five-point Likert scales (0, not at all; 4, extremely). Body mass (Seca 862, Hamburg, Germany) and resting blood lactate (Lactate Pro 2; Arkray Inc., Kyoto, Japan) were recorded before a standardized warm-up of 5-min running at 70% followed by 2 min at 80% of the maximum heart rate recorded during V˙O2max testing. Subjects were refamiliarized with the speed controls on the treadmill user terminal and practiced during each warm-up. On completion, subjects rested for 2 min before beginning the time trial. During each time trial, subjects could control the treadmill speed by manually manipulating the speed controls on the treadmill user terminal.
RPE (6–20 scale (28)) and affective valence (11-point Feeling Scale (29)) were recorded verbally at 200 m and each 400 m interval thereafter. For both scales, subjects were asked to respond for how they felt over the preceding 200 m. Heart rate was recorded continuously and averaged for the first 200 m and each subsequent 400 m. At each 1000-m interval, subjects verbally indicated the frequency of thoughts over the previous 800 m for three attentional focus categories; active self-regulation, internal sensory monitoring, and outward monitoring. Subjects responded on an 11-point Likert scale (0, never; 10, always (25)). All scales were projected on a screen 3.50 m in front of the treadmill and removed once subjects had responded. Blood lactate was also recorded immediately posttrial and attentional focus content was recounted during a brief posttrial interview (see Attentional focus interview).
During time trials, subjects were only able to view the distance display on the treadmill user terminal. However, a computer interface (h/p/cosmos pc software) allowed experimenters to record all pace-related data. Subjects received no other feedback or verbal encouragement. A fan was positioned at the front of the treadmill during all trials. Time trial 1 was a baseline (BL) trial and consisted of a self-paced 3000-m run on a 0% gradient. Time trials 2 and 3 were counterbalanced, randomized using a computer random number generator. Before a self-paced, known incline (KI) trial, expected to be a more difficult profile, subjects were informed that the treadmill gradient would remain at 0% until the final 800 m when the incline (i.e., difficulty) would increase to 7% until the finish (described to subjects as a “steep slope”). Subjects were reminded of the upcoming incline at 1800 m. Before a self-paced, unknown incline (UI) trial, expected to be less difficult, subjects were deceptively informed that the gradient would remain at 0% throughout (as during BL). At 1800 m, however, subjects were informed that the incline would increase to 7% at 2200 m until the finish. A 7% gradient was selected as typical of that used to investigate the biomechanics and physiology of uphill running (30). Finally, unbeknown to subjects, the treadmill speed was automatically reduced by 2 km·h−1 as the gradient increased to 7% during KI and UI time trials. Because data during the first 2200 m was of primary interest, the speed reduction was introduced following pilot testing for safety reasons and was based on a spontaneous decrease in running speed of 0.295 km·h−1 for every 1% increase in gradient in trained runners (31). Subjects were not informed of the reduction in treadmill speed.
Posttrial attentional focus interviews
After each time trial, subjects completed a brief interview during which they recounted their main thoughts for active self-regulation, internal sensory monitoring, and outward monitoring during each 1000-m interval. To assist recall, subjects could view their individual frequency ratings for each 1000-m interval and category items during the interview.
