Physical exercise and motor learning: A scoping review

32 Physical exercise, as an adjunct to motor task practice, can enhance motor learning by inducing 33 neurophysiological changes known to facilitate this process. This preregistered scoping review mapped 34 current knowledge on physical exercise’s effect on motor learning . The search strategy us ed indexed 35 terms and keywords across several databases: MEDLINE, EMBASE, SPORTDiscus, Web of Science, 36 PsycINFO, CINAHL Complete, ERIC and Dissertations & Theses Global (ProQuest) . A total of 66 37 sources were included, comprising 62 experimental studies and 4 review articles. Most studies involved 38 healthy populations (83%), while fewer focused on clinical populations (17%). Aerobic exercise was the 39 predominant type used, with lower -limb cycling the most common approach (73%). Among various 40 intensity levels, high -intensity exercise was most frequently investigated; however, in some studies, 41 reported exercise intensity did not accurately reflect the method prescribed (6%). In contrast, resistance 42 exercise was rarely examined (0.6%), highlighting a notable gap in existing literature. While some studies 43 assessed both motor skill acquisition and a retention test (55%), others only employed a retention test 44 (45%). Motor learning was measured at different time points including short-term or long-term delayed 45 retention tests, or both. This underscores the need for consistent inclusion of long-term delayed retention 46 tests to better capture lasting effects of physical exercise on motor learning. This review highlights gaps 47 in the literature, including underrepresentation of clinical populations, inaccurate reporting of exercise 48 intensity, scarce research on resistance exercise, and limited assessment of long-term retention tests, all 49 of which may affect interpretation of the effect of physical exercise on motor learning. 50 51 52

aerobic exercise, resistance exercise, motor skill acquisition, retention test, 53 performance 54 55 56 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 3 Key points 57 ▪ This scoping review provides perspectives for future research on the effects of physical exercise 58 on motor learning. 59 ▪ Despite the crucial role of motor learning for clinical populations, a smaller proportion of these 60 population groups was evaluated compared to healthy populations. 61 ▪ Lower-limb cycling was the most commonly used physical exercise. 62 ▪ High-intensity exercise was the most frequently studied intensity level. 63 ▪ Resistance exercise was underrepresented in the field. 64 ▪ Accurate prescription of exercise intensity is essential to ensure reliable findings. 65 ▪ Future research should more consistently use longer-term retention tests to better assess the 66 lasting effects of physical exercise on motor learning. 67 68 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 4 1. Introduction 69 Although th e terms “physical activity” and “physical exercise” are sometimes used in similar 70 contexts, they refer to different types of physical movements [1]. Physical activity is defined as any body 71 movement produced by skeletal muscles resulting in energy expenditure [2]. In contrast, physical exercise 72 consists of performing planned and structured physical activity to enhance physical fitness or maintain 73 overall health, and can be either chronic or acute [3]. There are different types of physical exercise (e.g. , 74 aerobic or resistance), such as running, cycling, or swimming for aerobic exercise, and weightlifting or 75 bodyweight exercises for resistance exercise, which could involve different modes of muscular 76 contractions (i.e., concentric, eccentric, or isometric). Aerobic exercise primarily targets the cardiovascular 77 system and metabolic health , leading to improvement in endurance performance. R esistance exercise 78 primarily targets the neuromuscular system and is essential for developing muscular strength and power. 79 Both aerobic exercise and resistance exercise are important for maintaining functional independence. 80 Moreover, physical exercise intensity may vary (e.g., low, moderate, and high), and the methodology used 81 to prescribe exercise intensity is different among studies. Importantly, physical exercise is not only 82 beneficial for maintaining physical function and overall health, as it has also been shown to exert beneficial 83 effects on brain health [4, 5]. 84 Recently, numerous studies in the field of exercise science have highlighted how external 85 interventions, such as physical exercise , can enhance nervous system function [6]. More specifically, 86 physical exercise has been shown to induce significant improvements in cognitive function [5], cognitive 87 performance [7] and memory [8]. Furthermore, physical exercise has been associated with changes in 88 neuroplasticity [9-13]. Interestingly, neuroplasticity changes , including changes in motor cortex 89 excitability, have been observed during the process of motor learning [14-16]. These observations suggest 90 that physical exercise may act as a priming mechanism for motor learning by facilitating these 91 neuroplasticity changes. Consequently, incorporating physical exercise as an adjunct intervention could be 92 an effective strategy to enhance motor learning [17, 18]. 93 A motor skill refers to the ability to perform a goal-directed movement that enables an individual 94 to achieve a specific outcome with accuracy and efficiency, using minimal time and effort [19]. Although 95 often used interchangeably, the terms motor performance and motor learning represent distinct concepts. 96 Motor performance refers to the observable execution of a voluntary action, wh ereas motor learning 97 involves practice-driven processes that lead to relatively lasting improvements in movement ability [20]. 98 Motor learning consists of two fundamental aspects: skill acquisition and skill maintenance. Skill 99 acquisition involves developing movement control through practice, and skill maintenance ensures long -100 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 5 term skill retention and adaptability of the learned skill under different con texts [21]. To study these 101 processes, several motor learning categories have been established, including sequence learning, motor 102 adaptation, motor acuity, and associative learning (for more details, see Figure S1 in supplementary 103

1). 104 Despite the well-established benefits of physical exercise on physical function and cognition [5, 7, 105 22], its impact on motor learning remains unclear, with divergent findings contributing to this uncertainty. 106 For example, some studies found that an acute bout of aerobic exercise only improve s the acquisition of 107 motor skills [23, 24] whereas other studies showed improvements in both motor acquisition and motor skill 108 retention [25-27]. Another group of studies found an enhancement only in motor skill retention [28-31]. In 109 contrast, some studies have found no effect on both skill acquisition and retention [32-37]. Interestingly, 110 the literature include s a wide range of exercise modalities (e.g., continuous and interval training), types 111 (e.g., aerobic and resistance), and intensities (e.g., low, moderate, and high), as well as varied delays in 112 timing of retention tests (i.e., ranging from hours to days), all of which may contribute to the divergent 113 effects of physical exercise on motor learning. Also, within aerobic exercise, multiple forms have been 114 used, including lower-limb cycling, running, walking, and upper -limb cycling. This diversity in exercise 115 types and protocols adds to the variability observed across studies and underscores the need for careful 116 comparison when interpreting results. 117 Considering these discrepancies, mapping the existing literature is crucial to better understand the 118 effect of physical exercise on motor learning. Understanding how different types and intensities of physical 119 exercise could impact motor learning is crucial for developing targeted exercise protocols that address the 120 specific needs of various populations, whether clinical populations in rehabilitation settings or healthy 121 individuals in sport. To optimize the effects of physical exercise on motor learning , it is important to 122 recognize that these populations have dist inct needs, and therefore, tailored exercise protocols may be 123 necessary to enhance motor learning. 124 This scoping review aims to explore and map the existing literature on the impact of physical 125 exercise on motor learning in humans. It examines the various populations studied, the types of physical 126 exercise implemented, and the timing of exercise interventions, specifically whether they were performed 127 before or after motor task practice. Additionally, this scoping review will also identify the different motor 128 learning categories used across studies. 129 130 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 6 2. Methods 131 This scoping review was pre-registered on the Open Science Framework (OSF) Website 132 (https://osf.io/4mv53) and was conducted according to the JBI scoping review guidelines and 133 recommendations [38]. 134 135 2.1 Search strategy 136 The database search was conducted by a librarian (DA) in collaboration with LY and consultation 137 with BP and JN . The search strategy in cluded a combination of keywords and terms indexed across 138 various databases, including MEDLINE (Ovid), EMBASE (Ovid), PsycINFO (Ovid), SportDiscus with 139 Full Text (Ebsco ), CINAHL Complete (Ebsco), ERIC (ProQuest), Web of Science (Clarivate), and 140 Dissertations & Theses Global (ProQuest). The complete search equations are provided in supplementary 141

2. The search process was conducted in February 2024 and updated in March 2025. The second 142 search led to the inclusion of two additional experimental studies. The search was limited to articles in 143 English and French, with no restrictions on the publication year. All identified sources were uploaded in 144 Endnote (Clarivate Analytics, PA, USA) and duplicates were removed. The screening of all sources was 145 then managed using Covidence software [39] and followed three steps: (1) remaining duplicates removal, 146 (2) title and abstract screening, and (3) full-text evaluation. Two researchers (LY and AOF) 147 independently reviewed articles at each stage, and any disagreements were resolved through discussion 148 with a third researcher (BP). A detailed flowchart outlining the selection process of sources can be found 149 in Figure 1. 150 151 2.2 Inclusion Criteria 152 All fully published, peer -reviewed articles evaluating the effect of physical exercise on motor 153 learning were included . Both experimental studies and reviews that incorporated experimental studies 154 were considered. The primary focus was the impact of physical exercise on motor learning, assessed in 155 either laboratory or field settings. Studies were included if they involved clearly defined motor tasks with 156 assessments of skill acquisition, with or without retention tests. However, following the classical 157 definition of motor learning [20], skill maintenance over time was not considered in studies where a 158 retention test was not included. Furthermore, all types of physical exercise interventions (aerobic or 159 resistance) at any intensity level (low, moderate, high) were included. Finally, the review included studies 160 involving all types of human populations (healthy and clinical) of all ages. 161 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 7 162 163 Fig . 1 Flowchart illustrating the selection process of sources. 