Perceived time during exercise is faster in individuals with higher cardiorespiratory fitness and is accompanied by differences in vagal-related autonomic regulation

Perceived time during exercise is faster in individuals with higher cardiorespiratory fitness and is accompanied by differences in vagal-related autonomic regulation | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 29 January 2026 V1 Latest version Share on Perceived time during exercise is faster in individuals with higher cardiorespiratory fitness and is accompanied by differences in vagal-related autonomic regulation Authors : Andrew Mark Edwards [email protected] , Anna Ling Yu Wong , Fenghua SUN 0000-0001-5251-4087 , Chi Ching Chow , and Chia-Hua Kuo Authors Info & Affiliations https://doi.org/10.22541/au.176967223.31854792/v1 152 views 61 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Exercise provides a powerful model for studying temporal experience under sustained physiological challenge, yet whether cardiorespiratory fitness shapes perceived time during exertion, and whether any such effect is linked to autonomic regulation, remains unclear. The present study tested whether fitness-related effects are most evident in task-embedded measures of temporal experience and progress monitoring versus a decontextualised interval timing probe, with heart rate variability (HRV) indexing vagal-related regulation. Twenty-five adults were grouped as higher fitness (HiFit; n=13; female=6) or lower fitness (LoFit; n=12; female=6) using sex-specific mean cut-points on directly measured VO 2 max and completed 20-min of cycling at 60% VO 2 max while performing a concurrent task designed to standardise attentional focus to external performance cues and minimise variability in attentional strategy. HRV was assessed pre- and post-exercise to index autonomic regulation and provide convergent physiological support for fitness-group differences in vagal-related control. Time perception outcomes comprised passage-of-time estimation (PoT; 0–100), an in-task estimate of temporal progress at 75% of the bout (DTPE; % elapsed), and a 30-s interval production task during cycling (IPT30). HiFit showed higher resting HRV than LoFit (SDNN p = .015; HF power p = .008), while HRV decreased post-exercise in both groups (ps ≤ .002). Task-embedded time perception differed by fitness: HiFit reported faster PoT (p = .029) and provided lower DTPE values at the 75% prompt (p = .027), indicating more calibrated progress monitoring. In contrast, IPT30 did not differ by group (p = .355). Across participants, vagal-related HRV indices tracked PoT and DTPE, but not IPT30. In summary, fitness-related time distortion during exercise was expressed in task-specific temporal experience and progress monitoring rather than generic interval timing and was accompanied by higher vagal-related autonomic regulation. Overall, fitter individuals experienced the same bout as passing faster despite matched relative intensity and standardised attentional focus. Title: Perceived time during exercise is faster in individuals with higher cardiorespiratory fitness and is accompanied by differences in vagal-related autonomic regulation Short title: Fitness shapes time experience during exercise Authors: Andrew Mark Edwards¹,²*; Anna Ling Yu Wong¹; Fenghua Sun¹; Chi Ching Chow¹; Chia-Hua Kuo¹,³ ¹ Department of Health and Physical Education, The Education University of Hong Kong (EdUHK), Hong Kong SAR, China ² School of Psychology and Life Sciences, Canterbury Christ Church University, Canterbury, United Kingdom ³ Laboratory of Biochemistry, The Education University of Hong Kong (EdUHK), Hong Kong SAR, China * Corresponding author: Professor Andrew Mark Edwards, Department of Health and Physical Education, Education University of Hong Kong, 10 Lo Ping Road, Tai Po, Hong Kong SAR, China Email: [email protected] Abstract word count: 299 Manuscript word count (excluding references, tables, and figures): 4660 Tables/Figures: 2 tables; 2 figures Keywords: Time perception; passage of time; exercise; cardiorespiratory fitness; heart rate variability; autonomic regulation Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Conflict of interest: The authors declare no conflicts of interest. Acknowledgements: the authors would like to thank the research students and assistants who made this project possible due to their hard work and diligence Title: Perceived time during exercise is faster in individuals with higher cardiorespiratory fitness and is accompanied by differences in vagal-related autonomic regulation Abstract Exercise provides a powerful model for studying temporal experience under sustained physiological challenge, yet whether cardiorespiratory fitness shapes perceived time during exertion, and whether any such effect is linked to autonomic regulation, remains unclear. The present study tested whether fitness-related effects are most evident in task-embedded measures of temporal experience and progress monitoring versus a decontextualised interval timing probe, with heart rate variability (HRV) indexing vagal-related regulation. Twenty-five adults were grouped as higher fitness (HiFit; n=13; female=6) or lower fitness (LoFit; n=12; female=6) using sex-specific mean cut-points on directly measured VO₂max and completed 20-min of cycling at 60% VO₂max while performing a concurrent task designed to standardise attentional focus to external performance cues and minimise variability in attentional strategy. HRV was assessed pre- and post-exercise to index autonomic regulation and provide convergent physiological support for fitness-group differences in vagal-related control. Time perception outcomes comprised passage-of-time estimation (PoT; 0–100), an in-task estimate of temporal progress at 75% of the bout (DTPE; % elapsed), and a 30-s interval production task during cycling (IPT30). HiFit showed higher resting HRV than LoFit (SDNN p = .015; HF power p = .008), while HRV decreased post-exercise in both groups (ps ≤ .002). Task-embedded time perception differed by fitness: HiFit reported faster PoT (p = .029) and provided lower DTPE values at the 75% prompt (p = .027), indicating more calibrated progress monitoring. In contrast, IPT30 did not differ by group (p = .355). Across participants, vagal-related HRV indices tracked PoT and DTPE, but not IPT30. In summary, fitness-related time