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Associations of activity, sedentary, and sleep behaviors with cognitive and social-emotional health in early childhood

Associations of activity, sedentary, and sleep behaviors with cognitive and social-emotional... Background Early childhood is important for cognitive and social‑ emotional development, and a time in which to promote healthy movement behaviors (sedentary behavior, physical activity, and sleep). Movement behaviors may have interactive influences on cognition and social‑ emotional factors in young children, but most previous research has explored them independently. The purpose of this study was to determine if movement behaviors are associated with measures of cognitive and social‑ emotional health in young children and if so, to describe optimal compositions of movement behaviors of a daily cycle for such outcomes. Methods Children (n = 388, 33 to 70 months, 44.6% female) from a clinical trial (ClinicalTrials.gov ID: NCT03285880, first posted September 18, 2017) wore accelerometers on their wrists for 24‑h for 9.56 ± 3.3 days. Movement behavior compositions consisted of time spent in sedentary behaviors, light intensity physical activity, moderate to vigorous intensity physical activity (MVPA), and sleep. Outcomes were cognitive (receptive vocabulary, declarative and proce‑ dural memory, and executive attention) and social‑ emotional measures (temperament and behavioral problems). Compositional linear regression models with isometric log ratios were used to investigate the relations between the movement behavior composition and the cognitive and social‑ emotional health measures. If a significant associa‑ tion was found between the composition and an outcome, we further explored the “optimal” 24‑h time ‑use for said outcome. Results Movement behavior compositions were associated with receptive vocabulary. The composition associated with the predicted top five percent of vocabulary scores consisted of 12.1 h of sleep, 4.7 h of sedentary time, 5.6 h of light physical activity, and 1.7 h of MVPA. Conclusions While behavior compositions are related to vocabulary ability in early childhood, our findings align with the inconclusiveness of the current evidence regarding other developmental outcomes. Future research exploring activities within these four movement behaviors, that are meaningful to cognitive and social‑ emotional development, may be warranted. Keywords Physical activity, Sedentary behavior, Sleep, Cognition, Social‑ emotional, Children *Correspondence: Rebecca M. C. Spencer rspencer@umass.edu Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. St. Laurent et al. Journal of Activity, Sedentary and Sleep Behaviors (2023) 2:7 Page 2 of 13 one’s attention during activities where there may conflict - Background ing information or stimuli present [18, 19]. Early childhood (i.e., under 6  years) is an important Movement behaviors have been independently asso- life phase in which to promote healthy behaviors of the ciated with cognitive outcomes in older children and 24-h cycle (i.e., sedentary behavior, physical activity, adults. There is evidence that both acute and chronic and sleep, hereafter collectively referred to as move- physical activity can improve cognitive outcomes such as ment behaviors). Specifically, minimizing time spent executive function and memory in these populations [2]. sedentary, participating in adequate levels of physical In older adults, physical activity benefits cognitive perfor - activity, and achieving sufficient sleep during child - mance, is associated with a lower risk of dementia, and is hood can positively impact outcomes such as improved beneficial to cognitive impairments in those with demen - mental and physical health, cognitive performance, and tia [2], while measures of sleep are associated with mem- overall quality of life [1–4]. The World Health Organi - ory and executive function [20]. There is some evidence zation (WHO) recommends that 4- to 5-year old chil- of associations of sedentary time and cognitive function dren be physically active for at least 180  min per day in this age group, but findings have been more variable (including at least 60  min of moderate- to vigorous- than for physical activity and sleep [21]. Although there intensity physical activity [MVPA]), should limit sed- has been some support of beneficial associations between entary time (i.e., no more than 60  min of screen time, physical activity and cognitive measures (and, to some not be restrained—such as sitting in a stroller or being extent, adverse associations with high sedentary time), a held—more than 1  h at a time, and avoid sitting for lack of sufficient studies limits the conclusions that could extended periods), and obtain 10 to 13  h of sleep in a be drawn for children under 6  years of age in the 2018 24-h period [5]. Although surveillance data on these Physical Activity Guidelines Advisory Committee Scien- health behaviors is limited in younger children, reports tific Report [2]. While a growing number of early child - indicate that many young children do not obtain suffi - hood studies have been conducted on this topic, recent cient physical activity and sleep, and therefore are likely reviews have indicated that associations between move- to engage in more sedentary behaviors [6, 7]. While ment behaviors and cognition are more variable than health promotion regarding these 24-h movement previous reports in older children [22–24]. In contrast, behaviors has traditionally been emphasized in older there is accumulating evidence that both daytime and children, early childhood may be an important time to overnight sleep are beneficial for cognitive functions in intervene given that sleep is unique during these early early childhood [25–28]. years (as children transition out of naps) and both sleep Children experience significant social and emotional and physical activity habits track through childhood development in the early years and many measures and even into adulthood [8–10]. within this domain have been explored as independent Early childhood also serves as an important phase for correlates of sedentary time, physical activity, and sleep cognitive development [11, 12]. Cognitive abilities at this [29–31]. Broadly, social-emotional health in early child- life stage underlie both current and future academic per- hood encompasses how young children view themselves formance [13, 14]. In the current study, we focused on and their world, their emotions, and their related behav- the cognitive domains of receptive vocabulary, declara- iors. Factors considered in the current paper include: (1) tive memory, procedural memory, and executive function temperament and (2) emotional and behavioral problems. (attention). Receptive vocabulary is the ability to under- According to Rothbart and Derryberry’s definition, tem - stand words and phrases and falls under the umbrella of perament consists of “constitutionally based individual language skills [15]. Declarative and procedural memory differences in reactivity and self-regulation, influenced are domains of overall memory that are characterized as over time by heredity and experience” [32]. Common long-term memory (e.g., seconds to days) and therefore indicators of temperament include surgency (e.g., high involves encoding, storage, and retrieval (as opposed to positive emotional reactivity levels), negative affectivity working memory which involves the ability to hold infor- (e.g., tendency to experience negative emotional reactiv- mation and manipulate it in the short term) [15]. Declar- ity), and effortful control (e.g., ability to regulate emo - ative memory (i.e., explicit memory) involves the ability tions) [33]. Emotional and behavioral problems in young to remember experiences, people, and things, whereas children are commonly categorized as externalizing (e.g., procedural memory is a form of implicit memory that behaviors that are presented and directed outside of the allows people to remember motor skills and actions child such as ‘acting out’ and non-compliance) and inter- [15–17]. In the preschool years (e.g., 2  years 9  months nalizing problems (e.g., behaviors that are directed within through 5  years), executive attention is a precursor to the child such as withdrawing or experiencing anxiety) executive function (e.g., set shifting, response inhibition, [34]. and working memory) and involves the ability to regulate S t. Laurent et al. Journal of Activity, Sedentary and Sleep Behaviors (2023) 2:7 Page 3 of 13 In older children, independent associations for sed- day), traditional statistical analysis methods that do not entary behavior (in the form of screen time), physical account for the compositional nature of such behaviors activity, and sleep social-emotional measure with social- (i.e., the co-dependence) are commonly applied. Compo- emotional indicators have been reported [35]. In observa- sitional data analysis can be used to provide information tional studies where 24-h behaviors have been separately about associations between a behavior of interest and a explored with social-emotional outcomes in young chil- health outcome while also accounting for time spent in dren, reported associations have been mixed, particularly other behaviors, as well as the effect on that relationship for waking behaviors. For example, in a 2017 review, 8 of when time is reallocated from one behavior to others the 11 included studies employed observational designs [46]. to examine physical activity and variables of psychoso- Following a trend in preadolescent research, stud- cial health in children under 5  years. Associations were ies have begun to explore relations between movement heterogeneous with favorable, unfavorable, mixed, and behaviors and cognitive and social-emotional health with null results and quality of evidence designated as “very compositional data analysis methods. In one cross-sec- low [31]”. Comparable findings were noted in a similar tional study, theoretical time reallocations replacing sleep 2017 review where sedentary behavior was the exposure or light physical activity with MVPA were associated of interest (i.e., 9 longitudinal and 7 cross-sectional stud- with improvements in inhibitory control (a measure of ies) [29]. In another review of sleep studies in toddlers executive function) [47]. Another recent study of Cana- and preschoolers (5 longitudinal and 17 cross-sectional dian preschoolers incorporated measures of the Early studies), although shorter sleep duration was more con- Years Toolbox to assess both cognitive (i.e., inhibitory sistently associated with poorer emotional regulation, control, visual-spatial working memory) and social-emo- overall associations were mixed, and the quality of evi- tional measures (i.e., sociability, externalizing, internaliz- dence was still designated as “low” [30]. ing, prosocial behavior, and self-regulation) [48]. Overall, Similar to recent findings in older children, it is pos - sedentary time was positively associated with inhibitory sible that movement behaviors may have interactive control and vocabulary, and MVPA was positively associ- influences on cognition and social-emotional factors in ated with sociability. When sleep time was theoretically young children. A number of mechanisms have been pro- replaced with sedentary time, this was positively associ- posed or supported in young children connecting sleep ated with vocabulary. Reallocating any behavior in place (e.g., duration, quality, timing, and routines) with cogni- of MVPA was positively associated with sociability and tive development and child behavior [25, 28]. For exam- self-regulation. ple, longer sleep is often accompanied by greater slow Further compositional data analysis studies building wave sleep, which contains physiological events (e.g., on these early reports in young children are necessary sleep spindles) that are associated with sleep dependent to provide greater insight into the interactive relations memory consolidation. Additionally, physiological (e.g., of 24-h movement behaviors with cognitive and social- changes in brain structure and physiology), psychosocial emotional health outcomes. Such studies would also (e.g., enhanced mood and self-perceptions), and behavio- determine if previous associations with cognitive and ral (e.g., coping and self-regulation skills) pathways have social-emotional health are consistent with different been proposed between physical activity and measures assessments that are commonly utilized by educators of brain health in children [36]. Sedentary behaviors may and clinicians, as well