High-intensity acute exercise impacts motor learning in healthy older adults

doi:10.21203/rs.3.rs-3122105/v1

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High-intensity acute exercise impacts motor learning in healthy older adults

https://doi.org/10.21203/rs.3.rs-3122105/v1

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published 17 Feb, 2024

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Healthy aging is associated with a decline in motor sequence learning. Acute cardiorespiratory exercise has emerged as a potential intervention to improve motor learning, however research in healthy older adults is limited. The current study investigated the impact of high-intensity interval exercise on a subsequent sequential motor learning task. Twenty-four older adults (aged 55–75 years) completed either 20-minutes of cycling, or an equivalent period of active rest before practicing a sequential force grip task. Skill learning was assessed during acquisition and at a 6-hour retention test. In contrast to expectation, exercise was associated with reduced accuracy during skill acquisition compared to rest, particularly for older participants. However, improvements in motor skill were retained in the exercise condition, while a reduction in skill was observed following rest. Our findings suggest the impact of high intensity exercise on motor performance varies depending on age. Importantly, our findings indicate that high-intensity exercise can facilitate early offline consolidation of a novel skill in older adults which may have implications for motor rehabilitation.

Biological sciences/Neuroscience/Learning and memory/Consolidation

Biological sciences/Psychology/Human behaviour

Biological sciences/Neuroscience/Motor control

Motor learning

exercise

ageing

acquisition

consolidation

Motor learning, the process by which motor tasks become more efficient and automatic with practice, is crucial for daily functioning. Motor learning involves both online acquisition of the new skill, when the skill improves during practice, and offline consolidation, which occurs between practice sessions. Formation of a ‘motor memory’ requires the learning trace to be encoded into memory and become resistant to interference¹. Aging has been associated with changes in both online acquisition and offline consolidation^2,3, which may impact the ability to acquire new skills, and reduce efficacy of motor rehabilitation ^4–6. Studies suggest the rate and magnitude of motor learning acquisition for older adults is similar to younger adults^7–11, however learning is diminished in tasks with increased complexity or cognitive demands^12–15. Similarly, older adults show reduced offline consolidation compared to younger adults, as demonstrated by poorer performance on retention tests^10,16−18 and greater susceptibility to interference¹⁹. One potential intervention to support motor learning is acute exercise, which has been shown to benefit young adults²⁰.

Increasing evidence suggests exercise can benefit both acquisition and consolidation stages of motor learning (for review see Wanner, Cheng & Stieb²⁰). While some studies show that a single bout of acute cardiorespiratory exercise can improve motor performance and online learning^21–23 accumulating evidence indicates that exercise is particularly beneficial in enhancing the offline consolidation of a new motor skill^24–27. Although several studies have found no apparent benefit of acute exercise on motor consolidation^28–31, a recent meta-analysis suggests this may be explained by variations in motor learning task and exercise characteristics Exercise intensity appears to be of particular importance, with greater benefits following high-intensity exercise^32,33. In addition to healthy young adults, there is emerging evidence that exercise can improve motor learning in clinical populations such as Parkinson’s disease³⁴, Huntington’s disease³⁵, or following a stroke³⁶, however research into the benefits of acute exercise for motor learning in healthy older adults is limited.

In older adults, greater cardiorespiratory fitness and increased engagement in leisure activities are associated with better motor sequence learning³⁷ and greater capacity to induce plasticity in the motor cortex³⁸. However, a single bout of exercise may be more accessible compared to longer exercise interventions for older adults, who face increased barriers to exercise³⁹. Furthermore, the effects of acute exercise on memory are distinct from those observed in chronic exercise⁴⁰ and the impact of acute exercise on motor learning in healthy older remains unclear. Hubner and colleagues²³ found that older adults improved performance on a precision grip force-matching task immediately following an acute bout of moderate-intensity exercise compared to non-exercised controls. However, they found no benefit of exercise on acquisition or consolidation of learning. Similarly, Greeley and colleagues⁴¹ found no effect of interval exercise on an implicit motor learning task in older adults. Notably, the motor tasks in these studies did not involve explicit sequence learning. Improvements in motor learning are proposed to relate to cognitive aspects of learning, rather than purely motoric improvements⁴², and exercise has been shown to benefit different aspects of cognition in older adults^43–45. Importantly, while these studies found no apparent effect of exercise on behavioural measures of motor learning, they did show evidence of increased cortical activity²³ and resting state functional connectivity⁴¹, respectively, in brain regions associated with motor learning. Therefore, the benefit of acute exercise in older adults may become evident at higher exercise intensities, and in motor learning tasks with greater cognitive demands.

