Family and Home Environment Predictors of Children’s 24-Hour Movement Guideline Adherence: A Mixed-Methods Study

Abstract

Background:

During summer, children may meet fewer 24 hours Movement Guidelines (24 hr-MGs) [moderate-vigorous physical activity (PA): ≥60 minutes/day, screen time: ≤2 hours/day, sleep: 9–11 hours/day) compared with the school year. Structured environments within community settings (e.g., summer programs) support guideline adherence. Information about the relationship between structured home environments and 24 hr-MGs is needed. This mixed-methods study examined which features of the family, home, and community environment supported children in meeting 24 hr-MGs during the school year and summer.

Methods:

Children’s PA and sleep data were estimated from wrist-worn accelerometry, and screen time was assessed via parent-reported nightly surveys (14 days) at two time points [school year: (March–April) and summer (July–August) of 2021]. Parents completed a survey at each time point with 13 measures of the family (e.g., screen time rules), home (e.g., bed sharing), and community (e.g., summer program enrollment) environment. Multilevel mixed effect logistic regression estimated the odds of meeting 24 hr-MGs at each time point. Parents (n = 20) completed a qualitative interview and thematic analysis revealed parents perceived facilitators and barriers to guideline adherence.

Results:

Summer program enrollment and bedtime rules were associated with greater odds of meeting the PA [odds ratios (ORs): 4.9, 95% confidence intervals (CIs): (1.4, 17.1)] and ≥two 24 hr-MGs [OR: 2.2, 95 CI: (1.2, 4.0)] during summer, respectively. Parents perceived family rules/routines supported guideline adherence and lack of access to summer programs was a barrier.

Conclusions:

Structured environments at home and in the community can support children in meeting 24 hr-MGs guidelines. Interventions that expand access to summer programming and encourage implementation of structured home routines may support meeting multiple 24 hr-MGs during summer.

Introduction

Meeting recommendations for physical activity (PA), sleep, and screen time is associated with many health benefits for children including reduced adiposity, cardiometabolic gains, and improved cognition.^1–4 Given that the amount of time spent in one movement behavior (e.g., screen time) may reduce time spent in another behavior [e.g., moderate-vigorous PA (MVPA)], the Canadian 24-hour Movement Guidelines (24 hr-MGs) for youth aged 5–17 years were developed to capture this interdependent relationship.^5,6 The guidelines suggest that children accumulate (a) 1 hour of MVPA, (b) 9–11 hours of sleep, and (c) less than 2 hours of recreational screen time within a 24-hour day. Few children (<8%) meet all three 24 hr-MGs, and more information is needed about the factors that support guideline adherence.⁷

Children’s engagement in movement behaviors varies between seasons (e.g., school year vs. summer).^8–13 Overall, children engage in more favorable behaviors during the traditional school year compared with the summer. The Structured Days Hypothesis posits that the removal of structured routines that occur during the school year (e.g., earlier bedtimes) may contribute to declines in children’s movement behaviors during summer.^14–16 Notably, in the absence of school, structure may be present within children’s family, home, and community social environments. Greater information about how these environments can be leveraged to support children in meeting 24 hr-MGs is needed.

Few studies examine associations between social context (e.g., family, home, and community) and meeting the 24 hr-MGs.⁷ A recent systematic review of 24 hr-MG prevalence in 23 countries reported that 50% (n = 9) of studies examined factors associated with guideline adherence among children (6–12 years).⁷ Among these, only two studies examined the impact of social context features on children’s guideline adherence.^7,17,18 The remaining studies examined associations with demographic characteristics (e.g., age and biological sex), which offer limited insight into modifiable factors for intervention. Consistent with the Social Ecological Model, factors at different ecological levels influence behavior.¹⁹ Understanding how features within the family, home, and community social ecological context influence children’s movement behaviors will provide meaningful targets for intervention.

The purpose of this convergent mixed-methods study was to qualitatively explore parent perceptions of facilitators and barriers to children’s guideline adherence during summer. Qualitative interviews provided rich contextual information about the home environment for children’s daily activities. Qualitative findings were compared with quantitative analyses that identified features of the family, home, and community ecological context associated with meeting 24 hr-MGs during the school year and summer.

