Abstract
Background/Objectives
This study examined the relationships between 24-h movement behaviors and physical fitness (PF) in preschool children.
Methods
The study was conducted on 474 children aged 3–6 years in Zhuhai. Physical activity (PA) and sedentary behavior (SB) were collected by the accelerometer, and sleep time was assessed through the parent-report questionnaire. Balance, cardiorespiratory fitness (CRF), flexibility, muscle strength, muscular endurance, and speed-agility were measured using a balance beam test, 20 m shuttle run test, sit and reach test, handgrip test, sit-ups, and 4 × 10 m shuttle run test respectively. The compositional data analysis was used to examine the association between 24-h movement behaviors and PF, and the compositional isotemporal substitution analysis was used for the time reallocation.
Results
The daily composition, adjusted for age, gender, and body mass index (BMI), was significantly associated with CRF (p < 0.001, r2 = 0.20), flexibility (p < 0.001, r2 = 0.07), muscular strength (p < 0.001, r2 = 0.37), muscular endurance (p < 0.001, r2 = 0.26), and speed-agility (p < 0.001, r2 = 0.26). The addition of moderate-to-vigorous PA (MVPA) at the expense of SB and sleep, MVPA at the cost of sleep, was associated with significant muscular strength and speed-agility improvements respectively. The impact of SB and sleep replacing MVPA is stronger than MVPA replacing SB and sleep on muscular strength.
Conclusion
These findings offer useful insight for the replacement of movement behaviors within the recommended range to facilitate PF development in early childhood.
Keywords: Composition analysis, 24-H movement behaviors, Physical fitness, Preschoolers
1. Introduction
Physical fitness (PF) refers to the general ability to perform physical activity (PA), including aerobic fitness, muscular strength, endurance, flexibility, speed, and balance.1 PF is one of the most powerful markers for early childhood health and daily life activities.2 Globally, preschool children's PF is declining at an alarming rate.3,4 From 2000 to 2020, the levels of sit and reach, standing long jump, and continuous jumping on both feet among Chinese preschool children have decreased,5 which is particularly disturbing given the short-term and long-term comorbidities associated with early childhood poor PF.6,7
High levels of physical activity, less sedentary behavior (SB), and adequate sleep are important for PF improvement in children.8, 9, 10 Moderate-to-vigorous PA (MVPA) seems to be a reliable predictor of children's PF.8 Physically active children generally display better PF than those who are inactive.11 The time spent on SB is negatively associated with cardiorespiratory fitness (CRF), muscular strength and endurance, and flexibility in children.9 Studies have shown that children with poor sleep quality and shorter sleep duration are more likely to show lower muscular endurance, flexibility, and CRF.10 This may be because sleep deprivation may lead to energy imbalance through altered hormonal regulation, reducing PF levels.12
From a movement perspective, PA, SB, and sleep represent a continuous range of movement distributed throughout 24 h; these three behaviors have been referred to collectively as movement behaviors.13 PA, SB, and sleep make up 1440 min of the day. All incumbent movement behaviors coexist as a whole or as components, so the change in time spent on one behavior entails changes in the time spent on other behaviors.14 The use of traditional linear regression analysis leads to pseudo-correlation and multi-collinearity between component data due to the limitations of non-negativity and summability of the composition data. Recently, studies have adopted compositional data analysis to investigate the association between movement behaviors and health outcomes, eliminating covariance between the original component data.15
The relationship between the aggregation of movement behaviors and PF among younger children is unclear due to the scarce findings and varying statistical approaches. A systematic review indicated that replacing SB with vigorous PA (VPA) was favorably associated with CRF, endurance, and speed among children aged from 3 to 5 years.16 Lemos et al., 2021; used compositional and isotemporal reallocation analysis to explore the relationship between movement behaviors and PF, suggesting that the addition of MVPA at the expense of any other behavior was associated with improvements in CRF and the reallocations between sleep and LPA was related to speed-agility and lower-body muscular strength in preschoolers.17 However, the study by Lemos et al., 2021; only investigated three tests of PF instead of all components. The relationship between all components of PF and the composition of movement behaviors needs further exploration since the separate examination of the different subcomponents of PF may add to our understanding of PF and its development.