The current study aimed to investigate whether acute high-intensity exercise can improve motor learning of a complex motor skill in healthy older adults. The sequential visual isometric pinch task (SVIPT) in the current study^26,35 required participants to learn explicitly cued sequences and an implicit force-to-cursor movement transformation, thereby relying on both explicit and implicit learning processes^46,47. Research utilising similar tasks identified engagement of cortical regions associated with distinct learning components including declarative sequence learning and sensorimotor mapping⁴⁸. The added complexity of this continuous force modulation task may be more comparable to real-life skills compared to discrete motor sequence tasks such as finger-tapping⁴². It was hypothesised that healthy older adults who completed a bout of high-intensity exercise would demonstrate improved acquisition and offline consolidation of a novel motor skill compared to those who completed an equivalent period of rest.

2.1. Participants

Participants were 24 healthy adults aged 55–75 years (M_age = 66.68 years, SD = 5.32) recruited from the Melbourne area via flyers and outreach to local community groups. All participants took part in the study voluntarily and provided written consent prior to participation. Exclusion criteria included a history of neurological conditions, contraindications to exercise (Adult Pre-Exercise Screening System)⁴⁹), and cognitive impairment (as determined by a score of < 26 on the Montreal Cognitive Assessment⁵⁰). One participant was excluded from analysis due to technical issues during data collection resulting in incomplete data. Participants were all right-handed, assessed using the Edinburgh Handedness Inventory⁵¹. The study was approved by the Monash University Human Research Ethics Committee.

2.2. Design

The study utilised a between-groups design, with participants randomly allocated to either an exercise or control group. All participants attended an initial session wherein their cardiorespiratory fitness level was determined by measuring peak oxygen consumption (VO₂peak) during a graded exercise protocol. After a delay of at least 48 hours, participants then returned for an experimental session involving a 20-minute bout of high-intensity interval exercise (exercise group) or an equivalent period of active rest (control group). Following exercise or rest, participants completed a novel computer-based task, a variant of the sequential visual isometric pinch task (SVIPT)²⁶ to assess motor skill acquisition (Fig. 1A). Retention of the novel motor skill was assessed on the same day following a 6-hour break during which participants were asked to refrain from exercise or sleep.

2.3. Cardiorespiratory Fitness Test

Cardiorespiratory fitness was assessed via evaluation of peak oxygen consumption (VO₂peak) during an incremental intensity exercise test on a stationary bicycle. All tests were supervised by an experienced exercise scientist and were conducted at least 48 hours prior to the experimental session. Prior to the test, heart rate reserve (HRR) was estimated for each participant based on age, and resting heart rate (RHR):

$$\text{H}\text{R}\text{R}={\text{H}\text{R}}_{age-predicted max}-\text{R}\text{H}\text{R}$$

where

${\text{H}\text{R}}_{age-predicted max}=208-(0.7\times \text{a}\text{g}\text{e})$⁵²

The test was preceded by a 5-minute warm up period, during which participants self-selected a pedalling cadence which was maintained for the duration of the test. The first two stages lasted 3 minutes each, with workload adjusted such that participants reached approximately 40% and 60% of their estimated HRR, respectively. This was followed by 1-minute stages wherein the workload was then increased by 10–15 Watts per stage. The test lasted between 8–12-minutes, terminating once participants were no longer able to maintain the self-selected cadence. Heart rate was monitored throughout the test using a Polar H10 heart rate monitor (Polar Electro, Finland). Expired air volume, and oxygen and carbon dioxide concentration were recorded using a Powerlab 16/35 and LabChart 7 data acquisition system (ADInstruments, Dunedin, New Zealand) configured to provide breath-by-breath analysis. Subjective exertion was evaluated each minute using Borg’s Rating of Perceived Exertion (RPE)⁵³.

