Comparing estimates of physical activity in children across different cut‐points and the associations with weight status

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-NC-ND 4.0 https://creativecommons.org/licenses/by-nc-nd/4.0/ Comparing estimates of physical activity in children across different cut‐points and the associations with weight status © 2022 The Authors. Scandinavian Journal of Medicine & Science In Sports published by John Wiley & Sons Ltd. Published version Leppänen, Marja H.; Migueles, Jairo H.; Abdollahi, Anna M.; Engberg, Elina; Ortega, Francisco B.; Roos, Eva Leppänen, M. H., Migueles, J. H., Abdollahi, A. M., Engberg, E., Ortega, F. B., & Roos, E. (2022). Comparing estimates of physical activity in children across different cut‐points and the associations with weight status. Scandinavian Journal of Medicine and Science in Sports, 32(6), 971-983. https://doi.org/10.1111/sms.14147 2022 Scand J Med Sci Sports. 2022;32:971–983. | 971 wileyonlinelibrary.com/journal/sms Received: 29 May 2021 | Revised: 14 February 2022 | Accepted: 16 February 2022 DOI: 10.1111/sms.14147 ORIGINAL ARTICLE Comparing estimates of physical activity in children across different cutpoints and the associations with weight status Marja H.Leppänen1,2 | Jairo H.Migueles3,4 | Anna M.Abdollahi1,5 | ElinaEngberg1,6 | Francisco B.Ortega4,7,8 | EvaRoos1,9,10 1Folkhälsan Research Center, Helsinki, Finland 2Faculty of Medicine, University of Helsinki, Helsinki, Finland 3Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden 4PROFITH “PROmoting FITness and Health Through Physical Activity” Research Group, Sport and Health University Research Institute (iMUDS), Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, Granada, Spain 5Department of Food and Nutrition, University of Helsinki, Helsinki, Finland 6Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Finland 7Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland 8Department of Biosciences and Nutrition, Karolinska Institutet, NEO, Huddinge, Sweden 9Department of Food Studies, Nutrition and Dietetics, Uppsala University, Uppsala, Sweden 10Department of Public Health, University of Helsinki, Helsinki, Finland Correspondence Marja Leppänen, Folkhälsan Research Center, Topeliuksenkatu 20, 00250 Helsinki, Finland. Email: [email protected] Funding information This research was funded by the Folkhälsan Research Center, University of Helsinki, Ministry of Education and Culture in Finland, Ministry of Social Affairs and Health, Academy of Finland (Grants: 285439, 287288, 288038), Juho Vainio Foundation, Signe and Ane Gyllenberg Foundation, Finnish Cultural Foundation/South Ostrobothnia Regional Fund, Päivikki and Sakari Sohlberg Foundation, Medicinska Föreningen Liv och Hälsa, Finnish Foundation for Nutrition Research, and Finnish Food Research Foundation This study aimed to compare sedentary time (SED) and intensityspecific physical activity (PA) estimates and the associations of SED and PA with body mass index (BMI) and waist circumference (WC) using three different sets of cutpoints in preschoolaged children. A total of 751 children (4.7±0.9years, boys 52.7%) wore an ActiGraph GT3X+BT accelerometer on their hip for 7days (24h). Euclidean norm −1 G with negative values rounded to zero (ENMO) and activity counts from vertical axis (VACounts) and vector magnitude (VMCounts) were derived. Estimates of SED and light, moderate, vigorous, and moderate- to- vigorous PA (MVPA) were calculated for commonly used cutpoints by Hildebrand et al., Butte et al., and Evenson et al. Furthermore, the prevalence of meeting the PA recommendation, 180min/day of which at least 60min/day being MVPA, were assessed for the cutpoints. Multilevel mixed analysis was used to examine associations of SED and PA with BMI and WC. In accordance with the results, SED and PA intensity estimates differed largely across cutpoints (i.e., SED = 22– 341min/day; light PA=52– 257min/day; moderate PA=5– 18min/day; vigorous PA=7– 17min/day; MVPA=13– 35min/day), and the prevalence of children meeting the PA recommendation varied from 4% to 70%. Associations of SED and PA with BMI or WC varied between the cutpoints. Our results indicate that SED and PA estimates in preschoolaged children between studies using these This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2022 