JoVE Journal > Behavior
Summary
Abstract
Introduction
Protocol
Results
Discussion
Disclosures
Acknowledgements
Materials
References
행동분석학

Physical Activity Measurement in Children Accepting Table Tennis Training

Published: July 27, 2022
doi:
10.3791/63937
, , , , ,
1Department of Sport,Inner Mongolia Medical University, 2Department of Neurology,The Seventh Medical Center of PLA General Hospital, 3Department of Neurology,NO 984 Hospital of PLA

Summary

This study proposes an accelerometer-based method to objectively measure physical activity (PA) and leisure time physical activity (LTPA) in Chinese children accepting table tennis training in clubs.

Abstract

An increasing body of evidence now shows that the majority of children in China experience lower levels of physical activity (PA) than the recommended guideline. Table tennis is a compound and technically difficult game that is popular in China; undertaking table tennis training in clubs can help children to elevate their levels of PA. Given that children cannot complete self-evaluated questionnaires themselves and caregiver-based observations are not suitable for children, we hypothesized that an actigraphy-based method can be an objective method to measure PA. In the present study, we describe a procedure that can be used to evaluate PA levels using an actigraphic device and software. Furthermore, since hip-worn devices are known to reduce compliance, we attempted to assess the agreement between hip-worn and wrist-worn device data. Collectively, our results indicate that these devices are suitable for measuring PA and leisure time physical activity (LTPA) levels. Together with subjective questionnaires, both hip-worn and wrist-worn devices are highly suitable for evaluating PA in Chinese children undergoing table tennis training in clubs.

Introduction

Physical activity (PA) is very important in childhood and is positively associated with physical and mental health. It is well documented that PA is associated with beneficial effects in school-going children with regards to obesity, bone health, mental wellbeing, cognitive function, and academic achievements1,2,3. However, most children in China still experience lower levels of PA than recommended for their age4; furthermore, sedentary time is known to increase with age. According to the National Physical Fitness and Health Surveillance Study for Students in China, the number of students with obesity has remained significantly high over the first two decades of the 21st century5.

International PA guidelines for children and adolescents recommend at least 60 min of moderate-to-vigorous physical activity (MVPA) per day and vigorous physical activity (VPA) on 3 days/week6 in order to achieve health benefits. Similarly, the latest version of the Physical Activity Guidelines for Chinese (2021)7 highlights that accumulated sedentary behavioral time should not last for more than 60 min, based on international PA guidelines. Participation in sports clubs or school activities is a highly beneficial way by which children can meet PA guidelines8. Table tennis is a compound and technically difficult game that is popular in China. Recent studies have confirmed that regular table tennis training has a positive effect on the health-related physical fitness of children and adolescents9,10. As such, table tennis club/school-based training is a very suitable method for children to increase their levels of PA11.

It is important to consider several issues that might impede the fulfillment of the recommendations made by international PA guidelines. For example, most surveys of PA in children are based on parent-reported questionnaires12; there is a significant lack of data acquired by objective methods in China. Furthermore, the activity patterns of children are characterized by relatively short bouts of spontaneous, but intense PA13,14. This type of pattern is difficult to summarize and report by observation alone; additionally, questionnaires or parental reports are prone to error15. Secondly, children spend a significant amount of leisure time at home, for example, during the evenings and weekends, and tend to accumulate a substantial part of their daily PA in a home-based setting. It is difficult to collect or estimate leisure time physical activity (LTPA) in children outside of school hours. LTPA is essential for health and is one of the most important components of total PA16. Thirdly, the PA of children may be influenced by gender differences and parental life style8. Collectively, this information highlights the need to acquire accurate measurements of PA to evaluate overall health, its social impact, and its use in policy making. If the activity levels of specific subpopulations (e.g., children undergoing table tennis training) are not correctly estimated, it is possible that the data may even misdirect policies and public health priorities12.