VO₂peak was defined as the maximum oxygen consumption rate identifiable in a 20-second averaged epoch. The incremental exercise was considered a maximal test if at least two of the following indicators were met: (1) respiratory exchange ratio of ≥ 1.1, (2) heart rate ≥ 95% estimated HRR, or (3) a self-reported RPE of ≥ 17 out of 20 ⁵⁴}. For twelve participants who did not meet these criteria, individual regression equations were used (Mean R² = .91) to derive a predicted VO₂peak value based on their estimated max heart rate⁵⁵.

2.4. Exercise Protocol

Both exercise and active rest protocols were completed on a Wattbike Atom stationary bicycle (Wattbike, 2017). Participant heart rate was monitored throughout using a Polar H10 heart rate monitor. Exercise intensity was calculated for each participant based on their incremental exercise test as a percentage of HRR. Participants in the exercise condition completed a 20-minute high-intensity interval protocol. The protocol alternated between 3-minute phases of low-intensity cycling (approximately 50%HRR) and 2-minute phases of moderate- to high-intensity cycling (up to 90%HRR)^56,57. This was followed by a low-intensity cool-down period. Participants in the control group completed 20 minutes of slow pedalling on the stationary bicycle, such that their heart rate remained within 15 beats per minute of heart rate measured at the commencement of the session.

2.5. Motor Learning Task

After the exercise or active rest protocol, participants completed the SVIPT task (Fig. 1B). Participants were seated before a computer holding a force transducer between their thumb and index finger of their dominant hand. Squeezing the force transducer produced a proportional on-screen cursor movement. Each trial commenced when 5 coloured targets appeared on screen. Participants were instructed to produce five pulses of force to move the cursor to the targets as quickly and accurately as possible according to a specified colour sequence (red-blue-green-yellow-white). Target locations were pseudorandomly shuffled among 3 different orders, requiring the learning and preparation of 3 different motor execution sequences. The amount of force required to reach the furthest target was set at 45% of each participant’s maximum voluntary pinch contraction (MVC).

Participants completed 9 initial trials to allow familiarisation with the task followed by 10 blocks of 12 trials, constituting the motor learning acquisition phase. To assess progress and encourage continual improvement, participants were shown a visual representation of their calculated skill level after completion of each block. Following a delay of 6 ± 1 hours (Rest M = 5.89 ± 0.36 hours, Exercise M = 5.77 ± 0.30 hours) participants returned to complete a retention test, comprising a warm-up (6 trials of the task to counteract the established warm-up decrement for this task⁵⁸) followed by four blocks of 12 trials.

2.6. Data Analysis

Performance on the SVIPT was assessed by calculating a skill measure, with higher values reflecting a shift in the speed-accuracy trade-off function towards faster and more accurate task performance^26,59. On each trial, accuracy was recorded as the summed distance from each of the five force peaks to their respective targets, resulting in a force error score, with lower force error indicating greater accuracy. The speed of each trial was calculated as the duration from trial onset to the end of the final force peak. The speed-accuracy trade-off function for the SVIPT has previously been defined as:

$$\text{S}\text{k}\text{i}\text{l}\text{l} \text{p}\text{a}\text{r}\text{a}\text{m}\text{e}\text{t}\text{e}\text{r}= \frac{1-force error}{force error \times \left(\text{l}\text{o}\text{g}{\left(duration\right)}^{a}\right)}$$

Where duration refers to the mean trial time for the block, and the value of a is 1.627²⁶. The same formula was applied to capture the speed-accuracy trade-off function, and to ensure homogeneity of variance across participants, the logarithm of this skill parameter was taken as the skill measure used for all analyses⁵⁹.

Data was assessed on a trial-by-trial basis. A trial was considered valid if it included 5 distinct force pulses within the timespan of the trial. Of the total 3864 trials, 267 (6.9%) were ineligible and excluded, and an additional 42 (1.1%) were identified as outliers (z= >±3.29) and Winsorized to eliminate bias prior to calculating mean performance across blocks for each participant.