164 165 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 8 2.3 Data extraction 166 The extracted data included details about the evaluated populations, the physical exercise 167 interventions, the motor learning parameters and additional key findings relevant to the objectives of this 168 scoping review. Data extraction was carried out by two independent reviewers (LY and AOF), with any 169 disagreements resolved through discussion. If consensus could not be reached, JN and BP were consulted 170 to provide a final resolution. A data extraction table was used to record the information from the included 171 studies. The initial table was tested on two articles by LY and AOF, then slightly refined before reaching 172 its final version presented in supplementary material 3. For the 6 2 experimental studies reviewed, the 173 following data were collected: population characteristics (age and type), details of the physical exercise 174 performed (type, duration, intensity) based on American College of Sports Medicine (ACSM) guidelines 175 [40], timing of exercise relative to motor task practice (before or after acquisition) , motor learning 176 category (sequence, motor acuity, motor adaptation, associative learning) as well as the result as reported 177 in each study (i.e, effect of physical exercise on skill acquisition and/or skill retention). 178 Although we followed the pre-registration for the review, two minor deviations were made during 179 the data extraction process to improve methodological clarity. First, we introduced an additional 180 distinction between studies regarding the methodology for exercise prescription. We decided to 181 dissociate prescriptions based on measured versus estimated parameters (e.g., estimated vs measured 182 maximal heart rate). This refinement was not initially planned but was added during data extraction to 183 better capture variability in exercise prescription methods across studies. Second, regarding the motor 184 learning categories [21], we originally intended to use predefined categories such as sequence learning 185 and adaptation learning. However, after examining the motor tasks used in the included studies, we found 186 that these predefined categories did not fully capture the distinct characteristics of the motor learning 187 constructs investigated. Therefore, we refined our categorization to better reflect the outcomes being 188 evaluated. The categorization used is detailed in Figure S1 of supplementary material 1. 189 In this scoping review, the number of experimental conditions exceeds the number of studies, as 190 several studies included multiple experimental conditions. For example, some studies involved different 191 exercise intensities or different populations. As a result, the mapping of the literature was also conducted 192 based on the number of experimental conditions relevant to each research question. In the manuscript, n 193 refers to a subtotal of the number of participants, k refers to a subtotal of the number of studies, g refers 194 to a subtotal of the number of groups, and e refers to a subtotal of the number of experimental conditions. 195 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 9 When these letters are capitalized, it refers to the grand total (e.g., N = the total number of participants 196 including all studies and all experimental conditions). 197 198 Motor learning categorization and results 199 To classify the motor learning categories used across studies, we identified four main categories 200 based on task characteristics and learning objectives [21]. Sequence learning , often referred to as 201 sequence-specific learning, was defined for motor tasks that involve comparing performance on a 202 repeated sequence or sequences with that o f random sequences (e.g., serial reaction time task) . This 203 comparison is necessary to identify learning that is specific to the practiced sequence rather than general 204 improvements in other aspects of the motor skill. However, if only a repeated sequence or sequences 205 were practiced without including random sequence s for comparison, the motor task was categorized as 206 motor acuity. Motor acuity refers to improvements in the accuracy and/or precision of performing a 207 particular movement or sequence of movements through practice (e.g., Rho task). In this case, the focus 208 is on refining the ability to execute the motor task more precisely and/or accurat ely. Motor adaptation 209 included tasks in which participants adjusted their movements in response to external perturbation (e.g., 210 visuomotor rotation task). Finally, associative learning referred to tasks requiring participants to form 211 arbitrary associations between stimuli and responses (e.g., conditional learning task) . The primary 212

of each category is shown in Figure S1 of supplementary material 1. This classification provides 213 a clear view of the distribution of motor learning categories across the included studies and the 214 identification of gaps in literature. 215 To provide a comprehensive understanding of the effect of physical exercise on motor learning, 216 we examined skill acquisition and skill retention separately based on when physical exercise was 217 performed relative to the motor task. The results for both skill acquisition and skill retention were 218 reported in studies that performed physical exercise prior to the motor task, while only results from 219 retention tests were reported in studies that conducted physical exercise after the motor task. 220 Furthermore, the timing of retention tests varied across studies. To address this potential challenge in the 221 synthesis of findings, we categorized the reported results from retention tests into three time windows: 222 before 24 hours, at 24 hours, and after 24 hours. Consequently, findings from some studies appear in 223 multiple retention time windows. Results were categorized based on exercise intensity and separated 224 according to population type. However, due to the complexity of intervention types and timing, all forms 225 of physical exercise were grouped together in the outcome of this scoping review, regardless of when the 226 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 10 exercise was performed. To accurately assess the effects of physical exercise on motor learning, a 227 comparison with a control condition (i.e., without physical exercise) is essential and was included in the 228 majority of the studies. Based on the results reported by the authors in the experimental studies, motor 229 learning effects were categorized as enhanced, no difference, or attenuated performance. Although 230 studies without a control condition were included in the scoping review and their data were extracted, 231 their findings were not considered in the outcome of this scoping review. 232 233 234 3. Results 235 3.1 Included studies 236 Following the systematic search in various databases, 66 sources were included (see Figure 1). 237 Following the JBI guidelines for categorization, 62 primary sources were experimental studies and 4 238 secondary sources were review articles [1 narrative review [17], 2 systematic reviews [41, 42], and 1 239 systematic review with meta-analysis [18]]. A timeline of the included sources is presented in Figure 2a. 240 The research interest on the effect of physical exercise on motor learning first appeared as early as 1970, 241 but after a long gap, studies in this area began to emerge more consistently starting in 2009, reaching a 242 peak in 2023 with 13 studies published that year. A world map illustrating the geographical distribution 243 of the included studies is presented in Figure 2b. T his scoping review revealed a worldwide interest in 244 the effects of physical exercise on motor learning, with most studies conducted in Canada (k = 16), the 245 United States (k = 11), Germany (k = 10) and Denmark (k = 6). 246 We extracted data from 62 experimental studies to address our specific research questions. The 247 following sections address our review questions, detailing the populations studied, the types of physical 248 exercise implemented, the motor learning parameters investigated, and the timing of physical exercise in 249 relation to motor task practice. An overview of the main characteristics of all experimental studies is 250 presented in Table 1 . Detailed characteristics of all experimental studies are provided in Table S1 of 251 supplementary material 1. 252 253 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 11 Table 1. Characteristics of the included studies related to the review questions 254 255 256 257 Study (1) What types of population were investigated? (2) What types of physical exercise were performed to impact motor learning? (Type, Intensity) (3) What types of motor learning categories were used? (4) When was the physical exercise performed relative to motor task practice? Andrews et al. (2023) [43] Huntington’s diseases Lower-limb cycling Moderate Motor acuity Before Andrushko et al. (2023) [44] Stroke Lower-limb cycling High Sequence Before Angulo-Barroso et al. (2019) [45] Healthy children Running High Motor adaptation Before Baird et al. (2018) [32] Healthy adults Lower-limb cycling Low and high Sequence Before Bartz and Smith (1970) [46] Healthy adults Walking Moderate Motor adaptation Before Bonuzzi et al. (2023) [47] Stroke and healthy adults Lower-limb cycling Moderate Sequence Before and after Bosch et al. (2020) [48] Healthy adults Lower-limb cycling Moderate and high Sequence After Chan et al. (2023) [49] Parkinson’s disease Lower-limb cycling High Sequence After Charalambous et al. (2018) [50] Stroke Treadmill walking and total body exercise High Motor adaptation Before and after Charalambous et al. (2019) [33] Healthy adults Whole-body cycling High Motor adaptation Before Chen et al. (2023) [51] Healthy adults Treadmill running Moderate Motor acuity Before Coco et al. (2016) [52] Healthy adults Lower-limb cycling High Motor acuity After Cristini et al. (2023) [53] Healthy adults Lower-limb cycling High Sequence After Dal Maso et al. (2018) [54] Healthy adults Lower-limb cycling High Motor acuity After Duchesne et al. (2016) [55] Parkinson’s disease and healthy older adults Lower-limb cycling High Sequence After Ferrer-Uris et al. (2017) [28] Healthy adults Shuttle run High Motor adaptation Before and after Ferrer-Uris et al. (2018) [56] Healthy children Shuttle run High Motor adaptation Before and after Greeley et al. (2021) [57] Healthy older adults Lower-limb cycling High Sequence Before Helm et al. (2017) [58] Healthy adults Upper-limb cycling High Motor adaptation Before Holman and Staines (2021) [59] Healthy adults Lower-limb cycling High Motor adaptation After Hubner et al. (2018) [60] Healthy older adults Lower-limb cycling Moderate Motor acuity Before Hung et al. (2021) [61] Healthy adults Lower-limb cycling High Motor acuity After James and Wang (2023) [34] Healthy adults Lower-limb cycling Moderate Motor adaptation Before Jespersen et al. (2023) [29] Healthy adults Lower-limb cycling Moderate and high Sequence Before and after Khan et al. (2022) [62] Healthy adults Lower-limb cycling High Motor acuity After Kuo et al. (2023a) [63] Healthy adults Lower-limb cycling Low, moderate and high Sequence After Kuo et al. (2023b) [64] Healthy adults Lower-limb cycling Moderate Sequence After Lehmann et al. (2020) [27] Healthy adults Lower-limb cycling High Motor adaptation Before Loras et al. (2020) [65] Healthy adults Lower-limb cycling Low and moderate Motor acuity Before Lundbye-Jensen et al. (2017) [66] Healthy children Floorball and running High Motor acuity After Mang et al. (2014) [25] Healthy adults Lower-limb cycling High Sequence Before Mang et al. (2016) [30] Healthy adults Lower-limb cycling High Sequence Before Munz et al. (2021) [67] Healthy children Lower-limb cycling High Motor acuity After Nepveu et al. (2017) [68] Stroke Lower-limb cycling High Motor acuity After .