Abstract
Practice of a given physical activity is known to improve the motor skills related to this activity. However, whether unrelated skills are also improved is still unclear. To test the impact of physical activity on an unpracticed motor task, 26 young adults completed the international physical activity questionnaire and performed a bimanual coordination task they had never practiced before. Results showed that higher total physical activity predicted higher performance in the bimanual task, controlling for multiple factors such as age, physical inactivity, music practice, and computer games practice. Linear mixed models allowed this effect of physical activity to be generalized to a large population of bimanual coordination conditions. This finding runs counter to the notion that generalized motor abilities do not exist and supports the existence of a “learning to learn” skill that could be improved through physical activity and that impacts performance in tasks that are not necessarily related to the practiced activity.
Introduction
Each year, physical inactivity is responsible for 13 million of disability-adjusted life-years worldwide and costs 67.5 billion of international dollars (). In this context, the promotion of physical activity appears strongly relevant. Such promotion could be initiated through the development of motor skills that have shown to be a primary factor of engagement in physical activity (; ; ). While engagement in a certain type of physical activity is known to improve the motor skills specific to this activity (; ), it is still unclear whether movement skills that are not directly related to this activity would also be improved. If such a generalization does occur, engagement in one type of physical activity could potentially promote engagement in other activities (Figure 1).
FIGURE 1
A potential explanatory mechanism of such overall improvement is the “learning to learn” phenomenon (; ), whereby problem-solving skills developed for one task/context generalize to similar tasks/contexts (; ). This generalization could be explained by adaptations at the level of executive functions (e.g., attention, response planning, decision making) that have been linked to physical activity (), and/or by improved proprioception (; ), which has shown to be critical for the control of movement (; ; ). Both improved proprioception and an increased ability to reduce limb stiffness, which would allow movements with larger amplitudes, would result in larger perceived errors to learn from (). Finally, as suggested by , this generalization could be explained by the improvement of the core skills required for the learning process, such as pattern and error detection and correction.
Here, we hypothesized that physical activity predicts motor skill proficiency in tasks that have never been practiced before such as a multi-frequency bimanual coordination task. We further posited that physical activity engagement and motor skill proficiency can potentially reinforce each other via a virtuous circle (Figure 1).
Materials and Methods
Participants
Twenty-six healthy young volunteers (age range 18–30 years; mean age 24 ± 3 years) participated in the study. All participants were right-handed according to the Edinburgh Handedness Inventory (). The International Physical Activity Questionnaire (IPAQ; ) was used to assess total physical activity and inactivity. Participants were asked how much time per week they spend sitting (physical inactivity), play a music instrument, and play computer games. All participants had normal or corrected-to-normal vision, and none reported neurological, psychiatric, or cardiovascular disorders. This study was carried out in accordance with the recommendations of the guidelines established by the ethics committee for biomedical research at KU Leuven. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the KU Leuven, Belgium.
Physical Activity
Total physical activity was assessed using the IPAQ, which assesses physical activity undertaken across a comprehensive set of domains including leisure time, domestic and gardening activities, and work-related and transport-related activities. The specific types of activity are walking, moderate-intensity activities, and vigorous-intensity activities. Frequency (days per week) and duration (time per day) are collected separately for each specific type of activity. The total score used to describe total physical activity required weighted summation of the duration (in minutes) and frequency (days) of walking, moderate-intensity, and vigorous-intensity activity. Each type of activity was weighted by its energy requirements defined in Metabolic Equivalent of Task (METs): 3.3 METs for walking, 4.0 METs for moderate physical activity, and 8.0 METs for vigorous physical activity ().
Experimental Setup
Skilled movement proficiency was assessed using a bimanual tracking task (; ; ; ). Participants were seated in front of a computer monitor with the forearms resting on a custom-made adjustable ramp. A 5-cm diameter dial was mounted at the end of each ramp. The task consisted in turning the handle of the dials with the thumb and index according to specific patterns without visual feedback of the upper limbs. Angular displacements were registered by means of non-ferromagnetic optical shaft encoders (Avago Technologies, sampling frequency = 100 Hz, accuracy = 0.089°) fixed to the rotation axes of the dials. The gain was set to 10 arbitrary units (au) per rotation indicating that drawing a vertical or horizontal line on a screen consisting of 162 au, required 16.2 complete rotations of the left or right dial, respectively. Rotating the dials moved a red cursor on the screen serving as online visual feedback.