Methods

Setting, Recruitment, and Participants

All study procedures were approved by the lead author’s university institutional review board (Pro00080382). Parents completed informed consent forms and children provided assent prior to enrollment. Children were recruited from 10 socioeconomically diverse elementary schools (K–5th) within two public school districts of a metropolitan city in the southeastern United States (55% White, 44% poverty, 180-day headcount data). Parents were recruited through one of three methods (1) study information flyers via short message service (SMS)-text messaging, (2) paper-based applications distributed in children’s classrooms, or (3) school-hosted parent outreach events. No exclusion criteria were used prior to recruitment.

Study Design

This study applied a convergent mixed-methods design and merging integration approach.²⁴ Data were gathered from a longitudinal study that examined differences in children’s 24-hour movement behaviors at two time points during the school year (March–April) and summer (July–August) of 2021. For context, the study period was 1 year into the COVID-19 pandemic and some summer programs remained closed.²⁵ Behavioral data and social ecological context data were collected during a 14-day period at each time point. Qualitative interviews were conducted between June and July of 2022. Data analysis occurred between August 2022 and May 2023.

Measures

Daily surveys

Each night, parents completed a survey (∼7 minutes) about their child’s activities, screen time, bed, and wake times.²⁶ Screen time was reported in 30 minute increments and assessed as the total time a child spent using screen devices via question items with acceptable reliability (prior test–retest reliability Intra-class correlation coefficient (ICC) = 0.66, 0.73, and 0.72, respectively).^27,28 Parents completed the survey via SMS text message weblink between 6 and 7:30 pm and were compensated $5 per survey.

Parent survey

Prior to each 14-day measurement period, parents completed one survey that contained validated questions about their child’s environmental context.^20,28–37 Measures mapped onto family, home, and community social ecological levels. A detailed description of all measures and available reliability and validity metrics are presented in Supplementary Table S1.^20,28–37 Parents completed the survey (∼25 minutes) using an SMS text message weblink and were compensated $25.

Individual (child) level measures

Demographic question items included child age, biological sex, and race.

Family ecological level measures

Family ecological level measures are related to family characteristics (e.g., income) or family practices (e.g., rules). Parents indicated their annual household income, implementation of PA, screen, bed, and wake time rules on weekdays, and other practices including child bed sharing and child screen ownership.

Home ecological level measures

Home ecological level measures related to the physical home environment (e.g., total screens at home). Questions were asked about the type of housing, bed sharing, noise disturbance around home, home disorganization, and the number of screen devices at home and in child’s bedroom.

Community ecological level measure

The community ecological level measure captured enrollment in out-of-school time (OST) programs. Parents indicated if their child was enrolled in any programs during the school year (e.g., after-school) or summer (e.g., summer day camp).

Accelerometry

Children wore an Actigraph GT9X accelerometer on their nondominant wrist for 24 hours/day during each 14-day measurement period to collect objective measures of PA and sleep. Participating children received a form of sports equipment (e.g., soccer ball) as compensation. Participants were mailed an envelope with their study information, an accelerometer, and a prestamped envelope for device returns. Accelerometers were initialized and downloaded using ActiLife software (version 6.13.4, Pensacola, FL). Details for initialization and processing can be found in Supplementary Table S2.

Qualitative interviews

Parents whose children met ≥2 guidelines (2MG, n = 10) or met 0 guidelines (0MG, n = 10) during summer completed a qualitative interview about perceived facilitators and barriers to meeting 24 hr-MGs. Some interview questions with 2MG parents included “What do you think explains why {XX} is physically active/sleeps 9 hours/meets Screen Time (ST) recommendations most days during the summer?”, “What challenges do you experience at bedtime?”, “How do you overcome them?”. 0MG parents were asked such questions as “What makes it challenging for {XX} to be physically active/sleep 9 hours/meet ST recommendations?”, “What challenges do you experience at bedtime?”, “What would make it easier for your child to reduce their screen use?”. All interviews (30 minutes) were conducted via phone by the lead author and participants received a $25 gift card as compensation.

