Effect of Childhood Developmental Coordination Disorder on Adulthood Physical Activity : Arvo Ylppö Longitudinal Study

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Effect of Childhood Developmental Coordination Disorder on Adulthood Physical Activity : Arvo Ylppö Longitudinal Study © 2022 The Authors. Scandinavian Journal of Medicine & Science In Sports published by John Wiley & Sons Ltd. Published version Tan, Jocelyn L.K.; Ylä‐Kojola, Anna‐Mari; Eriksson, Johan G.; Salonen, Minna K.; Wasenius, Niko; Hart, Nicolas H.; Chivers, Paola; Rantalainen, Timo; Lano, Aulikki; Piitulainen, Harri Tan, J. L., Ylä‐Kojola, A., Eriksson, J. G., Salonen, M. K., Wasenius, N., Hart, N. H., Chivers, P., Rantalainen, T., Lano, A., & Piitulainen, H. (2022). Effect of Childhood Developmental Coordination Disorder on Adulthood Physical Activity : Arvo Ylppö Longitudinal Study. Scandinavian Journal of Medicine and Science in Sports, 32(6), 1050-1063. https://doi.org/10.1111/sms.14144 2022 1050 | Scand J Med Sci Sports. 2022;32:1050–1063.wileyonlinelibrary.com/journal/sms Received: 23 June 2021 | Revised: 27 January 2022 | Accepted: 9 February 2022 DOI: 10.1111/sms.14144 ORIGINAL ARTICLE Effect of childhood developmental coordination disorder on adulthood physical activity; Arvo Ylppö longitudinal study Jocelyn L. K.Tan1,2 | AnnaMariYläKojola3 | Johan G.Eriksson4,5,6,7 | Minna K.Salonen4,8 | NikoWasenius4,5 | Nicolas H.Hart2,9,10,11 | PaolaChivers2,9,10 | TimoRantalainen2,9,10,12 | AulikkiLano3 | HarriPiitulainen13,14 1School of Health Sciences, University of Notre Dame Australia, Fremantle, Western Australia, Australia 2Western Australian Bone Research Collaboration, Perth, Western Australia, Australia 3Department of Child Neurology, Children’s Hospital, University of Helsinki, Helsinki, Finland 4Folkhälsan Research Center, Helsinki, Finland 5Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland 6Department of Obstetrics and Gynecology and Human Potential Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore 7Singapore Institute for Clinical Sciences, A*Star, Singapore, Singapore 8Unit of Chronic Disease Prevention, Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland 9Institute for Health Research, University of Notre Dame Australia, Fremantle, Western Australia, Australia 10School of Medical and Health Science, Edith Cowan University, Joondalup, Western Australia, Australia 11Centre for Healthcare Transformation, Queensland University of Technology, Brisbane, Queensland, Australia 12Gerontology Research Center, University of Jyväskylä, Jyväskylä, Finland 13Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland 14Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Aalto, Finland This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2022 The Authors. Scandinavian Journal of Medicine & Science In Sports published by John Wiley & Sons Ltd. Correspondence Harri Piitulainen, Faculty of Sport and Health Sciences, University of Jyväskylä, Seminaarinkatu 15, 40014 Jyväskylän yliopisto, Finland. Email: [email protected] Funding information Jane ja Aatos Erkon Säätiö; Australian Government Research Training Program; Arvo and Lea Ylppö Foundation; Academy of Finland, Grant/Award Number: 296240, 326988, 307250 and 327288; Finnish Foundation of Pediatric Research Individuals at risk of Developmental Coordination Disorder (DCD) have low levels of physical activity in childhood due to impaired motor competence; however, physical activity levels in adulthood have not been established. This study sought to determine the impact of DCD risk on physical activity levels in adults using accelerometry measurement. Participants (n=656) from the Arvo Ylppö Longitudinal Study cohort had their motor competence assessed at the age of five years, and their physical activity quantified via device assessment at the age of 25years. Between group differences were assessed to differentiate physical activity measures for individuals based on DCD risk status, with general linear modeling performed to control for the effects of sex, body mass index (BMI), and maternal education. Participants at risk of DCD were found to have a lower total number of steps (d=0.3, p=0.022) than those not at risk. Statistical modeling