distortion during exercise was expressed in task-specific temporal experience and progress monitoring rather than generic interval timing and was accompanied by higher vagal-related autonomic regulation. Overall, fitter individuals experienced the same bout as passing faster despite matched relative intensity and standardised attentional focus. Introduction Time is a fundamental human experience, yet subjective time often diverges from objective chronological time. Distortions in perceived duration and passage of time are shaped by factors including arousal, attention, emotion, and bodily state (Grondin, 2010; Wittmann, 2009). These distortions are not merely incidental: people use perceived temporal progression as information when evaluating experiences, such that faster perceived passage of time can signal enjoyment and engagement (Sackett et al., 2010), and approach-motivated positive states can compress perceived time (Gable & Poole, 2012). Exercise provides a strong and ecologically valid context in which to study these effects because it combines sustained physiological challenge with heightened interoceptive signalling, attentional control, and motivational regulation within a single behavioural bout and has been shown to distort perceived time across exercise contexts and tasks (Behm et al., 2020; Edwards & McCormick, 2017; Edwards et al., 2024a; Wittmann, 2009). However, despite extensive research on pacing and self-regulation in sport and exercise (e.g., Edwards & Polman, 2013; Tucker & Noakes, 2009), time perception remains comparatively underexamined as an integrated psychophysiological outcome during exercise, particularly in paradigms that constrain attentional strategy and standardise intensity relative to individual capacity. A consequential question is whether exercise is experienced as passing more quickly in fitter individuals even when workload is scaled to capacity. Classic accounts of prospective timing conceptualise duration judgements through internal-clock mechanisms, most prominently pacemaker–accumulator frameworks and scalar expectancy theory. In these models, pulses are generated by a pacemaker and accumulated to represent elapsed time; arousal can influence pacemaker rate, while attention to time determines how much temporal information is registered (Gibbon et al., 1984). The attentional-gate model formalises this idea by proposing that attention allocated to timing opens the gate to pulse accumulation, whereas attention allocated to competing task demands reduces the pulses counted and can contract perceived duration (Zakay & Block, 1995). Reviews consistently emphasise that prospective timing performance reflects both timing-specific processes and domain-general attentional demands (Block, 2014; Grondin, 2010). Contemporary perspectives extend these accounts by emphasising embodiment and interoception. From this viewpoint, subjective time is shaped in part by the sampling and interpretation of bodily signals (e.g., cardiovascular and autonomic cues), with the insular cortex frequently implicated as an integrative hub supporting the subjective experience of bodily states (Craig, 2009; Wittmann, 2009). Empirical work links interoceptive focus and bodily state to time distortions, supporting the broader proposal that bodily salience can modulate temporal experience (Meissner & Wittmann, 2011; Pollatos et al., 2014). Exercise therefore provides an informative testbed: it reliably intensifies bodily signals and shifts autonomic balance, but the direction and magnitude of distortion should depend on how bodily salience interacts with attention and motivation during the activity (Behm et al., 2020; Edwards et al., 2024a; Wittmann, 2009). A further reason for heterogeneity in the exercise literature is that time perception comprises dissociable components (e.g., prospective interval timing versus passage-of-time experience) (Block, 2014; Grondin, 2010; Tanaka & Yotsumoto, 2017; Wittmann, 2009). Behavioural timing tasks such as interval production (i.e., indicating when a specified duration has elapsed) test prospective timekeeping for a discrete, decontextualised interval, whereas passage-of-time judgements capture the experiential sense that time passed quickly or slowly during an event (Block, 2014; Grondin, 2010). A related but distinct class of judgement concerns perceived temporal progress within an ongoing activity (here termed task-embedded progress monitoring), which reflects how far through an activity a person believes they are while engaged in the task. These components can dissociate because they depend on partially separable processes: discrete timing tasks draw more heavily on timing and working memory operations, whereas passage-of-time and progress monitoring are more tightly coupled to engagement, affective appraisal, and sustained regulation of attention under load (Block, 2014; Grondin, 2010). In exercise contexts, this distinction is critical because task-embedded temporal experience and perceived progress are more directly tied to the psychological burden that shapes willingness to persist than performance on a decontextualised interval timing test. For example, strenuous exercise can distort subjective time even when pacing demands are central to performance, as effort-related bodily sensations become increasingly salient and interact with attention and affect during the ongoing task (Edwards & McCormick, 2017; Behm et al., 2020; Wittmann, 2009). A major limitation of prior experimental work is that participants are often treated as functionally homogeneous, with limited consideration of cardiorespiratory fitness as indexed by cardiorespiratory variables such as maximal oxygen uptake (VO₂max). This omission is theoretically consequential because fitness can shape how a given workload is experienced even when exercise is prescribed relative to individual capacity. For example, large-scale and experimental work indicates that perceived exertion is sensitive to individual factors, including cardiorespiratory fitness, and is not guaranteed to be identical even when intensity is standardised by conventional anchors (Grummt et al., 2024; Hill et al., 1987). Such individual differences should be especially important for experiential temporal outcomes (passage-of-time and task-embedded progress monitoring) because these judgements are closely linked to attentional