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American Journal of Clinical Nutrition, Vol. 86, No. 3, 735-742, September 2007
© 2007 American Society for Nutrition

ORIGINAL RESEARCH COMMUNICATION

APPLE Project: 2-y findings of a community-based obesity prevention program in primary school–age children1,2,3

Rachael W Taylor, Kirsten A McAuley, Wyn Barbezat, Amber Strong, Sheila M Williams and Jim I Mann

1 From the Departments of Human Nutrition (RWT, WB, AS, and JIM) and Preventive and Social Medicine (SMW) and the Edgar National Centre for Diabetes Research (KAM and JIM), University of Otago, Dunedin, New Zealand

2 Supported by funding from the Health Research Council, the National Heart Foundation, The Community Trust of Otago, The University of Otago, and the Otago Diabetes Research Trust

3 Reprints not available. Address correspondence to R Taylor, Department of Human Nutrition, University of Otago, PO Box 56, Dunedin 9054, New Zealand. E-mail: rachael.taylor{at}otago.ac.nz.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: Developing effective strategies for obesity prevention in children is urgently required.

Objective: We determined the effectiveness of a 2-y controlled community-based intervention to prevent excessive weight gain in 5–12-y-old children by enhancing opportunities for healthy eating and noncurricular physical activity.

Design: Children (n = 730) from 4 intervention and 3 control schools underwent measurements of height, weight, waist circumference, blood pressure, diet, and physical activity at baseline and at 1 and 2 y. Intervention components included nutrition education that targeted reductions in sweetened drinks and increased fruit and vegetable intake and activity coordinators who managed an activity program that focused on noncurricular lifestyle-based activities (eg, community walks).

Results: Body mass index (BMI; in kg/m2) z score was significantly lower in intervention children than in control children by a mean of 0.09 (95% CI: 0.01, 0.18) after 1 y and 0.26 (95% CI: 0.21, 0.32) at 2 y, but the prevalence of overweight did not differ. Waist circumference was significantly lower at 2 y (–1 cm), and systolic blood pressure was reduced at 1 y (–2.9 mm Hg). An interaction existed between intervention group and overweight status (P = 0.029), such that mean BMI z score was reduced in normal-weight (–0.29; 95% CI: –0.38, –0.21) but not overweight (–0.02; 95% CI: –0.16, 0.12) intervention children relative to controls. Intervention children consumed fewer carbonated beverages (67% of control intake; P = 0.04) and fruit juice or drinks (70%; P = 0.03) and more fruit (0.8 servings/3 d; P < 0.01).

Conclusion: A relatively simple approach, providing activity coordinators and basic nutrition education in schools, significantly reduces the rate of excessive weight gain in children, although this may be limited to those not initially overweight. This trial was registered at Australian Clinical Trials Registry as #12605000578606.

Key Words: Obesity prevention • body mass index • BMI • weight gain • child • physical activity • healthy eating


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Determining effective strategies for the prevention of childhood obesity is paramount, given the rising prevalence worldwide (1). To date, many obesity prevention initiatives in children have reported beneficial changes to behaviors for nutrition and physical activity, although few show significant improvement in anthropometric indexes (2-4). Moreover, debate continues as to the relative importance of alterations in energy intake compared with energy expenditure for managing obesity at the population level (5).

Several recent school-based interventions offer promise, showing significant benefits in population subgroups with a variety of interventions. Studies targeting increases in the amount or intensity of structured physical education (with or without nutrition components) have shown improvements in anthropometry in some (6-8) but not all (6, 9) interventions in preschool or school-age children. However, increasing pressures on curriculum time and teachers in schools worldwide suggest the need for approaches that do not place further demands on staff or teaching programs (10). Surprisingly, few studies have evaluated the effectiveness of noncurricular approaches (11, 12). We report here on the results of a 2-y controlled community intervention designed to prevent obesity in children by enhancing extracurricular opportunities for physical activity and reinforcing simple dietary messages in the school and local community. Preliminary findings relating to the first year of the intervention were previously published (13).


