Characteristics associated with changes in BMI and zBMI (2024)

How do outcomes associated with participation in MEND vary with the characteristics of children (sex, SEC and ethnicity), MEND centres (type of facility, funding source and programme group size) and areas where children live (in relation to area-level deprivation and the obesogenic environment)?

Background

To our knowledge, no studies have examined variations in outcomes from a childhood weight management intervention delivered under service conditions at the population level. In the two chapters which follow, we address this research gap. This chapter addresses the question for outcomes of change in body fatness measured by change in absolute BMI and zBMI.

As part of this analysis, we also aimed to benchmark changes in BMI estimated from service data on MEND 7–13 programmes delivered in the community against those observed in the RCT15 of the programme.

Data

This analysis draws upon the MEND service data described previously (see Chapter 2, MEND 7–13 service-level data). Chapter-specific outcomes and covariates are described below.

Outcomes

The primary outcome was change in BMI after participation in MEND. BMI was calculated at baseline and follow-up [using (weight/height)2]. BMI measures weight after taking account of an individual’s height, and where it exceeds the 91st centile of the UK 1990 growth charts14 it can be used as a measure of overweight, a proxy for having excess fat. A reduction in BMI can therefore be interpreted as a reduction in excess fat. Where this is an average value across a group such as the MEND 7–13 participants, it will likely reflect that the group predominantly reduced levels of excess fat (one of the aims of the intervention), but that a minority either maintained or even increased their levels. Change in BMI was calculated by subtracting baseline BMI from follow-up BMI, so that 0 on the measure indicates no change, a positive value indicates an increase in BMI and a negative value indicates a reduction in BMI. We took an average reduction in BMI to be evidence of the programme assisting families in child weight management at a population level.

To compare the changes with those commonly reported in reviews10 of childhood obesity interventions, we also measured change in BMI after standardising baseline and follow-up BMI for age and sex. This was done using the UK 1990 BMI growth reference,14 created using Cole’s lambda-mu-sigma (LMS) method,62 which standardises BMI for age and sex. Standardised BMI is termed zBMI. As with BMI, change in zBMI was calculated by subtracting baseline zBMI from follow-up zBMI. Also as with BMI, a fall in zBMI implies a reduction in excess fat, whereas a rise indicates increased excess fat.

In our analysis, the two outcome measures, change in BMI and change in zBMI, lead to broadly similar conclusions. Change in BMI is more comprehensible than change in zBMI, and this is why it is the main focus of the report.

Covariates

Participant-level variables included BMI at baseline, sex, age and ethnicity; characteristics of families included housing tenure, parental unemployment, lone parent or couple status; and characteristics of LSOAs included IDACI 2007 deciles, a measure of built environment, food outlet density and urban/rural status. Programme-level variables included group size, number of previous programmes per programme manager and rounding of height and weight measurements. These variables were described in Chapter 2 (see MEND 7–13 service-level data).

In this section we also examine whether participant-reported global self-esteem is associated with change in BMI. We measure global self-esteem using an adapted form of the widely validated Rosenberg self-esteem scale.35 The scale measures global self-esteem by asking MEND participants to state to what extent they agree with 10 statements about themselves (listed in Box 1). The Rosenberg scale was originally designed for use with adolescents35 and few studies have used it with children within the age range targeted by MEND 7–13. However, the response format was judged by staff at MEND Central to be too confusing for younger children, and so was modified to more fully reflect the cognitive abilities of younger children. In addition, some of the original constructs measured in the scale used American terminology, and these statements were modified by MEND Central to be more accessible to children in the UK (for example, including ‘happy’ in brackets as an alternative to ‘satisfied’ in statement 1 – all 10 MEND-modified statements are listed in Box 1 for reference). Responses to each statement were on a four-point symmetric agree–disagree scale where the coding is given in brackets: ‘a lot like me (0)’, ‘a bit like me (1)’, ‘not like me (2)’ or ‘not at all like me (3)’. Where statements were negative (i.e. statements 2, 5, 6, 8 and 9 in Box 1), these were reverse coded to be positive. The score was then calculated by summing the statements together so that a high value was indicative of high self-esteem.

