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Active Living in Kindergarten Predicts Children’s Lifestyle by End of Sixth Grade

Daniela González-Sicilia, Linda S. Pagani*

École de Psychoéducation, Université de Montréal, Canada

Abstract: Today’s youth favor less active leisure time and transport lifestyle habits. Physical activity has dramatically decreased in recent decades. Using birth cohort data, this study aims to examine prospective associations between active living at age 6 years and lifestyle and academic adjustment indicators in boys and girls at age 12 years. Participants are from the Quebec Longitudinal Study of Child Development (Canada) birth cohort. At kindergarten, mothers reported on seven indicators of physical activity (during leisure time and travel) and screen activity (computer, video games, and television). Indicators of lifestyle and academic adjustment outcomes were reported by sixth grade children and teachers, respectively. Academic adjustment was linearly regressed on active living, stratified by sex, while controlling for individual/family confounders. We found long-term net associated benefits of early active living for both girls and boys. An active kindergarten lifestyle was associated with more participation in leisure-time physical activity, less time spent on the computer and playing video games, and lower emotional distress and victimization by the end of sixth grade. Health and sport caregivers/coaches/teachers should provide parents guidelines for physical activity to their better chances of child flourishment. Communities can also adopt policies that promote and capitalize on the academic benefits in addition to the advantages to physical health.

Keywords: physical activity, early lifestyle, parenting, cardiovascular health, and early lifestyle

Received January 15, 2022; Revised February 18, 2022; Accepted February 27, 2022

Citation: Daniela González-Sicilia, Linda S. Pagani, Active Living in Kindergarten Predicts Children’s Lifestyle by End of Sixth Grade. Journal of Physical Activity Research, 7 (1). 27-36. January 2022. doi:10.12691/jpar-7-1-5

1. Introduction

Physical activity has decreased dramatically in recent decades, becoming a global public health concern across all age groups, especially youth. [1,2] Screen viewing has become one of the main leisure activities among children. [3] In Canada, only 35% of youth aged 5-17 accumulate the recommended 60 minutes of moderate to vigorous physical activity per day, and 49% meet the sedentary behavior guidelines that recommend no more than 2 hours of recreational screen time daily. [4] Physical activity and sedentary behaviour, both important determinants of lifestyle, seem to remain relatively stable, or even deteriorate, across time. [5,6]

Leisure time represents a crucial moment for children to engage in active endeavours, such as sports, which can have a positive influence on physical and psycho-social development. [7,8,9,10,11] Active transportation constitutes another important source of physical activity for children, but it has declined lately. [12] In Canada, only 21% of youth aged 5-17 walk or cycle to school. [4] Sociodemographic and built environment characteristics may facilitate or hinder children’s active mobility. [13,14]

Sedentary behavior and, more specifically, screen time also play an important role in lifestyle. Nowadays, children have regular access to screens and thus, the time they spend on screen-based activities has considerably increased. [3] Among Canadian youth aged 5-11 and 12-17, the average time spent daily in front of a screen is 2.3 and 4.1 hours, respectively. [4] Although sedentary behavior seems to increase from childhood to adolescence, individual and family environment factors (e.g. having a TV in the bedroom and parental rules) are also associated with screen viewing. [6,15,16]

The multiple health and psycho-social benefits of physical activity in youth have widely been investigated. A systematic review including 86 studies found dose-response relations for several health outcomes, with some differences between boys and girls. [10] In another systematic review including 30 studies, sport participation was associated with social interaction, higher self-esteem, and fewer depressive symptoms. [9] Evidence of positive cross-sectional and longitudinal associations between physical activity and academic achievement has also been found. [8,17,18] A recent systematic review including 162 studies found favorable associations between physical activity and body composition, cardiometabolic biomarkers, physical fitness, bone health, motor skill development, prosocial behavior, well-being, and cognition/academic achievement. [11] Nevertheless, the results are mixed and inconsistent, studies are mainly cross-sectional, and the methodological quality is very heterogeneous, making it difficult to appreciate the role that physical activity plays in the different outcomes.

