Do Fundamental Movement Skill Domains in Early Childhood Predict Engagement in Physical Activity of Varied Intensities Later at School Age? A 3-Year 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/ Do Fundamental Movement Skill Domains in Early Childhood Predict Engagement in Physical Activity of Varied Intensities Later at School Age? A 3-Year Longitudinal Study © 2023 The Authors Published version Kasanen, Maria; Laukkanen, Arto; Niemistö, Donna; Kotkajuuri, Jimi; Luukkainen, Nanne-Mari; Sääkslahti, Arja Kasanen, M., Laukkanen, A., Niemistö, D., Kotkajuuri, J., Luukkainen, N.-M., & Sääkslahti, A. (2023). Do Fundamental Movement Skill Domains in Early Childhood Predict Engagement in Physical Activity of Varied Intensities Later at School Age? A 3-Year Longitudinal Study. Journal of Motor Learning and Development, 11(3), 424-443. https://doi.org/10.1123/jmld.2023-0004 2023 Journal of Motor Learning and Development, 2023, 11, 424–443 https://doi.org/10.1123/jmld.2023-0004 ORIGINAL RESEARCH Do Fundamental Movement Skill Domains in Early Childhood Predict Engagement in Physical Activity of Varied Intensities Later at School Age? A 3-Year Longitudinal Study Maria Kasanen, Arto Laukkanen, Donna Niemistö, Jimi Kotkajuuri, Nanne-Mari Luukkainen, and Arja Sääkslahti Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland This study was conducted to determine how total fundamental movement skill (FMS) score and, separately, locomotor skill (LMS), and object control skill scores in children 3–8 years old predicted their specific-intensity physical activity 3 years later. Overall, 441 Finnish children (51.7% female, baseline mean age of 5.6 years) participated in the study. Total FMS, LMS, and object control skill scores were assessed using the Test of Gross Motor Development, third edition. The time spent engaged in physical activity of different intensities (light, moderate, vigorous, moderate-to-vigorous, light-to-vigorous, and sedentary behavior) was determined using accelerometers. A two-level regression model was used in the analysis, considering potential covariates and interactions. The results showed that moderate physical activity, vigorous physical activity, and moderate-to-vigorous physical activity were predicted by the total FMS score (β=0.177 to 0.203, p=.001–.003) and the LMS score (β=0.140 to 0.164, p=.004–.014), but not the object control skill score. Moreover, the LMS score inversely predicted sedentary behavior (β=−0.116, p=.042). In conclusion, higher FMS and, specifically, LMS scores seem to predict more engagement in moderate-to-vigorous physical activity and less sedentary behavior over time. However, most of the variance in physical activity remains unexplained. Keywords:children, infancy, motor development © 2023 The Authors. Published by Human Kinetics, Inc. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, CC BY 4.0, which permits unrestricted noncommercial and commercial use, distribution, and reproduction in any medium, provided the original work is properly cited, the new use includes a link to the license, and any changes are indicated. See http://creativecommons.org/licenses/by/4.0. This license does not cover any thirdparty material that may appear with permission in the article. Laukkanen https://orcid.org/0000-0002-9722-0258 Niemistö https://orcid.org/0000-0002-9198-9437 Kotkajuuri https://orcid.org/0000-0002-4195-8675 Luukkainen https://orcid.org/0000-0002-3204-2905 Sääkslahti https://orcid.org/0000-0003-4354-0990 Kasanen (maria.kasane[email protected]) is corresponding author, https://orcid.org/0000-0003-0057-3610 424 Corrected December 1, 2023—see https://doi.org/10.1123/jmld.2023-0070 Brought to you by JYVASKYLAN YLIOPISTO | Unauthenticated | Downloaded 12/01/23 10:46 AM UTC Evidence has shown the health benefits (e.g., bone health, reduced risk for being overweight) of engaging in physical activity (PA) during childhood (Pate et al., 2019; World Health Organization, 2019,2020). While moderate-to-vigorous physical activity (MVPA) is recommended to promote children’s health and well-being, lower levels of activity, such as light physical