Background/Aims: Cluster designs favor unbalanced baseline measures. The aim of the present study was to determine the frequency of unbalanced baseline BMI on school-based randomized controlled trials (RCT) aimed at obesity reduction and to evaluate the analysis strategies. We hypothesized that the adjustment of unbalanced baseline measures may explain the great discrepancy among studies. Methods: The source of data was the Medline database content from January 1995 until May 2012. Our search strategy combined key words related to school-based interventions with such related to weight and was not limited by language. The participants' ages were restricted to 6-18 years. Results: We identified 146 school-based studies on obesity prevention (or overweight or excessive weight change). Of the 146 studies, 36 were retained for the analysis after excluding reviews, feasibility studies, other outcomes, and repeated publications. 13 (35%) of the reviewed studies had statistically significant (p < 0.05) unbalanced measures of BMI at baseline. 11 studies with BMI balanced at baseline adjusted for the baseline BMI, whereas no baseline adjustment was applied to the 5 unbalanced studies. Conclusion: Adjustment for the baseline BMI is frequently done in cluster randomized studies, and there is no standardization for this procedure. Thus, procedures that disentangle the effects of group, time and changes in time, such as mixed effects models, should be used as standard methods in school-based studies on the prevention of weight gain.

What is already known: To date, interventions have been inconsistent in improving the BMI or body composition of children and adolescents. Uncertainty within the literature published thus far may be due to the heterogeneity of study populations and unrealistic expectations concerning the change in body mass.

What this study adds: Although randomized studies on average must be balanced, a greater percentage of cluster randomized studies on obesity showed statistically significant imbalance for baseline body mass. Adjusting for the baseline body mass is a wrong procedure in this scenario that increases the uncertainty. Also, few cluster randomized studies regarding obesity included around 1,000 participants - a sample size required due to the small effect on body mass that could be expected.

School-based interventions for obesity prevention have been conducted since the publication of two major studies on this topic in 1999: the Pathways Study conducted among American Indian school children [1] and the Planet Health Study conducted among students from Boston, MA, USA [2]. Both studies used a multicomponent intervention, had a high prevalence of obesity among the subjects, and disposed combination methods to better define obesity. Therefore, these two studies - conducted in populations with a high prevalence of obesity and using the state-of-the-art definition of obesity - were very likely to observe positive changes in obesity. However, neither study found an overall reduction in the prevalence of obesity. In the Planet Health Study, a statistically significant reduction in the prevalence of obesity was observed among girls only (decreasing from 23.6% to 20.3%), but the change was very small. Ever since these two studies were published, many more have been conducted in the USA and other countries; although many reviews have been published on the topic, the findings are still considered inconclusive, as indicated by Khambalia et al. [3]. These authors combined the findings of eight reviews, three meta-analyses, and five systematic reviews of school-based programs to prevent and control obesity and concluded that there was limited evidence to serve as a basis for recommendations on this matter. Methodological issues, such as inclusion criteria and outcome assessments, explain some of the discrepancies in these findings [4].

The heterogeneity of participants in cluster randomized trials is a potential problem in many fields of research, but it is particularly relevant in obesity studies because their outcome is almost always measured as weight or BMI change from baseline. Papers [5,6,7,8] and books [9,10] have called attention to the controversy about whether baseline measurements should be adjusted for in this context. A computer simulation study which compared the biases in the estimated treatment effect, with and without adjusting for measurement error at baseline and for different levels of baseline imbalance, concluded that adjusting for baseline leads to bias, especially when sample sizes are small [11].

The present study explores the imbalance of baseline groups and related methodological issues as another possible explanation for these discrepancies. This topic has not been considered in meta-analyses before, even in those accounting for the quality of the papers included. Unbalanced data at baseline in school-based studies are due to underestimated sample sizes and the cluster design, the latter because schools, not individual children, are randomized. In most studies, sample size calculations were based on a change of approximately 1 BMI unit, which is too large for most primary prevention trials. Thus, this analysis focuses on the evaluation of classes or schools for which unbalanced data in the comparison groups may represent an important source of bias.