Therefore, the present study aimed to 1) explore the relationship between the 24-h movement behaviors and PF among Chinese preschoolers, and 2) investigate the dose-response relationship between movement behaviors, reallocation time, and PF.
2. Methods
2.1. Participants
Kindergartens provide preschool education for children aged 3–6 years in mainland China. From July 2021 to September 2021, 45 registered kindergartens of the Zhuhai Early Childhood Education Association were invited for measurements. Four kindergartens agreed to participate in this study. The inclusion criteria were: (1) aged 3–6 years old; (2) without any disease or disability that prevents children from performing daily PA; and (3) would not transfer or drop out of preschool while participating in the assessment. A total of 910 preschoolers were invited to participate in this study, 730 parents signed the informed consent, and 725 preschoolers met the inclusion criteria. Finally, 474 eligible preschool children provided validated accelerometer data and sleep time information, and completed PF tests (Fig. 1).
Fig. 1.
Flowchart of participants.
2.2. Measures
2.2.1. Anthropometric measurements
Height and weight were measured with a stadiometer (Seca) and weight scale (Wujin, RGT-120), while the participants were barefoot and lightly clothed. Body mass index (BMI) was calculated by dividing body weight by the square of height (kg/m2).
2.2.2. Movement behaviors
PA was objectively monitored on a tri-axial accelerometry (Actigraph, model GT3-BT, Florida), which is valid to measure PA levels in preschool children. The kindergarten teachers and the parents received written and video instructions for the correct use of the accelerometer. In addition, parents were asked to register an activity diary for both wear and non-wear time. The accelerometer wear was checked by teachers on each school day. The device initialization and data analysis were performed using the ActiLife software (version 6.13).
The accelerometer was attached to the participant's right hip for 24 h daily for seven consecutive days during all activities, except for sleeping, water-related activities, or in cases when the accelerometer could be damaged, such as self-defensive or contact sports. A recording epoch of 15 s was used and valid wear time was considered to be at least 10 h of wear time over at least three days (two weekdays and one weekend day). Non-wear time was defined by 20 consecutive minutes of zero count/minute using ActiLife standard approaches.18 The accelerometer's activity counts were categorized into different intensities (i.e., SB, light, moderate, and vigorous PA) by using the cut-off points according to Butte et al. (2014)19: sedentary: <819 counts per minute (CPM); light: 820–3907 CPM; moderate: 3908–6111 CPM; vigorous: ≥6112 CPM.
Parents reported on their child's daily sleep time. They were asked to recall the children's average sleep hours as follows: “On weekdays, how many hours does your child usually sleep at night?” and “On weekends, how many hours does your child usually sleep at night?“. The total sleep time was calculated as follows: ((weekdays sleep*5) + (weekends sleep*2))/7. This method has been validated by sleep logs and objective actigraphy in preschoolers.20,21
2.2.3. Physical fitness
Dynamic balance was measured using a balance beam. The tester gave orders in front of the participant, started the stopwatch as the participant began and followed the participant to the finish line. As soon as either tiptoe of the participant crossed the finish line, the tester stopped the stopwatch. The test was repeated twice and the best result was recorded. The balance beam test has displayed adequate reliability and validity in younger children.22
CRF was measured using the 20 m shuttle run test. During the test, participants ran back and forth at an initial speed of 6.5 km/h on two tracks 20 m apart, and then in increments of 0.5 km/h per minute. During the test, participants ran back and forth according to the timing of a beep from the compact disc (CD) recorder. The test was conducted once. The 20 m shuttle run is considered more fun than running around a track, and it accurately reflects the cardiorespiratory fitness of younger children. Because we were working with younger children, the track was marked and a research assistant helped lead the children so they could master the pace of running. The 20 m shuttle run has good reliability (r = 0.73 to 0.93) and validity in children aged 3–5 years.23
Flexibility was assessed by sit and reach test. The participant sat on a mat with bare feet, feet straight, heels together, toes naturally apart, and palms resting on the test board. Then the participant was asked to slowly bend their body forward and push the edge of the moving board as far as possible with their arms without bending their knees. The test was repeated twice, and the maximum value was recorded. This measurement has been utilized in preschool children with good test-retest reliability (r = 0.75 to 0.93).4
