Proprioceptive and visual motion detection acuity contribute to children’s dynamic postural control

Proprioceptive and visual motion detection acuity contribute to children’s dynamic postural control | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Proprioceptive and visual motion detection acuity contribute to children’s dynamic postural control Antonella Iannotta , Scott J. Mongold , Esranur Yildiran Carlak , View ORCID Profile Christian Georgiev , Pierre Cabaraux , Dorine Van Dyck , Gilles Naeije , Marc Vander Ghinst , Jennifer Foucart , Nicolas Deconinck , Mathieu Bourguignon doi: https://doi.org/10.1101/2025.04.29.651175 Antonella Iannotta 1 Laboratory of Functional Anatomy, Faculty of Human Motor Sciences, Université libre de Bruxelles (ULB) , Brussels, Belgium 2 Laboratoire de Neuroanatomie et Neuroimagerie translationnelles, UNI – ULB Neuroscience Institute, Université libre de Bruxelles (ULB) , Brussels, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: antonella.iannotta{at}ulb.be Scott J. Mongold 1 Laboratory of Functional Anatomy, Faculty of Human Motor Sciences, Université libre de Bruxelles (ULB) , Brussels, Belgium 2 Laboratoire de Neuroanatomie et Neuroimagerie translationnelles, UNI – ULB Neuroscience Institute, Université libre de Bruxelles (ULB) , Brussels, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Esranur Yildiran Carlak 1 Laboratory of Functional Anatomy, Faculty of Human Motor Sciences, Université libre de Bruxelles (ULB) , Brussels, Belgium 2 Laboratoire de Neuroanatomie et Neuroimagerie translationnelles, UNI – ULB Neuroscience Institute, Université libre de Bruxelles (ULB) , Brussels, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christian Georgiev 1 Laboratory of Functional Anatomy, Faculty of Human Motor Sciences, Université libre de Bruxelles (ULB) , Brussels, Belgium 2 Laboratoire de Neuroanatomie et Neuroimagerie translationnelles, UNI – ULB Neuroscience Institute, Université libre de Bruxelles (ULB) , Brussels, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christian Georgiev Pierre Cabaraux 1 Laboratory of Functional Anatomy, Faculty of Human Motor Sciences, Université libre de Bruxelles (ULB) , Brussels, Belgium 3 Department of Neurology, Hôpital universitaire de Bruxelles (HUB), Université libre de Bruxelles (ULB) , Brussels, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dorine Van Dyck 4 Department of Occupational Science & Occupational Therapy, University of British Columbia , Vancouver, British Columbia, Canada 5 Brain, Behaviour, and Development, BC Children’s Hospital Research Institute , Vancouver, British Columbia, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gilles Naeije 2 Laboratoire de Neuroanatomie et Neuroimagerie translationnelles, UNI – ULB Neuroscience Institute, Université libre de Bruxelles (ULB) , Brussels, Belgium 3 Department of Neurology, Hôpital universitaire de Bruxelles (HUB), Université libre de Bruxelles (ULB) , Brussels, Belgium 6 Centre de Référence Neuromusculaire, Department of Neurology, CUB Hôpital Erasme, Université libre de Bruxelles (ULB) , Brussels, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marc Vander Ghinst 2 Laboratoire de Neuroanatomie et Neuroimagerie translationnelles, UNI – ULB Neuroscience Institute, Université libre de Bruxelles (ULB) , Brussels, Belgium 7 Service d’ORL et de chirurgie cervico-faciale, CUB Hôpital Erasme, Université libre de Bruxelles (ULB) , Brussels, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jennifer Foucart 8 Unité de recherche en Psychophysiologie de la santé et de la motricité humaine, Faculty of Human Motor Sciences, Université libre de Bruxelles (ULB) , Brussels, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nicolas Deconinck 3 Department of Neurology, Hôpital universitaire de Bruxelles (HUB), Université libre de Bruxelles (ULB) , Brussels, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mathieu Bourguignon 1 Laboratory of Functional Anatomy, Faculty of Human Motor Sciences, Université libre de Bruxelles (ULB) , Brussels, Belgium 2 Laboratoire de Neuroanatomie et Neuroimagerie translationnelles, UNI – ULB Neuroscience Institute, Université libre de Bruxelles (ULB) , Brussels, Belgium 9 WEL Research Institute , avenue Pasteur, 6, 1300 Wavre, Belgique Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Preview PDF ABSTRACT Acquiring efficient postural control strategies is key to children’s proper motor development. For that, the brain needs to continuously integrate sensory information and convert it into corrective motor commands. Although this entire process naturally hinges on the reliability of early senses, very few studies have investigated early sensory acuity and its role in postural stability during development. Clarifying this could lead to a better understanding of conditions, such as developmental coordination disorder (DCD), where the impairment of balance control is substantial. Here, we tested 25 