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Subjects
The APPLE (A Pilot Programme for Lifestyle and Exercise) Project was a 2-y community-based obesity prevention initiative, based in Otago, New Zealand. Ethical approval was obtained from the University of Otago Ethics Committee, and all parents or guardians and the older children themselves gave informed consent. Design and reporting procedures followed the Transparent Reporting of Evaluations with NonRandomized Designs statement (14). Our control and intervention communities were not randomly selected. Rather, we chose 2 semirural geographically separate areas to reduce contamination. The most recent census information (2001 New Zealand Census, Department of Statistics) confirmed that sociodemographic variables were broadly comparable in these areas. All 7 primary schools serving these 2 communities agreed to be involved in the study. They had Ministry of Education 2003 School Decile ratings of 3–7 [indicator of socioeconomic status in which possible range is from 1 (highest) to 10 (lowest)]. All children enrolled at the 4 intervention and 3 control schools as of August 2003, 2004, and 2005 were invited to participate. Figure 1Go refers to the number of children available at each time point (2003, 2004, 2005). Subjects who had their first measurement in 2005 were not included in the analysis because they did not have any follow-up measures. Subjects recruited in 2003 were followed up at 2004 and 2005. Subjects recruited in 2004 were followed up in 2005, with the 2004 measure being treated as baseline. Response rates at each measurement point were uniformly high, ranging from 81% to 89% in control schools and from 85% to 92% in intervention schools. Subjects were predominantly white (82.6%, 16.5% Maori, and <1% Pacific Islander), and ethnic distribution did not differ according to intervention allocation (P = 0.133, chi-square test) or sex (P = 0.108, chi-square test). Control schools received payment of $500–$1000 (depending on school size) for the purchase of school equipment as a reimbursement for the time required to conduct measurements on children at school.


Figure 1
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  		FIGURE 1.. Flow of participants in the study.

 
Physical measurements
All measurements were made in duplicate during school hours at baseline, 1 y, and 2 y. Height was measured with the use of a portable stadiometer (Wedderburn, Dunedin, New Zealand) to the nearest 0.1 cm, and weight was measured with the use of electronic scales (Tanita TI1618; Tanita, Tokyo, Japan) to the nearest 0.1 kg. Waist circumference was measured with the use of a metal diameter tape (Rabone, Sheffield, United Kingdom) at the highest point of the right iliac crest at minimal expiration. All measurements were completed with the children wearing light clothing and no shoes, with the use of the same equipment and procedures as used in the New Zealand National Children's Nutrition Survey (15). Duplicate measures of pulse rate and blood pressure were obtained with the use of an automated sphygmomanometer (Dinamap; GE Medical Systems, Waukesha, WI) (16) with children in a sitting position after a 5-min rest. Body mass index (BMI) was calculated as weight divided by height squared (kg/m2), and z scores were calculated according to the 2000 Centers for Disease Control and Prevention reference norms (CDC) (17). Children with a BMI ≥ 85th percentile for age and sex were classified as overweight.

Dietary intake was assessed during 3 d (including one weekend day) by validated short food questionnaire (SFQ) (18). Briefly, the SFQ elicited the frequency and portion size (standard size provided) of 33 specific foods, food groups, or beverages. Our SFQ was adapted for the New Zealand diet from a similar questionnaire used in the Pathways study (19). Because no nutrition interventions were introduced until the second year of the intervention, we compared the intake of specific foods of interest (beverages, fruit, and vegetables) from children completing the 3 questionnaires at 1 y (62% response rate) and 2 y (66% response rate).