BOX 1

Adapted items from the Rosenberg self-esteem scale I am satisfied (happy) with myself.

Missingness

Chapter 2 (see Missingness and multiple imputation) described how we used multiple imputation to address issues of missingness in variables required for the analysis. We used the same approach in the analysis reported in this chapter. We imputed values on covariates where BMI (and therefore zBMI) at baseline and follow-up, age, sex, neighbourhood and programme variables were completely observed (n = 9563).

In addition to exploring sources of variation in change in BMI and variation in change in self-esteem, we wanted to examine how baseline self-esteem related to change in BMI. For this specific model, we estimated models where baseline self-esteem and change in BMI were both completely observed (n = 4962).

Randomised controlled trial data and the randomised controlled trial-comparable subset of the MEND data

We pooled the service data and data from the intervention arm in the MEND RCT.15 The RCT was not part of the current study. The RCT data (previously unpublished in trial reports) relate to changes in BMI over the course of the programme. This period (10-week follow-up) is the same as that for the service data. The RCT was restricted to 8- to 12-year-old participants who were obese (exceeded the 98th centile of the UK 1990 growth charts14), whereas MEND 7–13 services in the community are available to those who are overweight and aged 7–13 years. To ensure a valid comparison, the pooled sample was restricted to participants in the RCT intervention arm (n = 47) and those in the service data who were obese and of the same age as RCT participants (n = 8054), for whom data were recorded at baseline and follow-up.

Methods

Regression to the mean

Data obtained before and after interventions are subject to measurement error, biological variation, intrasubject variability and other influences unrelated to the intervention. As a result, the correlations between serial measurements taken from individuals over time are less than perfect, and this leads to regression to the mean.63 Regression to the mean is a statistical phenomenon such that, on average, the change in BMI over time is greater in subjects whose BMI at baseline is more extreme; thus, there is an inverse association between BMI at baseline and BMI change even in the absence of any intervention effect.64 Regression to the mean confounds the interpretation of BMI change associated with an intervention. To address this, estimation of intervention effects needs to include adjustment of the outcome (change in BMI and zBMI in this chapter and change in secondary outcomes presented in Chapter 4) for the baseline value. We undertook this adjustment in all analysis models. The adjustment cannot adjust perfectly for confounding by regression to the mean as it accounts for two separate effects simultaneously: conventional regression to the mean, and unmeasured confounding by variables which are associated with BMI at baseline and change in BMI but are not measured by other covariates in the model.

Multilevel analysis of change in body mass index

In this chapter we used multilevel linear regression models to examine the outcomes of change in BMI and change in zBMI, as these outcomes were continuous. Multilevel regression models make the assumption that residuals at the participant and programme levels are normally distributed. We checked this assumption by graphing the residuals from the unadjusted models and found that residuals at both levels met these assumptions. We calculated the Bayesian information criterion (BIC) for models to assess whether the addition of parameters such as random intercepts and slopes, polynomials and interactions improved the fit of the model parsimoniously. The BIC is a measure of a model’s overall fit, penalised for its parametric complexity. The BIC decreases if parameters added to the model improve the fit over and above the increase in parametric complexity. Following recommendations for the use of Bayesian criteria for model selection, we considered a decrease in BIC of 1–2 as only weak evidence of improved fit while decreases ≥ 3 were considered positive to strong.65

Our preliminary analysis involved a five-stage series of multilevel models. Complete case data (n = 2150) were used to evaluate whether complex parameters (random intercepts and slopes, polynomials and interaction terms) were required in the multivariate model. Complete case data were used as the BIC cannot be computed for multiply-imputed data.