2.7. Statistical Analysis

Baseline performance was assessed using independent samples t-tests. Mean skill, force error, and trial time for block 1 was compared between exercise and rest groups. To evaluate differences in skill, force error, and trial time across the 10 blocks of learning acquisition between exercise and rest groups, linear mixed models (LMMs) were constructed using a model selection approach. A maximal model-based approach was considered inappropriate for the current study, given a sample size and risk of overfitting⁶⁰. The selected models included Participant as a random factor, random slopes for Block, and fixed effects of Group (rest, exercise) and Block (1–10). Additional variables of theoretical interest (Sex, Age, and Cardiorespiratory Fitness) and interactions were entered into the model and were retained if they significantly improved overall model fit as indicated by the Akaike Information Criterion (AIC)⁶¹. In cases where Chi-square comparisons between models were non-significant, the more parsimonious model was selected. For a detailed summary of model fitting, see Supplementary Material.

Retention of motor learning was assessed as the difference in performance at the end of acquisition (average across blocks 9 and 10) and the beginning of the retention test (average across blocks 11 and 12). LMMs were constructed for skill, trial time, and force error, with Participant included as a random effect and Time (end of learning, start of retention) as a fixed effect. Additional fixed effects and interactions were added according to the model fitting process described above. Significant effects were followed-up using Bonferroni-adjusted pairwise comparisons of model estimates. For ease of interpretation, significant relationships involving age were followed up by comparing scores at one standard deviation above and below the mean age (61.3 years, referred to as “younger-old”, and 71.9 years, referred to as “older”)⁶².

Models were fit in RStudio (version 2022.07.2) (RStudio Team, 2022) using the lme4 package⁶³. Model comparisons were conducted with maximum likelihood estimation, with restricted maximum likelihood estimation and a Satterthwaite adjustment to compute the degrees of freedom in the final models. Selected models are described in Table 1. Overall effects, interactions and p values were calculated using the lmerTest package⁶⁴. Effect sizes are represented by beta estimates and 95% confidence intervals. Results are reported as mean ± standard deviation, or model estimates and 95% confidence intervals. α was set to .05 for all analyses.

Table 1

Summary of Linear Mixed Models
Dependent Variable		Model Predictors	AIC	R²(m)	R²(c)
Acquisition
Skill		~ Group × Block × Age + Sex + Fitness + (Block \| Participant)	106.15	.39	.89
Force Error		~ Group × Block × Age + Sex + Fitness + (1\| Participant)	-778.72	.32	.87
Trial Time		~ Group × Block × Age + (Block \| Participant)	141.66	.19	.89
Retention
Skill	~ Group × Time + Age + Sex + Fitness + (1 \| Participant)		37.21	.33	.96
Force Error	~ Group × Time + Age + Sex + Fitness + (1 \| Participant)		-130.99	.25	.96
Trial Time	~ Group × Time + (1 \| Participant)		63.37	.02	.91
AIC = Akaike Information Criterion. Smaller AIC values reflect better model fit. AIC values are derived from models fit using restricted maximum likelihood (REML). R²(m) = marginal R squared. A larger R²(m) reflects a higher proportion of variance accounted by fixed factors alone. R²(c) = Conditional R squared. A larger R²(c) indicates a higher proportion of variance explained by both fixed and random factors.

3.1. Baseline

Group characteristics are summarised in Table 2. Groups were balanced on age, sex, body mass index, physical activity level, resting HR, and retention test delay (all p > .05). Baseline skill scores (block 1) did not differ across Group (t(21) = 0.13, p = .89), nor did force error (t(21) -0.22, p = .83) or trial time (t(21) = − 0.05, p = .97).

Table 2

Participant means and standard deviations for Rest and Exercise groups
	Rest	HIIT Exercise
n (female)	12 (7)	11 (8)
Age (years)	67.92 ± 4.10	65.27 ± 6.31
Retention test delay (hrs)	5.89 ± 0.36	5.77 ± 0.30
Body mass index	25.56 ± 3.59	27.19 ± 4.54
Self-report physical activity (IPAQ)	5789 ± 3888	8224 ± 3725
Maximal Fitness Test
VO₂peak (mL.kg^− 1.min^− 1)	36.68 ± 10.92	38.76 ± 11.31
n maximal test attained	4	7
Resting HR	64.25 ± 3.41	67.00 ± 11.51
Max HR (attained)	151.00 ± 14.85	158.45 ± 11.56
Max HR (estimated)	160.46 ± 2.87	162.31 ± 4.42
Max output (W)	187.27 ± 71.65	185.00 ± 97.25
Exercise Session Characteristics
Peak HR	82.82 ± 10.40	150.91 ± 13.64
Peak output (W)	-	150.03 ± 83.85
Peak %HRR	-	91.09 ± 10.46
RPE	-	16 ± 2.79
Resting heart rate (HR), max HR measured in beats per minute. Max output in watts (W), and heart rate reserve (HRR) obtained from graded exercise test. IPAQ (International Physical Activity Questionnaire) scores expressed as METs minutes/week. There were no significant group differences in demographics or maximal fitness parameters (all p > .05). Peak HR, output and Borg’s Rating of Perceived Exertion (RPE) for the acute exercise bout summarised for the exercise group only.

