the relationship between physical growth, the level of activity and the development of motor skills...

The relationship between physical growth, the level ofactivity and the development of motor skills in adolescence:

Di�erences between children with DCD and controls

Jan Visser *, Reint H. Geuze, Alex F. Kalverboer

Developmental and Experimental Clinical Psychology, University of Groningen, Grote Kruisstraat 2/1,

NL 9712 TS Groningen, The Netherlands

Abstract

This study uses a longitudinal design to explore the relationships between physical

growth, motor competence and level of participation in physical activity, during the adoles-

cent growth spurt. Thirty boys were selected, representing the range of motor competence.

Out of this sample two groups were formed, eight boys with clumsy child syndrome (DCD)

and 16 boys with adequate motor skills. The size of the DCD group was increased to 15 by

an additional selection. During a period of 2 12

years, starting from the age of 11 years and

six months, general motor skills were assessed half-yearly with the Movement Assessment

Battery for Children (ABC). Growth was measured monthly and a crude measure of level

of activity was obtained with a questionnaire. Multi-level regression modelling was used

to examine the relationships between these variables. Among the sample representing the

whole range of motor competence, the results support the view that high velocities in phys-

ical growth are negatively related to motor competence, with high levels of activity showing

a positive relationship with competence. Surprisingly, children with DCD do not seem to be

a�ected by the growth spurt. A majority of the children with DCD catches up with controls

to some extent and ®ve even reach full competence. The results are discussed in terms of

theories of both normal and atypical development. Ó 1998 Elsevier Science B.V. All rights

reserved.

Human Movement Science 17 (1998) 573±608

* Corresponding author. E-mail: [email protected].

0167-9457/98/$19.00 Ó 1998 Elsevier Science B.V. All rights reserved.

PII: S 0 1 6 7 - 9 4 5 7 ( 9 8 ) 0 0 0 1 4 - 1

PsycINFO classi®cation 2330; 2800; 3250

Keywords: Motor development; Growth spurt; Activity levels; Developmental coordination

disorder

1. Introduction

The word ``clumsy'' is used in many di�erent contexts. One such examplecan be found in the literature on the adolescent growth spurt, in which re-searchers have frequently characterized the movements of children, mainlyboys, as clumsy (e.g. Tanner, 1978; Shepard, 1981). What is being referredto here, of course, is not the enduring state of ``clumsiness'' experienced bythe children described in this theme issue, but a temporary disruption ofmotor coordination which is believed to occur during the period of rapidphysical growth experienced by all boys at some point in their adolescence.The precise relationship between physical growth and motor developmentduring this period is not well understood and forms one focus of the presentstudy. The issue which makes the paper relevant to the theme issue, however,is the relationship between clumsiness as a syndrome and clumsiness as aproduct of the adolescent growth spurt. If such disruption of motor perfor-mance truly occurs during adolescence, it seems possible that childrenwho have found the acquisition of motor skills di�cult throughout theirlives might be especially vulnerable to the e�ects of getting taller, heavierand faster.

Over the years a number of studies have been designed to investigate thephenomenon of adolescent clumsiness. These have not produced entirelyconsistent results. Among the earliest studies were investigations by Dimock(1935) and Espenschade (1940), both of which reported improved motor per-formance between the ages of 12 and 16. In contrast, two other studiesshowed a period of temporary regression in motor performance betweenthe ages of 11 and 17 (Schnabel, 1961; Winter, 1964). Consistent with thelater studies, Dimock (1935) did report that at all ages prepubescent boysin his study performed better than pubescent or post-pubescent boys, whichseems to suggest that some maturational factor is indeed involved in the de-velopment of motor competence. However, since none of these studies actu-ally identi®ed the growth spurt in the subjects being studied, all theycontribute is a rough estimate of change as a function of age. This limitationis elaborated upon in a statement by Roede and van Wieringen (1985) who

574 J. Visser et al. / Human Movement Science 17 (1998) 573±608

point out that the individual adolescent growth spurt shows great variationnot only in the age of onset, but also in duration, maximum value, and over-all gain. As a consequence, they note that cross-sectional studies producecurves which are incapable of re¯ecting the precise e�ects of the adolescentgrowth spurt because the degree of acceleration is ¯attened and its durationarti®cially prolonged.

In a more recent study, which takes account of this problem, Beunen andMalina (1988) aligned individual developmental pro®les of a large numberof boys, on the basis of ``Peak Height Velocity'' (the peak in increase inheight). Using this procedure they found that strength, speed, and ¯exibilityincreased rather rapidly during the growth spurt, leading them to concludethat there does not appear to be a period of ``adolescent awkwardness''.This conclusion seems to be somewhat rash, however, as the study didnot include tasks that truly measured motor coordination. It seems farmore likely, for example, that the negative e�ects of growth will be detect-able in coordination tasks which are dependent on a ®nely calibrated sen-sorimotor system for proper execution. In other words, as the metrical andbiomechanical changes produced by rapid physical growth disturb the cal-ibration, or ``®ne tuning'' of the sensorimotor system, so too will the child'sability to move easily and ¯uently be a�ected. Relevant to this idea is astudy by Jensen (1981), which showed that in a 12 year old adolescent, ex-periencing a growth spurt, body moments of inertia changed up to 46% inone year for the transverse axis. This means that the level of force requiredto achieve the same amount of rotation in a movement has to increase dra-matically. Although not exactly comparable, support for the idea that coor-dinated behaviour can only be preserved by a continuous calibration of thenervous system of the growing organism might be found in experimentalstudies of prism adaptation in which the ability to adapt to various kindsof disturbance of the normal relationship between sensory input and motoroutput are observed (e.g. Held and Hein, 1958; Redding and Wallace,1988). Further evidence of the e�ects of such disruption is provided by ar-ti®cial neural network studies, in which a changed input-output relationshipwithin an already calibrated eye±hand coordination system is simulated(Sporns and Edelman, 1993).

In this study, we consider the possibility that the impact and duration ofthe deterioration in performance in adolescent boys might depend upon threefactors: the actual velocity of growth, the adaptive quality of the nervous sys-tem, and the extent to which the child participates in physical activity duringthe critical period of growth. We will comment upon each in turn.

J. Visser et al. / Human Movement Science 17 (1998) 573±608 575

The actual velocity of growth is the ®rst factor of importance. It seemslikely that the e�ects of growth on motor coordination would be most eas-ily detectable during periods of very fast growth, as higher growth veloc-ities increase the demands on the adaptational qualities of the system.Infancy and adolescence are stages in development in which growth ratesare extremely high and negative e�ects of growth can be expected to showup especially during these periods. In this study, we attempt to improveupon previous studies of these relationships by employing a longitudinaldesign with frequent measurement of both physical growth and motorcompetence. Since very little is known about which kinds of activity aremost vulnerable to disruption we have elected to use a broad based testof motor competence, which includes both gross and ®ne motor tasks,the Movement Assessment Battery for Children (ABC) (Henderson andSugden, 1992). Although the manual of this test does not provide any in-formation pertinent to the repeated use of the test over an extended peri-od, our own experience with it suggests that it will serve our purposereasonably well.

Secondly, we consider the possibility that a lack of adaptiveness of the cen-tral nervous system (CNS) might exacerbate the negative e�ects associatedwith the adolescent growth spurt. To test this hypothesis, we have chosento study children who have found it di�cult to acquire adequate levels ofmovement skill throughout their lives. In the past, such children might havebeen described as su�ering from the ``clumsy child syndrome'' but more cor-rectly, they now bear the label, DCD (American Psychiatric Association,1994). There is little doubt that many children with DCD exhibit signs of cen-tral nervous system dysfunction, albeit minor. Some studies, for example,simply document a high incidence of so-called ``minor'' or ``soft'' neurolog-ical signs in general (Volman and Geuze, 1998), while others focus on speci®csigns such as dysdiadochokinesia (Iloeje, 1987; Losse et al., 1991), choreiformdyskinesia and mild di�use hypotonia (Hadders-Algra et al., 1986). Thus,children with DCD, already facing di�culties in sensorimotor performance,may be even more at risk during the adolescent growth spurt than their nor-mally developing peers.