Analysis

24-hour MGs classification

On each valid day of wear, children were classified as meeting each respective guideline if they accrued ≥1 hour of MVPA, ≤2 hours of screen time, or ≥9 hours of sleep.⁵ To compare guideline adherence between time points, a variety of data handling strategies can be used.³⁸ Calculating an average for each behavior across all measured days is common practice, but does not fully reflect the Canadian 24 hr-MGs guidance that each 24 hr-MG should be met daily.⁵ Previous research has used a 100% of days threshold, in which children must meet guidelines on all measured days.^38,39 Given the present study’s 14-day observation period, a 100% threshold was considered unreasonable and risked a misclassification of children who often met guidelines (e.g., met 24 hr-MGs on 13 of 14 days). Further, few children meet guidelines using a 100% threshold, which can limit generalizability and reduce statistical power.³⁸ To account for these risks, children who met a guideline on at least 75% of their measured days at each time point were classified as “Met Guideline”. Children who did not meet a guideline or met a guideline on less than 75% of their measured days were classified as “Not Meeting Guideline” at each time point. Associations with meeting a combination of guidelines (i.e., 2 and 3 guidelines) were intended for analysis. However, only 5% of children met three guidelines (on ≥75% of days) during summer. Consequently, associations with “Meeting at Least Two Guidelines” were examined to create a larger sample for analysis.

Quantitative analysis

All statistical analyses were performed using Stata software version 16.1 (College Station, TX). Multilevel mixed effects logistic regression models estimated the odds of meeting each 24 hr-MG with family, home, and community-level predictors and covariates (e.g., child age, sex, and race) at each time point. Random intercepts were used to account for the nesting of days within children and children within households. Covariates were included because of global differences in the prevalence of guideline adherence by age, sex, and race.^7,40 Importantly, race was included as a proxy for the pervasive effects of structural racism, which likely contribute to disparities in MGs adherence.⁴¹ For all models, the Benjamini–Hochberg procedure for multiple comparisons was applied.^42,43 Unadjusted and adjusted p values are presented as significant at their lowest false discovery rate threshold (i.e., p = 0.05, 0.1, or 0.2). All confidence intervals are unadjusted for multiple comparisons. Children with behavioral data at both time points were included in analysis. To investigate whether children who had behavioral measures at both time points were different than those with only a single time point, we conducted analyses to examine whether missing a behavioral outcome was associated with child and household/family characteristics. No statistically significant differences were found.

Qualitative analysis

Independent coders (R.D., T.W.) utilized a three-step latent coding technique for analysis.^44,45 Themes were identified using inductive thematic analysis and labeled as facilitators or barriers to guideline adherence. Facilitators and barriers were required to have a frequency of n = 3 to be considered a theme.⁴⁶

Mixed-methods integration

Integration was achieved by a quantitatively organized writing structure and integrating through narrative using the contiguous approach.^24,47 In the Results section, quantitative findings are presented in Tables 2–5 and compared with the qualitative findings. A joint display table (Table 6) elucidates the relationship between quantitatively and qualitatively identified facilitators of 24 hr-MGs adherence during summer.

Results

Descriptive data for all participants are presented in Table 1. Children (n = 417) were mostly male (53%), 9.2 ± 1.7 years old, and racially identified as White (67%). Children provided behavioral data for an average of 10.7 ± 3.2 days (School Year) and 10.1 ± 3.3 days (Summer). The prevalence of meeting 24 hr-MGs, individually and in combination (i.e., ≥2, 24 hr-MGs), is presented by time point in Table 1. Statistically significant associations between social ecological features and 24 hr-MG adherence are presented by time point in Tables 2–5. Overall, excluding sleep, fewer children met MGs during the summer compared with the school year. OST time program enrollment, bedtime and screen time rules, and total screens at home were associated with meeting 24 hr-MGs during the summer.

Table 1.