allocation, affect, and self-regulatory capacity. A complementary perspective is that perceived time during exercise may be shaped by the interaction between effort-related bodily salience and the degree to which attention is held by task goals. Subjective time progression is shaped by motivational and affective states (Gable & Poole, 2012; Sackett et al., 2010), and integrative accounts propose that both boredom and effort can slow the experience of time via interoceptive pathways. In exercise, the most relevant element of this framework is the prediction that internal attentional capture by effort-related signals slows temporal experience, whereas externally oriented attention/focus reduces explicit temporal monitoring (Wolff et al., 2025). In sport settings, time experience also covaries with session context and enjoyment, highlighting the applied relevance of passage-of-time effects for training evaluation (Edwards et al., 2024b). Accordingly, experimental designs that constrain attentional strategy by providing common, externally referenced performance cues may help to isolate fitness-linked physiological contributions to task-embedded time experience, rather than confounding temporal judgements with uncontrolled differences in associative focus (attention directed toward internal sensations such as effort, discomfort, and bodily signals) versus dissociative focus (attention directed away from internal sensations toward external cues, the environment, or other distractors) (Brick et al., 2014). Physiologically, autonomic regulation provides a plausible psychophysiological bridge linking fitness to temporal experience during exertion. Heart rate variability (HRV) indexes aspects of autonomic nervous control of heartbeats under standardised recording conditions and is typically summarised using time-domain and frequency-domain indices (Task Force, 1996; Shaffer & Ginsberg, 2017). In psychophysiological research, vagally mediated HRV is often discussed as reflecting, at least in part, functional integrity of brain–autonomic networks that support flexible regulation of attention and affect (Laborde et al., 2017; Thayer & Lane, 2009). Consistent with this interpretation, meta-analytic evidence indicates a small positive association between vagally mediated HRV and executive functioning, particularly inhibition and cognitive flexibility (Magnon et al., 2022). In exercise contexts, such regulatory capacity may be especially relevant to time experience: stronger vagal regulation may support sustained externally oriented attention and buffering of interoceptive salience, thereby reducing explicit temporal monitoring and promoting the experience of time passing quickly, whereas weaker regulation may predispose attention to shift inward toward effort and discomfort, amplifying temporal awareness and biasing task-embedded progress monitoring. HRV may therefore contribute both construct validity in characterising fitness-related regulatory physiology and mechanistic specificity in linking regulatory capacity to exercise-embedded temporal judgements rather than decontextualised timing performance. The present study therefore tested whether cardiorespiratory fitness and vagal-related autonomic regulation are reflected in exercise-embedded temporal experience and task-embedded progress monitoring during a standardised cycling bout. It was expected that fitness-related effects would be more evident in passage-of-time and task-embedded progress monitoring than in decontextualised interval timing performed during the same bout, and that vagal-related HRV would covary with the exercise-embedded temporal measures. Participants Twenty-six healthy adults (13 men, 13 women) participated in this study, with one participant (woman) not completing the full experiment for reasons unrelated to the study. Recruitment occurred via local advertisement. Eligibility required self-reported absence of diagnosed cardiovascular, neurological, or psychiatric disorder; non-smoking status; and no use of medication known to influence cardiovascular or autonomic function. All participants provided written informed consent prior to participation. Procedures were conducted in accordance with the Declaration of Helsinki and received approval from the local institutional research ethics committee. Participants were grouped into lower- and higher-fitness categories based on directly measured maximal oxygen uptake (VO₂max) obtained at a baseline visit. The overall sample mean VO₂max was 48.3 ml·kg⁻¹·min⁻¹. To minimise confounding by sex-related differences in VO₂max, fitness classification used sex-specific mean cut-points. Participants were classified as Low Fit (LoFit) if their VO₂max was below the female sample mean (46.6 ± 6.8 ml·kg⁻¹·min⁻¹) or below the male sample mean (51.0 ± 9.7 ml·kg⁻¹·min⁻¹), and as High Fit (HiFit) if their VO₂max was at or above the corresponding sex-specific mean (n = 6 women, n = 7 men). This approach ensured that fitness reflected position relative to same-sex peers rather than absolute VO₂max alone. Design and overview The study employed a mixed design with fitness group (LoFit vs HiFit) as a between-participants factor. Autonomic outcomes were assessed before and after exercise (pre vs post). Time perception during exercise was assessed using three complementary measures: (i) subjective passage-of-time rating (PoT), completed immediately after the bout; (ii) a task-embedded estimate of temporal progress at 75% of the bout duration (DTPE); and (iii) a decontextualised 30-s interval production task administered at the same 75% timepoint (IPT30). Participants attended two laboratory visits separated by 5–7 days: a baseline fitness assessment visit (Visit 1) and an experimental exercise visit (Visit 2). Cardiorespiratory fitness assessment (VO₂max) VO₂ max was assessed during visit 1 using a graded exercise test to volitional exhaustion on an electronically braked cycle ergometer (Lode Excalibur, Groningen, The Netherlands). Expired gases were collected breath-by-breath using a calibrated metabolic measurement system (Cortex Metalyser 4B; CORTEX Biophysik GmbH, Leipzig, Germany). Experimental exercise task (steady-state cycling with externally guided attentional focus task) During visit 2, participants completed a 20-min continuous cycling bout at 60% of their individual VO ₂max . The corresponding target workload