Thirdly, it is known that the level of activity is an important factor in thedevelopment of motor skills (e.g. Beitel and Kuhlman, 1992; Cleland andGallahue, 1993), although little is known about the speci®c nature of this re-lationship. Bullock and Grossberg assume that under normal learning condi-tions, that is with a su�cient amount of activity, calibration happens soquickly that negative e�ects of growth will hardly be noticed (Bullock and


Grossberg, 1988). However, the earlier mentioned experiments with prismglasses do show, that under certain circumstances, extreme disruption ofthe sensorimotor system can a�ect even the most simple of tasks such aspointing to a target (Redding and Wallace, 1988). Whether the adolescentgrowth spurt can be viewed as equivalent to such an arti®cial disruptionto the system has never been investigated. The concept of activity level israther di�cult to operationalize. In this study, we operationalize activitylevel as the amount of time a child spends participating in certain typesof physical activity. Since we could ®nd no suitable instrument to measurethis, we constructed our own questionnaire, including activities which, atleast loosely, relate to those activities formally assessed in the MovementABC.

In order to explore the relationship between these three factors, physicalgrowth, motor competence and activity level during the adolescent period,we employed a longitudinal design in which these variables were systemati-cally and frequently measured in groups of boys over a three year period.Each boy entered the study when he reached the age of 11:6 and was followeduntil the age of 14. This age span was chosen because we expected the major-ity of the boys to show a growth spurt during this period. Tanner, for exam-ple, reports a mean age of 12 for the onset of the spurt and 14 for PeakHeight Velocity, while the mean duration of the spurt is estimated at 4 years(Tanner et al., 1966; Tanner et al., 1976). As age related data provide littleinsight into development during the growth spurt, we aligned all individualdevelopmental curves on growth characteristics. The aligned data were ana-lyzed using multi-level regression analysis.

Speci®cally, the study addresses two main questions. The ®rst questionconcerns the relationship between growth, motor competence and activitylevel in the normal population of adolescent boys. We predict that the in-crease in growth velocity, which takes place during the adolescent growthspurt, temporarily disturbs performance on tasks involving sensorimotor co-ordination and that a high level of activity is bene®cial for the adaptation togrowth e�ects. Since the longitudinal design and the frequency of testing wehad elected to employ placed huge demands on our resources, we were onlyable to test these predictions on a small sample of 30 boys taken from ran-domly selected schools in Holland. Our second question concerns the possi-ble di�erential e�ects of growth and activity level on motor competence inboys with DCD. On the basis of our hypothesis that children with DCDare ``at risk'' during the adolescent growth spurt we predicted that theywould be a�ected more by increases in growth velocity then children with ad-


equate skills. To test this prediction, 15 boys with signs of DCD were com-pared with a control group of 16 boys.

2. Method

2.1. Subjects and selection procedures

A total of 37 boys, born between 1-7-1982 and 1-1-1983, participated in thestudy. All attended mainstream elementary schools in and around the city ofGroningen.

2.1.1. Reference groupThirty schools in the Groningen area were picked randomly from the tele-

phone book. The heads of 12 schools agreed to participate and teachers wereasked to select all the boys in their class who fell within the speci®ed agerange. Nearly all boys agreed to participate in the study, resulting in a sampleof 30 boys. For each boy, teachers completed a motor performance question-naire, the Groningen Motor Observation scale (GMO) (Van Dellen et al.,1993) and they were later tested on the Movement Assessment Battery forChildren. The GMO contains 20 items, describing a wide range of movementskills, each of which is rated on a four-point scale. GMO raw scores are thenconverted into deciles, with higher scores indicating worse performance. Themean decile score was 5.3, with a range of 1±10. For selection purposes theMovement ABC scores were converted into percentiles, higher scores indicat-ing better performance. In our sample the mean percentile score was 34.5(range 1±96).

2.1.2. DCD groupFifteen boys with DCD participated in the study. Eight of these were boys

from the reference group, who did poorly on both of the motor assessments.On the GMO, their scores fell above the 7th decile and on the MovementABC they fell below the 10th percentile. Five boys were obtained from a dif-ferent sample of mainstream schools, in which teachers were asked to selectthe boy with the clearest signs of motor di�culties in their class. Con®rma-tion of their di�culties was then obtained by examining their GMO andMovement ABC scores. These boys also obtained scores above the 7th decileon the GMO and below the 10th percentile on the Movement ABC. A further


two boys, who had participated in an earlier study completed this group.Both scored below the 10th percentile on the Movement ABC but werenot rated on the GMO. Three children received physiotherapy at some timeduring the study. None showed clear signs of neurological disorders, whilemental retardation was excluded by the fact that all boys were from main-stream schools.

2.1.3. Control groupTo compare the boys with DCD with boys of the same age who were rel-

atively well coordinated sixteen boys from the reference sample who obtainedscores above the 15th percentile on the Movement ABC were designated thecontrol group.

Table 1 shows the characteristics of the boys in the DCD group and thecontrol group.

Table 1

Characteristics of the individuals in the DCD group and the control group (Movement ABC scores are

percentiles, GMO scores are deciles)

Subject M-ABC-

score

GMO-

score

Worst of

class

Other

characteristics

Subject M-ABC GMO

DCD (N� 15) Controls

(N� 16)

1 1 8 ) 1 65 6

2 8 9 ) Very small for age

Obese

2 54 4

3 6 9 ) 3 45 3

4 2 10 ) 4 65 5

5 2 10 ) 5 60 3

6 1 7 ) 6 20 8

7 2 8 ) 7 54 5

8 6 8 ) 8 89 3

9 2 9 Yes 9 79 6

10 4 10 Yes 10 96 1

11 8 10 Yes 11 40 3

12 1 10 Yes 12 45 )13 3 10 Yes Physioth erapy 13 16 1

14 1 ) ) Physioth erapy 14 16 4

15 4 ) ) Physioth erapy 15 32 3

16 96 3

Mean 3.4 9.1 Mean 58.6 3.9


2.2. Procedure

Between the ages of 11;6 and 14;0 growth data were collected monthly bythe boys' parents. Motor performance was measured half-yearly at our lab-oratory, using the Movement ABC, resulting in six data points per subject.Activity levels were measured retrospectively at the end of the study, usinga questionnaire ®lled in by the boys and their parents.

2.2.1. Growth measuresAs measures of growth we used height velocity, weight velocity and the

Quetelet index, which is a weight±height ratio (weight/height 2). The Queteletindex is mostly used as an indication of underweight or overweight. However,we use it here as an index of change in body proportions, which we felt mightbe the variable which interacted most directly with the performance of certainskills.

Body height and body weight were measured monthly by the boys' par-ents. At the start of the study all participating families were visited by theexperimenter. They received identical measurement tools (a weighing scale,a tape measure with a length of 200 cm, and a metal triangle) and detailedinstruction on how to perform the measurements. Measurements of weightand height were performed ®rst thing in the morning. Parents were provid-ed with a drawing, showing the required body posture during the measure-ment of height, with the following instructions: the back should be againsta wall, with heels touching, arms against the body, the body as straight aspossible and the virtual line between the eye and the top of the ear in a hor-izontal position. The triangle was rested on top of the child's head, with its90° angle against the wall. The distance between the ¯oor and the base ofthe triangle was then measured with the tape measure. The measurementswere performed at the end of each month, by which time the parents hadreceived a postcard on which the required date of the measurements wasstated. The height and weight was ®lled in on this card, and returned toour institute.

Two procedures were used to check the reliability of the measurements ofheight. Firstly, height was measured twice on the ®rst measurement occasion.Secondly, height was also measured every six months by the experimenter,each time the children visited the laboratory.


2.2.2. General motor skillsUnfortunately, well-documented tests of general motor skills, suitable for

use with adolescents, are unavailable. In our study we used the MovementABC, a test designed for children from 4 to 12 years of age (Hendersonand Sugden, 1992). It is a well-standardized test and follow up studies haveshown that the items are suitable for adolescents, although one should beaware of the possibility of ``ceiling e�ects'' (Geuze and Borger, 1993; Losseet al., 1991).

The test contains items grouped under three headings, manual dexteri-ty, ball skills and balance, with a total of eight items at each age level.Scores on individual items are converted to scaled scores between 0 and5, with higher scores indicating worse performance. Item scores aresummed to obtain scores on subtests, while summation over the subtestsresults in a total score (for selection purposes the total score can be con-verted into a percentile score, as we did in selecting the DCD and thecontrol group). In our main analyses we used the total score and scoreson the subtests.