indicated that DCD risk status increased time spent in sedentary light activity (β=0.1, 95% CI 0.02 to 0.3, p=0.026) and decreased time spent in vigorous physical activity | 1051 TAN et al. 1 | INTRODUCTION Individuals with motor difficulties, manifesting clinically as developmental coordination disorder (DCD) in approximately five percent of the population, have difficulties with the performance of their motor skills to a degree that is impactful upon everyday functioning.1 In 75 to 80% of cases with DCD motor difficulties recognized in childhood persist into adulthood1 and although natural variation of motor competence in early childhood prevents diagnosis of DCD prior to the age of five, the presence of motor difficulties indicating DCD risk in preschoolaged children has been shown to be a good indicator of persistent motor difficulties.2 Preschoolaged children at risk of DCD have been identified to have physical activity deficits,3 similar to those reported throughout childhood and adolescence for individuals with DCD.1,4,5 As motor deficits associated with DCD usually continue into adulthood, along with negative physical activity beliefs4 and the use of avoidancebased coping mechanisms6 continued detriment of physical activity into adulthood would be anticipated. Although this has been reported via selfreport,6 there is currently an absence of deviceassessed measures of physical activity in this group. This absence is particularly pertinent, as studies in pediatric populations have reported a discrepancy between selfreport and deviceassessed measures of physical activity in children with DCD7 and as such physical activity selfreports in adults need confirmation. Due to the prevalence of DCD, continued low physical activity could have population level health repercussions given the increased risk of sedentary behaviorrelated chronic conditions later in life,8,9 and markers for these conditions have been reported in adults with DCD.10 As such, the absence of deviceassessed measures of physical activity in an adult population with DCD is a significant gap in the literature with the potential for significant health implications. In quantifying the differences in physical activity in adults with childhood DCD risk, the role of specific areas of impairment as a barrier to physical activity is a necessary avenue for investigation. Studies of physical activity in pediatric populations report varying levels of deficit,7 which may in part be due to the impact of a variety of factors known to impact upon physical activity such as gender, body mass index (BMI), and socioeconomic factors.11 However, a specific area affecting physical activity for individuals at risk of DCD is the frequent cooccurrence of impairments outside of pure motor competence issues,1,12 which may also act to impair physical activity. A common deficit among individuals at risk of DCD is visuomotor integration (VMI),13 the coordination of visual and motorrelated neuronal processing known to impact behavior and perception.13 Individuals with DCD and VMI deficits have been shown to have different areas of motor deficit than those with motor competence impairment only14,15 and decreasing diversity and intensity of physical activity with increasing VMI deficits has been shown in children with DCD.16 It is not known whether VMI plays a similar role for adults with a history of DCD risk; however, prior work using the Arvo Ylppö Longitudinal Study (AYLS) population established a link between decreased VMI and negative health outcomes in the form of increased body fat percentage and increased body mass index (BMI)17 of which lower levels of physical activity could be a causative factor. The potential for VMI impairments to reduce physical activity indicates a need for further investigation of the role of VMI on physical activity in a DCD population. This study aims to describe the relationship between childhood DCD risk status and VMI deficits defined at the age of approximately 5years, and physical activity levels recorded at the age of 25years in a young adult population by addressing the following two questions: (1) Does early DCD risk status have an impact upon physical activity levels into early adulthood? (2) Does