As the most widely used objective measurement for PA patterns in youths, accelerometers have been recognized as the gold standard for measuring PA in children17,18,19,20. With technological improvements, actigraphic devices have progressed into cost-effective capacitive sensors. In most cases, these devices need to be attached to the right hip21, an issue that might be a potential risk factor and lowers compliance22. Over recent years, several research studies have indicated that PA data derived from devices worn at other anatomical locations can be comparable when set-up appropriately23,24.

In the present study, we aimed to develop a wrist-worn actigraphy accelerometer-based method to assess PA in children undergoing table tennis training.

Protocol

This study was approved by the Academic Ethics Committee of Inner Mongolia Medical University in Hohhot, China. The parents of all children included in this study provided signed and informed consent. In the study, we used the Actigraph GT3X+ device which is referred to as an accelerometer hereafter.

1. General aspects of method development

  1. Obtain accelerometers to evaluate PA. The accelerometer is a small (3.3 cm x 4.6 cm x 1.5 cm, 19 g), watch-like unobtrusive device that measures acceleration in three axes: vertical, antero-posterior, and medio-lateral.
  2. Connect the device to a laptop PC with USB cable. Use an exclusive software for data recording, processing, and analyzing.
  3. Select participants according to the following inclusion/exclusion criteria.
    1. Include 20 children between 7-12 years of age who accept table tennis training as the Sports group. Include children who attend the table tennis club regularly, with three-to-five weekly training session,s with each training session lasting 2 h. Include children living majorly in a house or rented flat with their parents with a short home-to-club distance.
    2. Select 20 children from the same class as the Sports group as an age and gender matched Control group. Children of the Control group don't attend any sports club.
  4. Exclude participants whose parents do not know their children's PA information at school and home.
  5. Exclude participants who were diagnosed to have any neurodevelopment disorder such as Attention Deficit and Hyperactivity Disorder (ADHD), autism, Developmental Coordination Disorder (DCD), etc.

2. Initialization of data collection using the accelerometer

  1. Download and run the software for the device.
  2. Type in the duration for collecting data by clicking the button Select Start Time and entering the date (e.g., 2022/2/9) and time (e.g., 13:00).
  3. Click the button Enter Subject Info to enter the next step about demographic information setting. Type in the demographic information of the participant, including name, gender, height, weight, date of birth, ethnic, side (right), limb (waist), and dominance (dominant).
    NOTE: For the left-handed participants, in step 2.4 select the opposite side.
  4. Initialize the data collection by clicking Initialize 1 device. Make sure that the battery is charged to more than 80%, otherwise the initialization will fail. Initialize to record raw accelerations at a frequency of 30 Hz.
  5. Instruct the participants to wear the accelerometer on the right hip with an elastic waistband. Ensure that the accelerometer is positioned on the right mid-axilla line at the level of the iliac crest.
  6. Repeat step 2.2. Set the same start date (e.g., 2022/2/9) and time (e.g., 13:00), to ensure that the data from both devices is collected at the same time.
  7. Repeat 2.4 with the following modifications: side (left), limb (wrist), dominance (non-dominant).
    NOTE: For the left-handed participants, in step 2.8 select the opposite side.
  8. Instruct the participants to wear the accelerometer on the wrist of the non-dominant hand on a watch belt.
  9. Remind the participants to wear the devices all day long, except while bathing, swimming, and showering.
    NOTE: The duration of data collection should not be shorter than 7 days. (e.g., from 13:00, 2022/2/9 to 12:59, 2022/2/16).
  10. For the raw data collected, get the data confirmed by a physician, institutional researcher, or professional coach, according to the VM chart and counts (Figure 1).
  11. Delete any extreme data that is unexplained (e.g., from 21:41, 2022/2/12 to 22:07, 2022/2/12, the data was zero, and cannot be explained). Delete such data from the collected raw data.