Muscle strength was tested with a WCS-100 electronic dynamometer (Shanghai Wanqing Electronics Co., LTD.). Participants stood in a quiet environment, relaxed, with their arms hanging down naturally. They held the dynamometer, adjusted the grip distance, grasped the dynamometer once with maximum force, and the value was recorded. The left and right hands were measured twice each, and the maximum value was recorded. To allow for proper rest, the interval between the two tests was more than 15 s. This measurement has been utilized in preschool children with good reliability (r = 0.90 to 0.92) and validity.24
Sit-ups were used to assess the muscular endurance of the trunk. Participants lay on their backs with their knees bent and arms crossed over the other shoulder. They sat up and returned to the starting position. The research assistant held the child's feet. The number of lifts within 30 s that reached the sitting position correctly was recorded. Good reliability (r = 0.68 to 0.94) has been obtained from the tests in preschoolers.25
Speed-agility was assessed by the 4 × 10 m shuttle run test. When participants heard the start signal, they ran up and turned the 10-m track as fast as they could for a distance of 40 m. At the end of each section of the track, participants must touch the hand of one of the testers who had crossed the limit with their foot and returned as fast as they could. The best of the two tests was recorded. This test has been utilized in preschoolers with good reliability and validity.3
2.3. Procedure
Signed consent forms were obtained from parents and kindergartens before the study. A 1-h workshop was provided for the teachers of kindergartens about the study's purpose, protocol, and procedures. Another 3-h training session was conducted for the physical education teachers and research assistants to give detailed explanations and practices on the use of instruments and measurement. The assessments were conducted over two months (October 2021 and November 2021). Anthropometric data were assessed in the kindergarten activity rooms, and then PF tests including balance beam test, 20 m shuttle run, sit and reach, handgrip test, sit-ups, and 4 × 10 m shuttle run were assessed in turn. Children were given a 2-min break between each test to minimize fatigue after the test. Accelerometers were placed on children who wore the monitors for seven consecutive days.
2.4. Data analysis
Component data analyses were carried out in R (https://www.r-project.org/). Standard and component descriptive statistics were calculated for comparison. The compositional mean was computed by calculating the geometric mean for each behavior (sleep time, SB, LPA, and MVPA) and then normalizing the data to the same constant as the raw data, i.e. 1. This metric is consistent with the relative and symmetric scale of the data.26 A variance matrix was used to describe the dispersion of the daily composition.15 Since the variance of individual composition data does not capture the interdependence of the movement behaviors, the pairwise log-ratio variances were calculated to describe dispersion trends: when the value is close to zero, it indicates that the time spent in the two respective movement behaviors is highly proportional, while when the value is close to one, it indicates the opposite.
Multiple linear regression models were used to examine the relationship between the time-use component (explanatory variable) and PF (dependent variable). The composition was represented as a set of three isometric log-ratio (ilr) coordinates before inclusion in the regression model. Covariates (age, gender, and BMI) were included as explanatory variables. The outcome variables were balance (balance beam test), CRF (20 m shuttle run), flexibility (sit and reach), muscular strength (handgrip test), muscular endurance (sit-ups), and speed-agility (4 × 10 m shuttle run) respectively. The linearity, normality, homoscedasticity and outliers of the ilr multiple linear regression models were further examined.
The isotemporal reallocation analyses were conducted to predict differences in the outcome variables associated with the reallocation of fixed time duration between two movement behaviors, while the third and fourth remained unchanged. This was achieved by creating a new series of activity compositions to simulate a 5-min reallocation between all pairs of activity behaviors, using the average composite of samples as the baseline or starting composite. The new compositions were represented as sets of ilr coordinates, and each composition was subtracted from the mean ilr coordinates to obtain the ilr differences. These ilr differences were used to determine the estimated difference (95% CI) for all outcomes. Predictions were made for the 5-, 10- and 15-min pairwise reallocations respectively. The decision was made to limit the duration of reallocations to a maximum of 15 min to accurately reflect the actual change in MVPA.17
This model was repeated for the activity that had a significant effect on PF for 15-min pairwise reallocations. Dose-response of mutual reallocation of movement behaviors and PF were explored in increments of 5 min and continued for longer periods of up to 60 min to facilitate comparison with previous studies.14,27
3. Results
3.1. Descriptive statistics
The mean age of 474 children was 4.4 years and 265 were boys. The compositional means of the participants showed that 49.6% of the 24 h spent in sleep, 40.3% in SB, 4.8% in MVPA, and 5.3% in LPA (see details in Table 1).
