Accelerometry-Derived Physical Activity Correlations Between Parents and Their Fourth-Grade Child Are Specific to Time of Day and Activity Level

in Journal of Physical Activity and Health

Background: The purpose of this study was to employ high-frequency accelerometry to explore parent–child physical activity (PA) relationships across a free-living sample. Methods: We recorded 7 days of wrist-mounted accelerometry data from 168 dyads of elementary-aged children and their parents. Using a custom MATLAB program (Natick, MA), we summed child and parent accelerations over 1 and 60 seconds, respectively, and applied published cut points to determine the amount of time spent in moderate–vigorous PA (MVPA). Bivariate and partial correlations examined parent–child relationships between percentage of time spent in MVPA. Results: Weak to moderate positive correlations were observed before school (r = .326, P < .001), after school (r = .176, P = .023), during the evening (r = .213, P = .006), and on weekends (r = .231, P = .003). Partial correlations controlling for parent–child MVPA revealed significant relationships during the school day (r = .185, P = .017), before school (r = .315, P < .001), and on weekends (r = .266, P = .001). In addition, parents of more active children were significantly more active than parents of less active children during the evening. Conclusions: These data suggest that there is some association between parent–child PA, especially before school and on weekends. Future interventions aiming to increase PA among adults and children must consider patterns of MVPA specific to children and parents and target them accordingly.

Despite recognition that more physically active youth and adults maintain better overall health,1,2 physical activity (PA) levels continue to remain below the number of recommended daily minutes.3 Current PA guidelines urge children to engage in a minimum of 60 minutes of daily moderate–vigorous PA (MVPA) and adults to accumulate 30 minutes of MVPA on most days of the week; however, existing estimates of children’s activity levels suggest that only 42% of children aged 6–11 years and only 5% of adults actually achieve these daily goals.35 Given the lack of participation in regular PA among adults and youth, interventions aimed at increasing MVPA accumulation have become more frequent.610

Several interventions designed to increase MVPA at the family level have targeted either parents or children as agents of change.1113 However, this approach assumes that changing PA levels in one group will precipitate PA changes in the other group. Thus, for such interventions to be successful, a significant positive correlation between parent and child PA must exist. Previous explorations that have examined the correlation between parent and child PA levels using direct observation, self-report, and objective measures (ie, accelerometry) have generated mixed results.14,15

Accelerometry is the current best-practice technique for measuring free-living PA in adults and children.16,17 Body-worn accelerometers (ACCs) provide an objective measurement of the frequency, intensity, and duration of PA.17 The GENEActiv ACC (Activinsights Ltd, Cambridge, UK) is one such device that collects raw (ie, not processed) acceleration data and allows for a user-determined sampling frequency ranging from 10 to 100 Hz. This waterproof, wrist-mounted device has been validated for use in both adults and children.16,18 During ACC wear time, acceleration magnitudes are collected at a specific sampling frequency and are processed to output data at a user-determined epoch (period of measurement). Because adults tend to participate in purposeful, sustained bouts of MVPA, 1-minute epoch lengths are typically used to measure PA participation among adults.16,19 By contrast, children tend to participate in short bouts of movement, with the majority of bouts lasting between 3 and 22 seconds; thus, shorter epochs (eg, 1 s) are used in the pediatric population to ensure that sporadic bouts of MVPA are captured.20,21 Once accelerometry data are collected using respective epoch lengths for adults and children, these data can then be translated into estimates of PA participation.

Previous studies using ACCs to examine correlations between parent and child activity level have relied on hip-mounted devices sampling at epochs ranging from 10 to 60 seconds.14,15,22,23 Because of the sporadic nature of children’s activity and the recommendation that children’s MVPA be measured in 1-second epochs, it is likely that these investigations have not captured the true magnitude of children’s activity levels. Thus, to our knowledge, this study is the first to examine the relationship between parent–child MVPA levels using a wrist-mounted device and a 1-second measurement period for child MVPA estimates. The primary purpose of the study was to describe the correlation between parent–child activity levels during different periods of the week and weekend days. A secondary purpose was to quantify whether parents with higher activity levels have more active children than parents with lower activity levels and, by contrast, whether children with higher activity levels have more active parents than children with lower activity levels. The null hypothesis stated that parent and child percentage of time spent in MVPA is not correlated across parent–child dyads. Alternative hypotheses were as follows: (1) parent–child MVPA levels are positively correlated, particularly during times that parents and children are more likely to be together, (2) children’s MVPA accumulation is greater among those with more versus less active parents, and (3) parent’s MVPA accumulation is greater among those with more versus less active children.

Methods

Subjects

This investigation was approved by the Institutional Review Board for Human Subjects Research at Colorado State University. All children and parents/guardians provided written informed assent and consent, respectively, before commencement of participation. Subjects included 168 parent–child dyads (N = 336) who were participating in the Fuel for Fun child obesity prevention effort in Northern Colorado. Fuel for Fun is an experiential cooking and tasting program that combines the fourth-grade curriculum with knowledge and skills related to healthful cooking and eating.24 Fuel for Fun also includes (1) Sports, Play, and Active Recreation for Kids active recess component aimed at increasing levels of MVPA among participants25 and (2) About Eating, an online curricula focused on eating competence and healthful lifestyle behaviors.26 Percentage of students qualifying for free and reduced lunch at each of the 3 participating elementary schools ranged from 24% to 57% based on the specific school. Importantly, the data presented in the current investigation are baseline data. No intervention had been delivered to parents or children prior to data collection.

Experimental Procedures

Accelerometry

Detailed methods have been published previously.24 In brief, parents and children wore a waterproof GENEActiv ACC on their nondominant wrist for 7 days. Children and parents participated in their normal activities when wearing the ACC and did not remove the device for swimming or bathing. On the day that children received their ACC, children’s height and weight were measured with a standard scale and portable stadiometer; children were then classified as normal weight or overweight/obese based on the body mass index percentile (ie, children <85th percentile = normal weight and children ≥85th percentile =overweight/obese).27 On the seventh day of wear, a member of the research team collected the child and parent ACCs. Children’s gender, birth date, and race/ethnicity were obtained from classroom rosters provided by each school. Parents were asked to complete a short survey on parent height, weight, age, and sex, and return the survey with their ACC. Child data were analyzed at a 1-second resolution, and parent data were analyzed at a 1-minute resolution. The following accelerometry cut points were applied for children: sedentary <0.0935 gravity-subtracted signal vector magnitude (SVMg), moderate PA ≥0.1847 SVMg, and vigorous PA ≥0.4532 SVMg and for adults: sedentary <203 SVMg, moderate PA ≥605 SVMg, and vigorous PA ≥1696 SVMg.

