Associations of subjectively and objectively measured sedentary behavior and physical activity with cognitive development in the early years

Associations of subjectively and objectively measured sedentary behavior and physical activity with cognitive development in the early years

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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.

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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,

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T6G 2H9.

Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada, T6G

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Department of Psychology, University of Alberta, Edmonton, Alberta, Canada, T6G 2E9.

Author E-mail Addresses: VC: [email protected]

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SW: [email protected]

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AAR: [email protected]

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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

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Male Female

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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)†

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College, vocational, or trade school diploma

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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)

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Light-intensity physical activity (min/day; n=79)†

114.2 (76.7)

Organized physical activity (min/day; n=97)

13.0 (13.7)

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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.

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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

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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)*

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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

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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

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Associations of subjectively and objectively measured sedentary behavior and physical activity with cognitive development in the early years

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ACCEPTED MANUSCRIPT Abstract

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Purpose: To examine the associations of subjectively and objectively measured sedentary behavior

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and physical activity with cognitive development in a sample of 30 to 59 month olds.

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Methods: Cross-sectional findings are based on 100 early years children (43.4±9.4 months; 53%

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female) from Edmonton, Canada that were part of the Physical Activity and Cognition in Early

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Childhood (PACE) study. Sedentary time and physical activity (light-intensity, moderate- to vigorous-

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intensity, total) were objectively measured with an accelerometer. Sedentary behavior (television,

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video/computer games, screen time) and physical activity (organized, non-organized, total) were also

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subjectively measured with a parental questionnaire. Vocabulary was measured with the Peabody

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Picture Vocabulary Test, Fourth Edition, working memory was measured with the Nebraska Barnyard

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task, and response inhibition was measured with the Fish-Shark Go/No-Go task. Correlations and

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linear regression were used to examine associations.

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Results: Total subjective physical activity (r=0.31; p=0.018) and non-organized physical activity

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(r=0.27; p=0.035) were significantly positively correlated with vocabulary. Conversely, television

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viewing (r=-0.21; p=0.046) was significantly negatively correlated with vocabulary. These significant

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associations remained in linear regression models after adjusting for age. Objectively measured

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sedentary time and physical activity were not significantly associated with any cognitive development

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measure and no sedentary behavior or physical activity measure was associated with working memory

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or response inhibition.

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Conclusions: Television viewing may be detrimental and physical activity, especially non-organized,

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may be beneficial for vocabulary in early years children. Future research with larger sample sizes and

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longitudinal and experimental study designs are needed to confirm these findings and determine the

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mechanisms.

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Keywords: Physical activity, Television, Vocabulary, Memory, Response inhibition, Young children

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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

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through the early 20s (Christakis, 2009). The early years, the first five years of life, are characterized

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by significant growth and development of the brain (Khan & Hillman, 2014; Lenroot & Giedd, 2006).

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By age 2 the human brain has reached approximately 80% of its adult weight and by age 5 it has

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reached 90% of it adult weight (Lenroot & Giedd, 2006). Furthermore, the production of synapses

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rapidly increases from birth to 2 years (Lenroot & Giedd, 2006) followed by pruning of synapses at

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varying rates in different regions of the brain (Huttenlocher & Dabholkar, 1997; Lenroot & Giedd,

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2006). This rapid period of brain maturation in the early years (Khan & Hillman, 2014) makes it more

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sensitive to the immediate environment (Knudsen, 2004). As a result, early life experiences can have

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beneficial or detrimental long-term effects on brain structure and function (Greenough, Black, &

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Wallace, 1987). For instance, healthy brain development in the early years enables optimal cognitive

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development, including the growth of abilities and skills in domains such as vocabulary (Tomasello,

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2010) and executive functions (Garon, Bryson, & Smith, 2008). Therefore, identifying and targeting

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the factors that are beneficially associated with healthy brain development during the early years is

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critical to facilitating optimal cognitive development across domains.

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Physical activity may be one factor to consider for optimal brain and cognitive development in

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the early years, given its association with cognitive outcomes in older age groups. For instance,

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evidence from several reviews indicates physical activity in school-aged children and youth is

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beneficially associated with cognitive functioning (Biddle & Asare, 2011). Additionally, a number of

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mechanisms to explain this relationship have been identified primarily in animal models and older

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adults (Khan & Hillman, 2014; Voss, Car, Clark, & Weng, 2014). However, evidence in the early years

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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

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the relationship between physical activity and cognitive development (Carson, Hunter, et al., 2016).

