Pediatric Weight Management

Tami's Folder

Citation:

Fisher JO, Mitchell DC, Smiciklas-Wright H, Mannino ML, Birch LL. Meeting calcium recommendations during middle childhood reflects mother-daughter beverage choices and predicts bone mineral status. Am J Clin Nutr. 2004 Apr; 79(4): 698-706.

PubMed ID: 15051617
 
Study Design:
Prospective Cohort Study
Class:
B - Click here for explanation of classification scheme.
Quality Rating:
Positive POSITIVE: See Quality Criteria Checklist below.
Research Purpose:

To evaluate calcium intake across middle childhood as a function of mother-daughter beverage choices and as a predictor of bone mineral status.

Inclusion Criteria:
  • Girls from five counties in Central Pennsylvania with a mean age of 5.4±04 years and their parents
  • Girls living with both biological parents, absence of severe food allergies or chronic medical problems that would affect food intake and the absence of dietary restrictions involving animal products
  • Overweight = Body mass index (BMI) greater than the 95th percentile.
Exclusion Criteria:

None stated.

Description of Study Protocol:

Recruitment

Girls and parents enrolled in a health and development study of young girls who were recruited through flyers, newspaper advertisements and mailings within a five-county radius.

Design

  • Anthropometric data from girls were taken
  • Three 24-hour dietary recalls and a milk serving practices survey were administered to mothers with daughters' ages five, seven and nine years.

Statistical Analysis

  • Analysis of variance (age-related trends in girls’ calcium and beverage intakes)
  • Analysis of variance and covariance [differences in mother-daughter beverage intake patterns between girls who met the adequate intake for calcium and those who consumed less than the Adequate Intakes (AI)]
  • Multiple linear regression [relationship between calcium intake and bone mineral density (BMD) and bone mineral content (BMC)]
  • Logistic regression (evaluate predictors of girls’ milk intakes)
  • Spearman rank-order correlations (evaluate calcium tracking across time).
Data Collection Summary:

Timing of Measurements

Measured at baseline (five years), seven years and nine years of age.

Dependent Variables

Height, weight, BMD and BMC.

Independent Variables

  • Taken with three 24-hour recalls:
    • Mothers’ and daughters’ energy
    • Calcium
    • Milk
    • Fruit juice
    • Sweetened beverage
    • Non-energy-containing beverage intakes
  • Reports of milk serving practices
  • How frequently milk was made available to daughters at eating occasions.