Table 1.
Time-use, participant characteristics data, and physical fitness parameters.
| Variables | Mean (SD)/N (%)/Mean [%] | |
|---|---|---|
| Participant characteristics | ||
| Age (year) | 4.4 (0.9) | – |
| Height (cm) | 107.9 (7.5) | – |
| Weight (kg) | 18.2 (3.4) | – |
| BMI (kg/m2) | 15.5 (1.5) | – |
| Gender | ||
| Male | 265 (55.9%) | – |
| Female | 209 (44.1%) | – |
| Time-use | ||
| SB | 578.4 [40.1%] | 580.3 [40.3%] |
| LPA | 76.6 [5.3%] | 76.2 [5.3%] |
| MVPA | 71.3 [5.0%] | 68.7 [4.8%] |
| Sleep | 713.6 [49.6%] | 714.8 [49.6%] |
| Physical fitness parameters | ||
| Balance (s) | 9.6 (4.8) | – |
| CRF (laps) | 12.4 (11.0) | – |
| Flexibility (cm) | 9.9 (4.9) | – |
| Muscular strength (kg) | 5.2 (2.5) | – |
| Muscular endurance (number) | 8.2 (3.2) | – |
| Speed-agility (s) | 20.5 (6.4) | – |
Note: Time-use data are presented as linearly adjusted mean, compositional mean, and [%time per day]. Compositional mean cannot include the SD.
SB: sedentary behavior; LPA: light physical activity; MVPA: moderate-to-vigorous physical activity; CRF: cardiorespiratory fitness.
The variability of the data is summarized in the variation matrix (Table 2). The variance of log in SB and LPA was closest to 0 (In SB/LPA = 0.066), indicating that these two activities were the most highly correlated. The variance of log in MVPA and sleep was largest (In MVPA/sleep = 0.163), indicating that the association between MVPA and sleep was low.
Table 2.
Variation matrix.
| SB | LPA | MVPA | Sleep | |
|---|---|---|---|---|
| SB | 0.000 | 0.066 | 0.154 | 0.104 |
| LPA | 0.066 | 0.000 | 0.079 | 0.097 |
| MVPA | 0.154 | 0.079 | 0.000 | 0.163 |
| Sleep | 0.104 | 0.097 | 0.163 | 0.000 |
Note: A value approaching “0” indicates high proportionality between pairs of behaviors, whilst a value approaching “1” indicates the opposite.
SB: sedentary behavior; LPA: light physical activity; MVPA: moderate-to-vigorous physical activity.
3.2. Compositional analysis
The multiple linear regression analysis between time-use components and PF is shown in Table 3. The isometric log-ratio co-ordinates, adjusted for age, gender, and BMI, were significantly predictors of CRF, flexibility, muscular strength, muscular endurance, and speed-agility. The results showed that MVPA was positively associated with muscular strength and negatively associated with speed-agility. SB was negatively associated with flexibility and muscular strength.
Table 3.
Associations between movement behaviors and physical fitness.