typically developed school-aged children with a Visual Motion Detection test (VMDT), an ankle Joint Position Sense test (aJPST), force-plate assessed posturography, and the Movement Assessment Battery for Children - Second edition (MABC-2). We found a significant correlation between the balance score of the MABC-2 and both VMDT score ( r = 0.60, p = 0.003) and aJPST score ( r = -0.47, p = 0.02). However, no such relationship was found between the force-plate assessed sway amplitude during upright standing and the two sensory acuity scores. Importantly, the MABC-2 balance scores were associated with upright stability, but only to a limited extent. Given that the MABC-2 balance component factors in static and dynamic balance while posturography focuses only on static balance, our results point at a key role of early sensory acuity for dynamic balance. Together, these findings bring attention to possible clinical tools for motor impairment detection and subsequent rehabilitation strategies during development. 1. INTRODUCTION Postural stability refers to the ability to maintain a specific position of the body and achieve balance through coordinated actions, and it relies on three sensory systems: the visual, proprioceptive and vestibular systems. These three fundamental perceptual systems generate information that enables the localization of the body in space and more specifically, the position of each body segment [ 1 ], enabling the maintenance of the center of gravity within the body’s base of support. The efficiency of each of these three systems and their integration varies across individuals and according to different circumstances such as changes in environmental constraints (e.g., standing in darkness decreases visual weight) or even by top-down attention [ 2 ]. As in adults, postural control in children is the result of a complex process of integration of sensory information from these three main sensory systems [ 3 ]. However, children do not have the same postural responses as adults until middle childhood (6-11 years) or later [ 4 ]. Sensory systems mature at different rates, and each child preferentially uses information from one or more systems [ 5 ]. For instance, children between the ages of 4 and 6 rely predominantly on visual input for maintaining a bipedal stance [ 6 ]. It is only between 7 and 10 years of age that vestibular and proprioceptive inputs are integrated in a manner comparable to adults [ 7 ]. The critical roles of visual acuity and proprioception in coordinated movement and postural stability are well-documented [ 8 ]. However, the relationship between early sensory acuity and postural stability remains underexplored, leaving gaps in understanding how sensory deficits affect balance maintenance in children. Considering the above, the present study aims to evaluate to what extent postural stability hinges on early sensory acuity in typically developing children. Specifically, we evaluated typically developed school-aged children with a Visual Motion Detection test (VMDT), an ankle Joint Position Sense test (aJPST), posturography, and the Movement Assessment Battery for Children - Second edition (MABC-2; ref). The aJPST was performed at the ankle joint given its crucial role in balance maintenance [ 7 ], [ 9 ]. Likewise, the VMDT was selected given the relevance of visual motion detection acuity for upright balance maintenance [ 10 ], [ 11 ]. We hypothesized that poor visual motion detection acuity and ankle proprioceptive acuity would be associated with poor motor performances, especially in terms of postural stability. In addition, we hypothesized that the amplitude of postural sways obtained by posturography would be predictive of the MABC-2 score for postural stability. This research may guide future investigations into conditions marked by significant postural instability. A striking and understudied example of this is Developmental Coordination Disorder (DCD). This neurodevelopmental disorder affects around 5-6% of children worldwide and is characterized by poor motor abilities and difficulty learning new motor skills, including poor static and dynamic balance and poor coordination [ 12 ]. Moreover, children with DCD show weaker visual reweighting, no advanced multisensory integration and delayed responses to multisensory stimuli compared with typically developing children [ 13 ]. By highlighting the fundamental role of early sensory acuity in postural control, our findings may pave the way for targeted interventions that address sensory deficits to improve motor outcomes in children, particularly those with conditions such as DCD. 2. MATERIALS AND METHODS 2.1 Participants selection and study approval Twenty-five typically developing children (mean ± SD age, 8.3 ± 2.3 years, range 5-12 years, 12 females) were recruited to participate in the study. They had no history of movement disorders and were generally healthy as reported by their parents or legal guardian. The following exclusion criteria were employed: balance disorders, psychiatric disorders, and musculoskeletal injuries. The study was approved by the ULB-Hôpital Erasme Ethics Committee (P2023/313, CCB B4062023000180). Each child’s legal representative gave written informed consent before participation in accordance with the Declaration of Helsinki. The measurements were carried out at the ULB Hôpital Erasme, Brussels, Belgium. Participants received a gift card as compensation for their participation. 