Control Variables

  • Age
  • Baseline intake of sweetened beverages at age five years
  • Pubertal status.
Description of Actual Data Sample:
  • Initial N: 197 five-year-old girls and their mothers
  • Attrition (final N): 182 mother/daughter pairs
  • Age: Five years old at baseline
  • Ethnicity: White
  • Socioeconomic status: Approximately equal numbers of families reported income in three ranges:
    • $20,000 to $35,000
    • $35,000 to $50,000
    • $50,000
  • Anthropometrics: At baseline, 6.3% of girls were overweight
  • Location: Pennsylvania.
Summary of Results:
  • Average total calcium intake at each age was calculated as the mean daily intake from all foods, beverages and calcium-containing supplements. The girls’ calcium intakes were categorized as either meeting or falling below recommendations across the five-year period. Specifically, the girls’ calcium intakes at each age were expressed as a percentage of the recommended adequate intake for that particular age. 
  • 42% (N=78) girls were categorized as meeting the AI from ages five to nine years, with a mean calcium consumption of 124%±2% of the AI. Of the 59% of girls (N=114) who consumed less than the AI from ages five to nine years, the mean calcium intake was 78%±2% of the AI.
  • Calcium intake increase by about 10% from ages five to nine years (P<0.001) with mean intakes of 852±25, 876±22 and 930±23mg at ages five, seven and nine years, respectively. 55% of five-year-olds and 57% of seven-year-olds met the 800mg per day recommendations. In contrast, 10% of the total sample consumed the recommended 1,300mg per day at age nine years. The girls who met the AI at age five were 4.8 times (95% CI: 1.3, 17.0; P<0.05) as likely to meet the AI for calcium at age nine as those who consumed less than the AI at age five. The effect of calcium intake classification (meeting or consuming less than the AI) on girls’ calcium intakes did not vary significantly by age (P=0.06). When the calcium intake at age five years was controlled for, the girls who met the AI showed a 277mg per day increase from ages five to nine years, whereas consuming less than the AI showed a 67mg per day decrease (P<0.0001). 
  • Girls who met the AI had higher mean energy intakes from age five to nine than girls who did not meet the AI (P<0.0001); girls who met the AI were not heavier from age five to nine than the girls who consumed less than the AI for calcium P=0.83)
  • Mean calcium intake from ages five to nine was positively related to BMD at age nine after control for stage of pubertal development at age nine and was weakly related to BMC after control for pubertal development and height at age nine
  • Girls who met the AI consumed daily almost twice the amount of milk as did girls who consumed less than the AI (407 compared with 215g per day; P<0.0001). Milk intakes did not vary significantly with age (P=0.42). Juice intake decreased by 26% (P<0.001) while sweetened beverage intake increased by 21% (P<0.0001). Non-energy-containing beverages showed an age-related increase of more than 200% (P<0.0001), but was low in absolute amounts relative to intakes of milk and sweetened beverages. A main effect of calcium intake classification on beverage intakes from five to nine years was observed for milk (P<0.0001) and sweetened beverages (P<0.01) but not for juice (P=0.70) or non-energy-containing beverages (P=0.96). Girls who met the AI consumed daily almost twice the amount of milk as did girls who consumed less than the AI (407 compared with 215g per day; P<0.0001). Girls who met the AI for calcium consumed 18% fewer sweetened beverages from ages five to nine years (P<0.01) than did girls who consumed less than the AI for calcium. 
  • Milk constituted close to 50% of all beverages consumed (excluding water) by the girls who met the AI, which represented 11% of their total daily energy intake. Sweetened beverages represented close to 50% of all beverages consumed by the girls who failed to meet the AI, which represented 9% of their total daily energy intake.
  • When baseline milk intake at age five years was controlled for, greater decreases in milk intake from ages five to nine years were associated with a greater mean sweetened beverage intake, but were not associated with increases in sweetened beverage intake from ages five to nine years
  • Intakes of milk and sweetened beverages were positively associated with their mothers’ intake of those beverages. Similarly, girls who met the AI for calcium had mothers who drank more (P<0.05) milk. Girls who met the AI for calcium were also served milk more frequently at meals and snacks than were girls who consumed less than the AI (3.7±0.1 compared with 3.2±0.1, P<0.0001, N=181).
  • Both sweetened beverage intake and juice consumption were not analyzed in terms of a possible association with overweight.
Author Conclusion:
  • Girls’ calcium intakes from ages five to nine years reflected the relative proportions of milk and sweetened beverages in their diets
  • The girls who met calcium recommendations were served milk more frequently than were the girls who failed to meet calcium recommendations and had mothers who drank more milk than did the mothers of girls who did not meet calcium recommendations
  • Milk availability to the daughters at meals and snacks appeared to explain the mother-daughter similarities in milk intake
  • Calcium intake from age five to nine years predicted bone mineral status at age nine years, which is evidence that maternal influences on daughters’ beverage choices are relevant to the girls’ bone health.
Funding Source:
Government: NIH
Industry:
National Dairy Council
Commodity Group:
University/Hospital: Pennsylvania State University
Reviewer Comments:
  • Strengths:
    • Prospective design
    • Generalizable to white female population who are at risk for developing osteoporosis in adulthood on the basis of ethnicity and sex
    • Mean milk intake in the sample was similar to that reported for six- to 11-year-old female participants of the Continuing Survey of Food Intakes by Individuals 1994 to 1996
  • Limitations:
    • Influence of season on dietary intake
    • Not generalizable to other races, gender and subjects who are lactose intolerant.
Quality Criteria Checklist: Primary Research
Relevance Questions
  1. Would implementing the studied intervention or procedure (if found successful) result in improved outcomes for the patients/clients/population group? (Not Applicable for some epidemiological studies) N/A
  2. Did the authors study an outcome (dependent variable) or topic that the patients/clients/population group would care about? Yes
  3. Is the focus of the intervention or procedure (independent variable) or topic of study a common issue of concern to dieteticspractice? Yes
  4. Is the intervention or procedure feasible? (NA for some epidemiological studies) N/A
 