The Movement ABC was administered half-yearly at our laboratory (bythe principal investigator or a supervised student) resulting in six data pointsper subject. In our study we used the items that were constructed for age level11±12.

2.2.3. Activity levelsAs noted above, the concept of ``activity levels'' as it might apply to move-

ment skill development is extremely di�cult to operationalize and we could®nd no well-developed assessment instruments which might serve our pur-pose. As a substitute, therefore we used a short questionnaire, which we con-structed ourselves. Since we had no resources to develop this instrumentformally, it can only be regarded as a very crude measure of activity levels.The questionnaire focused on two broad classes of activities.

1. Physical exercise, including sports and physical education at school.2. Fine motor activities, including hobby work, such as model making,

and art/craft classes at school.At the end of the study, the boys and their parents were asked to completethe questionnaire by estimating the number of hours that were spent on av-erage per week on these classes of activities, around the time of the labora-tory measurements. This resulted in six retrospective estimates of activitylevels.


Additionally, we asked the subjects to note whether they were receivingphysiotherapy during the study and/or had su�ered periods of illness lastinglonger than a month. These factors may in¯uence motor performance andthereby confound the relationship between growth velocity and skill develop-ment. Such episodes were rare and we used these data only for descriptivepurposes.

2.3. Data analysis

2.3.1. Height, weight and height±weight ratioOf the monthly measurements on height and weight 2% were missing,

either because postcards were lost or because there was no time to performthe measurements (due to holidays etc.). These missing data were estimatedby linear interpolation. We then ®tted third degree polynomials to the result-ing data, yielding smoothed curves for height and weight (see also Largo etal., 1978). The Quetelet index could then be calculated from the ®tted data.

2.3.2. Growth velocityGrowth velocity refers to both height velocity and weight velocity. Height

velocity and weight velocity were determined from the smoothed, monthlydata on height and weight, by computing di�erence scores between the suc-cessive data points. Height velocity was also computed from the half-yearlymeasurements on height in our laboratory, for which we used a procedure de-scribed by Beunen and Malina (1988). In this procedure smoothing of thedata is based on the assumption that, at each measurement point, the growthcurve is described by a second degree polynomial (Beunen and Malina, 1988p. 14). This implies that the rate of growth at each measurement point is com-puted from the increase during the surrounding intervals. Note that with thisprocedure growth velocity at the ages of 11;6 and 14;0 can only be computedon the basis of, respectively, the following and preceding interval, leading toreduced reliability of estimated growth velocities at these ages.

2.3.3. Onset of the spurtTo study growth related development precisely we needed data that were

synchronized on the growth spurt, rather than on age. Therefore, we estimat-ed the onset of the spurt in height from the smoothed, monthly growth veloc-ity data and aligned the data on growth, motor performance and activitylevels for all individuals on this event. The onset of the spurt was de®nedas the ®rst of the six half-yearly laboratory measurements at which the in-


crease in height, as determined from the smoothed monthly growth velocitydata, exceeded 6 cm per year, provided this value was followed by a clear andmonotonous increase in height velocity.

Using these criteria the onset of the spurt could be clearly de®ned in 25 ofthe 30 boys in the reference group, in 14 of the 15 children with DCD and in13 of the 16 controls. The data from the remaining boys could not be used inanalyzing the relationship between growth and motor performance, as theydid not show a spurt in height during the course of the study. This fact doesnot decrease the power of our design as the relationship between growth ve-locity and motor performance can only be studied reliably in subjects whoshow a de®nite growth spurt.

Three remarks should be made with respect to the use of such synchro-nized data sets. First of all, as the timing of the onset of the spurt may varybetween measurements 1 and 6, the synchronization procedure results in adata set with 11 repeated measurements, with the onset of the spurt at the6th for each subject. Each individual only contributes to 6 sequential datapoints, with missing data for at least 5 measurement points, as is depictedin Fig. 1. The calculation of mean group curves is problematic with largenumbers of missing data. Therefore, to graphically present the mean groupdata we used di�erence scores to compute mean curves. This is illustratedin Appendix A. Secondly, the synchronization procedure implies that meancurves of group data are less reliable for data points which are more remotefrom the onset, as these are characterized by the occurrence of a relativelylarge number of missing data. And, thirdly, a large amount of missing dataplaces severe limitations on the statistical procedures that can be used to

Fig. 1. Alignment of data (X) of two subjects with the onset at the start and the end of the study, respec-

tively. The alignment procedure results in 11 data points, with missing data (0) at ®ve points for each in-

dividual.


analyze the data. This ®nal point will be further discussed in the followingsection.

2.3.4. Statistical proceduresFor age related data, univariate analysis of group di�erences (DCD vs.

controls), was conducted with ANOVA, while MANOVA repeated mea-sures analysis was used to study developmental trends (SPSS) (Nie,1975). In these analyses the data of all subjects were used, including thosesubjects who did not show a spurt in growth. The dependent variablesused were the Movement ABC scores (subtest scores and total score),the activity measures and the growth measures. We adopted a 5% levelof signi®cance.

For the aligned data, multilevel regression modelling, as implemented inthe program MLn (Woodhouse, 1996), was used to analyze the relationshipsbetween growth velocity, the activity levels and motor development. MLnuses computational procedures which allow for the estimation of regressioncoe�cients even in data sets with a fairly large number of missing values.Further, this technique, designed speci®cally to study hierarchical data sets,in which lower levels (in our case longitudinal measurements) are nested inhigher levels (in our case subjects), allows for explicit modelling of variabilitybetween and within subjects (Goldstein, 1995). In addition to ®xed regressioncoe�cients, which de®ne the intercept and slope of the mean group curve, theprogram estimates random intercepts and random slopes. The random inter-cepts re¯ect the individual deviations from the mean initial valp^;ue on a re-sponse variable, whereas the random slopes re¯ect individual deviations fromthe mean developmental trends. The estimation of random intercepts andslopes is essential if the between subject variance forms a fairly large propor-tion of the total variation in the sample. It not only leads to improved mod-elling of the data but it can also be used to search for individual di�erences inthe relationships between the variables. These di�erences can then be relatedwith other individual characteristics. For a general discussion of the useful-ness of multilevel modelling of longitudinal or repeated measures data, seeGoldstein (1995), Bryk and Raudenbush (1992), Woodhouse (1996) and Snij-ders (1996).

2.3.5. The regression modelAs predictors we used time (centered around 0, that is t�)5 to 5), for

which we considered linear, quadratic and cubic trends, the age at the onsetof the spurt (a dummy variable), the activity measures, the growth measures,


and the interactions between the activity and growth measures. A high scoreon one of the interaction components indicates a high growth velocity andlow activity levels. In the analyses of group di�erences between children withDCD and controls additional predictors were used, namely, group (a dum-my variable, with DCD� 1 and control� 0), interactions between group andthe time variables and between group and the covariates, and three-way in-teractions between group, the activity measures and the growth velocitymeasures.

To model the covariates at both level 1 and 2 we computed individualmeans for each covariate, as well as deviations from this mean. This resultsin two predictors per covariate: a level 2 predictor, which is constant overtime and a level 1 predictor, which is time dependent.

The following model was ®tted on the data

yij � aj � Rkbkj�PREDICTORk� � eij;

where yij is the motor performance score of subject j on measurement i. Fur-ther, aj is the estimated intercept for subject j and bkj the regression coe�cientof predictor k�k � 1; 2; 3; . . .� for subject j. The latter de®nes the slope of thecurves. Finally, eij represents the error term of the model. It is assumed thatthe eij are normally distributed with a mean of 0 and a variance r2. In thismodel both the intercept and the regression coe�cients of the predictorscan be split into two components, a ®xed component, which is constantacross individuals, and a random component, which varies between individ-uals and has a mean of 0.

Predictors were removed from the models if their e�ects did not reach sig-ni®cance. The statistical signi®cance of ®xed coe�cients is tested with t-tests,using the estimated value of the coe�cient divided by its standard error astest statistic. Signi®cance of random e�ects is tested with the likelihood ratiotest (Bryk and Raudenbush, 1992).

3. Results

3.1. Growth

This section presents data on the reliability of growth measurements, basedon the total sample, as well as age related and spurt related data for the re-spective groups.