The effects of condition (KI, UI, BL) on pretrial body mass, motivation, and mood states were analyzed using RM ANOVA. Before analysis, the running speed at each distance interval (200 m, 600 m, etc.) during KI and UI time trials was expressed as the percentage change relative to the speed at the equivalent interval during BL (i.e., relative running speed [RRS]). Accordingly, data for each distance interval, and mean data over the first 2200 m, the final 800 m, and the full 3000 m for RRS (%; KI and UI time trials only), absolute running speed, perceived effort, affective valence, heart rate, and attentional focus frequency ratings (at 1000, 2000, 3000 m) were analyzed using RM ANOVA. Blood lactate was analyzed using 3 (condition) × 2 (time) RM ANOVA. Greenhouse–Geisser corrections were used where sphericity assumptions were violated. Post hoc pairwise comparisons with Sidak-adjusted P values were conducted where significant F-ratios were observed. Relationships between RRS (KI and UI time trials only) and psychological variables, and between perceived effort, affect, and attentional focus data were investigated using bootstrapped (1000 samples) Pearson’s correlations where assumptions of linearity and normality (Kolmogorov–Smirnov test) were not met. For correlations, RRS and psychological variables were averaged for the 800 m preceding each 1000-m interval (reflecting attentional focus data). Statistical significance was accepted as P ≤ 0.05 for all tests. Cohen’s d values are provided to estimate between-trial effect sizes and interpreted as trivial (<0.20), small (0.21–0.60), moderate (0.61–1.20), large (1.21–2.00), and very large (2.01–4.00) (32). Ninety-five percent confidence intervals (CI) are reported for post hoc comparisons and for bootstrapped correlations (bias corrected and accelerated). Analyses were conducted using SPSS (IBM Statistics 24.0; SPSS Inc., Chicago, IL). Interview data are presented as percentage of subjects reporting attentional focus content at each 1000-m interval during KI and UI time trials only.
No differences were found between pretrial states (Table 2) for body mass, potential motivation, or total mood disturbance (all P > 0.05). Individual mood states did not differ between UI and KI time trials (all P > 0.05), but tension was higher before BL time trial than KI time trial (P = 0.026, d = 0.85) and UI time trial (P = 0.003, d = 1.21). Vigor did not differ between UI and KI time trials (P = 0.865) nor between UI and BL time trials (P = 0.229) but was higher before BL time trial than KI time trial (P = 0.038, d = 0.46). Confusion differed between trials F (1.64, 44.34) = 4.20, P = 0.028, ηp2 = 0.14, but did not differ between any pair of trials (all P > 0.05).
During KI time trial, pretrial motivation correlated with RRS at 1000 m (r = 0.458, (0.175 to 0.687), P = 0.014) and 2000 m (r = 0.422, (0.131 to 0.699), P = 0.025). Pretrial vigor correlated with RRS at 1000 m (r = 0.423, (0.153 to 0.611), P = 0.025). No pretrial psychological states correlated with RRS during UI (all P > 0.05).
Time trial performance
Relative running speed (Fig. 1A) was slower during KI time trial than UI time trial after 200 m until the 2200-m distance interval (all P ≤ 0.009, all d ≥−0.43 and ≤−0.53). Mean RRS over the first 2200 m was slower during KI time trial than UI time trial (mean difference [MD], −2.44%; 95% CI, −3.92 to −0.97; P = 0.002). In absolute terms (Fig. 1B), subjects completed the first 2200 m 13.96 s slower during KI time trial than UI time trial (P = 0.008). Exploratory analysis revealed no main order effect or condition–order interaction over the first 2200 m (all P > 0.05), but subjects who completed KI time trial first after BL time trial ran comparatively slower during KI time trial (d = −0.58; Fig. 1C) than those who completed UI time trial first after BL time trial (d = −0.24; Fig. 1D). Relative running speed over the final 800 m (Fig. 1A) did not differ between KI and UI time trials (P = 0.854) but was slower over the full 3000-m time trial during KI time trial than UI time trial (MD, −1.62%; 95% CI, −2.92 to −0.32; P = 0.017). In absolute terms (Fig. 1B), subjects completed the full 3000-m time trial 14.35 s slower during KI time trial than UI time trial (P = 0.044). Pairwise comparisons between KI and UI time trials are presented in Table 3.
Absolute running speed (Fig. 1B) was also slower during both KI and UI than BL over the final 800 m (all P < 0.001) and the full 3000 m (all P < 0.001). Absolute running speed did not differ over the first 2200 m segment between KI and BL time trials (P = 0.978) nor between UI and BL time trials (P = 0.092) (see Supplemental Digital Content 1 for a more complete analysis, https://links.lww.com/MSS/B448).