Custom Intervals

Based on class schedules completed by classroom teachers, the following custom intervals were used for these analyses: full day (children: 6:00 AM to 11:00 PM; parents: 5:00 AM to 11:59 PM), school day (school start time to school end time), before school (children: 6 AM to school start time; parents: 5 AM to school start time), after school (school end time to 5:00 PM), and evening (5:00 PM to 9:00 PM). School start and end times varied by school, but the typical school day was approximately 7 hours, starting at 8:30 AM and ending at 3:30 PM.

Statistical Analyses

All data were analyzed in SPSS (IBM SPSS Statistics 20, Somers, NY), and significance was set at P < .05. Data were assessed for normality, and nonnormal custom intervals (ie, parent before school percentage of time spent in MVPA, parent school-day percentage of time spent in MVPA, parent evening percentage of time spent in MVPA, and parent weekend percentage of time spent in MVPA) were log transformed. Pearson correlations examined the correlation between the percentage of time parents and children spent in MVPA during each custom interval of the day. To classify parents and children as more versus less active, parents and children were split at the median for full-day minutes of MVPA accumulated. Partial correlations controlling for parent and child PA dichotomization (ie, more vs less active) were then explored. Bivariate and partial correlations were then run for same-sex dyads versus dyads who were not the same sex, dyads in which the father wore the ACC versus dyads in which the mother wore the ACC, and dyads in which the child was classified as normal weight versus dyads in which the child was classified as overweight or obese. Finally, independent samples t tests were run to assess the difference in children’s MVPA accumulation by parent PA dichotomization, as well as the difference in parent’s MVPA accumulation by child PA dichotomization. Unless otherwise noted, data are presented as mean (SE).

Results

Demographics

Children (n = 168, all fourth graders; 52% male) had a mean age of 9.1 years (SD = 0.32); 77% were normal weight, 11% were overweight, and 12% were obese. Of parents who reported age (52% of the sample) and sex (70% of the sample), average age was 38.8 years (SD = 5.61), and 81% were female. Of dyads for whom parent and child sex was available (n = 118), 48% were of the same sex. Mean minutes of MVPA and percentage of time spent in MVPA during each custom interval are presented in Table 1. Mean full-day MVPA was 121 minutes for parents and 135 minutes for children. Median full-day MVPA was 116.3 minutes for parents and 135.6 minutes for children.

Table 1

Mean (SE) Percentage of Time and Minutes Spent in MVPA During Each Custom Interval for 168 Fourth-Grade Children and Their Parents

ChildrenParents
Full day
 Length of interval, min10201140
 Percentage of interval spent in MVPA13.28 (0.22)10.63 (0.36)
 Minutes in MVPA135.29 (2.21)120.93 (4.13)
 n168168
School day
 Length of interval, min∼420∼420
 Percentage of interval spent in MVPA14.93 (0.27)12.46 (0.57)
 Minutes in MVPA59.26 (1.06)49.73 (2.26)
 n168168
Before school
 Length of interval, min∼150∼210
 Percentage of interval spent in MVPA9.91 (0.29)10.47 (0.53)
 Minutes in MVPA15.83 (0.47)21.89 (1.08)
 n168166
After school
 Length of interval, min∼100∼100
 Percentage of interval spent in MVPA19.49 (0.51)13.97 (0.67)
 Minutes in MVPA19.97 (0.56)14.12 (0.70)
 n168167
Evening
 Length of interval, min240240
 Percentage of interval spent in MVPA15.30 (0.42)11.74 (0.52)
 Minutes in MVPA36.65 (1.00)28.14 (1.23)
 n168168
Weekend
 Length of interval, min10201140
 Percentage of interval spent in MVPA12.80 (0.32)10.78 (0.43)
 Minutes in MVPA130.07 (3.20)122.81 (4.85)
 n160160

Abbreviation: MVPA, moderate–vigorous physical activity.

Bivariate Correlations

Results from bivariate correlations between parent and child percentage of time spent in MVPA during each custom interval are displayed in Table 2 and Figure 1. For all dyads, significant correlations were revealed during the before school period, after school period, evening period, and weekend days. Among same-sex dyads, significant correlations were found for the full day, before school period, evening period, and weekend days. Among dyads who were not of the same sex, a significant correlation was found for weekend days only. Among dyads in which the father wore the ACC, significant correlations were found during the evening period and on weekend days; whereas among dyads in which the mother wore the ACC, a significant correlation was found during the before school period only. Finally, among dyads in which the child was classified as normal weight, significant correlations were found during the before school period, evening period, and on weekend days, and among dyads in which the child was classified as overweight or obese, significant correlations were found during the evening period only.

Table 2

Bivariate Correlations Between Parent and Child Percentage of Time Spent in MVPA During Each Custom Interval for 168 Fourth-Grade Children and Their Parents

All dyadsSame-sex dyadsNonsame-sex dyadsFather completedMother completedNW childOW/OB child
Full day
 n1685761239512538
r.123.297−.012.345.138.151.065
P.11.03.92.11.18.09.70
School daya
 n1685761239512538
r.056.232−.044.370.033.150−.151
P.47.08.74.08.75.10.37
Before schoola
 n1665761239512437
r.326.559.184.283.384.329.287
P.001.001.16.19.001.001.09
After school
 n1675761239512537
r.176.109.135.290.105.176.316
P.02.42.30.18.31.05.06
Eveninga
 n1685761239512538
r.213.286.197.449.189.182.391
P.01.03.13.03.07.04.02
Weekenda
 n1605758239312134
r.231.283.290.562.169.234.277
P.003.03.03.01.11.01.11

Note. Bolded P-values denote significance at P < .05.

Abbreviations: MVPA, moderate–vigorous physical activity; NW, normal weight; OW/OB, overweight/obese.

aParent data were log transformed to achieve normality for analyses.

Figure 1
Figure 1

—Bivariate correlations between parent and child percentage of time spent in MVPA during the full-day (A), school-day (B), before school (C), after school (D), evening (E), and weekend (F) periods. Child and parent percentages of time spent in MVPA during the before school, after school, evening, and weekend custom intervals were significantly correlated. Full-day and school-day periods were not significantly correlated. MVPA indicates moderate–vigorous physical activity.