3.1.1. Reliability of the monthly measurements of heightThird degree polynomials ®tted well on the monthly data on height and

weight, measured by the parents. For height we found a mean Pearson's Rof 0.99, with a minimum of 0.97. For weight the mean correlation was0.97, with a minimum of 0.88. The mean absolute di�erence scores betweenthe ®rst two measurements made by the parents was 0.52 cm, with a stan-dard deviation of 1.03 and a maximum value of 2.4 cm (the maximum val-ue was an outlier). As the mean height was 149.5 cm in bothmeasurements, the measurement error was 0.3%. Pearson's R correlationsbetween the ®tted curves of the monthly measurements and the ®ttedcurves of the half-yearly measurements, taken in our laboratory were be-tween 0.90 and 0.91.

3.1.2. Age related dataIn our reference sample the mean body height increases from 151 cm at

the age of 11;6 to 167 cm at the age of 14;0, while the mean body weightincreases from 37 to 50 kg. The Quetelet index increases linearly, from 16.4to 18. These data are quite comparable to the mean values (50th percentile)that were found in a Dutch national survey study conducted by Roede andvan Wieringen (1985). Height velocity increases linearly from 5 cm per yearat the age of 11;6 to approximately 8 cm per year at the age of 14;0, whileweight velocity increases from about 4 kg per year to 7 kg per year (seeFig. 2(a)).

Individual di�erences in timing of the spurt are large. Fig. 2(b) presentsthe velocity curves of two subjects in our study. As can be seen, subject Ashows an increase in the rate of growth, which continues beyond the age of14;0, while subject B has attained Peak Height Velocity at the age of 13;0already.

Over the entire duration of the study the children with DCD were con-sistently heavier than the controls, but not taller. This ®nding is re¯ected inthe Quetelet index, although the group di�erence with respect to this vari-able just fell short of signi®cance (F(1,29)� 3,76, p� 0.06). There were nosigni®cant interactions between group and age. Finally, there were no groupdi�erences in the timing of the onset of the spurt or in the velocity ofgrowth.

3.1.3. Alignment of the growth velocity data on the onset of the spurt in heightAs Fig. 3 shows the alignment of individual growth velocity data on the

onset of the spurt in height results in curves with a clear peak in height veloc-


ity (10.6 cm/year) as well as weight velocity (9 kg/year). The onset of the spurtin weight coincides nicely with the onset of the spurt in height, while the delaybetween the time of Peak Height Velocity and the time of Peak Weight Ve-locity is in accordance with data reported in the literature (e.g. Shepard,1981).

Fig. 2. (a) Mean growth velocity in the reference sample (N� 30) between the ages of 11;6 and 14;0. (b)

Individual di�erences in growth velocity. The data are from two boys in the reference sample.


3.2. General motor competence

Recall that we used the Movement ABC items for 11 to 12-year-olds andnorms for 12-year-olds.

3.2.1. Age related dataFig. 4(a)±(d) show the Movement ABC data from the DCD group and the

control group, in comparison to the scores from the reference group as awhole. In the reference group the improvement in total scores is not signi®-cant. Of the three subtests only ball skills improve signi®cantly, with a qua-dratic trend (F(1,28)� 8,3, p < 0.01) indicating that the improvement in thisarea of performance is rapid at ®rst, after which it levels out.

Comparison of the data for the DCD and control group showed a signif-icantly worse total score, as well as subtest scores for the DCD group. Themain e�ect of age is signi®cant for all variables except for manual dexterity.More importantly, the interactions between group and age were signi®cantfor all variables, indicating that the di�erence between the children withDCD and the controls decreases with age (total Movement ABC score:F(1,26)� 35,7, p < 0.001; manual dexterity: F(1,26)� 15.6, p < 0.001; ballskills: F(1,26)� 9.0, p < 0.01; balance: F(1,26)� 11.7, p < 0.01).

Further analyses of these data involving tests of the di�erences betweentwo consecutive measurements showed that children with DCD improve sig-

Fig. 3. Mean aligned curves of growth velocity in the reference sample (N� 25).


ni®cantly in total score between the ages of 11;6 and 12;0 (p < 0.001) and theages of 12;6 and 13;0 (p < 0.001), while the performance of the controls de-teriorated between the ages of 11;6 and 12;0 (p < 0.001). Similar results werefound for manual dexterity (with p < 0.01 for all e�ects). On ball skills chil-dren with DCD again show a signi®cant improvement in performance be-tween the ages of 11;6 and 12;0 and between 12;6 and 13;0 (p < 0.001),while the improvement in the control children is smaller and more gradual.On balance a large improvement can be seen in the DCD group betweenthe ages of 13;0 and 13;6 (p < 0.01). At the age of 13;6, the children withDCD even score better than the control children. In the control group thetotal scores, as well as the balance scores, did not show a normal distribution.On balance optimal scores are found in more than half of the subjects in thisgroup, suggesting the existence of ceiling e�ects.

3.2.2. Stability of DCDAlthough the data clearly show that the DCD group catches up with the

control group during the study, a number of children in the DCD group still

Fig. 4. Age related Movement ABC scores (total score and subtest scores) of the DCD (N� 13) and con-

trol group (N� 15) in comparison to the reference group (N� 29).


show very poor skills at later ages. Using the norms for 12-year-olds, four ofthe 15 children with DCD still have scores below the 5th percentile at the ageof 14;0 (representing de®nite motor di�culties), while six have scores betweenthe 5th and 15th percentile (borderline scores). Thus, only 5 out of 15 chil-dren score in the normal range at the age of 14;0! Of the 16 control childrentested at the age of 14;0, one has a score below the 5th percentile at this age,while three have borderline scores. Thus, while general motor skills improvein most children between the ages of 11;6 and 14;0, performance in some ofthe children actually deteriorates.

3.2.3. Alignment of the motor performance data on the onset of the spurtFig. 5, which presents the data on the Movement ABC after alignment on

the onset of the spurt in height, shows that performance of the control groupdeteriorates during the growth spurt, whereas performance in the DCDgroup shows continued improvement.

3.3. Activity levels

In our total sample, there were very few instances of children receivingphysiotherapy (maximally 2 subjects per measurement occasion) or experi-

Fig. 5. Spurt related Movement ABC scores (total score) of the DCD (N� 14) and control group (N� 13)

in comparison to the reference group (N� 25).


encing prolonged illness (maximally 1 subject per measurement occasion).Since these variables were very unlikely to have any substantial in¯uenceon the development of motor skills in our sample, we will not further com-ment upon them. The following paragraphs present age related and spurt re-lated data on the level of physical exercise and the level of ®ne motorpractice.

3.3.1. Age related dataIn our reference group there were no age e�ects for either of the activity

measures. With respect to di�erences between children with DCD and con-trols, we found a signi®cant e�ect of group (F(1,29)� 4,52, p < 0.05) onthe level of physical exercise but no age e�ect or interaction e�ect. On aver-age, the control children spent 58% more time on activities included in thephysical exercise section of our questionnaire than the children with DCD.In contrast, no signi®cant group di�erences were found on the ®ne motorcomponent. Age e�ects and interaction e�ects were also insigni®cant.

Fig. 6 shows the activity levels during the course of the study, for bothsubgroups in comparison to the data of the reference group. The ¯uctuationsin the activity levels between the age of 11;6 and 12 are possibly related to theentrance in secondary school.

3.3.2. Alignment of the activity levels data on the onset of the spurt in heightAlignment of the physical exercise data shows that a fairly large decrease

in the amount of physical exercise takes place in the control group, about 1;6years before the onset of the spurt. After this the level remains stable(Fig. 7(a)). A drop in the level of ®ne motor practice is found in the DCDgroup, which coincides with the onset of the spurt (Fig. 7(b)). We cannotthink of any logical explanation for this ®nding.

3.4. Multilevel regression modelling of the aligned Movement ABC data:Relationships between motor development, growth, and the activity levels

Separate analyses were conducted on the data from the reference groupand on the data from the DCD and the control group. We will ®rst discussthe results of the analyses on the data for the reference group.

3.4.1. Reference groupTable 2 presents the coe�cients of the regression models for the Move-

ment ABC and subtest data, together with their standard errors and an indi-


cation of the level of signi®cance of the ®xed e�ects. Mean curves were com-puted from the predicted scores based on these regression models, using in-dividual di�erence scores as illustrated in Appendix A. These curves arepresented in Fig. 8 as ``estimated scores'' and are contrasted with the ``ob-served scores''.

