Accepted Manuscript Associations of subjectively and objectively measured sedentary behavior and physical activity with cognitive development in the early years Valerie Carson, Aishah Abdul Rahman, Sandra A. Wiebe PII:
S1755-2966(17)30030-3
DOI:
10.1016/j.mhpa.2017.05.003
Reference:
MHPA 216
To appear in:
Mental Health and Physical Activity
Received Date: 6 April 2017 Revised Date:
23 May 2017
Accepted Date: 26 May 2017
Please cite this article as: Carson, V., Rahman, A.A., Wiebe, S.A., Associations of subjectively and objectively measured sedentary behavior and physical activity with cognitive development in the early years, Mental Health and Physical Activity (2017), doi: 10.1016/j.mhpa.2017.05.003. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
RI PT
Associations of subjectively and objectively measured sedentary behaviour and physical activity with cognitive development in the early years
Valerie Carsona, Aishah Abdul Rahmanb, Sandra A. Wiebeb,c
Faculty of Physical Education and Recreation, University of Alberta, Edmonton, Alberta, Canada,
SC
a
T6G 2H9.
Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada, T6G
M AN U
b
2E1
Department of Psychology, University of Alberta, Edmonton, Alberta, Canada, T6G 2E9.
Author E-mail Addresses: VC:
[email protected]
TE D
c
SW:
[email protected]
EP
AAR:
[email protected]
AC C
Corresponding Author: Valerie Carson, PhD
University of Alberta
Edmonton, AB, T6G 2H9 Phone: (780) 492-1004 Fax: (780) 492-1008
E-mail:
[email protected]
ACCEPTED MANUSCRIPT Table 1. Participant Characteristics Variables Age (months; n = 100)
43.4 (9.4)
Sex (%; n=100) 47
RI PT
Male Female
53
Highest Household Education (%; n = 97) Some high school
1.0 3.1
Some university/college (but did not receive degree)
Bachelor’s degree Graduate/professional degree Sedentary behaviour and Physical activity Sedentary time (min/day; n=79)†
M AN U
College, vocational, or trade school diploma
SC
High school diploma
5.2
20.6 38.1 32.0
328.6 (41.3) 261.1 (28.8)
Moderate- to vigorous-intensity physical activity (min/day; n=79)†
86.6 (25.3)
Total objective physical activity (min/day; n=79)†
347.7 (41.2)
Television viewing (min/day; n=97)
87.6 (61.2)
Computer/video games (min/day; n=97)
26.6 (28.2)
EP
TE D
Light-intensity physical activity (min/day; n=79)†
114.2 (76.7)
Organized physical activity (min/day; n=97)
13.0 (13.7)
AC C
Screen time (min/day; n=97)
Non-organized physical activity (min/day; n=97)
41.7 (16.4)
Total subjective physical activity (min/day; n=97)
54.7 (18.6)
Cognitive Development
Vocabulary (standard score; n=92)
114.1 (12.4)
Working Memory (summary score; n=88)
2.0 (0.7)
Response Inhibition (d-prime sensitivity score; n=81)
2.7 (0.7)
Data presented as mean (standard deviation) for continuous variables and percentages for categorical variables. †Corrected for wear time using the residuals method.
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
Table 2. Pearson correlations between objective and subjective measures of sedentary behaviour, physical activity, and cognitive development Vocabulary Working Memory Response Inhibition Sedentary time† r = -0.02 r = -0.04 r = 0.10 n = 75 n = 71 n = 65 LPA† r = 0.04 r = -0.09 r = -0.10 n = 75 n = 71 n = 65 MVPA† r = -0.01 r = 0.17 r = -0.06 n = 75 n = 71 n = 65 Total objective PA† r = -0.04 r = 0.04 r = -0.11 n = 56 n = 71 n = 65 Television viewing r = -0.14 r = -0.09 r = -0.21** n = 90 n = 85 n = 79 Video/computer games r = -0.07 r = 0.01 r = 0.12 n = 90 n = 85 n = 79 Screen time r = -0.11 r = -0.02 r = -0.19* n = 85 n = 79 n = 90 Organized PA r = 0.09 r = 0.06 r = -0.01 n = 90 n = 85 n = 79 Non-organized PA r = 0.12 r = 0.09 r = 0.27** n = 90 n = 85 n = 79 Total subjective PA r = 0.16 r = 0.07 r = 0.31** n = 90 n = 85 n = 79 LPA = light-intensity physical activity; MVPA = moderate- to vigorous-intensity physical activity; PA = physical activity †Corrected for wear time using the residuals method. **P<0.05; *P<0.10
ACCEPTED MANUSCRIPT
M AN U
SC
RI PT
Table 3. Unstandardized beta coefficients (95% Confidence Intervals) for the associations between objective and subjective measures of sedentary behaviour, physical activity, and cognitive development adjusted for age Vocabulary Working Memory Response Inhibition (n=90) (n=71) Β (95% CI) Β (95% CI) Β (95% CI) Sedentary time (min/day) -0.001 (-0.071, 0.069) 0.001 (-0.003, 0.005) 0.002 (-0.003, 0.006) LPA (min/day) 0.013 (-0.086, 0.113) -0.003 (-0.008, 0.003) -0.002 (-0.009, 0.004) MVPA (min/day) -0.016 (-0.133, 0.101) 0.002 (-0.005, 0.008) -0.001 (-0.009, 0.007) Total Objective PA (min/day) 0.001 (-0.069, 0.071) -0.001 (-0.005, 0.003) -0.002 (-0.006, 0.003) Television viewing (min/day) -0.001 (-0.004, 0.002) -0.046 (-0.090, -0.002)** -0.002 (-0.005, -0.000) Video/computer game (min/day) -0.034 (-0.129, 0.062) -0.001 (-0.006, 0.004) 0.003 (-0.003, 0.009) Screen time (min/day) -0.002 (-0.003, 0.000) -0.000 (-0.002, 0.002) -0.034 (-0.068, 0.001)*
AC C
EP
TE D
Organized PA (min/day) 0.085 (-0.127, 0.296) -0.002 (-0.013, 0.009) -0.000 (-0.012, 0.011) Non-organized PA (min/day) 0.008 (-0.001, 0.017) 0.004 (-0.006, 0.014) 0.216 (0.057, 0.375)** Total subjective PA (min/day) 0.005 (-0.003, 0.013) 0.003 (-0.006, 0.012) 0.209 (0.070, 0.349)** LPA = light-intensity physical activity; MVPA = moderate- to vigorous-intensity physical activity; PA = physical activity All models were adjusted for age **P<0.05; *P<0.10
ACCEPTED MANUSCRIPT References
AC C
EP
TE D
M AN U
SC
RI PT
Adamo, K. B., Prince, S. A., Tricco, A. C., Connor-Gorber, S., & Tremblay, M. (2009). A comparison of indirect versus direct measures for assessing physical activity in the pediatric population: a systematic review. Int J Pediatr Obes, 4(1), 2-27. doi:10.1080/17477160802315010 Bauer, P. J., Larkina, M., & Deocampo, J. (2010). Early Memory Development. In U. Goswami (Ed.), The Wiley-Blackwell Handbook of Childhood Cognitive Development, 2nd Edition (pp. 153179). Oxford, UK: Wiley-Blackwell. Becker, D. R., McClelland, M. M., Loprinzi, P., & Trost, S. G. (2014). Physical activity, selfregulation, and early academic achievement in preschool children. Early Educ Dev, 25, 56-70. Biddle, S. J., & Asare, M. (2011). Physical activity and mental health in children and adolescents: a review of reviews. British journal of sports medicine, 45(11), 886-895. Boudreau, D. (2005). Use of a parent questionnaire in emergent and early literacy assessment of preschool children. Language, speech, and hearing services in schools, 36(1), 33-47. Campbell, D. W., Eaton, W. O., & McKeen, N. A. (2002). Motor activity level and behavioural control in young children. International Journal Of Behavioral Development, 26(4), 289-296. Carson, V., Hunter, S., Kuzik, N., Wiebe, S. A., Spence, J. C., Friedman, A., . . . Hinkley, T. (2016). Systematic review of physical activity and cognitive development in early childhood. Journal of science and medicine in sport / Sports Medicine Australia, 19(7), 573-578. doi:10.1016/j.jsams.2015.07.011 Carson, V., & Janssen, I. (2012). Associations between factors within the home setting and screen time among children aged 0-5 years: a cross-sectional study. BMC Public Health, 12, 539. doi:10.1186/1471-2458-12-539 Carson, V., Kuzik, N., Hunter, S., Wiebe, S. A., Spence, J. C., Friedman, A., . . . Hinkley, T. (2015). Systematic review of sedentary behavior and cognitive development in early childhood. Preventive medicine, 78, 115-122. doi:10.1016/j.ypmed.2015.07.016 Carson, V., Rhodes, R. E., Rinaldi, C., Rodgers, W., Spence, J. C., & Hesketh, K. D. (2016). Psychometric properties of a parental questionnaire for assessing correlates of toddlers’ physical activity and sedentary behavior (submitted). Carson, V., Rosu, A., & Janssen, I. (2014). A cross-sectional study of the environment, physical activity, and screen time among young children and their parents. BMC Public Health, 14, 61. doi:10.1186/1471-2458-14-61 Carson, V., Tremblay, M., Spence, J. C., Timmons, B., & Janssen, I. (2013). The Canadian Sedentary Behaviour Guidelines for the Early Years (zero to four years of age) and screen time among children from Kingston, Ontario. Paediatr Child Health, 18(1), 25-28. Chevalier, N., James, T. D., Wiebe, S. A., Nelson, J. M., & Espy, K. A. (2014). Contribution of reactive and proactive control to children's working memory performance: Insight from item recall durations in response sequence planning. Developmental psychology, 50(7), 1999-2008. doi:10.1037/a0036644 Chevalier, N., Kelsey, K. M., Wiebe, S. A., & Espy, K. A. (2014). The temporal dynamic of response inhibition in early childhood: an ERP study of partial and successful inhibition. Dev Neuropsychol, 39(8), 585-599. doi:10.1080/87565641.2014.973497 Christakis, D. A. (2009). The effects of infant media usage: what do we know and what should we learn? Acta Paediatr, 98(1), 8-16. doi:APA1027 [pii] 10.1111/j.1651-2227.2008.01027.x Christakis, D. A. (2014). Interactive media use at younger than the age of 2 years: time to rethink the American Academy of Pediatrics guideline? JAMA pediatrics, 168(5), 399-400. doi:10.1001/jamapediatrics.2013.5081