Bimanual Tracking Task
Participants were instructed to track a white target dot moving along a target line by rotating both dials simultaneously. Four coordination patterns imposed by the target line direction were tested: both hands rotating inward, outward, clockwise, or counter-clockwise. The left and right hands, respectively, controlled movements on the vertical and horizontal axis. Each pattern was performed according to five frequency ratios: 1:1, 1:2, 1:3, 2:1, and 3:1 (left hand:right hand) resulting in 20 different target line directions (Figure 2A).
FIGURE 2
Procedure
Prior to data recording, participants performed 12 trials with different target lines for familiarization. Before each recorded trial, the target line appeared for 2 s. Then the target dot moved over the line at a constant speed from start (center of the screen) to end for 10 s. The goal was to match the white target dot movement with the red dot as accurately as possible in both space and time. The inter-trial interval was 3 s. Four 6-min blocks with 3 min rest in between were administered, each consisting of 24 trials, presented in a pseudorandom order. Therefore, all participants performed 96 recorded trials. Each block included all 20 distinctive target lines and an additional 1:1 trial for each coordination pattern.
Kinematic Data Analysis
Accuracy was assessed using the target deviation of the time series and was computed as follows for each trial:
where n is the number of data samples over a trial of 10 s (10 × 102), x2 and y2 are the respective position of the red cursor on the x- and y axis, and x1 and y1 are the respective position of the white target dot on the x- and y axis. Larger target deviation scores reflected poorer performance.
Statistical Analysis
The extent to which total physical activity, music practice, and computer gaming were predictive of target deviation was analyzed using a linear mixed model. Unlike traditional analyses of variance, linear mixed models take into account the sampling variability of both the participants and conditions, thereby limiting a large inflation of false positives (). Moreover, as illustrated in Figure 2B, treating both participants and conditions as random effects allows generalizing the results to the population of participants, but also to the population of conditions (). Finally, linear mixed models take each single observation into account, thereby avoiding information loss due to averaging. The linear mixed model specified participants (n = 26) and target lines (n = 20) as random factors and was built using the R language lmerTest package, version 2.0-301. Target deviation was normalized using the Box-Cox method. The predictive variable was total physical activity as measured by the IPAQ. The control variables were age, gender, session, trial order, physical inactivity, computer game practice, and music practice. The continuous variables were scaled and centered on zero.
Results
The distribution of physical activity, music practice, computer game practice, and physical inactivity are illustrated as a function of age in Figure 3.
FIGURE 3
Results of the linear mixed model (Table 1) showed a significant effect of physical activity (b = -0.335, p = 0.005) on target deviation with higher total physical activity predicting lower target deviation and thus better performance (Figure 4). This effect was observed while controlling for age (b = 0.142, p = 0.101), gender (b = -0.241, p = 0.178), session (b = -0.160, p < 0.001), trial order (b = -0.001, p = 0.660), physical inactivity (b = -0.140, p = 0.137), computer game practice (b = -0.021, p = 0.863), and music practice (b = -0.286, p = 0.009). These latter results showed that the number of hours of music practice per week, but not computer games practice, predicted lower target deviation.
Table 1
| Fixed effects | b | SE | p |
|---|---|---|---|
| Intercept | 2.499 × 100 | 1.200 × 10-1 | <0.001∗∗∗ |
| Age | 1.417 × 10-1 | 8.337 × 10-2 | 0.101 |
| Gender | -2.408 × 10-1 | 1.739 × 10-1 | 0.178 |
| Session (1–4) | -1.601 × 10-1 | 1.069 × 10-2 | <0.001∗∗∗ |
| Trial order (1–24) | -7.646 × 10-4 | 1.737 × 10-3 | 0.660 |
| Physical inactivity | -1.398 × 10-1 | 9.097 × 10-2 | 0.137 |
| Computer games | -2.078 × 10-2 | 1.197 × 10-1 | 0.863 |
| Music | -2.860 × 10-1 | 1.008 × 10-1 | 0.009ˆ** |
| Physical activity | -3.349 × 10-1 | 1.082 × 10-1 | 0.005ˆ** |
| Random effects | σ2 | ||
| Participant | |||
| Intercept | 1.187 × 10-1 | ||
| Condition | |||
| Intercept | 4.550 × 10-2 | ||
| Residual | 3.567 × 10-1 |
Predictors of target deviation.
∗∗p < 0.01, ∗∗∗p < 0.001.
FIGURE 4
Discussion
Here we investigated whether engagement in physical activity improves overall motor skill proficiency by means of an unpracticed bimanual coordination task. Results revealed that usual physical activity was predictive of performance in multiple conditions of this new and complex bimanual coordination task. This result is consistent with studies showing a relationship between the level of physical activity and motor speed. Specifically, in a discrete unimanual aiming task young active adults showed faster reaction times and movement times than sedentary young adults, while accuracy was similar (; ). Reaction times were also faster in active young and older adults than their sedentary peers ().