2.2 Experimental protocol Subjects underwent a VMDT, an aJPST and an evaluation of the bipedal stance through posturography. In addition, the MABC-2 [ 14 ] was used to assess motor skills. For the VMDT, participants sat in front of a 17-inch computer screen, with their eyes ∼90 cm away from the screen. The VMDT assessed their ability to recognize the vertical movement (either upwards or downwards) of a black–white Gabor patch oriented horizontally (full width at half maximum: 5.06 cm, corresponding to a visual angle of 3.22 degrees, spatial frequency: 1.16 cm -1 corresponding to 1.83 per degree of visual angle) and presented atop a gray background. In implementing the vertical movement, only the sine carrier was moved within a fixed Gaussian patch. The test included a total of 80 trials. Figure 1A illustrates the VMDT task sequence. Each trial started with a gray screen for 0.5 s, followed by the presentation of the moving Gabor patch for 2 s. Then the participant was prompted to record the perceived motion direction via mouse click. The next trial started after the participant’s response. Shift period, defined as the time it takes for a stripe to move by a spatial period, was varied following a two-down/one-up staircase procedure. The initial shift period was 1 s, which a successful trial led to multiply by and a failed trial to divide by √2. As a result, the shift period converged onto a threshold that reflected each participant’s visual motion detection acuity. Download figure Open in new tab Figure 1. A . Time-course of the VMDT task. Each of the 80 trials started with a gray screen, followed by the visual stimulus, and then by the response selection screen. The Gabor patch moved in either the upwards or downwards direction. The participant moved the cursor to indicate the perceived direction of movement (adapted from [ 15 ]). B . aJPST set-up. Participants’ dominant foot rested on a movable plank attached to a custom-made structure that allowed rotation of the plank in the plantar/dorsiflexion directions. A gyroscope attached to the movable plank continuously monitored the angle. C . Posturography set-up. Participants were equipped with a 64 channel EEG-cap and stood on a force plate across four experimental conditions. EEG data was not analyzed for this study. Figure 1B illustrates the setup for the aJPST. The test was conducted on the dominant leg, as determined by the subject’s self-report regarding the leg typically employed for kicking a ball. Participants sat on a chair with their legs at a 90° angle to the thighs. Participants were blindfolded using a mask. The tested foot was placed on a custom-made device consisting of a foot-sized plank that could rotate about an axis situated ∼6 cm underneath the ankle. A gyroscope (Wit motion HWT901B-RS485), featuring a precision of 0.05°, was fixed to a corner of the plank and used to record its orientation at 100 Hz during the whole duration of the procedure. Each trial consisted of three stages and began and ended with the ankle at the ‘neutral’ position, corresponding to the ankle oriented 90° relative to the shank. First, the ankle was passively mobilized to a predefined angle and held for ∼3 s. Second, the ankle was passively repositioned to the starting position for ∼ 3 s. Finally, the participant was asked to actively move their ankle to the previous position as accurately as possible, and to hold this position for ∼ 3 s. Passive mobilizations were performed at constant speed. Nine trials were performed in random order, at three different angles (dorsiflexion: 10° and 15°; plantar flexion: 10°). Figure 1C illustrates the posturography setup. Participants stood atop a force plate (AccuSway-O, AMTI, Watertown, MA, USA) and underwent four different balance conditions: with the eyes open on a hard surface, with the eyes closed on a hard surface, with the eyes open on foam pads (Domyos, Decathlon, Villeneuve-d’Ascq, France) and with the eyes closed on foam pads. Participants were asked to maintain their balance and stay relaxed. Each participant performed 8 randomized trials (each condition twice) of 5 minutes each, for a total of 40 minutes of recording. During each trial, the force plate measured the forces and force moments applied at ground level at a sampling frequency of 1000 Hz. The Movement Assessment Battery for Children – Second Edition (MABC-2) [ 14 ] assesses three major domains: manual dexterity, static and dynamic