Validity Questions
1. Was the research question clearly stated? Yes
  1.1. Was (were) the specific intervention(s) or procedure(s) [independent variable(s)] identified? Yes
  1.2. Was (were) the outcome(s) [dependent variable(s)] clearly indicated? Yes
  1.3. Were the target population and setting specified? Yes
2. Was the selection of study subjects/patients free from bias? Yes
  2.1. Were inclusion/exclusion criteria specified (e.g., risk, point in disease progression, diagnostic or prognosis criteria), and with sufficient detail and without omitting criteria critical to the study? Yes
  2.2. Were criteria applied equally to all study groups? Yes
  2.3. Were health, demographics, and other characteristics of subjects described? Yes
  2.4. Were the subjects/patients a representative sample of the relevant population? Yes
3. Were study groups comparable? Yes
  3.1. Was the method of assigning subjects/patients to groups described and unbiased? (Method of randomization identified if RCT) N/A
  3.2. Were distribution of disease status, prognostic factors, and other factors (e.g., demographics) similar across study groups at baseline? Yes
  3.3. Were concurrent controls or comparisons used? (Concurrent preferred over historical control or comparison groups.) Yes
  3.4. If cohort study or cross-sectional study, were groups comparable on important confounding factors and/or were preexisting differences accounted for by using appropriate adjustments in statistical analysis? Yes
  3.5. If case control study, were potential confounding factors comparable for cases and controls? (If case series or trial with subjects serving as own control, this criterion is not applicable.) N/A
  3.6. If diagnostic test, was there an independent blind comparison with an appropriate reference standard (e.g., "gold standard")? N/A
4. Was method of handling withdrawals described? Yes
  4.1. Were follow-up methods described and the same for all groups? Yes
  4.2. Was the number, characteristics of withdrawals (i.e., dropouts, lost to follow up, attrition rate) and/or response rate (cross-sectional studies) described for each group? (Follow up goal for a strong study is 80%.) Yes
  4.3. Were all enrolled subjects/patients (in the original sample) accounted for? Yes
  4.4. Were reasons for withdrawals similar across groups? Yes
  4.5. If diagnostic test, was decision to perform reference test not dependent on results of test under study? N/A
5. Was blinding used to prevent introduction of bias? ???
  5.1. In intervention study, were subjects, clinicians/practitioners, and investigators blinded to treatment group, as appropriate? N/A
  5.2. Were data collectors blinded for outcomes assessment? (If outcome is measured using an objective test, such as a lab value, this criterion is assumed to be met.) ???
  5.3. In cohort study or cross-sectional study, were measurements of outcomes and risk factors blinded? ???
  5.4. In case control study, was case definition explicit and case ascertainment not influenced by exposure status? N/A
  5.5. In diagnostic study, were test results blinded to patient history and other test results? N/A
6. Were intervention/therapeutic regimens/exposure factor or procedure and any comparison(s) described in detail? Were interveningfactors described? Yes
  6.1. In RCT or other intervention trial, were protocols described for all regimens studied? N/A
  6.2. In observational study, were interventions, study settings, and clinicians/provider described? N/A
  6.3. Was the intensity and duration of the intervention or exposure factor sufficient to produce a meaningful effect? Yes
  6.4. Was the amount of exposure and, if relevant, subject/patient compliance measured? Yes
  6.5. Were co-interventions (e.g., ancillary treatments, other therapies) described? N/A
  6.6. Were extra or unplanned treatments described? N/A
  6.7. Was the information for 6.4, 6.5, and 6.6 assessed the same way for all groups? N/A
  6.8. In diagnostic study, were details of test administration and replication sufficient? N/A
7. Were outcomes clearly defined and the measurements valid and reliable? Yes
  7.1. Were primary and secondary endpoints described and relevant to the question? Yes
  7.2. Were nutrition measures appropriate to question and outcomes of concern? Yes
  7.3. Was the period of follow-up long enough for important outcome(s) to occur? Yes
  7.4. Were the observations and measurements based on standard, valid, and reliable data collection instruments/tests/procedures? Yes
  7.5. Was the measurement of effect at an appropriate level of precision? Yes
  7.6. Were other factors accounted for (measured) that could affect outcomes? Yes
  7.7. Were the measurements conducted consistently across groups? Yes
8. Was the statistical analysis appropriate for the study design and type of outcome indicators? Yes
  8.1. Were statistical analyses adequately described and the results reported appropriately? Yes
  8.2. Were correct statistical tests used and assumptions of test not violated? Yes
  8.3. Were statistics reported with levels of significance and/or confidence intervals? Yes
  8.4. Was "intent to treat" analysis of outcomes done (and as appropriate, was there an analysis of outcomes for those maximally exposed or a dose-response analysis)? N/A
  8.5. Were adequate adjustments made for effects of confounding factors that might have affected the outcomes (e.g., multivariate analyses)? Yes
  8.6. Was clinical significance as well as statistical significance reported? Yes
  8.7. If negative findings, was a power calculation reported to address type 2 error? N/A
9. Are conclusions supported by results with biases and limitations taken into consideration? Yes
  9.1. Is there a discussion of findings? Yes
  9.2. Are biases and study limitations identified and discussed? Yes
10. Is bias due to study's funding or sponsorship unlikely? Yes
  10.1. Were sources of funding and investigators' affiliations described? Yes
  10.2. Was the study free from apparent conflict of interest? Yes