Citation: Journal of Physical Activity and Health 15, 6; 10.1123/jpah.2016-0645

Partial Correlations Controlling for Parent and Child Full-Day MVPA

Results from partial correlations controlling for parent and child full-day MVPA (less or more active) are shown in Table 3. Among all dyads, partial correlations revealed significant correlations for the school-day, before school, and weekend periods. Among same-sex dyads, partial correlations revealed significant correlations for the school-day and before school periods. Among dyads who were not of the same sex, partial correlations revealed significant correlations for weekend days only. Among dyads in which the father wore the ACC, significant correlations were found during the school-day, and among dyads in which the mother wore the ACC, significant correlations were found during the before school period only. Finally, among dyads in which the child was classified as normal weight, significant correlations were found during the full day, school day, before school period, and on weekend days; whereas among dyads in which the child was classified as overweight or obese, no significant correlations were found.

Table 3

Partial Correlations Controlling for Parent and Child MVPA Level Between Parent and Child Percentage of Time Spent in MVPA During Each Custom Interval for 168 Fourth-Grade Children and Their Parent

All dyadsSame-sex dyadsNonsame-sex dyadsFather completedMother completedNW childOW/OB child
Full day
df1645357199112134
r.111.156−.066.150.106.218−.184
P.15.26.62.52.31.02.28
School daya
df1645357199112134
r.185.355−.044.456.143.285−.124
P.02.01.74.04.17.001.47
Before schoola
df1625357199112033
r.315.544.150.058.386.332.296
P.001.001.26.80.001.001.09
After school
df1635357199112133
r.108.052.045.078.004.149.088
P.17.70.73.46.99.10.62
Eveninga
df1645357199112134
r.128.098.235.386.063.111.292
P.10.48.07.08.55.22.08
Weekenda
df1565354198811730
r.266.171.291.391.196.273.313
P.001.21.03.08.06.003.08

Note. Bolded P-values denote significance at P < .05.

Abbreviations: MVPA, moderate–vigorous physical activity; NW, normal weight; OW/OB, overweight/obese.

aParent data were log transformed to achieve normality for analyses.

MVPA by Parent and Child Dichotomization

Parents and children were split at the median number of full-day minutes of MVPA to dichotomize them as more versus less active. Table 4 and Figure 2 depict the percentage of time children spent in MVPA during each custom interval by parent MVPA dichotomization. No significant differences were found for the number of minutes of MVPA accumulated or the percentage of time spent in MVPA by children of more versus less active parents for any custom intervals. Table 4 and Figure 3 depict the percentage of time parents spent in MVPA during each custom interval by child MVPA dichotomization. During the evening period, parents of more active children spent a significantly greater percentage of time in MVPA than did parents of less active children (P = .025).

Table 4

Percentage of Time Spent in MVPA Compared Between More Versus Less Activea Parent (for Child) and More Versus Less Activea Child (for Parent)

Child MVPA (n = 168)Parent MVPA (n = 168)
Full day
 More active13.12 (0.30)10.09 (0.50)
 Less active13.45 (0.32)11.18 (0.53)
P.45.13
School dayb
 More active15.00 (0.35)12.64 (0.77)
 Less active14.85 (0.41)12.29 (0.83)
P.78.76
Before schoolb
 More active9.60 (0.38)9.62 (0.68)
 Less active10.22 (0.43)11.30 (0.80)
P.28.11
After school
 More active18.81 (0.75)12.77 (0.84)
 Less active20.16 (0.70)15.15 (1.02)
P.19.08
Eveningb
 More active14.74 (0.58)10.66 (0.70)
 Less active15.85 (0.59)12.81 (0.74)
P.18.03
Weekendb
 More active12.83 (0.47)10.31 (0.59)
 Less active12.76 (0.43)11.24 (0.62)
P.91.30

Note. The aforementioned P-values denote statistical significance between MVPA accumulation in children of more versus less active parents and in parents of more versus less active children. Bolded P-value denotes significance at P < .05.

Abbreviation: MVPA, moderate–vigorous physical activity.

aChildren and parents were classified as more versus less active by their full-day MVPA accumulation (median split).

bParent data were log transformed to achieve normality for analyses. Nontransformed mean values are presented above.

Figure 2
Figure 2

—Mean percentage of each custom interval spent in MVPA for children of more versus less active parents (ie, dichotomized by median split). Data are reported as mean percentage (SE). Percentage of time spent in MVPA did not differ between children of more versus less active parents during any custom interval (full day: t166 = −.33, P = .45; school day: t166 = .15, P = .78; before school: t166 = −.62, P = .28; after school: t166 = .19, P = −1.35; evening: t166 = −1.12, P = .18; and weekend: t162 = −.07, P = .91). MVPA indicates moderate–vigorous physical activity.

Citation: Journal of Physical Activity and Health 15, 6; 10.1123/jpah.2016-0645

Figure 3
Figure 3

—Mean percentage of each custom interval spent in MVPA for parents of more versus less active children (ie, dichotomized by median split). Data are reported as mean percentage (SE). Parent data for the school day, before school, evening period, and weekend days were log transformed to achieve normality for analyses. Nontransformed means are depicted in the figure. Parents of more active children spent a significantly greater percentage of time in MVPA during the evening (t166 = −2.26, P = .03) period than parents of less active children. Percentage of time spent in MVPA did not differ between parents of more versus less active children during the remaining custom intervals (full day: t166 = −1.52, P = .13; school day: t166 = .31, P = .76; before school: t164 = −1.60, P = .11; after school: t165 = −1.79, P = .08; and weekend: t161 = −1.04, P = .30). MVPA indicates moderate–vigorous physical activity. *Significance at P < .05.

Citation: Journal of Physical Activity and Health 15, 6; 10.1123/jpah.2016-0645

Discussion

Overall, these data demonstrate weak to moderate correlations between parent–child MVPA levels. Specifically, among all parent–child dyads, MVPA was significantly correlated during the before school, after school, evening, and weekend periods. When controlling for parent and child MVPA levels (eg, more vs less active), significant correlations were found during the school-day, before school, and weekend periods. Furthermore, child MVPA accumulation did not differ between children of less versus more active parents during any period of the weekday or weekends. However, parent MVPA level differed significantly by child MVPA level during the evening period, such that parents of more active children were significantly more active than parents of less active children during the evening.