The Authors. Scandinavian Journal of Medicine & Science In Sports published by John Wiley & Sons Ltd. 972 | LEPPÄNEN et al. 1 | INTRODUCTION Engaging in sufficient levels of physical activity (PA) has been connected to numerous health benefits, including lower adiposity in preschoolaged children (3– 5years).1 In accordance with the World Health Organization (WHO) PA recommendations, preschoolaged children should spend at least 180min a day engaging in PA at any intensity, with the inclusion of at least 60min of moderate- to- vigorous intensity PA (MVPA).2 Conflictingly, the proportion of preschoolers complying with the recommendation has been reported to vary between 11%– 93% when assessed with accelerometers.3– 5 In addition to actual variation in children's habitual PA, accelerometerdefined PA metrics6 and cutpoints7 used have been found to influence the proportions. Accelerometers are the most widely used objective method of assessing PA in research currently.8There are several types of accelerometers and data processing methods available to assess SED and PA, but the estimates of these different methods have been found inconsistent.9 Usually, accelerometers collect a raw acceleration signal at a prespecified frequency and they are cleaned (i.e., gravitational acceleration and noise are removed from the signal) and aggregated over a time period (i.e., epoch). The data processing to clean the signal has traditionally been performed directly by the manufacturers, with the activity counts by ActiGraph being the most frequent acceleration metrics used in previous literature. Activity counts are usually calculated as the vector magnitude of the three axes (VMCounts) or as the onedimensional vertical axis (VACounts). Thereafter, ageappropriate cutpoints are defined to distinguish intensityspecific PA.10,11 However, activity counts are not comparable between accelerometers from different manufacturers, or even between different generations of accelerometers from the same manufacturer.9,12 Cutpoints are usually calibrated in small studies with limited sample sizes and underrepresented activities of daily life.10,11,13,14Therefore, it is typical to observe large discrepancies in the estimation of PA intensities from various cutpoints when they are extrapolated to different settings and/or participants.15The development of opensource algorithms to clean the raw signal is now an alternative to activity counts.16,17The Euclidean Norm of the raw acceleration in the three axes Minus One G (ENMO, 1 G ~ 9.8m/s2) with negative values rounded to zero has become widely used and has shown a high agreement between brands,18 facilitating data harmonization across studies. Although, opensource raw accelerometer data processing has been warranted in order to increase equivalency of data outputs and improved comparability between studies using different devices,19 using activity counts provides better comparability with the majority of previous literature. Yet the comparability across commonly used cutpoints based on activity counts and opensource methods has only been studied in schoolaged children thus far warranting the need to confirm the findings in preschoolaged children. Several cutpoints have been used to classify PA intensity from hipworn accelerometers in preschoolers. VACounts have been frequently used to assess PA in children20 and the commonly used cutpoints by Evenson et al.11 have been crossvalidated among 5 to 15- yearold children.21The cutpoints by Butte et al.10 provide PA intensities based on the VMCounts measurements in preschoolaged children. The more recently developed ENMO measurements offer an opensource method increasing comparability between studies, and the cutpoints by Hildebrand et al.13,14 have been previously used in children and provide the most reference data.6,19,22 A previous study in preschoolers7 reported that cutpoints by Butte et al.10led to less sedentary time (SED) and more light PA and MVPA compared to estimates using cutpoints by Janssen et al.23 based on VACounts. In schoolaged children,6,22 Hildebrand et al.13,14has been shown to provide less moderate and vigorous PA compared to estimates using Evenson et al.11 and Romanzini et al.24 who used VMCounts. However, it has also been found that Hildebrand et al.13,14 