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 12 Table 1. Continued 258 259 260 Study (1) What types of population were investigated? (2) What types of physical exercise were performed to impact motor learning? (Type, Intensity) (3) What types of motor learning categories were used? (4) When was the physical exercise performed relative to motor task practice? Neva et al. (2019) [26] Healthy adults Lower-limb cycling Moderate Motor adaptation Before Ostadan et al. (2016) [69] Healthy adults Lower-limb cycling High Sequence After Opie and Semmler (2019) [70] Healthy adults Lower-limb cycling Moderate and high Motor acuity Before Perini et al. (2016) [71] Healthy adults Lower-limb cycling Moderate Motor acuity Before Pixa et al. (2021) [35] Healthy adults Lower-limb cycling Low and high Motor acuity Before Quaney et al. (2009) [72] Stroke Lower-limb cycling Moderate Sequence Before Quinlan et al. (2021) [36] Healthy adults Lower-limb cycling High Motor acuity Before Rhee et al. (2016) [73] Healthy adults Lower-limb cycling High Motor acuity After Roig et al. (2012) [74] Healthy adults Lower-limb cycling High Motor acuity Before and after Roig-Hierro and Batalla (2023) [75] Healthy adults Shuttle run High Motor acuity After Singh et al. (2016) [76] Healthy adults Lower-limb cycling Moderate Motor adaptation Before Skriver et al. (2014) [77] Healthy adults Lower-limb cycling High Motor acuity Before Snow et al. (2016) [23] Healthy adults Lower-limb cycling Moderate Motor acuity Before Statton et al. (2015) [24] Healthy adults Running High Motor acuity Before Stavrinos and Coxon (2017) [31] Healthy adults Lower-limb cycling High Motor acuity Before Steib et al. (2018) [78] Parkinson’s disease Lower-limb cycling Moderate Motor adaptation Before Stranda et al. (2019) [79] Healthy adults Lower-limb cycling Moderate Motor acuity Before Swarbrick et al. (2020) [80] Healthy adults Lower-limb cycling Low and high Motor acuity Before Taylor et al. (2024) [81] Healthy older adults Lower-limb cycling High Motor acuity Before Thomas et al. (2016) [82] Healthy adults Lower-limb cycling Low and high Motor acuity After Thomas et al. (2017) [83] Healthy adults Strength training, Circuit training and Hockey training High Motor acuity After Thompson et al. (2023) [84] Stroke Whole-body cycling High Motor adaptation After Tomporowski and Pendleton (2018) [85] Healthy adults Dancing Moderate Motor acuity Before and after Wanga et al. (2023) [86] Healthy adults Lower-limb cycling Low and high Sequence After Wanner et al. (2020) [37] Healthy adults Lower-limb cycling Low, moderate, and high Motor adaptation Before Wanner et al. (2021) [87] Parkinson’s disease Lower-limb cycling Moderate Motor adaptation Before Williams et al. (1985) [88] Healthy adults Upper-limb cycling Low and high Motor acuity Before Yildirim et al. (2024) [89] Healthy adults Running Low and moderate Motor acuity Before and after .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 13 261 262 263 Fig. 2 Temporal and geographical distribution of included sources. 264 (Panel a) Temporal distribution of all included sources. Orange bars represent experimental studies, while blue gradient bars 265 represent other types of sources (narrative review; systematic review; systematic review with meta -analysis). ( Panel b) 266 Geographical distribution of all included sources. The format used is the following: total number [other types of sources*]. 267 The total number of sources is shown first; when sources other than experimental studies are included, their number is 268 indicated in brackets. Darker colors indicate a higher number of sources reported from each country. n refers to the number 269 of participants reported from experimental studies conducted in that country. 270 271 272 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 14 3.2 What types of populations were investigated? 273 The majority of included studies investigated healthy populations (g = 53 ), while only a small 274 portion of studies (g = 11) focused on clinical populations (see Figure 3). Among the studies including 275 healthy populations, 45 studies involved young healthy adults (studies are listed in Table 1). Research 276 involving other age groups was limited, with only 4 studies investigating healthy children [45, 56, 66, 277 67] and 4 studies focusing on healthy older adults [57, 60, 81, 90] . Within the studies investigating 278 clinical populations, three neurological conditions were identified with 6 studies investigating individuals 279 with stroke [44, 47, 50, 68, 72, 84] , followed by 4 studies investigating individuals with Parkinson’s 280 disease [49, 78, 87, 90] . Additionally, a single study focused on individuals with Huntington’s disease 281 [43]. 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 Fig. 3 Types of populations evaluated across all included experimental studies. 315 Distribution of the 1,479 participants reported across the 62 experimental studies. Participants were categorized as either 316 healthy or clinical populations, with each category further divided into subcategories. Green gradients represent healthy 317 populations, and orange gradients represent clinical populations. k refers to the number of experimental studies, G refers to 318 the overall number of reported groups, g refers to the number of reported subgroups. 319 320 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 15 3.3 What types and intensities of physical exercise were used? 321 Among the 62 experimental studies, a total of 81 experimental conditions were conducted (E = 322 81). Interestingly, the vast majority involved aerobic exercise (e = 79), while only a small number focused 323 on resistance exercise (e = 2) (see Figure 4a). Of the aerobic exercise conditions, lower-limb cycling was 324 the main type of exercise used (e = 59). Other aerobic exercise modalities included running [24, 28, 45, 325 51, 56, 66, 75, 89] , walking [46, 50] , upper-limb cycling [58, 88] , combined upper and lower -limb 326 cycling (whole-body cycling) [33, 84] , and whole -body aerobic exercise [50]. Additionally, a small 327 number of studies employed less common physical exercise, including floorball [66], dancing [85], and 328 hockey [83]. 329 Of the 62 experimental studies included, 34 prescribed exercise intensity based on measured 330 physiological parameters reflecting individual capacity while 28 relied on estimated parameters (see 331 Figure 4b). No study used the perception of effort or other perceptual responses to prescribe the exercise 332 intensity. According to ACSM guidelines, the 81 experimental conditions were further classified as high 333 intensity (e = 48), moderate intensity (e = 23), and low intensity (e = 10) (see Figure 4c). When combining 334 exercise type and intensity, lower -limb cycling at high intensity was the most commonly used 335 combination, followed by lower-limb cycling at moderate intensity (see Figure 4d). 336 Among the 81 experimental conditions, five showed discrepancies between the reported and 337 actual exercise intensities , as defined by the ACSM guidelines. Specifically, two conditions were 338 reported as moderate intensity, but the prescribed workloads—70% age-predicted heart rate reserve [59] 339 and 65-85% age-predicted maximal heart rate with a reported average of 84% maximal heart rate [24]—340 fall within the high-intensity range. In another study, two conditions were labeled as moderate and high 341 intensity, but the prescriptions of 50% age-predicted maximal heart rate and 75% age-predicted maximal 342 heart rate respectively, actually correspond to low and moderate intensity based on ACSM guidelines 343 [65]. Also, one condition was described as low intensity, although the exercise was performed at 50% 344 age-predicted heart rate reserve, which is interpreted as moderate intensity (Opie & Semmler, 2019). 345 346 347 348 349 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 16 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 Fig. 4 Types and intensities of physical exercise reported across all included experimental studies. 392 (Panel a) Distribution of the 81 experimental conditions according to exercise type, categorized as aerobic or resistance. 393 Aerobic exercise is further subcategorized by specific types. ( Panel b) Prescription of exercise intensities across the 394 experimental studies, pink refers to measured capacity and turquoise refers to estimated capacity. Pink and turquoise gradients 395 refer to measured and estimated exersie parameters respectively. (Panel c) Exercise intensities used across all experiments 396 where red refers to high intensity, yellow refers to moderate intensity and green refers to low intensity. ( Panel d) Heatmap 397 showing the combination of exercise type and intensity where darker purple indicates a higher number of experimental 398 conditions using the specific combination. Others refers to all exercise types different from lower -limb cycling, running and 399 walking. E refers to the total number of experimental conditions reported across the 62 included experimental studies, e refers 400 to the number of experimental conditions per exercise type category and n refers to the number of participants. 401 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 17 3.4 Which categories of motor learning were investigated, and when was physical exercise 402 performed relative to motor task practice? 403 Three motor learning categories were used across the 62 included experimental studies ( see 404 Figure 5 a): 30 involved motor acuity, 1 6 focused on sequence learning, and 1 6 addressed motor 405 adaptation; no study investigated associative learning (studies are listed in Table 1). 406 Among the 73 experimental conditions related to the timing of the exercise performed (E = 73), 407 42 involved physical exercise performed before motor task practice and 3 1 after (see Figure 5b). In 408 healthy adults (e = 5 1), 30 conditions implemented exercise before motor task practice and 21 after. 409 Among older healthy adults (e = 4), three conditions performed exercise before and one after motor task 410 practice. In healthy children (e = 6), two conditions included exercise before and four after motor task 411 practice. In individuals with stroke (e = 7), four conditions involved exercise before and three after motor 412 task practice. In individuals with Parkinson’s disease (e = 4), two conditions implemented exercise before 413 and two after motor task practice. Finally, the single condition involving individuals with Huntington’s 414 disease (e = 1) performed exercise before motor task practice (see Figure 5c). 415 416 417 418 419 420 421 422 423 424 425 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 18 426 427 Fig. 5 Motor learning categories and timing of exercise relative to motor task practice. 428 (Panel a) Distribution of motor learning categories evaluated across the 62 experimental studies. (Panel b) Timing of exercise 429 in relation to motor task practice across the 73 experimental conditions, (Panel c) Timing of exercise in relation to motor task 430 practice across the 73 experimental conditions shown by population type. Light blue indicates exercise performed before the 431 motor task practice, and light red indicates exercise performed after the motor task practice. Before = exercise performed prior 432 to motor task practice; After = exercise performed after the motor task practice; n refers to the number of participants. 433 434 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 19 3.5 Effects of physical exercise on motor learning 435 The effects of physical exercise on motor learning, as reported by the authors in each included 436 experimental study, were categorized based on acquisition and retention tests (enhanced, no difference, 437 or attenuated) relative to a control condition. Results for the retention tests were separated into three time 438 windows: less than 24 hours, at 24 hours, and more than 24 hours. These results were categorized by 439 exercise intensity (high, moderate and low) and by population type (see Figure 6). Detailed results for 440 each study are also provided in Table S1 of supplementary material 1. 