early VMI impairment have an impact upon physical activity levels into early adulthood, either independently or in combination with DCD? It was hypothesized that both DCD risk status and VMI impairment will have a negative longterm effect on the via interaction with BMI (β=0.04, 95% CI 0.001 to 0.1, p=0.025). Sensitivity analysis found that visuomotor impairment did not significantly impact physical activity but did increase the role of DCD risk status in some models. This 20yearlongitudinal study indicated that DCD risk status continues to negatively impact on levels of physical activity into early adulthood. KEYWORDS accelerometry, developmental disability, motor competence 1052 | TAN et al. physical activity levels (increased sedentary behavior, decreased moderate to vigorous physical activity compared to nonaffected referents) that would still be evident at the age of 25years. 2 | METHODS 2.1 | Experimental design This is an analysis of participants from the AYLS, a longitudinal prospective cohort study.18 The current study explores the impact of DCD status and VMI impairment at the age of approximately five years on physical activity at the age of 25years using data from birth, 56months, and 25years. DCD risk status via motor competence assessment and VMI using the Beery scale were assessed at the age of 56months. Participants had anthropometry assessment (height and weight), and accelerometry performed at the age of 25years. 2.2 | Participants The AYLS comprised of infants born alive from seven maternity hospitals in the county of Uusimaa, Finland between March 15, 1985, and March 14, 1986. A total of 1535 participants were recruited who had been admitted to neonatal wards of obstetric units or the Neonatal Intensive Care Unit of Children's Hospital, Helsinki University Hospital, Finland, within ten days of their birth, with an additional 658 healthy control infants prospectively and randomly recruited via three maternity hospitals. Participants were invited to clinical followup visits at age 56months and 25years. As shown in Figure1, some participants attended at one clinical followup visit only, with about twenty percent of those with valid accelerometery data not attending at the age of 56months which is considered to be due to the mobility of the sample. Missing data analysis of participants who had valid accelerometery data at the age of 25years found no significant differences in gender, hospitalization rate, parental education level, birthweight, gestational age, or in sum scores for obstetric or neonatal optimality when assessed based upon attendance at 56 months. However, participants who were included in DCD classification at 56months but did not have accelerometery performed at the age of 25years were found to be more frequently male (57.5% compared to 48.9%, χ2=11.2, p<0.001), hospitalized following birth (70.5% compared to 63.5%, χ2=8.4, p=0.004), and had parents with a lower education level (maternal χ2=20.2 p<0.001; paternal χ2=12.7 p=0.005). The childhood protocol was approved by the ethics committees of the Women's Hospital and Children's Hospital of Helsinki University Hospital, the Helsinki City Maternity Hospital, and Jorvi Hospital, and in adulthood by the Coordinating Ethics Committee of the Helsinki and Uusimaa Hospital District. Informed consent was provided by parents in childhood and participants in adulthood. The current study reports on a subsample of 695 participants drawn from the AYLS cohort. Participants were excluded from analysis if they had an impairment that FIGURE 1 Participant flow through study, including exclusion points. *Some participants qualified for exclusion on more than one criterion Cohort at birth (n=2193) Hospitalised Control (n = 1535)(n=658) Participated at 56 month follow up (n=1600) Hospitalised Control ( n=1093 ) ( n=507 ) Attended at 25 years(n=1136) Deceased (n=68) Unable to attend clinic,health informationonly(n=245) Not traceable or unable to participate(n=280) Valid accelerometry data (n=854) Excluded (n=37)* •Visual impairment (n=4) •Major impairment or disability (n=21) •Intellectual impairment (n=14) Included in analysis (n=658) DCD classification (n=1505) Hospitalised Control (n=1012) (n=493) Motor assessment