Previous studies exploring the relationship between parent and child MVPA have been equivocal. Specifically, using an ActiGraph ACC (Pensacola Beach, FL), Jago et al22 found no association between parent and child activity levels among a sample of 10- to 11-year-old children and their parents. Similarly, Kalakanis et al23 explored MVPA levels of 10-year-old obese children and their parents during nonwork and nonschool hours, and concluded that parent activity levels do not predict children’s minutes of MVPA. By contrast, both Freedson and Evenson14 as well as Fuemmeler et al15 found that parent activity level is positively correlated with child activity level, such that children of more active parents are more likely to have higher activity levels than children of less active parents. Furthermore, Fuemmeler et al15 found correlations to be strongest during times that parents and children are more likely to be together (ie, periods outside of work and school).

Results of the current study most closely align with findings from Fuemmeler et al,15 in that parent–child PA is most strongly correlated during times that parents and children are likely to be together (ie, before school, after school, evening, and weekend periods). However, in contrast to Freedson and Evenson14 and Fuemmeler et al,15 the current study did not find children of more active parents to be more active overall than children of less active parents. Importantly, the difference in methodology must be noted between present and past studies. While the current investigation used a wrist-mounted ACC sampling at 75 Hz, previous studies have used a variety of subjective and objective measurements, none of which employed high-frequency wrist-mounted ACC.

In addition, the current study generated estimates of MVPA that are much larger than many previous MVPA estimates for both parents and children. This study estimated full-day MVPA to be ∼121 minutes per day for parents and ∼135 minutes per day for children; however, previous studies exploring parent–child MVPA correlations have provided MVPA estimates of ∼30–37 minutes and ∼35–145 minutes for parents and children, respectively.15,22 Two plausible explanations exist for the large magnitude of MVPA reported for the current sample. First, study participants were primarily white, affluent, Colorado residents. It has been well documented that Colorado residents are less likely to be overweight/obese and less likely to be physically inactive compared with individuals across the United States.2830 Therefore, it may be that the high activity levels and current findings are not representative of parent–child pairs living outside of the Colorado region. Second, although previous studies have utilized methodology that examines PA levels in longer epochs, the current study is the first to examine parent–child activity correlations using high-frequency resolution that does not require sustained bouts of PA participation.14,15,22,23 Thus, differences in methodology and MVPA estimates are likely to have contributed to differences in findings between current and past studies. However, best-practice recommendations suggest that studies exploring accelerometry data, particularly in children, adopt similar methodologies to those used in the current study.16,1921 Therefore, it is likely that the movement estimates presented in this study most closely represent true MVPA accumulation among this sample of children and adults.

Notably, among all dyads, the before school period demonstrated the strongest correlation between parent–child PA levels even when controlling for parent and child activity levels. Interestingly, this relationship holds steady among same-sex dyads, dyads in which the mother wore the ACC, and dyads in which the child was normal weight. The strength of this correlation may be due to transportation methods. For example, parents and children might either walk or bike to school together, or they may engage in passive transportation to school, such as commuting by car or bus. Mendoza et al31 have documented a positive relationship between active transport to school and MVPA in 12- to 19-year-old children. Future interventions aimed at increasing PA levels between both parents and children could focus on the before school period to engage more dyads in active transportation to school.

Interestingly, the percentage of time spent in MVPA did not differ between children of more versus less active parents. These results parallel those of Jago et al22 who reported that parents tended to engage in similar amounts of MVPA as children, but that it did not appear that they were engaging in this activity together. Thus, they propose that for older elementary-aged children (ie, 10- to 11-year-olds), parents might be better facilitators of PA through arranging PA transportation and supervision than modelers of PA.22 By contrast, during the evening period, parents of more active children spent a greater percentage of time in MVPA than did parents of less active children. Thus, it is likely that evenings were times when parents and children engaged in similar levels of PA, either together or separately. For instance, it might be that the more active children engaged in formal evening PA opportunities and that parents used this time to engage in PA of their own.

Given the weak correlation between parent–child PA levels, findings suggest that school-day PA, including recess and physical education periods, might be critical periods for PA accumulation and promotion among children. Several studies have noted the importance of the school day for PA accumulation among elementary-aged children, with students accumulating significantly greater MVPA during the school day compared with time spent at home and children participating in significantly greater sedentary time at home compared with time spent in school.3234 In addition, children encouraged to be physically active in school have been shown to be more active outside of school compared with children whose school-day PA was restricted, and other studies have shown children to be more active during the school week compared with weekend days.3537 These findings, in concert with the weak correlation between parent–child PA levels, corroborate the importance of school-day PA.

In response to the increasing prevalence of obesity and inactivity,2730 significant attention has turned to lifestyle interventions for both children and adults to improve the healthfulness of the nation. However, some groups have suggested that current PA interventions, particularly in children, are ineffective at significantly increasing PA participation,38 and thus, many interventionists are currently seeking more effective approaches to increase PA and subsequently decrease body mass index among children and adults. While some research groups have focused on interventions that target increased PA participation and improved health behaviors at the family level,39,40 others have utilized either parents or children as agents of change to increase healthfulness among all family members.4143 The current findings suggest that although PA is significantly correlated between children and parents during times that they are likely to be together, correlations are weak to moderate at best. Thus, these findings suggest that family-based PA interventions that target both children and parents might be most effective. In addition, although parents of more active children tended to be more active in the evenings compared with parents of less active children, parent activity level was not significantly associated with child activity during any period of the day. Notably, Erkelenz et al44 also did not find subjectively measured parental PA to be associated with children’s MVPA, although children of active parents were less likely to be overweight or obese compared with children of inactive parents. Together, these findings suggest that parental modeling of PA might not be as critical as parental support of PA participation when aiming to increase children’s PA levels.22,44

A significant strength of this study was the use of objective measures of PA via accelerometry, and particularly the high-resolution sampling at a 1-second epoch in children. This methodology allows us to obtain a more accurate picture of children’s MVPA based on their true movement patterns. Additionally, the relatively large sample size across 3 elementary schools, which varied in demographics and socioeconomic status, also aided in the generalizability of findings. Sample sizes reported in previous studies exploring the relationship between parent–child PA ranged from 30 to 340 dyads/triads (Fuemmeler et al15 utilized mothers and fathers when possible), with 3 of the 4 studies utilizing 51 or fewer parent–child dyads/triads. Given that the current study examined 168 parent–child pairs, it features one of the largest samples to explore the relationship between parent and child activity levels to date.14,15,22,23

However, this study was not without limitations. First, we were not able to collect parent demographics on the entire sample, and of those who reported their sex, a majority of parent participants were female. Because of this, the majority of same-sex dyads were mothers and daughters, and the majority of nonsame-sex dyads were mothers and sons. It may be that fathers and sons and fathers and daughters display different relationships in the percentage of time spent in MVPA compared with mothers and children. For example, if mothers are more likely to spend time with children before school, after school, and on the weekends, MVPA might be more strongly correlated between mothers and children compared with fathers and children. In addition, although parent–child MVPA was significantly correlated during the before school period for all dyads and dyads in which the mother wore the ACC, this correlation weakened when examining dyads in which the father wore the ACC.