Perceptual and affective responses
Perceived effort (Fig. 2A) over the first 2200 m did not differ between KI and UI time trials nor between UI and BL time trials (all P > 0.05) but was lower during KI than BL (P = 0.014, d = −0.41). Over the final 800 m, perceived effort did not differ between KI and UI nor between KI and BL (all P > 0.05) but was higher during UI than BL (P = 0.010, d = 0.58). Perceived effort over the full 3000 m did not differ between conditions (P = 0.200). Affective valence (Fig. 2B) did not differ between trials over the first 2200 m (P = 0.986). During the final 800 m affective valence did not differ between KI and UI time trials (P = 0.909) but was more negative during KI time trial (P = 0.008, d = −0.43) and UI time trial (P = 0.001, d = −0.50) than BL time trial. Over the full 3000-m affective valence did not differ between conditions (P = 0.127). Comparisons between KI and UI time trials are presented in Table 3.
Both perceived effort (r = 0.382, (−0.041 to 0.643), P = 0.045) and affective valence (r = −0.389; −0.692 to 0.089), P = 0.041) correlated with RRS at 2000 m during KI time trial. No such relationships existed at 1000 m during KI (all P > 0.05) or at any distance interval during UI time trial (all P > 0.05). Perceived effort was also inversely correlated with affective valence at each distance interval during KI (all r ≥ −0.572, all P ≤ 0.001) and UI (all r ≥ −0.559, all P ≤ 0.002) (see also Supplemental Digital Content 2, https://links.lww.com/MSS/B449).
Heart rate (Fig. 2C) over the first 2200 m did not differ between KI and UI nor between UI and BL (all P > 0.05) but was lower during KI than BL (P = 0.002, d = −0.35). Heart rate did not differ between any time trial over the last 800 m (P = 0.576) and differences over the full 3000 m did not reach statistical significance (P = 0.051). A main effect for time revealed that blood lactate (Fig. 2D) increased from pretrial to posttrial for all time trials (P < 0.001). A main effect for condition revealed that blood lactate was higher in UI than BL (P < 0.001) but did not differ between KI and UI trials (P = 0.519). Finally, a condition–time interaction (F (2, 54) = 8.50, P = 0.001, ηp2 = 0.24) revealed that blood lactate increased more after UI time trial than BL time trial (see Supplemental Digital Content 1, https://links.lww.com/MSS/B448).
Active self-regulation was more frequent over the final 3000-m segment during KI time trial than UI time trial (MD, 1.00; 95% CI, 0.11–1.89; P = 0.024, d = 0.45) but not at 1000 m (P = 0.637) or 2000 m (P = 0.809) (Fig. 3A). Active self-regulation did not differ between KI and BL time trials nor between UI and BL time trials (all P > 0.05). Internal sensory monitoring and outward monitoring frequency ratings (Fig. 3B and C) did not differ between time trials at any distance interval (all P > 0.05).
Interview data for active self-regulatory thought content revealed that more subjects focused on pacing during KI time trial than both UI and BL time trials at 1000 m (82.14% vs 67.86% and 57.14%, respectively) and at 2000 m (85.71% vs 67.86% and 64.29%, respectively). In contrast, fewer subjects focused on other self-regulatory thoughts such as relaxing (42.86% vs 60.71% and 53.57%) over the first 1000 m during KI time trial than both UI and BL time trials. During the final 800-m (incline) segment fewer subjects used motivational self-talk during KI time trial than UI time trial (42.86% vs 71.43%). In contrast, more subjects focused on relaxing (35.71% vs 17.86%) and chunking (42.86% vs 28.57%) during the final 800 m of KI than UI (see Supplemental Digital Content 3 for all attentional focus content, https://links.lww.com/MSS/B450).
Internal sensory monitoring and outward monitoring thought content did not differ greatly between time trials. During KI and UI time trials, most subjects focused on breathing and movement form at 1000 and 2000 m and breathing and overall effort/feel over the final 800-m segment. Outward monitoring during time trials was predominantly on the distance display.