missing (n=5) Excluded for intellectual impairment, cerebral palsy, genetic disease or syndrome, congenital malformation (n=60) Not approvable test performance (n=30) Contactable at 25 year follow up (n=1913) Objectivelymeasured PA ( n=991 ) Valid accelerometry data (n=695) | 1053 TAN et al. could impact upon their motor skills in accordance with criterion D of the DSMV criteria for DCD diagnosis19 as reported by parents or their medical records. Reasons for exclusion included intellectual impairment, cerebral palsy, genetic disease, and congenital malformations (Figure1). An additional four cases were excluded as they had visual impairment to a degree that may have impacted upon their VMI score. 2.3 | Assessment measures and tools 2.3.1 | Motor competence testing Motor competence was assessed by four experienced pediatricians (incl.AL) of the research team using a quantitative test of motor competence developed for the AYLS study. The test contained items similar to the Zurich Neuromotor Assessment,20 and each child was scored on whether their performance on each item was within normal range. Individual test items are listed in Appendix A. The Zurich Neuromotor assessment is designed for use in children from the age of five years, although adjusted versions of the test have been found to be reliable in children aged three to five.21 Test– retest correlations are between 0.66 to 0.80 in children aged between five and ten years of age21 and convergent validity with other motor tests established.21 As some children refused to perform all tasks, a percentage sum score of successful tasks to attempted tasks ( n(succesful tasks) n(attempted tasks) × 100 )22 was used to define the child's motor competence.23 For children who made insufficient task attempts (less than seven), the calculated percentage score on attempted tasks was only used if the score was outside of normal range, children who had insufficient attempts but whose percentage score was within normal range were excluded from analysis.23 DCD risk status was established based on the cutoff points where five percent and fifteen percent of the healthy control subjects in the original AYLS study population (n=493) failed, equivalent to a score of 68.75 and 78.95, respectively. Due to the children being below diagnostic age for DCD at the time of testing, the groups were classified as “at risk of DCD” (DCD5) at the five percent cutoff and “probably at risk of DCD” (DCD15) at the fifteen percent cutoff.24 The impact of motor skills upon activities of daily living was assessed via parental clinical interviews at child age 4.7years, including questions on ageappropriate activities of daily living (e.g., buttoning, dressing self), social relationships, play skills, and motor skill performance (running, catching a ball, riding a bike). 2.3.2 | Visuomotor integration (VMI) testing VMI was assessed using 12items of BeeryBuktenica Developmental Test of Visual Motor Integration where children are instructed to copy geometric forms which increase in complexity.25 Test scores were corrected for exact age at measurement and converted to have a mean of 100 and a standard deviation of 15, such that standardized scores represent the difference from the mean for healthy children born at term. The Beery VMI has convergent validity with other tests of visual perception26 and a reported interrater reliability of 0.92, internal consistency of 0.96, and test– retest reliability of 0.89.27 For consistency with DCD categorizations, VMI scores were categorized into the bottom 5th percentile, and 5– 15th percentile of scores, corresponding to cutoff scores of 75.6 and 82.6, respectively. 