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
Cliff, D. P., Reilly, J. J., & Okely, A. D. (2009). Methodological considerations in using accelerometers to assess habitual physical activity in children aged 0-5 years. Journal of science and medicine in sport / Sports Medicine Australia, 12(5), 557-567. doi:10.1016/j.jsams.2008.10.008 Cohen, J. (1992). A power primer. Psychological bulletin, 112(1), 155-159. Colley, R. C., Garriguet, D., Adamo, K. B., Carson, V., Janssen, I., Timmons, B. W., & Tremblay, M. S. (2013). Physical activity and sedentary behavior during the early years in Canada: a crosssectional study. The international journal of behavioral nutrition and physical activity, 10, 54. doi:10.1186/1479-5868-10-54 Draper, C. E., Achmat, M., ,, Forbes, J., & Lambert, E. V. (2012). Impact of a community-based programme for motor development on gross motor skills and cognitive function in preschool children from disadvantaged settings. Early Child Dev Care, 182(1), 137-152. Dunn, L. M., & Dunn, D. M. (2007). PPVT-4 :Peabody picture vocabulary test (4th edition). Minneapolis, MN: Pearson Assessments. Garon, N., Bryson, S. E., & Smith, I. M. (2008). Executive function in preschoolers: a review using an integrative framework. Psychological bulletin, 134(1), 31-60. doi:10.1037/0033-2909.134.1.31 Greenough, W. T., Black, J. E., & Wallace, C. S. (1987). Experience and brain development. Child Dev, 58(3), 539-559. Hinkley, T., O'Connell, E., Okely, A. D., Crawford, D., Hesketh, K., & Salmon, J. (2012). Assessing volume of accelerometry data for reliability in preschool children. Medicine and science in sports and exercise, 44(12), 2436-2441. doi:10.1249/MSS.0b013e3182661478 Hinkley, T., Salmon, J., Okely, A. D., Crawford, D., & Hesketh, K. (2012). Preschoolers' physical activity, screen time, and compliance with recommendations. Medicine and science in sports and exercise, 44(3), 458-465. doi:10.1249/MSS.0b013e318233763b Holmes, R. M., Pellegrini, A. D., & Schmidt, S. L. (2006). The effects of different recess timing regimens on preschoolers' classroom attention. Early Child Dev Care, 176(7), 735-743. Hunter, S., Leatherdale, S. T., Storey, K., & Carson, V. (2016). A quasi-experimental examination of how school-based physical activity changes impact secondary school student moderate- to vigorous- intensity physical activity over time in the COMPASS study. The international journal of behavioral nutrition and physical activity, 13, 86. doi:10.1186/s12966-016-0411-9 Huttenlocher, P. R., & Dabholkar, A. S. (1997). Regional differences in synaptogenesis in human cerebral cortex. The Journal of comparative neurology, 387(2), 167-178. Irwin, J. D., Johnson, A. M., Vanderloo, L. M., Burke, S. M., & Tucker, P. (2015). Temperament and Objectively Measured Physical Activity and Sedentary Time among Canadian Preschoolers. Prev Med Rep, 2, 598-601. doi:10.1016/j.pmedr.2015.07.007 Janssen, X., Cliff, D. P., Reilly, J. J., Hinkley, T., Jones, R. A., Batterham, M., . . . Okely, A. D. (2013). Predictive validity and classification accuracy of ActiGraph energy expenditure equations and cut-points in young children. PloS one, 8(11), e79124. doi:10.1371/journal.pone.0079124 Khan, N. A., & Hillman, C. H. (2014). The relation of childhood physical activity and aerobic fitness to brain function and cognition: a review. Pediatric exercise science, 26(2), 138-146. doi:10.1123/pes.2013-0125 Kirk, S. M., & Kirk, E. P. (2016). Sixty Minutes of Physical Activity per Day Included Within Preschool Academic Lessons Improves Early Literacy. J Sch Health, 86(3), 155-163. doi:10.1111/josh.12363 Kirkorian, H. L., Pempek, T. A., Murphy, L. A., Schmidt, M. E., & Anderson, D. R. (2009). The impact of background television on parent-child interaction. Child Dev, 80(5), 1350-1359. doi:10.1111/j.1467-8624.2009.01337.x Knudsen, E. I. (2004). Sensitive periods in the development of the brain and behavior. Journal of cognitive neuroscience, 16(8), 1412-1425. doi:10.1162/0898929042304796
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
Kowalski, K. C., Crocker, P. R. E., & Kowalski, N. P. (1997). Convergent validity of the physical activity questionnaire for adolescents. Pediatric exercise science, 9, 342-352. Lee, E. Y., Spence, J. C., & Carson, V. (2017). Television viewing, reading, physical activity and brain development among young South Korean children. Journal of science and medicine in sport / Sports Medicine Australia. doi:10.1016/j.jsams.2016.11.014 Lenroot, R. K., & Giedd, J. N. (2006). Brain development in children and adolescents: insights from anatomical magnetic resonance imaging. Neuroscience and biobehavioral reviews, 30(6), 718729. doi:10.1016/j.neubiorev.2006.06.001 Li, X., & Atkins, M. S. (2004). Early childhood computer experience and cognitive and motor development. Pediatrics, 113(6), 1715-1722. Mavilidi M-F, Okely, A. D., Chandler, P., Cliff, D. P., & Paas, F. (2015). Effects of integrated physical exercises and gestures on preschool children’s foreign language vocabulary learning. Educ Psychol Rev, 27(3), 413-426. Mota, J., Santos, P., Guerra, S., Ribeiro, J. C., Duarte, J. A., & Sallis, J. F. (2002). Validation of a physical activity self-report questionnaire in a Portuguese pediatric population. Pediatric exercise science, 14, 269-276. Tandon, P. S., Tovar, A., Jayasuriya, A. T., Welker, E., Schober, D. J., Copeland, K., . . . Ward, D. S. (2016). The relationship between physical activity and diet and young children's cognitive development: A systematic review. Prev Med Rep, 3, 379-390. doi:10.1016/j.pmedr.2016.04.003 Teixeira Costa, H. J., Abelairas-Gomez, C., Arufe-Giráldez, V., Pazos-Couto, J. M., & BarcalaFurelos, R. (2015). Influence of a physical education plan on psychomotor development profiles of preschool children. J Human Sport Exerc, 10(1), 126-140. Tomasello, M. (2010). Language Development. In U. Goswami (Ed.), The Wiley-Blackwell Handbook of Childhood Cognitive Development, 2nd Edition (pp. 239-257). Oxford, UK: WileyBlackwell. Voss, M. W., Car, L. J., Clark, R., & Weng, T. (2014). Revenge of the "sit" II: Does lifestyle impact neuronal and cognitive health through distinct mechanisms associated with sedentary behavior and physical activity? Mental Health and Physical Activity, 7(2014), 9-24. Walker, S. P., Wachs, T. D., Grantham-McGregor, S., Black, M. M., Nelson, C. A., Huffman, S. L., . . . Richter, L. (2011). Inequality in early childhood: risk and protective factors for early child development. Lancet, 378(9799), 1325-1338. doi:10.1016/S0140-6736(11)60555-2 Webster, E. K., Wadsworth, D. D., & Robinson, L. E. (2015). Preschoolers' time on-task and physical activity during a classroom activity break. Pediatric exercise science, 27(1), 160-167. doi:10.1123/pes.2014-0006 Wiebe, S. A., Clark, C. A., De Jong, D. M., Chevalier, N., Espy, K. A., & Wakschlag, L. (2015). Prenatal tobacco exposure and self-regulation in early childhood: Implications for developmental psychopathology. Dev Psychopathol, 27(2), 397-409. doi:10.1017/S095457941500005X Wiebe, S. A., Sheffield, T. D., & Espy, K. A. (2012). Separating the fish from the sharks: A longitudinal study of preschool response inhibition. Child Development, 83(4), 1245-1261. Wiebe, S. A., Sheffiled, T., Nelson, J. M., Clark, C. A. C., Chevalier, N., & Epsy, K. A. (2011). The structure of executive function in 3-year-olds. J Exp Child Psychol, 108(3), 436-458. Willett, W., & Stampfer, M. J. (1986). Total energy intake: implications for epidemiologic analyses. American journal of epidemiology, 124(1), 17-27. Zachopoulou, E., Trevlas, E., Konstadinidou, E., & Group, A. P. R. (2006). The design and implementation of a physical education program to promote children’s creativity in the early years. Int J Early Years Educ., 14(3), 279-294.