| ilr Regression models | Model fit |
||||
|---|---|---|---|---|---|
| B1 | SE | p value | R2 | p value | |
| Balance | |||||
| ilr MVPA/(LPA*SB*Sleep) | −0.90 | 1.00 | 0.366 | 0.000 | 0.434 |
| ilr LPA/(MVPA*SB*Sleep) | 1.24 | 1.52 | 0.415 | ||
| ilr SB/(MVPA*LPA*Sleep) | −0.79 | 1.09 | 0.472 | ||
| ilr Sleep/(MVPA*LPA*SB) | 0.44 | 0.89 | 0.614 | ||
| CRF | |||||
| ilr MVPA/(LPA*SB*Sleep) | −0.92 | 2.05 | 0.655 | 0.203 | <0.001 |
| ilr LPA/(MVPA*SB*Sleep) | 4.00 | 3.12 | 0.201 | ||
| ilr SB/(MVPA*LPA*Sleep) | −3.48 | 2.24 | 0.121 | ||
| ilr Sleep/(MVPA*LPA*SB) | 0.40 | 1.82 | 0.826 | ||
| Flexibility | |||||
| ilr MVPA/(LPA*SB*Sleep) | 1.08 | 0.98 | 0.274 | 0.067 | <0.001 |
| ilr LPA/(MVPA*SB*Sleep) | −0.08 | 1.50 | 0.955 | ||
| ilr SB/(MVPA*LPA*Sleep) | −2.24 | 1.08 | 0.039 | ||
| ilr Sleep/(MVPA*LPA*SB) | 1.24 | 0.87 | 0.156 | ||
| Muscular strength | |||||
| ilr MVPA/(LPA*SB*Sleep) | 1.15 | 0.42 | 0.006 | 0.366 | <0.001 |
| ilr LPA/(MVPA*SB*Sleep) | −0.42 | 0.64 | 0.508 | ||
| ilr SB/(MVPA*LPA*Sleep) | −1.29 | 0.46 | 0.005 | ||
| ilr Sleep/(MVPA*LPA*SB) | 0.56 | 0.37 | 0.135 | ||
| Muscular endurance | |||||
| ilr MVPA/(LPA*SB*Sleep) | 0.49 | 0.57 | 0.390 | 0.263 | <0.001 |
| ilr LPA/(MVPA*SB*Sleep) | −0.18 | 0.86 | 0.834 | ||
| ilr SB/(MVPA*LPA*Sleep) | −0.45 | 0.62 | 0.472 | ||
| ilr Sleep/(MVPA*LPA*SB) | 0.14 | 0.50 | 0.779 | ||
| Speed-agility | |||||
| ilr MVPA/(LPA*SB*Sleep) | −2.31 | 1.15 | 0.044 | 0.257 | <0.001 |
| ilr LPA/(MVPA*SB*Sleep) | 2.45 | 1.75 | 0.161 | ||
| ilr SB/(MVPA*LPA*Sleep) | −1.83 | 1.26 | 0.145 | ||
| ilr Sleep/(MVPA*LPA*SB) | 1.69 | 1.01 | 0.098 | ||
Note: All are adjusted for age, gender, and BMI.
CRF: cardiorespiratory fitness; LPA: light physical activity; MVPA: moderate-to-vigorous physical activity; SB: sedentary behavior. B1:unstandardized regression coefficient of the first ilr coordinate, referring to the association of time spent in one specific behavior relative to time spent in other movement components on physical fitness, e.g., ilr MVPA/(LPA*SB*Sleep denotes the association of MVPA time relative to LPA, SB, and sleep time on physical fitness outcomes; SE = standard error.
3.3. Isotemporal reallocation
Table 4 presents the estimated differences in PF outcomes for reallocations of 15 min between time-use behaviors. Reallocating 15 min from SB to sleep was associated with an increase in flexibility (p < 0.05). For muscular strength, reallocations between sleep and SB showed significant associations (p < 0.05), and the addition of MVPA at the expense of SB and sleep was associated with a significant increase in muscular strength (p < 0.05). Reallocating 15 min from MVPA to sleep was associated with a significant improvement in speed-agility (p < 0.05).
Table 4.
Isotemporal substitutions between movement behaviors.