activity (LPA) are also beneficial (World Health Organization, 2019,2020). However, the amount of PA that children engage in seems to decrease with age (Cooper et al., 2015;Farooq et al., 2020;Lounassalo et al., 2019), starting at an average age of 7.7 years (Lounassalo et al., 2019). Hence, insufficient PA as a risk factor for noncommunicable diseases and reduced quality of life is a global concern (Guthold et al., 2020;Tucker, 2008). In addition, reducing sedentary behavior (SB) also is important to preventing disease and improving public health (Park et al., 2020). For these reasons, the underlying mechanisms of PA behaviors are important to understand. Understanding that the underlying mechanisms of PA, such as fundamental movement skills (FMS), start in early childhood is crucial. FMS refer to the performance level of the neural, muscular, biomechanical, and perceptual mechanism involved in movement; the FMS development phase occurs in early childhood (Goodway et al., 2019, pp. 36, 118–119), in children aged eight or younger (UNICEF, 2022). Many theoretical models and frameworks (Brian et al., 2020;Hulteen et al., 2018;Robinson et al., 2015;Seefeldt, 1980;Stodden et al., 2008) have suggested that children with limited FMS in early childhood may have restricted opportunities for engagement in PA later in childhood because they lack the skills necessary to be adequately physically active. Early childhood is considered an effective period for promoting FMS (Brian et al., 2020) and PA behavior, as it can influence children’s future PA levels (Goldfield et al., 2012). According to systematic reviews, evidence of a longitudinal pathway from FMS score to PA level in early childhood (Jones et al., 2020;Xin et al., 2020), as well as across childhood (2–18 years old; Barnett et al., 2021), is limited, and more longitudinal studies on the link between FMS and PA are needed (Barnett et al., 2021;Jones et al., 2020;Xin et al., 2020). FMS include the skill domains of stability, locomotor skills (LMS), and object control skills (OCS; Goodway et al., 2019, pp. 36, 118). Considering the skill domains separately (Barnett et al., 2021) and considering different intensities of PA, including SB (Barnett, Lai, et al., 2016;Barnett, Salmon, & Hesketh, 2016;Xin et al., 2020), may provide a more detailed picture of the relationship between FMS and PA levels. In this study, FMS is defined in terms of gross motor skills, focusing on LMS and OCS. LMS and OCS may exhibit different longitudinal relationships with PA. LMS enable body movement relative to a fixed point on a surface, from point A to point B (Goodway et al., 2019, pp. 44, 118), which PA often necessitates. Thus, a lack of LMS can hinder PA engagement and increase SB, although physically active children do not necessarily demonstrate decreased SB (Pearson et al., 2014). This also supports considering SB as an independent outcome variable. From the perspective of OCS, Barnett et al. (2009) and Cohen et al. (2014) suggested that OCS are particularly related to MVPA via many games and activities (e.g., soccer), as measured by data gathered through questionnaires as part of a 6-year longitudinal study (baseline mean age =10.1 years, range =7.9–11.9 years; Barnett et al., 2009) and through accelerometers worn by students during the school day (n=460, mean age =8.5 years, SD =0.6) in a cross-sectional study (Cohen et al., 2014). JMLD Vol. 11, No. 3, 2023 LONGITUDINAL PATHWAYS FROM FMS TO PA INTENSITIES 425 Brought to you by JYVASKYLAN YLIOPISTO | Unauthenticated | Downloaded 12/01/23 10:46 AM UTC Based on longitudinal studies initiated in early childhood, the relationship between FMS or skill domains and PA measured by accelerometer does not entirely conform to previous findings. Multiple studies (Bürgi et al., 2011;Duncan et al., 2021;Foulkes et al., 2021;Gu, 2016;Gu et al., 2018;Lima et al., 2017;Melby et al., 2021;Nilsen et al., 2020;Schmutz et al., 2020) have investigated this relationship, beginning in early childhood. The findings have been partly contradictory regarding the pathways from FMS or LMS to PA, and none of the studies provided