We hypothesised that relationships between variables might vary with age, sex, ethnic group and whether the children lived with lone parents or parents in a couple. We tested these a priori interactions for change in BMI and change in zBMI (see the description of the stages of the analysis, below, for further details).

A series of models were fitted to estimate associations with change in BMI. To ensure that associations were not ruled out because of a lack of power, we used data that were completely observed for each pairwise comparison (i.e. for the age models, data were completely observed for change in BMI, baseline BMI and age).

The first five stages, using complete case data sets, are summarised below.

  1. Compare single-level and multilevel models to test whether a multilevel model is necessary (complete case data to compute change in BIC).

  2. Assess whether the relationship between BMI at baseline and change in BMI was linear or polynomial to determine how to adjust for baseline BMI in subsequent models (complete case data to compute change in BIC).

  3. Estimate ‘baseline-adjusted’ associations between covariates and change in BMI. These multilevel models were adjusted for BMI at baseline. Models were based on fully observed data for change in BMI, BMI at baseline and each covariate in turn to maximise power.

  4. Assess whether a random slope for each coefficient improves the fit compared with stage 3. The random slopes allow the coefficients to vary between programmes (complete case data to compute change in BIC).

  5. Assess whether prespecified interaction terms improve the fit of models over and above those including just the main effects specified in the interaction (i.e. the BIC of a model including an age-by-sex interaction was compared with the BIC of a model including age and sex but no interaction).

The sixth, seventh and eighth stages of the analysis used multiply-imputed data. As with the multilevel modelling described in Chapter 2 (see Methods, Determinants of completion of MEND 7–13), we fitted models in MLwiN 2.25 and combined analyses across all 10 data sets using Rubin’s rules54 as implemented by the Stata 11.2 multiple imputation commands. These stages are described below.

  • 6. Fit multivariable model including all significant covariates, and interactions or random slopes which improved fit with imputed data.

  • 7. Repeat multivariable model of change in BMI, replacing outcome with change in zBMI and replacing BMI at baseline with zBMI at baseline.

  • 8. We also explored whether self-esteem at baseline was associated with change in BMI, using the Rosenberg self-esteem scale data collected in 2009/10.

We report the intercept of the model, which describes change in BMI for a given ‘reference group’. We characterised this reference group as girls, and for other categorical variables (e.g. ethnicity or family structure) we chose the largest category as the reference category (e.g. white, parents living as a couple, etc.). For continuous variables (e.g. age), the reference category was the average value (e.g. 10.7 years).

Comparisons with randomised controlled trial results

The RCT and service data were also compared using a multilevel regression model. Again, the outcome was change in BMI. For this analysis, the principal covariate of interest was a binary variable indicating whether participants were included in the RCT or the service data. Two multivariable models were estimated, the first estimating differences in change in BMI between the RCT and service data after adjusting for any differences in baseline BMI, age and sex. The second also included ethnicity and housing tenure in addition to the first model, in order to adjust for differences in samples by ethnic group and SEC.

Findings

Description of MEND participants

On average, BMI declined by 0.7 kg/m2 among MEND participants, which equated to a reduction of 0.2 units of zBMI. The sample was approximately normally distributed in terms of BMI change (Figure 4). The histogram shows that although BMI was maintained or reduced for the majority of participants (n = 7661, 80.1%), it also increased for some participants (n = 1902, 19.9%). A larger majority maintained or decreased their zBMI (n = 8441, 88.3%) with a corresponding smaller minority increasing their zBMI (n = 1122, 11.7%).

FIGURE 4

Histogram of change in BMI (n = 9563).