3. Data collection from diary entries

  1. Ask the participants to wear the device all day long. Ask the trainers to maintain a diary of table tennis training, including the exact time schedule. For the children of the Control group, no diary of training is needed.
  2. Make sure that the participants performed their daily routines during data collection.
  3. Ask the parents to maintain a diary of leisure time at home. Instruct the parents to collect the data of sleep, the time to bed, and the time of waking up in the diary.

4. Accelerometer data output

  1. Take off the device from the right hip and connect it to a laptop/PC with a USB cable. Run the software of the device.
  2. Download the accelerometer data of the participant, by clicking Download. Analyze raw accelerometer data in 60 s epochs.
  3. Take off the device from the non-dominant hand and connect it to a laptop/PC with a USB cable. Repeat step 4.2.
  4. Raw acceleration outcome variables for the accelerometer are based on vector magnitude (VM) counts. Confirm the accelerometer data of LTPA according to the diary of training, leisure time, and sleep.

5. Scoring the data

  1. Open the scoring page of the software (Figure 2).
  2. Select Algorithms > Cut Points and MVPA > Puyau Children (2002) on the left of the page.
    NOTE: Other algorithms for the cut points of PA can be selected if necessary.
  3. Click Calculate and then Export, and the scoring output will be displayed automatically, including SBs (sedentary behaviors), LPAs (light physical activities), MPAs (moderate physical activities), and MVPAs (moderate-to-vigorous physical activities).
  4. Obtain everyday-LTPA by adding diary timing and defining leisure time (e.g., the leisure time of 2022/2/9 is from 19:00, 2022/2/9 to 21:00 2022/2/6, according to the diary). Then, define the mean VM counts during this time as 715.75, and the LTPA for this epoch as 715.75.
  5. Average all the everyday-LTPAs, to get the LTPA for the participant.

6. Statistical analysis

  1. Use Student's t-test to measure group differences with a P value less than 0.05 considered statistically significant. Use a commercially available statistical software package to conduct all statistics.
  2. Use the Bland-Altman procedures to assess agreement for each PA, including MPA, VPA, and MVPA, between hip-worn and wrist-worn devices based on raw data and counts. Calculate the mean difference between the two methods of measurement and 95% limit of agreement for the mean difference calculated.

Representative Results

Demographic data are shown in Table 1, including gender, age, height, weight, ethnicity, and dominant hand. As shown in Table 1, there were no significant differences between the groups with regards to gender, age, height, weight, and dominant hand. Furthermore, participants from the Sports group did not exhibit any significantly different parameters in terms of sedentary behaviors (SB; 441.05 ± 31.80 vs 442.25 ± 30.74, P = 0.904), LPA (213.10 ± 15.00 vs 215.65 ± 17.41, P = 0.623), MPA (42.55 ± 3.80 vs 40.70 ± 2.85, P = 0.090), as well as LTPA (1514.20 ± 146.10 vs 1587.70 ± 182.25, P = 0.167). In contrast, children in the Sports group exhibited a significantly higher VPA (21.65 ± 3.43 vs 17.15 ± 4.01, P = 0.0001) and MVPA (64.20 ± 2.33 vs 57.85 ± 3.36, P < 0.001) than those in the Control group.

The Bland-Altman plot was originally developed to compare data with two sets of measurements on one occasion. It was expected that 95% of the differences between the two measurement methods would fall within the 95% limit of agreement. As shown in Figure 3, Bland-Altman plots suggested that the agreement between hip-worn and wrist-worn accelerometer data was acceptable for MPA, VPA, and MVPA. There were two (10%), zero (0%), and three (15%) outliers from the 1.96 standard deviation value for MPA, VPA, and MVPA, respectively.

Figure 1
Figure 1: Vector magnitude counts (raw data) depicted as plots. The graphs on the left show the vector magnitude counts per day. The table on the right provides the exact vector magnitude count for each epoch (60 s). Four graphs for VM are magnified and shown at the bottom. Please click here to view a larger version of this figure.

