Exercise intervention and cardiovascular risk factors ...

http://informahealthcare.com/ahb ISSN: 0301-4460 (print), 1464-5033 (electronic) Ann Hum Biol, Early Online: 1–8 ! 2015 Informa UK Ltd. DOI: 10.3109/03014460.2015.1059889

RESEARCH PAPER

Exercise intervention and cardiovascular risk factors in obese children. Comparison between obese youngsters taking part in a physical activity school-based programme with and without individualised diet counselling: the ACORDA project

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Luı´sa Aires1,2, Gustavo Silva1, Clarice Martins1,3, Elisa Marques1,2, Maria Joaõ Lagoa1,2, Jose´ Carlos Ribeiro1, Carla Reˆgo4, Henrique Nascimento5,6, Petronila Rocha Pereira7, Alice Santos-Silva5,6, Luı´s Belo5,6, and Jorge Mota1 1

Research Centre in Physical Activity, Health and Leisure (CIAFEL) Faculty of Sports, University of Porto, Rua Dr Plaćido Costa, Porto, Portugal, University Institute of Maia (ISMAI), Av. Carlos Oliveira Campos, Casteˆlo da Maia, Maia, Portugal, 3Department of Physical Education – Federal Rural University of Pernambuco, CEP: 52171-900, Recife/PE, Brazil, 4Child and Adolescent Center, CUF Hospital, Department of Pediatrics, Faculty of Medicine, University of Porto, Porto, Portugal, 5Department of Biological Sciences, Faculty of Pharmacy, University of Porto, Porto, Portugal, 6 Institute for Molecular and Cell Biology (IBMC), University of Porto, Porto, Portugal, and 7Centro de Investigaçaõ em Cieˆncias da Sau´de, Universidade da Beira Interior, Covilha˜, Portugal 2

Abstract

Keywords

Aim: To determine the effects of a school-based exercise intervention programme on cardiovascular risk factors, including body fat (BF), metabolic profile and physical activity (PA) in children with and without individualised dietary counselling approach (IDC and WIDC). Subjects and methods: Forty-six overweight children from 6–16 years old (25 girls, 54.3%; age ¼ 10.3 ± 2.8) of six schools took part in an 8-month interdisciplinary, school-based intervention programme. All children were engaged in PA classes, but only one group was exposed to individualised counselling. Blood pressure (BP), lipids and lipoproteins, accelerometer-based PA, percentage of body fat (%BF) and trunk fat (%TF) measures were taken before and after intervention. General Linear Model (Repeated Measures ANOVA) adjusted for age, maturation and height change was used to analyse the longitudinal effect of individualised counselling between two evaluations in each group. Results: Favourable changes were observed for %BF, %TF, systolic BP and total cholesterol in the IDC group. Subjects WIDC only increased light and moderate–vigorous PA. In IDC, significant effects for time * group interactions were found for systolic BP, total cholesterol and LDL-cholesterol, indicating that counselling might add favourable changes in these markers, beyond those explained by PA and growth. Conclusion: School-based interventions can contribute to counteracting obesity in youth, particularly when individualised dietary counselling is provided. Therefore, the link between schools and professional counselling should be strengthened to ensure consolidated changes towards healthy behaviours.

Children and adolescents, diet counselling, dual-energy X-ray absorptiometry, obesity, physical activity

Introduction Childhood overweight and obesity are major concerns in terms of public health, in developed and developing countries (Olds et al., 2011) and this is a concern in Portugal (Sardinha et al., 2011). Current evidence shows that increased physical activity (PA) levels, particularly moderate to vigorous PA (MVPA), are associated with lower total and central adiposity (Franks et al., 2010), lower blood pressure (Gaya et al., 2009) and a more favourable lipid profile (Andersen et al., 2011). In Portugal, according to international recommendations of 60 minutes MVPA per day, only 36% of 10–11 year-old Correspondence: Luı´sa Aires, Research Centre in Physical Activity, Health and Leisure (CIAFEL) Faculty of Sports, University of Porto, Rua Dr Plaćido Costa, 91 4200-450 Porto, Portugal. Tel: +351 22.04.25.200. Fax: +351 225 500 689. E-mail: [email protected]

History Received 22 September 2014 Revised 13 April 2015 Accepted 28 May 2015 Published online 21 July 2015

children were considered sufficiently active (Baptista et al., 2012). Therefore, it is of major concern to explore effective and feasible intervention designs to promote PA to prevent and control weight-related comorbidities in early ages. Schools are one of the best venues for making these population-wide changes. However, the effectiveness of PA intervention is still inconsistent. There are a few encouraging evidences for targeting multidisciplinary school-based interventions (Barbeau et al., 2007; Kriemler et al., 2011), but there are also studies showing less optimistic results. In a comparative review and meta-analysis, Wang et al. (2013) showed inconsistency and insufficient strength of evidences: (i) moderate evidence that diet or PA alone are more effective at preventing obesity and (ii) insufficient evidence that a combination of diet and PA are more effective at preventing