Of 8368 children who were obese at baseline, 8.8% were overweight at follow-up and 0.07% (n = 6) were normal weight. Of 1195 children who were overweight at baseline, 0.4% (n = 38) were obese at follow-up, while 2.8% (n = 264) were normal weight. Demographically, MEND participants were normally distributed by age, centred on a mean of approximately 10 years (Table 9). A disproportionate number of MEND 7–13 participants were girls and from white ethnic groups. Socioeconomically, most MEND participants lived with a parent/carer who considered themselves to be in a couple, reported that they owned their residence and were employed. Higher proportions lived in neighbourhoods which were income deprived relative to England as a whole, and which were urban and built up.

TABLE 9

Description of sociodemographic composition in change in BMI and change in zBMI data sets (means and percentages of total sample, n = 9563)

Using the definitions of ‘completion’ chosen for this study (non-completers attended < 25%, partial completers attended 25–75% and completers attended > 75% of sessions), only a very small minority of the sample were non-completers. This is not surprising because to be part of the change in BMI sample (or any other change outcome sample), the children needed fully observed BMI data at baseline and follow-up. A quarter of the sample partially completed the programme whereas the majority completed the programme.

In terms of data quality, most programmes rounded height measurements to 0.5 or 1 cm. A smaller majority rounded weight measurements.

Examination of the overlap in CIs suggests that there are few differences between the imputed and complete case samples in regard to age, sex, ethnicity, housing tenure, residential neighbourhood deprivation, built environment or urban/rural status and programme group size. The imputed proportions were lower for completers and conversely higher for partial completers.

Modelling to inform multilevel models of change in body mass index

The first stage of the analysis compared an unadjusted single-level model of change in BMI with an unadjusted multilevel model of change in BMI. Estimating the between-programme intercept improved the fit of the model (single-level model BIC = 7434, multilevel model BIC = 7354, change in BIC = –79.9). Between-programme variation in change in BMI explained 16% of the overall variation.

The second stage of the analysis tested whether change in BMI was associated with BMI at baseline on a linear or quadratic scale. The multilevel model showed a significant quadratic effect of BMI at baseline. However, the BICs were similar (linear model BIC = 7304, quadratic model BIC = 7303).65 Thus, subsequent models in the analysis adjusted for BMI at baseline as a linear term only. On average, children with higher BMI at baseline declined slightly more in BMI than those with lower BMI at baseline. Figure 5 illustrates this relationship.

FIGURE 5

Relationship between BMI at baseline and change in BMI (n = 9563).

The third stage of the analysis used baseline-adjusted models to explore which variables to use in the final multivariate model. After adjustment for BMI at baseline, most variables were significantly associated with change in BMI (Table 10). Food environment variables for both healthy and unhealthy environments were non-significant and were omitted from later models, as were variables measuring rounding of height and weight.

TABLE 10

Baseline-adjusted associations between change in BMI and covariates (n varies)

Results were broadly similar for change in zBMI and change in BMI. However, sex and number of previous programmes per programme manager were not associated with the change in zBMI and so were not carried forward to the multivariable model.

A fourth set of models investigated whether the relationships between each covariate and the change in BMI varied randomly between programmes (Table 11). Adding a random slope increased BIC in every case so no random slopes were included. Similarly, the a priori-specified two-way interaction terms all increased BIC, so they too were omitted from the multivariable model.

TABLE 11

Multilevel models to test for random slopes of the variables in the multivariable model of baseline-adjusted change in BMI (n = 2150)

Multivariable models of change in BMI and zBMI

The multivariable models of change in BMI, based on the imputed and complete case data sets, appear in Table 12. Model 1 was based on 10 imputed data sets (n = 9563) where the missing covariates (including BMI at baseline) and missing change in BMI were imputed. The intercept shows that the average change in BMI associated with the intervention was −0.76 kg/m2 for those children belonging to the reference group. The characteristics of the reference group in interpreting this finding are therefore important. The group comprised white girls, of average BMI for MEND participants at baseline, living in favourable SEC (having parents who are in a couple, owner occupiers and employed) and who had completed at least 75% of the MEND programme. This group lived in urban neighbourhoods with average levels of deprivation and built environment and attended MEND programmes with around nine attendees at baseline where the programme manager had run around six previous programmes.