441 442 3.5.1 Skill acquisition 443 A total of 49 experimental conditions (E = 49) assessed the effects of physical exercise on motor 444 skill acquisition (see Figure 6a). In healthy children (e = 3; all high intensity), no significant effects were 445 observed. In healthy adults ( e = 35), the majority of experimental conditions showed no effect (e = 28; 446 across low, moderate, and high intensities), while six conditions reported enhancement (e = 6; three with 447 high intensity, three with moderate intensity), and one moderate -intensity condition led to attenuated 448 performance. In healthy older adults ( e = 3), two conditions showed no effect (high and moderate 449 intensity), and one high -intensity condition led to attenuated performance. Among individuals with 450 stroke ( e = 4), one moderate -intensity condition enhanced acquisition, while the others (two high 451 intensity and one moderate intensity) showed no effect. In individuals with Parkinson’s disease , three 452 conditions (e = 3; 2 moderate and 1 high intensity) reported no difference. Finally, the single 453 experimental condition involving individuals with Huntington’s disease (e = 1) reported improvement 454 following a moderate-intensity exercise. 455 456 3.5.2 Retention tests less than 24h 457 A total of 28 experimental conditions ( E = 28) assessed the effects of physical exercise on 458 performance within 24 hours following the exercise intervention ( see Figure 6b). In healthy children (e 459 = 3), two experimental conditions showed enhanced performance (both at high intensity), and one 460 condition at high intensity showed no difference. In healthy adults (e = 23), five conditions demonstrated 461 enhanced performance (three at high intensity, two at moderate intensity), 17 showed no difference 462 (across all three intensities), and one high -intensity condition showed attenuated performance at 463 retention. In healthy older adults ( e = 2), one condition showed enhanced performance (high intensity) 464 and one showed no difference (moderate intensity). Notably, no experimental conditions involving 465 clinical populations were reported in this retention time category. 466 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 20 3.5.3 Retention tests at 24h 467 A total of 49 experimental conditions (E = 49) assessed the effects of physical exercise on 468 performance at 24 hours following the exercise intervention (see Figure 6c). In healthy children (e = 4), 469 three conditions showed enhanced performance (all at high intensity), and one condition showed 470 attenuated performance (high intensity). In healthy adults (e = 36), 12 conditions demonstrated enhanced 471 performance (nine at high intensity, two at moderate intensity, and one at low intensity), while 24 472 conditions showed no difference (across all intensities). In healthy older adults ( e = 2), both conditions 473 showed no difference (one at high intensity and one at moderate intensity). Among individuals with 474 stroke (e = 4), one condition showed enhanced performance (high intensity), and three conditions showed 475 no difference (all at high intensity). In individuals with Parkinson’s disease (e = 3), one condition showed 476 enhanced performance (moderate intensity), and two conditions showed no difference (one at high 477 intensity and one at moderate intensity). 478 479 3.5.4 Retention tests more than 24h 480 A total of 31 experimental conditions (E = 31) assessed the effects of physical exercise on 481 performance beyond 24 hours following the exercise intervention (see Figure 6d). In healthy children (e 482 = 3), all conditions showed enhanced performance (all at high intensity). In healthy adults ( e = 21), 483 fourteen conditions showed enhanced performance (ten at high intensity, two at moderate intensity, and 484 two at low intensity), and seven conditions showed no difference (three at high intensity, three at 485 moderate intensity, and one at low intensity). In healthy older adults one condition showed no difference 486 (high intensity). Among individuals with stroke (e = 2), one condition showed no difference (moderate 487 intensity), and one condition showed attenuated performance (moderate intensity). In individuals with 488 Parkinson’s disease (e = 3), all conditions showed enhanced performance (two at high intensity and one 489 at moderate intensity). 490 491 492 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 21 493 Fig. 6 Effects of physical exercise interventions on motor learning. 494 Colored dots represent individual experimental conditions, with their position indicating whether motor learning was 495 enhanced, not different, or attenuated compared to a control condition. Only experiments including a control condition were 496 considered. All population types investigated are represented. Reported r esults related to acquisition are shown only for 497 experiments in which exercise was performed before motor task practice. Reported retention test results are shown regardless 498 of whether exercise was performed before or after motor task practice. ( Panel a) Reported results from acquisition. (Panel b) 499 Reported results on motor learning from retention test s conducted within 24 hours following exercise, ( Panel c) Reported 500

on motor learning from retention tests conducted at 24 hours following exercise ( Panel d) Reported results on motor 501 learning from retention tests conducted after 24 hours following exercise. Green dots indicate low -intensity exercise, orange 502 dots indicate moderate-intensity exercise, and red dots indicate high-intensity exercise. 503 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 22 3.6 Review articles previously published 504 Among the included sources, 4 secondary sources were review articles. Bonuzzi and colleagues 505 conducted a systematic review on aerobic exercise and motor learning in healthy individuals, 506 highlighting that exercise performed before or after motor task practice can improve motor learning [41]. 507 Hubner and colleagues conducted a systematic review examining the effects of physical activity level 508 and acute bouts of physical exercise on motor performance and motor learning of upper limb tasks in 509 older people. This systematic review highlighted the benefits of a high physical activity level in the early 510 phases of motor learning [42]. Taubert and colleagues performed a narrative review and discussed 511 behavioral and neurobiological evidence in favor of aerobic exercise as a neuromodulator strategy to 512 promote motor learning and neuroplasticity [17]. Wanner and colleagues performed a systematic review 513 and meta-analysis and evaluated how acute aerobic exercise affects the encoding and consolidation of 514 motor memory, highlighting the importance of timing and intensity [91]. 515 516 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 23 4. Discussion 517 To the best of our knowledge, this is the first scoping review to explore and map the existing 518 literature on the use of physical exercise to prime motor learning. We included studies involving any 519 population type, any form of physical exercise, and whether the physical exercise was performed before 520 or after any motor learning task. Although our scoping review focuses largely on experimental studies 521 (62 included studies), it should be noted that four previous reviews have explored specific aspects of the 522 topic (1 narrative review, 2 systematic reviews and 1 systematic review with meta -analysis). These 523 focused reviews each addressed important aspects of the topic, and our scoping review synthesis builds 524 on and integrates these contributions by examining a broader range of populations, exercise types, motor 525 learning categories and exercise timing in a single comprehensive framework. 526 The main outcomes of this scoping review were the following: (1) the evaluated population 527 consisted primarily of healthy individuals, with limited focus on clinical populations and children; (2) 528 the predominant type of physical exercise used was lower -limb cycling, potentially due to its ease of 529 implementation; (3) resistance exercise was rarely used, highlighting a significant gap in the literature 530 regarding its potential role in the promotion of motor learning; (4) in several studies, the prescribed 531 exercise intensity did not align accurately with ACSM guidelines , which may limit the ability to draw 532 accurate conclusions about the effects of specific exercise intensities on motor learning; (5) many studies 533 conducted a single retention test on the same day as the practice session; however, according to the 534 classical definition of motor learning, demonstrating maintenance in motor skill improvement over time 535 via delayed retention tests is essential [92], and hence these findings may not truly reflect motor learning. 536 537 4.1 Limited representation of clinical populations 538 Most of the included studies involved healthy individuals, with children and clinical populations 539 being significantly less evaluated. One reason could be due to practical limitations, as recruiting healthy 540 adults participants is usually less complicated and requires less time and resources. Yet, in order to 541 determine whether the results obtained in healthy individuals are transferable to children and individuals 542 living with specific health-related conditions, it is essential to replicate the interventions in children and 543 clinical populations. This is particularly of interest for children as this population is crucially developing 544 motor skills needed for their adult life. It is also crucial for clinical populations given that many 545 interventions improving motor learning are designed for rehabilitation purposes [93], and the clinical 546 relevance of these interventions should be tested within the clinical populations of interest. Furthermore, 547 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 24 given the limited number of studies involving clinical populations, the division across three distinct 548 conditions (stroke, Parkinson’s disease, and Huntington’s disease) further limits the applicability of the 549 findings to specific clinical settings, as each subgroup is represented by only a small number of studies. 550 Importantly, the underlying mechanisms of motor learning may differ between healthy 551 individuals and clinical populations due to different factors including impaired neuroplasticity, altered 552 sensory feedback, or compensatory motor strategies [94, 95]. For instance, in individuals with stroke , 553 cortical reorganization and the recruitment of alternative neural pathways may affect how motor skills 554 are re-acquired [96]. Similarly, in Parkinson’s disease, basal ganglia and sensorimotor -related 555 dysfunction may alter feedback-based learning mechanisms [97]. These differences imply that exercise 556 interventions that are effective in healthy individuals may not produce the same effects in the context of 557 clinical conditions, making it essential to investigate tailored strategies specific to each clinical 558 population. Therefore, prioritizing research in populations with motor dysfunction and rehabilitation 559 needs including individuals with stroke, or other populations living with neurodegenerative disorders, 560 would be particularly valuable. These population groups could benefit the most from interventions that 561 can enhance motor learning, recovery and maintenance of functional independence. By targeting clinical 562 populations, we can develop more effective, evidence -based interventions that address their specific 563 motor learning profiles. 