The Impact of Family Rules on Children's Eating Habits,Sedentary Behaviors,and Weight Status

Abstract

Background:

Family rules may be influential in helping children to modify their dietary and sedentary behaviors, which are important modifiable risk factors for childhood obesity. However, data examining family rules in relation to children's health behaviors and weight status are limited.

Methods:

This cross-sectional study examined differences in family rules by demographic characteristics of students enrolled in the HEROES (Healthy, Energetic, Ready, Outstanding, Enthusiastic Schools) Initiative, a school-based childhood obesity prevention program. It also investigated the relationship between eating and screen time family rules and six eating and screen time behaviors: fast food consumption; soft drink consumption; fruit and vegetable intake; television viewing; computer use; and video game use, in addition to the association between family rules and children's weight status. Measures included self-reported behavioral data and anthropometric data from students in fourth to eighth grade at 16 schools (N=2819) in a tri-state area of the United States in spring 2012.

Results:

Approximately one-third of students had each of the family rules examined. Whereas the profile of students who had specific rules varied, in general, younger, female, white, and low socioeconomic status students were more likely to have rules than their counterparts. Family rules were associated with healthier outcomes for each of the six behaviors examined (p<0.001), even after controlling for demographics (p<0.001). However, family rules were not associated with children's weight status.

Conclusions:

This study demonstrates that family rules are an underutilized strategy to promote healthier eating habits and reduce children's screen time hours and may serve as an intermediary mechanism to curb childhood obesity.

Introduction

Childhood obesity has tripled in the last 30 years and continues to increase in the United States.¹ It is associated with severe health issues, including increased risk for high blood pressure, type 2 diabetes, metabolic disorders, asthma, and psychological and social concerns.^2–4 Given its prevalence and consequences, it is imperative to examine ways to prevent or reduce this burden. Physical inactivity and dietary intake are modifiable primary determinants of childhood obesity.^4,5 Because parents are influential forces for behavioral change among children,^6–8 it is necessary to determine how parents can effectively intervene to modify these behaviors. An emerging area of research is the use of family rules as a mechanism for obesity protective behaviors among children. Some studies have examined the construct of parental control,^9–11 which involves parents controlling specific elements of their children's diet (e.g., removing access to certain foods).¹² This is conceptually different from family rules, which are familial-based policies for various behaviors that children are expected to observe and involve some level of self-regulation.

Family rules that previously have been explored in the literature include rules about television (TV) viewing and food consumption. Salmon and colleagues¹³ found that Australian children who had rules prohibiting TV during dinner watched less TV than children without these rules, and that girls with rules generally restricting TV hours watched less TV than girls who did not have such rules. Van Zutphen and colleagues¹⁴ found that Australian children with rules about TV viewing watched significantly less TV than children who did not have these rules. However, having TV rules was not associated with children's weight status. Contrary to these findings, Johnson and colleagues¹⁵ found that, among Australian students who were part of an obesity prevention initiative, lack of rules for TV viewing was significantly associated with increased zBMI in children. Verzeletti and colleagues¹⁶ found that the presence of family rules restricting unhealthy foods was the strongest variable associated with soft drink consumption in both Italian and Belgian adolescents in a cross-country comparative study. In a Dutch sample, family rules were also positively associated with decreased soft drink consumption.¹⁷ Associations with weight status were not explored in either of these studies nor were they explored in a systematic review by Pearson and colleagues,¹⁸ who found that having family rules about eating fruits and vegetables (FVs) was positively associated with children's FV consumption.

There is increasing evidence that family rules may provide a protective effect for obesity-related behaviors; however, there are limitations to this scholarship worth noting. First, study samples were primarily among European and Australian children. Second, the literature lacks measurement of weight status and largely focuses on the behaviors of TV viewing, soda consumption, and FV intake. Third, these studies are based on having family rules about one specific activity, rather than the role that a variety of family rules may have on children's behavior. The present study utilizes a cross-sectional quasi-experimental post-test–only no-treatment control group design by evaluating various obesity-related behaviors and the weight status of students with family rules compared to students who do not have family rules in a tri-state area of the United States. Our two hypotheses are as follows: H1: Students who have family rules related to diet and sedentary activities will have healthier corresponding behaviors in comparison to students who do not have these family rules; and H2: Having family rules related to diet and sedentary behaviors will be associated with a normal weight status in comparison to students who do not have family rules. This study adds to the previous literature by providing a demographic profile of students who are most likely to have family rules, examining multiple dietary behaviors and screen time behaviors and their association with family rules, and assessing the relationship between family rules and children's weight status. Ultimately, these findings can help determine whether family rules should be recommended to curb childhood obesity.