Active transportation seems to be another good predictor of health-related fitness and mental well-being in youth. [16] A systematic review including 27 studies, mostly cross-sectional, found significant associations between active travel and weight status, body composition, and cardiorespiratory fitness in youth. [19] A Chinese cross-sectional study found that children actively commuting to school were less likely to present depressive symptoms than those using passive transportation. [20] Yet, longitudinal studies are needed to better understand the long-term salutary influence of active transportation in children.

Increasing research suggests, in contrast, that sedentary behavior (screen time in particular) is detrimental to children’s health and psycho-social development. [3,15,16] A systematic review including 235 studies found that greater screen time was associated with an unfavorable body composition, higher cardiometabolic risk and conduct disorders, and lower fitness, pro-social behavior, and self-esteem. [21] However, studies were mainly cross-sectional and the quality of evidence was very low to moderate. Longitudinal evidence has shown that preschool TV viewing is prospectively associated with peer victimization and classroom disengagement by middle childhood and adolescence. [22,23]

Even if there is increasing support for the immediate and short-term childhood benefits of an active lifestyle and the risks associated with inactivity and sedentariness, most studies have solely focused on either participation in physical activity, use of active transportation, or screen time. The net potential long-term influence of multiple aspects of active living on positive youth development warrants further examination. A prospective-longitudinal design, adjusting for potential confounders, can better elucidate the role that active living may play on various spheres of children’s subsequent well-being.

Thus, the purpose of this study is to examine longitudinal associations between active living, as defined by physical activity and screen indicators at the beginning of schooling (age 6), and lifestyle and academic adjustment outcomes by early adolescence (age 12), both important transition periods. Moreover, given that being a boy or a girl may be an important determinant of health-related behaviors, we aim to focus on how both sexes experience benefits of active living. [13,24,25] It is expected that boys and girls with higher levels of active living will show more favorable outcomes subsequently, compared to their same-sex counterparts presenting lower levels of active living.

2. Methods

2.1. Participants

This institutional review board-approved study (CERAS-2017-18-129) relied on secondary data from the Quebec Longitudinal Study of Child Development (coordinated by the Institut de la Statistique du Québec), a representative, randomly selected, stratified sample of 2837 babies born in 1997-1998 in Quebec, Canada. [26] Of the eligible participants, 93 were deemed ineligible due to their First Nation status, 186 were untraceable/unreachable, and 438 were excluded because their parents refused participation at the 5-month baseline assessment. The remaining 2120 infants were eligible for longitudinal follow-up. Parental written consent was obtained at each follow-up during early childhood. For every school-age wave of data collection, informed written consent was obtained from parents, children, and teachers.

2.1.1. Predictor: Mother-reported Index of Active Living (Child Age 6 Years)

An index of active living was derived specifically for this secondary analysis of the birth cohort data using seven mother-reported indicators of physical activity and sedentariness (during leisure time and travel). Leisure-time physical activity (3 items): “In the last 12 months, outside of school hours, how often has your child participated in: a) sports with a coach/instructor; b) lessons or instruction in other organized physical activities with a coach/instructor such as dance, gymnastics, martial arts or circus arts; c) unstructured sports or physical activities without a coach/instructor?” Response choices for each item included: “almost never” (= 0), “once a month” (= 1), “once a week” (= 2), “few times per week” (= 3), and “almost every day” (= 4). Active transportation (1 item): “How does your child usually get to school?”. Children who walked or biked received a score of 1. Those who used other means of transportation (bus, car) received a score of 0. Screen time (3 items): “Outside of school hours, how often does your child spend time on a computer?” Response choices included: “almost every day” (= 0), “few times per week” (= 1), “once a week” (= 2), “once a month” (= 3), and “almost never” (= 4). They also reported how much time per week did their child spend, on average, playing video games, with the response choices being: “more than 5 hours/week” (= 0), “3-5 hours/week” (= 1), “1-3 hours/week” (= 2), “less than an hour/week” (= 3), and “not at all” (= 4). They indicated the time that their child spends watching TV daily. Response choices included: “more than 5 hours/day” (=0), “3-5 hours/day” (=1), “1-3 hours/day” (= 2), “less than an hour/day” (= 3), and “not at all” (= 4). All seven indicators were coded in the same direction (lower scores indicating less active/more sedentary; higher scores indicating more active/less sedentary). From the sum of these indicators, a continuous index score, ranging from 0 to 25, was computed for each child and used in the analyses. Higher scores represent more active living than lower scores.