Demographics of Participants by Time Point

	School year	Summer
Children (n)	417	417
Child age in years (mean ± SD)	9.1 ± 1.6 (Range: 5–13)	9.2 ± 1.7
Female (%)	47.9	47.9
Historically minoritized groups (%)	32.3	32.3
Meeting 24 hours movement guidelines (%)
≥2 guidelines	33.8	22.3
MVPA	40.0	27.6
Sleep	29.5	32.1
Screen time	40.8	28.1
Qualitative sample (n = 10)
Met MVPA + sleep guidelines	n/a	2
	3
Met MVPA + screen guidelines	n/a	5
Family ecological level
Below 200% FPL (%)	29.7	28.8
Screen time rules (mean ± Standard Deviation (SD))	12.3 ± 3.9	10.7 ± 4.6
Physical activity rules (mean ± SD)	10.7 ± 4.6	12.5 ± 3.0
Bedtime rule (mean ± SD)	2.9 ± 0.5	2.1 ± 0.9
Wakeup rule (mean ± SD)	2.2 ± 0.9	1.0 ± 0.9
Share bed (% yes)	16.1	15.2
Total screens owned (mean ± SD)	0.25 ± 0.4	1.1 ± 0.8
Home ecological level
House (% yes)	90.3	90.7
No noise (% yes)	63.1	65.4
Home disorganization (mean ± SD)	25.9 ± 7.0	26.2 ± 6.9
Total screens at home (mean ± SD)	7.6 ± 2.5	7.6 ± 2.6
Total screens in the bedroom (mean ± SD)	1.1 ± 1.5	1.1 ± 1.6
Community ecological level
Out-of-school time program enrollment (%)	74.5	70.2

The table presents child demographics, prevalence of 24 hr-MGs, family, and home context variables. Historically minoritized groups racial identities include Black or African American, White, American Indian or Alaskan Native, Asian Native Hawaiian or Pacific Islander, or other.

FPL = Federal Poverty Line; OST = Out of school time.

Table 2.

Odds Ratios and 95% Confidence Intervals from Mixed Effects Logistic Regression Predicting Meeting the MVPA 24-Hour Movement Guideline

Features	Odds ratio	95% confidence interval	Unadjusted p value	BH adjusted p value
MVPA guideline (school year)
Child age	0.6	(0.4, 0.7)	—	—
Child race: historically minoritized groups	0.5	(0.2, 1.2)	—	—
Child sex: female	0.2	(0.1, 0.5)	—	—
Below poverty (200% FPL)	1.2	(0.5, 3.3)	0.70	0.97
Enrolled OST program	5.9	(2.0, 18.0)	0.00	0.00***
House	1.0	(0.2, 3.8)	0.86	0.97
Total screens owned	1.3	(0.5, 3.3)	0.64	0.97
Total screens at home	1.0	(0.9, 1.2)	0.78	0.97
Total screens in the bedroom	0.9	(0.7, 1.3)	0.73	0.97
Screen time rules	1.0	(0.9, 1.1)	0.97	0.97
PA rules	1.0	(0.8, 1.2)	0.84	0.97
MVPA guideline (summer)
Child age	0.6	(0.4, 0.8)	—	—
Child race: historically minoritized groups	0.6	(0.2, 1.8)	—	—
Child sex: female	0.2	(0.1, 0.7)	—	—
Below poverty (200% FPL)	0.5	(0.2, 1.6)	0.25	0.56
Enrolled OST program	4.9	(1.4, 17.1)	0.01	0.08**
House	0.4	(0.1, 2.2)	0.28	0.56
Total screens owned	0.9	(0.5, 1.7)	0.82	0.89
Total screens at home	0.9	(0.8, 1.1)	0.53	0.70
Total screens in the bedroom	1.1	(0.8, 1.5)	0.42	0.67
Screen time rules	1.0	(0.9, 1.1)	0.89	0.89
PA rules	0.9	(0.8, 1.0)	0.08	0.32

The table presents the association between environmental features and the odds of meeting the MVPA guideline during the school year and summer. Statistically significant values are highlighted in bold using the Benjamini–Hochberg (BH) p value corrections (false discovery rate: *** = 0.05, ** = 0.1, * = 0.2). Covariates (child age, race, and sex) were included in the model but excluded from the p value corrections.

BH, Benjamini–Hochberg; FPL, federal poverty line; MPVA, moderate-vigorous physical activity; OST, out-of-school time; PA, physical activity.

Table 3.