Methods

Participants and Procedure

The data used to explore the hypotheses are from the HEROES (Healthy, Energetic, Ready, Outstanding, Enthusiastic Schools) Initiative, a school-based childhood obesity prevention program. Students were in grades 4–8 at 16 schools in southern Indiana, northwestern Kentucky, and southwestern Illinois. More information about the HEROES Initiative is available elsewhere.¹⁹

This study was approved by the authors' institutional review board. Data were collected in spring 2012 as part of an ongoing data collection process for HEROES. Behavioral data were attained using the Student Health Assessment Questionnaire (SHAQ), a 40-question instrument created for HEROES. The SHAQ is modeled after the validated School Physical Activity and Nutrition questionnaire²⁰ and includes questions related to dietary habits, physical activity (PA), and sedentary behaviors of individual students. Because many behaviors assessed in the SHAQ ask students to recall behaviors from the previous day, it was only administered Tuesday through Friday to ensure that data would represent behaviors students engaged in on school days. The family rules questions were developed by the authors and reviewed by a panel of school health experts, all of whom were previous K–12 teachers and worked in the geographic area in which the study took place. Physiological data, including height and weight, were collected by healthcare professionals and paraprofessionals. These individuals were trained and followed a protocol to ensure consistency among data collectors and measurements.

Inclusion criteria consisted of all students in HEROES schools in the fourth grade and higher. The SHAQ and physiological data sets were merged based on student ID number for each school and then aggregated into one overall data set for the study. There were initially N=4285 students in the sample. Once merged and aggregated, several students were missing either behavioral (22.7%) or physiological data (15.0%), likely owing to student absences on the days that data collection occurred. Human error related to inputting student ID numbers may have also accounted for a proportion of the missing data. However, given that the assessments were of a voluntary nature, a binary logistic regression was performed to ensure there were no systematic differences using weight status missing/not missing as the outcome variable and all other variables examined in the study as covariates. This analysis revealed no consequential differences in students who only participated in one portion of the data collection, and, as such, cases that did not have both physiological and behavioral data were then dropped from the data set.

Measures

Measures were selected based on the conceptual model developed by Lytle²¹ that was intended to capture the etiology of childhood obesity. According to Lytle, demographics are immutable factors that impact contextual factors and obesity risk. Familial socioenvironmental factors are a component of contextual factors, described as interpersonal dynamics and norms within families. This study proposes that family rules are a possible determinant within this category. Eating and PA or inactivity behaviors are behavioral factors. According to the conceptual model, these categories of factors collectively influence obesity risk.

Demographics

Demographic data were obtained through school records and included age, sex, race/ethnicity, and socioeconomic status (SES). Because the sample was primarily Caucasian, race/ethnicity was dichotomized as white/nonwhite. School lunch status was used as a proxy measure for SES, which was dichotomized as low SES for students eligible for free or reduced lunch and high SES for students who were not eligible for the free or reduced lunch program.

Family rules

Four measures were used to assess family rules: “Does your family have rules about… (1) “what you are allowed and not allowed to eat?”; (2) “how much time you can spend watching TV?”; (3) “how much time you spend on the computer?”; and (4) “how much time you can spend playing video games?” Response options were “yes,” “no,” or “I don't know.” After examining initial frequency distributions participants who selected “I don't know” were excluded from further analyses. A composite sedentary behavior family rules variable was created for students who had one or more of the three screen time family rules. A composite family rules variable was created for students who had one or more of the four family rules in order to differentiate outcomes between children who had families attempting to modulate their behavior in some way versus children who do not.