Second, we did not collect contextual information on parent occupation or times when parents and children were together. Thus, we inferred periods when parents and children were more likely to be together versus apart. Obtaining such contextual information would provide a richer picture of the factors that promote PA among families. Finally, our sample was significantly leaner than what is currently observed across the United States. While 77% of children in the current study were classified as normal weight, the national average classifies 66% of children aged 6–11 years across the United States as normal weight, with the remaining one-third classified as overweight or obese.27 Thus, because of differences in children’s weight status, findings from the current study are not necessarily generalizable to children across the United States.

Conclusions

Overall, the weak to moderate correlations in parent–child activity suggest that there is some association between parent and child PA levels. However, the finding that overall child PA accumulation did not differ by parent PA level suggests that children’s overall MVPA accumulation is largely independent of parent PA classification. Thus, it is necessary that future interventions aiming to increase PA among adults and children consider patterns of MVPA specific to children and parents and target these patterns accordingly. However, given that correlations between children and parents were generally strongest before school and on weekend days, interventions targeting parents and children together during these times might have a stronger chance of being effective.

Acknowledgments

Authors would like to thank members of the Physical Activity Energetics and Mechanics Lab at Colorado State University for their help in collecting these data. This material is based upon work that is supported by Agriculture and Food Research Initiative (grant no. 2012-68001-19603) from the US Department of Agriculture National Institute of Food and Agriculture, and Childhood Obesity Prevention: Integrated Research, Education, and Extension to Prevent Childhood Obesity (A2101). Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the US Department of Agriculture. Results of the present study do not constitute endorsement by the American College of Sports Medicine (ACSM). The authors declare no conflict of interest.

References

  • 1.

    Janssen I, Leblanc AG. Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. Int J Behav Nutr Phys Act. 2010;7(40):14795868.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 2.

    Pate RR, Pratt SN, Blair SN. Physical activity and public health: a recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine. JAMA. 1995;273:402407. PubMed doi:10.1001/jama.1995.03520290054029

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Troiano R, Berrigan D, Dodd K, Masse L, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181188. PubMed doi:10.1249/mss.0b013e31815a51b3

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4.

    Strong WB, Malina RM, Blimkie CJ, et al. Evidence based physical activity for school-age youth. J Pediatr. 2005;146(6):732737. PubMed doi:10.1016/j.jpeds.2005.01.055

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5.

    Centers for Disease Control and Prevention (CDC). School health guidelines to promote healthy eating and physical activity. MMWR Recomm Rep. 2011;60(RR-5):176. PubMed

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    Boudreau F, Walthouwer MJ, de Vries H, et al. Rationale, design and baseline characteristics of a randomized controlled trial of a web-based computer-tailored physical activity intervention for adults from Quebec City. BMC Public Health. 2015;15:1038. PubMed doi:10.1186/s12889-015-2364-3

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    Carlson JA, Engelberg JK, Cain KL, et al. Implementing classroom physical activity breaks: associations with student physical activity and classroom behavior. Prev Med. 2015;81:6772. PubMed doi:10.1016/j.ypmed.2015.08.006

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Poirier J, Bennett WL, Jerome GJ, et al. Effectiveness of an activity tracker- and internet-based adaptive walking program for adults: a randomized controlled trial. J Med Internet Res. 2016;18(2):34. PubMed doi:10.2196/jmir.5295

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9.

    Van Kann DH, Jansen MW, de Vries SI, de Vries NK, Kremers SP. Active living: development and quasi-experimental evaluation of a school-centered physical activity intervention for primary school children. BMC Public Health. 2015;15:1315. PubMed doi:10.1186/s12889-015-2633-1

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10.

    Yildirim M, Arundell L, Cerin E, et al. What helps children to move more at school recess and lunchtime? Mid-intervention results from Transform-Us! Cluster-randomised controlled trial. Br J Sports Med. 2014;48(3):271277. PubMed doi:10.1136/bjsports-2013-092466

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Newton RL Jr, Marker AM, Allen HR, et al. Parent-targeted mobile phone intervention to increase physical activity in sedentary children: randomized pilot trial. JMIR MHealth UHealth. 2014;2(4):e48. PubMed doi:10.2196/mhealth.3420

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    Rubin DA, Wilson KS, Wiersma LD, Weiss JW, Rose DJ. Rationale and design of active play @ home: a parent-led physical activity program for children with and without disability. BMC Pediatr. 2014;14(41):14712431.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13.

    Golan M, Kaufman V, Shahar DR. Childhood obesity treatment: targeting parents exclusively v. parents and children. Br J Nutr. 2006;95(5):10081015. PubMed doi:10.1079/BJN20061757

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14.

    Freedson PS, Evenson S. Familial aggregation in physical activity. Res Q Exerc Sport. 1991;62(4):384389. PubMed doi:10.1080/02701367.1991.10607538

  • 15.

    Fuemmeler BF, Anderson CB, Masse LC. Parent-child relationship of directly measured physical activity. Int J Behav Nutr Phys Act. 2011;8(17):14795868.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 16.

    Esliger DW, Rowlands AV, Hurst TL, Catt M, Murray P, Eston RG. Validation of the GENEA accelerometer. Med Sci Sports Exerc. 2011;43(6):10851093. PubMed doi:10.1249/MSS.0b013e31820513be

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    Rowlands AV. Accelerometer assessment of physical activity in children: an update. Pediatr Exerc Sci. 2007;19(3):252266. PubMed doi:10.1123/pes.19.3.252

  • 18.

    Phillips LR, Parfitt G, Rowlands AV. Calibration of the GENEA accelerometer for assessment of physical activity intensity in children. J Sci Med Sport. 2013;16(2):124128. PubMed doi:10.1016/j.jsams.2012.05.013

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    Evenson KR, Wen F. Performance of the ActiGraph accelerometer using a national population-based sample of youth and adults. BMC Res Notes. 2015;8:7. PubMed doi:10.1186/s13104-014-0970-2

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20.

    Baquet G, Stratton G, Van Praagh E, Berthoin S. Improving physical activity assessment in prepubertal children with high-frequency accelerometry monitoring: a methodological issue. Prev Med. 2007;44(2):143147. PubMed doi:10.1016/j.ypmed.2006.10.004

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21.