balance and the capacity of aiming and catching. Participants completed a total of eight subtests, according to their age group. Each participant was given a training session to familiarize themselves with the task. 2.3 Data processing Unless specified otherwise, the data analysis was performed using custom scripts in Matlab (version R2024a; Mathworks, Natick, MA, USA). VMDT The VMDT score was calculated using the geometric mean of the shift period in the last 30 trials. Participants with higher VMDT score are deemed to possess a better visual motion detection acuity compared to participants with lower scores. aJPST The aJPST score was calculated as the mean of the relative angle reproduction error across all trials. The relative angle reproduction error was itself the absolute value of the difference between the imposed and reproduced angles divided by the imposed angle. Increased aJPST scores indicate larger errors and thus correspond to worse proprioceptive acuity. Posturography The position of the center of pressure (CoP) was calculated using force plate data. CoP time-series were filtered between 0.2 and 10 Hz using a digital filter. To ensure that only data from the bipedal stance were processed, recordings were taken 10 seconds after ascent and 10 seconds before descent from the force plate. To quantify postural instability, we assessed the standard deviation of the CoP along the anterior-posterior axis (sdCoP AP ) for each recording separately. Higher sdCoP AP indicated greater postural instability. Asingle value per condition was obtained as the mean of sdCoP AP across the two trials of that condition. MABC-2 Motor performance data were obtained from the raw scores of the MABC-2 tests. These scores were converted to standard scores according to the age of the participants, using the conversion tables in the MABC-2 manual. The standard scores were aggregated within the same components assessing motor performance. Each component was converted into standard scores and percentiles using another conversion table, providing data on static and dynamic balance, aiming and catching ability, and manual dexterity. Finally, the sum of the standard scores was converted to percentiles using a final conversion table, providing an overall measure of each participant’s motor performance. These standard scores indicate an individual’s relative position within a reference distribution. 2.4 Statistical analyses Statistical analyses were performed using RStudio (version 2024.04.2+764). Prior to statistical modeling, VMDT, aJPST and sdCoP AP scores were log-transformed to better approximate a normal distribution, and to comply with the homoscedasticity assumption [ 15 ]. Scores were then corrected for outliers by setting values greater than 2.5 SD above or below the mean to this threshold. Pearson’s correlations were used to assess the dependence of VMD acuity and proprioceptive error on the results of each section of the MABC-2. We performed a linear mixed model analysis with lme4 to evaluate how sdCoP AP depends on five fixed effects: condition, age, VMDT score, aJPST score and MABC-2 balance score. We started with a null model that included only a different random intercept for each subject. The model was iteratively incremented with fixed effects and compared to the non-incremented model using a χ 2 test. Effects were tested in a planned order: (1) condition, (2) age and, (3) its interaction with condition, (4) VMDT score and, (5) its interaction with condition, (6) aJPST score and, (7) its interaction with condition, (8) MABC-2 balance score, and (9) its interaction with condition. At every step, the added fixed effect was retained in the model if deemed significant ( p < 0.05). Post-hoc comparisons for significant effects were conducted with t-tests for categorical fixed effects (condition) and Pearson’s correlations for continuous ones. 3. RESULTS 3.1 Relationship between motor abilities and sensory acuity Figure 2 presents the distribution of motor performance data from the MABC-2 expressed as a percentile of the scores in a reference population. Scores were 41 ± 28 % (manual dexterity; mean ± SD), 47 ± 27 % (aiming and catching), 61 ± 22 % (balance) and 48 ± 25 % (total score). The substantial standard deviation observed in the results could indicate that, even in the context of testing typically developed children, some may potentially be at risk of motor impairments, as indicated by the standardized score of the MABC-2. Two participants in the present study had a total score in the percentile 16, which is considered to be the upper limit of the “orange zone” [ 15 ]. This zone is characterized as the area in which children are at risk of motor impairments. Furthermore, both participants received a low score in one of the three sections (manual dexterity for one participant and aiming and catching for the other). With the exception of the aforementioned subjects, the remaining participants