Screen time and dietary behaviors

Six measures were used to assess sedentary and eating behaviors. The three measures that assessed sedentary behaviors directly corresponded with the measures regarding family rules: (1) “Yesterday, how many hours did you watch TV?”; (2) “Yesterday, how many hours did you spend on the computer away from school? (Include time spent surfing the internet and instant messaging)”; and (3) “Yesterday, how many hours did you spend playing video games like Nintendo, PlayStation, Xbox, Gameboy or arcade games away from school?” Response options included none; less than 1 hour; 1 hour; 2 hours; 3 hours; 4 hours; and 5 or more hours. A composite screen time measure was created that summed TV, computer, and video game hours. Three measures were used to examine dietary behavior and were selected based on evidence that they have important implications for obesity and health.^22–25 They were: (1) “Yesterday, did you drink regular (not diet) soft drinks?” Response options were: None, 1, 2, 3, and 4 or more times; (2) “How often do you eat fast food (such as McDonald's, Burger King, Taco Bell, Pizza Hut, etc.)?” Response options were: Never or almost never; 1 time per week; 2 times per week; 3 times per week; 4 times per week; 5 times per week; and 6 or more times per week; and (3) a composite measure of the number of FVs eaten on the preceding day.

Physical activity

Two PA measures were used in some analyses given the importance of PA to obesity prevention.^4,5 Vigorous-intensity PA was determined by the question “Last week, how many days did you exercise or participate in physical activity that made your heart beat fast and made you breathe hard for at least 30 minutes, such as basketball, soccer, running, swimming laps, fast bicycling, fast dancing, or similar activities?” Moderate-intensity PA was based on the question, “Last week, how many days did you exercise or participate in physical activity for at least 30 minutes that did NOT make your heart beat fast or make you breathe hard, such as walking, skating, or playing at a pool?” Response options for both items were 0 days, 1 day, 2 days, 3 days, 4 days, 5 days, and 6 or more days.

Weight category

BMI was computed based on measured height and weight (kg/m²) in the physiological data. Weight was measured using a digital scale that measured to the nearest tenth of a pound, and height was measured using a stadiometer that measured to the nearest eighth of an inch. Anthropometric data collection was consistent with state standards.²⁶ BMI was converted to the BMI percentile for sex and age, a standard measurement for children in the United States,²⁷ using CDC growth charts²⁸ and participants were then categorized into four groups: underweight (<5th percentile); normal (5th–84.9th percentile); overweight (85th–94.9th percentile); and obese (≥95th percentile).²⁹ After an initial frequency analysis, the small number of underweight participants (2.7%) was excluded from analyses and overweight and obese participants were combined, creating a binary outcome measure of normal weight and overweight/obese.

Statistical Analyses

Frequency analyses were conducted for all measures. To determine differences in family rules based on demographic characteristics, chi-square statistic tests were used for categorical demographic variables and an independent-samples t-test was conducted for age. A logistic regression was performed to see whether a predictive model could be used to determine students who had greater odds of having one or more family rule using demographic variables as covariates. Independent-samples t-tests were conducted for the individual rules to determine whether there were differences in each corresponding sedentary or dietary behavior between students who had the rule versus students who did not. Multiple regressions were conducted for each sedentary and dietary behavior to determine the impact of the corresponding rule once demographic variables were controlled for.

To examine the effect of family rules on weight status, cross-tabulations with the chi-square statistic were done to assess possible differences in weight category between students with and without rules. A logistic regression was conducted to examine the impact of family rules on overweight/obese weight status with the following covariates: demographics; rules; and dietary, screen time, and PA behaviors. Moderate PA and soft drink consumption were excluded from this model based on the high p values of the bivariate associations. Significance was set a priori at p<0.05. Analyses were conducted in IBM SPSS Statistics software (Version 20; IBM Corp, Armonk, NY).

Results

The final data set consisted of N=2819 students. Students' average age was 12.5 years (standard deviation [SD]=1.33) and ranged from 9 to 15 years. The same proportion of students was male and female; almost half were of low SES; and students were predominantly white. The majority of students reported at least one of the four family rules, and approximately one third of students had each specific rule. Other descriptive data can be found in Table 1.

Table 1.

Frequencies for Primary Variables of Interest (N=2819)