Odds Ratios and 95% Confidence Intervals from Mixed Effects Logistic Regression Predicting Meeting the Sleep 24-Hour Movement Guideline

Features	Odds ratio	95% confidence interval	Unadjusted p value	BH adjusted p value
Sleep guideline (school year)
Child age	0.6	(0.5, 0.8)	—	—
Child race: historically minoritized groups	0.3	(0.1, 0.9)	—	—
Child sex: female	1.8	(0.9, 3.7)	—	—
Below poverty (200% FPL)	1.2	(0.4, 3.5)	0.76	0.84
Enrolled OST program	1.3	(0.5, 3.7)	0.61	0.84
Bed sharing	0.7	(0.2, 2.2)	0.53	0.83
No noise	0.6	(0.3, 1.6)	0.33	0.72
Home disorganization	1.0	(0.9, 1.0)	0.26	0.72
Total screens owned	0.6	(0.2, 1.7)	0.31	0.72
Total screens at home	0.9	(0.8, 1.1)	0.37	0.72
Total screens in the bedroom	0.8	(0.6, 1.2)	0.31	0.72
Screen time rules	1.0	(0.9, 1.1)	0.70	0.84
Bedtime rule	1.0	(0.4, 2.7)	0.98	0.98
Wakeup rule	0.8	(0.5, 1.3)	0.39	0.72
Sleep guideline (summer)
Child age	0.7	(0.6, 0.9)	—	—
Child race: historically minoritized groups	0.4	(0.2, 1.1)	—	—
Child sex: female	0.6	(0.3, 1.1)	—	—
Below poverty (200% FPL)	0.9	(0.3, 2.1)	0.74	0.81
Enrolled OST program	2.1	(0.8, 5.1)	0.11	0.48
Bed sharing	0.7	(0.2, 2.2)	0.54	0.74
No noise	1.4	(0.6, 3.2)	0.43	0.68
Home disorganization	1.0	(0.9, 1.0)	0.32	0.68
Total screens owned	0.7	(0.4, 1.1)	0.11	0.48
Total screens at home	1.0	(0.9, 1.2)	0.99	0.99
Total screens in the bedroom	0.9	(0.7, 1.2)	0.42	0.68
Screen time rules	0.9	(0.9, 1.0)	0.22	0.61
Bedtime rule	1.5	(0.9, 2.4)	0.13	0.48
Wakeup rule	0.9	(0.6, 1.4)	0.70	0.81

The table presents the association between environmental features and the odds of meeting the screen time guideline during the school year and summer. Statistically significant values are highlighted in bold using the Benjamini–Hochberg (BH) p value corrections (false discovery rate: *** = 0.05, ** = 0.1, * = 0.2). Covariates (child age, race, and sex) were included in the model but excluded from the p value corrections.

Table 4.

Odds Ratios and 95% Confidence Intervals from Mixed Effects Logistic Regression Predicting Meeting the Screen Time 24-Hour Movement Guideline

Features	Odds ratio	95% confidence interval	Unadjusted p value	BH adjusted p value
Screen time guideline (school year)
Child age	0.8	(0.7, 1.1)	—	—
Child race: historically minoritized groups	0.3	(0.1, 1.0)	—	—
Child sex: female	1.9	(0.8, 4.5)	—	—
Below poverty (200% FPL)	0.6	(0.2, 2.2)	0.45	0.56
Enrolled OST program	9.0	(2.1, 37.2)	0.00	0.00***
Bed sharing	0.2	(0.0, 0.9)	0.04	0.10*
Total screens owned	0.6	(0.2, 2.0)	0.40	0.56
Total screens at home	0.8	(0.6, 1.0)	0.03	0.10*
Total screens in the bedroom	0.8	(0.6, 1.3)	0.40	0.56
Screen time rules	1.2	(1.1, 1.4)	0.01	0.05**
PA rules	0.9	(0.7, 1.2)	0.57	0.57
Bedtime rule	1.4	(0.4, 4.7)	0.55	0.57
Wakeup rule	0.7	(0.4, 1.2)	0.23	0.46
Screen time guideline (summer)
Child age	0.9	(0.6, 1.2)	—	—
Child race: historically minoritized groups	0.2	(0.0, 1.4)	—	—
Child sex: female	4.3	(1.4, 13.8)	—	—
Below poverty (200% FPL)	1.3	(0.3, 6.7)	0.73	0.85
Enrolled OST program	1.3	(0.3, 6.5)	0.72	0.85
Bed sharing	0.2	(0.0, 1.8)	0.15	0.28
Total screens owned	1.0	(0.4, 2.4)	0.99	0.99
Total screens at home	0.7	(0.6, 1.0)	0.03	0.15*
Total screens in the bedroom	0.7	(0.4, 1.2)	0.17	0.28
Screen time rules	1.2	(1.0, 1.4)	0.07	0.18*
PA rules	0.8	(0.6, 1.0)	0.05	0.17*
Bedtime rule	3.7	(1.4, 9.8)	0.00	0.00**
Wakeup rule	0.9	(0.4, 1.9)	0.77	0.85

Table 5.

