| Literature DB >> 30373557 |
Jennifer A Emond1,2,3, Lucy K Tantum4, Diane Gilbert-Diamond5, Sunny Jung Kim6, Reina K Lansigan5, Sara Benjamin Neelon5,7.
Abstract
BACKGROUND: Excess screen media use is a robust predictor of childhood obesity. Understanding how household factors may affect children's screen use is needed to tailor effective intervention efforts. The preschool years are a critical time for obesity prevention, and while it is likely that greater household disorder influences preschool-aged children's screen use, data on that relationship are absent. In this study, our goal was to quantify the relationships between household chaos and screen use in preschool-aged children.Entities:
Keywords: Childhood obesity; Household chaos; Preschoolers; Screen media use
Mesh:
Year: 2018 PMID: 30373557 PMCID: PMC6206857 DOI: 10.1186/s12889-018-6113-2
Source DB: PubMed Journal: BMC Public Health ISSN: 1471-2458 Impact factor: 3.295
Distribution of sample characteristics and a comparison of mean household chaos by those characteristics
| n (%) | Household chaos | ||
|---|---|---|---|
| Mean (SD) | |||
| Overall | 385 (100%) | 30.1 (8.1) | – |
| Child characteristics | |||
| Age, years, n (%) | |||
| 2 | 114 (29.6%) | 28.9 (7.9) | 0.18 |
| 3 | 103 (26.8%) | 31.3 (8.5) | |
| 4 | 97 (25.2%) | 30.5 (8.5) | |
| 5 | 71 (18.4%) | 30.0 (7.0) | |
| Gender, n (%) | |||
| Female | 188 (48.8%) | 29.6 (8.1) | 0.19 |
| Male | 197 (51.2%) | 30.7 (8.0) | |
| Ethnicity, n (%) | |||
| Hispanic or Latino | 33 (8.6%) | 28.9 (6.8) | 0.42 |
| Not Hispanic or Latino | 334 (86.8%) | 28.4 (8.8) | |
| Did not want to answer | 18 (4.7%) | 30.3 (8.2) | |
| Race, n (%) | |||
| White | 300 (77.9%) | 30.1 (7.9) | 0.98 |
| Black or African American | 30 (7.8%) | 30.1 (8.3) | |
| Asian | 12 (3.1%) | 31.1 (6.2) | |
| Othera | 43 (11.2%) | 29.9 (9.6) | |
| Parent characteristics | |||
| Age, years, n (%) | |||
| 18–29 | 139 (36.1%) | 29.1 (8.2) | 0.08 |
| 30–39 | 229 (59.5%) | 30.9 (7.9) | |
| 40–49 | 17 (4.4%) | 28.7 (8.9) | |
| Education level, n (%) | |||
| High school or less | 138 (35.8%) | 31.4 (8.8) | 0.09 |
| Associate’s degree | 53 (13.8%) | 30.1 (8.5) | |
| Bachelor’s degree | 117 (30.4%) | 29.7 (7.2) | |
| Graduate or professional school | 77 (20.0%) | 28.6 (7.6) | |
| Relationship to child | |||
| Mother | 365 (94.8%) | 30.2 (8.1) | 0.74 |
| Father | 16 (4.2%) | 29.3 (9.3) | |
| Other | 4 (1.0%) | 27.5 (6.1) | |
| Household characteristics | |||
| Annual household income, n (%) | |||
| Less than $25,000 | 51 (13.2%) | 31.2 (9.4) | 0.04 |
| $25,000–$64,999 | 158 (41.0%) | 31.2 (8.7) | |
| $65,000–$144,999 | 134 (34.8%) | 29.1 (7.0) | |
| $145,000 or more | 17 (4.4%) | 26.1 (5.5) | |
| Did not want to answer | 25 (6.5%) | 29.4 (7.4) | |
| Home ownership status, n (%) | |||
| Own | 214 (55.6%) | 29.1 (7.3) | < 0.001 |
| Rent | 146 (37.9%) | 30.8 (8.5) | |
| Other | 25 (6.5%) | 35.5 (10.0) | |
| Adults (≥18 years) in the home, n (%) | |||