Variable	N	%
Eating rules
Yes	998	35.5
No	1157	41.1
I don't know	658	23.4
TV rules
Yes	615	22.3
No	1759	63.8
I don't know	383	13.9
Computer rules
Yes	875	31.8
No	1527	55.5
I don't know	349	12.7
Video game rules
Yes	875	31.6
No	1495	54.0
I don't know	397	14.3
Number of family rules
None	1145	42.8
1	605	22.6
2	385	14.4
3	295	11.0
4	248	9.3
Yesterday soft drink consumption
None	1480	52.8
1 time	748	26.7
2 times	303	10.8
3 times	142	5.1
4+ times	128	4.6
Fast food eaten per week
None	716	25.4
1–2 times	1650	58.6
3–4 times	363	12.9
5–6 times	88	3.1
Yesterday FV consumption
None	374	13.4
1–2 times	1005	36.1
3–4 times	845	30.3
5+ times	566	20.3
Yesterday TV hours
None	410	14.7
1 hour or less	1192	42.7
2–3 hours	762	27.3
4–5 hours	278	10.0
6+ hours	151	5.4
Yesterday computer hours
None	1257	45.2
1 hour or less	996	35.8
2–3 hours	325	11.7
4–5 hours	113	4.1
6+ hours	92	3.3
Yesterday video game hours
None	1721	61.1
1 hour or less	616	22.2
2–3 hours	226	8.1
4–5 hours	115	4.1
6+ hours	101	3.6
Yesterday mean total screen time hours	2.99; SD=3.4
Last week vigorous intensity PA
0 days	194	6.9
1 day	270	9.6
2–3 days	846	30.0
4–5 days	771	27.4
6+ days	716	25.6
Last week moderate intensity PA
0 days	649	23.1
1 day	434	15.5
2–3 days	900	32.1
4–5 days	436	15.5
6+ days	383	13.7
Weight category
Underweight	75	2.7
Normal	1655	58.7
Overweight	469	16.6
Obese	620	22.0

N for each variable slightly differs because some participants did not respond to all questions. Valid percentages are presented.

TV, television; FV, fruit and vegetable; SD, standard deviation; PA, physical activity.

As shown in Table 2, various demographic variables were associated with having family rules. For the composite variable of one or more rules, age, sex, and SES were all significant. For each of the specific family rules, statistically significant demographic variables differed, although age was significant for each, with younger children tending to have family rules more than older children (p<0.001). Compared to male students, more female students had each rule (p<0.05), with the exception of video games. White students tended to have more eating and video game rules (p<0.05), compared to nonwhite students, whereas low-SES students had computer rules significantly more often than high SES students (p<0.05).

Table 2.

Differences in Family Rules by Students' Demographic Characteristics

Demographic	Total	Has rules	No rules	p value	Eating rules	No eating rules	p value	TV rules	No TV rules	p value	Computer rules	No computer rules	p value	Video game rules	No video game rules	p value
Age^a	12.51	12.35	12.73	<0.001	12.33	12.77	<0.001	12.20	12.67	<0.001	12.30	12.67	<0.001	12.28	12.66	<0.001
Sex^b				0.032			0.006			0.017			0.006
Male	49.7	55.2	44.8		43.4	56.6		23.8	76.2		33.6	66.4		37.1	62.9	0.816
Female	50.3	59.3	40.7		49.3	50.7		28.1	71.9		39.3	60.7		36.7	63.3
Race^b				0.599			0.012			0.229			0.524			0.044
White	87.1	57.4	42.6		47.3	52.7		26.3	73.7		37.7	63.3		37.7	62.3
Non-white	12.9	55.9	44.1		39.5	60.5		23.1	76.9		34.8	65.2		31.7	68.3
SES^b				0.030			0.395			0.592			0.049			0.725
Low SES	45.9	59.5	40.5		45.3	54.7		25.4	74.6		38.5	61.5		36.5	63.5
High SES	54.1	55.4	44.6		47.2	52.8		26.4	73.6		34.6	65.4		37.2	62.8

Independent-samples t-test for age; cross-tabulations with the chi-square significance tests for sex, race, and SES. “Has rules” is based on having one or more of the four rules assessed.

The unit of age is year.

Sex, race, and SES variables are displayed with percentage (%).

TV, television; SES, socioeconomic status.

Age remained the only significant predictor of children who had one or more family rules after controlling for demographic variables (adjusted odds ratio=0.80; 95% confidence interval, 0.76–0.85; p<0.001) in the logistic regression.

As shown in Table 3, having family rules about eating was associated with less fast food consumption, less soft drink consumption, and more FV consumption (p<0.001), in comparison to students who did not have family rules about eating. Students with family rules about TV, computer use, and video games used these devices significantly less than students without such rules (p<0.001). Similarly, students who had one or more rules related to the three screen time behaviors had significantly less total screen time hours than students who had none of these rules (p<0.001). Further, students who had one or more rules related to screen time behaviors engaged in significantly more vigorous intensity PA than students without these rules (p<0.001), although there was no relationship between screen time behavior rules and moderate intensity PA.

Table 3.