Figure 2
Figure 2: The scoring page shown in the device software. The Puyau Children (2002) options for Cut Points and MVPA are accessible in the Algorithms section on the left. Scoring output can be obtained automatically by clicking the Calculate and Export buttons. Please click here to view a larger version of this figure.

Figure 3
Figure 3: Bland-Altman plot for physical activities using hip-worn and wrist-worn actigraphic devices. (A) Bland-Altman plot for MPA using hip-worn and wrist-worn actigraphic devices. (B) Bland-Altman plot for VPA using hip-worn and wrist-worn actigraphic devices. (C) Bland-Altman plot for MVPA using hip-worn and wrist-worn actigraphic devices. Please click here to view a larger version of this figure.

Sports group Control group P value
Gender (male/female) 10 male/ 10 female 8 male/ 12 femal 0.537
Age (years) 9.85±1.34 9.80±1.36 0.908
Height (cm) 135.3±9.41 135.8±9.43 0.881
Weight (kg) 36.65±7.25 35.10±4.84 0.432
Dominant hand (right%) 15% 10% 0.643
SBs (minutes) 441.05±31.80 442.25±30.74 0.904
LPA (minutes) 213.10±15.00 215.65±17.41 0.623
MPA (minutes) 42.55±3.80 40.70±2.85 0.090
VPA (minutes) 21.65±3.43 17.15±4.01 0.001
MVPA (minutes) 64.20±2.33 57.85±3.36 <0.000
LTPA (VM counts/) 1514.20±146.10 1587.70±182.25 0.167

Table 1: Demographic and actigraphic data. The table provides the demographic and actigraphic data collected from the Sports group and the Control group. Abbreviations:cm = centimeters; kg = kilograms; SBs = sedentary behaviors; LPA = light physical activity; MPA = moderate physical activity; VPA = vigorous physical activity; MVPA = moderate-to-vigorous physical activity; LTPA = leisure time physical activity; VM = vector magnitude.

Discussion

As shown in Table 1, children in the Sports group exhibited a significantly higher VPA and MVPA (64.20 ± 2.33 vs 57.85 ± 3.36, P < 0.001) relative to those in the Control group. According to the findings of previous reports in both adolescents25 and young adults26, accelerometer devices represent an accurate method for the estimation of PA, relative to subjective surveys.

Bland-Altman plots demonstrated that there were high levels of agreement for MPA, VPA, and MVPA between hip-worn and wrist-worn accelerometer data (shown in Figure 3). This result indicated that these devices can also be worn on the wrist to assess PA. However, we must highlight that the agreement between hip-worn and wrist-worn accelerometer data for MPA was lower than that of VPA. This is because in low strength PA, such as sitting in the classroom or doing homework, the hip-worn accelerometer graph mainly reflects the movement of the body's center of gravity, while the wrist-worn accelerometer graph mainly reflects the movement of the non-dominant upper extremity. In addition, considering the different levels of compliance between hip-worn and wrist-worn devices, it is important to select the most appropriate device to evaluate PA in children undergoing table tennis training in clubs.

The critical steps in the protocol are to confirm the availability of the raw VM count data and the accelerometer data for LTPA. In other words, the main challenge will be to ensure that the data undergo quality control in a strict manner. It is highly recommended to use data plotting to monitor the data from each participant. Periods in which the device was not worn can be identified as long strings of zero counts and must be removed from the final dataset, even if the participants do not report this period as a time when they were not wearing the device. Leisure time and sleep diaries are useful for identifying LTPA; consequently, it is necessary that the parents or caregivers acknowledge their children's daily information in a precise manner.