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Ann Hum Biol, Early Online: 1–8

obesity or overweight, than any of them separately (Wang et al., 2013). Another review evidenced more effectiveness in obesity treatment when combining diet and physical activity education and behavioural therapy (van Hoek et al., 2014). Furthermore, the long-term impact of interventions is also unclear (Kelishadi & Azizi-Soleiman, 2014; Kriemler et al., 2010; Meyer et al., 2014). Indeed, no standard intervention can fit all schools and populations. Further alternatives can be found in the literature where the use of individualised targeted counselling in school-based interventions can be feasible and can have the potential to enhance behaviour change (Flattum et al., 2011). However, this type of study may be limited in sample size. Therefore, further research is needed to identify specific programme characteristics, which will influence the effectiveness of interventions. The aim of this study is to evaluate and compare the changes that occurred in several cardiovascular risk factors, including body composition, blood pressure (BP), lipid profile, glucose metabolism and in PA, in two groups: with (IDC) and without (WIDC) an individualised counselling approach, over 8 months of a multidisciplinary school-based intervention programme.

Methods Subjects We performed a longitudinal study, included in the ‘‘ACORDA Project’’ (i.e. Obese Children and Adolescent Involved in PA and Diet Programme), by analysing data from an 8-month multidisciplinary, school-based intervention programme. The ACORDA purpose is to change obesity-related behaviours in youth by providing easy access to supervised PA and associated nourishment counselling and clinical supervision. The nature, benefits and risks of the study were explained to the volunteers and parents and a parent’s written informed consent was obtained before any measurements were taken, consistent with the Helsinki Declaration. All subjects participated voluntarily. The experimental protocol was approved by the Review Committee of the Scientific Board of the Faculty of Sport, University of Porto, as well as by the Foundation of Science and Technology.

Participants under regular medication or with diabetes mellitus, endocrine disorders, inflammatory or infectious diseases were excluded from the study. Children attended the Faculty of Sports, University of Porto for testing at the beginning of the study and at the end of the intervention, under the same conditions, using the same protocols, instruments and evaluators. The study sample comprises two groups (Figure 1): Group without individualised dietary counselling (WIDC) From 19 elementary schools from a suburban area of Porto District (Faˆnzeres and Valongo), only six met the conditions of sports facilities and proximity to each other in order to organise schedules for the teachers and children. All schools were located in a deprived area, with low socio-economic status: 56.6% of the mothers or fathers were unemployed and over 60% of the mothers and 70% of the fathers had concluded the 9th grade or less. The prevalence of overweight and obesity was higher than the country average (46.4% girls and 47% boys). Therefore, all children, from these six elementary schools (1st to 9th grade) aged 6–16 years-old, were invited to participate in a multidisciplinary intervention programme supported by the Ministry of Health (General Health Board) aimed to increase PA levels and promote healthy food choices. The mean number of students per school was 152 (min ¼ 93; max ¼ 236). At the starting point, weight and height were taken to screen all children and those above the cut-points of overweight defined by Cole et al. (2000) were invited to participate. A letter was sent to all parents, acknowledging the mission of the project and inviting them to participate in a meeting where they would be informed in more detail about the aims, contents and evaluation to be accomplished. From a total of 420 children with overweight/ obesity, only 82 (19%) answered positively to participate in the project. After baseline data collection, children that accomplished all measurements were included in the group WIDC. At the end of intervention, four children did not attend more than 45% of classes and were excluded from the sample. Therefore, 12 girls and 11 boys comprised the WIDC group. At baseline,

ACORDA Project

Figure 1. Participants’ recruitment.

Recruited at school

Recruited at Paediatric Obesity Clinic

n = 420

n = 30

Accepted to participate

Accepted to participate

n = 82

n = 23

4 children did not attend more than 45% of classes All measurements completed

All measurements completed

12 girls and 11 boys in WIDC

13 girls and 10 boys in IDC

n = 23

n = 23


DOI: 10.3109/03014460.2015.1059889

School-based intervention for obesity in youth

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the percentages of children with overweight and obesity were 34.8% and 65.2%, respectively. A group of nurses specialist in Public Health organised the school year along with teachers promoting several activities with children, focusing on the quality of nutrients and water supply. A children’s symposium was held at the end of the school year, where all children presented their work.