| Add | Remove | CRF | Flexibility | Muscular strength | Muscular endurance | Speed-agility |
|---|---|---|---|---|---|---|
| 15 min reallocated | ||||||
| Sleep | SB | 0.09 (−0.05, 0.22) | 0.07 (0.01, 0.14)* | 0.04 (0.01, 0.07)* | 0.01 (−0.03, 0.05) | 0.07 (0.00, 0.15) |
| Sleep | LPA | −0.75 (−1.94, 0.43) | 0.04 (−0.53, 0.61) | 0.09 (−0.15, 0.33) | 0.04 (−0.29, 0.36) | −0.44 (−1.10, 0.23) |
| Sleep | MVPA | 0.20 (−0.67, 1.07) | −0.21 (−0.62, 0.21) | −0.24 (−0.41, −0.06)* | −0.10 (−0.34, 0.14) | 0.52 (0.04, 1.01)* |
| SB | Sleep | −0.08 (−0.22, 0.05) | −0.07 (−0.14, −0.01)* | −0.04 (−0.07, −0.01)* | −0.01 (−0.05, 0.03) | −0.07 (−0.15, 0.00) |
| SB | LPA | −0.84 (−2.06, 0.39) | −0.03 (−0.62, 0.56) | 0.05 (−0.20, 0.30) | 0.02 (−0.31, 0.36) | −0.51 (−1.19, 0.18) |
| SB | MVPA | 0.12 (−0.74, 0.97) | −0.28 (−0.69, 0.13) | −0.27 (−0.45, −0.10)* | −0.11 (−0.35, 0.12) | 0.45 (−0.03, 0.93) |
| LPA | Sleep | 0.62 (−0.36, 1.59) | −0.04 (−0.50, 0.43) | −0.08 (−0.28, 0.12) | −0.03 (−0.30, 0.24) | 0.35 (−0.19, 0.90) |
| LPA | SB | 0.70 (−0.31, 1.72) | 0.04 (−0.45, 0.53) | −0.04 (−0.24, 0.17) | −0.02 (−0.30, 0.26) | 0.42 (−0.15, 0.99) |
| LPA | MVPA | 0.82 (−0.84, 2.48) | −0.24 (−1.04, 0.55) | −0.31 (−0.65, 0.03) | −0.13 (−0.59, 0.33) | 0.88 (−0.05, 1.80) |
| MVPA | Sleep | −0.16 (−0.86, 0.53) | 0.16 (−0.17, 0.50) | 0.19 (0.04, 0.33)* | 0.08 (−0.11, 0.27) | −0.43 (−0.82, −0.04)* |
| MVPA | SB | −0.08 (−0.76, 0.61) | 0.24 (−0.09, 0.56) | 0.23 (0.09, 0.37)* | 0.09 (−0.10, 0.28) | −0.35 (−0.74, 0.03) |
| MVPA | LPA | −0.92 (−2.62, 0.79) | 0.20 (−0.62, 1.02) | 0.28 (−0.07, 0.63) | 0.12 (−0.35, 0.59) | −0.86 (−1.82, 0.09) |
Note: Data reported as total (95% CI). *p < 0.05, based on 95% CI.
SB: sedentary behavior; LPA: light physical activity; MVPA: moderate-to-vigorous physical activity; CRF: cardiorespiratory fitness.
3.4. Dose-response between 24-h movement behaviors and PF
For the movement behaviors with significant alterations, this study explored the dose-response relationship between reallocation time with PF outcomes in increments of 5 min and the duration was extended to 60 min.
Reallocations of MVPA to SB and sleep were associated with higher muscular strength, whereas reallocations from SB and sleep to MVPA were associated with lower muscular strength. The associations were asymmetric. The benefits of increasing time in MVPA at the cost of SB and sleep were lower than the negative effects of reallocating time away from MVPA to SB and sleep.
When MVPA replaced SB and sleep for 5 min, muscular strength increased by 0.080 cm and 0.067 cm respectively. During the subsequent 10–60 min period, the rise in muscular strength gradually slowed down, with the range of 0.050 cm–0.075 cm for SB, and 0.036 cm–0.062 cm for sleep. When SB and sleep reallocated MVPA for 5 min, muscular strength decreased by 0.085 cm and 0.072 cm respectively. In the following 10–60 min, the rate of decrease in muscular strength gradually accelerated, with the range of 0.091 cm–0.462 cm for SB, and 0.078 cm–0.450 cm for sleep respectively (Fig. 2).
Fig. 2.
Changes in handgrip test after MVPA isotemporal substitution for SB and sleep.
Reallocations of MVPA to sleep were associated with better speed-agility, whereas reallocations from sleep to MVPA were associated with worse speed-agility. The associations were asymmetric. The benefits of increasing time in MVPA at the cost of sleep were lower than the determinants of reallocating time away from MVPA to sleep.
When MVPA replaced sleep for 5 min, speed-agility time decreased by 0.151 s. During the subsequent 10–60 min period, the reduction in speed-agility time gradually slowed down, with the range of 0.090 s–0.142 s for sleep. When sleep reallocated MVPA for 5 min, speed-agility time increased by 0.161 s. In the following 10–60 min, the rate of increase in speed-agility time gradually accelerated, with the range of 0.174 s–0.919 s for sleep. Five minutes was the turning point of the change in speed-agility time for the reallocation between MVPA and sleep (Fig. 3).