provided more moderate PA and less vigorous PA compared to estimates using Evenson et al.11 in another study in schoolaged children.19The proportions of schoolaged children meeting the PA recommendation (i.e., at least 60min of MVPA per day) have been reported to vary considerably between different cutpoints.6,19 Yet, there is a lack of previous studies comparing acceleration estimates based on both traditional activity counts and opensource metrics and cutpoints in preschoolaged children, even though such knowledge would be essential for comparing SED and PA estimates between studies. To date, information on comparability between estimates of SED and PA in preschoolaged children based on different cutpoints is scarce, especially using opensource metrics. cutpoints are poorly comparable. Methods facilitating accelerometerderived PA estimate comparison between studies are highly warranted. KEYWORDS acceleration metrics, adiposity, exercise, sedentary behavior, youth 16000838, 2022, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/sms.14147 by Duodecim Medical Publications Ltd, Wiley Online Library on [06/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 973 LEPPÄNEN et al. Furthermore, there is a lack of studies examining whether associations of SED and PA with the most frequently used health indicators vary when using different SED and PA cutpoints based on the commonly used metrics. Overweight and obesity are closely connected to health.25Their associations with SED and PA have been widely investigated, although with inconsistent findings.26 Associations of SED and PA with overweight and obesity have been studied in children also compositionally taking the relative changes in the daily activities into account.27 However, due to the differences in the study designs and methods assessing SED, PA and weight status, conclusions about the associations should still be drawn with caution.26 Since body mass index (BMI) is the most common method to assess obesity and waist circumference (WC) is an important marker of central obesity, they have been recommended to be used as a routine measurement in clinical practice.28Therefore, it is of high importance to further examine how their associations with SED and PA are dependent on the cutpoints used based on data from the same study. Therefore, the aims of this study were to compare (1) SED and intensityspecific PA estimates and (2) the associations of SED and PA with anthropometrics (BMI and WC) when using three different cutpoints (VACountsEvenson, VMCountsButte, and ENMOHildebrand) in preschoolaged children. 2 | MATERIALS AND METHODS 2.1 | Study design and participants The present study utilizes crosssectional data from the Increased Health and Wellbeing in Preschools (DAGIS) study.29The study was conducted in early childhood education and care (ECEC) centers in southern and western Finland in 2015– 2016. The eligibility criteria for the ECEC centers in the study were: (1) having at least one group consisting of 3– 6- yearold children, (2) providing early education only during the daytime, (3) being Finnish or Swedish speaking (official languages of Finland), and (4) charging incomedependent fees. In total, 864 children (25% of the invited children, boys 52%) and their families, from 66 ECEC centers (43% of the invited ECEC centers) in 8 municipalities participated in the study. Guardians gave their written informed consent. The study was approved by the University of Helsinki Ethical Review Board in the Humanities and Social and Behavioral Sciences in February 2015 (#6/2015). 2.2 | Assessment of sedentary time and physical activity SED and PA were measured using a hipworn triaxial ActiGraph wGT3X- BT accelerometer (Pensacola, FL, USA) for 7 days, 24 h per day. The raw accelerations that were collected using 30 Hz were processed using ActiLife v.6.13.3 (ActiGraph, Pensacola, FL, USA) to obtain VMCounts and VACounts using the normal filter developed by ActiGraph. Additionally, we exported the raw accelerations in “.csv” files to process them in the GGIR R package v. 1– 5.1230 to obtain ENMO. For VMCounts and VACounts, we used ActiLife to obtain the SED and PA intensity metrics. For such purpose, periods of ≥10min of consecutive zeros were regarded as nonwear time and removed from the further analyses.31 In