(Christmas, Carnival and Easter). Attendance average was 85% (minimum of 73.6% and maximum of 88.9%). To increase parental support, parents could also participate in all exercise sessions and other activities. Furthermore, a basket with selected nutrients and recipes of low-cost meals was raffled for parents who attended a workshop about healthy food habits.

Group with individualised dietary counselling (IDC)

Measurements

Paediatricians from an outpatient obesity clinic of Porto were informed about the study and encouraged to invite their patients to participate in the ACORDA Programme. Of these children, those who were living in the area near the selected schools were assigned to the IDC group [n ¼ 23; girls ¼ 13 (56.5%)]. At baseline, the percentages of children with overweight and obesity were 52.2% and 47.8%, respectively (according to Cole’s criteria). The dietary counselling was delivered every 3 months, through medical appointments, and aimed to give healthy dietary knowledge for the whole family, along with work packages in training preferences/taste and self-efficacy. All sessions included a family-based intervention involving a multidisciplinary team (paediatrician, nutritionist and psychologist). A recorded-dietary plan was analysed, weak and strong points were discussed and an agreement for the next 3 months was assumed by the children/ adolescents, the family and the team. The attendance to counselling appointments was 100%. All together, 23 overweight and obese children from WIDC group and 23 from IDC (21 boys and 25 girls, X ¼ 10.3 ± 2.8 years-old) took part in this study (Figure 1).

Data were collected before [October 2011; Time Point 0 (TP0)] and after the intervention programme [June 2012; Time Point 1 (TP1)]. At the end, an individual report including all test results and a brief interpretation was provided to parents. If abnormal values were observed, paediatrics and parents/guardians were informed and children were encouraged to do a follow-up evaluation with their family doctors.

Physical activity intervention All participants were asked to modify their lifestyle habits and to participate in regular physical exercise classes. The planned programme added 2 hours of after-school sessions (1 hour each session) to the 3 hours of physical education (included in the curriculum) resulting in a total of 5 hours/ week, from October to June. Two graduates in Sport Sciences supervised sessions, under the guidance of two researchers, ensuring that the type and variety of exercises were performed according to what was previously planned. Physical exercise sessions included 15 minutes of warm-up with aerobic endurance and flexibility, 30 minutes of working circuits for aerobics, strength training, co-ordination and balance with balls, bows, strings and callisthenic exercises, 10 minutes of games to promote the enjoyment and 5 minutes of stretching. All activities were done in indoor sports facilities at school. Exercises and games were progressively intensified as individually tolerated. Training intensity and compliance between individuals were defined to induce heart rate (HR) higher than 80% of each child’s HRmax. To ensure this, 10 randomly selected children wore a portable HR monitor (Polar Team2 Pro, Polar, Finland) and an accelerometer (MTI, model GTX3, as described below) during sessions. To avoid dropouts, three bicycles were offered to those children who attended all sessions and simultaneously achieved the highest PA levels. To maintain enthusiasm, activities outside school were organised, such as surfing lessons, a boot camp during the weekend and thematic classes

Anthropometry Height and weight were measured with participants wearing only shorts and t-shirts. Height was measured using a Holtain stadiometer (Holtain Ltd., Crymmych, UK) and recorded in centimetres to the nearest millimetre. Weight was measured to the nearest 0.1 kg with the scale Tanita MC 180 MA. BMI was calculated by the ratio between weight and squared height (kg/m2). Body mass index (BMI) categories were set using Cole et al. (2000) cut-points. Waist circumference (WC) was measured to the nearest millimetre with a metallic tape at the superior border of the iliac crest, according to the protocol of the NHANES (The Third National Health and Nutrition Examination Survey, 1996). Blood pressure Systolic and diastolic blood pressure (SBP and DBP) were measured in the right arm, with the subjects in the fasting state with an automated oscillometric sphygmomanometer (Colin Press Mate Non-Invasive Blood Pressure Monitor model BP 8800p; Colin Medical Instruments Corporation, San Antonio, TX), using a standard technique (Duarte et al., 2000). A trained technician took the measurements. The subjects were in the sitting position (without their legs crossed), with the right arm at heart level. Three standard pressure cuffs of correct size (9 18, 12 22, 16 30 cm) were used according to the published guidelines for BP assessment in children (Pickering et al., 2005). The first and second measurements were taken after 5 and 10 minutes of resting, the mean of these measurements being considered for statistical purposes. If these two measurements differed by more than 2 mmHg, the protocol was repeated (two new measurements that could not exceed 2 mm Hg). Body composition Whole body Dual-energy X-ray absorptiometry (DEXA) was performed using a Hologic Explorer configured with software version 12.1 (Hologic, Bedford, MA). Measurements were analysed using Hologic APEX 3.1 software (Hologic) according to standard procedures set forth in the user’s

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guide for the DEXA instrument and %BF and trunk fat (%TF) were reported. Maturational stage To determine sexual maturational stage (ranging from Stage 1–5), each subject was asked to self-assess using a scale, previously validated in a similar sample (Mota et al., 2002).