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
Zimmerman, F. J., Christakis, D. A., & Meltzoff, A. N. (2007). Television and DVD/video viewing in children younger than 2 years. Archives of pediatrics & adolescent medicine, 161(5), 473-479. doi:10.1001/archpedi.161.5.473
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
Associations of subjectively and objectively measured sedentary behavior and physical activity with cognitive development in the early years
1
ACCEPTED MANUSCRIPT Abstract
2
Purpose: To examine the associations of subjectively and objectively measured sedentary behavior
3
and physical activity with cognitive development in a sample of 30 to 59 month olds.
4
Methods: Cross-sectional findings are based on 100 early years children (43.4±9.4 months; 53%
5
female) from Edmonton, Canada that were part of the Physical Activity and Cognition in Early
6
Childhood (PACE) study. Sedentary time and physical activity (light-intensity, moderate- to vigorous-
7
intensity, total) were objectively measured with an accelerometer. Sedentary behavior (television,
8
video/computer games, screen time) and physical activity (organized, non-organized, total) were also
9
subjectively measured with a parental questionnaire. Vocabulary was measured with the Peabody
M AN U
SC
RI PT
1
Picture Vocabulary Test, Fourth Edition, working memory was measured with the Nebraska Barnyard
11
task, and response inhibition was measured with the Fish-Shark Go/No-Go task. Correlations and
12
linear regression were used to examine associations.
13
Results: Total subjective physical activity (r=0.31; p=0.018) and non-organized physical activity
14
(r=0.27; p=0.035) were significantly positively correlated with vocabulary. Conversely, television
15
viewing (r=-0.21; p=0.046) was significantly negatively correlated with vocabulary. These significant
16
associations remained in linear regression models after adjusting for age. Objectively measured
17
sedentary time and physical activity were not significantly associated with any cognitive development
18
measure and no sedentary behavior or physical activity measure was associated with working memory
19
or response inhibition.
20
Conclusions: Television viewing may be detrimental and physical activity, especially non-organized,
21
may be beneficial for vocabulary in early years children. Future research with larger sample sizes and
22
longitudinal and experimental study designs are needed to confirm these findings and determine the
23
mechanisms.
AC C
EP
TE D
10
2
ACCEPTED MANUSCRIPT 24
25
Keywords: Physical activity, Television, Vocabulary, Memory, Response inhibition, Young children
AC C
EP
TE D
M AN U
SC
RI PT
26
3
ACCEPTED MANUSCRIPT 27 28
Introduction Human brains experience rapid growth and development during gestation (Lenroot & Giedd, 2006) but the brain is not fully developed at birth, with development continuing in some regions
30
through the early 20s (Christakis, 2009). The early years, the first five years of life, are characterized
31
by significant growth and development of the brain (Khan & Hillman, 2014; Lenroot & Giedd, 2006).
32
By age 2 the human brain has reached approximately 80% of its adult weight and by age 5 it has
33
reached 90% of it adult weight (Lenroot & Giedd, 2006). Furthermore, the production of synapses
34
rapidly increases from birth to 2 years (Lenroot & Giedd, 2006) followed by pruning of synapses at
35
varying rates in different regions of the brain (Huttenlocher & Dabholkar, 1997; Lenroot & Giedd,
36
2006). This rapid period of brain maturation in the early years (Khan & Hillman, 2014) makes it more
37
sensitive to the immediate environment (Knudsen, 2004). As a result, early life experiences can have
38
beneficial or detrimental long-term effects on brain structure and function (Greenough, Black, &
39
Wallace, 1987). For instance, healthy brain development in the early years enables optimal cognitive
40
development, including the growth of abilities and skills in domains such as vocabulary (Tomasello,
41
2010) and executive functions (Garon, Bryson, & Smith, 2008). Therefore, identifying and targeting
42
the factors that are beneficially associated with healthy brain development during the early years is
43
critical to facilitating optimal cognitive development across domains.
SC
M AN U
TE D
EP
Physical activity may be one factor to consider for optimal brain and cognitive development in
AC C
44
RI PT
29
45
the early years, given its association with cognitive outcomes in older age groups. For instance,
46
evidence from several reviews indicates physical activity in school-aged children and youth is
47
beneficially associated with cognitive functioning (Biddle & Asare, 2011). Additionally, a number of
48
mechanisms to explain this relationship have been identified primarily in animal models and older
49
adults (Khan & Hillman, 2014; Voss, Car, Clark, & Weng, 2014). However, evidence in the early years
50
is limited and has notable limitations. More specifically, a recent systematic review only identified 4
ACCEPTED MANUSCRIPT seven studies in early childhood (birth to 6 years) among apparently healthy children that had examined
52
the relationship between physical activity and cognitive development (Carson, Hunter, et al., 2016).
53
While some preliminary evidence was found for a positive relationship, the majority of studies were
54
rated as weak in quality (Carson, Hunter, et al., 2016) and only two studies included an objective
55
measure of physical activity (Becker, McClelland, Loprinzi, & Trost, 2014; Campbell, Eaton, &
56
McKeen, 2002). Similar findings were observed in a subsequent review (Tandon et al., 2016) on
57
physical activity, gross motor skills, diet and cognitive development, which included two additional
58
physical activity studies (Draper, Achmat, Forbes, & Lambert, 2012; Mavilidi M-F, Okely, Chandler,
59
Cliff, & Paas, 2015) that were not included in the earlier review.
SC
M AN U
60
RI PT
51
In comparison to physical activity, more research has examined the association between sedentary behavior, in particular screen-based sedentary behavior, and cognitive development in the
62
early years (Carson et al., 2015). It is thought that television viewing may have a detrimental impact on
63
brain development in the early years due to the overstimulation of the developing brain and reduced
64
interaction with caregivers (Christakis, 2009). A recent systematic review on sedentary behavior and
65
cognitive development in early childhood observed primarily null or detrimental effects for screen time
66
(Carson et al., 2015). However, the majority of studies were rated as weak in quality and none of the
67
studies included contemporary forms of screen time beyond television viewing as an exposure.