564 565 4.2 Types of aerobic exercise: a focus on lower-limb cycling and the need for diversity 566 Among the included studies, the predominant form of aerobic exercise was lower -limb cycling. 567 This type of aerobic exercise is likely favored due to its ease of implementation, low risk of injury, and 568 practicality in experimental settings. Particularly, lo wer-limb cycling effectively induces an aerobic 569 response by engaging the leg muscle s, the largest muscle group in the body , resulting in a substantial 570 cardiovascular activation [98, 99]. Also, lower-limb cycling allows the upper limbs to remain at rest, 571 which helps prevent the development of neuro muscular fatigue that could negatively impact motor 572 learning when upper-limb motor tasks are used [100]. 573 Other types of aerobic exercise such as running, walking, dancing and upper-limb cycling were 574 considerably less represented. Including these diverse forms of aerobic exercise in future research is 575 important for several reasons. Different types may activate distinct neuromuscular and cardiovascular 576 responses, influencing task-specific adaptations, and offer practical alternatives for various populations. 577 For example, running engages more complex coordination, while upper-limb cycling may be particularly 578 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 25 suitable for individuals with lower -limb limitations. Dancing, which is often practiced in groups and 579 especially popular among older adults also deserves attention, as it not only combines demands on the 580 aerobic system and coordination [101, 102] , but has also been associated with enhanced cognitive 581 function [103, 104]. Dancing also may boost motivation and adherence to regular engagement in physical 582 activity through social interaction [105]. In addition, music , which is an integral part of most dance 583 interventions, has itself been linked to improved cognitive function [106] and positive affect [107], 584 further supporting the potential of dance -based activities in promoting motor learning. Such variety of 585 physical exercise could help reveal how the specific demands of different types of exercise might affect 586 motor learning. 587 Importantly, all of these reported aerobic types of physical exercise primarily relied on concentric 588 muscle contractions, where the muscles shorten while producing a force. None of the included studies 589 investigated aerobic exercise involving primarily eccentric contractions, like eccentric cycling, in which 590 muscles lengthen while producing a force. 591 592 4.3 Eccentric and resistance exercise: underexplored avenues in motor learning research 593 Interestingly, none of the included studies investigated the effects of physical exercise primarily 594 involving eccentric muscle contractions on motor learning. Eccentric contractions, which involve muscle 595 lengthening while resisting a workload, are known to induce distinct neural adaptations compared to 596 concentric contractions [108, 109] . Despite this, research focusing specifically on eccentric -based 597 physical exercise remains limited, representing a notable gap in the literature. Specifically, eccentric 598 contractions have been associated with a prolonged decrease in intracortical inhibition [109], a 599 neurophysiological marker of altered motor cortex excitability. Also, it has been shown that eccentric 600 cycling elicits greater activation in sensorimotor -related cortical areas of the frontal and parietal lobes 601 during movement planning and execution [108], as well as increased activation in cognitive-related brain 602 regions [110]. Given the involvement of these brain regions in motor learning, eccentric cycling could 603 induce potential benefits to motor learning. 604 In fact, recent preliminary evidence from our laboratory [111] supports this idea. Th is study, 605 which was not included in the present scoping review due to its preprint status at the time of submission, 606 suggests that eccentric cycling enhances motor learning more than concentric cycling in healthy young 607 adults. These findings suggest that compared to concentric cycling, eccentric cycling may offer specific 608 advantages that promote motor learning. Beyond its potential effects on motor learning, eccentric cycling 609 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 26 also provides p hysiological advan tages. For a similar workload, eccentric cycling results in a lower 610 cardiovascular, respiratory, and metabolic cost compared to concentric cycling [112-115]. Eccentric 611 cycling is therefore a particularly appealing exercise modality for clinical populations with reduced 612 cardiorespiratory capacity [e.g., [116]], as well as for older adults [(e.g., [117]]. Since lower-limb cycling 613 is already the most employed exercise type in the reviewed literature, integrating eccentric cycling could 614 allow researchers to build on already established protocols while expanding the range of muscular 615 contractions studied. Finally, other eccentric -focused exercise types like downhill walking or running 616 also deserve attention. These exercise types involve distinct neuromuscular demands [118] and a lower 617 energy cost [119], which could be beneficial in rehabilitation . Given the potential benefits of eccentric 618 exercise on motor learning, its integration into future studies evaluating the effects of physical exercise 619 on motor learning appears both feasible and promising [120]. 620 Beyond eccentric-focused types of exercise , our findings also raise important considerations 621 about resistance exercise, which often incorporates eccentric contractions and shares similar 622 physiological mechanisms [121]. These findings also highlight a significant gap in the motor learning 623 literature, which is the underrepresentation of resistance exercise as priming exercise . Of the 81 624 experimental conditions included, only two involved resistance exercise, despite the fact that this exercise 625 type is widely used in rehabilitation and training. Resistance exercise may influence motor learning 626 through several neural mechanisms . I t has been shown that resistance exercise increases muscle 627 activation and neural drive to muscles, leading to improved force production capacity [122]. Resistance 628 exercise can also modulate corticospinal excitability, with evidence indicating that resistance training 629 induces neuroplastic changes within the motor cortex and corticospinal tract [123, 124]. Furthermore, 630 resistance exercise may enhance proprioceptive input, increasing sensory feedback, which is an essential 631 component of motor control and learning [125]. Supporting these observations, cerebral blood flow 632 during force exertion has been shown to correlate with activity in brain areas such as the primary motor 633 cortex, and the supplementary motor area [126]. These regions are also integral to motor learning 634 processes, suggesting a neuroanatomical overlap between areas recruited during resistance exercise and 635 those underlying motor learning. However, the direct role of the effects of resistance exercise on motor 636 learning remains largely unexplored. Investigating how resistance exercise influences motor learning 637 represents a promising avenue for future research. Taken together, these observations highlight the need 638 for future studies to incorporate both eccentric and resistance exercise types to better understand their 639 potential roles in enhancing motor learning across various populations. 640 641 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 27 4.4 Prescription of exercise intensities 642 Although all studies reported the exercise intensity used, we standardized intensity categorization 643 based on the exercise prescription details provided using the ACSM guidelines [40]. In 5 experimental 644 conditions, discrepancies were observed between the prescribed intensity and the actual workload 645 performed by participants. Some exercise appeared either lighter or more intense than prescribed. For 646 instance, Holman et al. (2021) and Statton et al. (2015) reported using moderate -intensity exercise, yet 647 the prescribed workloads correspond to high -intensity levels according to ACSM guidelines [24, 59]. 648 Similarly, Opie and Semmler (2019) described their exercise protocol as low-intensity, although it aligns 649 with moderate-intensity based on ACSM classifications [70]. In contrast, Loras et al. (2020) referred to 650 their two exercise conditions as moderate and high intensity exercise, but these would correspond to low 651 and moderate intensity, respectively, under ACSM classifications [65]. First, this inconsistency suggests 652 that following established exercise guidelines is essential for drawing accurate conclusions about the 653 effects of specific exercise intensities on motor learning. Second, it allows replicability across studies 654 which is an essential factor for building a consistent and reliable body of evidence. Additionally, while 655 many studies relied on predicted values (e.g., age-predicted maximal heart rate, age-predicted heart rate 656 reserve, estimated VO2max), and although we recognize the practicality of using predicted values in 657 certain research settings, prescribing intensities based on measured capacities are preferable to ensure 658 that the targeted intensity is truly achieved for each participant. Interestingly, none of the studies included 659 in this review employed self-paced exercise protocols. This is notable, as self-paced exercise, for example 660 prescribed with the intensity of perceptual responses such as effort [127, 128] , when matched for 661 workload with externally prescribed intensities [129], has been proposed to enhance affective responses, 662 which can be beneficial for promoting adherence to rehabilitation programs [130]. 663 664 4.5 Uneven representation of motor learning categories 665 While the studies included in this review examined various motor learning categories such as 666 sequence learning, motor acuity, and motor adaptation, none investigated the effects of physical exercise 667 on associative learning, which is a fundamental form of motor learning involving stimulus -response 668 associations. Given its relevance in real -world contexts and rehabilitation settings [131], investigating 669 how physical exercise influences associative learning could offer valuable insights. For instance, tasks 670 such as learning to respond appropriately to traffic signals while driving rely heavily on stimulus -671 response associations. 672 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 28 Additionally, the field would benefit from more balanced research across motor learning 673 categories. The current emphasis on motor acuity (n = 30) contrasts with the limited number of studies 674 evaluating sequence learning and motor adaptation (both n = 16). Sequence learning, for example, is 675 highly relevant for activities like playing a musical instrument or typing on a keyboard , while motor 676 adaptation is crucial in situations such as adjusting movements to a new sports technique or learning to 677 walk on uneven surfaces. More research in these underrepresented categories could allow for more robust 678 meta-analyses and potentially clarify whether the effects of physical exercise vary depending on the 679 motor learning type. 680 681 4.6 Timing of retention tests 682 When measuring skill retention, different time points were reported across the studies falling in 683 our defined time windows: less than 24 hours, at 24 hours, and more than 24 hours following motor task 684 practice. Some studies included two time points [29, 33, 35, 49, 54, 60, 62, 83, 86, 87, 89, 132] or all 685 three time points [28, 45, 56, 59, 66, 74, 77, 80, 85], which provides a clearer understanding of the effects 686 on motor learning. Other studies only assessed short-term retention at less than 24 hours [31, 34, 46, 48, 687 63, 64, 69, 81, 133]. However, measuring skill retention only in the short term ( e.g., same day as initial 688 practice) may not be sufficient to assess motor learning, as this may primarily reflect temporary rather 689 sustained performance improvements [92]. According to Schmidt et al. (2018), observing performance 690 after a sufficient delay provides stronger evidence of stable and relatively permanent changes in the 691 learner’s capability, which is the hallmark of motor learning [92]. 