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While some preliminary evidence was found for a positive relationship, the majority of studies were

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rated as weak in quality (Carson, Hunter, et al., 2016) and only two studies included an objective

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measure of physical activity (Becker, McClelland, Loprinzi, & Trost, 2014; Campbell, Eaton, &

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McKeen, 2002). Similar findings were observed in a subsequent review (Tandon et al., 2016) on

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physical activity, gross motor skills, diet and cognitive development, which included two additional

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physical activity studies (Draper, Achmat, Forbes, & Lambert, 2012; Mavilidi M-F, Okely, Chandler,

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Cliff, & Paas, 2015) that were not included in the earlier review.

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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

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early years (Carson et al., 2015). It is thought that television viewing may have a detrimental impact on

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brain development in the early years due to the overstimulation of the developing brain and reduced

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interaction with caregivers (Christakis, 2009). A recent systematic review on sedentary behavior and

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cognitive development in early childhood observed primarily null or detrimental effects for screen time

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(Carson et al., 2015). However, the majority of studies were rated as weak in quality and none of the

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studies included contemporary forms of screen time beyond television viewing as an exposure.

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Furthermore, no study included an objective measure of sedentary behavior (Carson et al., 2015). It is

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important to consider both objective and subjective measures as objective measures can more

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accurately measure total sedentary time and subjective measures can provide information on type and

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context of sedentary behavior (Lubans, Hesketh, et al., 2011).

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Future research examining the association between sedentary behavior, physical activity, and

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cognitive development that addresses current gaps and limitations is needed to strength the evidence

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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

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particular moderate- to vigorous-intensity physical activity (MVPA), in the early years (Colley et al.,

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2013; Hinkley, Salmon, Okely, Crawford, & Hesketh, 2012). Therefore, the objective of this study was

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to examine the associations of subjectively and objectively measured sedentary behavior and physical

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activity with cognitive development in a sample of early years children.

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Methods

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Participants

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Participants were 100 children aged 30 to 59 months from the Physical Activity and Cognition in Early

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Childhood (PACE) study. Data were collected between April, 2015 and December, 2016 in Edmonton,

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Canada. Families were recruited from existing databases, local media, online advertising, and flyers

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distributed to businesses serving families such as child care centres and doctor’s offices. Inclusion

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criteria for the study were: (1) children aged 30 to 59 months and (2) English-speaking and English-

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reading parents. The exclusion criteria for the study were: (1) children who are non-ambulatory; (2)

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children diagnosed with a new or recent chronic disease (e.g., Type 1 diabetes) where physical activity

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may be limited during the initiation of treatment; (3) children with a disability/impairment that limits

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their ability to be physically active; and (4) children with a developmental delay, diagnosed

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neurological or psychiatric disorder, or children with pre- or perinatal risk factors known to affect brain

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development (e.g., fetal alcohol spectrum disorder, preterm birth, low birth weight). Families that met

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eligible criteria and agreed to participate attended a laboratory appointment where cognitive

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development measures were conducted. At the appointment, parents completed a questionnaire and

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families were also given an accelerometer for their child to wear for 7 consecutive days. Verbal and

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written accelerometer instructions were provided to families. Ethics approval was obtained from the

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University of Alberta Human Research Ethics Board and all participating parents provided written

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informed consent.

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Objective measures of sedentary time and physical activity Sedentary time, light-intensity physical activity (LPA), and moderate- to vigorous-intensity physical

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activity (MVPA) were objectively measured with Actigraph wGT3X-BT accelerometers worn on an

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elastic waistband over the right hip for 7 consecutive days. Parents were instructed to only remove the

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accelerometer for overnight sleep and during swimming and bathing. Data was collected in 15 second

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epochs. To be included, participants were required to have ≥4 days with ≥ 1440 total 15 second

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intervals (equivalent to 6 hours) of wear time each day (Hinkley, O'Connell, et al., 2012). A weekend

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day was not required for the 4 days. Non-wear time was defined as ≥80 consecutive 15 second intervals

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of zero counts (equivalent to ≥20 minutes of consecutive zeros counts). Daytime naps were assumed to

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be removed with non-wear time. Sedentary time was defined as 0-25 counts per 15 seconds, LPA as

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26-419 counts per 15 seconds, and MVPA as ≥420 counts per 15 seconds (Janssen et al., 2013).