evidence of a longitudinal relationship from OCS to PA measured by accelerometer. A few studies (Duncan et al., 2021;Gu, 2016;Gu et al., 2018;Lima et al., 2017) evidenced at least some statistically significant longitudinal pathways from total FMS or skill domain scores to PA measured by accelerometer. Lima et al. (2017) discovered that the total FMS score, measured by Körperkoordinationstest für Kinder, was associated with vigorous PA (VPA) but not with MVPA in Danish children (n=298, baseline mean age =6.75, SD =0.37) over a 7-year follow-up. Duncan et al. (2021) found that some subtests of LMS, measured by the Test of Gross Motor Development 2nd edition (TGMD-2) at baseline, significantly predicted time spent engaged in MVPA, LPA, and SB 1 year later in British preschoolers (n=177, baseline mean age =4.46 years, SD =0.70). Gu et al. (2018) observed a positive longitudinal link from FMS total score, measured by PE Metrics related to physical education, to MVPA in school days, as well as a negative relationship to SB in Hispanic children (n=141, baseline mean age = 5.37 years, SD =0.485) over a 1-year follow-up. Another study (Gu, 2016) involving Hispanic children (n=256, baseline mean age =5.37 years, SD =0.48) also provided evidence of a statistically significant positive longitudinal pathway from FMS to MVPA and SB measured in the same manner used by Gu et al. (2018). This study also revealed that only higher LMS scores predicted a statistically significant greater degree of engagement in MVPA and VPA, and inversely LPA, after controlling for body mass index (BMI; Gu, 2016). On the other hand, some studies (Bürgi et al., 2011;Foulkes et al., 2021;Melby et al., 2021;Nilsen et al., 2020;Schmutz et al., 2020) did not find a longitudinal association between total FMS or skill domain scores and engagement in various PA intensities. Bürgi et al. (2011) and Schmutz et al. (2020) found no statistically significant relationship when FMS were measured by agility and dynamic balance test (Bürgi et al., 2011) or using the Zurich neuromotor assessment (Schmutz et al., 2020) in Swiss children (n=217–550) aged 2–6 years old (baseline) during a 9to 12-month follow-up. Moreover, Melby et al. (2021) found no statistically significant relationship between FMS measured by Körperkoordinationstest für Kinder at 6 years old and MVPA at 9 and 13 years (n=654). At the same time, Foulkes et al. (2021) found no longitudinal relationship between FMS, LMS, or OCS measured by TGMD-2 and MVPA in British preschoolers (n=75, baseline mean age =4.5 years, SD =0.6) over a 5-year follow-up. Similarly, Nilsen et al. (2020) reported finding no longitudinal relationship based on an examination of LMS and OCS measured by the TGMD-3 as predictors, with PA intensity and SB as outcome variables in Norwegian children (n=230, baseline mean age =4.7 years, SD =0.9) over a 2-year follow-up. The related literature lacks comprehensive longitudinal studies examining pathways from total FMS or skill domains scores to PA of varied intensities in early childhood; furthermore, existing results are contradictory. The current study aimed to provide a more accurate picture of the longitudinal pathways from FMS JMLD Vol. 11, No. 3, 2023 426 KASANEN ET AL. Brought to you by JYVASKYLAN YLIOPISTO | Unauthenticated | Downloaded 12/01/23 10:46 AM UTC scores to accelerometer-measured PA levels utilizing skill domains and multiple PA intensities (light-to-vigorous PA [LVPA], LPA, moderate PA [MPA], MVPA, VPA, and SB), considering the effect of age (Barnett, Lai, et al., 2016;Farooq et al., 2020;Lounassalo et al., 2019;Valentini et al., 2022), gender (Barnett, Lai, et al., 2016;Ricardo et al., 2022;Zheng et al., 2022), and BMI (Jago et al., 2020;Lopes et al., 2021). This study was guided by two research questions that asked how total FMS score and, separately, LMS and OCS scores in children 3–8 years old predict time spent engaged in PA at different intensities 3 years later. Social-level factors, like school, may interact with individual-level factors to determine children’sPA (Duncan et al., 2004); this is considered in a two-level regression