692 Ideally, studies could include both short -term and longer-term retention tests, with the 24 -hour 693 time point used as a standard. This would help determine whether performance improvements due to 694 practice are maintained over time, facilitate comparisons across studies, and provide insight into the 695 evolution of motor skill consolidation, which is the process by which newly acquired motor skills become 696 stable and resistant to interference. Using consistent methods to measure skill retention would also help 697 improve the quality of future studies in the field of physical exercise and motor learning. 698 699 700 701 702 703 704 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 29 4.7 Limitations 705 This scoping review has several limitations, particularly regarding the report ed results of motor 706 learning, due to the complexity of categorization. First, for retention tests, all studies were grouped 707 together regardless of whether physical exercise was performed before or after skill acquisition, as the 708 heterogeneity in study designs made it difficult to establish clear subcategories for the reported results 709 on motor learning. Second, no differentiation was made based on the type of exercise for the reported 710

on motor learning, which limits the ability to attribute specific effects to a particular type of 711 exercise. Third, because we examine the effects of physical exercise in general and as reported by the 712 authors, exercise frequency was not considered, and no distinction was made between acute and chronic 713 interventions. Future studies with more data may allow for a more refined mapping of the literature 714 addressing these aspects. 715 716 4.8 Perspectives for future studies 717 This scoping review highlighted several key areas for future research on the effects of physical 718 exercise on motor learning. First, accurately prescribing exercise intensity is essential. Many studies 719 showed inconsistencies between the reported and the actual prescribed intensity, which can affect the 720 reliability of findings. Using measured values rather than predicted ones, and adhering to established 721 guidelines like the ACSM, would improve consistency and accuracy across studies. As self -paced 722 exercise are proposed to enhance affective responses [127, 128] and have positive effect on exercise 723 adherence, future studies should consider testing the possibility to prime motor learning with exercise 724 prescribed based on perceptual responses, with particular interest on the perception of effort [127, 128]. 725 Furthermore, although most studies have concentrated on concentric lower -limb cycling, it is essential 726 to explore the impact of other types of exercise , such as eccentric cycling, on motor learning. 727 Additionally, resistance exercise was rarely used . Integrating resistance exercise modalities will enable 728 us to better understand its potential role in enhancing motor learning across various populations. 729 Moreover, measuring skill retention in motor learning studies in exercise science is another area to 730 improve. Studies testing retention only within a few hours may not fully capture the achievement and 731 maintenance of motor learning. Therefore, including longer retention time points, particularly at 24 hours 732 and more, might help in providing a more comprehensive understanding of the effects of physical 733 exercise on motor learning. Finally, more studies are needed where children, older adults and clinical 734 populations are involved . Increasing research on clinical population s is especially important as motor 735 learning can play a critical role in rehabilitation and recovery. 736 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 30 5. Conclusion 737 In conclusion, while the field of physical exercise and motor learning is growing, there are several 738 key areas that require improvement. Future research should prioritize accurate and consistent reporting 739 of exercise intensity and consider longer retention intervals to better capture lasting motor learning 740 effects and, consequently, better understand the impact of physical exercise on motor learning. 741 Additionally, greater inclusion of children, older adults and clinical populations is essential, as these 742 population groups may benefit most from motor learning interventions but remain underrepresented in 743 the current literature . Expanding the range of exercise modalities, more particularly types of exercise 744 involving eccentric contractions and resistance -based exercise, may also reveal unique advantages 745 relevant to motor learning in both healthy and clinical population s. Addressing these gaps will help in 746 building a more robust and applicable body of evidence to inform both clinical and non-clinical practice. 747 748 749 750 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 31 Author contributions 751 Layale Youssef: Conceptualization, Methodology, Formal analysis, Investigation, Writing – Original 752 Draft, Visualization. Amanda O’Farrell: Formal analysis, Investigation. Denis Arvisais: Methodology, 753 Investigation. Jason L. Neva: Conceptualization, Writing – Review & Editing, Visualization, 754 Supervision. Benjamin Pageaux: Conceptualization, Writing – Review & Editing, Visualization, 755 Supervision. 756 757 Funding 758 LY is supported by Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal 759 (CRIUGM), the Faculté de médecine at Université de Montréal and the Fonds de Recherche du Québec 760 - Nature et Technologies. AOF is supported by internal scholarships from Université de Montréal. BP is 761 supported by the Chercheur Boursier Junior 1 award from the Fonds de Recherche du Québec - Santé. 762 JLN is supported by the Chercheur Boursier Junior 1 award from the Fonds de Recherche du Québec - 763 Santé (FRQS #313769). 764 765 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 32

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It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 41 130. Ekkekakis P, Parfitt G, Petruzzello SJ. The pleasure and displeasure people feel when they exercise 1074 at different intensities: decennial update and progress towards a tripartite rationale for exercise intensity 1075 prescription. Sports Med. 2011;41(8):641-71. 1076 131. Le Pelley ME, Mitchell CJ, Beesley T, George DN, Wills AJJPb. Attention and associative learning 1077 in humans: An integrative review. 2016;142(10):1111. 1078 132. Thomas R, Johnsen LK, Geertsen SS, Christiansen L, Ritz C, Roig M, et al. Acute exercise and 1079 motor memory consolidation: The role of exercise intensity. PLoS ONE Vol 11(7), 2016, ArtID 1080 e0159589. 2016;11(7). 1081 133. Coco M, Perciavalle V, Cavallari P, Perciavalle V. Effects of an Exhaustive Exercise on Motor Skill 1082 Learning and on the Excitability of Primary Motor Cortex and Supplementary Motor Area. Medicine 1083 (Baltimore). 2016;95(11):e2978. 1084 1085 1086 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 42 Figure captions 1087 Fig. 1 Flowchart illustrating the selection process of sources. 1088 1089 Fig. 2 Temporal and geographical distribution of included sources. 1090 (Panel a) Temporal distribution of all included sources. Orange bars represent experimental studies, while 1091 blue gradient bars represent other types of sources (narrative review; systematic review; systematic 1092 review with meta-analysis). (Panel b) Geographical distribution of all included sources. The format used 1093 is the following: total number [other types of sources*]. The total number of sources is shown first; when 1094 sources other than experimental studies are included, their number is indicated in brackets. Darker colors 1095 indicate a higher number of sources reported from each country. n refers to the number of participants 1096 reported from experimental studies conducted in that country. 1097 1098 Fig. 3 Types of populations evaluated across all included experimental studies. 1099 Distribution of the 1,479 participants reported across the 62 experimental studies. Participants were 1100 categorized as either healthy or clinical populations, with each category further divided into 1101 subcategories. Green gradients represent healthy populations, and orange gradients represent clinical 1102 populations. k refers to the number of experimental studies, G refers to the overall number of reported 1103 groups, g refers to the number of reported subgroups. 1104 1105 Fig. 4 Types and intensities of physical exercise reported across all included experimental studies. 1106 (Panel a) Distribution of the 81 experimental conditions according to exercise type, categorized as 1107 aerobic or resistance. Aerobic exercise is further subcategorized by specific types. (Panel b) Prescription 1108 of exercise intensities across the experimental studies, pink refers to measured capacity and turquoise 1109 refers to estimated capacity. Pink and turquoise gradients refer to measured and estimated exersie 1110 parameters respectively. (Panel c) Exercise intensities used across all experiments where red refers to 1111 high intensity, yellow refers to moderate intensity and green refers to low intensity. (Panel d) Heatmap 1112 showing the combination of exercise type and intensity where darker purple indicates a higher number 1113 of experimental conditions using the specific combination. Others refers to all exercise types different 1114 from lower-limb cycling, running and walking. E refers to the total number of experimental conditions 1115 reported across the 62 included experimental studies, e refers to the number of experimental conditions 1116 per exercise type category and n refers to the number of participants. 1117 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 43 Fig. 5 Motor learning categories and timing of exercise relative to motor task practice. 1118 (Panel a) Distribution of motor learning categories evaluated across the 62 experimental studies. (Panel 1119 b) Timing of exercise in relation to motor task practice across the 73 experimental conditions, (Panel c) 1120 Timing of exercise in relation to motor task practice across the 73 experimental conditions shown by 1121 population type. Light blue indicates exercise performed before the motor task practice, and light red 1122 indicates exercise performed after the motor task practice. Before = exercise performed prior to motor 1123 task practice; After = exercise performed after the motor task practice; n refers to the number of 1124 participants. 1125 1126 Fig. 6 Effects of physical exercise interventions on motor learning. 1127 Colored dots represent individual experimental conditions, with their position indicating whether motor 1128 learning was enhanced, not different, or attenuated compared to a control condition. Only experiments 1129 including a control condition were considered. All population types investigated are represented. 