as with other cognitive and social- potentially play a role in such pathways either by indirect emotional health measures (e.g., receptive vocabulary effects on physical activity and sleep, or directly on some and procedural memory). More recently, given that com- mechanisms. Moreover, movement behaviors may influ - positions of behaviors may have differential benefits on ence one another [22, 37, 38] which in turn may facilitate various health outcomes, a novel use of compositional interactive influences on cognitive and social-emotional data analysis is to explore ‘optimal’ daily combinations health [35]. of behaviors for different measures [49, 50]. Studies in One approach researchers have used to examine inter- older children that have used this approach have found actions of movement behaviors is to explore compli- variations in best 24-h movement behavior composi- ance with guidelines (e.g., meeting national or WHO tions for physical, skeletal, mental, and cognitive health recommendations for all three 24-h behaviors) as the [51–53]. Therefore, the purpose of the present study was exposure of interest [35, 39–42]. Another method that to determine if movement behaviors were associated has been adopted in recent years by many public health with measures of cognitive and social-emotional health researchers is the use of compositional data analysis in early childhood. We hypothesized that 24-h movement [43–45]. Although behavioral data is typically presented behavior compositions would be associated with each as a component of a daily cycle (i.e., min/day or % of of the cognitive and social-emotional outcomes while St. Laurent et al. Journal of Activity, Sedentary and Sleep Behaviors (2023) 2:7 Page 4 of 13 accounting for age and sex. Additionally, we aimed to nap-promotion day, children were encouraged to nap describe optimal composition of movement behaviors for (e.g., with quiet encouragement and back-rubs) and on each of our cognitive and social-emotional measures. the wake-promotion day, the room was dim and quiet, similar to the nap condition, but children participated in Methods sedentary activities such as coloring and reading books. Overview Most participants completed a receptive vocabulary task The data in this study stem from a clinical trial examin - and at least one other cognitive task (e.g., declarative ing whether daytime naps contribute to immediate and memory, procedural memory, or executive function). delayed benefits on memory in early childhood (Clinical - Trials.gov ID: NCT03285880, first posted on September Participants 18, 2017). The Strengthening the Reporting of Obser - Preschool children were recruited from childcare and vational Studies in Epidemiology Statement (STROBE) preschool centers in western Massachusetts in the United checklist can be viewed in our supplementary materials States between 2013 and 2020. To be eligible for the par- (see Additional file  1). The study protocol was approved ent study, children: (1) were 33–71 months of age, (2) had by the University of Massachusetts Amherst Institutional normal or corrected-to-normal vision and hearing, (3) Review Board (approved December 15, 2011; protocol had no current or past diagnosis of a developmental dis- ID: 2011-1152). Written informed consent was obtained ability or sleep disorder, (4) had no use of psychotropic from adult caregivers for consent of their own participa- or sleep-affecting medications, and (5) had not recently tion as well as permission for their child’s participation. traveled outside of the local time zone. For the current Child participants provided verbal assent and childcare study, only participants with at least three days and three providers provided written informed consent (for com- nights of sufficient actigraphy data (see next section) and pleting daytime sleep diaries). at least one of the outcomes of interest were included In brief, the parent study followed a within-subjects (Fig. 2). design and was conducted over 16  days in preschool and childcare settings (Fig. 1). (See Spencer et al. [54] for 24‑h Movement behavior measures more details on the protocol.) At the beginning of the Wake and sleep behaviors were measured with actig- study, adult caregivers completed a questionnaire that raphy. Actiwatch Spectrum monitors were worn on the included demographics and behavior assessments and participants’ non-dominant wrists. The Actiwatch is children were asked to wear an accelerometer, which a triaxial accelerometer with off-wrist detection and was instructed to be worn for the full study period. Par- a button that can be pressed by participants to mark ticipants each completed two conditions (nap- and wake- events. Children were taught how to press the event promotion), 1 week apart in a randomized order. On the marker when they began trying to fall asleep and again Fig. 1 Study overview. Actigraphy was measured throughout the full period, but ‘experimental’ days were excluded because all children were encouraged to either nap or stay awake in the afternoon regardless of their typical routine. Cognitive health measures were collected on nap‑ and wake‑promotion days. Social‑behavioral health measures were derived from caregiver/parent complete questionnaires that were distributed on Day 1 and collected at the end of the study period S t. Laurent et al. Journal of Activity, Sedentary and Sleep Behaviors (2023) 2:7 Page 5 of 13 Fig. 2 Participant flow diagram and sample sizes of outcome measures. Participant flow diagram and sample size of outcome measures. (For cognitive health, receptive vocabulary was sought for all participants, but each other cognitive task was only completed on a subset of the participants.) when they woke. The monitors were configured to col - Actiware algorithm) were used to define sleep onset and lect data in 15-s epochs, with a sensitivity of < 0.01 g and the last five consecutive minutes of sleep were defined as 32 Hz sampling rate. Data was processed in the Actiware sleep offset. For our preliminary models, sleep time was software using the default algorithm to designate each calculated by subtracting wake bouts (i.e., wake after epoch categorized as sleep, wake, or off-wrist [55]. This sleep onset and sleep onset latency when diaries were algorithm has demonstrated good agreement with vid- available) from the rest intervals. For our compositional eosomnography in 28- to 73-month-old children and is models, the sum of time spent in rest intervals was used commonly employed in sleep studies of early childhood as a proxy for sleep time (i.e., we did not exclude wake [56, 57]. Each daily cycle (i.e., wake onset for day x until after sleep onset time). wake onset for day x + 1) was then partitioned into over- Sedentary time and physical activity. The Actiwatch night rest periods, daytime rest periods (when present), has been studied for validity and reliability in preado- and daytime wake. Therefore, it was possible for aver - lescent children as an estimate of energy expenditure aged daily cycles to fluctuate beyond or below 24  h (or [60]. Accelerometer activity count cut points were 1440  min). Daily cycles that included an experimental derived from to categorize wake behaviors into seden- condition (i.e., nap- or wake-promotion) as part of the tary time, light physical activity, and MVPA [60]. These larger study were excluded as these activities may have cut points were cross validated in preschool children differed from a child’s typical routine. against direct observation [61]. One recent recom- Sleep. In children, the Actiwatch is considered an mendation of daytime wear time for activity acceler- acceptable tool for sleep measurement and has demon- ometer estimation in early childhood is a minimum of strated validity compared to polysomnography [58, 59]. 600 min [62]. However, as much of our sample received Rest intervals (i.e., sleep time) for overnight periods and 60 to 120 min of daytime sleep (which was categorized daytime naps (when present) were defined using a com - as time in bed rather than wake), days with at least bination of marked events (i.e., button presses) and sleep 480 min of daytime wear were included. Therefore, day - diaries. If neither of these was available, the first three time intervals that were defined as wake by Actiware consecutive minutes of sleep (i.e., as categorized by the were further processed to estimate sedentary time and St. Laurent et al. Journal of Activity, Sedentary and Sleep Behaviors (2023) 2:7 Page 6 of 13 Social‑emotional health measures physical activity for days that had at least 480  min of Social-emotional health measures included three sub- actigraphy data. Using the Ekblom et al. [60] cut points, scale scores for temperament and two subscales for child daytime wake intervals were then classified as seden - behavior. Temperament was explored with scores from tary time (less than 79 counts), light physical activity the parent-reported Child Behavior Questionnaire Very (80 to 261 counts), or MVPA (262 counts or greater). Short Form (CBQ) [33], with higher scores indicating stronger characteristics reflected by each of the tem - perament subscales all with a possible score range of 1 Cognitive and Social‑emotional health measures to 7: surgency/extraversion (n = 325), negative affectiv - Cognitive health measures ity (n = 324), and effortful control (n = 327). In children A detailed description of the cognitive and socio- ages 3 to 8 years, internal consistency alpha levels of the emotional health measures is provided in Additional Very Short Form ranged between 0.70 and 0.76 for sur- file  2. Cognitive measures included receptive vocabu- gency, 0.66 and 0.70 for negative affect, and 0.62 and 0.78 lary, visuospatial memory, procedural memory, and for effortful control [33]. Correspondence corrected cor - executive attention. Receptive vocabulary was evalu- relations between the Very Short Form and the standard ated with the Peabody Picture Vocabulary Test, 4th Child Behavior Questionnaire were 0.83 for surgency, Edition (PPVT-IV) [63] (n = 328), a test that evaluates 0.75 for negative affect, and 0.83 for effortful control [33]. children’s ability to understand spoken words, and the Internalizing and externalizing behavior raw scores calculated raw score was used. Reliability of internal (n = 359 and 371, respectively) served as indicators of consistency of the items has been reported at 0.90 or social and emotional behavior problems from the parent- above across ages groups [63]. Possible scores can range completed Child Behavioral Checklist for Ages 1.5–5 from 2 to 228. This test is not typically used by itself (CBCL) [66]. Higher scores for both represented that for clinical interpretations regarding vocabulary devel- these types of problems were more typical for the child opment, but higher scores represent higher receptive [66]. For clinical purposes, scores under 12 for internal- vocabulary and comprehension of spoken English [63]. izing problems and under 18 for externalizing problems As an indicator of declarative memory, a visuospa- are often used as cut offs between ‘normal’ ranges and tial task similar to the game “Memory” was completed the ‘borderline clinical’ range [66]. (n = 62). Children were presented with a grid of images on a computer screen and then asked to recall the loca- tions of each. The average immediate accuracy score Covariates (for those that scored at least 30%) was used as our Several additional measures were collected to character- declarative memory outcome [17]. Thus, scores could ize the sample and potentially control for in our models. range from 30 to 100%, with a higher score represent- Age (months), sex, race and ethnicity were obtained from ing better memory performance. the caregiver questionnaires. Additionally, a compos- Procedural memory was assessed with a serial reac- ite score for socioeconomic status was calculated using tion time task (n = 41) [64]. In this task, children caregiver-reported education, employment status, and completed a sequence of finger presses on an elec - household income [67]. Daily total physical activity was tronic tablet and a learning score was calculated as parameterized as average activity counts/minute during median reaction time in the final sequence blocks wake from actigraphy. Actigraphy-measured sleep time minus median reaction time for surrounding random (actual estimated time asleep) was also determined for blocks. As such, higher learning scores indicate greater the average daily cycle (i.e., daytime and overnight sleep). sequence-specific learning [16, 64]. Finally, weekly nap frequency (number of days with nap Finally, we