The aim was to investigate the number of published school-based obesity intervention studies that used groups that were unbalanced for BMI at baseline and to study their approaches for handling the imbalance. This analysis may help researchers to better understand the uncertainty in the obesity intervention literature caused by clustering and improper analysis.

Types of Studies Considered in This Review

All randomized school-based intervention studies that focus on reducing excessive weight gain were included. Randomization of schools was accepted, whereas randomization of individuals was not.

Participants

The participants might be of either sex; and participants aged 6-18 years were included.

Types of Intervention

Studies dealing with intervention in terms of dietary advice for students intended to reduce weight were included in this review. Furthermore, studies that compare the effects of dietary advice versus no dietary advice or dietary advice versus physical activity advice were included.

Types of Outcome Measures

As the main outcome, we chose changes in weight over time or related measures, e.g., weight gain, overweight, obesity, BMI, or BMI z-score.

Secondary outcomes were changes in food consumption and physical activity.

Search Strategy for Identification of Studies

Medline was searched to identify relevant literature. There were no language restrictions for search terms or trial inclusion. The search strategy combined ‘intervention at school' or ‘school-based', and ‘randomized' or ‘clustered' with key words related to weight (‘obesity', ‘weight', ‘body mass index', ‘weight gain' or ‘overweight'). All articles published between January 1995 and May 2012 were regarded as eligible. In 1995, the first trial on the prevention of cardiovascular disease among children using a school-based design was published, and obesity-related, school-based studies have been appearing since then. The search started on May 25, 2012, and updates were included through June 8, 2012.

Review Methods

The papers were reviewed by the two authors (RS and DBC) independently. Relevant studies were determined by the initial search of electronic databases and subsequent screening by the lead reviewer (RS) and a double-check by the co-reviewer (DBC). During this initial screening, articles could be rejected if the reviewer inferred from the title and/or the abstract that it did not meet the inclusion criteria.

Both reviewers independently collected data from each study using a data extraction form. This form included authors, country and year of publication, number of schools randomized, sample characteristics (size and age, baseline data, and the main findings with and without adjustment. The balance of baseline measures was also investigated, and studies with unbalanced baselines were defined by a statistically significant degree of imbalance (p ≤ 0.05) for baseline BMI or related measures, such as bioimpedance or the prevalence of overweight and obesity.

Of 257 papers taken into account, 146 were related to the review subject according to their titles. These 146 works included 17 reviews, 37 papers that reported only the design of the study or pilot results, and 3 opinion or position papers. In 12 papers, randomization was not conducted at the school level, 2 articles reported no baseline data, 1 presented results of the experimental group only, 20 were repeated publications (differing in analysis or years of follow-up), and 18 only provided outcome data for diabetes behaviors that were not related to food intake or physical activity or outcome data for food sales (fig. 1). A total of 36 studies were included in the analysis (table 1).

Table 1

School-randomized studies for obesity prevention: comparison of intervention(s) and control groups - unbalanced data refer mainly to measures of body composition

School-randomized studies for obesity prevention: comparison of intervention(s) and control groups - unbalanced data refer mainly to measures of body composition
School-randomized studies for obesity prevention: comparison of intervention(s) and control groups - unbalanced data refer mainly to measures of body composition
Fig. 1

Flowchart of the selection of the studies.

Fig. 1

Flowchart of the selection of the studies.

Close modal

From these 36 studies, 13 displayed unbalanced outcome measures at baseline (table 2), and most of them were based on mean BMI or BMI classification. Adjustments varied across the analyzed material. Some of the studies balanced at baseline were adjusted for baseline values of BMI and other variables. Thus, in 11 studies with balanced anthropometric measures at baseline, an analysis was conducted after adjusting for BMI [2,12,13,14,15,16,17,18,19,20,21]. Conversely, no baseline adjustment was applied in the 5 studies that were unbalanced at baseline [22,23,24,25,26].

Table 2

Information about response, balancing, adjustment, and sample size of the selected school-randomized studies for obesity prevention

Information about response, balancing, adjustment, and sample size of the selected school-randomized studies for obesity prevention
Information about response, balancing, adjustment, and sample size of the selected school-randomized studies for obesity prevention

Our analysis showed that 35% of the reviewed studies used unbalanced baseline BMI. This indicates that the school clustering design poses a methodological challenge for analyzing the results because anthropometric measures at baseline are one of the most important factors to explain changes over time in BMI or related measures.