Physical activity was measured with the use of unidirectional Actical accelerometers (Mini-Mitter Co, Bend, OR) worn around the waist which provide an objective independent assessment of physical activity (20). Because of funding constraints, the accelerometers were worn by each child from 1 (74%) to 2 (26%) d at baseline and from 2 (68%) to 5 (32%) d at 1 and 2 y, respectively. Children were instructed to put the accelerometer on as soon as they woke and to take it off just before bed. Several belts were provided for each child so that the accelerometers could still be worn during bathing and swimming. Because of variation in sleeping patterns in children, accelerometry data were analyzed for each child from 0800 to 2000, and the average accelerometry counts during this period are presented. Physical activity and television viewing times were also assessed by 7-d recall questionnaire (Physical Activity Questionnaire for Older Children), which sums participation in a variety of activities to provide an overall activity rating that ranges from 1 (low) to 5 (high). Time spent watching television was assessed separately for Saturday, Sunday, and weekdays, and a weekly score was calculated (15).

Intervention components
Extensive community consultations were held (21), leading to the development of several intervention initiatives introduced at various stages of the 2-y intervention. The focus of the intervention was on encouraging healthy eating and increased levels of physical activity in all children rather than highlighting weight or obesity as issues, although participants (children, families, and schools) were not blinded to the fact that it was an obesity prevention initiative. The main intervention in both years was the provision of community activity coordinators (ACs) attached to each intervention school (0.5 full-time equivalent per school). Their main role was to encourage all children to be a little more physically active every day by increasing the variety and opportunities for physical activity beyond that which was currently provided in each school. They were used to increase noncurricular activity at recess, lunchtimes, and after school, with a particular focus on less traditional sports and more lifestyle-based activities such as outdoor games, household chores, gardening, beach hikes, and children's games from different countries. A full description of the role of the ACs was presented elsewhere (13). Other intervention initiatives in the first year included the development of a resource for teachers to facilitate short bursts of activity in class called "snacktivity" and the provision of cooled water filters to each intervention school.

Additional initiatives in the second year of the intervention were predominately nutrition based and focused on reducing the intake of sugary drinks and on increasing fruit and vegetable consumption. Students received science lessons highlighting the adverse health effects of sugary drinks, a healthy eating resource was developed and made available to all members of the intervention community, and a novel interactive card game, "GoTri," was developed. GoTri simulated completing a triathlon, and students were provided with a starter set of cards. They then had to complete specific physical activities, often with friends or a family member, or to follow particular dietary guidelines to earn 10 "missing" cards. Once students had obtained a complete set, they were able to play the game against each other. All resources were pretested, refined, and pilot tested before introduction into the intervention (A Strong, unpublished data, 2005). The remaining activity intervention introduced in year 2 was the increased promotion and availability of a variety of sport and play equipment at school breaks to enhance the level of "free" play in intervention children.

Statistics
Because this was a pilot study, power calculations were based on the number of students available in the 7 participating schools (250 in each area), an estimate of the intraclass correlation, and the correlation between repeated measures of BMI from an earlier study in Dunedin (22). These suggested that our study had the potential to detect an effect size of 0.3 in any of our measures with 80% power with the use of the 5% level of significance. Because the SD of BMI increases with age, we used z scores for BMI]derived from the CDC tables (17)], which take into account age and sex as the principal outcome measure. Means (±SDs) are presented for variables which were not normally distributed; the data were transformed before analysis. Because schools and not students were the sampling unit, generalized estimating equations with an exchangeable covariance matrix were used to analyze the data (23). Robust standard errors were used to estimate the CIs and P values. Generalized estimating equations, with the use of the Poisson distribution and robust SEs, were used to obtain relative risks for the variables based on counts and the categorical variable overweight obtained by using the CDC cutoff of the 85th percentile (17). The results are presented as relative risks. The model adjusted for age, sex, baseline television viewing, baseline participation in physical activity, and recruitment in 2004 rather than in 2003. The only subgroup analysis compared differences between the intervention and control groups for the BMI z score in normal and overweight children for both time periods. All data were analyzed with the use of STATA 161 software (Release 8.0; Stata Corp, College Station, TX).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Measurements were available on 384 intervention and 346 control children in total; 133 intervention and 127 control children who were only present at one measurement point were excluded. Only 3 participants dropped out of the study (one intervention family). The majority of the remainder left primary school to attend high school, and a small proportion shifted away from the area and could not be traced. Consequently, children with no follow-up measures were significantly older than those with follow-up data (8.4 compared with 7.7 y; P < 0.001), but their sex distribution (51% compared with 48%; P = 0.439) and z scores for height (0.23 compared with 0.13; P = 0.169), weight (0.52 compared with 0.53; P = 0.859), and BMI (0.63 compared with 0.70; P = 0.355) did not differ. This analysis concerns those children with at least 1 y of follow-up. Anthropometric and clinical measurements were obtained on 94–99% of children at each time point, and measurements of physical activity and television viewing were completed by 74–83% of the children.