Odds Ratios and 95% Confidence Intervals from Mixed Effects Logistic Regression Predicting Meeting ≥2, 24-Hour Movement Guideline

Features	Odds ratio	95% confidence interval	Unadjusted p value	BH adjusted p value
≥ 2, 24-hr guideline (school year)
Child age	0.6	(0.4, 0.7)	—	—
Child race: historically minoritized groups	0.4	(0.1, 1.2)	—	—
Child sex: female	0.7	(0.3, 1.5)	—	—
Below poverty (200% FPL)	1.1	(0.4, 3.2)	0.86	0.94
Enrolled OST program	5.5	(1.7, 17.7)	0.00	0.00**
House	2.2	(0.4, 11.7)	0.37	0.80
Bed sharing	0.8	(0.3, 2.4)	0.69	0.94
No noise	1.1	(0.5, 2.5)	0.87	0.94
Home disorganization	0.9	(0.9, 1.0)	0.01	0.07**
Total screens owned	0.8	(0.3, 2.3)	0.73	0.94
Total screens at home	1.0	(0.9, 1.2)	0.74	0.94
Total screens in the bedroom	0.8	(0.6, 1.1)	0.25	0.80
Screen time rules	1.0	(0.9, 1.1)	0.98	0.98
PA rules	0.9	(0.7, 1.1)	0.33	0.80
Bedtime rule	0.9	(0.3, 2.4)	0.86	0.94
Wakeup rule	0.7	(0.4, 1.1)	0.10	0.43
≥2, 24-hour guideline (summer)
Child age	0.7	(0.5, 0.9)	—	—
Child race: historically minoritized groups	0.5	(0.2, 1.6)	—	—
Child sex: female	0.5	(0.2, 1.1)	—	—
Below poverty (200% FPL)	0.6	(0.2, 1.9)	0.38	0.55
Enrolled OST program	1.3	(0.5, 3.4)	0.65	0.70
House	0.5	(0.1, 3.0)	0.47	0.57
Bed sharing	0.3	(0.1, 1.5)	0.15	0.35
No noise	1.6	(0.6, 4.0)	0.35	0.55
Home disorganization	0.9	(0.9, 1.0)	0.06	0.35
Total screens owned	0.7	(0.4, 1.3)	0.29	0.54
Total screens at home	0.9	(0.7, 1.0)	0.12	0.35
Total screens in the bedroom	0.7	(0.4, 1.1)	0.09	0.35
Screen time rules	1.0	(0.9, 1.1)	0.09	0.09
PA rules	0.9	(0.8, 1.0)	0.16	0.35
Bedtime rule	2.2	(1.2, 4.0)	0.01	0.13*
Wakeup rule	0.9	(0.5, 1.3)	0.48	0.56

The table presents the association between environmental features and the odds of meeting two or more guidelines during the school year and summer. Statistically significant values are highlighted in bold using the Benjamini–Hochberg (BH) p value corrections (false discovery rate: *** = 0.05, ** = 0.1, * = 0.2). Covariates (child age, race, and sex) were included in the model but excluded from the p value corrections.

Table 6.

Integration Through Merging Supportive Features for Meeting 24-Hour Movement Guidelines During Summer

Meeting 24-hour movement guideline	Qualitative findings (meeting ≥2, 24-hour MGs)	Quantitative findings	Similarities (QUANT + QUAL)
MVPA guideline	Facilitators Rules and routines around play and screen use Child supervision Sense of community within the neighborhood Barriers Work schedule challenges Neighborhood safety Lack of peer support	OST program enrollment (facilitator-spring and summer)	No similarities.
Sleep guideline	Facilitators Bedtime rules and routines Screen time rules Barriers Child bedtime resistance Late-night screen use	None	No similarities
Screen time guideline	Facilitators Screen time rules Limit access to screens Encourage non-screen activity options Barriers Relaxed screen time rules Lack of affordable summer programming	Screen time rules (Facilitator-spring and summer) Bedtime rules (Facilitator-summer) physical activity rules (Barrier- summer) Fewer screens at home (facilitator-spring and summer)	Screen time rules Bedtime rules Fewer screens at home

The table presents the integration of qualitative themes and quantitative findings of family, home, and community social ecological level features that support meeting 24 hr-MGs during summer. Features that were statistically significant are labeled (QUANT), and environmental features that were qualitative themes are labeled (QUAL).