evaluated executive attention with a sleep/number of days with usable actigraphy data × 7) Flanker task (n = 60) similar to a preschool age-adapted was derived from non-experimental days with actigraphy version that was used in McDermott et  al. [65]. Chil- measurement. dren were presented with an array of five fish on a com - puter screen and were instructed to select the direction Statistical analyses that the center fish was facing as quickly as possible. Analyses were performed in R version 4.1 [68], using Some trials were congruent (i.e., all fish faced the same the compositions [69] and car [70] packages. Standard direction) and some were incongruent (i.e., the outer descriptive statistics (means and standard deviations or fish faced the opposite direction of the center fish). as frequencies and percentages) were used to character- We included the accuracy score in our analysis. Higher ize the study population. Preliminary analyses to assess accuracy scores represented better executive attention associations between individual movement behaviors, performance. S t. Laurent et al. Journal of Activity, Sedentary and Sleep Behaviors (2023) 2:7 Page 7 of 13 each of the outcomes, and potential covariates were consisted of 5.2 h of sedentary time, 5.3 h of light physi- explored with Pearson correlations and multiple linear cal activity, 1.9 h of MVPA, and 11.6 h of sleep. For cog- regression models adjusting for age and sex. nitive health, children’s scores ranged from 10 to 141 We followed a compositional data analysis (CoDA) for receptive vocabulary performance, 33.0 to 91.7% for approach to describe the children’s 24-h time-use as visuospatial memory accuracy, − 0.0291 to 0.138.3 for well as investigate the relations between their move- procedural memory learning, and 38.2 to 98.7% execu- ment behaviors and each brain health measure [71]. The tive attention accuracy. For social-emotional health, the approach consisted of several steps. First, for each child, range in temperament scores indicated that participants 24-h time-use was defined as a 4-part composition con - varied in temperament characteristics, and 17% (n = 66) sisting of time spent sleeping, sedentary, in light physical and 17.3% (n = 67) of the sample had a score above ‘nor- activity, and in MVPA. Compositional means were used mal’ for externalizing and internalizing behaviors respec- to describe the 24-h time-use composition, obtained by tively, indicating potential behavior concerns for those calculating the geometric mean of each behavior and categories. then normalizing these to sum to 24 h [71, 72]. Second, compositional linear regression models were Preliminary analyses used to investigate the relationship between the 24-h As a first step in our model building, we explored rela - movement behavior composition and the cognitive and tions between relevant covariates (i.e., age and sex) with social-emotional health measures, respectively. This was our movement behaviors and brain health outcomes. In done by first expressing the 24-h time-use composi - respect to movement behaviors, females engaged in more tion as a set of three isometric log-ratio (ilr) coordinates light physical activity and less MVPA than males. Males [73], which were entered as independent variables in the had higher scores for vocabulary, effortful control, and regression models (i.e., one model per cognitive or social- surgency. There were no differences in age between the emotional health outcome). All models were adjusted for females and males. Correlations were explored between age, sex, and grid size/timing (for visuospatial memory age with the movement behaviors and outcomes. As only). Multiple regression parameters from the type III would be expected, age was positively associated with analysis of variances were used to assess if the 24-h com- light physical activity, MVPA, vocabulary, and executive position was associated with each brain health measure attention, and negatively correlated with sleep time. [74]. We next explored preliminary associations between Finally, if a significant relationship was found between individual absolute movement behaviors (i.e., sedentary the 24-h behavior composition and an outcome, we fur- time, light physical activity, MVPA, and 24-h sleep time) ther explored the “optimal” 24-h time-use for said out- with each cognitive and social-emotional health out- come, following the approach described in Dumuid et al. come. Pairwise unadjusted Pearson correlations demon- [51]. In short, this was done by predicting cognitive and strated some significant associations (Additional file  3). social health measures for all 24-h compositions rep- Each of the four movement behaviors were correlated resented in the dataset based on the linear regression with vocabulary development. Specifically, light physi - models. For each outcome measure, the optimal 24-h cal activity and MVPA were positively associated with composition was defined as the means of the composi - vocabulary score (r = 0.11 and 0.18, respectively) and tions associated with the top 5% outcome zone. sedentary time and sleep time were inversely associated (r = − 0.18 and − 0.16, respectively). Additionally, MVPA Results was positively associated with surgency scores (r = 0.11), Participant characteristics and sleep time was positively associated with procedural Descriptive characteristics of the 388 preschool-aged memory performance (r = 0.32). However, when we next participants and sample sizes for each outcome meas- ran multiple linear regression models for each independ- ure are presented in Table  1. Sample sizes were smaller ent movement behavior while adjusting for age and sex for the memory and executive attention measures due (but not other movement behaviors), only a few signifi - to the protocol of the parent study rather than compli- cant associations were observed (Additional file  4). Light ance (i.e., these measures were only collected in sub- physical activity was positively associated with vocabu- samples of children). Children ranged in age from 33 to lary (B = 0.71, p = 0.003). Total 24-h sleep time was nega- 70 months and our sample had slightly more males than tively associated with visuospatial memory (B = − 0.13, females (55.4% vs. 44.6%). Participants had an average of p = 0.018), but positively associated with procedural 739 ± 65.7 min of actigraph wear time during wake inter- memory (B = 0.0003, p = 0.038). An interaction of each vals and wore the devices for 9.56 ± 3.3 days (range: 3 to movement behavior and sex was initially included in each 15). The average 24-h movement behavior composition model, but the only significant interaction was between St. Laurent et al. Journal of Activity, Sedentary and Sleep Behaviors (2023) 2:7 Page 8 of 13 Table 1 Descriptive characteristics of the study sample Variables Mean (SD) or n (%) Range Sample characteristics (n = 388) Age (months) 51.5 (9.46) 33 to 70 Sex Female 173 (44.4) – Race White 237 (61.1) – Black/African American 33 (8.5%) – Asian 14 (3.6%) – Native Hawaiian/Pacific Islander 2 (0.5%) – Two or more racial groups 41 (10.6%) – Other 32 (8.2%) – Missing 29 (7.5%) – Hispanic ethnicity Yes 100 (25.8) No 268 (69.1) – Missing 20 (5.1) – Socioeconomic status (score) 4.56 (1.98) 1 to 7 24‑h Movement Behaviors (‘absolute’ means) Sedentary time (min) 311.7 (72.1) 129.8 to 575.8 Light physical activity (min) 312.5 (41.5) 169.3 to 434.7 MVPA (min) 114.3 (39.7) 18.9 to 292.3 Sleep time (min) 683.6 (45.6) 547.9 to 816.7 Daily cycle weartime (min) 1422.1 (62.3) 1159.0 to 1590.5 24‑h Movement Behaviors (compositional means) Sedentary time (min) 314.7 (−) – Light physical activity (min) 316.3 (−) – MVPA (min) 115.8 (−) – Sleep time (min) 693.2 (−) – Cognitive health Receptive vocabulary (PPVT score; n = 328) 86.1 (25.6) 10 to 141 Visuospatial memory accuracy score (%; n = 62) 68.9 (13.7) 33.0 to 91.7 Procedural memory learning score (n = 40) 0.0554 (0.0414) − 0.0291 to 0.138.3 Executive attention accuracy score (%; n = 60) 65.6 (14.5) 38.2 to 98.7 Social‑ emotional health Surgency/extraversion score (n = 325) 4.49 (0.763) 2 to 6.7 Negative affectivity score (n = 324) 3.75 (0.919) 1.4 to 6.5 Eor ff tful control score (n = 327) 5.00 (0.761) 2.2 to 6.8 Externalizing behavior problem score (n = 359) 8.51 (7.84) 0 to 44 Internalizing behavior problem score (n = 371) 5.11 (5.82) 0 to 52 MVPA: moderate- to vigorous-intensity physical activity Compositional linear regression models MVPA and sex for negative affectivity, but when stratified In the compositional data analysis approach, we used by sex, the association did not remain significant. Addi - compositional linear regression models to investigate tionally, including the interaction terms did not improve the relations between the 24-h movement behavior model fit and therefore were they not included in the compositions and the nine cognitive (i.e., receptive reported models. vocabulary, visuospatial memory, procedural memory, S t. Laurent et al. Journal of Activity, Sedentary and Sleep Behaviors (2023) 2:7 Page 9 of 13 Table 2 Associations between overall 24‑h movement behavior Discussion composition and cognitive/social‑ emotional health measures In this study, among the nine cognitive and social-emo- tional health measures that we examined, the daily time df F‑ value p‑ value use composition of movement behaviors was only asso- Cognitive health ciated with receptive vocabulary in our early childhood Receptive vocabulary 3 5.242 0.002 sample. A daily profile that is similar to current WHO Visuospatial memory 3 0.3573 0.784 recommendations for physical activity and sleep in pre- Procedural memory 3 0.131 0.941 school aged children [5] corresponded to the top five per - Executive attention 3 0.465 0.708 cent of receptive vocabulary scores (i.e., 12.1  h of sleep, Social‑ emotional health 4.7  h of sedentary time, 5.6  h of light physical activity, Surgency 3 1.252 0.291 and 1.7 h of MVPA). These findings align with the incon - Negative affectivity 3 0.025 0.995 clusiveness of the current evidence regarding the rela- Eor ff tful control 3 0.490 0.690 tions between daily movement behaviors and similar Externalizing behavior 3 0.195 0.900 components of brain health in early childhood. Internalizing behavior 3 0.035 0.991 The sole significant association in the current study All models included the four 24-h movement behaviors expressed as three for receptive vocabulary was particularly interesting as isometric log-ratio coordinates and were adjusted for age and sex. *The model PPVT scores may be a proxy for IQ, which is reflective of for visuospatial memory was also adjusted for grid size and timing brain development in the early years [26, 75]. Although early childhood vocabulary ability has been minimally explored in studies using compositional data analysis, one study had complementary findings for this cogni - Table 3 Optimal 24‑h movement behavior composition for tive domain [48]. Kuzik et al. [48] reported an association vocabulary score between the 24-h behavior composition of their pre- Compositional mean, hours/day [min; max] school sample and expressive vocabulary. While account- ing for the other behaviors, sedentary time (referred to as Sleep time 12.1 [10.2; 15.5] stationary time in their report) was positively associated Sedentary time 4.7 [2.3; 9.1] with vocabulary. Reallocating time from sleep to sed- Light physical activity 5.6 [3.8; 7.0] entary time was associated with estimated increases in MVPA 1.7 [0.3; 4.8] vocabulary. As they measured expressive vocabulary with a different tool (i.e., the Early Years Toolbox), it is nota - ble that between the two studies movement behaviors were related to both expressive and receptive vocabulary. and executive attention) and social-emotional health While early findings show agreement, this domain should outcomes (i.e., three subscales of temperament, and be further explored in young children to see if results are internalizing and externalizing behaviors). Type III generalizable across populations and assessment meth- analyses of variance F-test were used to determine if ods. While speculative, it is possible that children who the 24-h movement behavior composition was asso- are more active may have more opportunities to interact ciated with each of the outcomes (Table  2). The with others and thus practice and develop their vocabu- 24-h time use composition of our preschool partici- lary skills. Furthermore, physical activity may contribute pants was only associated with receptive vocabulary to better sleep (e.g., duration and quality) which in turn (F = 5.242, p = 0.002). is associated with vocabulary development [76, 