was derived from the individual VO₂–power relationship obtained during the incremental test. The steady-state bout was performed on the same ergometer. To preserve task immersion and avoid additional encumbrance, metabolic gas sampling was not collected during the steady-state bout; heart rate was monitored continuously. To standardise attentional focus and engagement across participants, exercise was paired with a goal-directed cognitive task providing continuous external performance cues. Participants received on-screen visual feedback indicating current cadence and power relative to target values and were instructed to maintain both within predefined tolerance bands throughout the bout (power within ±5% of target workload and cadence (80 rpm) within ±5 rpm of the target cadence). Target cadence was standardised for all participants. A performance score (GameScore) began at 100 and decreased by 10 points for each continuous 10-s period outside the permitted range for one of cadence or power. Higher GameScore values reflected more stable task control across the bout. Perceived exertion was recorded immediately post-exercise using Borg’s 6–20 rating of perceived exertion scale (Borg, 1998; Williams, 2017). RPE was treated as an index of perceived exertional/effort-related experience at the end of the bout, consistent with psychobiological accounts emphasising perceived effort as a central determinant of endurance-related experience and behaviour (Marcora, 2009; Pageaux, 2014). Autonomic measures R–R intervals were recorded using a validated chest-strap monitor (Polar H10; Polar Electro Oy, Kempele, Finland). The Polar H10 has demonstrated strong agreement with ECG for commonly used linear HRV indices under resting conditions (Schaffarczyk et al., 2022). Data were inspected for artefact and corrected prior to analysis using Kubios HRV software (Kubios Oy, Kuopio, Finland) (Tarvainen et al., 2014). HRV was quantified over two 5-min seated epochs: (i) pre-exercise baseline rest (eyes closed; spontaneous breathing) and (ii) post-exercise recovery commencing 60 s following cessation of exercise (seated, eyes closed, breathing normally). Time-domain HRV was indexed using the standard deviation of normal-to-normal intervals for overall HRV (SDNN; ms). Frequency-domain measures were derived and summarised as absolute high-frequency power (HF power; 0.15–0.40 Hz; ms²) and low-frequency power (LF power; 0.04–0.15 Hz; ms²), consistent with standards for short-term HRV measurement and reporting (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996). HF power was interpreted primarily as a marker of vagal modulation under standardised seated rest, whereas LF power was treated as reflecting mixed autonomic influences (Task Force, 1996). Engagement Immediately after the exercise bout, participants rated task engagement on a single-item 7-point Likert scale anchored from 1 (“very low engagement”) to 7 (“very high engagement”). Participants were instructed to rate how mentally engaged and focused they had been on the externally guided task during the bout. Single-item ratings are commonly used as brief manipulation checks and are considered appropriate when the construct is concrete and unidimensional, with evidence that they can be valid in psychological research (Allen et al., 2022). Time perception outcomes Subjective passage of time during exercise (PoT) Immediately post-exercise, participants completed a 0–100 visual analogue rating of perceived passage of time during the bout, anchored at 0 (“time passed very slowly”) and 100 (“time passed very quickly”). The use of a 0–100 visual analogue format is consistent with established passage-of-time measurement practice using analogue scales scored from 0 to 100 (Tanaka & Yotsumoto, 2017). Higher scores indicated faster subjective passage of time. Dynamic time progression estimation (DTPE; 75% prompt) At 75% of the planned bout duration, participants were briefly shown a prompt asking them to estimate how much of the exercise duration had elapsed (0–100%). Participants responded verbally with a single number while continuing to cycle. This task-embedded progress estimate was selected to minimise disruption to task engagement whilst sampling late-bout progress monitoring under exertion, consistent with prior exercise-based progress-estimation paradigms (Edwards & McCormick, 2017). Behavioural timing during exercise: interval production (IPT30) Immediately following DTPE at the 75% timepoint, participants completed a decontextualised interval production task during cycling. Participants were instructed to estimate when 30 s had elapsed without access to external timing cues and to indicate completion verbally (“stop”). The experimenter timed productions using a concealed timer. Produced duration (s) served as a generic measure of prospective interval timing during exertion, conceptually distinct from task-embedded progress monitoring. The inclusion of IPT30 was motivated by prior evidence that decontextualised interval timing can be systematically altered during exercise relative to non-exercise contexts (Edwards et al., 2024a). Statistical analysis A priori power calculations (G*Power 3.1) indicated that a total sample of approximately 22–24 participants would provide 80% power (α = .05, two-tailed) to detect a large between-group effect (d ~ 1.0) in the primary exercise-embedded time-perception outcomes (PoT and DTPE), consistent with effect magnitudes reported in exercise-based time-perception studies (e.g., Edwards & McCormick, 2017; Edwards et al., 2024a). Recruitment therefore targeted 26 participants to allow for attrition. Linear mixed-effects modelling was used for outcomes with repeated measures (HRV indices). For each repeated outcome (SDNN, HF power, LF power), fixed effects included group (LoFit vs HiFit), time (pre vs post), and their interaction. Single-time outcomes (PoT, DTPE, IPT30, GameScore, engagement, RPE) were compared between groups using independent-samples t-tests; Welch’s t-test was used where indicated by heteroscedasticity. Between-group standardised mean differences were quantified using Hedges’ g (reported as HiFit − LoFit) to provide an effect-size metric that is less biased in small samples. Effect sizes for PoT, DTPE, and IPT30 were compared descriptively to characterise the relative sensitivity of the three time-perception