analysis (Hox et al., 2010) in this study. Despite contradictory research results, the authors hypothesized a positive longitudinal pathway from total FMS score to time spent engaged in various PA intensities based on previous literature (Brian et al., 2020; Hulteen et al., 2018;Robinson et al., 2015;Seefeldt, 1980;Stodden et al., 2008)and research findings (Gu, 2016;Gu et al., 2018;Lima et al., 2017). The authors also hypothesized that LMS may have a statistically significant longitudinal relationship with engagement in various PA intensities according to previous research results (Duncan et al., 2021;Gu, 2016), as opposed to the link to OCS, of which no evidence was found in previous studies (Duncan et al., 2021;Foulkes et al., 2021; Gu, 2016;Nilsen et al, 2020). Materials and Methods Design and Participants The baseline (T1) data for this longitudinal study were derived from the Skilled Kids study (2015–2016, n=1,238), which was based on a geographic cluster randomization of childcare centers in Finland (Laukkanen et al., 2018). Of the children who participated in the measurement at T1, only 950 were contacted in the follow-up (T2) for the Active Family study (2018–2020) because complete data on the main study variables of the Active Family study (parental support for child’s PA and child’s outdoor time on weekdays and weekends) were available at T1. Among these 950 children, 675 (from 97 schools) participated in the follow-up assessment, and only for this group did researchers have permission to combine T1 and T2 data. The difference between the contacted and participating children (275 out of 950) was acceptable (29%) from the bias perspective in the follow-up (Kristman et al., 2004). However, out of these 675 participants, LMS and OCS levels were measured in 592 children. Acceptable PA measurements (at least 10 hr recording time per day on at least two weekdays and one weekend day) were missing for 144 participants, and data on at least one other model-involved variable were missing for seven children. All participants with available results for all variables used in the analysis were included in the study (n=441, 46.4% of initial sample of T2, girls =228 [51.7%]; at T1: mean age =5.6 years, SD =1.1, in 37 childcare centers; at T2: mean age =8.8 years, SD =1.1, in 52 schools). The data were missing completely at random (Little’s missing completely at random (MCAR) test chi-square =31.983, df =22, p=.078), considering all model-involved variables. This indicates that the model included observations (n=441) represent a random subset of all the observations (n=675) and JMLD Vol. 11, No. 3, 2023 LONGITUDINAL PATHWAYS FROM FMS TO PA INTENSITIES 427 Brought to you by JYVASKYLAN YLIOPISTO | Unauthenticated | Downloaded 12/01/23 10:46 AM UTC that the distributions of the missing and observed values will be comparable (Bhaskaran & Smeeth, 2014). Thus, the complete case analysis is valid (Ross et al., 2020). Both T1 and T2 studies received ethical approval from the Ethics Committee of the University of Jyväskylä, on October 30, 2015, for T1 and on June 28, 2018, for T2. Participation was voluntary, and appropriate permissions were obtained to conduct the research from the parents for their own and their children’s participation before the beginning of data collection at both T1 and T2. Measurements Fundamental Movement Skills FMS were measured using the TGMD-3 (Ulrich, 2013,2019), a process-oriented measure developed for children aged 3–11 years old, which included six LMS subtests that produced a maximum total of 46 points and examined the following skills: run (0–8 points), gallop (0–8), hop (0–8), skip (0–6), horizontal jump (0–8), and slide (0–8). OCS levels were measured by seven subtests (two-hand strike of a stationary ball [0–10 points], one-hand forehand strike [0–8], one-hand stationary dribble [0–6], two-hand catch [0–6], kicking a stationary ball [0–8], overhand throw [0–8], and underhand throw [0–8]) for a maximum total score of 54 points. Total FMS score was defined by the gross motor index of the TGMD-3 measure, reflecting a sum of the LMS and OCS subtest scores; the