24 hr-MGs, 24-hour Movement Guidelines.

Qualitative Findings

Parents (2MG or 0MG) described facilitators and barriers to their child’s ability to meet 24 hr-MGs during summer. Descriptions aligned with multiple levels of the social ecological model. 2MG parents implemented rules and routines for each movement behavior. 0MG parents referenced upstream factors including neighborhood safety, lack of childcare, and summer program closures as barriers to guideline adherence. Qualitative findings are presented as perceived facilitators and barriers to meeting each 24 hr-MG.

Moderate-Vigorous PA

Facilitators

2MG parents identified features within the family, home, and community ecological level that supported guideline adherence. 2MG parents implemented rules and routines (family-level) to limit screen time and encourage outdoor play. Some parents limited options for video games (home-level), which made PA an attractive alternative for children. Some parents perceived their ability to stay at home during the summer enabled supervision of their child’s activities. Others described a sense of community around their home as neighbors shared swimming pools and children played in the cul-de-sac (community-level).

Barriers

Some 0MG parents noted a traditional work schedule (e.g., 9 am–5 pm) resulted in the lack of a caregiver at home during the day (family-level). Others described neighborhood safety concerns (community-level) and required children to stay indoors, which limited PA engagement. Others noted that they lived in a neighborhood with few children, which limited outdoor play opportunities (community-level).

Sleep

Facilitators

2MG parents perceived the implementation of bedtime rules and routines (family-level) supported their child in meeting the sleep guidelines. Parents implemented a consistent bedtime (year-round), strategic meal-timing, relaxation activities (e.g., yoga), and screen-use rules (e.g., no screens in the bedroom).

Barriers

Most 0MG parents did not identify any challenges with bedtime and were unaware their child did not meet sleep duration recommendations. Some 0MG parents discussed their child’s personal and behavioral characteristics as barriers to meeting the sleep guidelines. Some children were described as avid screen users and resistant to bedtime. Parents noted that delayed sleep start times due to late-night screen use may have contributed to the shorter sleep duration.

Screen Time

Facilitators

Most 2MG parents credited the implementation of screen access restrictions for their children’s screen time guideline adherence. Parents set specific timeframes for screen use, created bargaining options for screen time (e.g., 30 minutes of reading to access screens), and required intermittent breaks from screen use (family-level). They also provided nonscreen activity options and encouraged children to find alternative activities (family-level).

Barriers

Some 0MG parents discussed they tended to relax screen time rules during the summer (family-level). 0MG parents valued providing their children with a respite from the structured routines they experienced during the school year. Some parents reasoned the absence of planned activities the next day removed the justification for screen time limitations. Several parents discussed the lack of affordable summer programming as a barrier to guideline adherence (community-level). They perceived that children who attended summer programs would be physically active and consequently, avoid screen use.

Discussion

This study examined the association between features of the family, home, and community environment and adherence to 24 hr-MGs during school and summer. During school, OST time program enrollment was associated with meeting the MVPA, screen time, and two or more 24 hr-MGs. During summer, OST program enrollment and parent rules and routines were significantly associated with meeting the MVPA and screen time guidelines, respectively. Qualitative findings revealed that, during summer, a confluence of resources at multiple social ecological levels supported children in meeting guidelines. At the interpersonal level, supportive resources included parent supervision of child activities, a sense of neighborhood community, and the implementation of rules and routines around movement behaviors. At the community level, barriers to guideline adherence encompassed upstream factors including a lack of childcare, neighborhood safety concerns, unaffordable summer programs, and relaxed screen time rules.