The software used by the accelerometer contains several algorithms that are suitable for children, including the Puyau Children (2002) algorithm, the Freedson Children (2005) algorithm, and the Mattock Children (2007) algorithm. The Everson Children (2008) algorithm was previously chosen to evaluate the PA of children and adolescents in Tibet27, while the Pate Preschool (2006) algorithm was chosen to evaluate PA in preschoolers residing in Shanghai, China28. In our present study, we used the Puyau Children (2002) algorithm because it is the most useful method with which to classify children according to body mass index and fat mass percentage29.

In addition, we needed to elucidate the exact equations to use; this was determined by the specific type of accelerometer device used to acquire the raw data (see the equation below).

VM = Equation 1

In the equation, X, Y, and Z are the vector magnitude counts for the X-axis, Y-axis, and Z-axis, respectively. LTPA represents the average PA during leisure time.

The triaxial VM counts per minute cut-off for different PA intensities were determined by the Puyau Children (2002) algorithm, as follows: sedentary behaviors <799; light PA = 800 to 3199; moderate PA = 3200 to 8199; vigor PA >8200; and moderate and vigor PA >3200. In future, different types of algorithms will be modified to provide an optimized algorithm for the specific characteristics of subjects.

The accelerometer device has three main limitations that need to be considered. First, the hip-worn method is thought to be the best choice for reflecting PA; however, this method shows poorer compliance relative to wrist-worn devices, especially for young children30. Second, the complexity and high price of the device (including software) can impede the utility of the accelerometer device and software in a home environment. Otherwise, clinical staff, institutional researchers, and sports-club coaches can easily manage this method, and the associated cost will decrease if the device is widely reused. Third, accelerometer devices only have a basic waterproof guarantee; thus, these devices should not be used for some sports participants, such as those undertaking sailing, rowing, and swimming.

There are some alternative methods that could be used instead of accelerometers. For example, many cell phones have similar functions for measuring PA, albeit with relatively low reliability and validity. Other studies have reported more cost-effective pedometers that are suitable for individuals31. Further research needs to identify the reliability and validity of all alternative methods.

Collectively, our results indicate that both hip-worn and wrist-worn accelerometers can effectively measure PA and are highly suitable for Chinese children undertaking table tennis training in clubs. These methods also can be used to evaluate PA in both healthy individuals and children with developmental disorders such as cerebral palsy32, autism33, and ADHD34.

The device used here is considered as the gold standard for measuring PA, as mentioned earlier. However, preliminary reports suggest that these devices can also measure sleep quality, circadian rhythm, and rest-activity rhythm in clinical practice35,36. Further investigations are now needed to widen the scope and application of these devices. These devices can also be helpful in monitoring PA in children undertaking table tennis training in clubs. Together with subjective questionnaires, such as the Health Behavior in School-aged Children Questionnaire and the International Physical Activity Questionnaire, this method is capable of demonstrating the PA of children in a highly effective manner.

Disclosures

The authors have nothing to disclose.

Acknowledgements

We thank Ms Shuo Tian for the digital technology support. This study was supported by the Wu Jieping Foundation (Grant No. 320.6750.18456).

Materials

Actigraph  ActiGraph Corp  GT3X+ device
ActiLife ActiGraph Corp  v6.13.3 software
SPSS 22.0 software statistical analysis software
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References