2.3.3 | Quantification of physical activity Physical activity was measured with SenseWear Pro 3 Armband (Body Media, Inc., Pittsburgh, PA, USA), a multisensory body monitor including a twoway axis accelerometer.28 The SenseWear Armband has been found to be valid for physical activity measurements in young adults in resting conditions, exercise conditions, and field monitoring.29,30 Participants were instructed to wear the armband on their right triceps for ten consecutive days. Participants were included if they had more than three valid days, weekday or weekend, with a valid day having more than ten hours of wear. This criterion was designed to maximize sample size while providing measurement reliability.31,32 The device logged physical activity based on the acceleration recordings minute by minute, which was combined with subject's characteristics such as gender, age, and BMI to estimate intensity of physical activity, distance of data points from the mean (mean amplitude deviation), and number of steps, using manufacturer algorithms (SenseWear Professional Software, v6.1). Following the removal of any measurements indicated by the device to be sleep, each minute was classified into sedentary light (under 3metabolic equivalent[MET]), moderate (3 to under 6MET), vigorous (6 to under 9MET), or very vigorous (above 9MET).28 Vigorous and very vigorous minutes were pooled into the vigorous category, and a moderatevigorous category (MVPA) created by pooling moderate and vigorous categories. Mean durations in minutes per day are reported as the outcome. Physical activity was assessed as minutes per day, and percentage of total wear time. Minutes per day for MVPA was converted to a weekly duration by multiplying by seven, which was then categorized to determine whether participants met 1054 | TAN et al. World Health Organisation (WHO) Guidelines for physical activity. Cutoffs for meeting guidelines were set at 150minutes for MVPA, covering minimum requirements for moderate and vigorous activity.33 2.3.4 | Anthropometric and background measures Researchers collected information about pre- , peri- , and neonatal conditions from medical records on daily ward visits. Information about parental educational status was collected via parental interviews at wards and 56month clinical visits. Anthropometric measures for height in centimeters and weight in kilograms were taken by trained research nurses during clinical visits at 56 months and 25years. Height was measured to the nearest 0.1cm and weight in light indoor clothing to the nearest 0.1kg. As some participants did not attend at the exact age for each visit, corrections were made for exact age by linear regression. BMI was calculated as weight (kg)/height (m)2 and categorized into weight status for age and gender using the WHO standards for childhood measures34 and the Centre for Disease Control standards for adult measurements.35 2.3.5 | Data analysis All analysis was performed in IBM SPSS, version 26, excepting effect size measures which used the Psychometrica online calculator.36 Alpha was set at 0.05. All variables were assessed for normality using visual assessment and Shapiro– Wilk test. Data were assessed to be missing at random. Descriptive between group differences for confounders by risk group were assessed using either an independent ttest, Kruskal– Wallis, Mann– Whitney U, or chisquare tests. Between group differences were assessed for age, BMI, and accelerometery via Mann– Whitney U as the data had a nonparametric distribution. BMI categories, change in BMI categories between time points, and meeting of physical activity guidelines were assessed via chisquare analysis. Age, BMI, and accelerometery measurements were described using mean (M), median (Md), and standard deviation (SD). Parental age, birthweight, gestational age, and VMI scores were described with M and SD. Pre- , peri- , and neonatal risk factors as well as socioeconomic factors as reflected by parental (paternal and maternal) education level were described as frequencies in each risk category. Motor competence measures were described as both group frequencies for anomalous measures and M, Md, and SD for continuous scores. Cohen's d effect sizes were calculated and classified as small d=0.2, medium d=0.5, and large d=0.8. As following assessment, no significant difference was shown between the DCD5 and DCD15 categories, and in accordance with International Clinical Practice recommendations where the 16th percentile is set as a cutoff for DCD,1 the groups were combined into a single risk category (DCD) and general linear modeling was done at this level. Accelerometery and BMI measurements were performed for the entire risk group, as well as at the 5th and 15th percentile, while confounder assessment was done at the 5th and 15th percentile