The characteristics of the study population at baseline, year 1, and year 2 are shown in Table 1Go. At baseline, intervention and control children did not differ in age, sex distribution, height, pulse, and blood pressure. However, intervention children were leaner (P = 0.004) with smaller waist circumferences (P = 0.001).


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  		TABLE 1. Characteristics of the study population at each time point and adjusted differences in outcome variables at 1 and 2 y1

 
The effect on outcome variables after 1 and 2 y of intervention, adjusting for baseline, is also shown in Table 1Go. Mean BMI z score was significantly lower in intervention children than in control children by 0.09 (95% CI: 0.01, 0.18) after 1 y and 0.26 (95% CI: 0.21, 0.32) at 2 y. Changes in BMI did not result from variation in height z scores but rather from differences in relative weight between intervention and control children over time. Waist circumference was also significantly lower at 2 y in intervention children (–1.0 cm), and systolic blood pressure was lower at 1 year, although this was no longer significant at 2 y. Although the prevalence of overweight was lower in intervention children in univariate analyses (data not shown), differences were not significant once adjusted for baseline values (Table 1). Analyses conducted with only those children who were present at each of the 3 measurement points reported similar effects as the overall analysis (Table 2). The mean BMI z scores at each time point in intervention and control children according to weight status at baseline are shown in Figure 2Go. The subgroup analysis (adjusted for age, sex, clustering, baseline z score, and 1-y z score) showed a significant interaction between intervention group and classification of overweight (P = 0.029). Mean BMI z score was significantly lower at both 1 (–0.08; 95% CI: –0.12, –0.04) and 2 (–0.29; 95% CI: –0.38, –0.21) y in the intervention children than in the control normal-weight children. By contrast, no intervention effect was observed in overweight children at either time point [–0.02 (95% CI: –0.11, 0.07) at 1 y and –0.02 (95% CI:–0.16, 0.12) at 2 y].


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  		TABLE 2. Adjusted differences in outcome variables at year 1 and year 2 in those children present at all 3 measurement points (n = 135 for control group, n = 147 for intervention group)1

 

Figure 2
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  		FIGURE 2.. Mean BMI z score in normal-weight ({blacksquare}) and overweight (•) intervention (solid line) and control (dotted line) children at baseline, 1 y, and 2 y. A significant interaction effect existed between intervention group and weight status (P = 0.029).

 
The differences in reported dietary intake at year 2 between intervention and control children adjusted for age, sex, and year-1 intakes are shown in Table 3. Intervention children consumed significantly fewer carbonated beverages (67% of that consumed by control children; P = 0.04) and fruit juice or drinks (70%; P = 0.03), whereas intake of flavored milk and water did not differ. Intervention children also consumed 0.8 more servings of fruit during 3 d (P < 0.01), whereas no intervention effect was observed for vegetable intake.