Associations between Family Rules and Their Corresponding Behaviors

Rules/behaviors	Has rule M (SD)	No rule M (SD)	p value
Eating family rules
Soft drink consumption, times/day	0.59 (.90)	1.07 (1.25)	<0.001
Fast food consumption, times/week	1.17 (1.13)	1.60 (1.14)	<0.001
FV consumption, times/day	3.12 (2.07)	2.54 (2.01)	<0.001
TV family rules
TV time, hours/day	1.10 (1.51)	1.78 (1.84)	<0.001
Computer family rules
Computer time, hours/day	0.62 (1.34)	0.91 (1.61)	<0.001
Video game family rules
Video time, hours/day	0.46 (1.16)	0.85 (1.68)	<0.001
One or more sedentary behavior family rules
Total screen time, hours/day	2.44 (3.06)	3.39 (3.55)	<0.001
Vigorous PA, days/week	3.80 (1.87)	3.53 (1.98)	<0.001
Moderate PA, days/week	2.48 (2.01)	2.44 (2.08)	0.599

Independent-samples t-tests conducted. Means are presented and standard deviations are in parentheses.

FV, fruit and vegetable; TV, television; PA, physical activity; M, mean; SD, standard deviation.

As shown in Table 4, for all six regression models, family rules were consistently associated with their corresponding behaviors (p<0.001).

Table 4.

Impact of Family Rules on Dietary and Sedentary Behaviors after Controlling for Demographics

	Predictor variable	Beta	SE	Adjusted R²
TV hours, hours/day	Having TV rule	−0.158^***	0.08	0.049
	Age	0.032	0.03
	Female	−0.075^***	0.07
	Non-white	0.091^***	0.11
	Low SES	0.068^**	0.08
Computer hours, hours/day	Having computer rule	−0.076^***	0.06	0.033
	Age	0.127^***	0.02
	Female	−0.017	0.06
	Non-white	0.058^**	0.10
	Low SES	0.069^**	0.06
Video game hours, hours/day	Having video game rule	−0.119^***	0.06	0.114
	Age	0.023	0.02
	Female	−0.301^***	0.06
	Non-white	0.000	0.09
	Low SES	0.108^***	0.06
Soft drink consumption, times/day	Having eating rule	−0.199^***	0.05	0.077
	Age	0.026	0.02
	Female	−0.131^***	0.05
	Non-white	−0.016	0.07
	Low SES	0.132^***	0.05
Fast food consumption, times/week	Having eating rule	−0.159^***	0.06	0.045
	Age	0.002	0.02
	Female	−0.041	0.05
	Non-white	0.113^***	0.08
	Low SES	0.045^*	0.06
Fruit and vegetable consumption, times/day	Having eating rule	0.135^***	0.09	0.023
	Age	−0.052^*	0.03
	Female	−0.042^*	0.09
	Non-white	0.028	0.13
	Low SES	0.021	0.09

Multiple regressions performed for each behavior after controlling for demographics.

p<0.05; ^**p<0.01; ^***p<0.001.

TV, television; SES, socioeconomic status; SE, standard error.

Bivariate analyses examined whether the four individual family rules and having one or more family rules were associated with children's weight status; there were no significant relationships. Correspondingly, having one or more family rules was not significant as a covariate in the multivariate logistic regression that examined predictors of children's overweight/obese weight status.

Discussion

This study investigated the impact of family rules on dietary and screen time behaviors and the possible influence of family rules on children's weight status. Over 40% of students did not have rules related to what they were or were not allowed to eat and their screen time use. Approximately one third of students had each individual rule examined. Interestingly, children used TV more than any other form of entertainment, and the smallest number of students had rules related to this medium. Students' mean screen time on the preceding school day was almost 3 hours. This is less daily screen time hours than a national study determined for children,³⁰ but still beyond the limit of 1–2 hours of daily media time recommended by the American Academy of Pediatrics.³¹

This study provides the first known examination of the demographic differences among children with and without family rules. Younger age was the only demographic predictor of students with family rules. This may be because as children get older, parents offer more autonomy and opportunities for nondirected self-regulation. Whereas associated demographic variables varied by individual rule, female students, white students, and students of lower SES had some rules more often than their counterparts. Though the reasons are unclear, it is possible that guardians may be more protective of female children and therefore institute different familial policies.³² Because children in low-SES households may be at home unsupervised owing to family members' work schedules, guardians may attempt to implement policies in their absence in order to moderate their children's behavior. Yet, given that low SES is often associated with poorer obesogenic outcomes,^33,34 this finding merits further attention.