68
Furthermore, no study included an objective measure of sedentary behavior (Carson et al., 2015). It is
69
important to consider both objective and subjective measures as objective measures can more
70
accurately measure total sedentary time and subjective measures can provide information on type and
71
context of sedentary behavior (Lubans, Hesketh, et al., 2011).
AC C
EP
TE D
61
72
Future research examining the association between sedentary behavior, physical activity, and
73
cognitive development that addresses current gaps and limitations is needed to strength the evidence
74
base in this area. Understanding these relationships is of particular importance given current trends of 5
ACCEPTED MANUSCRIPT high sedentary behavior, in particular screen-based sedentary behavior, and low physical activity, in
76
particular moderate- to vigorous-intensity physical activity (MVPA), in the early years (Colley et al.,
77
2013; Hinkley, Salmon, Okely, Crawford, & Hesketh, 2012). Therefore, the objective of this study was
78
to examine the associations of subjectively and objectively measured sedentary behavior and physical
79
activity with cognitive development in a sample of early years children.
80
Methods
81
Participants
82
Participants were 100 children aged 30 to 59 months from the Physical Activity and Cognition in Early
83
Childhood (PACE) study. Data were collected between April, 2015 and December, 2016 in Edmonton,
84
Canada. Families were recruited from existing databases, local media, online advertising, and flyers
85
distributed to businesses serving families such as child care centres and doctor’s offices. Inclusion
86
criteria for the study were: (1) children aged 30 to 59 months and (2) English-speaking and English-
87
reading parents. The exclusion criteria for the study were: (1) children who are non-ambulatory; (2)
88
children diagnosed with a new or recent chronic disease (e.g., Type 1 diabetes) where physical activity
89
may be limited during the initiation of treatment; (3) children with a disability/impairment that limits
90
their ability to be physically active; and (4) children with a developmental delay, diagnosed
91
neurological or psychiatric disorder, or children with pre- or perinatal risk factors known to affect brain
92
development (e.g., fetal alcohol spectrum disorder, preterm birth, low birth weight). Families that met
93
eligible criteria and agreed to participate attended a laboratory appointment where cognitive
94
development measures were conducted. At the appointment, parents completed a questionnaire and
95
families were also given an accelerometer for their child to wear for 7 consecutive days. Verbal and
96
written accelerometer instructions were provided to families. Ethics approval was obtained from the
97
University of Alberta Human Research Ethics Board and all participating parents provided written
98
informed consent.
AC C
EP
TE D
M AN U
SC
RI PT
75
6
ACCEPTED MANUSCRIPT 99
Objective measures of sedentary time and physical activity Sedentary time, light-intensity physical activity (LPA), and moderate- to vigorous-intensity physical
101
activity (MVPA) were objectively measured with Actigraph wGT3X-BT accelerometers worn on an
102
elastic waistband over the right hip for 7 consecutive days. Parents were instructed to only remove the
103
accelerometer for overnight sleep and during swimming and bathing. Data was collected in 15 second
104
epochs. To be included, participants were required to have ≥4 days with ≥ 1440 total 15 second
105
intervals (equivalent to 6 hours) of wear time each day (Hinkley, O'Connell, et al., 2012). A weekend
106
day was not required for the 4 days. Non-wear time was defined as ≥80 consecutive 15 second intervals
107
of zero counts (equivalent to ≥20 minutes of consecutive zeros counts). Daytime naps were assumed to
108
be removed with non-wear time. Sedentary time was defined as 0-25 counts per 15 seconds, LPA as
109
26-419 counts per 15 seconds, and MVPA as ≥420 counts per 15 seconds (Janssen et al., 2013).
110
Minutes per day of sedentary time, LPA, and MVPA were derived by dividing the number of 15
111
second intervals classified as wear time by 4 and then dividing the total minutes in each intensity by the
112
total number of valid days. To adjust for wear time, sedentary time, LPA, and MVPA variables were
113
standardized by using the residuals obtained when regressing sedentary time, LPA, and MVPA
114
separately on wear time (Willett & Stampfer, 1986).
115
Subjective measures of sedentary behavior and physical activity
116
Television viewing, video/computer games, and overall screen time were subjectively measured via the
117
parental questionnaire. Parents reported the average hours and minutes per weekday and weekend day
118
that their child: (1) “watches television, videos, or DVDs on a television, computer, or portable device”
119
and (2) “plays video/computer games on devices such as a learning laptop, leapfrog leapster, computer,
120
laptop, tablet, cell phone, the internet, Playstation, or XBOX”. All four questions were open ended.
121
Average minutes per day of both television and video/computer games were derived by calculating
AC C
EP
TE D
M AN U
SC
RI PT
100
7
ACCEPTED MANUSCRIPT weighted averages for weekday and weekend responses ([weekday*5 + weekend*2]/7). Average
123
minutes per day of screen time was derived by summing the average minutes per day of television and
124
video/computer games. The television viewing and video/computer game questions originally came
125
from a national survey in Canada (Colley et al., 2013) and were modified for a previous study in early
126
years children (Carson & Janssen, 2012; Carson, Rosu, & Janssen, 2014). The modified questions have
127
shown good 1-week test re-test reliability (Intra-class correlation=0.82) in another sample of early
128
years children (Carson, Rhodes, et al., 2016).
SC
129
RI PT
122
Organized physical activity, non-organized physical activity, and total physical activity were subjectively measured via the parental questionnaire. Participants reported the hours per week their
131
child usually takes part in physical activity (that makes him/her out of breath or warmer than usual)
132
while participating in: (1) “organized activities (e.g., swimming lessons, skating lessons, gymnastics)”
133
and (2) “non-organized activities (e.g., going for a walk, drop-in skating, playing at a splash pad or
134
wading pool, bike or tricycle ride, playing at the park or in the yard)”. There were 5 response options
135
for both questions (never, less than 2 hours per week, 2-3 hours per week, 4-6 hours per week, 7+
136
hours per week). Consistent with previous research (Colley et al., 2013), where applicable, the mid-
137
points of the response category (i.e., 0, 1, 2.5, 5, 7) was calculated for organized and non-organized
138
activity variables. Minutes per day of organized and non-organized physical activity were derived by
139
multiplying hours per week variables by 60 and dividing total weekly minutes by 7. Total minutes per
140
day of physical activity was derived by summing organized and non-organized physical activity
141
variables. The questions were adopted from a national survey in Canada (Colley et al., 2013). Total
142
subjective physical activity was significantly correlated with total objective physical activity (r=0.31;
143
p=0.005) and MVPA (r=0.33; p=0.003) in the sample. Additionally, non-organized physical activity
144
was significantly correlated with total objective physical activity (r=0.28; p=0.01) and MVPA in this
AC C
EP
TE D
M AN U
130
8
ACCEPTED MANUSCRIPT sample (r=0.34; p=0.002). No significant correlations were observed between organized physical
146
activity and objective measures.
147
Cognitive Development
148
Language and executive functions were the cognitive development domains assessed in this study,
149
informed by two systematic reviews on the association between physical activity, sedentary behavior
150
and cognitive development in early childhood (Carson et al., 2015; Carson et al., 2016). To assess
151
executive functions, which encompasses the cognitive abilities that support goal-directed behavior
152
(Miyake, Friedman, Emerson, Witzki, Howerter & Wager, 2000), we included tasks measuring
153
children's ability to hold and manipulate information in working memory and to inhibit prepotent
154
responses. To assess language, we included a measure of children's receptive vocabulary.
SC
M AN U
155
RI PT
145
Working memory was measured with the Nebraska Barnyard task (Wiebe et al., 2011). This is a sensitive computerized measure of working memory used in multiple studies of early childhood
157
development (Chevalier, James, Wiebe, Nelson, & Espy, 2014; S. A. Wiebe et al., 2015). Children are
158
asked to press buttons on a touch screen corresponding to animal names, in the same order as they are
159
spoken by the examiner. The task begins with two-item sequences, and progressively increase in length
160
until the child responds incorrectly to all sequences at a level. A summary score was created by
161
summing the child’s proportion of correct responses at each level across all completed levels. In
162
previous research with preschool children, this measure showed high 9-month longitudinal stability (r
163
= .64-.71; unpublished data) and loaded significantly on a latent executive function factor (λ = .41-.52)
164
(Wiebe et al., 2011).