TABLE 12

Multivariable models examining associations between covariates and change in BMI

Body mass index fell more in children with higher baseline BMI. Demographically, BMI fell more in younger rather than older children, boys rather than girls and white rather than Asian or black ethnic groups. Socioeconomically, BMI fell more in children whose parents were employed, those living in areas with low proportions of income-deprived families (i.e. low IDACI 2007 scores) and those who attended groups that were larger at baseline. BMI fell less in partial completers and non-completers compared with completers.

Model 2 used complete data (n = 2150) (see the final column of Table 12). The results for models 1 and 2 were similar in direction, though coefficients in the latter model were non-significant for age, sex, black groups, IDACI 2007 decile, group size and non-completers, reflecting the smaller sample size. One coefficient, the number of previous programmes per programme manager, was only significant in model 2.

Results for change in zBMI (Table 13) were broadly similar to the results for change in BMI, except that the baseline BMI/zBMI coefficient was negative for BMI and positive for zBMI, and both were highly significant.

TABLE 13

Multivariable models examining associations between covariates and change in zBMI

Magnitude of changes in BMI and zBMI

In order to interpret how the results from the multivariable models related to changes in BMI and zBMI we calculated the amount of change which would be expected for key subgroups after adjustment for covariates. We selected subgroups on the basis that they were significantly different from the intercept in the model, and using extreme but meaningful categories (for example, 7 and 13 years were chosen as the extremes of the intended age range for MEND 7–13).

The findings (Table 14) show that all subgroups reduced in BMI and zBMI on average. The top 10% of the sample in terms of BMI showed the greatest BMI reduction overall (0.90 kg/m2). Non-completers reduced the least but still reduced their BMI by 0.55 kg/m2 and their zBMI by 0.15 kg/m2. Other groups reduced in BMI by 0.61 kg/m2 (Asian children), 0.70 kg/m2 (children with unemployed parents) and 0.73 kg/m2 (children living in the most deprived neighbourhoods). The range of the reduction in BMI for group sizes ranging from two to 18 participants was −0.68 kg/m2 to −0.82 kg/m2.

TABLE 14

Modelled changes in BMI and zBMI by selected subgroups (those which were significant in the change in BMI model are presented in Table 12)

Self-esteem at baseline and change in body mass index

Table 15 explores the relationship between change in BMI and self-esteem, using the imputed self-esteem data set (n = 4962) (models 1 and 2). Model 1 omits baseline self-esteem while model 2 includes it. Model 2 shows that self-esteem at baseline is weakly associated with change in BMI; for each extra unit of self-esteem (measured on a 0–30 scale), BMI falls by 0.0048 kg/m2 less. This means that, after adjustment for covariates, participants whose level of self-esteem at baseline was average (16.78) reduced in BMI by −0.79 kg/m2. For those whose self-esteem was one standard deviation (6.89) above the mean at baseline, BMI reduced less (by −0.76 kg/m2), and for those whose self-esteem was one standard deviation below, BMI reduced more (by −0.83 kg/m2). Model 3 in Table 15 shows that in the complete case model baseline self-esteem was not significantly related to change in BMI.

TABLE 15

Moderation of change in BMI by baseline self-esteem (imputed n = 4962, complete case n = 1815)

Results were broadly similar to those shown in Table 12 (change in BMI in a larger sample size of 9563, with no baseline self-esteem included). However, whereas associations between change in BMI and IDACI 2007 decile, non-completers versus completers and group size were non-significant in Table 15, they were significant in Table 12.

Sensitivity analyses of modelling of change in body mass index

We considered it essential to adjust for BMI at baseline before selecting the other covariates. However, we anticipate that, for comparison with previous work, some readers may be interested in the univariable relationships between change in BMI and the covariates without adjustment for BMI at baseline. Associations from multilevel models with the imputed data but without adjustment for BMI at baseline are presented in Appendix 8 (see Table 34).