    Ekelund U, Tomkinson G, Armstrong N. What proportion of youth are physically active? Measurement issues, levels and recent time trends. Br J Sports Med. 2011;45(11):859865. doi:10.1136/bjsports-2011-090190

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22.

    Jago R, Fox KR, Page AS, Brockman R, Thompson JL. Parent and child physical activity and sedentary time: do active parents foster active children? BMC Public Health. 2010;10(194):14712458.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 23.

    Kalakanis LE, Goldfield GS, Paluch RA, Epstein LH. Parental activity as a determinant of activity level and patterns of activity in obese children. Res Q Exerc Sport. 2001;72(3):202209. doi:10.1080/02701367.2001.10608953

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24.

    Cunningham-Sabo L, Lohse B, Smith S, et al. Fuel for Fun: a cluster-randomized controlled study of cooking skills, eating behaviors, and physical activity of 4th graders and their families. BMC Public Health. 2016;16(1):444. doi:10.1186/s12889-016-3118-6

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 25.

    Sallis JF, McKenzie TL, Alcaraz JE, Kolody B, Faucette N, Hovell MF. The effects of a 2-year physical education program (SPARK) on physical activity and fitness in elementary school students. Sports, Play and Active Recreation for Kids. Am J Public Health. 1997;87(8):13281334. PubMed doi:10.2105/AJPH.87.8.1328

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26.

    Lohse B, Belue R, Smith S, Wamboldt P, Cunningham-Sabo L. About Eating: an online program with evidence of increased food resource management skills for low-income women. J Nutr Educ Behav. 2015;47(3):265272.e1. PubMed doi:10.1016/j.jneb.2015.01.006

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27.

    Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA. 2014;311(8):806814. PubMed doi:10.1001/jama.2014.732

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28.

    Ward ZJ, Long MW, Resch SC, et al. Redrawing the US obesity landscape: bias-corrected estimates of state-specific adult obesity prevalence. PLoS ONE. 2016;11(3):e0150735. doi:10.1371/journal.pone.0150735

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29.

    Trust for America’s Health, Robert Wood Johnson Foundation. The state of obesity: states with the highest rates of physical inactivity. 2016. http://stateofobesity.org/lists/least-physically-active-states/. Accessed July 27, 2017.

    • Export Citation
  • 30.

    Sherry B, Blanck HM, Galuska DA, Pan L, Dietz WH. Vital signs: state-specific obesity prevalence among adults—United States, 2009. MMWR. 2010;59(30):951955.

    • Search Google Scholar
    • Export Citation
  • 31.

    Mendoza JA, Watson K, Nguyen N, Cerin E, Baranowski T, Nicklas TA. Active commuting to school and association with physical activity and adiposity among US youth. J Phys Act Health. 2011;8(4):488495. PubMed doi:10.1123/jpah.8.4.488

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32.

    Beck J, Chard CA, Hilzendegen C, Hill J, Stroebele-Benschop N. In-school versus out-of-school sedentary behavior patterns in U.S. children. BMC Obes. 2016;3:34.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33.

    Brooke HL, Corder K, Atkin AJ, van Sluijs EM. A systematic literature review with meta-analyses of within- and between-day differences in objectively measured physical activity in school-aged children. Sports Med. 2014;44(10):14271438. PubMed. doi:10.1007/s40279-014-0215-5

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34.

    Nyberg GA, Nordenfelt AM, Ekelund U, Marcus C. Physical activity patterns measured by accelerometry in 6- to 10-yr-old children. Med Sci Sports Exerc. 2009;41(10):18421848.

    • Search Google Scholar
    • Export Citation
  • 35.

    Groffik D, Sigmund E, Frömel K, Chmelík F, Nováková Lokvencová P. The contribution of school breaks to the all-day physical activity of 9- and 10-year-old overweight and non-overweight children. Int J Public Health. 2012;57(4):711718. doi:10.1007/s00038-012-0355-z

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36.

    Dale C, Corbin CB, Dale KS. Restricting opportunities to be active during school time: do children compensate by increasing physical activity levels after school? Res Q Exerc Sport. 2000;71(3):240248. PubMed doi:10.1080/02701367.2000.10608904

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37.

    Dauenhauer BD, Keating XD. The influence of physical education on physical activity levels of urban elementary students. Res Q Exerc Sport. 2011;82(3):512520. PubMed doi:10.1080/02701367.2011.10599784

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38.

    Metcalf B, Henley W, Wilkin T. Effectiveness of intervention on physical activity of children: systematic review and meta-analysis of controlled trials with objectively measured outcomes (EarlyBird 54). BMJ. 2012;345:35888.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 39.

    Teder M, Mörelius E, Nordwall M, et al. Family-based behavioural intervention program for obese children: an observational study of child and parent lifestyle interpretations. PLoS ONE. 2013;8(8):e71482. PubMed doi:10.1371/journal.pone.0071482

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40.

    Viitasalo A, Eloranta A, Lintu N, et al. The effects of a 2-year individualized and family-based lifestyle intervention on physical activity, sedentary behavior and diet in children. Prev Med. 2016;87:8188. PubMed doi:10.1016/j.ypmed.2016.02.027

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41.

    Faith MS, Van Horn L, Appel LJ, et al. Evaluating parents and adult caregivers as “agents of change” for treating obese children: evidence for parent behavior change strategies and research gaps. Circulation. 2012;125:11861207. PubMed doi:10.1161/CIR.0b013e31824607ee

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.

    Golan M, Weizman A, Fainaru M. Impact of treatment for childhood obesity on parental risk factors for cardiovascular disease. Prev Med. 1999;29:519526. PubMed doi:10.1006/pmed.1999.0584

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 43.

    Wingert K, Zachary DA, Fox M, Gittelsohn J, Surkan P. Child as change agent. The potential of children to increase healthy food purchasing. Appetite. 2014;81:330336. PubMed doi:10.1016/j.appet.2014.06.104

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44.

    Erkelenz N, Kobel S, Kettner S, Drenowatz C, Steinacker JM, The Research Group “Join the Healthy Boat–Primary School”. Parental activity as influence on children’s BMI percentiles and physical activity. J Sports Sci Med. 2014;13:645650.

    • PubMed
    • Search Google Scholar
    • Export Citation

If the inline PDF is not rendering correctly, you can download the PDF file here.