maximum score was 100 points (Ulrich, 2013,2019). The TGMD-3 evaluates qualitative information on children’s total FMS level and, separately, their LMS and OCS levels. The qualitative evaluation of each skill is based on three to five performance criteria, depending on the skill (Ulrich, 2013, 2019). Observing performance with the naked eye is effective for investigating FMS levels (Goodway et al., 2019, p. 36). Each performance is scored according to the defined criteria by a trained observer. If the criterion is met, one point is allocated instead of zero, and vice versa. Children perform each activity twice. The sum of the scores from these two trials for each performance criterion constitutes the total score for the skill (Ulrich, 2013,2019). The TGMD-3 is recommended for FMS testing because of low cost, feasibility, and strong psychometric properties. According to one meta-analysis, the interrater reliability of TGMD-3 was above 0.9 in about 70% of the reported statistics (19 studies; Rey et al., 2020). Regarding data collection for the current study, the interrater reliability was determined to be good (interclass correlation = .88, 95% confidence interval [.85, .92]) based on the performance of 167 children before data collection in T1 (Niemistö et al., 2019). According to the metaanalysis, the intrarater reliability of the TGMD-3 was greater than 0.9 in 85% of reported statistics (n=13 studies). Moreover, the internal consistency of the TGMD-3 was between acceptable and excellent (Cronbach’s alpha .7–.9), and test–retest reliability was over 0.8 (Rey et al., 2020). Accelerometer-Based Physical Activity Intensities An accelerometer can reliably measure the PA of children (Pate et al., 2010). PA was measured at T2 using triaxial accelerometers (UKK RM-42, UKK JMLD Vol. 11, No. 3, 2023 428 KASANEN ET AL. Brought to you by JYVASKYLAN YLIOPISTO | Unauthenticated | Downloaded 12/01/23 10:46 AM UTC Terveyspalvelut Oy). Participants were instructed to attach the accelerometer to the right side of the waist with an elastic belt for seven consecutive days during waking hours, excluding sick days. They were further instructed to remove the accelerometer at bedtime and during showering, saunas, and swimming. Accelerometer data collected between 6 a.m. and 10 p.m. were included for analysis. Nonwear time was determined as a mean amplitude deviation (MAD) of the acceleration below 0.02gfor 60-min periods. An accelerometer recording time of at least 10 hr per day, which is a recommended criterion, was considered acceptable (Migueles et al., 2017). Valid accelerometer data for at least two weekdays (on average 4.2 weekdays) and one weekend day (on average 1.9 weekend days) was an inclusion criterion, as this has been shown to provide an acceptable (62%) level of reliability (Penpraze et al., 2006). MATLAB software was used for the accelerometer data analysis. Accelerometer data (100 Hz) were classified according to MAD based on nonoverlapping epochs of 5-s periods. The MAD is considered suitable for analyzing PA intensity for children (Aittasalo et al., 2015;Gao et al., 2019). Moreover, MAD cut-points in the analysis were under 29 for SB, 29 for LPA, 338 for MPA, and 604 for VPA (Aittasalo et al., 2015). The sum of the time spent engaged in LPA, MPA, and VPA constitutes the LVPA. Gender, Age, Body Mass Index, and Time Between Measurements Parents reported the child’s gender (options: girl or boy) and date of birth by completing a questionnaire at T1. The exact age of each child was calculated using the date of birth and the test date. BMI-for-age was calculated using weight (seca 877, seca GmbH & Co. KG.) and height (HM200P, Charder Electronic Co., Ltd.) measured directly to the nearest decimal. BMI SD scores were calculated according to Finnish children’s national standards (Saari et al., 2011). The time between baseline and follow-up measurements (T1–T2) was calculated as the difference between measurement dates in T1 and T2. Data Analyses The predictive effect of total FMS score and of LMS and OCS scores on time spent engaged in PA of varied intensities was investigated using