Comparison of change in body mass index observed in service and randomised controlled trial settings

The mean change in BMI in service-level data for 8- to 12-year-old obese children was −0.71 kg/m2 after adjustment for BMI at baseline, age and sex (Table 16). The coefficient for the RCT shows that the BMI of those in the RCT reduced by 0.17 kg/m2 more, but this difference was not statistically significant. After adjustment for ethnicity and housing tenure the change in BMI in service-level data was −0.79 kg/m2, and for those in the RCT it reduced by 0.25 kg/m2 more. Again, this difference was not statistically significant.

TABLE 16

Difference in change in BMI between RCT and service-level participants (those exceeding the 98th centile of the UK 1990 growth charts only). Models use complete case data set adjusted for demographic (n = 5599), and demographic, ethnicity (more...)

Discussion

Changes in body mass index associated with MEND 7–13 delivered under service conditions

Our principal objective in this chapter was to investigate how outcomes associated with MEND 7–13 varied according to participant, family, neighbourhood and programme factors. However, before the results relating to this objective are discussed, we discuss whether or not MEND 7–13, when delivered under service conditions at the population level, was associated with any reduction in BMI.

Our findings show that MEND 7–13, as delivered across the country in the service setting, was associated with a mean reduction in BMI of 0.76 kg/m2. Previous work suggests that there are unlikely to be large increases in height over a 10-week follow-up period,66 which means that a reduction in BMI associated with MEND suggests that the programme may be associated with weight maintenance or weight loss. An outcome of weight maintenance/loss is in line with current national guidance on paediatric weight management interventions from the National Institute for Health and Care Excellence (NICE).9

Beyond knowing that average BMI is reduced in the programme, it may also be instructive to consider how much of a reduction in BMI might be important from a clinical perspective. However, there is little consensus as to what constitutes a clinically significant effect. Meta-analyses reported by Cochrane reviewers10 of four lifestyle treatments for childhood overweight and obesity suggest that treatment interventions for children aged < 12 years lead to changes in zBMI of −0.06 at 6-month follow-up. This reduction in zBMI was described by the authors as both ‘statistically significant and clinically relevant’ (p. 16). Therefore, the magnitude of the reduction of 0.18 in zBMI observed in the MEND 7–13 service might also be considered ‘clinically relevant’ but it is important to note that the findings reported here relate to a shorter follow-up (10 weeks) than the Cochrane review findings (6-month follow-up). As a targeted intervention, the magnitude of the reductions in BMI and zBMI noted here might be important for the health of overweight and obese children at the population level. However, as with the debate on whether the findings are clinically relevant, there are few sources available which suggest by how much BMI might need to reduce following a scaled-up intervention for it to be important at the population level. Changes observed in the service data were not significantly different from the change in BMI observed in the intervention arm of the MEND RCT (Sacher et al.15). This suggests that BMI fell by a similar amount under research and service conditions during the programme (and that changes were of a similar order of magnitude), although these results should be treated cautiously owing to the small sample size of the RCT intervention arm. As we discuss above, it would be interesting to consider whether this difference was important from a clinical or population health perspective. However, in the absence of a clear consensus view on the size of a clinically relevant difference between groups, or what constitutes an important reduction at the population level, we cannot comment further.

In the MEND RCT, the reduction in BMI seen by the end of the intervention was maintained at 12 months.15 We did not have service data at a comparable follow-up to assess whether changes were maintained after MEND 7–13 was delivered in service settings (discussed in more depth below; see Strengths and limitations of the study).

We note that our findings do not reflect an assessment of the effectiveness of MEND 7–13, or a comparative analysis of MEND 7–13 versus alternative weight management or health promotion schemes.