| 1 | 22 (5.7%) | 27.7 (7.7) | 0.12 |
| 2 | 263 (68.3%) | 29.9 (8.2) | |
| 3 or more | 100 (26.0%) | 31.3 (7.9) | |
| Children under the age of 12 years in the home, n (%) | |||
| 1 | 81 (21.0%) | 27.6 (8.2) | < 0.001 |
| 2 | 169 (43.9%) | 29.8 (7.2) | |
| 3 or more | 130 (33.8%) | 32.4 (8.5) | |
| Adolescents aged 12–17 years in the home, n (%) | |||
| 0 | 329 (85.5%) | 30.0 (7.9) | 0.43 |
| 1 | 38 (9.9%) | 31.3 (9.8) | |
| 2 or more | 10 (2.6%) | 27.7 (8.4) | |
Among 385 parents with preschool-aged children recruited via social media. P-values from ANOVA assessments except for gender where a two-sample t-test was used
aOther race includes American Indian, Alaskan Native, Pacific Islander, and other
Preschool-aged children’s screen use and reading by level of household chaos
| Any screen use n (%) | Weekly screen use Mean (SD) | Correlation with household chaosa | |
|---|---|---|---|
| Total screen use | 383 (99.5%) | 31.0 (23.8) | 0.17† |
| By type of electronic media activity | |||
| Watching shows or movies | 322 (83.6%) | 14.7 (12.1) | 0.19† |
| Using apps | 253 (65.7%) | 4.8 (7.3) | 0.08 |
| Viewing online videos (e.g., YouTube) | 236 (61.3%) | 5.5 (8.7) | 0.13* |
| Video calls (e.g., Facetime, Skype) | 137 (35.6%) | 1.2 (3.8) | −0.01 |
| Listening to streaming music | 100 (26.0%) | 2.2 (5.9) | −0.01 |
| Browsing e-books | 61 (15.8%) | 0.9 (3.6) | −0.07 |
| Playing Internet video games | 38 (9.9%) | 0.8 (3.4) | 0.10 |
*P < 0.05; †P < 0.001
Among 385 parents with preschool-aged children recruited via social media
aPearson’s correlation coefficient
Adjusted associations between household chaos and obesogenic screen use in preschool-aged children
| Outcomea | ||||||||
|---|---|---|---|---|---|---|---|---|
| Median | n | Weekly screen use (hours)b | Screen use within an hour of bedtimec | Screen use in the Bedroomc | ||||
| beta (95% CI) | RR (95% CI) | RR (95% CI) | ||||||
| Household chaos score, quartiles | ||||||||
| Quartile 1: < 24 | 21 | 104 | 0 (Reference) | 1.00 (Reference) | 1.00 (Reference) | |||
| Quartile 2: 25–29 | 27 | 91 | 7.7 (1.4, 14.1) | 0.02 | 1.28 (0.92, 1.79) | 0.14 | 1.15 (0.81, 1.62) | 0.44 |
| Quartile 3: 30–35 | 32 | 96 | 9.1 (2.7, 15.6) | < 0.01 | 1.42 (1.04, 1.95) | 0.03 | 0.84 (0.58, 1.22) | 0.35 |
| Quartile 4: > 35 | 40 | 94 | 14.4 (7.8, 21.0) | < 0.001 | 1.69 (1.25, 2.29) | < 0.001 | 1.43 (1.04, 1.97) | 0.03 |
Among 385 parents with preschool-aged children recruited via social media
aAll models also adjusted for child age, race, ethnicity and gender, parent age and education level, annual household income, homeownership status, and the number of adults and children under the age of 12 years in the home
bAdjusted beta coefficient from a linear regression model. Adjusted R2 = 0.17
cAdjusted relative risk (RR) and 95% confidence interval from a Poisson regression model with robust standard error estimates
dP for linear trend based on simple linear regression fitting point estimates on quartile medians