measures to fitness-group differences. Associations between cardiorespiratory fitness (VO₂max), autonomic indices (baseline and post-exercise HRV), and exercise-embedded time perception outcomes (PoT and DTPE) were examined using Pearson correlations. Statistical significance was set at p < .05 (two-tailed). Data are presented as means and standard deviations unless otherwise stated. AI tool disclosure AI-assisted tools (ChatGPT, OpenAI) were used sparingly to improve clarity and readability of the manuscript; no AI tools were used to generate data, perform analyses, or create figures/tables. Results Participant characteristics and cardiorespiratory fitness classification Twenty-five participants were included (LoFit n = 12; HiFit n = 13). Groups were comparable in age, height, and body mass (all ps > .10; Table 1). HiFit displayed substantially higher VO₂max than LoFit (p < .001; Hedges’ g = 3.64), confirming clear separation in cardiorespiratory fitness. Time perception during exercise (PoT, DTPE, IPT30) HiFit reported faster passage of time (PoT) than LoFit (p = .029; g = 0.90) (Figure 1). DTPE at the 75% timepoint also differed, with LoFit providing higher progress estimates than HiFit (p = .027; g = −0.92). IPT30 did not differ between groups (Welch p = .355; g = 0.37). Collapsed across participants, IPT30 productions were shorter than the 30 s target (one-sample p = .014). Task performance, perceived exertion, and engagement GameScore was higher in HiFit than LoFit (p = .037; g = 0.91), whereas RPE and engagement did not differ between groups (ps ≥ .172; Table 1). Autonomic indices (HRV) HRV differed by fitness group and changed from pre- to post-exercise (Figure 2). At baseline, HiFit showed higher SDNN, HF power, and LF power than LoFit (ps ≤ .015). Following exercise, each HRV index decreased from pre to post in both groups (within-group ps ≤ .002). Group × time interactions were not statistically significant (all ps ≥ .60), indicating no evidence that the magnitude of the pre–post reduction differed by group. Between-group differences were also present in early recovery (ps ≤ .002). Associations between fitness, autonomic regulation, and time-perception outcomes VO₂max correlated positively with PoT (r = .40, p = .050) (Table 2) and negatively with DTPE (r = −.47, p = .019). PoT was associated with baseline HF power (r = .46, p = .019), post-exercise SDNN (r = .52, p = .008), and post-exercise HF power (r = .51, p = .010). DTPE was associated with baseline SDNN (r = −.53, p = .006) and post-exercise HF power (r = −.42, p = .035). IPT30 was not associated with VO₂max, HRV indices, or engagement (all ps > .10; Table 2). Relative sensitivity of the three time-perception measures Standardised between-group differences (Hedges’ g; HiFit − LoFit) were larger for the task-embedded measures (PoT g = 0.90; DTPE g = −0.92) than for IPT30 (g = 0.37), indicating greater sensitivity of PoT and DTPE to fitness-group differences under these conditions. Primary analyses were repeated with sex included as a covariate; conclusions were unchanged. Discussion This study provides the first evidence that cardiorespiratory fitness and autonomic regulation jointly shape perceived time during exercise, with fitness-related differences emerging selectively in task-embedded temporal judgements. HiFit participants experienced time as passing faster and judged their progress differently at the 75% timepoint than LoFit participants during a 20-min cycling bout performed at the same relative intensity (60% VO₂max) and with attentional demands standardised using a common externally cued task. These fitness-related differences were present in exercise-embedded measures of temporal experience and task-embedded progress monitoring (PoT and DTPE) (Figure 1), but were not observed in a decontextualised interval production probe (IPT30) delivered at the same point in the bout. In parallel, HRV indices differentiated the fitness groups at baseline and early recovery (Figure 2), and vagal-related HRV covaried with PoT and DTPE rather than with IPT30 (Table 2). Collectively, the findings indicate that fitness-related differences in perceived time during exercise are expressed most clearly in task-embedded judgements and are accompanied by systematic differences in vagal-related autonomic regulation, supporting exercise time perception as a psychophysiological phenomenon rather than a purely cognitive timing effect. A central implication from this study is that time perception during exercise is best understood as multi-component. Prospective timing tasks such as interval production are often discussed through internal-clock and attentional-gate models, where arousal and attention alter timekeeping for a discrete interval (Gibbon et al., 1984; Zakay & Block, 1995; Grondin, 2010). However, the present dissociation indicates that fitness does not simply shift a single timing mechanism that would be expressed uniformly across measures. Instead, the results suggest that fitness modulates perceived time most strongly when temporal judgements are embedded within ongoing goal-directed behaviour, rather than reflecting a global alteration in generic interval timing. Passage-of-time judgements and task-embedded progress monitoring appear especially sensitive to the broader regulatory context in which timing occurs: how attention is held on goal demands, how bodily sensations are sampled, and how the affective meaning of effort evolves over the bout (Block, 2014; Wittmann, 2009). This distinction matters in exercise because the most adherence-relevant temporal experiences are not usually about estimating an abstract interval, but about whether the bout seems to be dragging and how much remains. DTPE is informative because it samples in-task estimates of progress during sustained effort (Edwards & McCormick, 2017) (Figure 1, panel B). This fits psychophysiological accounts of pacing and awareness that emphasise brain regulation of exercise behaviour under load (Edwards & Polman, 2013). In this framing, PoT and DTPE index how the exerciser experiences the unfolding bout, rather than simply the accuracy of prospective timekeeping. The pattern observed here (slower PoT in LoFit alongside higher DTPE at the 75% prompt) indicates that lower fitness was associated with a different temporal experience of the same objectively timed period. Importantly, these