only. The relationship of VMI and DCD category with physical activity levels was explored using a general linear model. Predictors included in the final model were sex, BMI, socioeconomics as reflected by mother's educational attainment, DCD or VMI category, and an interaction variable between risk category and BMI. Three other models were also conducted: Model one included predictors of sex and risk category only, model two included predictors of sex, BMI, and risk category, and model three contained predictors of sex, BMI, mother's educational attainment, and risk category. The interaction variable predictor was included after prior models indicated that the addition of BMI removed the effect of risk category. Figures of the interaction effect were derived from the final model presented in the manuscript. All other predictors were chosen as significant predictors for physical activity via accelerometery in young adults based on prior literature,11 with mother's educational attainment included as it is the most commonly used indicator of socioeconomic status.37 Age was not included in the model as the mean between group difference in age at time of accelerometery was 0.7months and hence not clinically relevant at the age of 25years. The final model was chosen based on Akaike information criterion (AIC), with the most complex model showing the best AIC fit. Residual plots for each model were visually assessed and determined to violate the assumption of normality, and as such, accelerometery data were transformed via natural log. Model residuals for the transformed data showed no violations although slight deviations were seen in the tails of some models. Due to reported sex effects on physical activity in this group,5 subgroup analysis was performed limiting the analysis by sex. A sensitivity analysis was also performed to determine the effects of using a minimum of three rather than four days as inclusion criteria in order to maximize sample size. 3 | RESULTS 3.1 | Motor competence 3.1.1 | Motor competence measures Motor competence testing indicated 30 participants (23 male, 7 female) as DCD5, with an additional 53 | 1055 TAN et al. participants (43 male, 10 female) being categorized as DCD15, and 575 participants (250 male, 325 female) as norisk. Both risk groups (DCD5M=88.1 [SD=13.91, MD=89.7]; DCD15M=97.0 [SD=11.9, Md=96.8]) showed detriments in their VMI score compared to the norisk group (M=102.1 [SD=14.1, Md=103.9]). These differences were statistically significant when compared at the 5th percentile (t=−4.9, p<0.001) and the 15th percentile (t=−4.8, p<0.001) to the norisk group. DCD risk groups were shown to have increased difficulty with motor skill performance at 5years old with a higher proportion of the atrisk group being reported to have difficulties in ball catching (DCD5 36.7%; DCD15 30.2% compared to 14.1% in norisk, χ2=18.4, p<0.001) and running (16.7% DCD5; 5.7% DCD15 vs. 2.6% in norisk, χ2=17.5, p<0.001). 3.1.2 | Background variables Motor competence groups were of similar health levels at birth with no differences detected in infant or maternal risk factors (Table1), including gestational age. No differences between DCD groups were detected for parental education maternally (χ2=3.9, p=0.685) or paternally (χ2=5.4, p=0.496). No difference in adiposity as assessed by BMI was found between groups in either score or corresponding category at either five or 25years of age, although the group as a whole increased in adiposity with a total of 32.7% being overweight or obese at age 25 compared to 15.8% at age five. Change in adiposity as indicated by BMI category change between fiveyear assessment and 25year assessment did not detect a difference for the DCD5group (χ2=1.1, p=0.896) nor the DCD15group (χ2=2.7, p=0.604). Between group differences at 5years of age are shown in Table2. 3.2 | Visuomotor integration (VMI) measures 3.2.1 | Visuomotor integration (VMI) Division of groups based on VMI testing found 23 participants (16male, 7 female) in the bottom 5th percentile, 32 (18male, 14 female) in the 5th to 15th percentile and 579 above the 15th percentile (272male, 309 female), with no difference in motor competence (<5th percentile M= 98.2[SD=4.0], 5 to 15th percentile M=99.0 [SD=2.3], >15th percentile M=99.2 [SD=2.5], H=5.0, p=0.083). 