165
AC C
EP
TE D
156
Response inhibition was measured with the Fish-Shark Go/No-Go task (Chevalier, Kelsey,
166
Wiebe, & Espy, 2014; Wiebe et al., 2015; Wiebe, Sheffield, & Espy, 2012). This is a child-friendly
167
adaptation of one of the main tasks used to assess response inhibition in adults. Children are instructed 9
ACCEPTED MANUSCRIPT to press a button to catch fish that appear on the computer screen (go trials), but to avoid pressing the
169
button when a shark appears (no-go trials); 75% of trials are go trials to promote children’s tendency to
170
respond. The standardized difference between the hit rate and the false alarm rate, called the d-prime
171
sensitivity, was calculated, reflecting children’s success in following both task rules. Previously, this
172
measure showed moderate but significant 9-month longitudinal stability in preschool children (r = .30-
173
.46; unpublished data) and loaded significantly on latent executive function (λ = .37-.38) (Wiebe et al.,
174
2011).
SC
175
RI PT
168
Vocabulary was measured with the Peabody Picture Vocabulary Test, Fourth Edition (PPVT-4) [Pearson Assessments, Minneapolis, MN]. It is a standardized test that measures receptive vocabulary
177
in children aged 30 months and up (Dunn & Dunn, 2007). Children are asked to point to the picture (of
178
four options) corresponding to each vocabulary item. An age-normed standard score was calculated.
179
This test has good 4-week test-retest reliability (r = .94-.95) and convergent validity (r = .80-.84 with
180
productive vocabulary measures) (Dunn & Dunn, 2007).
181
Statistical Analyses
182
All statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC). Descriptive
183
statistics were calculated. All continuous variables were checked for outliers (≥±3 standard deviations)
184
and television viewing was truncated below 3 standard deviations for two participants. The assumption
185
of normality for correlation and linear regression was assessed by examining residuals for physical
186
activity, sedentary behavior, and cognitive development variables. No variables needed to be
187
transformed. Pearson correlations between each sedentary behavior and physical activity variable with
188
each cognitive development variables were conducted. Next, multiple linear regression models were
189
run to examine the associations between each sedentary behavior and physical activity variable with
AC C
EP
TE D
M AN U
176
10
ACCEPTED MANUSCRIPT each cognitive development variable while adjusting for age in months. Statistical significance was
191
defined as a p< 0.05.
192
Results
193
Participant characteristics are provided in Table 1. Of the 100 participants, 97 had complete
194
questionnaire data, 92 had vocabulary data, 88 had working memory data, 81 had response inhibition
195
data, and 79 had complete accelerometer data. On average, the 79 participants had 6.6 valid days and
196
11.3 hours/day of total wear time (data not shown). Pairwise deletion was used to handle missing data.
197
The average age of the sample was 43.4 months and 53% were female. According to the accelerometry
198
data, children participated in an average 347.7 min/day of total physical activity (i.e., LPA and
199
MVPA); whereas, according to parental-report, children participated in an average of 54.7 min/day of
200
total physical activity (i.e., organized and non-organized). Furthermore, according to the parental-
201
report, children participated in an average of 114.2 min/day of screen time.
M AN U
SC
RI PT
190
Pearson correlations between objective and subjective measures of sedentary behavior, physical
203
activity, and cognitive development are provided in Table 2. Total subjective physical activity (r=0.31;
204
p=0.018) and non-organized physical activity (r=0.27; p=0.035) were significantly positively correlated
205
with vocabulary. The effect size for these correlations is considered medium/large and small/medium,
206
respectfully (Cohen, 1992). Additionally, television viewing was significantly negatively correlated
207
with vocabulary (r=-0.21; p=0.046), with a small/medium effect size (Cohen, 1992). All other
208
correlations had a p-value ≥0.10.
EP
AC C
209
TE D
202
Linear regression results for associations between objective and subjective measures of
210
sedentary behavior, physical activity, and cognitive development adjusted for age in months are
211
provided in Table 3. For every additional minute per day of television viewing, vocabulary was 0.046
212
(95%CI: 0.090, 0.002; p=0.040) units lower, respectively. For every additional minute per day of non-
213
organized physical activity and total subjective physical activity, vocabulary was 0.216 (95%CI: 0.057, 11
ACCEPTED MANUSCRIPT 0.375; p=0.008) and 0.209 (95%CI: 0.070, 0.349; p=0.004) units higher, respectively. All other
215
associations had a p-value ≥0.10.
216
Discussion
217
This study aimed to determine the associations of sedentary behavior and physical activity, measured
218
both objectively and subjectively, with vocabulary, working memory, and response inhibition in a
219
sample of 30-59 month olds. Sedentary behavior and physical activity, measured objectively via an
220
accelerometer, were not associated with any measure of cognitive development. However, associations
221
were observed between subjective measures of sedentary behavior and physical activity and cognitive
222
development. Specifically, higher television viewing was significantly associated with poorer
223
vocabulary scores, and higher non-organized physical activity and total physical activity were
224
significantly associated with better vocabulary scores. No associations were observed with working
225
memory and response inhibition.
SC
M AN U
The findings from the present study align with a recent systematic review on the relationship
TE D
226
RI PT
214
between sedentary behavior and cognitive development in early childhood (birth to 6 years), where the
228
majority of associations between television viewing and cognitive development across 37 studies were
229
detrimental or null (Carson et al., 2015). The null or detrimental findings were consistent across
230
different domains of cognitive development within the review; however, language (e.g., vocabulary)
231
was the most commonly assessed domain (Carson et al., 2015). Overall, findings suggest that television
232
viewing is not beneficial for cognitive development and it may be detrimental.
AC C
233
EP
227
The present study builds on the existing body of evidence for sedentary behavior and cognitive
234
development in several ways. Within the previous systematic review (Carson et al., 2015), only two
235
studies assessed video/computer game use (Boudreau, 2005; Li & Atkins, 2004) so conclusions could
236
not be made in terms of type of screen time. Findings of the present study suggest type of screen time 12
ACCEPTED MANUSCRIPT may be important to consider in regard to cognitive development, given associations were found for
238
television viewing but not for video/computer game use. One potential reason for the different findings
239
between these two exposures may be the different amount of time spent engaging in them. For
240
example, of the 114 average minutes of screen time, approximately 76% was spent watching television
241
and 24% was spent using video/computer games. Furthermore, the present study incorporated
242
contemporary examples within the screen time measures, including tablets and cell phones for
243
video/computer games. There is some speculation that interactive games found in a number of apps on
244
tablets and smartphones may be less detrimental than passive television viewing (Christakis, 2014).
245
Since there is no empirical evidence to support this, it is an important area for future research
246
(Christakis, 2014).
SC
M AN U
247
RI PT
237
The inclusion of an objective measure of sedentary time is another important addition to the current evidence base as no studies in the previous sedentary behavior review included this exposure
249
(Carson et al., 2015). The finding that total sedentary behavior was not associated with cognitive
250
development may again support the notion that not all types of screen time and non-screen time
251
pursuits are equal in terms of their impact on cognitive development. For instance, within the previous
252
review, reading/being read to was found to be consistently positively associated with cognitive
253
development, specifically within the language domain (Carson et al., 2015). Given children cannot
254
spend all waking hours being physically active, when they are sedentary in discretionary time, certain
255
pursuits may be better to promote than others for optimal cognitive development. While reading/being
256
read to appears to be one valuable pursuit (Carson et al., 2015), future research should examine if there
257
are other sedentary pursuits that are positive for cognitive development. Furthermore, given this was
258
the first study to examine the association between objectively measured sedentary time and cognitive
259
development in early years children, results need to be confirmed in other samples.
AC C
EP
TE D
248
13
ACCEPTED MANUSCRIPT 260
The findings of the present study also align with previous reviews on physical activity and cognitive development in early childhood where beneficial associations between physical activity and
262
at least one cognitive outcome were observed in eight out of nine studies (Carson, Hunter, et al., 2016;
263
Tandon et al., 2016). Other experimental studies either published since these reviews were conducted
264
or not included within these previous reviews have observed similar findings (Holmes, Pellegrini, &
265
Schmidt, 2006; Kirk & Kirk, 2016; Teixeira Costa, Abelairas-Gomez, Arufe-Giráldez, Pazos-Couto, &
266
Barcala-Furelos, 2015; Webster, Wadsworth, & Robinson, 2015; Zachopoulou, Trevlas,
267
Konstadinidou, & Group, 2006), though both null (Irwin, Johnson, Vanderloo, Burke, & Tucker, 2015)
268
and beneficial associations (Lee, Spence, & Carson, 2017) have been reported from observational
269
studies not captured in the previous review. Overall, these findings suggest that physical activity is not
270
detrimental for cognitive development and it is likely beneficial. This is consistent with what is
271
observed in older children (Biddle & Asare, 2011).