1130 Reported results related to acquisition are shown only for experiments in which exercise was performed 1131 before motor task practice. Reported retention test results are shown regardless of whether exercise was 1132 performed before or after motor task practice. (Panel a) Reported results from acquisition. (Panel b) 1133 Reported results on motor learning from retention tests conducted within 24 hours following exercise, 1134 (Panel c) Reported results on motor learning from retention tests conducted at 24 hours following 1135 exercise (Panel d) Reported results on motor learning from retention tests conducted after 24 hours 1136 following exercise. Green dots indicate low -intensity exercise, orange dots indicate moderate -intensity 1137 exercise, and red dots indicate high-intensity exercise. 1138 1139 1140 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 44 Supplementary Material 11141 1142 Figure S1. Classification of the task categories investigated to infer motor learning. This figure illustrates 1143 four commonly used motor learning task categories, classified according to their primary objective. The 1144 examples shown represent typical experimental tasks used within each category to assess motor learning. 1145 1146 Studies cited in Figure S1: 1147 Alderman, R. B. (1965, 1965/05/01). Influence of Local Fatigue on Speed and Accuracy in Motor Learning. Research Quarterly. American Association for 1148 Health, Physical Education and Recreation, 36(2), 131-140. https://doi.org/10.1080/10671188.1965.10614670 1149 Balsters, J. H., & Ramnani, N. (2011, Feb 9). Cerebellar plasticity and the automation of first -order rules. Journal of Neuroscience, 31(6), 2305 -2312. 1150 https://doi.org/10.1523/jneurosci.4358-10.2011 1151 Neva, J. L., Ma, J. A., Orsholits, D., Boisgontier, M. P., & Boyd, L. A. (2019, Apr). The effects of acute exercise on visuom otor adaptation, learning, and 1152 inter-limb transfer [Empirical Study; Quantitative Study]. Experimental Brain Research, 237(4), 1109 -1127. 1153 https://doi.org/http://dx.doi.org/10.1007/s00221-019-05491-5 1154 Robertson, E. M. (2007). The Serial Reaction Time Task: Implicit Motor Skill Learning? , 27(38), 10073-10075. https://doi.org/10.1523/JNEUROSCI.2747-1155 07.2007 %J The Journal of Neuroscience 1156 1157 1158 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 45 Table S1: Characteristics of the studies included in the scoping review. 1159 CONT = continuous; HRR = Heart rate reserve; W = Watts; HIIT = high-intensity interval training; kcals = kilocalories; bpm = beats per minute; HR = Heart Rate; MHR = maximal 1160 heart rate; PPO = peak power output; estim. = estimated; Before = exercise was performed before the motor task practice; Afte r = exercise was performed after the motor task 1161 practice; min = minutes; h = hours; d = days; NA = not available; ↗ = significant enhancement following exercise; ↘ = significant decrease following exercise ; ↔ = no change 1162 following exercise. 1163 Study Total N Type of population (n of exercise groups; age ± SD years) Physical exercise Exercise parameters Motor Learning Category Exercise Timing to Acquisition Retention Exercise effect on motor learning compared to control Acquisition Retention Andrews et al., 2023 20 Huntington’s disease (n = 10, 48.4 ± 13) Lower-limb cycling Moderate 20 min - CONT 50-55% age-predicted HRR Motor acuity Before 7d ↗ ↔ Andrushko et al., 2023 25 Stroke patients (n = 14, 66 ± 11) Lower-limb cycling (5 sessions) High 23 min - HIIT 3min at 75% Wpeak 3 min at 10W Sequence Before 24h ↔ ↔ Angulo- Barroso et al., 2019 71 Healthy children (n = 46, 9.2 ± 0.9) Running High 16 min (LONG) ; 5 min (SHORT) - HIIT LONG: 3 min at 85% estim. VO2max ; 2 min at 60% estim. VO2max SHORT: 2 min at 85% estim. VO2max ; 1 min at 60% estim. VO2max Motor adaptation Before 1h, 24h, 7d ↔ ↗ for LONG and SHORT Baird et al., 2018 48 Healthy adults (n = 32, 23 ± 3.1) Lower-limb cycling Low CONT: 40% measured max resistance until 200 kcals were expended (average: 28.67 min) High CONT: 80% measured max resistance until 200kcals were expended (average 16.75 min) Sequence Before 24h ↔ ↔ Bartz et al., 1970 20 Healthy adults (n = 10, 18-20 yrs) Walking Moderate 4 min - CONT HR target: 120-170 bpm Motor adaptation Before <10 min ↔ ↔ Bonuzzi et al., 2023 90 Stroke patients (n = 15; 51.6 ± 9.2) Lower-limb cycling (3 sessions) Moderate 20 min - CONT 50-70% age-predicted HRR Sequence Before 10d ↔ ↘ Stroke patients (n = 15; 49.6 ± 15.9) After ↔ ↔ Healthy adults (n = 15; 51.6 ± 9.2) Before ↘ ↔ Healthy adults (n = 15; 49.6 ± 15.9) After ↔ ↔ Bosch et al., 2020 15 Healthy adults (n = 15; 23.7 ± 4.0) Lower-limb cycling Moderate 30 min - CONT 70% measured MHR High 15 min - CONT 80% measured MHR Sequence After 2.5h NA ↗ for High ↔ for Moderate Chan et al., 2023 30 Parkinson’s disease (n = 15; 62.4 ± 8.5) Lower-limb cycling High 20 min - CONT 60-75% measured HRR Sequence After 1d, 7d ↔ ↗ at Day 7 Charalambou s et al., 2018 (active control) 37 Stroke patients (n=12; 55 ± 16) Treadmill walking High 5 min - CONT 70-85% age-predicted MHR Motor adaptation Before 24h ↔ ↔ Stroke patients (n=12; 62 ± 10) Total body exercise After ↔ ↔ Charalambou s et al., 2019 26 Healthy adults (n = 13; 21.8 ± 0.5) Whole-body cycling High 5 min - CONT 77-94% age-predicted MHR Motor adaptation Before 1d, 7d ↔ ↔ Chen et al., 2023 24 Healthy adults (n = 12; 20.9 ± 0.7) Treadmill Running Moderate 30 min - CONT 65-85% age-predicted MHR (average was 75%) Motor acuity Before 24h ↔ ↔ Coco et al., 2016 (no control) 28 Healthy adults (n = 28; 37.4 ± 5.8) Lower-limb cycling High CONT: 3 minutes of unloaded cycling with 30W increase every 3min until exhaustion Motor acuity After < 1h NA ↘ Cristini et al., 2023 56 Healthy adults (n = 20; 23.9 ± 3.4) Lower-limb cycling High 16 min - HIIT 3 min at 85–90 % of measured PPO 2min at 25 % of PPO. Sequence After 24h NA ↔ Dal Maso et al., 2018 25 Healthy adults (n = 12; 24.3 ± 3.6) Lower-limb cycling High 15 min - HIIT 3min at 90% measured HRR 2min at 50W Motor acuity After 8h, 24h NA ↔ for 8h ↗ at 24h Duchesne et al., 2015 39 Parkinson’s disease (n = 19; 59 ± 7.1) Lower-limb cycling (3x/week for 12 weeks) High CONT Start: 20 min at 60% measured PPO End: 40 min at 80% measured PPO Sequence After 12 weeks NA ↗ Healthy older adults (n = 20; 64 ± 8.2) After ↗ .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 46 Table S1. continued 1164 CONT = continuous; HRR = Heart rate reserve; W = Watts; HIIT = high-intensity interval training; HR = Heart Rate; MHR = maximal heart rate; PPO = peak power output; PWC 1165 = physical working capacity; Before = exercise was performed before the motor task practice; after = exercise was performed a fter the motor task practice; min = minutes; h = 1166 hours; d = days; NA = not available; ↗ = significant enhancement following exercise; ↘ = significant decrease following exercise ; ↔ = no change following exercise. 1167 1168 1169 Study Total N Type of population (n of exercise groups; age ± SD years) Physical exercise Exercise parameters Motor Learning Category Exercise Timing to Acquisition Retention Exercise effect on motor learning compared to control Acquisition Retention Ferrer-Uris et al., 2017 29 Healthy adults (n = 10, 20.9 ± 1.8) Shuttle run High 13 min - HIIT 3min at 85% estimated VO2max 2min at 60% estimated VO2max Motor adaptation Before 1h, 24h, 7d ↔ ↗ at 1h Healthy adults (n = 10, 20.5 ± 1.8) After NA ↗ at 1h Ferrer-Uris et al., 2018 33 Healthy children (n = 10, 9.2 ± 1.1) Shuttle run High 13 min - HIIT 3min at 85% estimated VO2max 2min at 60% estimated VO2max Motor adaptation Before 1h, 24h, 7d ↔ ↗ Healthy children (n = 12, 9.1 ± 0.8) After NA ↔ Greeley et al., 2021 44 Healthy older adults (n = 19; 68.3 ± 9.2) Lower-limb cycling (5 sessions) High 23 min - HIIT 3min at 75% measured Wpeak 3min at 10W Sequence Before 24h, 35 days ↔ ↔ Helm et al., 2017 54 Healthy adults (n = 27; 24.5 ± 2.8) Upper-limb cycling High 5 min - HIIT 1 min at 80% age-predicted MHR 1 min at half of cycling power Motor adaptation Before 24h ↔ ↔ Holman et al., 2021 33 Healthy adults (n = 17; 21.6 ± 2.3) Lower-limb cycling High 5 min - CONT 70% age-predicted HRR Motor adaptation After 30min, 24h, 7d NA ↗ at 7d Hubner et al., 2018 38 Healthy older adults (n = 17; 68.2 ± 3.2) Lower-limb cycling Moderate 25 min - CONT 60% measured Wpeak Motor acuity Before 30 min, 24h ↔ ↔ Hung et al., 2021 44 Healthy adults (n = 22; 23 ± 2) Lower-limb cycling High 25 min - HIIT 1 min at 90% measured Wpeak ; 1 min 25% measured Wpeak Motor acuity After 24h NA ↔ James and Wang., 2023 16 Healthy adults (n = 16; 24.6 ± 4.7 ) Lower-limb cycling Moderate 25 min - CONT 65-70% age-predicted MHR Motor adaptation Before < 30min ↔ ↔ Jespersen et al., 2023 64 Healthy adults (n = 16; 24.1 ± 3.9) Lower-limb cycling Moderate 20 min - HIIT 3min at 45% measured Wpeak ; 2 min at 50W Sequence Before <5 min, 7d ↔ ↗ at 7d Healthy adults (n = 16; 23.8 ± 4.2) High 20 min - HIIT 3min at 90% measured Wpeak; 2 min at 50W After NA ↗ at 7d Khan et al., 2022 48 Healthy adults (n = 23; 22.9 ± 4.9) Lower-limb cycling High 20 min - HIIT 3min at 90% measured Wpeak 2min at 50W Motor acuity After 24h, 7d NA ↗ at 7d Kuo et al., 2023a 26 Healthy adults (n = 26; 27.2 ± 1.0) Lower-limb cycling Low 20 min - CONT 40-60% age-predicted MHR Moderate 20 min - CONT 61-74% age-predicted MHR High 20 min - CONT 75-95% age-predicted MHR Sequence After 0-5 min NA ↗ for Moderate Kuo et al., 2023b 20 Healthy adults (n = 20; 26.4 ± 1.0) Lower-limb cycling Moderate 20 min - CONT 61-74% age-predicted MHR Sequence After 0-5 min NA ↗ Lehmann et al., 2020 31 Healthy adults (n = 15; 23 ± 7) Lower-limb cycling (7 sessions) High 20 min - HIIT 2x3min at 170 (PWC) 4 min at 120 (PWC) Motor adaptation Before 8 weeks ↗ ↗ Loras et al., 2020 (no control) 40 Healthy adults (n = 40; 23.8 ± 2.0) Lower-limb cycling Low 25 min - CONT 50% age-predicted MHR Moderate 25 min - CONT 75% age-predicted MHR Motor acuity Before 24h ↔ ↔ Lundbye- Jensen et al., 2017 77 Healthy children (n = 26; 10.4 ± 0.7) Floorball High 15 min - HIIT 3 min at high intensity 2 min at low intensity (measured via HR monitoring) Motor acuity After 1h, 24h, 7d ↔ ↗ at 7d Healthy children (n = 25; 10.8 ± 0.8) Running After ↘ at 1h ↗ at 7d Mang et al., 2014 16 Healthy adults (n = 16; 23.9 ± 3.7) Lower-limb cycling High 20 min - HIIT 3min at 90% measured PPO 2min at 50W Sequence Before 24h ↗ ↗ Mang et al., 2016 16 Healthy adults (n = 16; 25.7 ± 4.6) Lower-limb cycling High 20min - HIIT 3min at 90% measured PPO 2min at 50W Sequence Before 24h ↔ ↗ .