Total Movement ABC-score: From Table 2 it can be seen that the totalMovement ABC score improves linearly with time. Further, the random partof the regression model shows that there is a signi®cant amount of variance inslopes for the linear time e�ect. This means that individuals di�er signi®cant-ly in the rate of improvement. Other e�ects that reach signi®cance are the

Fig. 6. Age related level of activity in the DCD (N� 13) and control group (N� 15) in comparison to the

reference group (N� 29).


mean amount of physical exercise, which is positively related to performance,and mean height velocity, as well as increases in height velocity, which arenegatively related to performance (recall that a reduction in MovementABC-score indicates improved performance).

The mean predicted values associated with this model are presented inFig. 8(a) as ``estimated scores''. The individual predicted values correlatehighly with the original aligned data (Pearson's R� 0.88), which suggeststhat a fairly large part of the variance in the data can be explained by meansof the four predictors (for a critical review of explained variance in multilevelmodels the reader is referred to Snijders and Bosker, 1994).

Fig. 7. Spurt related level of activity in the DCD (N� 14) and control group (N� 13) in comparison to the

reference group (N� 25).


Manual dexterity: The aligned data in Fig. 8(b) show that performance onthe manual dexterity subtest ¯uctuates heavily over time. However, none ofthe time components reaches signi®cance in our regression equation. Rather,manual dexterity is positively related to the mean physical exercise level, themean ®ne motor practice level, and the mean Quetelet index, while perfor-mance is negatively related to increases in weight velocity (see Table 1).The model contains random intercepts but no random slopes, indicating thatthe e�ects of the predictors were essentially the same for all subjects. The pre-dicted values associated with this model correlate highly with the original da-ta (Pearson's R� 0.79).

Ball skills: Fig. 8(c) shows the aligned data on ball skills. In the regressionmodel providing the best ®t on the data the scores on the ball skills subtest

Table 2

Multilevel regression models ®tted on the Movement ABC and subtest data

Parameter Total score estimate Manual dexterity

estimate

Ball skills esti-

mate

Balance estimate

Fixed part

Intercept 8.11 (0.74) 4.32 (0.34) 1.17 (0.27) 2.61 (0.33)

Time )0.92 (0.22)*** )0.58 (0.18)** )0.40 (0.10)***

Time2 0.03 (0.01)*

Mean covariates (level 2)

Physical exercise )0.46 (0.20)** )0.32 (0.09)***

Fine motor practice )0.98 (0.29)***

Height velocity 1.3 (0.67)* 0.50 (0.30)*

Quetelet index )0.62 (0.27)**

Time dependent covariates

(level 1)

Physical exercise )0.17 (0.07)**

Height velocity 0.51 (0.17)** 0.16 (0.09)*

Weight velocity 0.16 (0.08)*

Random part

Intercept variance 12.08 (3.9) 2.14 (0.82) 1.68 (0.51) 2.24 (0.76)

Slope variance: time 0.40 (0.23) 0.004 (0.02)

Interc.±slope covariance )0.23 (0.65) )0.22 (0.08)

Residual variance 7.35 (1.0) 4.36 (0.55) 1.17 (0.16) 2.65 (0.34)

Parameter estimates are given, with their standard errors between brackets. Covariates in the ®xed part of

the model are split up into a mean value per subject (level 2 predictors) and individual deviations from this

mean (level 1 predictors). Signi®cance levels of the coe�cients in the ®xed part are indicated by asterisks,

where ***� p < 0.001, **� p < 0.01 and *� p < 0.05. Predictors not included in the table did not reach

signi®cance for any of the dependent variables. The models are based on the data of 25 subjects with a

total of 149 data points at level 1.


show a linear improvement over time, with a negative quadratic trend (seeTable 2). During the spurt, the rate of improvement levels o� but this e�ectis unrelated to the spurt itself, as none of the growth components or interac-tion components reaches signi®cance. Instead, ceiling e�ects might play a rolehere. The development of ball skills is signi®cantly related to changes in thelevel of physical exercise experienced during the study, but not to the level of®ne motor practice.

The ®nal model contains random intercepts, as well as random slopes forthe linear e�ect of time: the improvement in ball skills is smaller in individualswith high scores at the start of the study, which is demonstrated by the co-variance between the intercept and the random slopes for the linear e�ectof time. This is further proof that ceiling e�ects played a role in this subtest.The three predictors provide a reasonable description of the data, as a corre-lation of 0.79 was found between the predicted scores and the original aligneddata.

Balance: Fig. 8(d) shows that balance improves in general, with a smallsetback at the onset of the spurt. The regression model, presented in Ta-

Fig. 8. Mean aligned Movement ABC data (observed scores) of the reference sample (N� 25) and estimat-

ed scores, based on the regression models.


ble 1, shows that balance improves linearly with time, whereas no quadraticor third degree components were found. The activity level measures did notappear to be related to performance on the balance subtest, although thee�ect of the mean physical exercise level is close to signi®cance (a coe�cientvalue of 0.10, with a standard error of 0.07 (p� 0.07)). Of the growth com-ponents, increases in height velocity are negatively related to performanceon the balance subtests, while the coe�cients for weight velocity and theQuetelet index are not signi®cant. The model providing the best ®t onthe data contains random intercepts but no random slopes. The correlationbetween the predicted values resulting from this model and the original da-ta is 0.76.

3.4.2. Di�erences between the DCD group and the control groupTable 3 shows the coe�cients of the predictors used in the modelling of the

data of the DCD group and the control group. To improve the interpretationof the interaction terms, we also included insigni®cant main e�ects of predic-tors with signi®cant interaction terms. Predictors not included in the table didnot reach signi®cance for any of the dependent variables.

Total Movement ABC-score: For the total Movement ABC score we founda signi®cant e�ect of group and an interaction between group and time. Asthe main e�ect of time is insigni®cant the improvement over time is limitedto the children with DCD. With regard to the covariates we found signi®cantmain e�ects for the mean level of physical exercise and the mean level of ®nemotor practice: subjects with a high mean level of activity perform relativelywell on the Movement ABC. Further, a high mean weight velocity is relatedto poor performance, while a high mean Quetelet index is related to goodperformance. Signi®cant interactions were found between group and the ac-tivity level measures. These e�ects indicate that the positive e�ect of a highmean level of activity during the course of the study is limited to childrenin the control group.

The interactions between group and the growth velocity measures did notreach signi®cance. Thus, children with DCD do not di�er from control chil-dren with respect to the e�ects of the growth spurt, which contradicts ouroriginal hypothesis. Finally, the signi®cant group by mean Quetelet index in-teraction indicates that the relationship between the Quetelet index and mo-tor performance is limited to the control group. Three-way interactionsbetween group, the activity levels and growth velocity did not reach signi®-cance. The estimated scores, based on this model, correlate highly with theobserved scores (Pearson's R� 0.88).


Manual dexterity: Multilevel regression analysis of the dexterity datashowed a signi®cant e�ect of group as well as time and no group by timeinteraction. A signi®cant group by mean Quetelet index interaction isfound, with a small and insigni®cant main e�ect of the Quetelet index. Thisindicates that the negative relationship between the Quetelet and manual

Table 3

Multilevel regression models ®tted on the Movement ABC and subtest data of the DCD group and the

control group

Parameter Total score

estimate

Dexterity estimate ball skills estimate balance estimate

Fixed part

Intercept 4.74 (0.72) 2.91 (0.51) 0.56 (0.31) 1.64 (0.38)

Time 0.08 (0.20) )0.27 (0.14)* 0.05 (0.09) 0.12 (0.11)

Group (DCD� 1,

control� 0)

8.07 (1.01)*** 3.12 (0.65)*** 2.06 (0.43)*** 2.11 (0.53)***

Group X time )1.60 (0.27)*** )0.43 (0.11)*** )0.33 (0.15)*

Mean covariates

Physical exercise )0.34 (0.19)*

Fine motor practice )1.51 (0.61) 0.59 (0.11)***

Height velocity 0.51 (0.25)*

Weight velocity 2.59 (0.48)***

Quetelet index )2.74 (0.56)*** )0.02 (0.24)