Finally, during KI time trial, perceived effort correlated with both internal sensory monitoring and outward monitoring at 2000 m (r = 0.401; 0.064–0.680; P = 0.034, and r = 0.376; −0.070 to 0.685; P = 0.048, respectively) and 3000 m (r = 0.452; 0.115–0.742; P = 0.016, and r = 0.467; 0.167–0.729; P = 0.012, respectively). During UI time trial, perceived effort correlated with internal sensory monitoring only at 2000 m (r = 0.374; −0.260 to 0.837; P = 0.050) and 3000 m (r = 0.431; 0.044–0.714; P = 0.022). No relationships were found between perceived effort and active self-regulation during time trials (all P > 0.05). Relationships between attentional focus categories are presented in Supplemental Digital Content 2, https://links.lww.com/MSS/B449.
This was the first study to investigate the effects of anticipated task difficulty on pacing, psychological, and physiological responses during endurance activity. In agreement with the first hypothesis, a novel finding was that subjects ran 2.44% slower over the first 2200 m of a 3000-m time trial anticipated to become more difficult (KI time trial) in comparison with a similar, equidistant time trial deceptively expected to be less difficult (UI time trial) (Fig. 1A–B). The difference in running speed between time trials, expressed as the percentage change relative to a BL time trial (RRS) was larger than the coefficient of variation (1.50%) and the smallest worthwhile change (0.50%) suggested for shorter endurance events (<12,000 m) (33). As such, the slower speed during KI time trial than UI time trial represents a real and substantial difference in performance. Despite this, perceived effort, affective valence, heart rate, and blood lactate did not differ between KI and UI time trials (Fig. 2A–D). The similar ratings of perceived effort between KI and UI time trials support the second hypothesis in that the reduction in running speed did not result in a lower perception of effort.
The findings of this study add to theoretical understandings of factors that impact on pace regulation during endurance activity. The slower running speed during KI than UI suggests that, in addition to knowledge of task distance/duration (15,16,18), the anticipated difficulty of the task also influences pace-related decision making and endurance performance. In agreement with existing perspectives (4,9,10,12), there was evidence that previous task experience modulated pacing during time trials. Specifically, although no order effect was found, a larger reduction in RRS during the first 2200 m of KI was noted for those who completed the KI time trial first after BL (Fig. 1C) in comparison with those who completed the KI time trial last (Fig. 1D). As such, these findings support assertions that an athlete’s pacing strategy relates to the likely demands of an activity (1,2,14) and greater demand uncertainty can result in a larger reduction in work rate during task performance (3,6).
Relationships between RRS and subjects’ psychological responses provide further insight into factors determining pacing during time trials. Specifically, RRS during KI time trial was positively associated with pretrial motivation (at 1000 m and 2000 m), vigor (at 1000 m), and within-trial perceived effort (at 2000 m). No such relationships existed during UI. These data suggest that the initial running speed during KI was influenced by the subjects’ pretrial motivation and success-related drives (4,5,9). More so, motivated behavior is characterized by higher vigor and the exertion of effort during goal-directed action (34). Consequently, perception of effort also appeared to play an important regulatory role in pace-related decision making during KI (4,5,9,14). It has been suggested that the experience of effort provides important information about the difficulty of a task and a need to conserve energy for future activity (35). As such, the slower initial speed during KI may reflect an elevated perception of task-related difficulty during that time trial. Coupled with more subjects reporting a self-regulatory focus on pacing, the findings suggest a consciously aware, effort-based decision to reduce pace and conserve energy during KI time trial (2–4,9,35). In contrast, the faster initial running speed during UI, unrelated to pretrial motivation or perceived effort and completed with a reduced focus on pacing, suggests a more automated, sub-aware form of regulatory control (2). This may indicate that subjects followed a preplanned pacing strategy during UI time trial, unaffected by expectations of increasing task difficulty and perceived need to conserve energy (3,23).