2.1.2. Outcomes: Child-reported Lifestyle and Teacher-Reported Academic Adjustment Indicators (Child Age 12 Years)

Children indicated the number of days per week that they usually did leisure-time physical activity, with the response choices going from 1 (“1 day/week”) to 7 (“7 days/week”). Children also reported how much time they had usually spent in a typical week, during the past 3 months, on a computer (including on the Internet, playing games, doing homework, or chatting) and playing video games. Response choices for these two items included: “none” (=0), “less than 1 hour/week” (=1), “1-2 hours/week” (=2), “3-5 hours/week” (=3), “6-10 hours/week” (=4), “11-14 hours/week” (=5), “15-20 hours/week” (=6), and “more than 20 hours/week” (=7).

Sixth grade teachers rated global academic performance according to five possible response choices: “near the top of the class” (=2), “above the middle of the class, but not at the top” (=1), “in the middle of the class” (=0), “below the middle of the class, but above the bottom” (=-1), “near the bottom of the class” (=-2). [27] They also responded to 5 questions regarding the child’s level of emotional distress over the past 6 months: “Would you say that this child: preferred to play alone rather than with other children; was not as happy as other children; had no energy, was feeling tired; had trouble enjoying himself/herself; and was unable to make decisions?” For each item, three response choices were possible: “never/not true” (=0), “sometimes/somewhat true” (=1), “often/very true” (=2). The five items were then summed up to create an emotional distress scale (α= .76). [22] Teachers also responded to 3 questions regarding the child’s experience of victimization by classmates over the past 6 months: “Would you say that this child made fun of by other children; hit/pushed by other children; and called names by other children?”. Response choices were the same as those from the previous scale and summed (α= .80). [22]

2.1.3. Individual and Family Control Variables (from Child Age 5 months to 6 years, 1998-2004)

Control variables were coded in a way that a score of 1 always represents the risk group.

Individual. Sex, directly obtained from birth records, was included as a control to account for differences between girls (=0) and boys (=1). At age 1.5, mothers reported on their child’s temperament. Higher scores indicate a more difficult temperament than lower scores (below the median= 0, above the median= 1). At age 2, the Imitation Sorting Task was used to evaluate the child’s working memory and attention.²⁸ At age 3, verbal ability was assessed through the Peabody Picture Vocabulary Test.²⁹ Both cognitive variables were coded so that lower scores reflect better skills than higher scores (above the median= 0, below the median= 1). At age 4, children were asked if they had a TV in their bedroom (no= 0, yes= 1), to account for screen time as an indicator of sedentariness. At age 6, mothers were asked to rate their child’s level of physical condition in comparison to other children (same or higher= 0, lower= 1). Weight and height were directly assessed by a research assistant and the body mass index (BMI) was estimated for each 6-year-old child (normal weight= 0, overweight/obese= 1).

Family. At the 5-month baseline assessment, mothers reported on their level of education (completed high school= 0, no high school diploma= 1). At age 6, they reported on the family configuration (two parents= 0, single parent= 1).

2.2. Data Analytic Strategies

For this secondary analysis of birth cohort data, we computed a series of linear regression analyses using SPSS software. To reduce the possibility of competing explanations, the model was adjusted for individual and family potential confounders. Thus, our adjusted model aims to examine the unique contribution of active living at age 6 on the prediction of lifestyle and academic adjustment by age 12, above and beyond other confounding variables. An alpha level of 0.05 (two-tailed) was used to indicate statistical significance.

Analyses stratified by sex were also conducted to examine specific outcomes for girls and boys. For each outcome (age 12), the same linear regression model including the active living predictor (age 6) and all the control variables was used for girls and boys, separately. This more person-centered approach to analysis allowed us to compare more active girls/boys with their less active same-sex counterparts (see evidencebasedmentoring.org for details). Post hoc and attrition analyses are reported in the online appendix.

3. Results

Descriptive statistics are reported in Table 1. Bivariate correlations between each of the seven indicators used to create the active living predictor at age 6 are reported in the online appendix (Table A.3).