Group X phys. exercise 0.50 (0.28)*

Group X ®ne motor pr. 1.76 (0.66)**

Group X Quetelet index 2.22 (0.57)*** 0.46 (0.30)*

Time dependent covariates

Fine motor practice )0.80 (0.37)*

Height velocity 0.57 (0.13)*

Weight velocity )0.14 (0.06)**

Group X ®ne motor pr. 1.02 (0.43)**

Group X height velocity )0.74 (0.16)***

Random part

Intercept variance 3.35 (1.34) 2.36 (0.82) 0.96 (0.33) 1.36 (0.51)

Slope variance: time 0.18 (0.09)

Intercept±slope covariance 0.54 (0.22)

Residual variance 9.05 (1.11) 2.96 (0.40) 1.52 (0.19) 3.04 (0.37)

Parameter estimates are given, with their standard errors between brackets. Covariates in the ®xed part of

the model are split up into a mean value per subject (level 2 predictors) and individual deviations from this

mean (level 1 predictors). Signi®cance levels of coe�cients in the ®xed part are indicated by asterisks,

where ***� p < 0.001, **� p < 0.01 and *� p < 0.05. The models are based on the data of 27 subjects,

with a total of 162 data points at level 1.


dexterity is present only in the DCD group. With regard to the time depen-dent covariates, we found signi®cant e�ects of the level of ®ne motor prac-tice and of height velocity. The interactions between these variables andgroup inclusion were also signi®cant and indicate that children in the con-trol group bene®t from increases in ®ne motor practice, while children withDCD do not, and that children in the control group are negatively in¯u-enced by increases in height velocity, whereas children with DCD arenot. The latter ®nding indicates that in our regression model the di�erencebetween the groups in the rate of development (which was apparent fromthe MANOVA analyses) is attributed to the interaction between groupand height velocity. The model contains random slopes for the e�ect oftime. The intercept-slope covariance shows that the improvement in perfor-mance with time is larger in subjects with low initial scores. The predictedscores, estimated on the basis of this model correlate highly with the ob-served values (R� 0.87).

Ball skills: Multilevel regression analysis of the ball skills data showed asigni®cant group e�ect, as well as a group by time interaction and no maine�ect of time. Thus, the improvement over time is limited to children withDCD. Surprisingly, the mean level of ®ne motor practice is negatively relatedto ball skills, while increases in weight velocity show a positive relationshipwith this variable. We cannot think of a logical explanation for these results.The correlation between the estimated scores and the observed scores is high(R� 0.86).

Balance: Multilevel regression analysis on the balance data showed a sig-ni®cant main e�ect of group and an interaction between group and time.Again, the improvement over time is limited to children with DCD. Of themean covariates both height velocity and weight velocity are negatively relat-ed to balance, but only if they are entered in the model separately. Thus,these variables share explained variance. Subjects with a high mean growthrate during the study show a poor control of balance, but from the modelit is unclear if the predictor responsible for this e�ect is height velocity, weightvelocity or a combination of both. The table only shows the coe�cient valueof height velocity. The correlation between the estimated scores and the ob-served values is reasonably high (R� 0.74).

Summarizing: the multilevel analyses con®rm that children with signs ofDCD catch up with controls during the age range 11;6±14;0, although onlyfew reach normal competence. With respect to the covariates, that is, theactivity levels and physical growth, we did not ®nd fully consistent results.The data suggest, however, that children with DCD are less a�ected by the


spurt than children with adequate skills, which clearly contradicts our pre-dictions.

4. Discussion

In this study, we had two major objectives. The ®rst was to examine thee�ect of the adolescent growth spurt on the development of motor compe-tence in boys who were believed to be developing normally. As part of thisenquiry, we also took the opportunity to determine whether the amount ofphysical activity undertaken during this period had any e�ect on this rela-tionship. Our second objective was to determine whether boys who are poor-ly coordinated are a�ected similarly by the growth spurt or not. To addressthese questions, motor competence was measured with six-month intervals,using a standardized test, while activity levels were estimated using a simplequestionnaire. Height and weight were measured monthly by the boys' par-ents, a measurement frequency which is higher than in any other study weknow. Also, these growth measurements were checked in a laboratory settingat six monthly intervals and were shown to be highly reliable. For 84% of theboys participating in the study, we were able to determine the onset of thespurt in height and thereby synchronize the data on growth velocity, motorperformance and activity levels on this event. These growth spurt related dataenabled us to study the e�ects of growth on motor development, for which weused multilevel regression analysis.

Before turning to our main ®ndings and their interpretation, it might beuseful to deal with two possible limitations to the study. The ®rst concernsthe composition of the sample of boys we labelled the reference group. De-spite our attempt to obtain a sample which would be representative of therange of motor competence in the population at large, we found that an un-expectedly high proportion of these boys (26%) scored below the 10th percen-tile point on our objective measure of motor competence, the MovementABC. One possible explanation of this outcome is that, in spite of our in-structions, some teachers actually selected boys with motor problems. Anoth-er is that we were simply unlucky and the high preponderance of ratherpoorly coordinated boys occurred by chance. One implication of this prob-lem, of course, is that it may reduce any di�erences we might ®nd betweenthe children we describe as typically developing and those who have DCD.

The second limitation of the study concerns the measure of activity levelsemployed in the study. We have already noted that our questionnaire was a


rather crude instrument put together speci®cally for this study. The boyscompleted the inventory retrospectively. They were asked to estimate theamount of time they spent engaged in two di�erent types of activity at severalpoints in time. We acknowledge that retrospective measurements are alwaysless reliable than concurrent procedures. However, we believe that the mea-surements had some validity. First of all, as far as the level of physical exer-cise was concerned, children and their parents could easily trace the amountof activity undertaken in this area, as it related to the number of hours ofPhysical Education, sports, and sport training o�ered at school. More impor-tant, however, was the fact that the two sections of the inventory correlatedsigni®cantly with di�erent sections of the Movement ABC test. Whereas thephysical activity section of the inventory was related with the ball skills clus-ter, the ®ne motor section related only to the manual dexterity cluster, whichis the only subtest involving ®ne manipulative skills.

On the assumption then that neither of these limitations seriously a�ectedthe outcome of our study, we now proceed to the major ®ndings.

4.1. The growth spurt, motor competence and activity levels in typicallydeveloping boys

Multilevel regression analyses of data that were aligned on the onset of thespurt in height, using growth velocity and activity level measures as covari-ates, showed that general motor competence, as measured by the MovementABC, develops signi®cantly between the ages of 11;6 and 14;0 and is to someextent a�ected by the type and amount of physical activity undertaken duringthis time. When these e�ects were examined in more detail, we found that sig-ni®cant improvement on the Movement ABC was con®ned to the ball skillsand balance sections of the test. The boys' estimate of their level of partici-pation in sports and Physical Education was related to ball skills but notto balance. One possible reason for this distinction may be that sports relatedskills contained in our inventory were too far removed from the static anddynamic balance tasks in the Movement ABC for any correlation to emerge.The level of ®ne motor practice was only related to performance on the man-ual dexterity cluster. In general, therefore, we can state that activity levels areimportant variables in the development of motor skills and that the nature ofthis relationship depends upon both the type of activity and the task understudy.

As well as being interesting in their own right, the above-mentioned dem-onstrations of age related e�ects on the Movement ABC and correlations


with activity levels serve another important function. They con®rm our beliefthat the measures employed in the study were sensitive enough to re¯ect vari-ation in motor competence during the adolescent period. This means that wecan proceed with con®dence to our main concern, the possible negative e�ectof growth velocity on the development of motor skill. In those boys forwhom we could determine clear signs of a growth spurt, we were able to dem-onstrate a negative relationship between height velocity and total scores onthe Movement ABC. In fact, both a high mean height velocity and the in-crease in height velocity during the growth spurt were signi®cantly relatedto development. In contrast, weight velocity appeared to play no role in thisrelationship.

We were also able to demonstrate consistent e�ects on the sub-sections ofthe Movement ABC. Whereas height velocity was related to a dip in the de-velopment of balance, weight velocity emerged as being related to a deterio-ration in manual dexterity.