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Minutes per day of sedentary time, LPA, and MVPA were derived by dividing the number of 15

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second intervals classified as wear time by 4 and then dividing the total minutes in each intensity by the

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total number of valid days. To adjust for wear time, sedentary time, LPA, and MVPA variables were

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standardized by using the residuals obtained when regressing sedentary time, LPA, and MVPA

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separately on wear time (Willett & Stampfer, 1986).

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Subjective measures of sedentary behavior and physical activity

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Television viewing, video/computer games, and overall screen time were subjectively measured via the

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parental questionnaire. Parents reported the average hours and minutes per weekday and weekend day

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that their child: (1) “watches television, videos, or DVDs on a television, computer, or portable device”

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and (2) “plays video/computer games on devices such as a learning laptop, leapfrog leapster, computer,

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laptop, tablet, cell phone, the internet, Playstation, or XBOX”. All four questions were open ended.

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Average minutes per day of both television and video/computer games were derived by calculating

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ACCEPTED MANUSCRIPT weighted averages for weekday and weekend responses ([weekday*5 + weekend*2]/7). Average

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minutes per day of screen time was derived by summing the average minutes per day of television and

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video/computer games. The television viewing and video/computer game questions originally came

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from a national survey in Canada (Colley et al., 2013) and were modified for a previous study in early

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years children (Carson & Janssen, 2012; Carson, Rosu, & Janssen, 2014). The modified questions have

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shown good 1-week test re-test reliability (Intra-class correlation=0.82) in another sample of early

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years children (Carson, Rhodes, et al., 2016).

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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

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child usually takes part in physical activity (that makes him/her out of breath or warmer than usual)

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while participating in: (1) “organized activities (e.g., swimming lessons, skating lessons, gymnastics)”

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and (2) “non-organized activities (e.g., going for a walk, drop-in skating, playing at a splash pad or

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wading pool, bike or tricycle ride, playing at the park or in the yard)”. There were 5 response options

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for both questions (never, less than 2 hours per week, 2-3 hours per week, 4-6 hours per week, 7+

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hours per week). Consistent with previous research (Colley et al., 2013), where applicable, the mid-

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points of the response category (i.e., 0, 1, 2.5, 5, 7) was calculated for organized and non-organized

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activity variables. Minutes per day of organized and non-organized physical activity were derived by

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multiplying hours per week variables by 60 and dividing total weekly minutes by 7. Total minutes per

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day of physical activity was derived by summing organized and non-organized physical activity

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variables. The questions were adopted from a national survey in Canada (Colley et al., 2013). Total

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subjective physical activity was significantly correlated with total objective physical activity (r=0.31;

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p=0.005) and MVPA (r=0.33; p=0.003) in the sample. Additionally, non-organized physical activity

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was significantly correlated with total objective physical activity (r=0.28; p=0.01) and MVPA in this

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ACCEPTED MANUSCRIPT sample (r=0.34; p=0.002). No significant correlations were observed between organized physical

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activity and objective measures.

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Cognitive Development

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Language and executive functions were the cognitive development domains assessed in this study,

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informed by two systematic reviews on the association between physical activity, sedentary behavior

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and cognitive development in early childhood (Carson et al., 2015; Carson et al., 2016). To assess

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executive functions, which encompasses the cognitive abilities that support goal-directed behavior

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(Miyake, Friedman, Emerson, Witzki, Howerter & Wager, 2000), we included tasks measuring

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children's ability to hold and manipulate information in working memory and to inhibit prepotent

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responses. To assess language, we included a measure of children's receptive vocabulary.

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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

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development (Chevalier, James, Wiebe, Nelson, & Espy, 2014; S. A. Wiebe et al., 2015). Children are

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asked to press buttons on a touch screen corresponding to animal names, in the same order as they are

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spoken by the examiner. The task begins with two-item sequences, and progressively increase in length

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until the child responds incorrectly to all sequences at a level. A summary score was created by

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summing the child’s proportion of correct responses at each level across all completed levels. In

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previous research with preschool children, this measure showed high 9-month longitudinal stability (r

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= .64-.71; unpublished data) and loaded significantly on a latent executive function factor (λ = .41-.52)

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(Wiebe et al., 2011).