Associations between the active living predictor at age 6 and the lifestyle and academic outcomes at age 12 are reported in Table 2. Active living was positively associated with subsequent physical activity and negatively associated with screen time. More specifically, every unit increase in active living corresponded to 0.04-unit increases in the number of days per week engaging in leisure-time physical activity and to 0.05- and 0.06-unit decreases in the time spent in front of a computer and playing videogames, respectively. Unit increases in the active living score also corresponded to 0.08- and 0.05-unit decreases in emotional distress and victimization, respectively. All these associations were statistically significant (p≤.05). In contrast, global academic performance at age 12 was not significantly related to active living at age 6.

To examine sex-specific outcomes, we used the fully adjusted model with all the individual and family control variables for girls and boys, separately. This allowed comparisons between the more active girls/boys and their less active same-sex counterparts. Results from the analyses stratified by sex are reported in Table 3. For girls, higher active living scores were significantly associated with greater participation in leisure-time physical activity subsequently (unstandardized b= 0.05) and with lower levels of victimization (unstandardized b= -0.05), compared to the girls with lower active living scores. For boys, every unit increase in active living scores corresponded to 0.08- and 0.09-unit decreases in the weekly time spent on the computer and playing video games, respectively. Furthermore, boys’ unit increases in active living predicted 0.09- and 0.06-unit decreases in emotional distress and victimization, respectively.

Table 1. Descriptive statistics for active living predictor, control variables, and lifestyle and academic adjustment outcomes

Table 2. Associations between the active living predictor and subsequent lifestyle and academic adjustment outcomesa

Table 3. Sex-specific associations between the active living predictor and lifestyle and academic adjustment outcomesa

The results from the post-hoc analyses that we conducted to examine the individual and relative contribution of each of the exposure variables (physical activity and screen indicators used to determine the active living index) on the prediction of the different outcomes are reported in Table 4 and Table 5, respectively. In order to properly interpret these results, it should be noted that physical activity indicators at age 6 were coded in a way that higher scores represented a greater participation, but screen indicators at age 6 were coded inversely meaning that higher scores reflected less screen time (i.e. less sedentariness). Lifestyle outcomes at age 12 were coded in a way that higher scores represent greater participation in leisure-time physical activity, whereas higher scores on the screen variables (computer and video games) reflect greater time spent on these activities.

As reported in Table 4, higher participation in unstructured physical activities at the beginning of schooling was significantly associated with greater participation in leisure-time physical activity subsequently (unstandardized b= 0.09). Spending less time on the computer at age 6 was significantly associated with more participation in leisure-time physical activity (unstandardized b= 0.09) and with less time spent on the computer (unstandardized b= -0.12) at age 12. Spending less time playing video games at an early age was significantly associated with less time spent on the computer (unstandardized b= -0.29) and less time playing video games (unstandardized b= -0.18) later in life. Spending less time watching TV was significantly associated with less time spent playing video games six years later (unstandardized b= -0.13).

As reported in Table 5, the contribution of some of the physical activity and screen indicators remained significant even when all the other exposure variables were included in the same adjusted model. Higher participation in unstructured physical activities at age 6 remained significantly associated with greater participation in leisure-time physical activity at age 12 (unstandardized b= 0.08). Similarly, spending less time playing video games remained significantly associated with less time spent on the computer later in life (unstandardized b= -0.22), whereas spending less time watching TV remained significantly associated with less time playing video games subsequently (unstandardized b= -0.13).

Table 4. Individual contribution of each of the seven physical activity and screen indicators at age 6 on the prediction of each lifestyle and academic adjustment outcomes at age 12a

Table 5. Relative contribution of each of the seven physical activity and screen indicators at age 6 on the prediction of each lifestyle and academic adjustment outcomes at age 12a

4. Discussion

Promoting active living from an early age can foster positive youth development and prevent the multiple short- and long-term risks associated with inactivity and sedentariness. [2,9,15,16,21,22,23,31] Yet, due to advances in technology and the increasing accessibility to screens, many children spend their time in sedentary activities, instead of engaging in behaviors that require energy expenditure. [3] Given that the hours spent in school and study time are generally little or not physically active, leisure time plays a key role for children to get involved in sports or active play, rather than sitting in front of a screen to watch TV, play video games, or surf on the Internet. [32,33,34] Travel time represents another opportunity for children to be active. Walking or cycling to/from school, instead of using motorized transportation (such as a car or a bus), can help increase children’s overall physical activity levels. [35,36]

This study sought to examine the benefits associated with active living at the beginning of schooling across several spheres of development by early adolescence. We also wanted to explore if boys and girls experienced these benefits in the same way. Active living was defined according to the level of participation in sports, other organized and unstructured physical activities during leisure time, the use of active transportation to go to/from school, and the time spent in front of a screen (computer, video games, TV).