addition, the times between parentreported sleep onset and wakeup were excluded. A valid day was defined as ≥600min of wearing time during waking hours, and children with valid data on at least 3weekdays and 1 weekend day were included in the analyses. A 15- s epoch length was used for VACounts and the Evenson et al. cutpoints were applied (VACountsEvenson).11VMCounts were aggregated in 60- s epochs and the Butte et al. cutpoints were used (VMCountsButte)10 (Table1). The epoch and cutpoints decisions were based on the practical considerations reported in a previous systematic review.20 The GGIR R package was used to obtain the SED and PA intensity metrics based on ENMO.30 The processing methods involved the following: (1) Autocalibration of the data according to the local gravity.32 (2) Detection of the nonwear time based on the raw acceleration of the three axes.17 In brief, each 15- min block was classified as nonwear time if the standard deviation of 2 out of the 3 axes was lower than 13mg during the surrounding 60- min moving window, or if the value range for 2 out of the 3 axes was lower than 50mg. (3) Detection of sustained abnormal TABLE 1 Children's ageappropriate cutpoints for the estimation of sedentary time (SED) and physical activity (PA) intensities (N=751) References Acceleration metric Epoch length SED/Light PA Light PA/ Moderate PA Moderate PA/ Vigorous PA Hildebrand et al.13,14 ENMO 5s 40mg 140mg 465mg Butte et al.10 VMCounts 60s 820 c 3908 c 6112 c Evenson et al.11 VACounts 15s 26 c 574 c 1003 c Abbreviations: c, Activity counts; ENMO, Euclidean norm −1g; VACounts, Vertical axis counts; VMCounts, Vector magnitude counts. 16000838, 2022, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/sms.14147 by Duodecim Medical Publications Ltd, Wiley Online Library on [06/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 974 | LEPPÄNEN et al. high accelerations higher than 5.5g. (4) Calculation of the ENMO as (~9.8 m/s2) with negative values rounded to zero. (5) Imputation of detected nonwear time and abnormal high accelerations by means of the acceleration for the rest of the recording period during the same time interval as the affected periods. (6) Identification of waking and sleeping hours using an automatized algorithm guided by parentreported sleep times.33 Finally, estimation of SED and PA intensities were calculated using the Hildebrand et al.13,14 cutpoints for ENMO (ENMOHildebrand) (Table1). Mean daily SED and PA intensity levels were then calculated as: (mean of available weekdays*5 + mean of available weekend days*2)/7. Furthermore, meeting the WHO recommendation for PA (i.e., 180min/day at any PA intensity including at least 60min MVPA)34 was assessed for VACountsEvenson, VMCountsButte, and ENMOHildebrand. 2.3 | Assessment of anthropometrics Weight and height were measured by trained researchers, and thereafter, BMI was calculated as body weight (kg) / height2 (m). The threshold for being overweight/obese was defined using the age- and sexspecific BMI cutoffs of the International Obesity Task Force criteria.35WC was measured over one layer of clothing twice to the nearest 0.1cm with measuring tapes (SECA 201) and the mean of these values was calculated. Waist was defined as the midpoint between the top of the iliac crest and the lower margin of the last palpable rib. 2.4 | Covariates Children's age and sex were reported by the parents. Families participated in the study during different seasons and, therefore, the research time was divided into three categories: fall (September- October), winter (November- December) and spring (January- April). The educational level of both parents was inquired by a questionnaire and further categorized as low educational level (i.e., comprehensive, vocation, or high school), middle educational level (i.e., bachelor's degree or college), or high educational level (i.e., master's degree or licentiate/doctor). 