differences emerged under externally structured attentional demands. This suggests the effects cannot be reduced to a simple account in which fitter individuals spontaneously adopt a more distracted focus while less fit individuals focus inward; instead, fitness-related differences in temporal experience persisted even when the task was designed to keep attention oriented toward the same external performance cues. The HRV findings make three contributions to interpretation. First, they provide convergent psychophysiological support for the fitness grouping by demonstrating higher baseline and early-recovery HRV in HiFit (Figure 2), consistent with established links between cardiorespiratory fitness and higher vagal-related HRV under standardised conditions, alongside robust post-exercise reductions in both groups (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996; Sandercock & Brodie, 2006; Shaffer & Ginsberg, 2017). Although LF power showed the same directional group and time effects (Figure 2), interpretation focused on SDNN and HF power as the most defensible vagal-related indices in short-term recordings, whereas LF is typically treated as reflecting mixed autonomic influences under these conditions (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996; Shaffer & Ginsberg, 2017). Secondly, HRV showed mechanistic specificity: vagal-related indices covaried with PoT and DTPE but not with IPT30 (Table 2), mirroring the dissociation between exercise-embedded temporal experience and decontextualised interval timing. Thirdly, this pattern is consistent with neurovisceral integration perspectives in which vagal-related HRV is linked to regulatory processes supporting sustained attentional control and affect regulation under challenge (Thayer & Lane, 2009; Laborde et al., 2017). As such, the HRV data support the interpretation that the fitness effect on PoT and DTPE sits within a broader regulatory phenotype characterised by higher vagal-related autonomic control. In this framework, autonomic regulatory capacity provides a plausible connection between fitness and the ability to sustain task-focused processing as interoceptive signalling increases, influencing perceived temporal progression within the bout. These associations should be interpreted cautiously, but they indicate that autonomic indices track the exercise-embedded time measures more closely than generic interval timing. Notably, the absence of group × time interactions for HRV indices suggests that fitness differences were expressed primarily as higher tonic autonomic regulation at baseline and early recovery, rather than as a reduced magnitude of exercise-induced autonomic withdrawal. Attentional standardisation is central to the contribution of this research. Exercise can alter time perception partly because it changes attentional allocation and the ongoing regulation of competition between interoceptive signals and external task demands (Wittmann, 2009; Behm et al., 2020). Individuals also differ in the extent to which they adopt associative monitoring of effort and discomfort versus externally oriented focus during endurance exercise (Brick et al., 2014; Lohse et al., 2011; Vitali et al., 2019). In this literature, associative focus refers to attention directed toward internal sensations (e.g., effort/discomfort), whereas dissociative focus refers to attention directed away from such internal cues toward external information or distraction (Brick et al., 2014). Accordingly, the cycling bout was paired with a goal-directed external cueing task designed to keep attention anchored to performance cues and reduce variability in attentional strategy, including disengaged dissociation and time counting (Hutchinson & Karageorghis, 2013; Lohse et al., 2011). The absence of group differences in self-reported engagement and perceived exertion supports broadly comparable task involvement, and the GameScore provides an objective indication that participants attempted to meet the same externally specified demands. The higher GameScore in HiFit is consistent with better task control under load rather than reduced engagement. Collectively, these features make it less likely that group differences in PoT and DTPE reflect simple differences in attentional drift, strengthening the inference that fitness is linked to exercise-embedded temporal experience. The IPT30 findings further clarify what appears to be general versus fitness-specific (Figure 1, panel C). Across participants, interval productions were significantly shorter than the 30-s target, consistent with prior demonstrations that interval production can be shorter during exercise relative to non-exercise contexts (Edwards et al., 2024a). However, the lack of a fitness-group difference and the absence of meaningful associations with VO₂max and HRV suggest that this generic timing bias may reflect exercise-related influences that are shared across participants under the present dual-task demands, rather than indexing the processes that differentiate exercise-embedded temporal experience across individuals. In contrast, the measures most closely tied to the ongoing task (how fast time seems to be passing and how progress is monitored) were where fitness-related differences were expressed most strongly. This reinforces the value of distinguishing exercise-embedded temporal experience from decontextualised timing tasks when the aim is to understand perceived burden and persistence during exercise (Block, 2014; Grondin, 2010; Edwards & McCormick, 2017). From an applied perspective, the findings support perceived time as a plausible pathway linking fitness to sustained exercise behaviour. If lower-fitness individuals experience a bout as passing more slowly, the perceived temporal cost of exercise may be greater than objective duration would suggest, even when intensity is prescribed relative to capacity. Conversely, the accelerated temporal experience observed in fitter individuals may help explain why habitual exercisers often report that sessions pass quickly and are easier to sustain. This aligns with evidence that faster perceived time progression relates to enjoyment and positive evaluation of experiences (Sackett et al., 2010) and that approach-motivated engagement can compress perceived time (Gable & Poole, 2012). This is consistent with accounts suggesting that effort and salient