77]. Thus, exploring temporality of these behaviors and vocabu- lary outcomes, as well as exploring contexts and specific Optimal time‑use for receptive vocabulary activities within the movement behaviors, would provide Given the significant association between the move- greater insight into this association. ment behavior composition and receptive vocabu- Movement behavior compositions were not associated lary, we further explored the ‘optimal’ 24-h time-use with either of our memory outcomes or executive atten- for that specific cognitive health measure. The 24-h tion, impeding our ability to determine optimal com- composition associated with the 5% best vocabulary positions for these measures. In contrast to our finding, scores (i.e., raw scores of 122 to 141) consisted of Kuzik et al. [48] reported that memory performance was 12.1  h of sleep, 4.7  h of sedentary time, 5.6  h of light associated with movement behavior compositions with a physical activity and 1.7 h of MVPA (Table 3). The top similar visuospatial task, albeit to assess working mem- 5% represented 23% of our sample (n = 77 out of 328 ory rather than declarative memory as in the current children). St. Laurent et al. Journal of Activity, Sedentary and Sleep Behaviors (2023) 2:7 Page 10 of 13 study. To our knowledge no other study has explored to the high-income countries sampled for Kuzik et  al. declarative memory outcomes in early childhood sam- and the present study (Canada and the United States), ples. Interestingly, compositional data analyses of move- and environmental differences may also contribute to ment behaviors and memory in older children have also the associations between movement behaviors and the been scarce and limited to working memory measures outcomes. [78]. The lack of associations is somewhat surprising as Also similar to memory, early childhood studies reports of physical activity and, more consistently, sleep exploring behavior compositions with social-emotional have been correlated to memory performance when measures are limited and like the current findings, gener - explored independently [79, 80]. An important consid- ally null. For example, Kuzik et  al. [48] explored numer- eration to the current approach measure of sleep time ous subscale scores of the Child Self-Regulation and in the compositional models did not exclude wake bouts Behaviour Questionnaire (i.e., behavioral self-regulation, while in bed. Indeed, actual sleep time, rather than time cognitive self-regulation, emotional self-regulation, in bed (or in the present study total time in rest inter- externalizing, internalizing, sociability, and prosocial vals), may be more influential on memory [80]. Given behavior). Although some time reallocations were sig- that healthy sleepers of this age have generally low time nificantly associated with estimated changes in some out - spent in wake after sleep onset [81], this is not likely a come measures, movement behavior compositions were substantial concern. However, in addition to investigating not significant with any of these scores. In the current memory domains outside of working memory in future study we explored social-emotional variables as separate work, exploring subcomponents of sleep (e.g., sleep onset outcomes. However, one consideration for future work is latency, sleep duration, and wake after sleep onset) may that the relation between sleep and emotional regulation be warranted. may be moderated by temperament [82, 83]. Addition- While evidence regarding associations of movement ally, it is important to further breakdown components of behavior compositions and executive functions is also wake behaviors to consider contexts and modalities that limited, one study in older children suggested favorable may be relevant to social-emotional development. associations with some indicators of executive function Although movement behavior composition studies are [78], whereas findings in younger children have been also somewhat limited in older children, there appears mixed. In a study in Brazilian preschoolers, the move- to be some emerging support of an association between ment behavior composition was associated with inhibi- behaviors and social-emotional outcomes for preado- tory control [47]. When time was reallocated from sleep lescents. For example, in one cross-sectional analysis, or light physical activity to MVPA, this corresponded the time-use composition of 10- to 12-year-old Austral- with estimated improvements in inhibitory control. ian children was associated with internalizing behaviors However, comparable to our findings, Kuzik et  al. [48] and total difficulties scores [84]. Specifically, in relation reported no association between the movement behavior to other behaviors, sleep was negatively associated with composition and response inhibition. Interestingly, these internalizing problems and total difficulty scores, sed - two comparison studies both used the Go/No-Go task entary time was positively associated with internalizing from the Early Years Toolbox, but had conflicting results. problems, and light physical activity was positively asso- Differences in sample movement behavior composi - ciated with internalizing problems and total difficulties tions could possibly contribute to this discrepancy. The scores. Another study in 9- to 13-year-old British chil- time-use composition in the current study is similar to dren noted that the sample’s movement behavior com- the behavior profile in Kuzik et  al. [48] (i.e., 6.05  h sed - position was associated with internalizing problems and entary time, 5.09  h of light physical activity, 1.75  h of prosocial behavior, but only in primary school students. MVPA, and 11.2 h of sleep), whereas Bezzera’s composi- Specifically, sedentary time was positively associated with tion had greater levels of sedentary time and lower levels internalizing problems and negatively associated proso- of MVPA (7.6  h of sedentary time, 4.2  h of light physi- cial behavior. In the current analysis, we may not have a cal activity, 0.84 h of MVPA, 11.4 h of sleep). Given that generalizable range of child behavior scores (e.g., chil- socioeconomic status is often inversely associated with dren generally had low behavioral problem scores), which both physical activity and sleep of children, differences could in turn influence our findings. This could be related in compositions could be related to social-demographic to both the inclusion criteria of the parent study (i.e., no differences of the sample given that Bezzara et  al. [47] diagnosed sleep disorders or developmental disabilities) studied children from families that reported lower socio- and the possibility of participation bias (e.g., families with economic status. Additionally, the sample in the Bezerra children presenting more behavior challenges may be less et al. study lived in a middle-income country, as opposed likely to enroll in the study). S t. Laurent et al. Journal of Activity, Sedentary and Sleep Behaviors (2023) 2:7 Page 11 of 13 While the current study examined a range of cogni- Supplementary Information tive and social-emotional measures, some considerations The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s44167‑ 023‑ 00016‑6. should be taken into account. In addition to the potential limitations noted for behavioral measures, current find - Additional file 1. STROBE statement cross‑sectional study checklist. ings may not be generalizable to other early childhood Additional file 2. Detailed methods for the cognitive and social‑ emo‑ populations given the general healthy characteristics of tional health assessments our sample. As with other accelerometry devices, there Additional file 3. Pairwise correlations between absolute movement is room for misclassification of behaviors given differ - behaviors and cognitive/social‑ emotional health outcomes. ent data configuration and processing protocols, and Additional file 4. Associations between absolute movement behaviors that the waist is generally preferable for physical activ- and cognitive/social‑ emotional health outcomes with linear regression models. ity metrics [85], whereas the wrist is recommended for sleep [86]. However, wearing one device generally leads Acknowledgements to better compliance [62] and the time spent in different The authors would like to thank Chloe Andre, Robert Batsevich, Elizabeth behaviors of the present study are comparable to those in Monger, and Leah Hannon for their assistance with data processing and Kuzik et al.’s [48] sample of Canadian preschool children. preparation. Additionally, it may be that our behavioral composition Author contributions components were too ‘broad’ for these cognitive and Conceptualization: CWS and RMCS; Methodology and investigation: CWS, CR, social-emotional outcomes, and future researchers could JFH, ACC, LBFK, PCD, and RMCS; Data curation: JFH, ACC, LBFK, PCD, RMCS; Statistical analysis: CR; Writing—original draft: CWS; Writing—review and edit‑ look at more nuanced measures (i.e., subcomponents of ing: CR, JFH, ACC, LBFK, PCD, and RMCS; Supervision: RCMS. All authors read behaviors). For example, reading and traditional learn- and approved the final manuscript. ing activities that my help with cognitive performance are Funding most likely to consist of sedentary behaviors, and some This study was supported by NIH R01 HL111695 (PI: Spencer). CWS was sup‑ types of physical activity may be more beneficial than ported by NIH F32 HD105384. others (e.g., cognitively engaging or incorporating execu- Availability of data and materials tive functions skills) [87]. Dose, modality, intensity, and The datasets used and/or analyzed during the current study are available from timing could all play a role here, but our data could not the corresponding author on reasonable request. tease apart those potential moderators. Finally, we were unable to compare ‘best’ compositions across outcomes Declarations as some measures were only collected on a subgroup of Ethics approval and consent to participate participants. All procedures were approved by the University of Massachusetts Intuitional Review Board. Parental consent and permission and child verbal assent were obtained prior to participation. Conclusions Consent for publication In this sample of preschool aged children, 24-h move- Not applicable. ment behavior compositions of sedentary time, light Competing interests physical activity, MVPA, and sleep were generally not CLR serves as an editorial board member for the Journal of Activity, Sedentary associated with cognitive and social-emotional health and Sleep Behaviors. The remaining authors declare that they have no com‑ outcomes. However, consistent with other studies, the peting interests. time-use of these behaviors does appear to be related to Author details vocabulary knowledge. While the present findings are Department of Psychological and Brain Sciences, University of Massachusetts generally in alignment with other early childhood reports Amherst, 135 Hicks Way, Tobin Hall, Amherst, MA 01003, USA. Depar tment of Public Health and Nursing, Norwegian University of Science and Technol‑ that utilized compositional data analysis with the same ogy, Trondheim, Norway. Department of Physical Education and Sport four movement behaviors and mental health related out- Sciences, University of Limerick, Limerick, Ireland. Faculty of Physical Culture, comes, future work should consider activities within the Palacký University Olomouc, Olomouc, Czech Republic. Department of Psy‑ chology, Assumption College, Worcester, MA, USA. Department of Psychol‑ broad behaviors—as they may be more meaningful to ogy, Merrimack College, North Andover, MA, USA. Sensing, Perception, such outcomes—in more health diverse samples. and Applied Robotics Division, Charles River Analytics, Cambridge, MA, USA. Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, MA, USA. Abbreviations CBCL Child Behavioral Checklist for Ages 1.5–5 Received: 6 December 2022 Accepted: 27 January 2023 CBQ Questionnaire Very Short Form MVPA Moderate to vigorous intensity physical activity St. Laurent et al. 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Accelerometer data collection and processing Publisher’s Note criteria to assess physical activity and other outcomes: a systematic Springer Nature remains neutral with regard to jurisdictional claims in pub‑ review and practical considerations. Sports Med. 2017;47(9):1821–45. lished maps and institutional affiliations. 63. Dunn LM, Dunn DM, Bulheller S. Peabody Picture Vocabulary Test: PPVT. Swets Test Services; 2003. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Activity Sedentary and Sleep Behaviors Springer Journals