two-level regression analyses (Hox et al., 2010) due to data clustering. The two-level hierarchical data structure included the participants (first level) nested within the schools (second level). Individual-level predictive variables in T1 were measured in childcare centers, which is why school level predictive variables were not used in the longitudinal analysis. Participants were nested in 52 schools (on average, 5.3 students per school, range 1–23) in T2. The predictor variables for the analyses were total FMS score or LMS and OCS scores. In addition to the main effects of age and gender, interactions between total FMS score, LMS, and OCS with gender (total FMS score-by-gender, LMSby-gender interaction, OCS-by-gender interaction) and age (total FMS score-byage, LMS-by-age interaction, OCS-by-age interaction) also were used as predictor variables. Interactions were used to analyze gender and age differences in the models. If interactions were statistically significant, more detailed analyses were JMLD Vol. 11, No. 3, 2023 LONGITUDINAL PATHWAYS FROM FMS TO PA INTENSITIES 429 Brought to you by JYVASKYLAN YLIOPISTO | Unauthenticated | Downloaded 12/01/23 10:46 AM UTC performed. Otherwise, the interactions were removed from the analysis, and only the main effects were reported. BMI SD scores were used as a covariate. All these variables were measured at baseline (T1). Also, the time between measurements (T1–T2, mean 3.2 years, range 2.39–4.55 years) was used as a covariate in the analysis due to the considerable variability between participants. The outcome variables LVPA, LPA, MPA, MVPA, VPA, and SB were measured, on average, 3 years later (T2). Descriptive statistics and analyses were performed using SPSS (version 28) software. Differences between the means for girls and for boys were tested with the Mann–Whitney Utest. The statistical significance level was set at p<.05 for all analyses. All outcome variables were tested for normality, and VPA was Box-Cox power transformed due to skewness. Standardized values of the variables were used in the analyses. The two-level regression analyses were executed by linear mixed model using restricted maximum likelihood estimation and Satterthwaite approximation. If school level was not statistically significant in the two-level regression analysis, a linear regression analysis was performed. Results The descriptive statistics of the study population are presented in Table 1. Gender differences were found in the total FMS scores (p=.037), LMS scores, OCS scores, and PA intensities (p<.001). Girls obtained higher LMS scores, and they engaged in more LPA. Correspondingly, boys obtained higher total FMS and OCS scores and engaged in more MPA, MVPA, and VPA. The school level, according to the interclass correlation of the null model, explained 8.0% (p=.040) of the variance for LVPA, 11.4% (p=.020) for LPA, 13.3% (p=.014) for MPA, 13.8% (p=.010) for MVPA, and 12.3% (p=.004) for VPA. School level results were not statistically significant for SB (interclass correlation =.041, p=.224); a linear regression analysis was performed for SB. The interaction of total FMS score with gender was statistically significant (β=−0.177, p=.044) only in the pathway from total FMS score to LPA. Thus, the analyses were performed separately with both genders as a reference group (coded 0). Therefore, the main effects of the total FMS score could be obtained for both genders. Despite this, the main effect of the total FMS score did not statistically significantly predict LPA (girls: β=0.060, p=.443; boys: β=−0.117, p=.087; see Table 2). Gender and its interactions are reported in Table 2with girls as the reference group (girls =0). For boys, the estimates for gender and its interactions were the same, except negative values changed to positive. Interactions of total FMS score, LMS score, and OCS score with gender or age were not statistically significant (p=.117–.856) regarding time spent engaged in other levels of PA intensity. Thus, only the results of the main effects are reported. Results for the pathways from total FMS score to LVPA, MPA, MVPA, and VPA are presented