3.2.2 | Background variables The VMI groups showed some significant differences in risk factors in the neonatal period with those with lower scores having more neonatal complications and a lower gestational age. These differences are shown in Appendix B. VMI category did not impact on BMI or BMI category but impacted upon BMI change, with a significant difference being found for those in the bottom 15th percentile of VMI compared to those above the 15th percentile. The ≤15th percentile group was more likely to change category both down (18.8% ≤15th percentile vs 10.6% >15th percentile) and up (30.2% ≤15th vs. 26.5% >15th percentile) compared to those above the 15th percentile (χ2=15.0 p=0.005). 3.3 | Physical activity At 25 years of age, between group difference tests for the entire DCD group showed fewer steps taken compared to the norisk group (Md = 9083.4 compared to Md=9927.9, d=0.3, U=20161.0, p=0.022). The entire DCD group spent a higher proportion of time in sedentary light physical activity than the norisk group constituting a mean of 62.8% of their total measured time (SD=6.0, Md=63.7) compared to 61.2% for the norisk group (SD=6.4, Md=61.8) (U=20205.0, d=−0.3, p = 0.024). This difference in sedentary physical activity was also found in the DCD15group for proportion of time in sedentary light activity (Md=63.7 compared to Md 61.8, d=−0.3, U=20205.0, p=0.024) and total sedentary physical activity (M=872.1minutes [SD=92.6, Md=877.5] vs. M = 836.5 [SD= 105.3, Md=853.7]) (d=−0.3, U=12272.0, p=0.019). No other differences in physical activity measures were detected between the DCD5 and norisk group. No differences were found between risk groups in the frequency of participants meeting WHO physical activity guidelines for MVPA.33 Subgroup analysis restricting by sex found that no physical activity differences were statistically significant based on DCD risk status for either sex when analyzed separately. Between group difference measures are reported in Tables3 and 4. Of the eight participants with only three days measurement, one was in the DCD5 group and two in the DCD15group; however, there was no significant difference between risk groups for number of days included or total number of minutes recorded. A sensitivity analysis removing participants with less than four recorded days found no significant effect on any analysis, aside from the model for DCD risk and sedentary light activity. Results from sensitivity analysis are detailed in Appendix G. 1056 | TAN et al. GLM modeling of physical activity variables showed a significant role for the DCD group in sedentary light physical activity (β=0.1, p=0.027) when sex, BMI, DCD risk, maternal education, and BMItoDCD interaction were included in the model, as shown in Appendix C. A statistically significant role was also seen in the sedentary light model for BMI (β=0.01, p<0.001) and a nonsignificant effect for BMItoDCD interaction (β=−0.01, p=0.057). The BMItoDCD effect became significant in sensitivity analysis when participants with less than four recorded days were removed (β=−0.01, p=0.048). The interaction, depicted in Figure2, was such that the nonDCD group increased time spent in sedentary light activity at a faster trajectory than the DCD group. The model for vigorous physical activity suggested a role for DCD via its interaction with BMI (β=0.04, p=0.050), although not significant, with an additional nonsignificant role for DCD risk category (β=−0.9, p=0.062). This model, shown in Figure2, showed time spent in vigorous physical activity decreased at differing rates between groups with the nonDCD group losing more time in vigorous physical activity as BMI increased than the DCD group. Models including VMI as a continuous variable, shown in Appendix D, found a statistically significant effect for DCD risk in more models, although no significant effect was detected for VMI. DCD had a statistically significant effect in the models for sedentary light activity (β=0.2, p=0.007), moderate activity (β=−0.6, p=0.020), and MVPA (β=−0.7, p=0.014) such that sedentary light activity levels were higher for those in the DCD group while moderate and MVPA levels