Variations in outcomes associated with MEND 7–13

The extent of the reduction in BMI varies with BMI at baseline, demographic group, family SEC and neighbourhood deprivation, group size and levels of attendance at the programme. Similar variations were also observed for models using change in zBMI as an outcome and where complete data were used. Our findings suggest that reductions in excess fat associated with this weight management intervention were smaller for those living in less favourable SEC and in black and Asian groups. However, statistically significant variation between groups does not necessarily equate to variation which is clinically relevant. As discussed above, a reduction in zBMI of 0.06 can be considered a ‘clinically relevant’ threshold. Even the groups which experienced statistically significantly smaller reductions in average BMI (for example, older children, girls or Asian children) still reduced their zBMI by more than 0.06 on average (see Table 14).

Higher baseline zBMI was associated with a smaller reduction in zBMI, whereas higher baseline BMI was associated with a larger reduction in BMI. We investigated the underlying reasons for this, finding that these different patterns might be expected given the ways that age and sex standardisation impact differentially on the variability of baseline BMI/zBMI and on the relationships between age, sex and change in BMI/zBMI. These patterns are discussed in more detail in Appendix 10.

We have found no studies which have systematically tested how the impact of weight management interventions varies according to demographic factors. Review articles have suggested that this is a key area for future research. A recent review of reviews23 examining the differential effectiveness of interventions by SEC found no studies which examined this question for change in BMI or change in zBMI (although diet and physical activity interventions were found; see Chapter 4, Change in lifestyle behaviours associated with MEND 7–13).

Our study investigated whether or not self-esteem at baseline was associated with change in BMI after adjustment for BMI at baseline and other covariates. We found that higher levels of self-esteem were associated with slightly smaller reductions in BMI; conversely, children with lower self-esteem reduced in BMI more. This may be because children who have lower self-esteem before the programme have more potential to benefit from MEND than children whose self-esteem is already high. Although several intervention studies have examined self-esteem as a secondary outcome in weight management intervention studies (see Griffiths et al.67 for a recent review), we have found none which examined self-esteem as a moderator of weight management interventions.

Baseline-adjusted models showed that change in BMI associated with MEND was not associated with variations in local food environments. Thus, participants living in areas with a high density of fast food and other unhealthy food outlets did not reduce in BMI less than those living in areas with a lower density. Conversely, participants living in areas with high proportions of healthy outlets did not reduce in BMI more than those living in areas with less healthy outlets. This suggests that any changes associated with the programme were, in the short term, apparently resistant to differential exposure to food environments that either promote or constrain a healthy diet. This is perhaps not surprising in the UK context; despite the popular characterisation of ‘food deserts’ (areas where healthy food is less readily available), the evidence that variations in the availability of healthy food in the local environment are associated with levels of overweight is relatively weak.68,69

Change in BMI was not independently associated with characteristics of the built environment or with urban/rural status. Although participants living in more built-up areas did reduce less in BMI, and those in rural and suburban areas reduced more, when these variables were included in a model which also adjusted for neighbourhood deprivation (IDACI 2007 decile) and family socioeconomic status, these relationships were attenuated to non-significance. This suggests that the unadjusted differences may have been due to differences between urban and rural areas in terms of families’ SEC.

Change in BMI was associated with levels of attendance at the programme; completers showed greater reductions in BMI than partial completers and non-completers. This supports the assertion that the variations in the outcome are attributable to participation in the intervention. This is important because our study did not have access to measurements for controls who did not receive the intervention. This means that we cannot make causal assertions about whether or not changes in BMI observed among MEND participants were attributable to the intervention itself (as opposed to another temporal change).

Strengths and limitations of the study

This analysis was based on a service-level data set collected under service (or ‘real world’) conditions across all regions of England. The data set was large enough to have substantial statistical power to identify associations between change in BMI and factors at the individual, family, neighbourhood and programme levels when modelled in a multivariable regression. The availability of such data to examine how interventions operate is a major strength as it allows us to quantify changes associated with such programmes delivered under a wide range of circ*mstances across the country.