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Response inhibition was measured with the Fish-Shark Go/No-Go task (Chevalier, Kelsey,

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Wiebe, & Espy, 2014; Wiebe et al., 2015; Wiebe, Sheffield, & Espy, 2012). This is a child-friendly

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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

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button when a shark appears (no-go trials); 75% of trials are go trials to promote children’s tendency to

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respond. The standardized difference between the hit rate and the false alarm rate, called the d-prime

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sensitivity, was calculated, reflecting children’s success in following both task rules. Previously, this

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measure showed moderate but significant 9-month longitudinal stability in preschool children (r = .30-

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.46; unpublished data) and loaded significantly on latent executive function (λ = .37-.38) (Wiebe et al.,

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2011).

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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

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in children aged 30 months and up (Dunn & Dunn, 2007). Children are asked to point to the picture (of

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four options) corresponding to each vocabulary item. An age-normed standard score was calculated.

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This test has good 4-week test-retest reliability (r = .94-.95) and convergent validity (r = .80-.84 with

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productive vocabulary measures) (Dunn & Dunn, 2007).

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Statistical Analyses

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All statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC). Descriptive

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statistics were calculated. All continuous variables were checked for outliers (≥±3 standard deviations)

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and television viewing was truncated below 3 standard deviations for two participants. The assumption

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of normality for correlation and linear regression was assessed by examining residuals for physical

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activity, sedentary behavior, and cognitive development variables. No variables needed to be

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transformed. Pearson correlations between each sedentary behavior and physical activity variable with

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each cognitive development variables were conducted. Next, multiple linear regression models were

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run to examine the associations between each sedentary behavior and physical activity variable with

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ACCEPTED MANUSCRIPT each cognitive development variable while adjusting for age in months. Statistical significance was

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defined as a p< 0.05.

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Results

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Participant characteristics are provided in Table 1. Of the 100 participants, 97 had complete

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questionnaire data, 92 had vocabulary data, 88 had working memory data, 81 had response inhibition

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data, and 79 had complete accelerometer data. On average, the 79 participants had 6.6 valid days and

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11.3 hours/day of total wear time (data not shown). Pairwise deletion was used to handle missing data.

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The average age of the sample was 43.4 months and 53% were female. According to the accelerometry

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data, children participated in an average 347.7 min/day of total physical activity (i.e., LPA and

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MVPA); whereas, according to parental-report, children participated in an average of 54.7 min/day of

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total physical activity (i.e., organized and non-organized). Furthermore, according to the parental-

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report, children participated in an average of 114.2 min/day of screen time.

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Pearson correlations between objective and subjective measures of sedentary behavior, physical

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activity, and cognitive development are provided in Table 2. Total subjective physical activity (r=0.31;

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p=0.018) and non-organized physical activity (r=0.27; p=0.035) were significantly positively correlated

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with vocabulary. The effect size for these correlations is considered medium/large and small/medium,

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respectfully (Cohen, 1992). Additionally, television viewing was significantly negatively correlated

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with vocabulary (r=-0.21; p=0.046), with a small/medium effect size (Cohen, 1992). All other

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correlations had a p-value ≥0.10.

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Linear regression results for associations between objective and subjective measures of

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sedentary behavior, physical activity, and cognitive development adjusted for age in months are

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provided in Table 3. For every additional minute per day of television viewing, vocabulary was 0.046

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(95%CI: 0.090, 0.002; p=0.040) units lower, respectively. For every additional minute per day of non-

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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

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associations had a p-value ≥0.10.

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Discussion

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This study aimed to determine the associations of sedentary behavior and physical activity, measured

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both objectively and subjectively, with vocabulary, working memory, and response inhibition in a

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sample of 30-59 month olds. Sedentary behavior and physical activity, measured objectively via an

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accelerometer, were not associated with any measure of cognitive development. However, associations

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were observed between subjective measures of sedentary behavior and physical activity and cognitive

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development. Specifically, higher television viewing was significantly associated with poorer

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vocabulary scores, and higher non-organized physical activity and total physical activity were

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significantly associated with better vocabulary scores. No associations were observed with working

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memory and response inhibition.

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between sedentary behavior and cognitive development in early childhood (birth to 6 years), where the

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majority of associations between television viewing and cognitive development across 37 studies were

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detrimental or null (Carson et al., 2015). The null or detrimental findings were consistent across

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different domains of cognitive development within the review; however, language (e.g., vocabulary)

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was the most commonly assessed domain (Carson et al., 2015). Overall, findings suggest that television

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viewing is not beneficial for cognitive development and it may be detrimental.