Having family rules was associated with healthier outcomes for all of the dietary and screen time behaviors assessed. Students with family rules about what they were and were not allowed to eat had significantly less soft drink and fast food consumption and more FV intake than those without these rules. These findings confirm previous studies that examined the impact of rules on soft drink and FV consumption.^16–18 Students with rules about the three screen time behaviors spent significantly less time using these media than students without these rules. These findings confirm and expand upon the previous studies that determined that TV rules were associated with less TV viewing.^13–15 Further, students who had rules governing screen time reported engaging in significantly more vigorous intensity PA in the previous week, lending credence to the supposition that time spent doing sedentary activities offsets time spent being physically active.¹⁵ Though this relationship did not exist for moderate intensity PA, it has been shown that vigorous PA is actually more beneficial for health.³⁵ Further, after controlling for demographic characteristics, family rules continued to contribute to healthier dietary and screen time behaviors. Given these outcomes, it appears that family rules may be an underutilized strategy to successfully reduce sedentary and unhealthy eating behaviors among children.

Despite these positive implications, no associations were found between having family rules and children's weight status. This finding confirms that of van Zutphen and colleagues,¹⁴ but runs contrary to the findings of Johnson and colleagues.¹⁵ Overall, these findings partially support the original hypotheses. Students who had family rules related to dietary and sedentary activities did have healthier corresponding behaviors, in comparison to those who did not have these family rules. However, family rules related to dietary and sedentary behaviors were not associated with a healthier weight status. It may be that, given the multiplicity of determinants that impact weight status, family rules, though important, may not be the most influential. It is also possible that though children's behavior may change immediately after the implementation of family rules, a modification in weight status may be a longer-term outcome that is difficult to assess in a cross-sectional study. Future research should investigate the longitudinal impact of family rules.

These results have important implications. It has been suggested that obesity prevention efforts should work at multiple levels in order to effect change.⁴ The family is one such domain where change can occur. This study provides support for the implementation of family rules related to dietary and sedentary behaviors given their ability to positively regulate behaviors that are protective against childhood obesity. These family-determined policies may become even more influential as part of multifaceted obesity prevention efforts where students receive consistent messaging in multiple realms. Healthcare providers, educators, and other professionals who work with children and families should promote instituting family rules with parents in their dialogues about improving children's well-being, and organizations that aim to enhance children's health can include family rules as a valuable strategy in educational and skill-building materials and programming.

These findings are subject to some limitations. The sample was predominantly white and from the Midwestern region of the United States. Findings therefore may not be generalizable to other populations. Behavioral data were self-reported and could have been vulnerable to social desirability. Because most data were based on behaviors that occurred during school days, it is possible that findings may be different had weekend behavior been assessed. There are also some considerations related to the measurement of family rules. Although the family rules measures asked children to report if they had rules, they did not determine how well the rules were enforced. It is possible that examining the presence of rules versus their level of implementation may yield different findings. Also, because this study assessed general categories of family rules, it is unclear what children's specific rules were. Although identifying precise rules was outside the scope of this study, doing so would be valuable for future research. Further, though the sedentary behaviors analyzed corresponded directly with screen time rules, eating behaviors did not. Although having eating rules played a significant role in the three dietary behaviors assessed, it is possible that they could be even more influential had they been measured more explicitly given the suggestion that specific rules may be more effective than general rules at changing dietary behavior.¹⁶

Despite these limitations, this study makes an important contribution to the literature on parents' ability to modify their children's behavior through the use of family rules. Future research should include additional questions related to the specific types of family rules children have and their level of enforcement. The parental perspective regarding family rules would also be beneficial.³⁶ Examining the impact of family rules specifically among children of lower SES would be a useful application of the current findings. The present study replicates previous findings on the impact of family rules and children's behaviors, but additional research is needed to examine whether this extends to weight status. Longitudinal analysis would likely be the best approach for this examination. Further, given the increasing capabilities of technology (e.g., smartphones), traditional screen time activities may transition to these devices, which should be taken into account in forthcoming research.

Footnotes

Acknowledgments

This project was supported by the Welborn Baptist Foundation. The authors thank Dr. Hsien-Chang Lin for his assistance.

Author Disclosure Statement

No competing financial interests exist.

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