Similar to perceived effort, affective valence did not differ between time trials despite the slower running speed during KI time trial (Fig. 2B). A negative relationship was observed between affective valence and RRS during the initial stages of KI, however, and between perceived effort and affective valence throughout time trials. These findings support contentions that affective state may also play a primary and/or mediatory role in pace-regulation during endurance activity (7,13). Relevant to the present context, anticipated affective responses also impact on behavioral decisions and the slower initial speed during KI time trial may further reflect a preemptive pacing tactic to minimize reductions in hedonic state over the final 800 m (36). Collectively, the findings suggest that alongside a consciously aware attempt to regulate pacing behavior, effort- and affect-based heuristics (i.e., mental “shortcuts” used to make decisions during situations of increased complexity or uncertainty) may have influenced pace-related decision making during KI (3,6,7,37). As such, heuristic decision making may have allowed subjects solve a pacing conundrum during KI resulting from increased task uncertainty and a perception that the risks of running faster (e.g., feeling very bad, having to stop) outweighed any perceived benefits (8,35–37).
In addition to an increased focus on pacing during KI, other attentional focus findings also provided some important insights. During the initial stages of UI time trial, for example, more subjects reported focusing on other self-regulatory thoughts, such as relaxing. Relaxation has been shown to improve movement economy during endurance activity (17,38) and may account in part for the similar physiological responses during UI time trial as KI time trial, despite a faster running speed. Furthermore, associations between perceived effort and both internal sensory monitoring (KI and UI time trials) and outward monitoring (KI time trial only) add a more nuanced understanding of the relationship between effort perceptions and associative thought content when manipulating task-related variables (15,16,18). Specifically, during both KI and UI time trials, the data suggest that effort perceptions were positively associated with internal sensory monitoring (24). In addition, the relationship between effort perceptions and outward monitoring during KI strengthens our contention that pace-regulation during KI time trial resulted from a consciously aware decision making processes that incorporated a focus on the time trial distance information. Finally, during UI many more subjects reported using motivational self-statements, such as “keep it going” or “you’ve done this before,” to complete the unexpected 800-m incline. Motivational self-talk has been shown to improve endurance performance (39) and is typically used in contexts where the activity feels more challenging yet maintaining a desired level of performance is a priority (23). As such, subjects’ self-talk during UI may reflect a facilitative, goal-directed response to maintain goal-pursuit (i.e., complete the time trial as quickly as possible) and avoid task disengagement (i.e., stopping or slowing excessively) after the unexpected increase in task difficulty (7,23).
Despite the findings, some limitations should be acknowledged. Because time trials were completed on a motorized treadmill and subjects were required to manually manipulate the treadmill pace controls, the findings may have reduced ecological validity (1). This may be particularly relevant for attentional focus outcomes (e.g., outward monitoring) and subsequent pace-related decision making in more naturalistic settings. The reduction in running speed enforced by experimenters during the final 800-m incline segment of KI and UI time trials should also be highlighted and may have impacted on subjects pacing tactics over the final 800 m. However, it is noteworthy that the majority of subjects did not indicate an awareness of this intervention as most subjects simultaneously attempted to slow the treadmill using the treadmill speed controls at this point. After the time trials, only four subjects reported any awareness of the experimenter intervention but described noticing a change in the noise of the treadmill motor rather than a change in treadmill speed. These subjects were deceptively informed that the change in treadmill motor noise was normal when the treadmill incline increased.
Collection of psychological data during time trials may also have impacted on subjects’ usual attentional processes. However, data collection was consistent between time trials and subjects responded quickly (~10 s) at each distance interval, minimizing intrusiveness and possible interference. Furthermore, collecting attentional focus frequency ratings at 1000 m intervals was important to inform posttime trial interviews and overcome delayed recall of within-trial attentional focus content (17). Finally, the small number of female subjects (n = 5) should also be considered. Although not of primary interest in this study, exploratory analyses revealed no differences in pacing, psychological, or physiological outcomes between sexes, although males engaged in more active self-regulation during UI. Given that male and female runners may pace longer-distance athletic competitions differently (40), future researchers may wish to consider potential sex influences during investigations of pace-related decision making during endurance activity.