3.2. Acquisition

SVIPT performance across all blocks is summarised in Fig. 2A. For skill scores, the LMM analysis revealed several main effects, including Group (F(1, 17.00) = 5.71, p = .03) with exercise participants scoring a total of 6.58 lower than rest across all blocks (95% CI [-12.01, -1.15]), and Age (F(1, 17.00) = 5.74, p = .03) as younger-old participants scored 0.71 higher than older participants (95% CI [0.13, 1.29]). However, these main effects were superseded by the interaction effects in the model. A full summary of main effects, interactions and fixed effects estimates for all LMMs can be found in the Supplementary Materials, but most notably, there was a significant three-way interaction between Group, Block, and Age (F(1, 19.00) = 4.61, p = .04). Figure 3 shows model estimates for change in skill across Block for the rest and exercise groups at different ages. Within this older adult sample, younger-old age was associated with significant improvement across both rest ($\widehat{\beta }$ = 0.06, 95% CI [0.01, 0.11], p = .03) and exercise groups ($\widehat{\beta }$ = 0.06, 95% CI [0.03, 0.09], p = .001). In contrast, older age was associated with improvement in skill across Block in the rest group ($\widehat{\beta }$ = 0.08, 95% CI [0.03, 0.12], p = .001), but not following exercise ($\widehat{\beta }$ = − .001, 95% CI [-0.04, 0.04], p > .99).

Differences in skill were driven by changes in the accuracy subcomponent of the skill measure. Force error scores showed a three-way interaction between Group, Block and Age (F(1, 195.00) = 5.70, p = .02). Younger-old participants reduced force error following rest ($\widehat{\beta }$ = -0.01, 95% CI [-0.01, -0.001], p = .02) and exercise ($\widehat{\beta }$ = -0.01, 95% CI [-0.01, -0.003], p < .001), while older participants reduced error following rest ($\widehat{\beta }$ = -0.01, 95% CI [-0.01, -0.002], p = .004), but not exercise ($\widehat{\beta }$ = 0.002, 95% CI [-0.002, 0.01], p = .45).

Analysis of trial time revealed no significant main effect of Group (F(1, 19.00) = 0.72, p = .40), though there was a main effect of Block (F(1, 19.00) = 6.29, p = .02, $\widehat{\beta }$ = 0.83, 95% CI [0.15, 1.51]) and a Block by Age interaction (F(1, 19.00) = 7.62, p = .01). Model estimates indicate that older participants decreased speed over time ($\widehat{\beta }=$-0.09, 95% CI [-0.14, - 0.04]) while younger-old did not ($\widehat{\beta }$ = -0.002, 95% CI [-0.05, 0.05]), however this did not vary across rest and exercise (Group × Block × Age: F(1, 19.00) = 2.18, p = .16).

3.4. Retention

Assessment of skill retention, summarised in Fig. 2B, revealed no main effect of Group (F(1, 18) = 1.79, p = .20), however there was a Group by Time interaction (F(1, 21) = 5.98, p = .03), with participants in the rest group showing a reduction in skill between learning and retention ($\widehat{\beta }=$-0.09, 95% CI [-0.34, -0.11]) while participants in the exercise group showed no change in skill over the delay ($\widehat{\beta }=$-0.03, 95% CI [-0.15, 0.55]). However, skill scores did not differ significantly between exercise and rest groups at the end of acquisition ($\widehat{\beta }=$0.19, 95% CI [-0.32, 0.70]) or the start of the retention test ($\widehat{\beta }=$-0.22, 95% CI [0.73, 0.29]).