bodily sensations can make time seem to pass more slowly, whereas engaging tasks matched to capability can make time seem to pass more quickly (Wolff et al., 2025). Interventions that support an external focus and a sense of control may therefore help reduce the perceived temporal cost of exercise, even when chronological duration is unchanged. Several limitations should be acknowledged. The sample size was modest, limiting precision for correlational estimates and smaller effects. HRV was assessed at baseline and immediately post-exercise rather than continuously during the bout, so it indexes resting regulation and early recovery dynamics rather than within-bout autonomic fluctuations. Fitness grouping was based on a mean split, although the sample showed clear separation with no borderline cases. Finally, the design is cross-sectional, so causal conclusions about training-induced changes in time experience require longitudinal intervention studies. In summary, task-embedded temporal measures (PoT and DTPE) were substantially more sensitive to fitness-related differences than the decontextualised interval timing probe (IPT30), providing methodological guidance for future work. When participants exercised at the same relative intensity and attentional demands were structured around shared external task cues, fitter individuals experienced time as passing faster and monitored progress differently within the bout, whereas a decontextualised interval timing probe showed no fitness-related difference. HiFit participants also showed higher vagal-related HRV at baseline and early recovery, and these HRV indices tracked PoT and DTPE rather than IPT30, linking autonomic regulation to exercise-embedded temporal experience. Overall, time is perceived as passing faster in fitter individuals even when intensity is matched to capacity and attentional focus is constrained. Data and code availability De-identified data and analysis code are available from the corresponding author on reasonable request, subject to institutional ethics requirements and data-protection considerations. References 1. Allen, M. S., Iliescu, D., & Greiff, S. (2022). Single-item measures in psychological science . European Journal of Psychological Assessment, 38 (1), 1–5. https://doi.org/10.1027/1015-5759/a000699 2. Behm, D. G., Carter, T. B., Cugliari, G., Bompa, T. O., & Chaouachi, A. (2020). Effect of exercise-related factors on the perception of time. Frontiers in Physiology, 11 , 770. https://doi.org/10.3389/fphys.2020.00770 3. Block, R. A. (2014). Time perception, attention, and memory: A selective review. Acta Psychologica, 149 , 129–133. https://doi.org/10.1016/j.actpsy.2013.11.003 4. Borg, G. (1998). Borg’s perceived exertion and pain scales . Human Kinetics. 5. Brick, N. E., MacIntyre, T. E., & Campbell, M. J. (2014). Attentional focus in endurance activity: New paradigms and future directions. International Review of Sport and Exercise Psychology, 7 (1), 106–134. https://doi.org/10.1080/1750984X.2014.885554 6. Craig, A. D. B. (2009). How do you feel—now? The anterior insula and human awareness. Nature Reviews Neuroscience, 10 (1), 59–70. https://doi.org/10.1038/nrn2555 7. Edwards, A. M., & Polman, R. C. J. (2013). Pacing and awareness: brain regulation of physical activity. Sports Medicine, 43 (11), 1057–1064. https://doi.org/10.1007/s40279-013-0091-4 8. Edwards, A. M., & McCormick, A. (2017). Time perception, pacing and exercise intensity: Maximal exercise distorts the perception of time. Physiology & Behavior, 180 , 98–102. https://doi.org/10.1016/j.physbeh.2017.08.009 9. Edwards, A. M., Coleman, D., Fuller, J., Kesisoglou, A., & Menting, S. G. P. (2024b). Time perception and enjoyment of professional soccer players in different training sessions: Implications for assessment of session-RPE and training load. Journal of Strength and Conditioning Research, 38 (12), e754–e760. https://doi.org/10.1519/JSC.0000000000004928 10. Edwards, A. M., Menting, S. G. P., Elferink-Gemser, M. T., & Hettinga, F. J. (2024a). The perception of time is slowed in response to exercise, an effect not further compounded by competitors: Behavioral implications for exercise and health. Brain and Behavior, 14 (4), e3471. https://doi.org/10.1002/brb3.3471 11. Gable, P. A., & Poole, B. D. (2012). Time flies when you’re having approach-motivated fun: Effects of motivational intensity on time perception. Psychological Science, 23 (8), 879–886. https://doi.org/10.1177/0956797611435817 12. Gibbon, J., Church, R. M., & Meck, W. H. (1984). Scalar timing in memory. Annals of the New York Academy of Sciences, 423 (1), 52–77. https://doi.org/10.1111/j.1749-6632.1984.tb23417.x 13. Grondin, S. (2010). Timing and time perception: A review of recent behavioral and neuroscience findings and theoretical directions. Attention, Perception, & Psychophysics, 72 (3), 561–582. https://doi.org/10.3758/APP.72.3.561 14. Grummt, M., Berndt, F., Kelm, M., Spillner, P., & Heitkamp, H.-C. (2024). Rating of perceived exertion: A large cross-sectional study of standardized cycle ergometer exercise tests. Sports Medicine – Open, 10 , 46. https://doi.org/10.1186/s40798-024-00729-1 15. Healy, S., Patterson, F., Biddle, S., Dumuid, D., Glorieux, I., Olds, T., Woods, C., Bauman, A. E., Gába, A., Herring, M. P., Kastelic, K., Lachapelle, U., Volpe, S. L., Tomat, S. B., & Pedisic, Z. (2024). It’s about time to exercise: Development of the Exercise Participation Explained in Relation to Time (EXPERT) model. British Journal of Sports Medicine, 58 (19), 1131–1144. https://doi.org/10.1136/bjsports-2024-108500 16. Heesch, K. C., & Mâsse, L. C. (2004). Lack of time for physical activity: Perception or reality for African American and Hispanic women? Women & Health, 39 (3), 45–62. https://doi.org/10.1300/J013v39n03_04 17. Hill, D. W., Cureton, K. J., Collins, M. A., & Grisham, S. C. (1987). Effect of training on the rating of perceived exertion at the ventilatory threshold. European Journal of Applied Physiology and Occupational Physiology, 56 (2), 206–211. 