were lower. A DCDtoBMI interaction was seen in models for sedentary light activity (β=−0.01, p=0.019), moderate activity (β=0.03, p=0.027), and MVPA (β=0.03, p=0.018), illustrated in Figure2, which resulted in a more rapid reduction/increase in physical activity with increasing BMI. Sensitivity analysis to determine whether VMI risk had similar impact to DCD status on accelerometry found that the <5th percentile group indicated less vigorous activity (Md=1.1 compared to Md=3.8minutes a day) DCD5 DCD15 Not at risk Group difference p% % % χ2 Pre and perinatal risk factors Maternal severe chronic illness 6.7 9.4 5.6 1.3 0.513 Multiple pregnancy 6.7 1.9 4.9 1.2 0.539 Preeclampsia 23.3 11.3 12.0 3.4 0.180 Fetal distress during pregnancy 10.0 5.7 7.3 0.5 0.766 Fetal distress during birth 26.7 15.1 16.2 2.4 0.307 Small for gestational age 6.7 3.8 6.1 0.5 0.780 Neonatal risk factors/ complications Hospitalized 56.7 69.8 62.3 1.7 0.437 Intubation or ventilator treatment 10.0 7.5 9.4 0.2 0.898 Suspicion/verified septic infection 6.7 5.7 5.7 0.05 0.977 Surgical operation 3.4 1.9 1.2 1.1 0.588 Severe anemia requiring blood transfusion 6.7 3.8 4.0 0.5 0.767 Apnea 6.7 3.8 2.3 2.5 0.283 Clinical seizures 0.0 0.0 1.6 1.3 0.518 IVH grade 1– 2 3.3 0.0 1.1 2.0 0.364 TABLE 1 Prenatal, perinatal, and neonatal characteristics by DCD risk category | 1057 TAN et al. when compared to the >15th percentile group (d=0.2, U = 4780.0, p = 0.021), while the 5 to 15th percentile showed reduced moderate physical activity compared to the >15th percentile group (Md=156.9minutes a day compared to Md=121.2, d=0.3, U=7349.0, p=0.046). Combining the groups to <15th percentile found no significant differences on any physical activity measure. No significant differences were found between risk groups in frequency of meeting physical activity guidelines. Subgroup analysis by sex found differing physical activity effects for each sex, with the vigorous activity effect for the <5th percentile group being only significant for males, and the 5 to 15th percentile reduced moderate physical activity differences only being significant for females. Additional significant effects were found for females only at the 5 to 15th percentile of reduced MVPA, percentage time in moderate and MVPA, and total steps (Appendix F). GLM modeling of <15th percentile did not detect a significant role for VMI risk in any model, although VMItoBMI interaction effect was significant in the model for mean amplitude deviation, such that mean amplitude deviation decreased more rapidly with increasing BMI for the VMI risk group. Between group difference test and model results for VMI scores can be found in Appendix E. 4 | DISCUSSION DCD risk status in early childhood was found to impact upon some aspects of physical activity in early adulthood, with a small to medium effect on total steps and sedentary light physical activity. Controlling for VMI impairment TABLE 2 Characteristics at 56months followup DCD5 DCD15 Not at risk Group difference M (SD) M (SD) M (SD) H p Age (y) 4.7 (0.05) 4.7 (0.03) 4.7 (0.04) 1.2 0.547 Weight (kg) 18.5 (3.3) 18.4 (2.5) 18.2 (2.5) 20.0 <0.001 BMI 15.7 (2.1) 15.5 (1.5) 15.4 (1.3) 0.6 0.748 VMI (% sum score) 88.1 (13.9) 97.0 (11.9) 102.1 (14.1) 32.2 <0.001 %%%χ2p Hardly able to catch a ball 36.7 30.2 14.1 18.4 <0.001 Running, only slowly 16.7 5.7 2.6 17.5 <0.001 BMI grouping Underweight 3.3 1.9 1.4 9.5 0.149 Healthy 73.3 76.9 83.9 Overweight 13.3 19.2 12.4 Obese 10.0 1.9 2.3 TABLE 3 Accelerometry differences between DCD risk groups DCD5 N=30 DCD15 N=53 Not at risk N=573 H statistic pM (SD) M (SD) M (SD) Sedentary Light (min/day) 843.2 (120.8) 872.1 (92.6) 836.5 (105.3) 5.4 0.067 Moderate (min/day) 129.6 (69.3) 130.7 (65.5) 139.1 (79.0) 0.4 0.802 Vigorous (min/day) 5.4 (6.3) 6.4 (7.8) 6.6 (8.2) 0.2 0.889 MVPA (min/day) 135.0 (71.1) 137.0 (68.3) 145.7 (82.5) 0.5 0.796 % Sedentary light activity 62.9 (6.2) 62.8 (5.9) 61.2 (6.4) 5.2 0.074 % Moderate activity 9.5 (4.8) 9.4 (4.6) 10.2 (5.7) 0.7 0.713 % Vigorous activity 0.4 (0.5) 0.5 (0.6) 0.5 (0.6) 0.2 0.918 % MVPA 9.9 (5.0) 9.8 (4.8) 10.7 (6.0) 0.7 0.714 Steps 9136.1 (3205.3) 9436.9 (3430.3) 10335.8 (3642.4) 5.3 0.070 Mean amplitude deviation 0.96 (0.3) 0.96 (0.3) 0.99 (0.3) 0.9 0.633