Our findings show that, as expected, children with a more active/less sedentary lifestyle at age 6 presented more favorable lifestyle and school-related outcomes by age 12 than those with a less active/more sedentary lifestyle. Active living at an early age forecasted greater participation in leisure-time physical activity and less screen time, emotional distress, and victimization, subsequently. However, contrary to our expectations, active living at the beginning of schooling did not predict better academic achievement by early adolescence. This could be explained, at least in part, by the fact that only a measure of teacher-rated global academic performance was used. This single measure may not necessarily reflect children’s actual performance in the various school subjects. Another plausible explanation is that some of the screen-based sedentary behaviors used to determine the active living index (e.g. certain video games) could have a positive influence on cognitive functions. [37,38,39,40] Therefore, even if more active children would be expected to better perform in school, [8] certain sedentary activities may also contribute to their academic achievement. By spending more time playing certain type of video games, some children could develop better cognitive skills (such as attention, problem solving, creativity, and more efficient information processing), which could enhance their school performance.

Previous studies have shown that organized or structured physical activities may promote positive youth development. [7,9,31,41] Sports, for instance, can improve children’s physical health by allowing them to hone motor skills while being physically active. When the right conditions are in place, sports can also enhance cognitive and emotional development by providing children with challenging yet amusing opportunities, while teaching them important life skills (such as self-control, cooperation, and leadership). [31] These skills may then be transferred to other contexts, contributing to personal and socioemotional adjustment later in life. This may explain, at least in part, why active living at age 6 is associated with more favorable outcomes at age 12. It is noteworthy though that, in our study, other types of physical activity (including unstructured activities and active transportation), and less time dedicated to screen-related sedentary behaviors also seem to contribute to healthier lifestyles and better psycho-social functioning over the long term. These findings add to the growing evidence on the benefits of physical activity and the risks associated to inactivity and sedentariness. [11,19,20,21,23,23]

Although there is evidence of a potential salutary influence for both sexes, girls and boys seem to experience the benefits of active living differently. For girls, active living predicted more leisure-time physical activity subsequently. For boys, the benefits of active living included less time spent on the computer or playing videogames and less emotional distress. Both girls and boys who were more active/less sedentary at age 6 experienced less victimization later in life than their less active/more sedentary same-sex counterparts. These findings suggest that girls and boys experience active living in a sex-specific way.

When physical activity and screen indicators at age 6 are considered separately, their contribution to the prediction of lifestyle and academic adjustment outcomes at age 12 is limited. In fact, our findings suggest that only unstructured physical activities (but neither participation in organized physical activities nor the use of active transportation) predict participation in leisure-time physical activity subsequently. Sedentary behaviors also seem to remain relatively stable over time, even if the type of screen-based activity might change from childhood to early adolescence. Moreover, less time spent on sedentary activities at the beginning of schooling seems to predict more leisure-time physical activity six years later. Children spending less time on screens at an early age would probably have more time to engage in more active leisure activities, as suggested by the displacement theory, [42,43,44] and this could explain why they would tend to engage in more physical activity during their leisure time later in life.

Interestingly, even if the physical activity and screen indicators used to create the active living index at age 6 were associated to subsequent lifestyle outcomes, they did not make any individual or relative contribution to academic adjustment indicators. Nonetheless, when viewed as a whole, active living at an early age was significantly associated to less emotional distress and victimization by early adolescence. These findings suggest that the combination of multiple aspects of active living (including physical activity during leisure time and travel, as well as limited screen time) may have a more favorable impact on later development than isolated behaviors.