Assessing the force error subcomponent of skill, there was no effect of Group (F(1, 18) = 0.45, p = .21). However, a Group by Time interaction (F(1,2 1) = 4.50, p = .05) revealed that force error increased at the retention test for the rest group ($\widehat{\beta }=$0.03, 95% CI [0.01, 0.04]) but not for the exercise group ($\widehat{\beta }=$-.001, 95% CI [-0.02, 0.02]).

The current study aimed to investigate the effect of acute high-intensity exercise on motor learning in healthy older adults. The current study had two main findings. First, high-intensity exercise did not benefit acquisition of motor learning in healthy older adults. Contrary to expectation, exercise did not facilitate motor learning acquisition, and for some participants exercise had a negative impact. Second, the reduction in skill observed in the rest group between the end of acquisition and the retention test was eliminated in the exercise group, suggesting that HIIT exercise improved consolidation of the motor skill. As discussed below, these promising findings can inform the application of acute high-intensity exercise to support motor learning in older populations.

4.1. Exercise and Online Learning

Consistent with extant literature regarding young adults²⁰, the current study found no benefit of acute exercise on online learning of a motor sequence learning task in healthy older adults. Indeed, exercise had a detrimental impact on motor performance during learning, though this was specific to the oldest participants in our study. This contrasts with findings from Hubner and colleagues²³ who reported improved motor performance in healthy older adults following a moderate-intensity exercise intervention. The differing results in the current study may relate to the use of high-intensity exercise. Although increased physiological arousal associated with exercise has been shown to enhance basic cognitive process such as attention, rapid decision making, and automatic responses^65,66, the impact on higher-order, complex cognitive processes (i.e., those required for online acquisition of our modified SVIPT task) is less clear. The relationship between arousal and complex cognitive functions is thought to be U-shaped^67–69 and higher intensity exercise may be detrimental, at least to some types of cognition⁷⁰. The findings of the current study show that high-intensity exercise may be detrimental to performance of cognitively complex motor learning tasks by healthy older adults. This effect varied according to age within our sample, supporting the importance of optimising acute exercise interventions based on individual characteristics.

The current study found no overall difference in motor skill between groups at the start of the retention test. This indicates that learning of the motor sequences occurred for both groups, suggesting that high-intensity exercise may have negatively impacted motor performance for older participants, but not necessarily learning. Older age is associated with reductions in online motor learning relative to younger adults^71–73 which may reflect age-related changes in cortical and subcortical activity⁷⁴. For example, several studies suggest older adults show a differential pattern of cortical activation in response to motor demands^75–78 and following motor learning⁷⁹ relative to young adults. Furthermore, King and colleagues¹⁵ found that changes in functional connectivity in older adults, e.g. reduced segregation of large-scale resting networks, are associated with poorer motor performance, particularly in cognitively complex tasks. Notably, there is some evidence that high-intensity exercise may have a differential impact on the activity of these networks during attentionally-demanding tasks, compared to moderate-intensity or no exercise⁸⁰. One interpretation of the current results is that older participants were more susceptible to arousal-related deficits following exercise, which impacted their performance during motor skill acquisition.

4.2. Exercise and Offline Learning

The reported age-related deficits in motor learning in the literature likely reflect changes in offline consolidation, as older adults tend to show reduced offline improvements following motor learning compared to younger adults^2,18, and are more sensitive to interference¹⁹. In young adults, exercise is most frequently reported to benefit consolidation of motor learning, rather than online acquisition, particularly following high-intensity exercise³³. Importantly, the current results suggest that high-intensity exercise also benefits motor learning consolidation in older populations.

At the retention test, a reduction in performance was observed in the rest group, but not the exercise group, who showed no significant change in skill following a delay. This is evidence that exercise supported early consolidation processes, allowing for greater stabilisation of the newly learned motor skill. Although skill was retained ~ 6 hours following exercise, this is in contrast to previous findings using a similar experimental protocol in younger adults, which found an improvement in skill relative to control at the retention test²⁶. This likely reflects changes in consolidation associated with aging, as offline improvements in a motor skill observed in younger adults are often reduced in older participants^3,17, and can be replaced by offline forgetting⁸¹. The current study shows that high-intensity exercise benefits early consolidation processes relative to rest in older adults by reducing offline forgetting, rather than facilitating offline improvement.