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The present study builds on the existing body of evidence for sedentary behavior and cognitive

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development in several ways. Within the previous systematic review (Carson et al., 2015), only two

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studies assessed video/computer game use (Boudreau, 2005; Li & Atkins, 2004) so conclusions could

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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

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television viewing but not for video/computer game use. One potential reason for the different findings

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between these two exposures may be the different amount of time spent engaging in them. For

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example, of the 114 average minutes of screen time, approximately 76% was spent watching television

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and 24% was spent using video/computer games. Furthermore, the present study incorporated

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contemporary examples within the screen time measures, including tablets and cell phones for

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video/computer games. There is some speculation that interactive games found in a number of apps on

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tablets and smartphones may be less detrimental than passive television viewing (Christakis, 2014).

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Since there is no empirical evidence to support this, it is an important area for future research

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(Christakis, 2014).

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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

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(Carson et al., 2015). The finding that total sedentary behavior was not associated with cognitive

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development may again support the notion that not all types of screen time and non-screen time

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pursuits are equal in terms of their impact on cognitive development. For instance, within the previous

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review, reading/being read to was found to be consistently positively associated with cognitive

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development, specifically within the language domain (Carson et al., 2015). Given children cannot

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spend all waking hours being physically active, when they are sedentary in discretionary time, certain

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pursuits may be better to promote than others for optimal cognitive development. While reading/being

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read to appears to be one valuable pursuit (Carson et al., 2015), future research should examine if there

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are other sedentary pursuits that are positive for cognitive development. Furthermore, given this was

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the first study to examine the association between objectively measured sedentary time and cognitive

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development in early years children, results need to be confirmed in other samples.

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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

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at least one cognitive outcome were observed in eight out of nine studies (Carson, Hunter, et al., 2016;

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Tandon et al., 2016). Other experimental studies either published since these reviews were conducted

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or not included within these previous reviews have observed similar findings (Holmes, Pellegrini, &

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Schmidt, 2006; Kirk & Kirk, 2016; Teixeira Costa, Abelairas-Gomez, Arufe-Giráldez, Pazos-Couto, &

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Barcala-Furelos, 2015; Webster, Wadsworth, & Robinson, 2015; Zachopoulou, Trevlas,

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Konstadinidou, & Group, 2006), though both null (Irwin, Johnson, Vanderloo, Burke, & Tucker, 2015)

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and beneficial associations (Lee, Spence, & Carson, 2017) have been reported from observational

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studies not captured in the previous review. Overall, these findings suggest that physical activity is not

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detrimental for cognitive development and it is likely beneficial. This is consistent with what is

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observed in older children (Biddle & Asare, 2011).

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physical activity and cognitive development in young children (Becker et al., 2014; Irwin et al., 2015).

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In contrast to the present study, Becker and colleagues (2014) found higher MVPA during recess was

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significantly associated with higher self-regulation but consistent with the overall findings of the

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present study MVPA was not associated with literacy and math achievement (Becker et al., 2014).

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Similarly, total physical activity and MVPA were not associated with attention span in another study

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(Irwin et al., 2015). However, no previous study has examined the association between both objective

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and subjective measures of physical activity and cognitive development in the same study in the early

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years age group. Though significant correlations between objective and subjective measures of physical

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activity were observed in the present study, large differences in absolute values were observed when

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comparing subjective and objective measures, with all subjective measures being lower than all

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objective measures. The fact that objective measures are considered more robust (Adamo, Prince,

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ACCEPTED MANUSCRIPT Tricco, Connor-Gorber, & Tremblay, 2009) suggests the subjective measures were capturing a subset

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of physical activity. Given the definition (i.e., that makes him/her out of breath or warmer than usual)

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and examples (i.e., going for a walk, drop-in skating, playing at a splash pad or wading pool, bike or

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tricycle ride, playing at the park or in the yard) provided in the questionnaire, it is likely more

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purposeful physical activity was being captured versus incidental physical activity. Therefore, the

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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

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the current literature base as previous research has been heavily focused on the language domain

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(Carson, Hunter, et al., 2016; Carson et al., 2015). Caregiver-child interactions may be one potential

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explanation for the observed associations between television viewing, subjective physical activity, and

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vocabulary but not between these behaviors and working memory and response inhibition. Previous

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work has found a negative association between television viewing and caregiver-child interactions

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(Christakis, 2009) because parents often use television as a strategy to occupy their children when they

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need to get other things done (e.g., cooking, household chores) (Carson, Tremblay, Spence, Timmons,