demonstrated a percentile score that did not indicate any risk of motor impairment. Download figure Open in new tab Figure 2. Distribution of the MABC-2 scores, expressed in percentile, across all participants. The red line in each graph represents the mean for each section. Sensory acuity varied considerably between children, with VMDT scores characterized by a coefficient of variation of 76.8 % (mean ± SD shift period, 32.5 ± 24.9 s) and aJPST score by a coefficient of variation of 64.5 % (mean ± SD relative error, 52.2 ± 33.7 %). Figure 3 presents the results of the correlations between, on the one hand, VMDT and aJPST scores, and, on the other hand, the results of each section of the MABC-2. VMDT score correlated significantly with the balance score ( r = 0.60, p = 0.003) and the total score ( r = 0.49, p = 0.018). aJPST score correlated significantly with the balance score ( r = -0.47, p = 0.02). Download figure Open in new tab Figure 3. Relationship between sensory acuity (VMDT and aJPST scores) and MABC-2 scores. Black dots indicate individual participant’s values and the regression line across all participants is in red. Correlation value and the corresponding significance level for each association is indicated in the upper or bottom right corner. 3.2 Instability assessed with posturography Figure 4 presents the distribution of sdCoP AP across participants in the four balance conditions. The linear mixed model revealed a significant effect of condition on sdCoP AP ( χ (3) 2 = 87.1, p < 0.0001). The result of post-hoc comparisons between conditions is presented in Figure 4 . As expected, these comparisons revealed that sdCoP AP increased with condition complexity. Download figure Open in new tab Figure 4. Distribution of sdCoP AP in the four different conditions on a logarithmic scale. Children showed higher instability in the hardest condition (eyes-closed foam). Black dots indicate individual participants’ values and red dots the mean within conditions. Asterisks represent significance levels with: *, p < 0.05 and * * , p < 0.01 The linear mixed model revealed a significant effect of age on sdCoP AP ( χ (1) 2 = 4.96, p = 0.02), which was not modulated by the standing condition ( χ (3) 2 = 5.15, p = 0.16). With increasing age, sdCoP AP decreased in all conditions, indicating improved stability (see Figure 5 ). Download figure Open in new tab Figure 5. Relationship between sdCoP AP and age in the four different conditions. Black dots indicate individual participants’ values and the regression line across all participants is in sred. 3.3 Relationship between instability and sensory acuity The linear mixed model analysis assessing sdCoP AP did not identify a significant effect of sensory acuity (VMDT score, χ (1) 2 = 2.09, p = 0.14; aJPST score, χ (1) 2 = 1.03, p = 0.30), nor a significant interaction thereof with the condition (VMDT score, χ (4) 2 = 7.88, p = 0.09; aJPST score, χ (4) 2 = 5.89, p = 0.20). 3.4 Relationship between instability and MABC-2 The linear mixed model analysis assessing sdCoP AP identified a significant effect of the MABC-2 balance score ( χ (1) 2 = 3.67, p = 0.05), and no significant interaction thereof with the condition ( χ (4) 2 = 7.85, p = 0.09). Figure 6 . presents the correlation between the MABC-2 balance score and sdCoP AP . The correlation was negative in all conditions, albeit significant only in the eyes-closed hard condition ( r = -0.42; p = 0.04). 4. DISCUSSION In this study, we examined the relationship between sensory acuity and balance control, assessed with the MABC-2 and force plate-based posturography. We found that visual motion detection acuity and ankle proprioceptive acuity were significantly associated with the balance score of the MABC-2 (VMDT p = 0.003; aJPST p = 0.02), but not with force plate-assessed sway amplitude (VMDT p = 0.14, aJPST p = 0.30). Furthermore, the latter two measures of balance showed only a mild degree of association. Finally, confirmatory in nature, postural stability assessed with sway amplitude decreased with condition complexity, but improved with age within the 5-12 year age range. Download figure Open in new tab Figure 6. Relationship between sdCoP AP and the balance score of the MABC-2 in the four different conditions. Graph layout as in Figure 5. Correlation value and corresponding significance level for each association is indicated in the upper right corner. 