This research has certain limitations that merit discussion. First, as with most longitudinal studies, missing data was inevitable. We conducted multiple imputation to reduce attrition bias. Second, these findings are from a cohort of children born in 1997-1998. Children born in recent years are a lot more exposed to screens and, therefore, a lot more at risk. Third, although characteristics in the built environment may be important determinants of children’s involvement in physical activity and sedentary behavior, neighborhood variables were not considered in this study. Fourth, the active living predictor was a naturally occurring phenomenon. It was the children or their parents that chose to engage in physical activity, active transportation, or sedentary behaviors. Due to the non-experimental nature of the design, causal links cannot be inferred. Finally, our active living index was created solely for this study and therefore has not been validated before. The index only included active transportation to/from school and physical activity/sedentary behaviors during leisure time. Time spent by children being physically active or sedentary during school time was not considered. Furthermore, the different indicators used to calculate the active living index were not all given the same weight : participation in different forms of leisure-time physical activity and the time spent on screen-based sedentary behaviors made a greater contribution to the index than did the use of active transportation, for instance. Despite this, our active living predictor combines variables that have shown their predictive value in previous studies and that may be present simultaneously in the lives of young people.

Notwithstanding these limitations, to our knowledge, this is the first study to examine longitudinal associations between multiple aspects of active living and different spheres of youth development over several years. The prospective nature of the study design constitutes a major strength. Controlling for individual and family confounding variables allowed us to reduce the possibility of competing explanations and to estimate the unique contribution of active living at age 6 on child outcomes at age 12.

5. Practical Implications: Care of Children and Families

In conclusion, our findings indicate that active living at an early age leads to multiple benefits later in life. Health and sport care providers who are interested in developing adolescent attitudes, intervention strategies, and motivations in increasing physical activity to adequate levels can focus on its benefits for mental health. [45] Girls and boys who engage in more leisure-time physical activity, use active transportation, and spend less time in sedentary behaviors at age 6 present healthier lifestyles and less socio-emotional problems at age 12, compared to their less active/more sedentary same-sex counterparts. Even if the effect sizes were modest, they remain important considering their implications across the lifespan. This research sheds light on the importance of informing parents and developing communities that provide children with diverse opportunities to stay active. The extant literature has established links with fitness markers, but this research suggests that emphasis should be placed on the benefits for mental health. [45,46]

Parental attitudes and behaviors must be shaped by their knowledge of pediatric guidelines. Strategies also must include promoting participation in active endeavors and improving access to recreational facilities and transportation systems so that children can incorporate physical activity into their daily lives. [47] Previous research suggests that youth either personal desire to engage in being active because the behavior is fun and enjoyable, because they identify as athletic, or because it represents an important part of the adolescent identity as being healthy. [45,46] These three motivations should influence how community care professionals approach and educate youth in addressing the maintenance of a healthy lifestyle by physical activity.

Acknowledgements and Financial Support

This research was funded by the Social Sciences and Humanities Research Council of Canada (LSP, grant number 435-2017-0784) and by the Fonds de recherche du Québec – Société et culture (DGS, doctoral research scholarship number 2019-B2Z-257997). The Quebec Longitudinal Study of Child Development was made possible thanks to the funding provided by the Fondation Lucie et André Chagnon, the Institut de la Statistique du Québec, the Ministère de l’Éducation et de l’Enseignement supérieur (MÉES), the Ministère de la Famille (MF), the Institut de recherche Robert-Sauvé en santé et en sécurité du travail (IRSST), the Centre hospitalier universitaire Sainte-Justine, and the Ministère de la Santé et des Services sociaux du Québec (MSSS). Source: Data compiled from the final master file ‘E1-E20’ from the Quebec Longitudinal Study of Child Development (1998-2017), ©Gouvernement du Québec, Institut de la statistique du Québec. The funding sources did not have any involvement in: (1) the study design; (2) the collection, analysis and interpretation of data; (3) the writing of the manuscript; and (4) the decision to submit the manuscript for publication.

Conflict of Interest

None of the authors have conflicts of interest to disclose. The authors have no competing interests to declare. No financial disclosures were reported by the authors of this paper.

Ethical Approval

This study was approved by the Institutional Review Board of the University of Montreal (CERAS-2017-18-129). Parental written consent was obtained at each follow-up during early childhood. For every school-age wave of data collection, informed written consent was obtained from parents, children, and teachers.