  1. Janssen, I., LeBlanc, A. G. Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. The International Journal of Behavioral Nutrition and Physical Activity. 7 (1), 1-16 (2010).
  2. Biddle, S., Asare, M. Physical activity and mental health in children and adolescents: a review of reviews. British Journal of Sports Medicine. 45 (11), 886-895 (2011).
  3. Donnelly, J., et al. Physical activity, fitness, cognitive function, and academic achievement in children: a systematic review. Medicine and Science in Sports and Exercise. 48 (6), 1197-1222 (2016).
  4. Tremblay, M. S., et al. Global Matrix 2.0 Research Team. Global Matrix 2.0: report card grades on the physical activity of children and youth comparing 38 countries. Journal of Physical Activity and Health. 13, 343-366 (2016).
  5. Zhu, Z., Chen, P., Zhuang, J. Predicting Chinese children and youth’s energy expenditure using ActiGraph accelerometers: a calibration and cross-validation study. Research Quarterly for Exercise and Sport. 84, 56-63 (2013).
  6. Composing and Editorial Board of Physical Activity Guidelines for Chinese. Physical Activity Guidelines for Chinese (2021). Zhonghua Liu Xing Bing Xue Za Zhi. 43 (1), 5-6 (2022).
  7. Kokko, S., et al. Does sports club participation contribute to physical activity among children and adolescents? A comparison across six European countries. Scandinavian Journal of Public Health. 47 (8), 851-858 (2019).
  8. Lee, E. J., So, W. Y., Youn, H. S., Kim, J. Effects of school-based physical activity programs on health-related physical fitness of Korean adolescents: a preliminary study. International Journal of Environmental Research and Public Health. 18 (6), 2976 (2021).
  9. Pradas, F., Ara, I., Toro, V., Courel-Ibanez, J. Benefits of regular table tennis practice in body composition and physical fitness compared to physically active children aged 10-11 years. International Journal of Environmental Research and Public Health. 18 (6), 2854 (2021).
  10. Xiao, Y., Huang, W., Lu, M., Ren, X., Zhang, P. Social-ecological analysis of the factors influencing Shanghai adolescents’ table tennis skills: a cross-sectional study. Frontiers in Psychology. 11, 1372 (2020).
  11. Yang, X., Leung, A. W., Russell, J., Yu, S. C., Zhao, W. H. Physical activity and sedentary behaviors among Chinese children: recent trends and correlates. Biomedical and Environmental Sciences. 34 (6), 425-438 (2021).
  12. Must, A., Barish, E. E., Bandini, L. G. Modifiable risk factors in relation to changes in BMI and fatness: what have we learned from prospective studies of school-aged children. International Journal of Obesity. 33 (7), 705-715 (2009).
  13. Wilks, D. C., Besson, H., Lindroos, A. K., Ekelund, U. Objectively measured physical activity and obesity prevention in children, adolescents and adults: a systematic review of prospective studies. Obesity Reviews. 12 (5), 119-129 (2011).
  14. Brouwer, S. I., et al. Parental physical activity is associated with objectively measured physical activity in young children in a sex-specific manner: the GECKO Drenthe cohort. BMC Public Health. 18 (1), 1033 (2018).
  15. Salli, J. F., Prochaska, J. J., Taylor, W. C. A review of correlates of physical activity of children and adolescents. Medicine and Science in Sports and Exercise. 32 (5), 963-975 (2000).
  16. Vanderloo, L. M., Di Cristofaro, N. A., Proudfoot, N. A., Tucker, P., Timmons, B. W. Comparing the Actical and ActiGraph approach to measuring young children’s physical activity levels and sedentary time. Pediatric Exercise Science. 28 (1), 133-142 (2016).
  17. Cain, K. L., Sallis, J. F., Conway, T. L., Van Dyck, D., Calhoon, L. Using accelerometers in youth physical activity studies: A review of methods. Journal of Physical Activity and Health. 10 (3), 437-450 (2013).
  18. Nelson, M. B., et al. Raw and count data comparability of hip-worn ActiGraph GT3X+ and link accelerometers. Medicine and Science in Sports and Exercise. 50 (5), 1103-1112 (2018).