Associations of activity, sedentary, and sleep behaviors with cognitive and social-emotional health in early childhood

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Springer Journals
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Copyright © The Author(s) 2023
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2731-4391
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10.1186/s44167-023-00016-6
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Abstract

Background Early childhood is important for cognitive and social‑ emotional development, and a time in which to promote healthy movement behaviors (sedentary behavior, physical activity, and sleep). Movement behaviors may have interactive influences on cognition and social‑ emotional factors in young children, but most previous research has explored them independently. The purpose of this study was to determine if movement behaviors are associated with measures of cognitive and social‑ emotional health in young children and if so, to describe optimal compositions of movement behaviors of a daily cycle for such outcomes. Methods Children (n = 388, 33 to 70 months, 44.6% female) from a clinical trial (ClinicalTrials.gov ID: NCT03285880, first posted September 18, 2017) wore accelerometers on their wrists for 24‑h for 9.56 ± 3.3 days. Movement behavior compositions consisted of time spent in sedentary behaviors, light intensity physical activity, moderate to vigorous intensity physical activity (MVPA), and sleep. Outcomes were cognitive (receptive vocabulary, declarative and proce‑ dural memory, and executive attention) and social‑ emotional measures (temperament and behavioral problems). Compositional linear regression models with isometric log ratios were used to investigate the relations between the movement behavior composition and the cognitive and social‑ emotional health measures. If a significant associa‑ tion was found between the composition and an outcome, we further explored the “optimal” 24‑h time ‑use for said outcome. Results Movement behavior compositions were associated with receptive vocabulary. The composition associated with the predicted top five percent of vocabulary scores consisted of 12.1 h of sleep, 4.7 h of sedentary time, 5.6 h of light physical activity, and 1.7 h of MVPA. Conclusions While behavior compositions are related to vocabulary ability in early childhood, our findings align with the inconclusiveness of the current evidence regarding other developmental outcomes. Future research exploring activities within these four movement behaviors, that are meaningful to cognitive and social‑ emotional development, may be warranted. Keywords Physical activity, Sedentary behavior, Sleep, Cognition, Social‑ emotional, Children *Correspondence: Rebecca M. C. Spencer rspencer@umass.edu Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. St. Laurent et al. Journal of Activity, Sedentary and Sleep Behaviors (2023) 2:7 Page 2 of 13 one’s attention during activities where there may conflict - Background ing information or stimuli present [18, 19]. Early childhood (i.e., under 6  years) is an important Movement behaviors have been independently asso- life phase in which to promote healthy behaviors of the ciated with cognitive outcomes in older children and 24-h cycle (i.e., sedentary behavior, physical activity, adults. There is evidence that both acute and chronic and sleep, hereafter collectively referred to as move- physical activity can improve cognitive outcomes such as ment behaviors). Specifically, minimizing time spent executive function and memory in these populations [2]. sedentary, participating in adequate levels of physical In older adults, physical activity benefits cognitive perfor - activity, and achieving sufficient sleep during child - mance, is associated with a lower risk of dementia, and is hood can positively impact outcomes such as improved beneficial to cognitive impairments in those with demen - mental and physical health, cognitive performance, and tia [2], while measures of sleep are associated with mem- overall quality of life [1–4]. The World Health Organi - ory and executive function [20]. There is some evidence zation (WHO) recommends that 4- to 5-year old chil- of associations of sedentary time and cognitive function dren be physically active for at least 180  min per day in this age group, but findings have been more variable (including at least 60  min of moderate- to vigorous- than for physical activity and sleep [21]. Although there intensity physical activity [MVPA]), should limit sed- has been some support of beneficial associations between entary time (i.e., no more than 60  min of screen time, physical activity and cognitive measures (and, to some not be restrained—such as sitting in a stroller or being extent, adverse associations with high sedentary time), a held—more than 1  h at a time, and avoid sitting for lack of sufficient studies limits the conclusions that could extended periods), and obtain 10 to 13  h of sleep in a be drawn for children under 6  years of age in the 2018 24-h period [5]. Although surveillance data on these Physical Activity Guidelines Advisory Committee Scien- health behaviors is limited in younger children, reports tific Report [2]. While a growing number of early child - indicate that many young children do not obtain suffi - hood studies have been conducted on this topic, recent cient physical activity and sleep, and therefore are likely reviews have indicated that associations between move- to engage in more sedentary behaviors [6, 7]. While ment behaviors and cognition are more variable than health promotion regarding these 24-h movement previous reports in older children [22–24]. In contrast, behaviors has traditionally been emphasized in older there is accumulating evidence that both daytime and children, early childhood may be an important time to overnight sleep are beneficial for cognitive functions in intervene given that sleep is unique during these early early childhood [25–28]. years (as children transition out of naps) and both sleep Children experience significant social and emotional and physical activity habits track through childhood development in the early years and many measures and even into adulthood [8–10]. within this domain have been explored as independent Early childhood also serves as an important phase for correlates of sedentary time, physical activity, and sleep cognitive development [11, 12]. Cognitive abilities at this [29–31]. Broadly, social-emotional health in early child- life stage underlie both current and future academic per- hood encompasses how young children view themselves formance [13, 14]. In the current study, we focused on and their world, their emotions, and their related behav- the cognitive domains of receptive vocabulary, declara- iors. Factors considered in the current paper include: (1) tive memory, procedural memory, and executive function temperament and (2) emotional and behavioral problems. (attention). Receptive vocabulary is the ability to under- According to Rothbart and Derryberry’s definition, tem - stand words and phrases and falls under the umbrella of perament consists of “constitutionally based individual language skills [15]. Declarative and procedural memory differences in reactivity and self-regulation, influenced are domains of overall memory that are characterized as over time by heredity and experience” [32]. Common long-term memory (e.g., seconds to days) and therefore indicators of temperament include surgency (e.g., high involves encoding, storage, and retrieval (as opposed to positive emotional reactivity levels), negative affectivity working memory which involves the ability to hold infor- (e.g., tendency to experience negative emotional reactiv- mation and manipulate it in the short term) [15]. Declar- ity), and effortful control (e.g., ability to regulate emo - ative memory (i.e., explicit memory) involves the ability tions) [33]. Emotional and behavioral problems in young to remember experiences, people, and things, whereas children are commonly categorized as externalizing (e.g., procedural memory is a form of implicit memory that behaviors that are presented and directed outside of the allows people to remember motor skills and actions child such as ‘acting out’ and non-compliance) and inter- [15–17]. In the preschool years (e.g., 2  years 9  months nalizing problems (e.g., behaviors that are directed within through 5  years), executive attention is a precursor to the child such as withdrawing or experiencing anxiety) executive function (e.g., set shifting, response inhibition, [34]. and working memory) and involves the ability to regulate S t. Laurent et al. Journal of Activity, Sedentary and Sleep Behaviors (2023) 2:7 Page 3 of 13 In older children, independent associations for sed- day), traditional statistical analysis methods that do not entary behavior (in the form of screen time), physical account for the compositional nature of such behaviors activity, and sleep social-emotional measure with social- (i.e., the co-dependence) are commonly applied. Compo- emotional indicators have been reported [35]. In observa- sitional data analysis can be used to provide information tional studies where 24-h behaviors have been separately about associations between a behavior of interest and a explored with social-emotional outcomes in young chil- health outcome while also accounting for time spent in dren, reported associations have been mixed, particularly other behaviors, as well as the effect on that relationship for waking behaviors. For example, in a 2017 review, 8 of when time is reallocated from one behavior to others the 11 included studies employed observational designs [46]. to examine physical activity and variables of psychoso- Following a trend in preadolescent research, stud- cial health in children under 5  years. Associations were ies have begun to explore relations between movement heterogeneous with favorable, unfavorable, mixed, and behaviors and cognitive and social-emotional health with null results and quality of evidence designated as “very compositional data analysis methods. In one cross-sec- low [31]”. Comparable findings were noted in a similar tional study, theoretical time reallocations replacing sleep 2017 review where sedentary behavior was the exposure or light physical activity with MVPA were associated of interest (i.e., 9 longitudinal and 7 cross-sectional stud- with improvements in inhibitory control (a measure of ies) [29]. In another review of sleep studies in toddlers executive function) [47]. Another recent study of Cana- and preschoolers (5 longitudinal and 17 cross-sectional dian preschoolers incorporated measures of the Early studies), although shorter sleep duration was more con- Years Toolbox to assess both cognitive (i.e., inhibitory sistently associated with poorer emotional regulation, control, visual-spatial working memory) and social-emo- overall associations were mixed, and the quality of evi- tional measures (i.e., sociability, externalizing, internaliz- dence was still designated as “low” [30]. ing, prosocial behavior, and self-regulation) [48]. Overall, Similar to recent findings in older children, it is pos - sedentary time was positively associated with inhibitory sible that movement behaviors may have interactive control and vocabulary, and MVPA was positively associ- influences on cognition and social-emotional factors in ated with sociability. When sleep time was theoretically young children. A number of mechanisms have been pro- replaced with sedentary time, this was positively associ- posed or supported in young children connecting sleep ated with vocabulary. Reallocating any behavior in place (e.g., duration, quality, timing, and routines) with cogni- of MVPA was positively associated with sociability and tive development and child behavior [25, 28]. For exam- self-regulation. ple, longer sleep is often accompanied by greater slow Further compositional data analysis studies building wave sleep, which contains physiological events (e.g., on these early reports in young children are necessary sleep spindles) that are associated with sleep dependent to provide greater insight into the interactive relations memory consolidation. Additionally, physiological (e.g., of 24-h movement behaviors with cognitive and social- changes in brain structure and physiology), psychosocial emotional health outcomes. Such studies would also (e.g., enhanced mood and self-perceptions), and behavio- determine if previous associations with cognitive and ral (e.g., coping and self-regulation skills) pathways