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  		TABLE 3. Three-day intakes of beverages, fruit, and vegetables and differences in intake at study end1

 
At baseline, intervention and control children reported similar amounts of television viewing and their physical activity scores did not differ (data not shown), although mean accelerometry counts were significantly higher in the intervention children (1165 compared with 944; P = 0.001). No intervention effect was observed for television viewing time, but average accelerometry counts were significantly higher in the intervention children then in the controls at 1 y (mean difference: 167; 95% CI: 4, 329), although differences were no longer significant at 2 y (–75; 95% CI: –215, 65). By contrast, data from the Physical Activity Questionnaire for Older Children showed that the intervention children reported less physical activity than did the control children at both 1 (–0.2; 95% CI: –0.4, –0.1) and 2 (–0.2; 95% CI: –0.4, –0.0) y.

The intraclass correlation for BMI, or the ratio of the between-school variance to the total variance, was 0.04. The SDs for the changes in BMI and BMI z score were 1.2 and 0.4, respectively.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
This controlled community intervention has shown that enhancing opportunities for noncurricular physical activity combined with simple messages promoting healthy eating can slow the rate of excessive weight gain in primary school-age children, although possibly only in those who were initially of normal weight. At 2 y, significant differences were observed between intervention and control children in both BMI z score (–0.26) and waist circumference (–1.0 cm). Although the differences might appear relatively small, such a difference in BMI z score between 2 population groups has the potential to translate to large benefits in terms of population health (24). It is difficult to translate the difference in BMI z score into weight because variation in age and height affects the estimates. However, assuming a child is of median height, a difference of 0.26 BMI z score between intervention and control groups equates to a difference of {approx}0.5 BMI value in a 7-y-old and 0.7 BMI value in an 11-y-old.

Most of the recent school-based interventions have predominantly targeted improvements in healthy eating and physical activity by curriculum initiatives. Two studies in preschool-age children provided additional activity sessions each week (7, 9), and one study held a concurrent nutrition education program (7). No intervention effect was observed by Reilly et al (9), whereas significant reductions in BMI z score of the intervention children were apparent at 1- and 2-y follow-ups in Fitzgibbon et al (7). Studies in school-age children which used multifaceted approaches to improving physical education and school food provision provide encouragement; one study reported significant effects on BMI in boys but not in girls (6), whereas the other study showed a reduction in obesity prevalence but no change in relative BMI (8). Few studies have evaluated the effectiveness of noncurricular approaches for obesity prevention (11, 12), although some have included components such as active recess or short exercise breaks in the classroom (6, 25). One-year results from the FitKid project showed that higher attendance at an after-school activity program was associated with favorable changes in percentage body fat and fat-free mass but not in BMI (12). The APPLE project was designed to concentrate on approaches that would not involve an increased workload for teachers but use the schools as a physical base for community intervention. The ACs were primarily charged with developing activity sessions for noncurricular times, focusing on lifestyle-based activity rather than on traditional sports whenever possible. Such activities included golf, tae kwondo, community walks, beach hikes, school triathlons, line dancing, and children's games from other countries and were run by the ACs, older children, or other community volunteers (13). We observed a significant benefit to BMI after only 1 y, which was considerably enhanced at 2 y, perhaps in part because of the introduction of several simple messages that highlighted healthy eating.

The commitment of the wider school community to the APPLE project was shown by the high response rates in both control and intervention schools at each time point. Feedback from the school communities highlighted the importance of having an additional staff member dedicated to improving opportunities for activity for children, being a "face" for activity in the school, acting as a point of contact for parents and other community members to volunteer their time and expertise, being an initiator of ideas and activities, and contributing to the reported reduction in bullying in intervention schools. Although the focus was intended to be on noncurricular activity, so that ACs simply did not replace what teachers were currently doing, in practice, some of the ACs did contribute to curricular-based activities. Results from the concurrent process evaluation showed that these schools felt this contribution enhanced rather than lessened active opportunities for their children (J Bassett, J Simpson, unpublished data, 2006). Follow-up analyses are planned to determine the sustainability and reach of APPLE initiatives in the wider community after the cessation of the intervention. However, initial impressions are encouraging. Informal feedback from food outlet operators not formally involved in the intervention suggest that sales of healthier fast-food options increased during the intervention, despite no specific targeting of this behavior, and that such practice has continued.