in Table 3, and results on the association between LMS and OCS with the same PA intensities are conveyed in Table 4.TheSB results are presented in Tables 5and 6. JMLD Vol. 11, No. 3, 2023 430 KASANEN ET AL. Brought to you by JYVASKYLAN YLIOPISTO | Unauthenticated | Downloaded 12/01/23 10:46 AM UTC Table 1 Descriptive Statistics Variable MSD Gender differences (p) Age (years) 5.60 1.05 .484 Girls 5.57 1.03 Boys 5.62 1.08 Body mass index SDS 0.19 1.05 .443 Girls 0.22 1.13 Boys 0.16 0.96 T1–T2 (years) 3.22 0.37 .935 Girls 3.22 0.35 Boys 3.23 0.39 Total FMS score (0–100 points) 54.94 13.85 .037 Girls 53.75 12.57 Boys 56.22 15.03 Locomotor skills (0–46 points) 28.95 7.33 <.001 Girls 30.23 6.88 Boys 27.57 7.57 Object control skills (0–54 points) 26.00 8.71 <.001 Girls 23.52 7.59 Boys 28.65 9.06 Light-to-vigorous PA (min/day) 452.10 60.74 .993 Girls 452.19 60.25 Boys 452.01 61.41 Light PA (min/day) 282.44 44.55 <.001 Girls 293.45 42.25 Boys 270.66 44.03 Moderate PA (min/day) 156.18 36.01 <.001 Girls 147.23 33.44 Boys 165.76 36.27 Moderate-to-vigorous PA (min/day) 169.66 41.89 <.001 Girls 158.74 38.62 Boys 181.35 42.18 Vigorous PA (min/day) 13.48 10.50 <.001 Girls 11.51 9.50 Boys 15.59 11.11 Sedentary behavior (min/day) 334.02 59.22 .482 Girls 336.24 60.52 Boys 331.65 57.84 Note.pvalues of gender differences under .05 are shown in bold. SDS =standard deviation score; T1–T2 =time between baseline (T1) and follow-up (T2) measurements; FMS =fundamental movement skills; PA =physical activity. JMLD Vol. 11, No. 3, 2023 431 Brought to you by JYVASKYLAN YLIOPISTO | Unauthenticated | Downloaded 12/01/23 10:46 AM UTC However, this study also has its limitations. One major limitation is that PA was not assessed at T1. Also, despite the use of high-quality measures in assessing accelerometer-based PA, these measures have their own challenges. For instance, they do not account for water activities, as the device must be removed before entering the water. Moreover, measuring PA over two weekdays and one weekend day may not provide an accurate representation of habitual PA. Another noted limitation of accelerometers is their inability to accurately measure the quality, context, and type of PA (Barnett, Salmon, & Hesketh, 2016). The dropout factor constitutes a final limitation of the current study, as some of the participants at T1 did not participate in the follow-up phase of the research at T2. However, this is a rather frequent phenomenon in longitudinal studies that are based on voluntary participation (Hogan et al., 2004). To conclude, the lacking data were missing completely at random. In total, 675 children provided consent to combine data from T1 and T2, prohibiting the comparison of the analyzed sample (n=441) with the Skilled Kids study sample (n=1,238). More studies are needed in the future that examine the longitudinal relationship between FMS and PA from early childhood to adolescence and beyond, with comparable measures if possible. Moreover, future longitudinal studies should examine different reciprocal aspects of the relationship between FMS and PA with the same study population at multiple time points, which makes the growth curve analysis method possible. The use of skill domains and PA intensities, including SB, in future studies can provide a more detailed picture of this relationship. Also, by comparing processand product-oriented measurements, a more detailed relationship between FMS and PA can be obtained. Acknowledgments Research data collection for the Skilled Kids study and the Active Family study was funded by the Finnish Ministry of Education and Culture (OKM/48/626/2014 and OKM/59/626/ 2017). We thank all participants and their parents for their involvement in the study. We also thank those who participated in the collection of the data. References Aittasalo, M., Vähä-Ypyä, H., Vasankari, T., Husu, P., Jussila, A.-M., & Sievänen, H. (2015). Mean amplitude deviation calculated from raw acceleration data: A novel method for classifying the intensity of adolescents’physical activity irrespective of accelerometer brand. 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