2.5 | Statistical analysis Descriptive statistics were calculated as means, standard deviations (SD), and percentages (%). Comparing the time estimates of SED, light PA, moderate PA, vigorous PA, and MVPA between each pair of VACountsEvenson, VMCountsButte, and ENMOHildebrand estimations were conducted using pairedsamples ttest, Bland- Altman plots with their limits of agreement (LOA), mean absolute percent error (MAPE), and Lin's concordance correlation coefficient (LCCC). Since VACountsEvenson is the most traditional acceleration metric and cutpoints from these three, it was used as a reference against VMCountsButte and ENMOHildebrand in the analyses regarding MAPE. As no criterion measure exists, VMCountsButte was randomly selected as the reference cutpoints between VMCountsButte and ENMOHildebrand. Multilevel mixed model at the ECEC centers level was used to assess the associations of SED and PA based on VACountsEvenson, VMCountsButte, and ENMOHildebrand with BMI and WC. All models were adjusted for child's age and sex, research season, parental educational level, and accelerometer wear time. Assumptions were visually checked and they were not violated. In this study, the interest was which one of the multilevel mixed models, or both, in each pairwise comparisons contained the correct set of regressors and were more suitable to model BMI or WC. Therefore, we used the J test36 to examine whether the associations of SED and PA with BMI and WC differed statistically between each pair of the cutpoints used in estimating SED and PA. To control for differences in wear time, pairedsamples ttest, MAPE, and LCCC as sensitivity analysis were performed by standardizing SED estimates for wear time previously proposed.37The analyses were performed in SPSS statistical software (version 26.0) and in R software. Statistical significance was considered when p<0.05. 3 | RESULTS Valid accelerometer data were obtained for 751 children with 4 (0.7%), 5 (3.7%), 6 (19.0%), and 7 (76.6%) days, and on average the children wore the accelerometer for 6.7days (SD 0.57). Background characteristics as well as the time spent in SED and various PA intensities based on the different cutpoints are reported in Table2. The differences between SED and PA intensities estimated from the different cutpoints expressed in min/day are graphically presented in Figure1. Moreover, the proportion of children meeting the PA recommendations varied from ENMOHildebrand 3.6% to VMCountsButte 46.1% and VACountsEvenson 69.5% (Figure2). The prevalence was higher for boys than for girls, regardless of the used cutpoints. Further comparisons between SED and PA intensities estimates are shown in Table3. All pairwise comparisons were significantly different (p<0.05). The various mean daily estimations differed between 22– 341 min/ day for SED, 52– 257min/day for light PA, 5– 18min/day for moderate PA, 7– 17min/day for vigorous PA, and 13– 35min/day for MVPA, respectively. The lowest MAPE was 16000838, 2022, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/sms.14147 by Duodecim Medical Publications Ltd, Wiley Online Library on [06/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 975 LEPPÄNEN et al. TABLE 2 Descriptive characteristics of participating children All Boys Girls pe NMean±SD NMean±SD NMean±SD Age (years) 751 4.7±0.9 396 4.8±0.9 355 4.7±0.9 0.24 Height (cm) 720 109.5±7.8 372 110.5±7.9 348 108.6±7.7 0.001 Weight (kg) 719 19.2±3.5 372 19.6±3.5 347 18.8±3.4 0.003 BMI (kg/m2) 719 15.9±1.4 372 15.9±1.3 347 15.8±1.4 0.43 Overweight or obesea (N, %) 719 83 (11.5) 372 40 (10.8) 347 43 (12.4) 0.50 Waist circumference (cm) 719 53.7±4.0 372 54.0±3.8 347 53.5±4.2 0.076 Parental education levelb (N, %) 747 396 351 0.11 Low 161 (21.6) 89 (22.5) 72 (20.5) Middle 320 (42.8) 156 (39.4) 164 (46.7) High 266 (35.6) 151 (38.1) 115 (32.8) Research seasonc (N, %) 751 396 354 0.79 Fall 306 (40.7) 163 (41.2) 143 (40.3) Winter 285 (37.9) 153 (38.6) 132 (37.2) Spring 160 (21.3) 80 (20.2) 80 (22.5) PA and SED (min/day)d SED ENMOHildebrand 751 711.1±50.3 396 709.6±50.0 355 712.8±50.6 0.39 VMCountsButte 751 370.2±54.7 396 366.5±54.7 355 374.2±54.5 0.055 VACountsEvenson 751 392.4±46.6 396 385.2±46.7 355 400.4±45.2 <0.001 Light PA ENMOHildebrand 751 122.8±23.4 396 126.9±23.4 355 118.3±22.7 <0.001 VMCountsButte 751 379.6±41.6 396 378.3±41.1 355 381.1±42.3 0.36 VACountsEvenson 751 327.5±34.7 396 330.2±34.3 355 324.6±34.9 0.029 Moderate PA ENMOHildebrand 751 33.7±10.6 396 34.9±11.0 355 32.3±10.1 0.001 VMCountsButte 751 46.4±18.6 396 51.3±18.6 355 40.9±17.0 <0.001 VACountsEvenson 751 51.9±14.6 396 56.1±14.7 355 47.1±13.0 <0.001 Vigorous PA ENMOHildebrand 751 3.4±2.5 396 3.4±2.6 355 3.4±2.3 0.97 VMCountsButte 751 