Strutz is with the Colorado School of Public Health, Colorado State University, Fort Collins, CO. Browning is with Nike, Beaverton, OR. Smith is with the Dept of Nutrition and Dietetics, University of Northern Colorado, Greeley, CO. Lohse is with Rochester Institute of Technology, Rochester, NY. Cunningham-Sabo is with the Dept of Food Science and Human Nutrition, Colorado State University, Fort Collins, CO.

Cunningham-Sabo (Leslie.Cunningham-Sabo@colostate.edu) is corresponding author.
  • View in gallery

    —Bivariate correlations between parent and child percentage of time spent in MVPA during the full-day (A), school-day (B), before school (C), after school (D), evening (E), and weekend (F) periods. Child and parent percentages of time spent in MVPA during the before school, after school, evening, and weekend custom intervals were significantly correlated. Full-day and school-day periods were not significantly correlated. MVPA indicates moderate–vigorous physical activity.

  • View in gallery

    —Mean percentage of each custom interval spent in MVPA for children of more versus less active parents (ie, dichotomized by median split). Data are reported as mean percentage (SE). Percentage of time spent in MVPA did not differ between children of more versus less active parents during any custom interval (full day: t166 = −.33, P = .45; school day: t166 = .15, P = .78; before school: t166 = −.62, P = .28; after school: t166 = .19, P = −1.35; evening: t166 = −1.12, P = .18; and weekend: t162 = −.07, P = .91). MVPA indicates moderate–vigorous physical activity.

  • View in gallery

    —Mean percentage of each custom interval spent in MVPA for parents of more versus less active children (ie, dichotomized by median split). Data are reported as mean percentage (SE). Parent data for the school day, before school, evening period, and weekend days were log transformed to achieve normality for analyses. Nontransformed means are depicted in the figure. Parents of more active children spent a significantly greater percentage of time in MVPA during the evening (t166 = −2.26, P = .03) period than parents of less active children. Percentage of time spent in MVPA did not differ between parents of more versus less active children during the remaining custom intervals (full day: t166 = −1.52, P = .13; school day: t166 = .31, P = .76; before school: t164 = −1.60, P = .11; after school: t165 = −1.79, P = .08; and weekend: t161 = −1.04, P = .30). MVPA indicates moderate–vigorous physical activity. *Significance at P < .05.

  • 1.

    Janssen I, Leblanc AG. Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. Int J Behav Nutr Phys Act. 2010;7(40):14795868.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 2.

    Pate RR, Pratt SN, Blair SN. Physical activity and public health: a recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine. JAMA. 1995;273:402407. PubMed doi:10.1001/jama.1995.03520290054029

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Troiano R, Berrigan D, Dodd K, Masse L, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181188. PubMed doi:10.1249/mss.0b013e31815a51b3

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4.

    Strong WB, Malina RM, Blimkie CJ, et al. Evidence based physical activity for school-age youth. J Pediatr. 2005;146(6):732737. PubMed doi:10.1016/j.jpeds.2005.01.055

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5.

    Centers for Disease Control and Prevention (CDC). School health guidelines to promote healthy eating and physical activity. MMWR Recomm Rep. 2011;60(RR-5):176. PubMed

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    Boudreau F, Walthouwer MJ, de Vries H, et al. Rationale, design and baseline characteristics of a randomized controlled trial of a web-based computer-tailored physical activity intervention for adults from Quebec City. BMC Public Health. 2015;15:1038. PubMed doi:10.1186/s12889-015-2364-3

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    Carlson JA, Engelberg JK, Cain KL, et al. Implementing classroom physical activity breaks: associations with student physical activity and classroom behavior. Prev Med. 2015;81:6772. PubMed doi:10.1016/j.ypmed.2015.08.006

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Poirier J, Bennett WL, Jerome GJ, et al. Effectiveness of an activity tracker- and internet-based adaptive walking program for adults: a randomized controlled trial. J Med Internet Res. 2016;18(2):34. PubMed doi:10.2196/jmir.5295

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9.

    Van Kann DH, Jansen MW, de Vries SI, de Vries NK, Kremers SP. Active living: development and quasi-experimental evaluation of a school-centered physical activity intervention for primary school children. BMC Public Health. 2015;15:1315. PubMed doi:10.1186/s12889-015-2633-1

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10.

    Yildirim M, Arundell L, Cerin E, et al. What helps children to move more at school recess and lunchtime? Mid-intervention results from Transform-Us! Cluster-randomised controlled trial. Br J Sports Med. 2014;48(3):271277. PubMed doi:10.1136/bjsports-2013-092466

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Newton RL Jr, Marker AM, Allen HR, et al. Parent-targeted mobile phone intervention to increase physical activity in sedentary children: randomized pilot trial. JMIR MHealth UHealth. 2014;2(4):e48. PubMed doi:10.2196/mhealth.3420

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    Rubin DA, Wilson KS, Wiersma LD, Weiss JW, Rose DJ. Rationale and design of active play @ home: a parent-led physical activity program for children with and without disability. BMC Pediatr. 2014;14(41):14712431.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13.

    Golan M, Kaufman V, Shahar DR. Childhood obesity treatment: targeting parents exclusively v. parents and children. Br J Nutr. 2006;95(5):10081015. PubMed doi:10.1079/BJN20061757

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14.

    Freedson PS, Evenson S. Familial aggregation in physical activity. Res Q Exerc Sport. 1991;62(4):384389. PubMed doi:10.1080/02701367.1991.10607538

  • 15.

    Fuemmeler BF, Anderson CB, Masse LC. Parent-child relationship of directly measured physical activity. Int J Behav Nutr Phys Act. 2011;8(17):14795868.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 16.

    Esliger DW, Rowlands AV, Hurst TL, Catt M, Murray P, Eston RG. Validation of the GENEA accelerometer. Med Sci Sports Exerc. 2011;43(6):10851093. PubMed doi:10.1249/MSS.0b013e31820513be

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    Rowlands AV. Accelerometer assessment of physical activity in children: an update. Pediatr Exerc Sci. 2007;19(3):252266. PubMed doi:10.1123/pes.19.3.252

  • 18.

    Phillips LR, Parfitt G, Rowlands AV. Calibration of the GENEA accelerometer for assessment of physical activity intensity in children. J Sci Med Sport. 2013;16(2):124128. PubMed doi:10.1016/j.jsams.2012.05.013

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    Evenson KR, Wen F. Performance of the ActiGraph accelerometer using a national population-based sample of youth and adults. BMC Res Notes. 2015;8:7. PubMed doi:10.1186/s13104-014-0970-2

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20.