One remark should be made with regard to the regression model that waschosen to describe the data with respect to total Movement ABC scores. Inthis model the temporary drop in the rate of motor development is explainedby the negative e�ect of height velocity. Alternatively, it could also be mod-elled by ®tting polynomials (that is, quadratic and cubic trends). In fact, amodel with linear, quadratic and cubic time components had equal descrip-tive power as the model consisting of a linear trend and a negative e�ect ofheight velocity. This is a common problem in multilevel regression analysesof longitudinal data with time dependent covariates (like height velocity).In these cases decisions about the inclusion of predictors in the model haveto be based not only on statistics, but also on theoretical grounds (Snijders,1996). We argue that the model using height velocity as the dependent vari-able is theoretically more plausible than the model using only time compo-nents, as in our model the trends can be related to a precisely de®nedevent in the development of the child, namely the spurt in height, while amodel with only time components has little explanatory power. It is clear,however, that it is extremely di�cult to separate time e�ects from growth ef-fects, even with the alignment procedure used here.

While readers familiar with the scoring system of the Movement ABC testmight wonder whether ceiling e�ects a�ected our ®ndings in this study, weconsider that all of the results we have presented so far refute this idea.The test seems to have been su�ciently sensitive to small changes in perfor-mance to allow the relationships of interest to emerge. Ceiling e�ects in thiscase would simply have reduced our chances of revealing these relationships


rather than enhanced them. We are con®dent, therefore, that our ®nding, re-garding the negative e�ect of growth velocity on the development of motorcompetence is quite robust.

A rather surprising ®nding from the regression analyses was that manualdexterity was positively related to the mean Quetelet index. Recall that thisindex was used as a measure of body proportions. We have found one oth-er study which shows an e�ect of body proportions on ®ne manipulativeability (Peters et al., 1990). In a study with college students it was foundthat sex di�erences on the Purdue Pegboard task disappeared when ®ngerand thumb thickness was used as a covariate, indicating that subjects withthicker ®ngers and thumbs performed relatively poorly on this task. How-ever, our ®nding seems to contradict the results of Peters' study, as a highQuetelet index indicates relatively thick limbs and subjects with a high Que-telet index performed relatively well on the manual dexterity tasks. It re-mains unclear how body characteristics, more speci®cally the height/weight ratio, might in¯uence ®ne motor skills. But the positive e�ect ofthe Quetelet index underlines the fact that it is the rate of growth, ratherthan changes in body characteristics per se, which negatively in¯uencesthe development of skills. It seems that higher rates of change require moreadaptation at the neural level.

Although we have demonstrated clear relationships between activity levelsand motor competence during the adolescent period, our hypothesis concern-ing an interaction between growth velocity and activity levels was not con-®rmed. The interaction e�ects were small and not signi®cant, whichindicates that the activity levels did not interact with the negative e�ect ofa high growth rate. It is possible, however, that the power of our designwas insu�cient to measure these e�ects.

4.2. Di�erences between children with DCD and controls

The analyses of age related data showed that children with signs of DCDimproved signi®cantly on each sub-section of the Movement ABC, while thecontrol children only showed improvement on ball skills. As a group, theDCD children appeared to catch up with the control group. When this groupe�ect was examined further, however, we found that the major part of thisimprovement could be accounted for by the performance of ®ve children,who showed good skills at the age of 14;0. In contrast, four of the childrenin the DCD group continued to show clear signs of DCD at the age of14;0, even failing to meet the pass/fail criteria set for 12-year-olds (recall that


norms for 14-year-olds are not yet available). These results are in line with®ndings presented in the literature with respect to the development of chil-dren with DCD (Geuze and Borger, 1993; Cantell et al., 1994).

It is possible that methodological problems, particularly ``regression to themean'' and ``ceiling e�ects'' have a�ected our analyses of group di�erences tosome extent. With respect to regression to the mean, one can expect a groupof children with extremely low scores at a ®rst measurement (the DCDgroup) to improve more on a second measurement than a group of childrenwith moderate or high scores (the control group). Recall, however, that per-formance in the DCD group continued to improve throughout the entire du-ration of the study, whereas performance in the control group did not. This isevidence that the group by age interactions re¯ect true developmental phe-nomena. With respect to ceiling e�ects, the relatively high number of optimalscores on the ball skills subtest in the control group suggest that the possibil-ities for development in this group were limited, possibly enhancing thegroup by age interaction. This explanation does not hold for the manual dex-terity subtest, however, as optimal scores were rarely reached on this test.Nevertheless, the group by age interaction was strongest for the manual dex-terity subtest.

It seems plausible, therefore, that the group di�erences in age related devel-opment were caused by di�erences in the e�ects of growth rate and activitylevels. Multilevel regression analyses on the data that were aligned on theonset of the spurt in height showed that the nature of the relationship be-tween physical growth, motor competence and activity levels clearly di�eredbetween the groups. This was apparent especially on the manual dexteritysubtest. First of all, performance of the control group was positively relatedto increases in the level of ®ne motor practice, whereas this relationship wasnot found in the DCD group. Further, in the control group performance wasnegatively related to increases in growth rate, whereas the interaction be-tween group and height velocity suggests that performance of children withDCD even improves with higher growth rates. Finally, performance of theDCD group was negatively related to the mean Quetelet index. Although thisclearly contradicts the results with respect to the data of the reference group,who showed a positive e�ect of a high mean Quetelet index, it may be ex-plained by the group di�erences in mean Quetelet index. In the referencegroup, as well as in the control group, Quetelet values were in the normalrange, whereas they were relatively high in the DCD group. Negative e�ectsof a high Quetelet index can be expected to show up especially in the higherrange of Quetelet values.


The di�erential e�ects of growth velocity on children with DCD and chil-dren with adequate motor skills need explanation. These ®ndings are in linewith results reported by Beunen et al. (1988), who found that the rate of de-velopment during the growth spurt was related to the level of performancebefore the spurt. In their study, performance decreased in a number of chil-dren, particularly those who performed relatively well at the beginning of thegrowth spurt. It seems that children who are well coordinated, rather thanchildren who are poorly coordinated, are at risk during the growth spurt.One might state that the growth spurt cannot disturb the coordination ofchildren with DCD as they are poorly coordinated anyway. But this doesnot explain the rapid development of some of the children in the DCD group.Five of the children initially diagnosed as DCD, on the basis of both teacherratings and Movement ABC scores, showed good or even excellent motorskills at the age of 14;0. It may be that these children were su�ering from aneurodevelopmental delay and that they bene®tted from e�ects co-occurringwith the adolescent growth spurt. This suggestion is supported by a longitu-dinal study conducted by Soorani-Lunsing (1993). This study reports a de-crease in the incidence of signs of minor neurological dysfunction (MND)with the onset of puberty. Signs of MND are often reported in connectionwith DCD and may be related to the motor di�culties found in these chil-dren. The decrease in the occurrence of signs of MND suggests a transforma-tion in the central nervous system, which might be bene®cial especially forchildren with `soft signs' of neurological disorder.

Discontinuities in brain development during adolescence have also been re-ported by Huttenlocher, who found that synaptic density in the human cere-brum declines dramatically between the ages of 10 and 15. This wasinterpreted as ®ne tuning of the neurological system (Huttenlocher, 1979).Thus, the possibility of a transformation in brain development during adoles-cence should be considered. If such a transformation truly exists it is possiblethat it a�ects some children with DCD di�erently than controls.

4.3. Individual di�erences

Our data show that individual di�erences in development during thegrowth spurt are large. We tried to model this between subject variabilitywith a multilevel regression approach, allowing the estimation of separate co-e�cients for each individual. Random slopes for the e�ect of time could onlybe estimated reliably from the ball skills data and the total test scores of thereference sample. With respect to the ball skills data, this analysis revealed


that individuals with high scores at the start of the study showed relativelylittle improvement. The individual di�erences in the slopes of the curves rep-resenting the total test score were not related to initial scores, however. Un-fortunately, random slopes for the e�ects of growth velocity or activity levelscould not be estimated for any of the motor measures. Thus, we could notdescribe di�erences in the size of the e�ects in terms of di�erences in individ-ual characteristics. Apparently, the between subject variability in the e�ectsof the time dependent covariates was relatively small.