18. Hutchinson, J. C., & Karageorghis, C. I. (2013). Moderating influence of dominant attentional style and exercise intensity on responses to asynchronous music. Journal of Sport and Exercise Psychology, 35 (6), 625–643. https://doi.org/10.1123/jsep.35.6.625 19. Laborde, S., Mosley, E., & Thayer, J. F. (2017). Heart rate variability and cardiac vagal tone in psychophysiological research: Recommendations for experiment planning, data analysis, and data reporting. Frontiers in Psychology, 8 , 213. https://doi.org/10.3389/fpsyg.2017.00213 20. Lohse, K. R., & Sherwood, D. E. (2011). Defining the focus of attention: Effects of attention on perceived exertion and fatigue. Frontiers in Psychology, 2 , 332. https://doi.org/10.3389/fpsyg.2011.00332 21. Magnon, V., Vallet, G. T., Benson, A., Mermillod, M., Chausse, P., Lacroix, A., Bouillon-Minois, J.-B., & Dutheil, F. (2022). Does heart rate variability predict better executive functioning? A systematic review and meta-analysis. Cortex, 155 , 218–236. https://doi.org/10.1016/j.cortex.2022.07.008 22. Marcora, S. M. (2009). Perception of effort during exercise is independent of afferent feedback from skeletal muscles, heart, and lungs. Journal of Applied Physiology, 106 (6), 2060–2062. https://doi.org/10.1152/japplphysiol.90378.2008 23. Meissner, K., & Wittmann, M. (2011). Body signals, cardiac awareness, and the perception of time. Biological Psychology, 86 (3), 289–297. https://doi.org/10.1016/j.biopsycho.2011.01.001 24. Pageaux, B. (2014). The psychobiological model of endurance performance: An effort-based decision-making theory to explain self-paced endurance performance. Sports Medicine, 44 (9), 1319–1320. https://doi.org/10.1007/s40279-014-0198-2 25. Pollatos, O., Yeldesbay, A., Pikovsky, A., & Rosenblum, M. (2014). Interoceptive focus shapes the experience of time. PLOS ONE, 9 (1), e86934. https://doi.org/10.1371/journal.pone.0086934 26. Sackett, A. M., Nelson, L. D., Meyvis, T., Converse, B. A., & Sackett, A. L. (2010). You’re having fun when time flies: The hedonic consequences of subjective time progression. Psychological Science, 21 (1), 111–117. https://doi.org/10.1177/0956797609354832 27. Sandercock, G. R. H., & Brodie, D. A. (2006). The use of heart rate variability measures to assess autonomic control during exercise. Scandinavian Journal of Medicine & Science in Sports, 16 (5), 302–313. https://doi.org/10.1111/j.1600-0838.2006.00556.x 28. Schaffarczyk, M., Rogers, B., Reer, R., & Gronwald, T. (2022). Validity of the Polar H10 sensor for heart rate variability analysis during resting state and incremental exercise in recreational men and women. Sensors, 22 (17), 6536. https://doi.org/10.3390/s22176536 29. Shaffer, F., & Ginsberg, J. P. (2017). An overview of heart rate variability metrics and norms. Frontiers in Public Health, 5 , 258. https://doi.org/10.3389/fpubh.2017.00258 30. Tanaka, R., & Yotsumoto, Y. (2017). Passage of time judgments is relative to temporal expectation. Frontiers in Psychology, 8 , 187. https://doi.org/10.3389/fpsyg.2017.00187 31. Tarvainen, M. P., Niskanen, J.-P., Lipponen, J. A., Ranta-aho, P. O., & Karjalainen, P. A. (2014). Kubios HRV—Heart rate variability analysis software. Computer Methods and Programs in Biomedicine, 113 (1), 210–220. https://doi.org/10.1016/j.cmpb.2013.07.024 32. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. European Heart Journal, 17 (3), 354–381. https://doi.org/10.1093/oxfordjournals.eurheartj.a014868 33. Thayer, J. F., & Lane, R. D. (2009). Heart rate variability, prefrontal neural function, and cognitive performance: The neurovisceral integration perspective on self-regulation, adaptation, and health. Annals of Behavioral Medicine, 37 (2), 141–153. https://doi.org/10.1007/s12160-009-9101-z 34. Tucker, R., & Noakes, T. D. (2009). The physiological regulation of pacing strategy during exercise: A critical review. British Journal of Sports Medicine, 43 (6), e1. https://doi.org/10.1136/bjsm.2009.057562 35. Vitali, F., Tarperi, C., Cristini, J., Rinaldi, A., Zelli, A., Lucidi, F., Schena, F., Bortoli, L., & Robazza, C. (2019). Action monitoring through external or internal focus of attention does not impair endurance performance. Frontiers in Psychology, 10 , 535. https://doi.org/10.3389/fpsyg.2019.00535 36. Williams, N. (2017). The Borg rating of perceived exertion (RPE) scale. Occupational Medicine, 67 (5), 404–405. https://doi.org/10.1093/occmed/kqx063 37. Wittmann, M. (2009). The inner experience of time. Philosophical Transactions of the Royal Society B: Biological Sciences, 364 (1525), 1955–1967. https://doi.org/10.1098/rstb.2009.0003 38. Wolff, W., Özay-Otgonbayar, S., Danckert, J., Bieleke, M., & Martarelli, C. S. (2025). Effort and boredom shape our experience of time. Neuroscience & Biobehavioral Reviews, 178 , 106375. https://doi.org/10.1016/j.neubiorev.2025.106375 39. Zakay, D., & Block, R. A. (1995). An attentional-gate model of prospective time estimation. In M. Richelle, V. De Keyser, G. d’Ydewalle, & A. Vandierendonck (Eds.), Time and the dynamic control of behavior (pp. 167–178). Université de Liège. Supplementary Material File (psychophysiology figure 1.docx) Download 56.67 KB File (psychophysiology figure 2.docx) Download 62.16 KB File (psychophysiology tables.docx) Download 16.69 KB Information & Authors Information Version history V1 Version 1 29 January 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Andrew Mark Edwards [email protected] The Education University of Hong Kong Department of Health and Physical Education View all articles by this author Anna Ling Yu Wong The Education University of Hong Kong Department of Health and Physical Education View all articles by this author Fenghua SUN 0000-0001-5251-4087 The Education University of Hong Kong Department of Health and Physical Education View all articles by this author Chi Ching Chow The Education University of Hong Kong Department of Health and Physical Education View all articles by this author Chia-Hua Kuo The Education University of Hong Kong Department of Health and Physical Education View all articles by this author Metrics & Citations Metrics Article Usage 152 views 61 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Andrew Mark Edwards, Anna Ling Yu Wong, Fenghua SUN, et al. Perceived time during exercise is faster in individuals with higher cardiorespiratory fitness and is accompanied by differences in vagal-related autonomic regulation. Authorea . 29 January 2026. DOI: https://doi.org/10.22541/au.176967223.31854792/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.176967223.31854792/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fdfb77a082058f4',t:'MTc3OTE1ODIxNA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();