Adjusting for baseline measures was frequently utilized in the observed studies, even among those with balanced BMI at baseline. However, adjusting for baseline BMI may bias the results - an issue that has been well discussed [5,6,7,8,9,10,11]. For example, a book by Fitzmaurice et al. [9] discussed adjustment for baseline response using measures on the weight gain of infants aged 12 to 24 months. In this specific case, data were unbalanced at baseline because boys are heavier than girls. Both sexes gained the same amount of weight within 12 months, and it was concluded that there was no gender effect on body weight change. However, if the analysis includes adjustment for baseline values, boys gain more weight than girls. Findings such as this one, known as Lord's paradox, have generated a heated debate among analysts. In 1967, Lord described this paradox within a linear model framework: E(XA) = E(YA) and E(XB) = E(YB), but E(YB|XB = x) - E(YA|XA = x) > 0, uniformly in x (i.e., E(YB - XB|XB = x) - E(YA - XA|XA = x) > 0). According to Lord, the marginal group means seem to indicate no group effect (i.e., E(YB - XB) = E(YA - XA)), yet the comparison of conditional expectations appears to contradict the lack of a group differential effect [51].

For experimental designs, the same principles apply with the additional challenge that data are expected to be balanced at baseline. The reviewed data show that cluster designs favor imbalance, unless the number of clusters is high. We have also shown that adjustment for BMI at baseline is frequently performed.

Although adjusting for the baseline values of parameters that are highly influenced by baseline values is a standard procedure, this approach can bias the results towards an alternative hypothesis - if the control group has a greater BMI - or towards the null - if the experimental group has a greater BMI. Therefore, by forcing a baseline balance in experimental studies, a spurious relationship between treatment and outcome can be observed. Using procedures such as those from mixed-effects models represents a better way to attain results. This modeling allows testing the time effect, the treatment effect per se, and time × treatment effect (the variable that indicates change). In this type of analysis, change over time can be tested clearly without needing to adjust for baseline. The authors of the present study observed this bias in an unbalanced cluster school-randomized study, where the adjustment for baseline changed the result from a lack of association to a statistically significant association [52].

Other possible sources of discrepancy were related to the use or interpretation of the outcome measures. Table 1 shows that the reviewed studies used BMI change, BMI z-score change, prevalence of overweight and obesity, or a combination of these. In relation to the use of BMI z-score change, two studies demonstrated that changes in BMI were less subject to error compared to BMI z-scores [27,28]. The differences were due to abrupt changes in BMI z-scores and changes in the variance of BMI z-scores with growth.

Another source of discrepancy in the studies using prevalence as an outcome was the analysis of overweight and obesity as independent outcomes. For example, in the prevention trial in American Indian children [26], the intervention was not associated with statistically significant changes in variables measured on a continuous scale: BMI, BMI z-scores, skinfolds, and percentage of body fat. However, analysis of BMI as a categorical variable showed a significant decrease only in the prevalence of overweight, and the authors concluded a need for primary prevention because overweight but not obesity was reduced. However, the overall prevalence of excessive weight (overweight plus obesity at the end of the study) decreased from 42.88% to 41.13%, similar to the results from the analysis of BMI as a continuous variable. In addition, studies have shown that interventions have a greater effect on those who are overweight or obese at the beginning of the study [3,29], indicating that primary prevention has not been achieved.

In conclusion, unbalanced BMI values at baseline, the inadequacy of z-scores as an outcome, and a misleading definition of primary prevention of obesity may explain the controversial results of school-based obesity interventions. A pooled analysis of these studies, using mixed-effects models without adjustment for baseline, may help to better summarize these results.

This review was funded by the Brazilian National Research Council (CNPq senior fellowship) through a sabbatical at Harvard University to RS and a fellowship to DBC from the Brazilian Federal Agency for the Improvement of Higher Education (CAPES).

The authors have read and approved this version of the manuscript. None of the authors have any conflicts of interest.

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