12.6±8.6 396 13.4±9.1 355 11.7±7.8 0.006 VACountsEvenson 751 19.9±9.4 396 21.2±9.9 355 18.6±8.8 <0.001 MVPA ENMOHildebrand 751 37.1±12.4 396 38.3±12.8 355 35.8±11.9 0.006 VMCountsButte 751 59.0±24.5 396 64.8±24.9 355 52.6±22.5 <0.001 VACountsEvenson 751 71.8±22.2 396 77.3±22.6 355 65.7±20.1 <0.001 Wearing time during waking hours ENMOHildebrand 751 862.8±41.8 396 865.7±40.7 355 859.5±42.9 0.040 VMCountsButte 751 808.8±34.4 396 809.6±34.2 355 807.9±34.6 0.51 VACountsEvenson 751 791.7±36.0 396 792.5±36.3 355 790.8±35.6 0.50 Abbreviations: ENMO, Euclidean norm −1g; MVPA, Moderate- to- vigorous physical activity; PA, physical activity; SED, sedentary time; VACounts, Vertical axis counts; VMCounts, Vector magnitude counts. aAccording to Cole and Lobstein35. bLow educational level included comprehensive, vocation, or high school; middle educational level included bachelor's degree or college; and high educational level included master's degree or licentiate/doctorate. cFall was defined as September– October, winter was defined as November– December, and spring was defined as January– April. dCutpoints by Hildebrand et al.13,14 for ENMOHildebrand, Butte et al.10 for VMCountsButte, and Evenson et al.11 for VACountsEvenson. eT- test for continuous variables and chisquare test for categorized variables. Significant values are bolded. 16000838, 2022, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/sms.14147 by Duodecim Medical Publications Ltd, Wiley Online Library on [06/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 976 | LEPPÄNEN et al. between VACountsEvenson and VMCountsButte in SED (8.7), while the highest MAPE was between VACountsEvenson and ENMOHildebrand in vigorous PA (785.9). Furthermore, the strongest LCCC was between VACountsEvenson and VMCountsButte in moderate PA (0.82), while the weakest LCCC was between VMCountsButte and ENMOHildebrand in light PA (0.01). Bland- Altman plots displayed in Figure3 illustrate the differences in SED and PA estimates between each pair of the cutpoints. The mean bias for SED and all PA intensities were large when comparing the cutpoints. At the individual level, LOA were the widest for SED and light PA between VACountsEvenson and VMCountsButte with ENMOHildebrand, while LOA was the smallest for vigorous PA between VMCountsButte and VACountsEvenson. Moreover, regarding vigorous PA there was a trend between VACountsEvenson and VMCountsButte with ENMOHildebrand showing a greater difference in estimates when the mean increased. Table4shows associations of SED and PA intensities with BMI and WC. Using ENMOHildebrand, SED was inversely and light PA directly associated with BMI (both p < 0.05). Using VACountsEvenson or VMCountsButte, all associations with BMI were nonsignificant. Using ENMOHildebrand, vigorous PA and MVPA were inversely associated with WC (both p < 0.05). Similarly, using VMCountsButte, moderate PA and MVPA were inversely associated with WC. Using VACountsEvenson, light PA was directly associated with WC (p=0.010), respectively. TableS1 presents which multilevel mixed models contained the correct set of regressors in accordance with the FIGURE 1 Mean daily time spent (min) and standard deviations (error bars) in sedentary time and physical activity intensitites considering different cutpoints (N=751). Cutpoints expressed in the legend with the acceleration metric used and the first author of the validation study in subscripts, that is, Butte et al.10, Hildebrand et al.13,14, and Evenson et al.11 VMCounts: Vector magnitude counts; VACounts: Vertical axis counts; ENMO: Euclidean norm −1g FIGURE 2 Proportion of children meeting the physical activity recommendation (at least 180min a day engaging in physical activity at any intensity, with the inclusion of at least 60min of moderate- to- vigorous PA considering different cutpoints (Hildebrand et al.13,14 for ENMOHildebrand, Butte et al.10 for VMCountsButte, and Evenson et al.11 for VACountsEvenson). VMCounts: Vector magnitude counts; ENMO: Euclidean norm −1g; VACounts: Vertical axis counts 16000838, 2022, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/sms.14147 by Duodecim Medical Publications Ltd, Wiley Online Library on [06/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 977 LEPPÄNEN et al. J test. Regarding associations with BMI, ENMOHildebrand