Fig. 3.
Changes in 4*10 m shuttle run after MVPA isotemporal substitution for sleep.
The reallocation from SB to sleep revealed a symmetric reduction in flexibility and muscular strength. For every 5-min increase in SB reallocation to sleep over 60 min, flexibility decreased by 0.024 cm, whereas flexibility increased by 0.024 cm when the time was taken away from SB to sleep. For every 5-min increase in SB reallocation to sleep over 60 min, muscular strength decreased by 0.012 kg, whereas muscular strength increased by 0.012 kg when the time was taken away from SB to sleep (Fig. 4).
Fig. 4.
Changes in sit and reach test and handgrip after SB isotemporal substitution for sleep.
4. Discussion
This study investigated the association between 24-h movement behaviors and PF among Chinese preschoolers and discerned the effect of isotemporal substitution of movement behaviors. The main findings showed that when considering the behaviors as a 24-h movement composition, it could significantly explain 20.3%, 6.7%, 36.6%, 26.3%, and 25.7% of the variation in CRF, flexibility, muscular strength, muscular endurance, and speed-agility respectively. The reallocation of the individual components was related to changes in flexibility, muscular strength, and speed-agility. Specifically, the dose-response analysis showed that the substitution between MVPA, SB, and sleep had asymmetric effects on muscular strength. The effect of substitution between SB and sleep on flexibility and muscular strength was symmetric.
The results of this study indicated that increasing MVPA at the expense of SB yielded positive outcomes for muscular strength. MVPA typically includes movements such as crawling, running, and jumping, which require high levels of muscular strength. By performing these activities frequently, preschool children's muscular strength levels can be practiced and improved.28 In addition, MVPA promotes bone growth and development in young children, which in turn enhances the overall strength of the skeletal-muscular system.29 The prevalence of prolonged SB is common among preschool children, resulting in a slowed metabolism, energy accumulation, and insulin resistance.30 A longitudinal study exploring the relationship between PA and PF found that MVPA at 6.6 years was positively associated with muscular strength at follow-up.31 The existing PA guidelines for preschool children advocate increasing PA levels and reducing SB to provide an opportunity to shape healthy habits through childhood, adolescence, and adulthood.32 This finding highlights that the importance of promoting MVPA and reducing SB in preschool children's daily lives may have beneficial long-term effects on muscular strength.
Studies using compositional and isotemporal substitution analysis have highlighted that encouraging MVPA and decreasing SB can lead to better PF performance.13,33 The importance of sleep for PF was not emphasized. This study found that reallocating 15 min from SB to sleep was associated with an increase in flexibility and muscular strength, which is consistent with previous studies. The risk of low PF in children with sleep abnormalities is 1.1 times higher than in children with normal sleep.34 Barrios et al.found that preschool children with persistent sleep deprivation showed more attention deficits, hyperactivity disorder, and lower levels of PF compared to normal children.35 However, the mechanisms of the association between sleep and PF in children are still unclear.36
Furthermore, the benefits to muscular strength for reallocation of time from SB and sleep to MVPA were smaller in magnitude than the negative effects of time reallocation from MVPA to SB and sleep, which is consistent with previous studies.37,38 The relative contributions of reallocation time in the total time of the different behaviors partially explain the asymmetry of this association. Specifically, the time extracted from MVPA accounts for only a large proportion of the total MVPA time (68.7 min in this sample), whereas the time taken from sleep and SB represents a small amount (714.8 min and 580.3 min in this sample respectively) of daily sleep and SB time.27,37 Another explanation is the reversibility of PF improvement. Specifically, the physiological adaptation associated with overloading exercise capacity (i.e. participating in more MVPA) occurs slowly, leading to smaller improvements in health. Conversely, the effects of de-training and reversibility (i.e. increased SB) display quickly and may have an adverse influence on health.13 A study has indicated that the secular trend in muscular fitness among children decreases over time.39 Therefore, there is a need to promote a healthy lifestyle, for example, by encouraging preschoolers to participate in PA, and to stop and divert the negative trend of PF.