The current findings contrast with those of Greeley and colleagues⁴¹, who found no benefit of exercise on implicit motor sequence learning in older adults. Key methodological differences in the current study may explain these divergent findings. Most notably, the current study utilised a higher intensity exercise protocol, with participants reaching up to 90% of their estimated maximum heart rate during high-intensity epochs, resulting in greater power output and overall exertion compared to Greeley and colleagues⁴¹. Although high-intensity exercise may impact motor skill acquisition, there appears to be a dose-response relationship in favour of higher-intensity exercise in supporting motor consolidation^32,33. This may relate to the cascade of neurochemical changes induced by high-intensity exercise, which may maintain an interval environment favourable for memory consolidation, including increased circulation of catecholamines⁸², lactate⁸³, and brain-derived neurotrophic factor (BDNF)⁸⁴.

4.3. Limitations

The current study employed a moderate sample size, determined by power analysis based on previous studies using comparable motor learning tasks²⁶. We note that while the groups were matched overall, the demographic variables of age and cardiorespiratory fitness were found to be relevant to the study outcomes. These warrant further investigation, as age-related changes in motor learning may not occur in a linear fashion across the age range included in the current study⁷¹. Additional studies with larger samples are required to robustly examine interactions between these demographic variables on observed outcomes.

This study also utilised a same-day retention test, minimising potential confounding factors of sleep quality on subsequent motor skill consolidation and providing novel insight into the benefit of exercise on early consolidation. While sleep is not necessary for early motor consolidation²⁶, we note that sleep-dependent consolidation is a key aspect of motor learning^85,86 and changes in sleep architecture may play a role in age-related declines in motor learning^87,88. Moreover, previous exercise and motor learning studies suggest the benefits of exercise on consolidation become more pronounced over days and weeks after learning in younger adults²⁰. Further research is required to examine the longer-term impact of HIIT exercise on complex motor learning in older adults.

4.4. Conclusions

For our motor skill learning task, exercise did not facilitate online acquisition and may even have inhibited online acquisition for the oldest participants. However, we observed preserved motor learning following a delayed retention test for participants who exercised, in contrast to participants who did not exercise that demonstrated a performance decrement. Overall, these results demonstrate the importance of individual factors such as age when designing exercise interventions. Furthermore, these results suggest that the benefits of high-intensity exercise on early motor consolidation extend to older adult populations.

VO₂peak_,peak oxygen consumption; SVIPT, sequential visual isometric pinch task; HRR, heart rate reserve; RHR, resting heart rate; MVC, maximal voluntary contraction; RPE, rating of perceived exertion; LMM, linear mixed model.

Acknowledgements

We thank Sophie Thong and Emily Brooks for their support with data collection, and Tamar De Moel, Sarah Wallis and Sarah Cohen for their assistance with participant recruitment.

Authorship contribution statement

Eleanor M. Taylor: Conceptualization, Methodology, Formal analysis, Investigation, Writing –

Original Draft, Visualisation. Claire J. Cadwallader: Analysis, Investigation, Writing - Review & Editing.Dylan Curtin: Analysis, Investigation, Writing - Review & Editing. Joshua J. Hendrikse: Methodology, Investigation, Writing - Review & Editing.Trevor T-J. Chong: Conceptualization, Methodology, Writing - Review & Editing, Funding acquisition. James P. Coxon: Conceptualization, Methodology, Formal analysis, Investigation, Writing - Review & Editing, Visualisation, Funding acquisition.

Data Availability

De-identified behavioural data and code used for analysis available upon request to the study authors.

Funding

This research was supported by the Australian Research Council Grant, DP200100234, awarded to J.C., and funding awarded to T.T-J.C and J.P.C by the Office of Naval Research (Global). T.T-J.C is supported by the Australian Research Council (DP180102383, FT220100294).

Competing interests

The authors report no competing interests.

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High-intensity acute exercise impacts motor learning in healthy older adults

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Abstract

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1. Introduction

2. Methods

2.1. Participants

2.2. Design

2.3. Cardiorespiratory Fitness Test

2.4. Exercise Protocol

2.5. Motor Learning Task

2.6. Data Analysis

2.7. Statistical Analysis

3. Results

3.1. Baseline

3.2. Acquisition

3.4. Retention

4. Discussion

4.1. Exercise and Online Learning

4.2. Exercise and Offline Learning

4.3. Limitations

4.4. Conclusions

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