  19. Clevenger, K. A., Pfeiffer, K. A., Montoye, A. H. Cross-generational comparability of hip- and wrist-worn ActiGraph GT3X+, wGT3X-BT, and GT9X accelerometers during free-living in adults. Journal of Sports Science. 38 (24), 2794-2802 (2020).
  20. Wyszyńska, J., et al. Obesity and body composition in preschool children with different levels of Actigraphy-derived physical activity-A cross-sectional study. Journal of Clinical Medicine. 9 (4), 1210 (2020).
  21. McLellan, G., Arthur, R., Buchan, D. S. Wear compliance, sedentary behaviour and activity in free-living children from hip-and wrist-mounted ActiGraph GT3X+ accelerometers. Journal of Sports Science. 36 (21), 2424-2430 (2018).
  22. Rhudy, M. B., Dresibach, S. B., Moran, M. D., Ruggiero, M. J., Veerabhadappa, P. Cut points of the Actigraph GT9X for moderate and vigorous intensity physical activity at four different wear locations. Journal of Sports Science. 38 (5), 503-510 (2020).
  23. McLellan, G., Arthur, R., Donnelly, S., Buchan, D. S. Segmented sedentary time and physical activity patterns throughout the week from wrist-worn ActiGraph GT3X+ accelerometers among children 7-12 years old. Journal of Sport and Health Science. 9 (2), 179-188 (2020).
  24. Zelener, J., Schneider, M. Adolescents and self-reported physical activity: an evaluation of the modified godin leisure-time exercise questionnaire. International Journal of Exercise Science. 9 (5), 587-598 (2016).
  25. Lagersted-Olsen, J., et al. Comparison of objectively measured and self-reported time spent sitting. International Journal of Sports Medicine. 35 (6), 534 (2014).
  26. Nie, M. J., et al. Accelerometer-measured physical activity in children and adolescents at altitudes over 3500 meters: A cross-sectional study in Tibet. International Journal of Environmental Research and Public Health. 16 (5), 686 (2019).
  27. Quan, M., et al. Are preschool children active enough in Shanghai: an accelerometer-based cross-sectional study. BMJ Open. 9 (4), 024090 (2019).
  28. Gába, A., Dygryn, J., Mitas, J., Jakubec, L., Fromel, K. Effect of accelerometer cut-off points on the recommended level of physical activity for obesity prevention in children. PLoS One. 11 (10), 0164282 (2016).
  29. Fairclough, S. J., et al. Wear compliance and activity in children wearing wrist- and hip-mounted accelerometers. Medical and Science Sports and Exercise. 48 (2), 245-253 (2016).
  30. Moniruzzaman, M., et al. Relationship between step counts and cerebral small vessel disease in Japanese men. Stroke. 51 (12), 3584-3591 (2020).
  31. Xing, R., Huang, W. Y., Sit, C. H. P. Validity of accelerometry for predicting physical activity and sedentary time in ambulatory children and young adults with cerebral palsy. Journal of Exercise Science and Fitness. 19 (1), 19-24 (2021).
  32. Sung, Y. S., Loh, S. C., Lin, L. Y. Physical activity and motor performance: A comparison between young children with and without autism spectrum disorder. Neuropsychiatric Disease and Treatment. 17, 3743-3751 (2021).
  33. James, M. E., et al. Effects of comorbid developmental coordination disorder and symptoms of attention deficit hyperactivity disorder on physical activity in children aged 4-5 years. Child Psychiatry Human Devlopment. , 1-11 (2021).
  34. Tang, Q., Zhao, X., Feng, Z., Zhao, H. Executive performance is associated with rest-activity rhythm in nurses working rotating shifts. Frontiers in Neuroscience. 16, 805039 (2022).
  35. Rensen, N., et al. Actigraphic estimates of sleep and the sleep-wake rhythm, and 6-sulfatoxymelatonin levels in healthy Dutch children. Chronobiology International. 37 (5), 660-672 (2020).

Tags

Physical Activity MeasurementAccelerometerTable Tennis TrainingWrist-wornHip-wornRaw Acceleration DataData CollectionParticipant InstructionsDemographic InformationLeft-handedNon-dominant HandSleepLeisure Time

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Zhang, X., Xia, C., Zhao, X., Liu, Y., Zhào, H., Huang, Y. Physical Activity Measurement in Children Accepting Table Tennis Training. J. Vis. Exp. (185), e63937, doi:10.3791/63937 (2022).
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