Our results demonstrate that higher total physical activity enhances performance on an unpracticed task. This result supports previous findings showing transfer between unrelated skills of a unimanual joystick tracking () or aiming task () and reveals that such transfer can be demonstrated using a metric encompassing multiple skills (i.e., total physical activity). These findings run counter to the notion that motor abilities are highly specific and that practice effects are skill and context specific (e.g., ). Conversely, our findings support the existence of a “learning to learn” skill (; ) that could be improved through physical activity and that would impact performance in tasks that are not necessarily related to the practiced activity. Future studies should investigate, whether this impact of physical activity is related to the participants’ experience with a wide range of activities or to their overall level of fitness. Here, we showed that this impact was significant, irrespective of music practice, and computer gaming. Future research should also follow up on the significant relation between music practice and bimanual coordination to see if this relation is mediated by different processes or mechanisms other than those that underlie the relation between total physical activity and bimanual coordination.
Finally, as motor skills are known to be a primary factor for engagement in physical activity, these results are encouraging for the promotion of physical activity. Indeed, they reveal that once engaged in physical activity, this engagement could potentially be auto-reinforced through an overall improvement of motor skill proficiency.
Statements
Author contributions
Experimental design: SS and LS. Experimental conduct: LS. Data analysis: MB. First draft preparation: MB. Manuscript preparation: MB and SS.
Acknowledgments
MB is supported by a research grant (1504015N) and a post-doctoral fellowship of the Research Foundation–Flanders (FWO). This study was supported by the FWO (G0721.12; G0708.14), the Interuniversity Attraction Poles Program initiated by the Belgian Science Policy Office (P7/11), and the KU Leuven Research Fund (C16/15/070).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Footnotes
References
1
AdamoD. E.AlexanderN. B.BrownS. H. (2009). The influence of age and physical activity on upper limb proprioceptive ability.J. Aging Phys. Act.17272–293. 10.1123/japa.17.3.272
2
AinsworthB. E.HaskellW. L.WhittM. C.IrwinM. L.SwartzA. M.StrathS. J. (2000). Compendium of physical activities: an update of activity codes and MET intensities.Med. Sci. Sports Exerc.32S498–S504. 10.1097/00005768-200009001-00009
3
BachmanJ. C. (1961). Specificity vs. generality in learning and performing two large muscle motor tasks.Res. Q.323–31. 10.1080/10671188.1961.10762064
4
BarnettL. M.van BeurdenE.MorganP. J.BrooksL. O.BeardJ. R. (2009). Childhood motor skill proficiency as a predictor of adolescent physical activity.J. Adolesc. Health44252–259. 10.1016/j.jadohealth.2008.07.004
5
BarrD. J.LevyR.ScheepersmC.TilyH. J. (2013). Random effects structure for confirmatory hypothesis testing: keep it maximal.J. Mem. Lang.68255–278. 10.1016/j.jml.2012.11.001
6
BeetsI. A.GooijersJ.BoisgontierM. P.PauwelsL.CoxonJ. P.WittenbergG.et al (2015). Reduced neural differentiation between feedback conditions after bimanual coordination training with and without augmented visual feedback.Cereb. Cortex251958–1969. 10.1093/cercor/bhu005
7
BockO.SchneiderS.BloombergJ. (2001). Conditions for interference versus facilitation during sequential sensorimotor adaptation.Exp. Brain Res.138359–365. 10.1007/s002210100704
8
BoisgontierM. P.ChevalB. (2016). The anova to mixed model transition.Neurosci. Biobehav. Rev.681004–1005. 10.1016/j.neubiorev.2016.05.034
9
BurdetE.OsuR.FranklinD. W.MilnerT. E.KawatoM. (2001). The central nervous system stabilizes unstable dynamics by learning optimal impedance.Nature414446–449. 10.1038/35106566
10
CraigC. L.MarshallA. L.SjostromM.BaumanA. E.BoothM. L.AinsworthB. E.et al (2003). International physical activity questionnaire: 12-country reliability and validity.Med. Sci. Sports Exerc.351381–1395. 10.1249/01.MSS.0000078924.61453.FB
11
DingD.LawsonK. D.Kolbe-AlexanderT. L.FinkelsteinE. A.KatzmarzykP. T.van MechelenW.et al (2016). The economic burden of physical inactivity: a global analysis of major non-communicable diseases.Lancet3881311–1324. 10.1016/S0140-6736(16)30383-X
12
HarlowH. F. (1949). The formation of learning sets.Psychol. Rev.5651–65. 10.1037/h0062474
13
HelsenW. F.Van HalewyckF.LevinO.BoisgontierM. P.LavrysenA.ElliottD. (2016). Manual aiming in healthy aging: does proprioceptive acuity make the difference?Age38:45. 10.1007/s11357-016-9908-z
14
HigbieE. J.CuretonK. J.WarrenG. L.IIIPriorB. M. (1996). Effects of concentric and eccentric training on muscle strength, cross-sectional area, and neural activation.J. Appl. Physiol.812173–2181.