have social-emotional health are consistent with different been proposed between physical activity and measures assessments that are commonly utilized by educators of brain health in children [36]. Sedentary behaviors may and clinicians, as well as with other cognitive and social- potentially play a role in such pathways either by indirect emotional health measures (e.g., receptive vocabulary effects on physical activity and sleep, or directly on some and procedural memory). More recently, given that com- mechanisms. Moreover, movement behaviors may influ - positions of behaviors may have differential benefits on ence one another [22, 37, 38] which in turn may facilitate various health outcomes, a novel use of compositional interactive influences on cognitive and social-emotional data analysis is to explore ‘optimal’ daily combinations health [35]. of behaviors for different measures [49, 50]. Studies in One approach researchers have used to examine inter- older children that have used this approach have found actions of movement behaviors is to explore compli- variations in best 24-h movement behavior composi- ance with guidelines (e.g., meeting national or WHO tions for physical, skeletal, mental, and cognitive health recommendations for all three 24-h behaviors) as the [51–53]. Therefore, the purpose of the present study was exposure of interest [35, 39–42]. Another method that to determine if movement behaviors were associated has been adopted in recent years by many public health with measures of cognitive and social-emotional health researchers is the use of compositional data analysis in early childhood. We hypothesized that 24-h movement [43–45]. Although behavioral data is typically presented behavior compositions would be associated with each as a component of a daily cycle (i.e., min/day or % of of the cognitive and social-emotional outcomes while St. Laurent et al. Journal of Activity, Sedentary and Sleep Behaviors (2023) 2:7 Page 4 of 13 accounting for age and sex. Additionally, we aimed to nap-promotion day, children were encouraged to nap describe optimal composition of movement behaviors for (e.g., with quiet encouragement and back-rubs) and on each of our cognitive and social-emotional measures. the wake-promotion day, the room was dim and quiet, similar to the nap condition, but children participated in Methods sedentary activities such as coloring and reading books. Overview Most participants completed a receptive vocabulary task The data in this study stem from a clinical trial examin - and at least one other cognitive task (e.g., declarative ing whether daytime naps contribute to immediate and memory, procedural memory, or executive function). delayed benefits on memory in early childhood (Clinical - Trials.gov ID: NCT03285880, first posted on September Participants 18, 2017). The Strengthening the Reporting of Obser - Preschool children were recruited from childcare and vational Studies in Epidemiology Statement (STROBE) preschool centers in western Massachusetts in the United checklist can be viewed in our supplementary materials States between 2013 and 2020. To be eligible for the par- (see Additional file  1). The study protocol was approved ent study, children: (1) were 33–71 months of age, (2) had by the University of Massachusetts Amherst Institutional normal or corrected-to-normal vision and hearing, (3) Review Board (approved December 15, 2011; protocol had no current or past diagnosis of a developmental dis- ID: 2011-1152). Written informed consent was obtained ability or sleep disorder, (4) had no use of psychotropic from adult caregivers for consent of their own participa- or sleep-affecting medications, and (5) had not recently tion as well as permission for their child’s participation. traveled outside of the local time zone. For the current Child participants provided verbal assent and childcare study, only participants with at least three days and three providers provided written informed consent (for com- nights of sufficient actigraphy data (see next section) and pleting daytime sleep diaries). at least one of the outcomes of interest were included In brief, the parent study followed a within-subjects (Fig. 2). design and was conducted over 16  days in preschool and childcare settings (Fig. 1). (See Spencer et al. [54] for 24‑h Movement behavior measures more details on the protocol.) At the beginning of the Wake and sleep behaviors were measured with actig- study, adult caregivers completed a questionnaire that raphy. Actiwatch Spectrum monitors were worn on the included demographics and behavior assessments and participants’ non-dominant wrists. The Actiwatch is children were asked to wear an accelerometer, which a triaxial accelerometer with off-wrist detection and was instructed to be worn for the full study period. Par- a button that can be pressed by participants to mark ticipants each completed two conditions (nap- and wake- events. Children were taught how to press the event promotion), 1 week apart in a randomized order. On the marker when they began trying to fall asleep and again Fig. 1 Study overview. Actigraphy was measured throughout the full period, but ‘experimental’ days were excluded because all children were encouraged to either nap or stay awake in the afternoon regardless of their typical routine. Cognitive health measures were collected on nap‑ and wake‑promotion days. Social‑behavioral health measures were derived from caregiver/parent complete questionnaires that were distributed on Day 1 and collected at the end of the study period S t. Laurent et al. Journal of Activity, Sedentary and Sleep Behaviors (2023) 2:7 Page 5 of 13 Fig. 2 Participant flow diagram and sample sizes of outcome measures. Participant flow diagram and sample size of outcome measures. (For cognitive health, receptive vocabulary was sought for all participants, but each other cognitive task was only completed on a subset of the participants.) when they woke. The monitors were configured to col - Actiware algorithm) were used to define sleep onset and lect data in 15-s epochs, with a sensitivity of < 0.01 g and the last five consecutive minutes of sleep were defined as 32 Hz sampling rate. Data was processed in the Actiware sleep offset. For our preliminary models, sleep time was software using the default algorithm to designate each calculated by subtracting wake bouts (i.e., wake after epoch categorized as sleep, wake, or off-wrist [55]. This sleep onset and sleep onset latency when diaries were algorithm has demonstrated good agreement with vid- available) from the rest intervals. For our compositional eosomnography in 28- to 73-month-old children and is models, the sum of time spent in rest intervals was used commonly employed in sleep studies of early childhood as a proxy for sleep time (i.e., we did not exclude wake [56, 57]. Each daily cycle (i.e., wake onset for day x until after sleep onset time). wake onset for day x + 1) was then partitioned into over- Sedentary time and physical activity. The Actiwatch night rest periods, daytime rest periods (when present), has been studied for validity and reliability in preado- and daytime wake. Therefore, it was possible for aver - lescent children as an estimate of energy expenditure aged daily cycles to fluctuate beyond or below 24  h (or [60]. Accelerometer activity count cut points were 1440  min). Daily cycles that included an experimental derived from to categorize wake behaviors into seden- condition (i.e., nap- or wake-promotion) as part of the tary time, light physical activity, and MVPA [60]. These larger study were excluded as these activities may have cut points were cross validated in preschool children differed from a child’s typical routine. against direct observation [61]. One recent recom- Sleep. In children, the Actiwatch is considered an mendation of daytime wear time for activity acceler- acceptable tool for sleep measurement and has demon- ometer estimation in early childhood is a minimum of strated validity compared to polysomnography [58, 59]. 600 min [62]. However, as much of our sample received Rest intervals (i.e., sleep time) for overnight periods and 60 to 120 min of daytime sleep (which was categorized daytime naps (when present) were defined using a com - as time in bed rather than wake), days with at least bination of marked events (i.e., button presses) and sleep 480 min of daytime wear were included. Therefore, day - diaries. If neither of these was available, the first three time intervals that were defined as wake by Actiware consecutive minutes of sleep (i.e., as categorized by the were further processed to estimate sedentary time and St. Laurent et al. Journal of Activity, Sedentary and Sleep Behaviors (2023) 2:7 Page 6 of 13 Social‑emotional health measures physical activity for days that had at least 480  min of Social-emotional health measures included three sub- actigraphy data. Using the Ekblom et al. [60] cut points, scale scores for temperament and two subscales for child daytime wake intervals were then classified as seden - behavior. Temperament was explored with scores from tary time (less than 79 counts), light physical activity the parent-reported Child Behavior Questionnaire Very (80 to 261 counts), or MVPA (262 counts or greater). Short Form (CBQ) [33], with higher scores indicating stronger characteristics reflected by each of the tem - perament subscales all with a possible score range of 1 Cognitive and Social‑emotional health measures to 7: surgency/extraversion (n = 325), negative affectiv - Cognitive health measures ity (n = 324), and effortful control (n = 327). In children A detailed description of the cognitive and socio- ages 3 to 8 years, internal consistency alpha levels of the emotional health measures is provided in Additional Very Short Form ranged between 0.70 and 0.76 for sur- file  2. Cognitive measures included receptive vocabu- gency, 0.66 and 0.70 for negative affect, and 0.62 and 0.78 lary, visuospatial memory, procedural memory, and for effortful control [33]. Correspondence corrected cor - executive attention. Receptive vocabulary was evalu- relations between the Very Short Form and the standard ated with the Peabody Picture Vocabulary Test, 4th Child Behavior Questionnaire were 0.83 for surgency, Edition (PPVT-IV) [63] (n = 328), a test that evaluates 0.75 for negative affect, and 0.83 for effortful control [33]. children’s ability to understand spoken words, and the Internalizing and externalizing behavior raw scores calculated raw score was used. Reliability of internal (n = 359 and 371, respectively) served as indicators of consistency of the items has been reported at 0.90 or social and emotional behavior problems from the parent- above across ages groups [63]. Possible scores can range completed Child Behavioral Checklist for Ages 1.5–5 from 2 to 228. This test is not typically used by itself (CBCL) [66]. Higher scores for both represented that for clinical interpretations regarding vocabulary devel- these types of problems were more typical for the child opment, but higher scores represent higher receptive [66]. For clinical purposes, scores under 12 for internal- vocabulary and comprehension of spoken English [63]. izing problems and under 18 for externalizing problems As an indicator of declarative memory, a visuospa- are often used as cut offs between ‘normal’ ranges and tial task similar to the game “Memory” was completed the ‘borderline clinical’ range [66]. (n = 62). Children were presented with a grid of images on a computer screen and then asked to recall the loca- tions of each. The average immediate accuracy score Covariates (for those that scored at least 30%) was used as our Several additional measures were collected to character- declarative memory outcome [17]. Thus, scores could ize the sample and potentially control for in our models. range from 30 to 100%, with a higher score represent- Age (months), sex, race and ethnicity were obtained from ing better memory performance. the caregiver questionnaires. Additionally, a compos- Procedural memory was assessed with a serial reac- ite score for socioeconomic status was calculated using tion time task (n = 41) [64]. In this task, children caregiver-reported education, employment status, and completed a sequence of finger presses on an elec - household income [67]. Daily total physical activity was tronic tablet and a learning score was calculated as parameterized as average activity counts/minute during median reaction time in the final sequence blocks wake from actigraphy. Actigraphy-measured sleep time minus median reaction time for surrounding random (actual estimated time asleep) was