| Literature DB >> 21477500 |
Anisha I Patel1, Laura M Bogart, Marc N Elliott, Sheila Lamb, Kimberly E Uyeda, Jennifer Hawes-Dawson, David J Klein, Mark A Schuster.
Abstract
INTRODUCTION: Although several studies suggest that drinking water may help prevent obesity, no US studies have examined the effect of school drinking water provision and promotion on student beverage intake. We assessed the acceptability, feasibility, and outcomes of a school-based intervention to improve drinking water consumption among adolescents.Entities:
Mesh:
Year: 2011 PMID: 21477500 PMCID: PMC3103565
Source DB: PubMed Journal: Prev Chronic Dis ISSN: 1545-1151 Impact factor: 2.830
Sociodemographic Characteristics of Intervention and Comparison Middle Schools, Los Angeles, California, 2008a
|
| Intervention School (n = 1,669), % | Comparison School (n = 1,924), % |
|---|---|---|
|
| ||
| API/other | 23 | 23 |
| African American | 19 | 9 |
| Hispanic | 53 | 62 |
|
| 15 | 18 |
|
| 72 | 66 |
Abbreviations: API, Asian or Pacific Islander; NSLP, National School Lunch Program.
Data obtained from Education Data Partnership (16).
Students who report a primary language other than English and who have been determined by the state of California to lack clearly defined English language skills necessary to succeed in the school's regular instructional programs.
Refers to students who are eligible for free or reduced-cost lunch through the NSLP.
FigureWater bottle and filtered tap water dispensed as part of school environmental changes to promote student water intake, Los Angeles, California, 2008.
Baseline Characteristics of Study Participants From Intervention and Comparison Middle Schools in Los Angeles, California, 2008
|
| Intervention School (n = 405) | Comparison School (n = 471) |
|
|---|---|---|---|
|
| 12.8 (0.75) | 12.9 (0.46) | .27 |
|
| 56 | 54 | .57 |
|
| |||
| Hispanic | 53 | 63 | .001 |
| Asian/Pacific Islander | 22 | 22 | .90 |
| African American | 14 | 6 | .001 |
| Other | 11 | 9 | .31 |
|
| |||
| English only | 37 | 29 | .01 |
| English plus another | 50 | 55 | .11 |
| No English | 10 | 14 | .05 |
|
| 63 | 63 | .92 |
Abbreviation: NSLP, National School Lunch Program.
P values are based on 2-sample t-tests that compared sociodemographic variables by school (intervention vs comparison).
Refers to students who are eligible for free or reduced-cost lunch through the NSLP.
Consumption of Water, Nondiet Soda, Sports Drinks, and 100% Fruit Juice Among Los Angeles Middle School Students, Preintervention and 2 Months Postintervention, 2008
| Behavior on the Previous Day | Preintervention, n (%) | 2 Months Postintervention, n (%) | Percentage Change, Unadjusted |
| AOR (95% CI) |
|
|---|---|---|---|---|---|---|
|
| ||||||
| Comparison | 340 (79.1) | 324 (75.4) | −3.7 | .006 | 1.76 (1.20-2.57) | .003 |
| Intervention | 279 (76.9) | 300 (82.6) | 5.7 | |||
|
| ||||||
| Comparison | 235 (54.7) | 224 (52.1) | −2.6 | .03 | 1.45 (1.05-1.99) | .02 |
| Intervention | 185 (51.0) | 207 (57.0) | 6.0 | |||
|
| ||||||
| Comparison | 16 (3.7) | 30 (7.0) | 3.3 | .14 | 1.59 (0.93-2.73) | .09 |
| Intervention | 16 (4.4) | 39 (10.7) | 6.3 | |||
|
| ||||||
| Comparison | 133 (30.9) | 142 (33.0) | 2.1 | .65 | 1.03 (0.75-1.41) | .87 |
| Intervention | 125 (34.4) | 126 (34.7) | 0.3 | |||
|
| ||||||
| Comparison | 45 (10.5) | 38 (8.8) | −1.7 | .003 | 1.99 (1.23-3.20) | .005 |
| Intervention | 35 (9.6) | 57 (15.7) | 6.1 | |||
|
| ||||||
| Comparison | 219 (50.9) | 241 (56.1) | 5.2 | .10 | 0.89 (0.66-1.20) | .46 |
| Intervention | 202 (55.7) | 195 (53.7) | −2.0 | |||
|
| ||||||
| Comparison | 229 (53.3) | 216 (50.2) | −3.1 | .12 | 1.31 (0.97-1.75) | .08 |
| Intervention | 185 (51.0) | 199 (54.8) | 3.8 | |||
|
| ||||||
| Comparison | 185 (43.0) | 136 (31.6) | −11.4 | .01 | 1.28 (0.94-1.76) | .12 |
| Intervention | 129 (35.5) | 127 (35.0) | −0.5 | |||
Abbreviations: AOR, adjusted odds ratio; CI, confidence interval.
Values are unadjusted percentages.
P values were calculated by using paired t tests for differences in change from preintervention to postintervention (between intervention school and comparison school).
P values were calculated by using multivariable logistic regression models to predict the odds of drinking various beverages at 2 months postintervention, separately, controlling for intervention status, preintervention student consumption of beverages at school, age, sex, race/ethnicity, primary language spoken at home, and National School Lunch Program eligibility.