The health implications of changing from inactive ones to MVPA are significant.40 This study indicated that the reallocation of MVPA and sleep appeared to confer significant positive changes on muscular strength and speed-agility. Generally, MVPA is not advocated as a replacement for sleep due to the important role of sleep in the health and development of preschool children.41 However, Xiong et al., 2022; found an inverted U-shaped relationship between sleep duration and PF levels in preschoolers,34 with both insufficient and excessive sleep time increasing the detection of low PF levels. In addition, this study showed that 5 min was the turning point for dose-response change in MVPA replacement, suggesting that an increase in MVPA of 5 min per day may have the most efficient impact on muscular strength and speed-agility improvement. The preschoolers of this study slept for an average of 713.6 min per day, meeting the WHO recommendation of 10–13 h of sleep time. Thus, replacing 5 min of sleep with MVPA can not only enhance fitness levels but also ensure that preschool children's sleep and MVPA time is within the recommended range.
However, this study showed that there were no significant relationships between either CRF or muscular endurance and movement behaviors in preschoolers, which is inconsistent with previous studies.17,42 During the sit-ups and 20 m shuttle run test, since preschoolers’ neuromuscular systems are less well developed, there is greater variability in their movement patterns, which may contribute to the instability of the assessment and make it more difficult to accurately determine the relationship between movement behaviors and PF.17 In addition, there are large individual differences in CRF and muscular endurance in the early childhood years.3 Therefore, it may not be possible to capture the relationship between CRF, muscular endurance and movement behaviors over a relatively short period. A longitudinal study found that the relationship between fitness and PA strengthened over time.43 Longitudinal studies are needed to explore the changes in the relationship between CRF, muscular endurance, and movement behaviors in the future.
This study indicated that replacing SB with MVPA time was positively associated with preschool children's PF. Kindergartens and parents can contribute to the improvement of PF by appropriately reducing the amount of time spent on SB, such as learning, screen-based behaviors, and musical instrument practice, and replacing them with outdoor activities or organized sports. Notably, the findings of this study showed that reallocation of MVPA and sleep also had positive changes in PF. This finding reminds us that when using MVPA as the main component of interventions to improve preschool children's PF, attention should also be paid to sleep duration. Sleep plays a crucial role in cognitive function, emotional regulation, and physical health during early childhood.44 Therefore, PF interventions should be used while ensuring that sleep duration is within the recommended range.
The main strength of this study is the larger sample size than previous studies.17,43 Another strength is the use of compositional and isotemporal approaches based on objective measurements of PA, SB, and more comprehensive measurements of PF to assess the relationship between movement behaviors and PF in preschoolers. However, this study also has some limitations. Firstly, this study did not register the entire 24-h accelerometry data. It is difficult for younger children to ensure that accelerometers are worn consistently during the night.20 To ameliorate this problem, we used a reliable and valid method for screening sleep in preschoolers.21 However, parent-reported sleep duration may result in the vulnerable recall of results and social desirability bias. Future research can improve the accuracy of sleep duration measurements. Finally, this study is cross-sectional, which limits clarification of the benefits of replacing time in one behavior with another.
5. Conclusion
This study found that the variation in PF was associated with the 24-h movement composition, in which muscular strength was the most affected variable, followed by muscular endurance, speed-agility, CRF, and flexibility. Reallocating SB and sleep for MVPA was significantly positive for muscular strength, and reallocating sleep for MVPA was associated with speed-agility improvement, whereas the reallocation between MVPA and sleep on flexibility and muscular strength was similar. Promoting MVPA and reducing non-active behaviors in daily life may have a long-term beneficial influence on preschoolers’ fitness levels and prevent the decreasing trend of PF.
Ethics statement
This study was approved by the Research Ethics Committee of Hong Kong Baptist University (Ref. No.: NCT05290584).
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author statement
Conceptualization: Huiqi Song, Jingjing Wang; Data curation: Huiqi Song, Lei Shi; Formal analysis: Huiqi Song, Yunfei Liu, Yi Song; Methodology: Huiqi Song, Patrick WC. Lau; Project administration: Lei Shi; Resources: Patrick WC. Lau, Lei Shi; Software: Huiqi Song, Yunfei Liu, Yi Song; Supervision: Patrick WC. Lau, Lei Shi; Visualization: Huiqi Song; Writing - original draft: Huiqi Song, Jingjing Wang, Patrick WC. Lau; Writing - review and editing: Huiqi Song, Patrick WC. Lau.
Acknowledgements
None.
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