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& Janssen, 2013; Zimmerman, Christakis, & Meltzoff, 2007). Furthermore, the purposeful physical

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activity captured by the questionnaire facilitates caregiver-child interactions through play (Lee et al.,

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2017). Though positive caregiver-child interactions have been identified as an critical factor to protect

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and enhance overall cognitive development (Walker et al., 2011), the verbal stimulation that

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characterizes these interactions could have a stronger impact on the language domain than other

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cognitive domains (Kirkorian, Pempek, Murphy, Schmidt, & Anderson, 2009). In addition, infant

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studies have reported parallels in the development of motor skills and language acquisition (Ejiri, 1998;

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Locke, Bekken, McMinn-Larson, & Wein, 1995). It is suggested that gains in motor skills allow

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greater exploration of the surrounding world; thereby, aiding language development (Iverson, 2010). A

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similar mechanism could explain the association observed between purposeful physical activity and

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ACCEPTED MANUSCRIPT language development in this older early years sample. However, future research is needed to better

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understand the mechanisms of the relationships between sedentary behavior, physical activity, and

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multiple domains of cognitive development in this age group (Khan & Hillman, 2014), including the

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potentially mediating role of caregiver-child interactions.

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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

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age groups of children and the accelerometer-derived measures of sedentary behavior and physical

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activity are the gold standard measures for field-based research (Adamo et al., 2009; Cliff, Reilly, &

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Okely, 2009). Though subjective sedentary behavior and physical activity measures were also used,

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which are more prone to biases, the screen time measures had good reliability and the physical activity

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measures had moderate validity, which is consistent with other subjective physical activity measures in

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young people (Hunter, Leatherdale, Storey, & Carson, 2016; Kowalski, Crocker, & Kowalski, 1997;

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Mota et al., 2002). However, the subjective measure of physical activity likely captured more

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purposeful physical activity than incidental physical activity, which is common for physical activity

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questionnaires in children (Adamo et al., 2009). Furthermore, given the high quality measures and the

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time and cost demands associated with them, the sample size was modest, limiting the power to adjust

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for multiple potential confounders in the regression models apart from age. Therefore, it is not possible

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to rule out residual confounding. It was also not possible to examine effect modification due to power

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constraints. Furthermore, the demands and participant burden of the high quality measures, including

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traveling to the lab for an hour appointment and wearing the accelerometer for a week, may have

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contributed to the higher SES sample. Consequently, it is unclear whether findings can be generalized

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to all early years children. Another main limitation of the study was the cross-sectional design, which

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precludes causal inferences regarding associations observed.

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Conclusion

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ACCEPTED MANUSCRIPT The early years span a critical period of brain development. Understanding the factors that impact

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healthy brain development in the early years is needed to inform future interventions aiming to promote

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optimal cognitive development. Building on previous research, the findings from the present study

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suggest, television viewing may be detrimental and physical activity, especially non-organized, may be

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beneficial for vocabulary. Future research with larger sample sizes as well as longitudinal and

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experimental study designs are needed to confirm these findings and understand the potential

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mechanisms.

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Acknowledgements

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The authors are grateful to all the children and parents who took part in the study. The authors would

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like to thank all study staff who helped with data collection and data entry, including Stephanie

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Constantin, Luciano Hood, Dorothea Hui, Danielle Pertschy, Madeline Smith-Ackerl, Nasim Switzer,

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Alice Yan, Nicholas Kuzik, Stephen Hunter, Eun-Young Lee, and Helena Lee. This study was funded

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by the Palix Foundation, Alberta Family Wellness Initiative (AFWI), PolicyWise for Children &

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Families, and the Women and Children’s Health Research Institute (WCHRI). VC is supported by a

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Canadian Institutes of Health Research (CIHR) new investigator salary award. The funding sources had

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no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the

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manuscript; and in the decision to submit the article for publication.

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Conflict of Interest

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The authors have no conflict of interests to declare.

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ACCEPTED MANUSCRIPT Table 1. Participant Characteristics Variables Age (months; n = 100)

43.4 (9.4)

Sex (%; n=100) 47

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Male Female

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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)

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Sedentary time (min/day; n=79)†

5.2

20.6

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College, vocational, or trade school diploma

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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)

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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)

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Data presented as mean (standard deviation) for continuous variables and percentages for categorical variables. †Corrected for wear time using the residuals method.

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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

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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)

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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

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ACCEPTED MANUSCRIPT References

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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

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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

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