SC
M AN U
Only two observational studies have examined the association between accelerometer-derived
TE D
272
RI PT
261
physical activity and cognitive development in young children (Becker et al., 2014; Irwin et al., 2015).
274
In contrast to the present study, Becker and colleagues (2014) found higher MVPA during recess was
275
significantly associated with higher self-regulation but consistent with the overall findings of the
276
present study MVPA was not associated with literacy and math achievement (Becker et al., 2014).
277
Similarly, total physical activity and MVPA were not associated with attention span in another study
278
(Irwin et al., 2015). However, no previous study has examined the association between both objective
279
and subjective measures of physical activity and cognitive development in the same study in the early
280
years age group. Though significant correlations between objective and subjective measures of physical
281
activity were observed in the present study, large differences in absolute values were observed when
282
comparing subjective and objective measures, with all subjective measures being lower than all
283
objective measures. The fact that objective measures are considered more robust (Adamo, Prince,
AC C
EP
273
14
ACCEPTED MANUSCRIPT Tricco, Connor-Gorber, & Tremblay, 2009) suggests the subjective measures were capturing a subset
285
of physical activity. Given the definition (i.e., that makes him/her out of breath or warmer than usual)
286
and examples (i.e., going for a walk, drop-in skating, playing at a splash pad or wading pool, bike or
287
tricycle ride, playing at the park or in the yard) provided in the questionnaire, it is likely more
288
purposeful physical activity was being captured versus incidental physical activity. Therefore, the
289
context of physical activity may be important for cognitive development in this age group.
The inclusion of executive functions in the current study is another important contribution to
SC
290
RI PT
284
the current literature base as previous research has been heavily focused on the language domain
292
(Carson, Hunter, et al., 2016; Carson et al., 2015). Caregiver-child interactions may be one potential
293
explanation for the observed associations between television viewing, subjective physical activity, and
294
vocabulary but not between these behaviors and working memory and response inhibition. Previous
295
work has found a negative association between television viewing and caregiver-child interactions
296
(Christakis, 2009) because parents often use television as a strategy to occupy their children when they
297
need to get other things done (e.g., cooking, household chores) (Carson, Tremblay, Spence, Timmons,
298
& Janssen, 2013; Zimmerman, Christakis, & Meltzoff, 2007). Furthermore, the purposeful physical
299
activity captured by the questionnaire facilitates caregiver-child interactions through play (Lee et al.,
300
2017). Though positive caregiver-child interactions have been identified as an critical factor to protect
301
and enhance overall cognitive development (Walker et al., 2011), the verbal stimulation that
302
characterizes these interactions could have a stronger impact on the language domain than other
303
cognitive domains (Kirkorian, Pempek, Murphy, Schmidt, & Anderson, 2009). In addition, infant
304
studies have reported parallels in the development of motor skills and language acquisition (Ejiri, 1998;
305
Locke, Bekken, McMinn-Larson, & Wein, 1995). It is suggested that gains in motor skills allow
306
greater exploration of the surrounding world; thereby, aiding language development (Iverson, 2010). A
307
similar mechanism could explain the association observed between purposeful physical activity and
AC C
EP
TE D
M AN U
291
15
ACCEPTED MANUSCRIPT language development in this older early years sample. However, future research is needed to better
309
understand the mechanisms of the relationships between sedentary behavior, physical activity, and
310
multiple domains of cognitive development in this age group (Khan & Hillman, 2014), including the
311
potentially mediating role of caregiver-child interactions.
312
RI PT
308
A main strength of the study was the use of high quality exposure and outcome measures. Specifically, the cognitive development measures had established psychometric properties in similar
314
age groups of children and the accelerometer-derived measures of sedentary behavior and physical
315
activity are the gold standard measures for field-based research (Adamo et al., 2009; Cliff, Reilly, &
316
Okely, 2009). Though subjective sedentary behavior and physical activity measures were also used,
317
which are more prone to biases, the screen time measures had good reliability and the physical activity
318
measures had moderate validity, which is consistent with other subjective physical activity measures in
319
young people (Hunter, Leatherdale, Storey, & Carson, 2016; Kowalski, Crocker, & Kowalski, 1997;
320
Mota et al., 2002). However, the subjective measure of physical activity likely captured more
321
purposeful physical activity than incidental physical activity, which is common for physical activity
322
questionnaires in children (Adamo et al., 2009). Furthermore, given the high quality measures and the
323
time and cost demands associated with them, the sample size was modest, limiting the power to adjust
324
for multiple potential confounders in the regression models apart from age. Therefore, it is not possible
325
to rule out residual confounding. It was also not possible to examine effect modification due to power
326
constraints. Furthermore, the demands and participant burden of the high quality measures, including
327
traveling to the lab for an hour appointment and wearing the accelerometer for a week, may have
328
contributed to the higher SES sample. Consequently, it is unclear whether findings can be generalized
329
to all early years children. Another main limitation of the study was the cross-sectional design, which
330
precludes causal inferences regarding associations observed.
331
Conclusion
AC C
EP
TE D
M AN U
SC
313
16
ACCEPTED MANUSCRIPT The early years span a critical period of brain development. Understanding the factors that impact
333
healthy brain development in the early years is needed to inform future interventions aiming to promote
334
optimal cognitive development. Building on previous research, the findings from the present study
335
suggest, television viewing may be detrimental and physical activity, especially non-organized, may be
336
beneficial for vocabulary. Future research with larger sample sizes as well as longitudinal and
337
experimental study designs are needed to confirm these findings and understand the potential
338
mechanisms.
339
Acknowledgements
340
The authors are grateful to all the children and parents who took part in the study. The authors would
341
like to thank all study staff who helped with data collection and data entry, including Stephanie
342
Constantin, Luciano Hood, Dorothea Hui, Danielle Pertschy, Madeline Smith-Ackerl, Nasim Switzer,
343
Alice Yan, Nicholas Kuzik, Stephen Hunter, Eun-Young Lee, and Helena Lee. This study was funded
344
by the Palix Foundation, Alberta Family Wellness Initiative (AFWI), PolicyWise for Children &
345
Families, and the Women and Children’s Health Research Institute (WCHRI). VC is supported by a
346
Canadian Institutes of Health Research (CIHR) new investigator salary award. The funding sources had
347
no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the
348
manuscript; and in the decision to submit the article for publication.
349
Conflict of Interest
350
The authors have no conflict of interests to declare.
SC
M AN U
TE D
EP
AC C
351
RI PT
332
17
ACCEPTED MANUSCRIPT Table 1. Participant Characteristics Variables Age (months; n = 100)
43.4 (9.4)
Sex (%; n=100) 47
RI PT
Male Female
53
Highest Household Education (%; n = 97) Some high school
1.0 3.1
Some university/college (but did not receive degree)
Bachelor’s degree Graduate/professional degree Sedentary behavior and Physical activity Objective Measures
38.1 32.0
328.6 (41.3)
Light-intensity physical activity (min/day; n=79)†
261.1 (28.8)
TE D
Sedentary time (min/day; n=79)†
5.2
20.6
M AN U
College, vocational, or trade school diploma
SC
High school diploma
Moderate- to vigorous-intensity physical activity (min/day; n=79)†
86.6 (25.3)
Total objective physical activity (min/day; n=79)†
347.7 (41.2)
Subjective Measures
87.6 (61.2)
Computer/video games (min/day; n=97)
26.6 (28.2)
Screen time (min/day; n=97)
114.2 (76.7)
AC C
EP
Television viewing (min/day; n=97)
Organized physical activity (min/day; n=97)
13.0 (13.7)
Non-organized physical activity (min/day; n=97)
41.7 (16.4)
Total subjective physical activity (min/day; n=97)
54.7 (18.6)
Cognitive Development
Vocabulary (standard score; n=92)
114.1 (12.4)
Working Memory (summary score; n=88)
2.0 (0.7)
Response Inhibition (d-prime sensitivity score; n=81)
2.7 (0.7)
18
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
Data presented as mean (standard deviation) for continuous variables and percentages for categorical variables. †Corrected for wear time using the residuals method.