CONCLUSIONS AND APPLIED RECOMMENDATIONS
This was the first study to investigate the effects of anticipated task difficulty on pacing, psychological, and physiological responses during endurance activity. The main findings were that subjects ran slower when they anticipated a 3000-m time trial would become more difficult in comparison with a time trial deceptively expected to be less difficult. Despite this slower running speed, perceptual, affective, and physiological responses did not differ between time trials. From a theoretical perspective, this study adds to our awareness and understanding of factors that influence pacing and endurance performance. More so, the findings highlight a specific context (i.e., anticipation of task difficulty) that may influence the regulatory processes underpinning pace-related decision making. Based on the findings, some important recommendations for endurance athletes and coaches can be suggested. First, the reduced speed during KI time trial appeared to result from increased demand uncertainty. However, previous experience reduced this negative impact for those who completed the KI time trial last. As such, adequate familiarization with route elevations or other challenges (e.g., terrain (20)) before performance may help to reduce uncertainty and optimize pacing. Other self-regulatory cognitive strategies, such as chunking (i.e., mentally breaking a longer distance activity into smaller segments) and strategically setting more proximal pace-related sub-goals (23,24) may also reduce the deleterious effects of anticipated increases in task difficulty. Finally, the greater use of motivational self-talk during the final 800 m of UI emphasizes the importance of employing situationally appropriate cognitive strategies to maintain goal pursuit (7,23) during the challenging and often unexpected events that occur during endurance activity.
The authors wish to thank Noel McLaughlin, Aimee Gorman, Meghann McKeever, Nazareth Jorvinah, and Carla Hamilton for their invaluable assistance during data collection. We also thank Patrick Lynch and the Dingle Marathon, Ireland for providing the inspiration for this study.
This study received no sources of funding. The authors have no conflicts of interest to report. The results do not constitute endorsement by the ACSM and are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.
1. Smits BL, Pepping GJ, Hettinga FJ. Pacing and decision making in sport and exercise: the roles of perception and action in the regulation of exercise intensity. Sports Med
2. Edwards AM, Polman RC. Pacing and awareness: brain regulation of physical activity. Sports Med
3. Renfree A, Martin L, Micklewright D, St Clair Gibson A. Application of decision-making theory to the regulation of muscular work rate during self-paced competitive endurance activity. Sports Med
4. Pageaux B. The psychobiological model of endurance performance: an effort-based decision-making theory to explain self-paced endurance performance. Sports Med
5. St Clair Gibson A, Swart J, Tucker R. The interaction of psychological and physiological homeostatic drives and role of general control principles in the regulation of physiological systems, exercise and the fatigue process—the integrative governor theory. Eur J Sport Sci
6. Micklewright D, Kegerreis S, Raglin J, Hettinga FJ. 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
7. Venhorst A, Micklewright D, Noakes TD. Towards a three-dimensional framework of centrally regulated and goal-directed exercise behaviour: a narrative review. Br J Sports Med
8. Micklewright D, Parry D, Robinson T, et al. Risk perception influences athletic pacing strategy. Med Sci Sports Exerc
9. Marcora SM. Counterpoint: afferent feedback from fatigued locomotor muscles is not an important determinant of endurance exercise performance. J Appl Physiol
10. St Clair Gibson A, Noakes TD. Evidence for complex system integration and dynamic neural regulation of skeletal muscle recruitment during exercise in humans. Br J Sports Med
11. Pageaux B. Perception of effort in exercise science: definition, measurement and perspectives. Eur J Sport Sci