15
JacksonG. M.JacksonS. R.HusainM.HarveyM.KramerT.DowL. (2000). The coordination of bimanual prehension movements in a centrally deafferented patient.Brain123380–393. 10.1093/brain/123.2.380
16
KraftE. (2012). Cognitive function, physical activity, and aging: possible biological links and implications for multimodal interventions.Neuropsychol. Dev. Cogn. B Aging Neuropsychol. Cogn.19248–263. 10.1080/13825585.2011.645010
17
LupinacciN. S.RikliR. E.JonesC. J.RossD. (1993). Age and physical activity effects on reaction time and digit symbol substitution performance in cognitively active adults.Res. Q. Exerc. Sport64144–150. 10.1080/02701367.1993.10608791
18
MaesC.GooijersJ.Orban de XivryJ. J.SwinnenS. P.BoisgontierM. P. (2017). Two hands, one brain, and aging.Neurosci. Biobehav. Rev.75234–256. 10.1016/j.neubiorev.2017.01.052
19
NougierV.BardC.FleuryM.TeasdaleN.ColeJ.ForgetR.et al (1996). Control of single-joint movements in deafferented patients: evidence for amplitude coding rather than position control.Exp. Brain Res.109473–482. 10.1007/BF00229632
20
OldfieldR. C. (1971). The assessment and analysis of handedness: the Edinburgh inventory.Neuropsychologia997–113. 10.1016/0028-3932(71)90067-4
21
SainburgR. L.GhilardiM. F.PoiznerH.GhezC. (1995). Control of limb dynamics in normal subjects and patients without proprioception.J. Neurophysiol.73820–835.
22
SeidlerR. D. (2004). Multiple motor learning experiences enhance motor adaptability.J. Cogn. Neurosci.1665–73. 10.1162/089892904322755566
23
SeidlerR. D. (2010). Neural correlates of motor learning, transfer of learning, and learning to learn.Exerc. Sport Sci. Rev.383–9. 10.1097/JES.0b013e3181c5cce7
24
SerbruynsL.GooijersJ.CaeyenberghsK.MeesenR. L.CuypersK.SistiH. M.et al (2015). Bimanual motor deficits in older adults predicted by diffusion tensor imaging metrics of corpus callosum subregions.Brain Struct. Funct.220273–290. 10.1007/s00429-013-0654-z
25
SistiH. M.GeurtsM.ClerckxR.GooijersJ.CoxonJ. P.HeitgerM. H.et al (2011). Testing multiple coordination constraints with a novel bimanual visuomotor task.PLoS ONE6:e23619. 10.1371/journal.pone.0023619
26
StoddenD. F.GoodwayJ. D.LangendorferS. J.RobertonM. A.RudisillM. E.GarciaC.et al (2008). A developmental perspective on the role of motor skill competence in physical activity: an emergent relationship.Quest.60290–306. 10.1080/00336297.2008.10483582
27
Van HalewyckF.LavrysenA.LevinO.BoisgontierM. P.ElliottD.HelsenW. F. (2014). Both age and physical activity level impact on eye-hand coordination.Hum. Mov. Sci.3680–96. 10.1016/j.humov.2014.05.005
Summary
Keywords
bimanual coordination, computer games, health, music, physical activity
Citation
Boisgontier MP, Serbruyns L and Swinnen SP (2017) Physical Activity Predicts Performance in an Unpracticed Bimanual Coordination Task. Front. Psychol. 8:249. doi: 10.3389/fpsyg.2017.00249
Received
09 November 2016
Accepted
08 February 2017
Published
20 February 2017
Volume
8 - 2017
Edited by
Maarten A. Immink, University of South Australia, Australia
Reviewed by
David Ian Anderson, San Francisco State University, USA; Jason B. Boyle, The University of Texas at El Paso, USA
Updates
Copyright
© 2017 Boisgontier, Serbruyns and Swinnen.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Matthieu P. Boisgontier, matthieu.boisgontier@kuleuven.be
This article was submitted to Movement Science and Sport Psychology, a section of the journal Frontiers in Psychology
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.