19
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
Table 2. Pearson correlations between objective and subjective measures of sedentary behavior, physical activity, and cognitive development Vocabulary Working Memory Response Inhibition Objective Measures (n=75) (n=71) (n=65) Sedentary time† r = -0.02 r = -0.04 r = 0.10 LPA† r = 0.04 r = -0.09 r = -0.10 MVPA† r = -0.01 r = 0.17 r = -0.06 Total objective PA† r = -0.04 r = 0.04 r = -0.11 Subjective Measures (n=90) (n=85) (n=79) Television viewing r = -0.14 r = -0.09 r = -0.21* Video/computer games r = -0.07 r = 0.01 r = 0.12 Screen time r = -0.11 r = -0.02 r = -0.19 Organized PA r = 0.09 r = 0.06 r = -0.01 Non-organized PA r = 0.12 r = 0.09 r = 0.27* Total subjective PA r = 0.16 r = 0.07 r = 0.31* LPA = light-intensity physical activity; MVPA = moderate- to vigorous-intensity physical activity; PA = physical activity †Corrected for wear time using the residuals method. *P<0.05
20
ACCEPTED MANUSCRIPT
M AN U
SC
RI PT
Table 3. Unstandardized beta coefficients (95% Confidence Intervals) for the associations between objective and subjective measures of sedentary behavior, physical activity, and cognitive development adjusted for age Vocabulary Working Memory Response Inhibition Β (95% CI) Β (95% CI) Β (95% CI) Objective Measures (n=75) (n=71) (n=65) Sedentary time (min/day) -0.001 (-0.071, 0.069) 0.001 (-0.003, 0.005) 0.002 (-0.003, 0.006) LPA (min/day) 0.013 (-0.086, 0.113) -0.003 (-0.008, 0.003) -0.002 (-0.009, 0.004) MVPA (min/day) -0.016 (-0.133, 0.101) 0.002 (-0.005, 0.008) -0.001 (-0.009, 0.007) Total Objective PA (min/day) 0.001 (-0.069, 0.071) -0.001 (-0.005, 0.003) -0.002 (-0.006, 0.003) Subjective Measures (n=90) (n=85) (n=79) Television viewing (min/day) -0.001 (-0.004, 0.002) -0.046 (-0.090, -0.002)** -0.002 (-0.005, -0.000) Video/computer game (min/day) -0.034 (-0.129, 0.062) -0.001 (-0.006, 0.004) 0.003 (-0.003, 0.009) Screen time (min/day) -0.034 (-0.068, 0.001) -0.002 (-0.003, 0.000) -0.000 (-0.002, 0.002)
AC C
EP
TE D
Organized PA (min/day) 0.085 (-0.127, 0.296) -0.002 (-0.013, 0.009) -0.000 (-0.012, 0.011) Non-organized PA (min/day) 0.008 (-0.001, 0.017) 0.004 (-0.006, 0.014) 0.216 (0.057, 0.375)** Total subjective PA (min/day) 0.005 (-0.003, 0.013) 0.003 (-0.006, 0.012) 0.209 (0.070, 0.349)** LPA = light-intensity physical activity; MVPA = moderate- to vigorous-intensity physical activity; PA = physical activity All models were adjusted for age. **P<0.05
21
ACCEPTED MANUSCRIPT References
AC C
EP
TE D
M AN U
SC
RI PT
Adamo, K. B., Prince, S. A., Tricco, A. C., Connor-Gorber, S., & Tremblay, M. (2009). A comparison of indirect versus direct measures for assessing physical activity in the pediatric population: a systematic review. Int J Pediatr Obes, 4(1), 2-27. doi:10.1080/17477160802315010 Becker, D. R., McClelland, M. M., Loprinzi, P., & Trost, S. G. (2014). Physical activity, selfregulation, and early academic achievement in preschool children. Early Educ Dev, 25, 56-70. Biddle, S. J., & Asare, M. (2011). Physical activity and mental health in children and adolescents: a review of reviews. British journal of sports medicine, 45(11), 886-895. Boudreau, D. (2005). Use of a parent questionnaire in emergent and early literacy assessment of preschool children. Language, speech, and hearing services in schools, 36(1), 33-47. Campbell, D. W., Eaton, W. O., & McKeen, N. A. (2002). Motor activity level and behavioural control in young children. International Journal Of Behavioral Development, 26(4), 289-296. Carson, V., Hunter, S., Kuzik, N., Wiebe, S. A., Spence, J. C., Friedman, A., . . . Hinkley, T. (2016). Systematic review of physical activity and cognitive development in early childhood. Journal of science and medicine in sport / Sports Medicine Australia, 19(7), 573-578. doi:10.1016/j.jsams.2015.07.011 Carson, V., & Janssen, I. (2012). Associations between factors within the home setting and screen time among children aged 0-5 years: a cross-sectional study. BMC Public Health, 12, 539. doi:10.1186/1471-2458-12-539 Carson, V., Kuzik, N., Hunter, S., Wiebe, S. A., Spence, J. C., Friedman, A., . . . Hinkley, T. (2015). Systematic review of sedentary behavior and cognitive development in early childhood. Preventive medicine, 78, 115-122. doi:10.1016/j.ypmed.2015.07.016 Carson, V., Rhodes, R. E., Rinaldi, C., Rodgers, W., Spence, J. C., & Hesketh, K. D. (2017). Psychometric properties of a parental questionnaire for assessing correlates of toddlers’ physical activity and sedentary behavior. Measurement in Physical Education and Exercise Science. Carson, V., Rosu, A., & Janssen, I. (2014). A cross-sectional study of the environment, physical activity, and screen time among young children and their parents. BMC Public Health, 14, 61. doi:10.1186/1471-2458-14-61 Carson, V., Tremblay, M., Spence, J. C., Timmons, B., & Janssen, I. (2013). The Canadian Sedentary Behaviour Guidelines for the Early Years (zero to four years of age) and screen time among children from Kingston, Ontario. Paediatr Child Health, 18(1), 25-28. Chevalier, N., James, T. D., Wiebe, S. A., Nelson, J. M., & Espy, K. A. (2014). Contribution of reactive and proactive control to children's working memory performance: Insight from item recall durations in response sequence planning. Developmental psychology, 50(7), 1999-2008. doi:10.1037/a0036644 Chevalier, N., Kelsey, K. M., Wiebe, S. A., & Espy, K. A. (2014). The temporal dynamic of response inhibition in early childhood: an ERP study of partial and successful inhibition. Dev Neuropsychol, 39(8), 585-599. doi:10.1080/87565641.2014.973497 Christakis, D. A. (2009). The effects of infant media usage: what do we know and what should we learn? Acta Paediatr, 98(1), 8-16. doi:APA1027 [pii] 10.1111/j.1651-2227.2008.01027.x Christakis, D. A. (2014). Interactive media use at younger than the age of 2 years: time to rethink the American Academy of Pediatrics guideline? JAMA pediatrics, 168(5), 399-400. doi:10.1001/jamapediatrics.2013.5081 Cliff, D. P., Reilly, J. J., & Okely, A. D. (2009). Methodological considerations in using accelerometers to assess habitual physical activity in children aged 0-5 years. Journal of science 22
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
and medicine in sport / Sports Medicine Australia, 12(5), 557-567. doi:10.1016/j.jsams.2008.10.008 Cohen, J. (1992). A power primer. Psychological bulletin, 112(1), 155-159. Colley, R. C., Garriguet, D., Adamo, K. B., Carson, V., Janssen, I., Timmons, B. W., & Tremblay, M. S. (2013). Physical activity and sedentary behavior during the early years in Canada: a crosssectional study. The international journal of behavioral nutrition and physical activity, 10, 54. doi:10.1186/1479-5868-10-54 Draper, C. E., Achmat, M., ,, Forbes, J., & Lambert, E. V. (2012). Impact of a community-based programme for motor development on gross motor skills and cognitive function in preschool children from disadvantaged settings. Early Child Dev Care, 182(1), 137-152. Dunn, L. M., & Dunn, D. M. (2007). PPVT-4 :Peabody picture vocabulary test (4th edition). Minneapolis, MN: Pearson Assessments. Ejiri, K. (1998). Relationship between rhythmic behavior and canonical babbling in infant vocal development. Phonetica, 55(4), 226-237. doi:28434 Garon, N., Bryson, S. E., & Smith, I. M. (2008). Executive function in preschoolers: a review using an integrative framework. Psychological bulletin, 134(1), 31-60. doi:10.1037/0033-2909.134.1.31 Greenough, W. T., Black, J. E., & Wallace, C. S. (1987). Experience and brain development. Child Dev, 58(3), 539-559. Hinkley, T., O'Connell, E., Okely, A. D., Crawford, D., Hesketh, K., & Salmon, J. (2012). Assessing