We have noted challenges associated with using the MEND service data in terms of missingness and our methods of mitigating these issues using multiple imputation (see Chapter 2, Strengths and weaknesses of the study). The same discussion also applies to these analyses of change in BMI and change in zBMI.

The statistical power to relate sociodemographic and programme factors to change in BMI was a strength of the study. However, as with most secondary data sets, there were other variables which we would have liked to examine which were not measured at the family level, such as parental BMI and parental lifestyle factors which may have moderated the programme’s relationship with participants’ BMI. In addition, there was substantial unexplained random variation between programmes accounting for approximately 16% of the variation in change in BMI. This may well be explained by features which were unmeasured in the service data, such as variations in facilitator qualifications and experience, or features of the venues where sessions were delivered. Again, it would have been useful to explore how these factors related to changes in outcome, and service organisations might consider how to routinely collect such information.

MEND Central recommends anthropometry equipment and trains delivery partners in measurement. However, programmes did not record what equipment was used and so we could not check how far programmes adopted this guidance. In addition, when we analysed the height data, many measurements appeared to have been rounded. As this study was based on service data, the staff delivering programmes were also responsible for the data collection. Therefore, it is possible that observer bias may have influenced the measurement of results. This is not uncommon in routine data collection and it has been demonstrated to impact on estimates of prevalence.70 Our analysis considered height and weight rounding at the programme level, and these were not found to be associated with change in BMI in baseline-adjusted models; however, it is possible that observer bias may remain.

The analysis allowed us to estimate variations in change in BMI as the primary outcome. In the absence of controls we cannot attribute changes in BMI to the programme itself, as children may have been participating in other interventions elsewhere. However, it is unlikely that secular changes would account for changes in such a short time period. In addition, although there were issues with data quality in terms of height and weight rounding, it is unlikely that these could systematically have biased the results. That said, as with all studies, and especially those utilising data not collected under research conditions, there may have been other, unmeasured sources of error which may have introduced bias and we cannot assess how this may have impacted on our findings.

Most interventions measured under research conditions report change at periods of follow-up, typically 6 or 12 months. In this study, data were not available beyond the end of the programme. This limitation is probably common if using service-level data. Although the estimates derived here cannot be used to comment on the sustainability of the intervention effect over a longer time period, the intervention’s effectiveness was assessed under research conditions at 6 months and 1 year in a RCT.15 The change in BMI and zBMI at the end of the programme was similar in both the RCT and our analysis.

Headline findings from Chapter 3

Participation in MEND 7–13 was associated with ‘clinically relevant’ average reductions in BMI overall, and in all socioeconomic and ethnic groups.

Although all groups reduced in BMI by a ‘clinically relevant’ amount, reductions were greater for:

  • children who had higher BMI at the start of the programme

  • boys

  • younger children

  • white children

  • children living in a family where the primary earner was employed

  • children living in less deprived neighbourhoods where a social gradient in change in BMI was observed independently of family SEC

  • children who attended a smaller MEND 7–13 programme

  • children who completed the programme, compared with those who were partial or non-completers.

This suggests that, although referral to, participation in and completion of MEND 7–13 may enhance equity (see Chapter 2), the outcomes associated with the programme may have the potential to widen inequalities.

There were differences between MEND programmes in terms of children’s average reduction in BMI. However, characteristics of the food and built environment did not independently moderate changes in BMI associated with the programme. BMI reductions under service and RCT conditions were of similar magnitude.

Characteristics associated with changes in BMI and zBMI (2024)
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Hobby: Nordic skating, Lacemaking, Mountain biking, Rowing, Gardening, Water sports, role-playing games

Introduction: My name is Fredrick Kertzmann, I am a gleaming, encouraging, inexpensive, thankful, tender, quaint, precious person who loves writing and wants to share my knowledge and understanding with you.