also determined for blocks. As such, higher learning scores indicate greater the average daily cycle (i.e., daytime and overnight sleep). sequence-specific learning [16, 64]. Finally, weekly nap frequency (number of days with nap Finally, we evaluated executive attention with a sleep/number of days with usable actigraphy data × 7) Flanker task (n = 60) similar to a preschool age-adapted was derived from non-experimental days with actigraphy version that was used in McDermott et  al. [65]. Chil- measurement. dren were presented with an array of five fish on a com - puter screen and were instructed to select the direction Statistical analyses that the center fish was facing as quickly as possible. Analyses were performed in R version 4.1 [68], using Some trials were congruent (i.e., all fish faced the same the compositions [69] and car [70] packages. Standard direction) and some were incongruent (i.e., the outer descriptive statistics (means and standard deviations or fish faced the opposite direction of the center fish). as frequencies and percentages) were used to character- We included the accuracy score in our analysis. Higher ize the study population. Preliminary analyses to assess accuracy scores represented better executive attention associations between individual movement behaviors, performance. S t. Laurent et al. Journal of Activity, Sedentary and Sleep Behaviors (2023) 2:7 Page 7 of 13 each of the outcomes, and potential covariates were consisted of 5.2 h of sedentary time, 5.3 h of light physi- explored with Pearson correlations and multiple linear cal activity, 1.9 h of MVPA, and 11.6 h of sleep. For cog- regression models adjusting for age and sex. nitive health, children’s scores ranged from 10 to 141 We followed a compositional data analysis (CoDA) for receptive vocabulary performance, 33.0 to 91.7% for approach to describe the children’s 24-h time-use as visuospatial memory accuracy, − 0.0291 to 0.138.3 for well as investigate the relations between their move- procedural memory learning, and 38.2 to 98.7% execu- ment behaviors and each brain health measure [71]. The tive attention accuracy. For social-emotional health, the approach consisted of several steps. First, for each child, range in temperament scores indicated that participants 24-h time-use was defined as a 4-part composition con - varied in temperament characteristics, and 17% (n = 66) sisting of time spent sleeping, sedentary, in light physical and 17.3% (n = 67) of the sample had a score above ‘nor- activity, and in MVPA. Compositional means were used mal’ for externalizing and internalizing behaviors respec- to describe the 24-h time-use composition, obtained by tively, indicating potential behavior concerns for those calculating the geometric mean of each behavior and categories. then normalizing these to sum to 24 h [71, 72]. Second, compositional linear regression models were Preliminary analyses used to investigate the relationship between the 24-h As a first step in our model building, we explored rela - movement behavior composition and the cognitive and tions between relevant covariates (i.e., age and sex) with social-emotional health measures, respectively. This was our movement behaviors and brain health outcomes. In done by first expressing the 24-h time-use composi - respect to movement behaviors, females engaged in more tion as a set of three isometric log-ratio (ilr) coordinates light physical activity and less MVPA than males. Males [73], which were entered as independent variables in the had higher scores for vocabulary, effortful control, and regression models (i.e., one model per cognitive or social- surgency. There were no differences in age between the emotional health outcome). All models were adjusted for females and males. Correlations were explored between age, sex, and grid size/timing (for visuospatial memory age with the movement behaviors and outcomes. As only). Multiple regression parameters from the type III would be expected, age was positively associated with analysis of variances were used to assess if the 24-h com- light physical activity, MVPA, vocabulary, and executive position was associated with each brain health measure attention, and negatively correlated with sleep time. [74]. We next explored preliminary associations between Finally, if a significant relationship was found between individual absolute movement behaviors (i.e., sedentary the 24-h behavior composition and an outcome, we fur- time, light physical activity, MVPA, and 24-h sleep time) ther explored the “optimal” 24-h time-use for said out- with each cognitive and social-emotional health out- come, following the approach described in Dumuid et al. come. Pairwise unadjusted Pearson correlations demon- [51]. In short, this was done by predicting cognitive and strated some significant associations (Additional file  3). social health measures for all 24-h compositions rep- Each of the four movement behaviors were correlated resented in the dataset based on the linear regression with vocabulary development. Specifically, light physi - models. For each outcome measure, the optimal 24-h cal activity and MVPA were positively associated with composition was defined as the means of the composi - vocabulary score (r = 0.11 and 0.18, respectively) and tions associated with the top 5% outcome zone. sedentary time and sleep time were inversely associated (r = − 0.18 and − 0.16, respectively). Additionally, MVPA Results was positively associated with surgency scores (r = 0.11), Participant characteristics and sleep time was positively associated with procedural Descriptive characteristics of the 388 preschool-aged memory performance (r = 0.32). However, when we next participants and sample sizes for each outcome meas- ran multiple linear regression models for each independ- ure are presented in Table  1. Sample sizes were smaller ent movement behavior while adjusting for age and sex for the memory and executive attention measures due (but not other movement behaviors), only a few signifi - to the protocol of the parent study rather than compli- cant associations were observed (Additional file  4). Light ance (i.e., these measures were only collected in sub- physical activity was positively associated with vocabu- samples of children). Children ranged in age from 33 to lary (B = 0.71, p = 0.003). Total 24-h sleep time was nega- 70 months and our sample had slightly more males than tively associated with visuospatial memory (B = − 0.13, females (55.4% vs. 44.6%). Participants had an average of p = 0.018), but positively associated with procedural 739 ± 65.7 min of actigraph wear time during wake inter- memory (B = 0.0003, p = 0.038). An interaction of each vals and wore the devices for 9.56 ± 3.3 days (range: 3 to movement behavior and sex was initially included in each 15). The average 24-h movement behavior composition model, but the only significant interaction was between St. Laurent et al. Journal of Activity, Sedentary and Sleep Behaviors (2023) 2:7 Page 8 of 13 Table 1 Descriptive characteristics of the study sample Variables Mean (SD) or n (%) Range Sample characteristics (n = 388) Age (months) 51.5 (9.46) 33 to 70 Sex Female 173 (44.4) – Race White 237 (61.1) – Black/African American 33 (8.5%) – Asian 14 (3.6%) – Native Hawaiian/Pacific Islander 2 (0.5%) – Two or more racial groups 41 (10.6%) – Other 32 (8.2%) – Missing 29 (7.5%) – Hispanic ethnicity Yes 100 (25.8) No 268 (69.1) – Missing 20 (5.1) – Socioeconomic status (score) 4.56 (1.98) 1 to 7 24‑h Movement Behaviors (‘absolute’ means) Sedentary time (min) 311.7 (72.1) 129.8 to 575.8 Light physical activity (min) 312.5 (41.5) 169.3 to 434.7 MVPA (min) 114.3 (39.7) 18.9 to 292.3 Sleep time (min) 683.6 (45.6) 547.9 to 816.7 Daily cycle weartime (min) 1422.1 (62.3) 1159.0 to 1590.5 24‑h Movement Behaviors (compositional means) Sedentary time (min) 314.7 (−) – Light physical activity (min) 316.3 (−) – MVPA (min) 115.8 (−) – Sleep time (min) 693.2 (−) – Cognitive health Receptive vocabulary (PPVT score; n = 328) 86.1 (25.6) 10 to 141 Visuospatial memory accuracy score (%; n = 62) 68.9 (13.7) 33.0 to 91.7 Procedural memory learning score (n = 40) 0.0554 (0.0414) − 0.0291 to 0.138.3 Executive attention accuracy score (%; n = 60) 65.6 (14.5) 38.2 to 98.7 Social‑ emotional health Surgency/extraversion score (n = 325) 4.49 (0.763) 2 to 6.7 Negative affectivity score (n = 324) 3.75 (0.919) 1.4 to 6.5 Eor ff tful control score (n = 327) 5.00 (0.761) 2.2 to 6.8 Externalizing behavior problem score (n = 359) 8.51 (7.84) 0 to 44 Internalizing behavior problem score (n = 371) 5.11 (5.82) 0 to 52 MVPA: moderate- to vigorous-intensity physical activity Compositional linear regression models MVPA and sex for negative affectivity, but when stratified In the compositional data analysis approach, we used by sex, the association did not remain significant. Addi - compositional linear regression models to investigate tionally, including the interaction terms did not improve the relations between the 24-h movement behavior model fit and therefore were they not included in the compositions and the nine cognitive (i.e., receptive reported models. vocabulary, visuospatial memory, procedural memory, S t. Laurent et al. Journal of Activity, Sedentary and Sleep Behaviors (2023) 2:7 Page 9 of 13 Table 2 Associations between overall 24‑h movement behavior Discussion composition and cognitive/social‑ emotional health measures In this study, among the nine cognitive and social-emo- tional health measures that we examined, the daily time df F‑ value p‑ value use composition of movement behaviors was only asso- Cognitive health ciated with receptive vocabulary in our early childhood Receptive vocabulary 3 5.242 0.002 sample. A daily profile that is similar to current WHO Visuospatial memory 3 0.3573 0.784 recommendations for physical activity and sleep in pre- Procedural memory 3 0.131 0.941 school aged children [5] corresponded to the top five per - Executive attention 3 0.465 0.708 cent of receptive vocabulary scores (i.e., 12.1  h of sleep, Social‑ emotional health 4.7  h of sedentary time, 5.6  h of light physical activity, Surgency 3 1.252 0.291 and 1.7 h of MVPA). These findings align with the incon - Negative affectivity 3 0.025 0.995 clusiveness of the current evidence regarding the rela- Eor ff tful control 3 0.490 0.690 tions between daily movement behaviors and similar Externalizing behavior 3 0.195 0.900 components of brain health in early childhood. Internalizing behavior 3 0.035 0.991 The sole significant association in the current study All models included the four 24-h movement behaviors expressed as three for receptive vocabulary was particularly interesting as isometric log-ratio coordinates and were adjusted for age and sex. *The model PPVT scores may be a proxy for IQ, which is reflective of for visuospatial memory was also adjusted for grid size and timing brain development in the early years [26, 75]. Although early childhood vocabulary ability has been minimally explored in studies using compositional data analysis, one study had complementary findings for this cogni - Table 3 Optimal 24‑h movement behavior composition for tive domain [48]. Kuzik et al. [48] reported an association vocabulary score between the 24-h behavior composition of their pre- Compositional mean, hours/day [min; max] school sample and expressive vocabulary. While account- ing for the other behaviors, sedentary time (referred to as Sleep time 12.1 [10.2; 15.5] stationary time in their report) was positively associated Sedentary time 4.7 [2.3; 9.1] with vocabulary. Reallocating time from sleep to sed- Light physical activity 5.6 [3.8; 7.0] entary time was associated with estimated increases in MVPA 1.7 [0.3; 4.8] vocabulary. As they measured expressive vocabulary with a different tool (i.e., the Early Years Toolbox), it is nota - ble that between the two studies movement behaviors were related to both expressive and receptive vocabulary. and executive attention) and social-emotional health While early findings show agreement, this domain should outcomes (i.e., three subscales of temperament, and be further explored in young children to see if results are internalizing and externalizing behaviors). Type III generalizable across populations and assessment meth- analyses of variance F-test were used to determine if ods. While speculative, it is possible that children who the 24-h movement behavior composition was asso- are more active may have more opportunities to interact ciated with each of the outcomes (Table  2). The with others and thus practice and develop their vocabu- 24-h time use composition of our preschool partici- lary