volume of accelerometry data for reliability in preschool children. Medicine and science in sports and exercise, 44(12), 2436-2441. doi:10.1249/MSS.0b013e3182661478 Hinkley, T., Salmon, J., Okely, A. D., Crawford, D., & Hesketh, K. (2012). Preschoolers' physical activity, screen time, and compliance with recommendations. Medicine and science in sports and exercise, 44(3), 458-465. doi:10.1249/MSS.0b013e318233763b Holmes, R. M., Pellegrini, A. D., & Schmidt, S. L. (2006). The effects of different recess timing regimens on preschoolers' classroom attention. Early Child Dev Care, 176(7), 735-743. Hunter, S., Leatherdale, S. T., Storey, K., & Carson, V. (2016). A quasi-experimental examination of how school-based physical activity changes impact secondary school student moderate- to vigorous- intensity physical activity over time in the COMPASS study. The international journal of behavioral nutrition and physical activity, 13, 86. doi:10.1186/s12966-016-0411-9 Huttenlocher, P. R., & Dabholkar, A. S. (1997). Regional differences in synaptogenesis in human cerebral cortex. The Journal of comparative neurology, 387(2), 167-178. Irwin, J. D., Johnson, A. M., Vanderloo, L. M., Burke, S. M., & Tucker, P. (2015). Temperament and Objectively Measured Physical Activity and Sedentary Time among Canadian Preschoolers. Prev Med Rep, 2, 598-601. doi:10.1016/j.pmedr.2015.07.007 Iverson, J. M. (2010). Developing language in a developing body: the relationship between motor development and language development. J Child Lang, 37(2), 229-261. doi:10.1017/S0305000909990432 Janssen, X., Cliff, D. P., Reilly, J. J., Hinkley, T., Jones, R. A., Batterham, M., . . . Okely, A. D. (2013). Predictive validity and classification accuracy of ActiGraph energy expenditure equations and cut-points in young children. PloS one, 8(11), e79124. doi:10.1371/journal.pone.0079124 Khan, N. A., & Hillman, C. H. (2014). The relation of childhood physical activity and aerobic fitness to brain function and cognition: a review. Pediatric exercise science, 26(2), 138-146. doi:10.1123/pes.2013-0125 Kirk, S. M., & Kirk, E. P. (2016). Sixty Minutes of Physical Activity per Day Included Within Preschool Academic Lessons Improves Early Literacy. J Sch Health, 86(3), 155-163. doi:10.1111/josh.12363 23
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
Kirkorian, H. L., Pempek, T. A., Murphy, L. A., Schmidt, M. E., & Anderson, D. R. (2009). The impact of background television on parent-child interaction. Child Dev, 80(5), 1350-1359. doi:10.1111/j.1467-8624.2009.01337.x Knudsen, E. I. (2004). Sensitive periods in the development of the brain and behavior. Journal of cognitive neuroscience, 16(8), 1412-1425. doi:10.1162/0898929042304796 Kowalski, K. C., Crocker, P. R. E., & Kowalski, N. P. (1997). Convergent validity of the physical activity questionnaire for adolescents. Pediatric exercise science, 9, 342-352. Lee, E. Y., Spence, J. C., & Carson, V. (2017). Television viewing, reading, physical activity and brain development among young South Korean children. Journal of science and medicine in sport / Sports Medicine Australia. doi:10.1016/j.jsams.2016.11.014 Lenroot, R. K., & Giedd, J. N. (2006). Brain development in children and adolescents: insights from anatomical magnetic resonance imaging. Neuroscience and biobehavioral reviews, 30(6), 718729. doi:10.1016/j.neubiorev.2006.06.001 Li, X., & Atkins, M. S. (2004). Early childhood computer experience and cognitive and motor development. Pediatrics, 113(6), 1715-1722. Locke, J. L., Bekken, K. E., McMinn-Larson, L., & Wein, D. (1995). Emergent control of manual and vocal-motor activity in relation to the development of speech. Brain Lang, 51(3), 498-508. Lubans, D.R., Hesketh, K., Cliff, D.P., Barnett, L.M., Salmon, J., Dollman, J., Morgan, P.J., Hills, A.P., & Hardy, L.L. (2011). A systematic review of the validity and reliability of sedentary behaviour measures used with children and adolescents. Obesity Reviews, 12 (10), 781-799 Mavilidi M-F, Okely, A. D., Chandler, P., Cliff, D. P., & Paas, F. (2015). Effects of integrated physical exercises and gestures on preschool children’s foreign language vocabulary learning. Educ Psychol Rev, 27(3), 413-426. Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41(1), 49-100. Mota, J., Santos, P., Guerra, S., Ribeiro, J. C., Duarte, J. A., & Sallis, J. F. (2002). Validation of a physical activity self-report questionnaire in a Portuguese pediatric population. Pediatric exercise science, 14, 269-276. Tandon, P. S., Tovar, A., Jayasuriya, A. T., Welker, E., Schober, D. J., Copeland, K., . . . Ward, D. S. (2016). The relationship between physical activity and diet and young children's cognitive development: A systematic review. Prev Med Rep, 3, 379-390. doi:10.1016/j.pmedr.2016.04.003 Teixeira Costa, H. J., Abelairas-Gomez, C., Arufe-Giráldez, V., Pazos-Couto, J. M., & BarcalaFurelos, R. (2015). Influence of a physical education plan on psychomotor development profiles of preschool children. J Human Sport Exerc, 10(1), 126-140. Tomasello, M. (2010). Language Development. In U. Goswami (Ed.), The Wiley-Blackwell Handbook of Childhood Cognitive Development, 2nd Edition (pp. 239-257). Oxford, UK: WileyBlackwell. Voss, M. W., Car, L. J., Clark, R., & Weng, T. (2014). Revenge of the "sit" II: Does lifestyle impact neuronal and cognitive health through distinct mechanisms associated with sedentary behavior and physical activity? Mental Health and Physical Activity, 7(2014), 9-24. Walker, S. P., Wachs, T. D., Grantham-McGregor, S., Black, M. M., Nelson, C. A., Huffman, S. L., . . . Richter, L. (2011). Inequality in early childhood: risk and protective factors for early child development. Lancet, 378(9799), 1325-1338. doi:10.1016/S0140-6736(11)60555-2 Webster, E. K., Wadsworth, D. D., & Robinson, L. E. (2015). Preschoolers' time on-task and physical activity during a classroom activity break. Pediatric exercise science, 27(1), 160-167. doi:10.1123/pes.2014-0006 24
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
Wiebe, S. A., Clark, C. A., De Jong, D. M., Chevalier, N., Espy, K. A., & Wakschlag, L. (2015). Prenatal tobacco exposure and self-regulation in early childhood: Implications for developmental psychopathology. Dev Psychopathol, 27(2), 397-409. doi:10.1017/S095457941500005X Wiebe, S. A., Sheffield, T. D., & Espy, K. A. (2012). Separating the fish from the sharks: A longitudinal study of preschool response inhibition. Child Development, 83(4), 1245-1261. Wiebe, S. A., Sheffield, T., Nelson, J. M., Clark, C. A. C., Chevalier, N., & Epsy, K. A. (2011). The structure of executive function in 3-year-olds. J Exp Child Psychol, 108(3), 436-458. Willett, W., & Stampfer, M. J. (1986). Total energy intake: implications for epidemiologic analyses. American journal of epidemiology, 124(1), 17-27. Zachopoulou, E., Trevlas, E., Konstadinidou, E., & Group, A. P. R. (2006). The design and implementation of a physical education program to promote children’s creativity in the early years. Int J Early Years Educ., 14(3), 279-294. Zimmerman, F. J., Christakis, D. A., & Meltzoff, A. N. (2007). Television and DVD/video viewing in children younger than 2 years. Archives of pediatrics & adolescent medicine, 161(5), 473-479. doi:10.1001/archpedi.161.5.473
25
ACCEPTED MANUSCRIPT
Highlights
EP
TE D
M AN U
SC
RI PT
Objective measures of behavior were not associated with cognitive development Television viewing was unfavorably associated with vocabulary Non-organized and total physical activity were favorably associated with vocabulary The behaviors were not associated with working memory and response inhibition
AC C
• • • •