Children who enrolled in OST programs during the school year or summer were more likely to meet the MVPA guideline. This finding aligns with studies that indicate children who participate in OST programs report more favorable movement behaviors than children who do not.^8,21–23,48 As proposed by the Structured Days Hypothesis, summer programs are beneficial because they offer a structured environment in which children can engage in preplanned, segmented, adult-supervised activities.¹⁴ Qualitative interviews found that during summer, parents’ capacity to supervise children during the day impacted their children’s opportunity to play outdoors. Increasing access to affordable summer programming may be a useful strategy to address barriers to supervised PA.

Features of the home sleep environment were associated with meeting the screen time and ≥2 MGs. During school, children who shared a bed with someone were less likely to meet the screen time guidelines. During summer, children whose parents set bedtime rules were more likely to meet the screen time guidelines and ≥2 MGs. Maintenance of consistent bedtime routines is associated with adherence to multiple MGs and children who meet the screen time guideline are more likely to meet the sleep guideline during the school year.^40,49 Given that parent implementation of bedtime rules has been associated with child sleep duration, it was unexpected to find bedtime rules did not predict sleep guideline adherence.^20,31,50 This diverges from qualitative findings in which parents perceived a combination of bedtime and screen time rules was supportive of meeting the sleep guideline. Although several studies link bedtime rules with child sleep duration, most sleep data were collected via parent report.^{20,31,40,50,51} It is possible that bedtime rules encourage longer sleep, but not long enough to meet the sleep guideline.

During school, children who were enrolled in OST programs and children whose parents implemented screen time rules were more likely to meet screen time guidelines. This aligns with previous movement behavior studies among youth on school versus nonschool days.^9,13,23 School, OST program enrollment, and parent-directed screen time limits may have created the optimal daily routine for screen time guideline adherence. Notably, a greater number of screen devices at home reduced the likelihood of meeting the screen time guideline.^52–55 As proposed in the 24 hr-MG framework, an interdependent relationship exists between movement behaviors in which they influence each other.⁴ The study findings build upon this premise and suggest features of the home and community environment interact to support children in meeting 24 hr-MGs. The findings also extend the Structured Days Hypothesis. Among families for whom summer programming is unaffordable, structured home environments—where parents set rules around sleep and screen time—may be equally protective for children’s movement behaviors. Future research could examine a potential synergy between summer program enrollment and structured home environments on 24 hr-MG adherence.

Interpretation of the findings should be considered in light of the study’s limitations and strengths. Multiple environmental features in each model likely explained the same variance (e.g., screen time rules and screen devices in the bedroom) in MGs. As a result, no unique effect was determined for some conceptually linked predictors. The convenience sampling approach resulted in a modest oversampling of children who racially identified as White compared to the school's racial composition, thereby limiting generalizability. This study also has several strengths, which include a large sample, the use of objective measures of PA and sleep, and an examination of modifiable features of children’s environmental context.

Conclusions

Structured environments, both outside and inside the home, protect children from unfavorable movement behavior changes between the school year and summer. A continuity of structure within a child’s community (e.g., OST programs) and home environment (e.g., bedtime rules) may support children in meeting 24-hour MGs during summer. Future interventions should examine the combined effect of expanded access to summer programs and structured home routines on children’s movement behaviors during summer.

Impact Statement

This study reveals the importance of structure within the home and community environment to support children in meeting 24-hour movement guidelines. Family rules and routines around screen/bedtime and access to out-of-school time programs were found to be protective for guideline adherence during the school year and summer.

Authors’ Contributions

R.D., M.W.B., R.G.W., B.A., and A.C.M.L.: Conceptualization. R.D., M.W.B., R.G.W., A.C.M., and B.A.: Methodology. R.D., M.W.B., and R.G.W.: Formal analysis. R.D. and M.W.B.: Investigation. R.D., X.Z., S.B., L.v.K., H.P., J.W., and C.D.P.: Data curation. R.D. and M.W.B.: Writing—original draft preparation. R.D., M.W.B., S.B., X.Z., R.G.W., B.A., A.C.M.L., C.D.P., L.v.K., H.P., and J.W.: Writing—reviewing and editing. R.D. and M.W.B.: Funding acquisition. All authors have read and approved the final version of the article and agree with the order of presentation of the authors.

Footnotes

Acknowledgements

Funding Information

This work was supported by the NIDDK under Award Number R01DK116665 and NICHD (5F31HD102045). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author Disclosure Statement

The authors declare that they have no competing interests.

Supplementary Material

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