Interestingly, subgroup analyses showed that beneficial changes to BMI were only observed in children who were not overweight at baseline. Outcome in relation to initial weight status has been examined infrequently (6, 7). One study in preschool-age children reported similar benefit in those above and below the 85th percentile of BMI (7), whereas an intervention in older Chilean children showed a more favorable benefit in those who were overweight (6). It is conceivable that more intensive intervention than was offered in the present study or a longer period of follow-up is required before benefit is apparent for children who are already overweight. It seems unlikely that differences in physical activity or diet are responsible, given that both normal-weight and overweight children in both intervention groups made similar changes in terms of physical activity (whether measured by questionnaire and accelerometry) and diet (intake of fruit, vegetables, and sugary drink). The ACs did not monitor attendance at sessions by individual children but were briefed to encourage less-active children to get involved. Alternatively, our study is relatively small as befitting a demonstration project. However, given the general lack of success in treating obesity in children even with the use of intensive individualized approaches (26), it is perhaps not surprising that community-based approaches such as APPLE may be insufficient in reducing relative weight in children who are overweight. However, although the intervention did not appear to significantly affect BMI z score in overweight children, the findings nevertheless provide some encouragement even for this group. Given rising obesity rates both in New Zealand (27) and internationally (28), an increase in relative weight might have been expected in overweight as well as normal weight children. This did not occur.

Assessing physical activity patterns and dietary intake in large numbers of children is exceptionally difficult. This multifaceted intervention was neither designed nor powered to identify whether individual components were likely to explain the changes in the primary outcome measures. The dietary data suggested that intervention children consumed fewer sweet drinks than did control children at follow-up, primarily as a result of increases in intake by control children rather than declining consumption by intervention children. Interestingly, these differences are similar to those observed by James et al (29) in their campaign to reduce consumption of carbonated beverage. Similarly, the increases in fruit intake we observed (0.8 of a serving during 3 d) reflect typical intervention efforts seen elsewhere (30-32). It is possible that these differences contributed to benefits observed in BMI, although interpretation of the dietary data should be made with some caution, given that our poor response rates for this measure (62–66%) and the relatively crude nature of the assessment tool. Similarly, although we used accelerometry, an objective measure of physical activity in children (20, 33), funding constraints meant we collected limited data for each participant which may be insufficient to represent their habitual activity (34). Regardless of these potential limitations, the fact remains that some combination of initiatives in our intervention did have an impact on body weight in children, a reliable and valid outcome measure.

Studies with nonrandomization of intervention and control groups are susceptible to bias from differences between groups which might otherwise be eliminated or at least reduced with randomization. However, randomized controlled studies are not always feasible or indeed appropriate in public health (35). In practice, community interventions such as ours are complex, and developing community-driven partnerships and initiatives take considerable time (36). Others have published guidelines (Transparent Reporting of Evaluations with NonRandomized Designs statement) that assist researchers with the design and reporting of nonrandomized interventions (14), in a similar way to the Consolidated Standards of Reporting Trials statement (37).

Several different approaches have been suggested to reduce the epidemic of childhood obesity (28). Legislative and policy measures have been widely advocated as those most likely to succeed. However, these are generally unattractive to governments, regardless of their political persuasion (38). Thus, it is reassuring to discover that a relatively simple approach, the provision of ACs dedicated to promoting increased extracurricular physical activity combined with basic nutrition education, can significantly have an impact on the rate of weight gain in children during a relatively short time period.


   ACKNOWLEDGMENTS
 
We thank the many children, families, schools, and persons who participated in this project.

The author's responsibilities were as follows—RWT, KAM, and JIM (principal investigators): participated in the study conceptualization and ongoing project management; SMW: completed all statistical analyses; WB: was the project coordinator; and AS: undertook research and analysis and completed her Master's of Science degree in this project. All authors contributed to writing of the manuscript. None of the authors had a personal or financial conflict of interest.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication February 26, 2007. Accepted for publication April 20, 2007.




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