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 47 Table S1. continued 1170 Study Total N Type of population (n of exercise groups; age ± SD years) Physical exercise Exercise parameters Motor Learning Category Exercise Timing to Acquisition Retention Exercise effect on motor learning compared to control Acquisition Retention Munz et al., 2021 12 Healthy children (n = 12; 10.2 ± 1.4) Lower-limb cycling High 40 min – CONT (4 blocks of 10 min) 85-90% of age-predicted MHR Motor acuity After 24h NA ↗ Nepveu et al., 2017 22 Stroke patients (n = 11; 64.7 ± 11.6) Lower-limb cycling High 15 min - HIIT 3 min at 100% measured Wpeak 2min at 25% measured Wpeak Motor acuity After 24h NA ↗ Neva et al., 2019 17 Healthy adults (n = 17; 24 ± 3) Lower-limb cycling Moderate 25 min - CONT 65-70% age-predicted MHR Motor adaptation Before 24h ↗ ↗ Ostadan et al., 2016 38 Healthy adults (n = 25 ; 22.4 ± 3.9) Lower-limb cycling High 15 min - HIIT 3 min at 85-90% measured VO2peak 2 min at 25% measured Wpeak Sequence After 8h NA ↔ Opie and Semmler., 2019 13 Healthy adults (n = 13 ; 24.0 ± 3.3) Lower-limb cycling Moderate 30 min - CONT 50% age-predicted HRR High 30 min - HIIT 77% age-predicted HRR Motor acuity Before 24h ↗ for Mod ↗ for Mod Perini et al., 2016 38 Healthy adults (n = 18; 22.9 ± 2.2) Lower-limb cycling Moderate 30 min - CONT 70% measured MHR Motor acuity Before NA ↗ NA Pixa et al., 2021 27 Healthy adults (n = 30; 23.7 ± 4.4) Lower-limb cycling Low 25 min - CONT 20% measured PPO High 23 min - HIIT 3 min at 90% measured Wpeak 2 min at 60% measured Wpeak Motor acuity Before 15 min, 30min, 24h ↔ ↔ Quaney et al., 2009 21 Stroke patients (n = 19; 64.1 ± 12.3) Lower-limb cycling (24 sessions) Moderate 45 min - CONT 70% measured MHR Sequence Before 8 weeks ↗ ↔ Quinlan et al., 2021 29 Healthy adults (n = 15; 45 ± 5) Lower-limb cycling High 20 min - HIIT 4 min at 90% measured Wpeak 2min of active recovery at 50-80W Motor acuity Before 5-7d ↔ ↔ Rhee et al., 2016 60 Healthy adults (n = 22; 19.8 ± 1.3) Lower-limb cycling High 21 min - CONT 18min at 80% measured MHR 3min at 100% measured MHR Motor acuity After 24h NA ↗ Roig et al., 2012 48 Healthy adults (n = 16; 24.1 [21-33]) Lower-limb cycling High 15 min – HIIT 3min at high intensity 2min at 50W Based on measured lactate level Workload (200-315) Motor acuity Before 1h, 24h, 7d ↔ ↗ for 24h and 7d (higher when exercise is performed after) Healthy adults (n = 16; 24.4 [20-30]) After NA Roig-Hierro et al., 2023 32 Healthy adults (n = 30; 22.9 ± 0.3) Shuttle run High 13 min - HIIT 3 min at 80% age-predicted MHR 2min at 60% age-predicted MHR Motor acuity After 7d NA ↗ Singh et al., 2016 23 Healthy adults (n = 13; avg = 27 yrs) Lower-limb cycling Moderate 20 min - CONT 65-70% age-predicted MHR Motor adaptation Before NA ↔ NA Skriver et al., 2014 32 Healthy adults (n = 16; 24.1 ± 3.4) Lower-limb cycling High 20 min – HIIT 3min at high intensity 2min at 50W Based on measured lactate level Workload (200-315) Motor acuity Before 1h, 24h, 7d ↔ ↗ for 24h and 7d Snow et al., 2016 16 Healthy adults (n = 16; 25.7 ± 3.1) Lower-limb cycling Moderate 30 min - CONT 60% measured VO2 peak Motor acuity Before 24h ↗ ↔ Statton et al., 2015 24 Healthy adults (n = 8; 22.4 ± 1.1) Running High 30 min - CONT 65-85% age-predicted MHR (average was 84% MHR) Motor acuity Before NA ↗ NA 20 Healthy adults (n = 8; 24.9 ± 1.1) Running (3 sessions) 24h ↗ ↔ Stavrinos & Coxon 2017 24 Healthy adults (n = 12; 24.8 ± 5.8) Lower-limb cycling High 20 min - HIIT 2min at 90% age-predicted HRR 3min at 50% age-predicted HRR Motor acuity Before 5h ↔ ↗ Steib et al., 2018 17 Parkinson’s disease (n = 17; 64.4 ± 6.2) Lower-limb cycling Moderate 30 min - CONT 60-70% measured MHR Motor adaptation Before 24h ↔ ↗ Stranda et al., 2019 26 Healthy adults (n = 13; 25.3 ± 2.5) Lower-limb cycling (12 sessions) Moderate 20 min - CONT 65% age-predicted MHR Motor acuity Before 7d ↔ ↔ CONT = continuous; HRR = Heart rate reserve; W = Watts; HIIT = high-intensity interval training; HR = Heart Rate; MHR = maximal heart rate; PPO = peak power output; Before 1171 = exercise was performed before the motor task practice; After = exercise was performed after the motor task practice; min = minutes; h = hours; d = days; NA = not available; ↗ 1172 = significant enhancement following exercise; ↘ = significant decrease following exercise ; ↔ = no change following exercise. 1173 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 48 Table S1. continued 1174 CONT = continuous; HRR = Heart rate reserve; W = Watts; HIIT = high -intensity interval training; LIIT = light -intensity interval training; HR = Heart Rate; MHR = maximal 1175 heart rate; PPO = peak power output; rpm = revolutions per minute; bpm = beats per minute; RM = repetition maximum; Before = exercise was performed before the motor task 1176 practice; After = exercise was performed after the motor task practice; min = minutes; h = hours; d = days; NA = not availabl e; ↗ = significant enhancement following exercise; ↘ 1177 = significant decrease following exercise ; ↔ = no change following exercise. 1178 Study Total N Type of population (n of exercise groups; age ± SD years) Physical exercise Exercise parameters Motor Learning Category Exercise Timing to Acquisition Retention Exercise effect on motor learning compared to control Acquisition Retention Swarbrick et al., 2020 (no control) 25 Healthy adults (n = 12; 21.5 ± 2.1) Lower-limb cycling Low 19 min - LIIT 2 min at 8% Wpeak 3 min at 12% Wpeak Motor acuity Before 1h, 24h. 7d ↔ ↔ Healthy adults (n = 13; 22.2 ± 3.1) High 19 min - HIIT 2 min at 60% measured Wpeak 3 min at 90% measured Wpeak Taylor et al., 2024 (Active rest) 23 Healthy older adults (n = 11; 65.3 ± 6.3) Lower-limb cycling High 20 min - HIIT 3 min at 50% measured HRR 2 min at 90% measured HRR Motor acuity Before 6h ↘ ↗ Thomas et al., 2016 36 Healthy adults (n = 12; 23.5 ± 2.3) Lower-limb cycling Low 17 min - LIIT 2 min at 25% measured Wpeak 3 min at 45% measured Wpeak Motor acuity After 24h, 7d NA ↗ for High at 24h ↗ for Low and High at 7d Healthy adults (n = 12; 24.3 ± 2.3) High 17 min - HIIT 2 min at 60% measured Wpeak 3 min at 90% measured Wpeak Thomas et al., 2017 40 Healthy adults (n = 10; 24.7 ± 3.5) Strength training High 45-50 min Resistance training program (4 RM and 8 RM) Motor acuity After 1h, 24h NA ↘ at 1h ↗ at 1d Healthy adults (n = 10; 25.9 ± 4.5) Circuit training High 45-50 min (30% of 1 RM) After Healthy adults (n = 10; 25.9 ± 3.7) Hockey training High 48 min 5 x 7 min; 2 min rest After Thompson et al., 2023 45 Stroke patients (n = 15; 68.1 ± 7.1) Whole-body cycling High 5 min- CONT 70-85% age-predicted MHR Motor adaptation After 24h NA ↔ Stroke patients (n = 15; 61.8 ± 7.5) High 15min - HIIT 3 min at 70%-85% age-predicted MHR 2 min at < 70% age-predicted MHR Tomporowski and Pendleton, 2018 32 Healthy adults (n = 11; 22.4 ± 3.0) Dancing Moderate 10 min - CONT Simple Dance Dance Revolution Intensity determined based on HR Motor acuity Before 13min, 24h, 7d ↔ ↔ Healthy adults (n = 10; 22.9 ± 2.9) Moderate 10 min - CONT Complex Dance Dance Revolution Intensity determined based on HR After NA ↗ at 7d Wanga et al., 2023 33 Healthy adults (n = 11; 20.5 ± 2.3) Lower-limb cycling Low 23 min - HIIT 3min at 45% measured Wpeak 2min at 25% measured Wpeak Sequence After 24h, 48h NA ↗ (higher for Low) Healthy adults (n = 11; 20.3 ± 2.3) High 23 min - HIIT 2min at 60% measured Wpeak 3min at 90% measured Wpeak Wanner et al., 2020 50 Healthy adults (n = 17; 25.3 ± 2.7) Lower-limb cycling Low 17 min - CONT 25W Motor adaptation Before 24h ↔ ↔ Healthy adults (n = 18; 25.1 ± 2.3) Moderate 17 min - HIIT 2min at 25% measured Wpeak 3min at 45% measured Wpeak Healthy adults (n = 15; 25.7 ± 3.6) High 17 min - HIIT 2min at 60% measured Wpeak 3min at 90% measured Wpeak Wanner et al., 2021 17 Parkinson’s disease (n = 8; 59.3 ± 5.4) Lower-limb cycling Moderate 25 min - CONT 60% maximal Wpeak Motor adaptation Before 1d, 7d ↔ ↗ at 7d Williams et al., 1985 90 Healthy adults (n = 30; 20.9 ± 1.9) Arm cranking Low 5 min - CONT WO maintained at 10W (30 rpm) (110-125 bpm) Motor acuity Before 24h ↔ ↔ Healthy adults (n = 30; 20.9 ± 1.9) High 5 min - CONT WO maintained at 75W (90 rpm) (165-180 bpm) Yildirim et al., 2024 70 Healthy adults (n = 15; 20.5 ± 0.7) Running Low 30 min- CONT 57%-63% estimated MHR Motor acuity Before 24h, 7d ↔ ↔ Healthy adults (n = 15; 20.1 ± 0.5) After ↔ ↔ Healthy adults (n = 15; 20.8 ± 1.5) Moderate 30 min- CONT 64%-76% estimated MHR Before ↔ ↔ Healthy adults (n = 15; 20.9 ± 1.0) After ↔ ↔ .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 49 Supplementary Material 2 1179 Search equations 1180 1181 Web of Science Core Collection : A&HCI , ESCI , CPCI-SSH , CPCI-S , SCI-EXPANDED , SSCI 1182 1183 (exercise OR training) NEAR/5 ( "motor learning" OR "motor skill" OR "motor skills" OR "motor adaptation" OR 1184 "motor acuity" OR "motor coordination" OR "de novo learning" OR "motor control" OR "motor sequence" OR 1185 "motor behavior" OR "motor behaviour" OR "sensorimotor learning" OR "motor competence" OR "motor 1186 competences") (Topic) 1187 1188 - 1189 1190 ERIC (ProQuest) 1191 1192 ((MAINSUBJECT.EXACT("Perceptual Motor Learning") OR MAINSUBJECT.EXACT("Movement Education") OR 1193 MAINSUBJECT.EXACT("Perceptual Motor Coordination") OR MAINSUBJECT.EXACT("Multisensory Learning") OR 1194 MAINSUBJECT.EXACT("Psychomotor Skills")) AND MAINSUBJECT.EXACT("Exercise")) AND PEER(yes) 1195 OR 1196 noft((exercise OR training) NEAR/5 ("motor learning" OR "motor skill" OR "motor skills" OR "motor adaptation" OR 1197 "motor acuity" OR "motor coordination" OR "de novo learning" OR "motor control" OR "motor sequence" OR "motor 1198 behavior" OR "motor behaviour" OR "sensorimotor learning" OR "motor competence" OR "motor competences")) 1199 AND PEER(yes) 1200 1201 Limité par : Revu par les pairs 1202 1203 - 1204 SPORTDiscus with Full Text 1205 # Question S7 S5 OR S6 Opérateurs de restriction - Relu par un comité de lecture; Type de publication: Academic Journal Opérateurs dʼexpansion - Appliquer des sujets équivalents Modes de recherche - Booléen/Phrase S6 TI ( exercise OR training) N5 ( "motor learning" OR "motor skill" OR "motor skills" OR "motor adaptation" OR "motor acuity" OR "motor coordination" OR "de novo learning" OR "motor control" OR "motor sequence" OR "motor behavior" OR "motor behaviour" OR "sensorimotor learning" OR "motor competence" OR "motor competences") ) OR AB ( exercise OR training) N5 ( "motor learning" OR "motor skill" OR "motor skills" OR "motor adaptation" OR "motor acuity" OR "motor coordination" OR "de novo learning" OR "motor control" OR "motor sequence" OR "motor behavior" OR "motor behaviour" OR "sensorimotor learning" OR "motor competence" OR "motor competences") ) S5 S3 AND S4 S4 DE "MOTOR learning" OR DE "PERCEPTUAL motor learning" S3 S1 OR S2 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 50 S2 DE "EXERCISE" + S1 DE "PHYSICAL training & conditioning" + 1206 - 1207 Database(s): APA PsycInfo 1806 to March 2025 Week 2 1208 Search Strategy: 1209 # Searches 1 athletic training/ 2 exp exercise/ 3 1 or 2 4 Motor Coordination/ or Motor Skills/ or Motor Control/ or Perceptual Motor Coordination/ or Perceptual Motor Learning/ or Gross Motor Skill Learning/ or Fine Motor Skill Learning/ 5 3 and 4 6 ((exercise or "physical training" or "endurance training" or "resistance training") adj6 ("motor learning" or "motor skill" or "motor skills" or "motor adaptation" or "motor acuity" or "motor coordination" or "de novo learning" or "motor control" or "motor sequence" or "motor behavior" or "motor behaviour" or "sensorimotor learning" or "motor competence" or "motor competences")).ab,id,ti. 7 5 or 6 8 limit 7 to human 1210 - 1211 Database(s): Ovid MEDLINE(R) ALL 1946 to March 18, 2025 1212 Search Strategy: 1213 # Searches 1 ((exercise or "physical training" or "endurance training" or "resistance training") adj6 ("motor learning" or "motor skill" or "motor skills" or "motor adaptation" or "motor acuity" or "motor coordination" or "de novo learning" or "motor control" or "motor sequence" or "motor behavior" or "motor behaviour" or "sensorimotor learning" or "motor competence" or "motor competences")).ab,kf,ti. 2 exp animals/ not humans/ 3 1 not 2 - 1214 1215 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 51 1216 Database(s): Embase 1974 to 2025 March 18 1217 Search Strategy: 1218 # Searches 1 ((exercise or "physical training" or "endurance training" or "resistance training") adj6 ("motor learning" or "motor skill" or "motor skills" or "motor adaptation" or "motor acuity" or "motor coordination" or "de novo learning" or "motor control" or "motor sequence" or "motor behavior" or "motor behaviour" or "sensorimotor learning" or "motor competence" or "motor competences")).ab,kf,ti. 2 exp exercise/ 3 resistance training/ or endurance training/ 4 2 or 3 5 motor learning/ 6 4 and 5 7 1 or 6 8 (exp animal/ or nonhuman/) not exp human/ 9 7 not 8 1219 1220 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint Draft version #1 – 2025/07/25, submitted for peer review 52 Supplementary Material 3 1221 Data extraction sheet is attached to this manuscript on BioRxiv. 1222 1223 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted July 29, 2025. ; https://doi.org/10.1101/2025.07.25.666852doi: bioRxiv preprint