4.4. Concluding remarks

The present study has provided clear evidence of the fact that the growthspurt a�ects the development of motor coordination in boys. The amount ofphysical activity undertaken during the period of study appears to be a sec-ond factor in¯uencing development. Moreover, both the size and the direc-tion of the e�ect were related to the quality of motor performance beforethe growth spurt. The ®nding that some children with DCD seem to pro®tfrom the growth spurt, possibly because of enhanced maturation of someparts of the CNS during puberty, is intriguing and requires further study.It should be noted, however, that most of the children with DCD still showedpoor skills at the age of 14, which is in line with ®ndings from previous stud-ies (Losse et al., 1991; Geuze and Borger, 1993; Cantell et al., 1994).

Acknowledgements

We would like to acknowledge Prof. T. Snijders for his advise in the mul-tilevel analyses.


Appendix A

An example of how to calculate a mean aligned curve on the basis of heightvelocity data from two individuals

Step 1: Take the height velocity values coinciding with the laboratory mea-surements and determine the onset of the spurt (underlined data).

Step 2: Align the data on the onset of the spurt.

Step 3: Compute di�erence scores between the consecutive measurements.

Step 4: Compute the mean absolute score at the onset of the spurt (7.15cm/year) and use the mean di�erence scores to calculate a mean curve fromthis point, in both directions.

References

American Psychiatric Association, 1994. Diagnostic and Statistical Manual of Mental Disorders, fourth

edition. American Psychiatric Association, Washington, DC.

Beitel, P.A., Kuhlman, J.S., 1992. Relationships among age, sex, and depth of sport experience with initial

open task performance by 4- to 9-year-old children. Perceptual and Motor Skills 74, 387±396.

Beunen, G., Malina, R.M., 1988. Growth and physical performance relative to the timing of the

adolescent spurt. In: Pandolf, K.B. (Ed.), Exercise and Sport Sciences Reviews, vol. 16. Macmillan,

New York.

subject 1 5.8 7:8 9.1 9.3 8.7 6.9subject 2 5.0 4.1 4.2 4.7 6:5 8.9

onsetsubject 1 ) ) ) 5.8 7:8 9.1 9.3 8.7 6.9subject 2 5.0 4.1 4.2 4.7 6:5 8.9 ) ) )

onsetsubject 1 ) ) ) 2.0 1.3 0.2 )0.5 )1.8subject 2 )0.9 0.1 0.5 1.8 2.4 ) ) )

mean aligned onsetcurve 5.55 4.65 4.75 5.25 7:15 9.0 9.2 8.7 6.9


Bryk, A.S., Raudenbush, S.W., 1992. Hierarchical linear models, applications and data analysis methods.

Sage, London.

Bullock, D., Grossberg, S., 1988. Neural dynamics of planned arm movements: Emerging invariants and

speed-accuracy properties during trajectory formation. Psychological Review 95, 49±90.

Cantell, M.H., Smyth, M.M., Ahonen, T., 1994. Clumsiness in adolescence: Educational, motor, and

social outcomes of motor delay, detected at 5 years. Adapted Physical Activity Quarterly 11, 115±129.

Cleland, F.E., Gallahue, D.L., 1993. Young children's divergent movement ability. Perceptual and Motor

Skills 77, 535±544.

Dimock, H.S., 1935. A research in adolescence I. Pubescence and physical growth. Child Development 6,

177±195.

Espenschade, A., 1940. Motor performance in adolescence: Including the study of relationships with

measures of physical growth and maturity. Monographs of the Society for Research in Child

Development, vol. 5, no. 1.

Geuze, R.H., B�orger, H., 1993. Children who are clumsy: Five years later. Adapted Physical Activity

Quarterly 10, 10±21.

Goldstein, H., 1995. Multilevel Statistical Models. 2nd ed. Edward Arnold, London.

Hadders-Algra, M., Touwen, B.C.L., Huisjes, H.J., 1986. Neurologically deviant newborns: neurological

behavioral development at the age of six years. Developmental Medicine and Child Neurology, 28,

569±578.

Held, R., Hein, A., 1958. Adaptation to disarranged hand-eye coordination contingent upon rea�erent

stimulation. Perceptual and Motor Skills 8, 87±90.

Henderson, S.E., Sugden, D.A., 1992. Movement Assessment Battery for Children. Psychological

Corporation Ltd., Kent.

Huttenlocher, P.R., 1979. Synaptic density in human frontal cortex-developmental changes and e�ects of

aging. Brain Research 163, 195±205.

Iloeje, S.O. 1987., Developmental apraxia among Nigerian children in Enugu, Nigeria. Developmental

Medicine and Child Neurology, 29, 502±507.

Jensen, R.K., 1981. The e�ect of a 12-month growth period on the body moments of inertia of children.

Medicine and Science in Sports and Exercise 13 (4), 238±242.

Largo, R.H., Gasser, Th., Prader, A., Stuetzle, W., Huber, P.J., 1978. Analysis of the adolescent growth

spurt using smoothing spline functions. Annals of Human Biology 5, 421±434.

Losse, A., Henderson, S.E., Elliman, D., Hall, D., Knight, E., Jongmans, M., 1991. Clumsiness in children

± Do they grow out of it? A 10-year follow-up study. Developmental Medicine and Child Neurology

33, 55±68.

Nie, N.H., 1975. SPSS: Statistical Package for the Social Sciences, 2nd ed. McGraw-Hill, New York.

Peters, M., Servos, P., Day, R., 1990. Marked sex di�erences on a ®ne motor skill task disappear when

®nger size is used as a covariate. Journal of applied psychology 75 (1), 87±90.

Redding, G.M., Wallace, B., 1988. Adaptive mechanisms in perceptual-motor coordination: Components

of prism adaptation. Journal of Motor Behavior 20 (3), 242±254.

Roede, M.J., van Wieringen, J.C., 1985. Growth diagrams 1980; third nation wide survey. Supplement of

the Tijdschrift voor Sociale Gezondheidszorg 63, 1±34.

Schnabel, G., 1961. Zur bewegungscoordination in der Pubessenz. Theorie und Praxis der K�orperkultur:

Wissenschaftliches Organ des Staatliches Komitee f�ur K�orperkultur und Sport der Deutschen

Demokratischen Republik 11/12, 1070±1079.

Shepard, R.H., 1981. Cardiovascular limitations in the aged. In: Smith, E.L., Serfass, R.C. (Eds.), Exercise

and Aging. Enslow, Hillside, NJ.

Snijders, T.A.B., 1996. Analysis of longitudinal data using the hierarchical linear model. Quality and

Quantity 30, 427±448.

Snijders, T.A.B., Bosker, R.J., 1994. Modelled variance in two-level models. Sociological Methods and

Research 22 (3), 342±363.


Soorani-Lunsing, R.J., 1993. Neurobehavioral relationships and puberty: Another transformation? Early

Human Development 34, 59±67.

Sporns, O., Edelman, G.M., 1993. Solving Bernstein's problem: A proposal for the development of co-

ordinated movement by selection. Child Development 64, 960±981.

Tanner, J.M., 1978. Foetus into Man: Physical Growth from Conception to Maturity. Open Books,

London.

Tanner, J.M., Whitehouse, R.H., Marubini, E., Resele, L.F., 1976. The adolescent growth spurt of boys

and girls of the Harpenden growth study. Annals of Human Biology 3 (2), 109±126.

Tanner, J.M., Whitehouse, R.H., Takaishi, M., 1966. Standards from birth to maturity for height, weight,

height velocity and weight velocity; British children 1965. Archives of Disease in Childhood 41, 454±

471; 613±635.

Van Dellen, T., Vaessen, W., Schoemaker, M.M., 1993. Clumsiness: de®nition and selection of subjects. In

Kalverboer, A.F., (Ed.), Developmental biopsychology: experimental and observational studies in

children at risk. (pp. 135±152), Ann Arbor: The University of Michigan Press.

Volman, M.J.M., Geuze, R.H., 1998. Relative phase stability of bimanual and visuomanual rhythmic

coordination patterns in children with a Developmental Coordination Disorder. Human Movement

Science 17, 541±572.

Winter, R., 1964. Zur entwicklung der laufbewegungen bei knaben und m�adchen in schulalter. Theorie

und Praxis der K�orperkultur: Wissenschaftliches Organ des Staatliches Komitee f�ur K�orperkultur und

Sport der Deutschen Demokratischen Republik 13, 754±758.

Woodhouse, G., 1996. Multilevel Modelling Applications. A Guide for Users of MLn. Institute of

Education, London.


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