Highlights

• Multiple aspects of active living (including participation in leisure-time physical activity, use of active transportation, and limited screen time) at the beginning of schooling forecast more favorable lifestyle and and academic-related outcomes several years later.

• Gender-specific associations suggest that although there is evidence of a potential salutary influence for both genders, girls and boys experience the benefits of active living differently.

• Communities should provide children with diverse opportunities to stay active so that they can enjoy the benefits of physical activity and prevent the risks associated to inactivity and sedentariness.

References

Appendix

Data used in this longitudinal study was obtained from multiple sources and waves. An attrition analysis was conducted to compare the 1124 retained cases with data on the active living predictor at age 6 and the 996 non retained cases from the original sample (n = 2120) on the control variables. Results from attrition analyses comparing cases with and without complete data are available in the online appendix (Table A.1 and Table A.2). As reported in Table A.1, no significant differences were found between the retained and the nonretained sample. We imputed all missing data using SPSS multiple imputation. [30] The reported results are corrected for attrition bias. Missing data was imputed using SPSS multiple imputation. Probable values were generated to create several imputed datasets that were first used to produce individual outputs of the results emanating from the fully controlled model. Then, these complete datasets were combined so that the resulting pooled output averaged the estimated results as if the original dataset had been complete. Bivariate correlations between each of the seven indicators used to create the active living predictor at age 6 are reported in Table A.3.

The proportion of participants with complete data on lifestyle and academic outcomes ranged between 37% and 61%. An attrition analysis was conducted to compare complete and incomplete cases for each of the outcomes at age 12 on the active living predictor at age 6. As reported in Table A.2, between-group differences were only significant for two of the outcomes. Active living mean scores were higher for participants with complete data on leisure-time physical activity ( = 12.01 vs 11.16; t₁₁₂₂ = 4.601; p = .000) and video games ( = 11.77 vs 11.30; t₁₁₂₂ = 2.093; p = .037), compared to those with incomplete data.

Descriptive statistics are reported in Table 1. Bivariate correlations between each of the seven indicators used to create the active living predictor at age 6 are reported in the online appendix (Table A.3). The three types of leisure-time physical activity (sports, other organized physical activities, and unstructured physical activities) were weakly correlated with each other (r <0.3), but they were not correlated with active transportation. Regarding screen activities, the time spent on video games was correlated with the time spent on computer (r =0.5) and on television (r <0.1), but the latest were not correlated with each other. Television was correlated with sports and unstructured physical activities, but these correlations were weak (r <0.1). This suggest that there were no multicollinearity issues.

Furthermore, post-hoc analyses were conducted to examine the individual and relative contribution of each of the physical activity and screen indicators used to create the active living predictor at age 6 on each of the lifestyle and academic outcomes at age 12. First, the individual contribution was estimated using separate linear regression models for each of the seven exposure variables (sports, other organized physical activities, unstructured physical activities, active transportation, computer, video games, and television) and each of the six outcomes (leisure-time physical activity, computer, video games, global academic performance, emotional distress, and victimization), while controlling for the individual and family confounding variables. Then, the relative contribution was estimated using a single linear regression model that included the seven exposure variables simultaneously, along with the individual and family control variables, for each of the outcomes.

Attrition. Data used in this longitudinal study was obtained from multiple sources and waves. Results from attrition analyses comparing cases with and without complete data are available in the online appendix (Table A.1 and Table A.2). We imputed all missing data using SPSS multiple imputation. [30] The reported results are corrected for attrition bias. Missing data was imputed using SPSS multiple imputation. Probable values were generated to create several imputed datasets that were first used to produce individual outputs of the results emanating from the fully controlled model. Then, these complete datasets were combined so that the resulting pooled output averaged the estimated results as if the original dataset had been complete. Bivariate correlations between each of the seven indicators used to create the active living predictor at age 6 are reported in Table A.3.

Table A.1. Means of control variables for participants with complete/incomplete data on active livinga

Table A.2. Active living means for participants with complete/incomplete data on each of the outcomesa

Table A.3. Bivariate correlations between each of the seven physical activity and screen indicators used to create the active living predictor at age 6a

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