was more suitable compared to VMCountsButte or VACountsEvenson in SED and light PA, while all three cutpoints were equally suitable in moderate PA, vigorous PA, and MVPA. Regarding associations with WC, ENMOHildebrand was more suitable compared to VMCountsButte or VACountsEvenson in SED and light PA, except VACountsEvenson was equally suitable in SED. ENMOHildebrand and VACountsEvenson were generally more suitable in models regarding moderate PA, vigorous PA, and MVPA. In accordance with the main findings, sensitivity analyses found SED estimates differing between the cutpoints after standardizing for wear time (all p≤0.001). In addition, MAPE was the lowest between VACountsEvenson and VMCountsButte (9.7) and the highest between VMCountsButte and ENMOHildebrand (44.6). LCCC was the strongest between VACountsEvenson and VMCountsButte (0.71) and the weakest between ENMOHildebrand against VMCountsButte and VACountsEvenson (both 0.02). 4 | DISCUSSION This study aimed to compare (1) SED and intensityspecific PA estimates and (2) the associations of SED and PA with anthropometrics (BMI and WC) when using three different sets of cutpoints, VACountsEvenson, VMCountsButte, and ENMOHildebrand, in preschoolaged children. All SED and PA estimates varied largely between the cutpoints, although VACountsEvenson and VMCountsButte were more consistent compared to ENMOHildebrand. Furthermore, the proportion of children meeting the PA recommendations as well as the associations of SED and PA intensities with BMI and WC were highly discrepant across the different cutpoints. TABLE 3 Comparison between sedentary time (SED) and physical activity (PA) calculated from different cutpoints (N=751) Difference (min/d) LOA MAPE (%) LCCCMeana (95% CI) SD SED VACountsEvenson vs. ENMOHildebrand −319 (−322 to −315) 45.6 −408 to −229 44.8 0.02 VACountsEvenson vs. VMCountsButte 22 (20 to 24) 29.5 −36 to 80 8.7 0.76 VMCountsButte vs. ENMOHildebrand −341 (−344 to −338) 46.2 −431 to −250 48.0 0.03 Light PA VACountsEvenson vs. ENMOHildebrand 205 (203 to 207) 30.2 145 to 264 173.9 0.02 VACountsEvenson vs. VMCountsButte −52 (−54 to −50) 22.7 −97 to −8 13.6 0.43 VMCountsButte vs. ENMOHildebrand 257 (254 to 259) 36.1 186 to 327 217.3 0.01 Moderate PA VACountsEvenson vs. ENMOHildebrand 18 (17 to 19) 12.0 −5 to 42 62.9 0.28 VACountsEvenson vs. VMCountsButte 5 (5 to 6) 8.8 −12 to 23 24.5 0.82 VMCountsButte vs. ENMOHildebrand 13 (12 to 14) 14.5 −16 to 41 47.8 0.40 Vigorous PA VACountsEvenson vs. ENMOHildebrand 17 (16 to 17) 8.1 1 to 32 786.3 0.08 VACountsEvenson vs. VMCountsButte 7 (7 to 8) 3.9 −0 to 15 413.3 0.68 VMCountsButte vs. ENMOHildebrand 9 (9 to 10) 7.4 −5 to 24 89.3 0.15 MVPA VACountsEvenson vs. ENMOHildebrand 35 (34 to 36) 16.3 3 to 67 102.3 0.21 VACountsEvenson vs. VMCountsButte 13 (12 to 13) 9.8 −6 to 32 64.0 0.79 VMCountsButte vs. ENMOHildebrand 22 (21 to 23) 18.0 −13 to 57 31.1 0.35 Data are presented as mean differences, 95% confident interval (CI), and standard deviation (SD) as well as limits of agreement (LOA), mean absolute percent error (MAPE), and Lin's concordance correlation coefficient (LCCC). Cutpoints expressed with the acceleration metric used; Evenson et al.11, Hildebrand et al.13,14, and Butte et al.10. Abbreviations: ENMO, Euclidean norm −1g; MVPA, Moderate- to- vigorous physical activity; VACounts, Vertical axis counts; VMCounts, Vector magnitude counts. aBased on pairedsamples ttest, all mean differences were p≤0.001. 16000838, 2022, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/sms.14147 by Duodecim Medical Publications Ltd, Wiley Online Library on [06/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 978 | LEPPÄNEN et al. FIGURE 3 Bland- Altman plots with their limits of agreements using three different cutpoints (Hildebrand et al.13,14 for ENMOHildebrand, Butte et al.10 for VMCountsButte, and Evenson et al.11 for VACountsEvenson), N=751 Sedentary VACountsEvenson vs. ENMOHildebrand VMCountsButte vs. ENMOHildebrand VACounts Evensonvs. VMCounts Butte Light Moderate Difference Difference Difference Vigorous MVPA MeanMeanMean Difference Difference 16000838, 2022, 6, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/sms.14147 by Duodecim Medical Publications Ltd, Wiley Online Library on [06/10/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License