skills. Furthermore, physical activity may contribute pants was only associated with receptive vocabulary to better sleep (e.g., duration and quality) which in turn (F = 5.242, p = 0.002). is associated with vocabulary development [76, 77]. Thus, exploring temporality of these behaviors and vocabu- lary outcomes, as well as exploring contexts and specific Optimal time‑use for receptive vocabulary activities within the movement behaviors, would provide Given the significant association between the move- greater insight into this association. ment behavior composition and receptive vocabu- Movement behavior compositions were not associated lary, we further explored the ‘optimal’ 24-h time-use with either of our memory outcomes or executive atten- for that specific cognitive health measure. The 24-h tion, impeding our ability to determine optimal com- composition associated with the 5% best vocabulary positions for these measures. In contrast to our finding, scores (i.e., raw scores of 122 to 141) consisted of Kuzik et al. [48] reported that memory performance was 12.1  h of sleep, 4.7  h of sedentary time, 5.6  h of light associated with movement behavior compositions with a physical activity and 1.7 h of MVPA (Table 3). The top similar visuospatial task, albeit to assess working mem- 5% represented 23% of our sample (n = 77 out of 328 ory rather than declarative memory as in the current children). St. Laurent et al. Journal of Activity, Sedentary and Sleep Behaviors (2023) 2:7 Page 10 of 13 study. To our knowledge no other study has explored to the high-income countries sampled for Kuzik et  al. declarative memory outcomes in early childhood sam- and the present study (Canada and the United States), ples. Interestingly, compositional data analyses of move- and environmental differences may also contribute to ment behaviors and memory in older children have also the associations between movement behaviors and the been scarce and limited to working memory measures outcomes. [78]. The lack of associations is somewhat surprising as Also similar to memory, early childhood studies reports of physical activity and, more consistently, sleep exploring behavior compositions with social-emotional have been correlated to memory performance when measures are limited and like the current findings, gener - explored independently [79, 80]. An important consid- ally null. For example, Kuzik et  al. [48] explored numer- eration to the current approach measure of sleep time ous subscale scores of the Child Self-Regulation and in the compositional models did not exclude wake bouts Behaviour Questionnaire (i.e., behavioral self-regulation, while in bed. Indeed, actual sleep time, rather than time cognitive self-regulation, emotional self-regulation, in bed (or in the present study total time in rest inter- externalizing, internalizing, sociability, and prosocial vals), may be more influential on memory [80]. Given behavior). Although some time reallocations were sig- that healthy sleepers of this age have generally low time nificantly associated with estimated changes in some out - spent in wake after sleep onset [81], this is not likely a come measures, movement behavior compositions were substantial concern. However, in addition to investigating not significant with any of these scores. In the current memory domains outside of working memory in future study we explored social-emotional variables as separate work, exploring subcomponents of sleep (e.g., sleep onset outcomes. However, one consideration for future work is latency, sleep duration, and wake after sleep onset) may that the relation between sleep and emotional regulation be warranted. may be moderated by temperament [82, 83]. Addition- While evidence regarding associations of movement ally, it is important to further breakdown components of behavior compositions and executive functions is also wake behaviors to consider contexts and modalities that limited, one study in older children suggested favorable may be relevant to social-emotional development. associations with some indicators of executive function Although movement behavior composition studies are [78], whereas findings in younger children have been also somewhat limited in older children, there appears mixed. In a study in Brazilian preschoolers, the move- to be some emerging support of an association between ment behavior composition was associated with inhibi- behaviors and social-emotional outcomes for preado- tory control [47]. When time was reallocated from sleep lescents. For example, in one cross-sectional analysis, or light physical activity to MVPA, this corresponded the time-use composition of 10- to 12-year-old Austral- with estimated improvements in inhibitory control. ian children was associated with internalizing behaviors However, comparable to our findings, Kuzik et  al. [48] and total difficulties scores [84]. Specifically, in relation reported no association between the movement behavior to other behaviors, sleep was negatively associated with composition and response inhibition. Interestingly, these internalizing problems and total difficulty scores, sed - two comparison studies both used the Go/No-Go task entary time was positively associated with internalizing from the Early Years Toolbox, but had conflicting results. problems, and light physical activity was positively asso- Differences in sample movement behavior composi - ciated with internalizing problems and total difficulties tions could possibly contribute to this discrepancy. The scores. Another study in 9- to 13-year-old British chil- time-use composition in the current study is similar to dren noted that the sample’s movement behavior com- the behavior profile in Kuzik et  al. [48] (i.e., 6.05  h sed - position was associated with internalizing problems and entary time, 5.09  h of light physical activity, 1.75  h of prosocial behavior, but only in primary school students. MVPA, and 11.2 h of sleep), whereas Bezzera’s composi- Specifically, sedentary time was positively associated with tion had greater levels of sedentary time and lower levels internalizing problems and negatively associated proso- of MVPA (7.6  h of sedentary time, 4.2  h of light physi- cial behavior. In the current analysis, we may not have a cal activity, 0.84 h of MVPA, 11.4 h of sleep). Given that generalizable range of child behavior scores (e.g., chil- socioeconomic status is often inversely associated with dren generally had low behavioral problem scores), which both physical activity and sleep of children, differences could in turn influence our findings. This could be related in compositions could be related to social-demographic to both the inclusion criteria of the parent study (i.e., no differences of the sample given that Bezzara et  al. [47] diagnosed sleep disorders or developmental disabilities) studied children from families that reported lower socio- and the possibility of participation bias (e.g., families with economic status. Additionally, the sample in the Bezerra children presenting more behavior challenges may be less et al. study lived in a middle-income country, as opposed likely to enroll in the study). S t. Laurent et al. Journal of Activity, Sedentary and Sleep Behaviors (2023) 2:7 Page 11 of 13 While the current study examined a range of cogni- Supplementary Information tive and social-emotional measures, some considerations The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s44167‑ 023‑ 00016‑6. should be taken into account. In addition to the potential limitations noted for behavioral measures, current find - Additional file 1. STROBE statement cross‑sectional study checklist. ings may not be generalizable to other early childhood Additional file 2. Detailed methods for the cognitive and social‑ emo‑ populations given the general healthy characteristics of tional health assessments our sample. As with other accelerometry devices, there Additional file 3. Pairwise correlations between absolute movement is room for misclassification of behaviors given differ - behaviors and cognitive/social‑ emotional health outcomes. ent data configuration and processing protocols, and Additional file 4. Associations between absolute movement behaviors that the waist is generally preferable for physical activ- and cognitive/social‑ emotional health outcomes with linear regression models. ity metrics [85], whereas the wrist is recommended for sleep [86]. However, wearing one device generally leads Acknowledgements to better compliance [62] and the time spent in different The authors would like to thank Chloe Andre, Robert Batsevich, Elizabeth behaviors of the present study are comparable to those in Monger, and Leah Hannon for their assistance with data processing and Kuzik et al.’s [48] sample of Canadian preschool children. preparation. Additionally, it may be that our behavioral composition Author contributions components were too ‘broad’ for these cognitive and Conceptualization: CWS and RMCS; Methodology and investigation: CWS, CR, social-emotional outcomes, and future researchers could JFH, ACC, LBFK, PCD, and RMCS; Data curation: JFH, ACC, LBFK, PCD, RMCS; Statistical analysis: CR; Writing—original draft: CWS; Writing—review and edit‑ look at more nuanced measures (i.e., subcomponents of ing: CR, JFH, ACC, LBFK, PCD, and RMCS; Supervision: RCMS. All authors read behaviors). For example, reading and traditional learn- and approved the final manuscript. ing activities that my help with cognitive performance are Funding most likely to consist of sedentary behaviors, and some This study was supported by NIH R01 HL111695 (PI: Spencer). CWS was sup‑ types of physical activity may be more beneficial than ported by NIH F32 HD105384. others (e.g., cognitively engaging or incorporating execu- Availability of data and materials tive functions skills) [87]. Dose, modality, intensity, and The datasets used and/or analyzed during the current study are available from timing could all play a role here, but our data could not the corresponding author on reasonable request. tease apart those potential moderators. Finally, we were unable to compare ‘best’ compositions across outcomes Declarations as some measures were only collected on a subgroup of Ethics approval and consent to participate participants. All procedures were approved by the University of Massachusetts Intuitional Review Board. Parental consent and permission and child verbal assent were obtained prior to participation. Conclusions Consent for publication In this sample of preschool aged children, 24-h move- Not applicable. ment behavior compositions of sedentary time, light Competing interests physical activity, MVPA, and sleep were generally not CLR serves as an editorial board member for the Journal of Activity, Sedentary associated with cognitive and social-emotional health and Sleep Behaviors. The remaining authors declare that they have no com‑ outcomes. However, consistent with other studies, the peting interests. time-use of these behaviors does appear to be related to Author details vocabulary knowledge. While the present findings are Department of Psychological and Brain Sciences, University of Massachusetts generally in alignment with other early childhood reports Amherst, 135 Hicks Way, Tobin Hall, Amherst, MA 01003, USA. Depar tment of Public Health and Nursing, Norwegian University of Science and Technol‑ that utilized compositional data analysis with the same ogy, Trondheim, Norway. Department of Physical Education and Sport four movement behaviors and mental health related out- Sciences, University of Limerick, Limerick, Ireland. Faculty of Physical Culture, comes, future work should consider activities within the Palacký University Olomouc, Olomouc, Czech Republic. Department of Psy‑ chology, Assumption College, Worcester, MA, USA. Department of Psychol‑ broad behaviors—as they may be more meaningful to ogy, Merrimack College, North Andover, MA, USA. Sensing, Perception, such outcomes—in more health diverse samples. and Applied Robotics Division, Charles River Analytics, Cambridge, MA, USA. Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, MA, USA. Abbreviations CBCL Child Behavioral Checklist for Ages 1.5–5 Received: 6 December 2022 Accepted: 27 January 2023 CBQ Questionnaire Very Short Form MVPA Moderate to vigorous intensity physical activity St. Laurent et al. 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Journal

Journal of Activity Sedentary and Sleep BehaviorsSpringer Journals

Published: Apr 3, 2023

Keywords: Physical activity; Sedentary behavior; Sleep; Cognition; Social-emotional; Children

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