4.1 Importance of sensory acuity for motor performance Although the present study was conducted in a cohort of typically developing children, notable variability in motor performance was observed, suggesting that even within a non-clinical population, subtle motor difficulties may be present. A critical finding is the role of early sensory acuity in maintaining balance. That is, children with a higher score on the MABC-2 balance component displayed higher visual motion detection acuity and higher ankle proprioceptive acuity (i.e., lower errors on the aJPST). The association with visual motion detection acuity is in line with findings that children with DCD often present with visual deficits such as difficulties with fixation, abnormal eye movements, and binocular vision issues [ 16 ]. In the same vein, Cheng and colleague [ 17 ] reported that in children with DCD, poor visual performance has a negative impact on MABC-2 motor performance and daily life activities. As well, the association of the MABC-2 balance score with ankle proprioceptive acuity is in line with the fundamental role of proprioception in motor control. Ankle proprioception was demonstrated to contribute to balance, with extensive benefits of higher acuity, as has been reported in the context of sport performance and limitation of risks of injury [ 18 ] as well as for the limitation of balance impairment following stroke [ 19 ]. Critically, our data show that even in typically developing children, proprioception acuity at the ankle joint contributes to postural stability. These results align with the presence of proprioceptive deficits in individuals with DCD [ 20 ], [ 21 ]. Altogether, our findings support the notion that effective balance control may depend on the reliability of both the visual and proprioceptive systems. Therefore, future studies should determine the added value of VMDT and aJPST scores in clinical evaluations as predictive indicators for the early identification of motor deficits. No significant correlations emerged between our measures of early sensory acuity and the two MABC-2 components other than balance. Given that our sensory tests assessed aspects of perception that are most relevant for balance maintenance, and less so for manual dexterity and catching abilities, our results are not surprising. Proprioceptive acuity of upper limb joints and aspects of visual acuity subtending visuomotor coordination could be relevant for manual dexterity and catching ability. Accordingly, wrist proprioceptive acuity was found to be altered both in adults and children with DCD [ 22 ], [ 23 ].This alteration correlated with the manual dexterity component of the MABC-2 in children with DCD [ 22 ], and with levels of body coordination in adults, as assessed with the Bruininks-Oseretsky Test of Motor Proficiency [ 23 ]. Likewise, fine motor skills in healthy adults were reported to be associated with a combined measure of near visual acuity, near contrast sensitivity, and disability glare [ 24 ]. 4.2 Difference between MABC-2 and force plate-derived posturography Although there was a negative trend between sway amplitude and MABC-2 scores, the correlation between these two aspects was only significant in the eyes closed condition, with a modest magnitude. Two main factors could explain the weakness of this association: the limited reproducibility of the measures, and the difference in balance tasks used. The MABC-2 scores are subject to some degree of bias since the examiner can influence the child’s performance through disparities in instructional, motivational and rating styles [ 25 ]. Still, the test has been demonstrated to possess excellent test-retest reliability, with intraclass correlation coefficients (ICCs) ranging from 0.83 to 0.99 [ 26 ], [ 27 ]. In contrast, force plates provide fully objective measures of balance. Despite that fact, the reliability of force plate-based posturography is similar to that of the MABC-2. Indeed, ICCs of around 0.80–0.95 were reported for measures tightly related to sdCoP AP and vCoP AP assessed in stroke patients [ 28 ] and in older adults [ 29 ]. Accordingly, the similarity in ICCs reported for MABC-2 balance scores and force plate-derived measures suggests that the limited association between the two is explained by other sources of difference. The explanation could be the difference in the balance tasks for the two tests, where posturography focuses solely on static balance while the MABC-2 integrates more dynamic tasks. In line with this idea, Liu et al. [ 30 ] found weak correlations between static and dynamic balance skills in a cohort of 4 to 5 years old children. The same conclusion was reached by a meta-analysis conducted over the lifespan [ 31 ]. 4.3 Differential role of sensory acuity for static and dynamic balance In our data, early sensory acuity was predictive of stability assessed with the MABC-2 but not with posturography. In light of the considerations developed above, this suggests that such acuity is more important for dynamic than static tasks. This distinction aligns with the broader understanding that dynamic balance relies heavily on feedback control [ 32 ]. In such tasks, the nervous system uses continuous sensory input to update internal models and generate timely motor corrections. As a result, accurate sensory information is essential for effective dynamic balance control. In contrast, static balance typically requires less real-time sensory feedback and receives a contribution from