    Baquet G, Stratton G, Van Praagh E, Berthoin S. Improving physical activity assessment in prepubertal children with high-frequency accelerometry monitoring: a methodological issue. Prev Med. 2007;44(2):143147. PubMed doi:10.1016/j.ypmed.2006.10.004

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21.

    Ekelund U, Tomkinson G, Armstrong N. What proportion of youth are physically active? Measurement issues, levels and recent time trends. Br J Sports Med. 2011;45(11):859865. doi:10.1136/bjsports-2011-090190

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22.

    Jago R, Fox KR, Page AS, Brockman R, Thompson JL. Parent and child physical activity and sedentary time: do active parents foster active children? BMC Public Health. 2010;10(194):14712458.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 23.

    Kalakanis LE, Goldfield GS, Paluch RA, Epstein LH. Parental activity as a determinant of activity level and patterns of activity in obese children. Res Q Exerc Sport. 2001;72(3):202209. doi:10.1080/02701367.2001.10608953

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24.

    Cunningham-Sabo L, Lohse B, Smith S, et al. Fuel for Fun: a cluster-randomized controlled study of cooking skills, eating behaviors, and physical activity of 4th graders and their families. BMC Public Health. 2016;16(1):444. doi:10.1186/s12889-016-3118-6

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 25.

    Sallis JF, McKenzie TL, Alcaraz JE, Kolody B, Faucette N, Hovell MF. The effects of a 2-year physical education program (SPARK) on physical activity and fitness in elementary school students. Sports, Play and Active Recreation for Kids. Am J Public Health. 1997;87(8):13281334. PubMed doi:10.2105/AJPH.87.8.1328

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26.

    Lohse B, Belue R, Smith S, Wamboldt P, Cunningham-Sabo L. About Eating: an online program with evidence of increased food resource management skills for low-income women. J Nutr Educ Behav. 2015;47(3):265272.e1. PubMed doi:10.1016/j.jneb.2015.01.006

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27.

    Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA. 2014;311(8):806814. PubMed doi:10.1001/jama.2014.732

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28.

    Ward ZJ, Long MW, Resch SC, et al. Redrawing the US obesity landscape: bias-corrected estimates of state-specific adult obesity prevalence. PLoS ONE. 2016;11(3):e0150735. doi:10.1371/journal.pone.0150735

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29.

    Trust for America’s Health, Robert Wood Johnson Foundation. The state of obesity: states with the highest rates of physical inactivity. 2016. http://stateofobesity.org/lists/least-physically-active-states/. Accessed July 27, 2017.

    • Export Citation
  • 30.

    Sherry B, Blanck HM, Galuska DA, Pan L, Dietz WH. Vital signs: state-specific obesity prevalence among adults—United States, 2009. MMWR. 2010;59(30):951955.

    • Search Google Scholar
    • Export Citation
  • 31.

    Mendoza JA, Watson K, Nguyen N, Cerin E, Baranowski T, Nicklas TA. Active commuting to school and association with physical activity and adiposity among US youth. J Phys Act Health. 2011;8(4):488495. PubMed doi:10.1123/jpah.8.4.488

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 32.

    Beck J, Chard CA, Hilzendegen C, Hill J, Stroebele-Benschop N. In-school versus out-of-school sedentary behavior patterns in U.S. children. BMC Obes. 2016;3:34.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33.

    Brooke HL, Corder K, Atkin AJ, van Sluijs EM. A systematic literature review with meta-analyses of within- and between-day differences in objectively measured physical activity in school-aged children. Sports Med. 2014;44(10):14271438. PubMed. doi:10.1007/s40279-014-0215-5

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34.

    Nyberg GA, Nordenfelt AM, Ekelund U, Marcus C. Physical activity patterns measured by accelerometry in 6- to 10-yr-old children. Med Sci Sports Exerc. 2009;41(10):18421848.

    • Search Google Scholar
    • Export Citation
  • 35.

    Groffik D, Sigmund E, Frömel K, Chmelík F, Nováková Lokvencová P. The contribution of school breaks to the all-day physical activity of 9- and 10-year-old overweight and non-overweight children. Int J Public Health. 2012;57(4):711718. doi:10.1007/s00038-012-0355-z

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36.

    Dale C, Corbin CB, Dale KS. Restricting opportunities to be active during school time: do children compensate by increasing physical activity levels after school? Res Q Exerc Sport. 2000;71(3):240248. PubMed doi:10.1080/02701367.2000.10608904

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37.

    Dauenhauer BD, Keating XD. The influence of physical education on physical activity levels of urban elementary students. Res Q Exerc Sport. 2011;82(3):512520. PubMed doi:10.1080/02701367.2011.10599784

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 38.

    Metcalf B, Henley W, Wilkin T. Effectiveness of intervention on physical activity of children: systematic review and meta-analysis of controlled trials with objectively measured outcomes (EarlyBird 54). BMJ. 2012;345:35888.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 39.

    Teder M, Mörelius E, Nordwall M, et al. Family-based behavioural intervention program for obese children: an observational study of child and parent lifestyle interpretations. PLoS ONE. 2013;8(8):e71482. PubMed doi:10.1371/journal.pone.0071482

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40.

    Viitasalo A, Eloranta A, Lintu N, et al. The effects of a 2-year individualized and family-based lifestyle intervention on physical activity, sedentary behavior and diet in children. Prev Med. 2016;87:8188. PubMed doi:10.1016/j.ypmed.2016.02.027

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41.

    Faith MS, Van Horn L, Appel LJ, et al. Evaluating parents and adult caregivers as “agents of change” for treating obese children: evidence for parent behavior change strategies and research gaps. Circulation. 2012;125:11861207. PubMed doi:10.1161/CIR.0b013e31824607ee

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.

    Golan M, Weizman A, Fainaru M. Impact of treatment for childhood obesity on parental risk factors for cardiovascular disease. Prev Med. 1999;29:519526. PubMed doi:10.1006/pmed.1999.0584

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 43.

    Wingert K, Zachary DA, Fox M, Gittelsohn J, Surkan P. Child as change agent. The potential of children to increase healthy food purchasing. Appetite. 2014;81:330336. PubMed doi:10.1016/j.appet.2014.06.104

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44.

    Erkelenz N, Kobel S, Kettner S, Drenowatz C, Steinacker JM, The Research Group “Join the Healthy Boat–Primary School”. Parental activity as influence on children’s BMI percentiles and physical activity. J Sports Sci Med. 2014;13:645650.

    • PubMed
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 18 18 0
Full Text Views 175 175 16
PDF Downloads 61 61 9