12. Micklewright D, Papadopoulou E, Swart J, Noakes TD. Previous experience influences pacing during 20 km time trial cycling. Br J Sports Med
13. Renfree A, West J, Corbett M, Rhoden C, St Clair Gibson A. Complex interplay between determinants of pacing and performance during 20-km cycle time trials. Int J Sports Physiol Perform
14. Ulmer HV. Concept of an extracellular regulation of muscular metabolic rate during heavy exercise in humans by psychophysiological feedback. Experientia
15. Rejeski WJ, Ribisl PM. Expected task duration and perceived effort: an attributional analysis. J Sport Psychol
16. Baden DA, Warwick-Evans L, Lakomy J. Am I nearly there? The effect of anticipated running distance on perceived exertion and attentional focus
. J Sport Ex Psychol
17. Brick N, MacIntyre T, Campbell M. Attentional focus
in endurance activity: new paradigms and future directions. Int Rev Sport Exerc Psychol
18. Baden DA, McLean TL, Tucker R, Noakes TD, St Clair Gibson A. Effect of anticipation during unknown or unexpected exercise duration on rating of perceived exertion, affect, and physiological function. Br J Sports Med
19. Faulkner J, Parfitt G, Eston R. The rating of perceived exertion during competitive running scales with time. Psychophysiology
20. Reese RJ. Just how bad is Heartbreak Hill? (Internet)
; 2013 Apr 15 (cited 2018 July 30). Available from: https://www.runnersworld.com/run-the-numbers/just-how-bad-is-heartbreak-hill
21. Zamparo P, Perini R, Orizio C, Sacher M, Ferretti G. The energy cost of walking or running on sand. Eur J Appl Physiol Occup Physiol
22. Davies CT. Effects of wind assistance and resistance on the forward motion of a runner. J Appl Physiol Respir Environ Exerc Physiol
23. Brick N, MacIntyre T, Campbell M. Metacognitive processes in the self-regulation of performance in elite endurance runners. Psychol Sport Exerc
24. Brick NE, Campbell MJ, Metcalfe RS, Mair JL, MacIntyre TE. Altering pace control and pace regulation: attentional focus
effects during running. Med Sci Sports Exerc
25. de Pauw K, Roelands B, Cheung SS, de Geus B, Rietjens G, Meeusen R. Guidelines to classify subject groups in sport-science research. Int J Sports Physiol Perform
26. Matthews G, Campbell SE, Falconer S. Assessment of motivational states in performance environments. Proc Hum Fact Ergon Soc Annu Meeting
27. Terry PC, Lane AM, Fogarty GJ. Construct validity of the profile of mood state—adolescents for use with adults. Psychol Sport Exerc
28. Borg GA. Psychophysical bases of perceived exertion. Med Sci Sports Exerc
29. Hardy CJ, Rejeski WJ. (1989). Not what, but how one feels: the measurement of affect during exercise. J Sport Exerc Psychol
30. Vernillo G, Giandolini M, Edwards WB, et al. Biomechanics and physiology of uphill and downhill running. Sports Med
31. Townshend AD, Worringham CJ, Stewart IB. Spontaneous pacing during overground hill running. Med Sci Sports Exerc
32. Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc
33. Hopkins WG, Hewson DJ. Variability of competitive performance of distance runners. Med Sci Sports Exerc
34. Salamone JD, Correa M, Yohn SE, et al. Behavioral activation, effort-based choice, and elasticity of demand for motivational stimuli: basic and translational neuroscience approaches. Motivation Science
35. Preston J, Wegner DM. Elbow grease: when action feels like work. In: Morsella E, Bargh JA, Gollwitzer PM, editors. Oxford Handbook of Human Action
. Oxford, UK: Oxford University Press; 2009. p. 569–86.
36. Mellers B, Schwartz A, Ritov I. Emotion-based choice. J Exp Psychol Gen
37. Finucane ML, Alhakami A, Slovic P, Johnson SM. The affect heuristic in judgements of risks and benefits. J Behav Dec Making
38. Caird SJ, McKenzie AD, Sleivert GG. Biofeedback and relaxation techniques improves running economy in sub-elite long distance runners. Med Sci Sports Exerc
39. Blanchfield AW, Hardy J, de Morree HM, Staiano W, Marcora SM. Talking yourself out of exhaustion: the effects of self-talk on endurance performance. Med Sci Sports Exerc
40. Hanley B. Pacing, packing and sex-based differences in Olympic and IAAF World Championship marathons. J Sports Sci