feedforward control strategy [ 33 ]. The difference in sensory demands between dynamic and static balance is also reflected in previous findings on DCD. Indeed, dynamic postural control was shown to be a great challenge for children suffering from DCD [ 34 ], whereas under normal conditions, static balance control is not [ 35 ]. Only under difficult, unattended, or novel situations do such children seem to suffer from increased postural sway [ 36 ]. Although we did not include children with DCD, our findings may provide some important elements of information for existing theories of this disorder. One influential hypothesis attributes DCD to deficits in internal models [ 37 ]. However, given the link between sensory acuity and dynamic balance, our data suggests that impaired sensory acuity may be a more fundamental issue. Such a deficit could compromise the development of accurate internal models, thereby contributing to the motor difficulties observed in DCD. Finally, another potential explanation for the lack of significant relationship between sensory acuity and static balance stability may lie in the postural control strategy of the central nervous system. Although it would appear natural that sway amplitude be minimized, some models of feedback control suggest that, instead, humans attempt to minimize muscle activity [ 32 ], [ 38 ] with feedback control operating in an intermittent manner [ 39 ], [ 40 ]. Accordingly, sway amplitude may reflect an accepted level of instability rather than the minimal instability theoretically achievable through precise sensory feedback generated corrections. 4.4 Maturation of postural stability Force plate posturography results showed that instability increases under more complex conditions,such as on unstable surfaces or when visual information is absent, and decreases as children age. The effect of condition complexity is a classic finding in the field and has been reported extensively in adults [ 15 ], [ 41 ], [ 42 ] and children [ 43 ], [ 44 ]. The maturation with age is also well documented [ 45 ], [ 46 ], especially between age 6 and 10 [ 47 ], with adult-like behavior appearing around age 7–8 [ 48 ], [ 49 ]. 4.5 Limitations and perspectives The main limitation of our study lies in that we did not control for the role of attention in our sensory and balance assessments. SClearly, some children found it challenging to sustain concentration and complete each task block, which required ∼5 minutes of continuous attention. Future studies should consider shortening individual tasks and adapting them into more child-friendly implementations using colours/cartoons. Further research is required to elucidate the developmental trajectory of sensory-motor integration and explore the potential of sensory acuity measures as predictive markers for motor impairments, particularly in populations at risk for DCD. Notwithstanding the methodological limitations, these results lay the foundation for targeted intervention strategies and future longitudinal research. This can further refine diagnostic tools and therapies, thereby reinforcing the paradigm that robust sensory integration is the basis of effective motor function 4.6 Conclusions In conclusion, our study suggests that both visual motion detection and proprioceptive acuity are associated with balance control, with age playing a significant role in this relationship. The results further highlight the key distinction between static and dynamic postural control, where only the later seems to benefit from early and better sensory acuity. Collectively, our results indicate that future studies should determine the added value of early sensory acuity assessments as clinical tools to benefit diagnosis and treatment of children with motor disorders. Acknowledgment Scott Mongold was supported by an Aspirant Research Fellowship awarded by the F.R.S.-FNRS (Brussels, Belgium; grant FC 46249). Christian Georgiev was supported by an Aspirant Research Fellowship awarded by the Fonds de la Recherche Scientifique (F.R.S.- FNRS; Brussels, Belgium; grant 1.A.211.24F). Pierre Cabaraux was supported by a Clinical Researcher Fellowship awarded by the F.R.S.-FNRS (Brussels, Belgium; grant 40024164). Gilles Naeije is postdoctorate Clinical Master Specialist at the FRS-FNRS (Brussels, Belgium). The project was supported by grants of the Fonds de la Recherche Scientifique (F.R.S.-FNRS, Brussels, Belgium; grant MIS F.4504.21), and of the Brussels-Wallonia Federation (Collective Research Initiatives grant) awarded to Mathieu Bourguignon. REFERENCES [1]. ↵ L. B. Rowell , & J. T. Sheperd “ Horak , F. B. , & Macpherson , J. M. ( 1996 ). Postural Orientation and Equilibrium . In L. B. Rowell , & J. T. Sheperd (Eds.), Handbook of Physiology, Section 12. Exercise: Regulation and Integration of Multiple Systems (pp. 255 – 292 ). New York : Oxford University Press.” [2]. ↵ “ Peterka RJ. 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