Blood sampling and serum measurements After an overnight fast of at least 12 hours, blood was collected by venepuncture into ethylenediaminetetraacetic acid (EDTA) containing tubes and processed within 2 hours. Aliquots of plasma were made and stored at 80 C until assayed. Lipid and lipoprotein analysis was performed in an autoanalyser (Cobas Integra 400 plus, Roche, Basel, Switzerland) using commercially available kits. Total cholesterol (TC) and triglyceride (TG) concentrations were determined by enzymatic colorimetric tests (CHOD-PAP and GPO-PAP methods, Roche, respectively). High-density lipoprotein cholesterol (HDLc) was measured using enzymatic colorimetric tests (Direct HDLCholesterol, Roche). Low-density lipoprotein cholesterol (LDLc) and very low-density lipoprotein cholesterol (VLDLc) concentrations were calculated using Friedwald formula (LDLc ¼ TC – HDLc – (TG/5); VLDLc ¼ TG/5) (Friedewald et al., 1972). The determination of circulating levels of glucose and insulin were performed using routine automated technology (ABX Micros 60-OT, HORIBA ABX, Marseille, France). Daily PA Manufacturing Technology Inc. (MTI), model GTX3, formerly known as the Computer Science Applications activity monitor (Shalimar, FL) was used as an objective measure of daily PA. For the current study, the accelerometer was worn on the hip secured by an elastic waist belt. The epoch period was set at 10 seconds and the output was expressed as counts per minute (counts/min). Participants were provided with written instructions regarding care and placement of the accelerometers. A data sheet was given to each participant providing instructions to remove the accelerometers each time they performed any restricted activities like showering and swimming. Activity counts were summed for each hour that the accelerometer was worn between 7:00 h and 24:00 h to provide a representative picture of daily activity. Criteria for a successful recording were a minimum of 4 days of the week and 1 day of the weekend and more than 600 minutes per day (Colley et al., 2010; Trost et al., 2000). Time periods of at least 10 consecutive minutes of zero counts were considered as periods when the monitor was not worn and, thus, disregarded before analysis. The data were processed with specific software ‘‘ActiLife, version 6.8’’. Specific cut-points from Evenson et al. (2008) were used: for sedentary intensity 100; light 4100; moderate 2296; and vigorous 4012 counts/min. Statistics At baseline, chi-square was used to analyse differences in proportions between IDC and WIDC groups according to


gender and maturation, while independent sample t-test was used to compare age. General Linear Model (GLM)— Repeated Measures ANOVA—was used to analyse longitudinal effects of PA interventions. The two-levels longitudinal (within subjects) factor was time (TP0 and TP1), while the two-levels between subjects factor was dietary counselling (IDC and WIDC). Within and between groups’ differences were analysed with Bonferroni adjustments. For descriptive purposes, relative changes (%D) were calculated as [(TP1 TP0)/TP0]*100 for each variable. No differences were observed for the proportions of genders between groups (2 ¼ 0.088; p ¼ 0.767). Therefore, no adjustments were made for gender. Preliminary tests indicated differences between IDC and WIDC groups (p50.05) in age, maturation and %D height. Thus, GLM was carried out with adjustments for those variables as covariates. The main results for GLM were based in the analyses of time*group interactions, which were observed for each dependent variable. If time*group interaction was significant, it was considered to be indicative of a significant treatment (dietary counselling) effect. All analyses were performed using the SPSS 21 (SPSS Inc., Chicago, IL) for Mac OSX and significance level was set at 5%.

Results Table 1 shows the general subject characteristics in both time points. Both groups are distributed rather heterogeneously through Tanner’s criteria. At baseline, participants in the group WIDC represented a significantly higher percentage in stage 1. There were also differences (p50.05) between IDC and WIDC for age (11.8 ± 3.1 vs 8.8 ± 1.2 years-old), height (148.3 ± 15.6 vs 134.7 ± 6.7 cm) and weight (63.7 ± 23.2 vs 41.9 ± 8.1 kg), respectively. There was no gender difference between groups (2 ¼ 0.088; p ¼ 0.767). When comparisons at the baseline were adjusted for age and Tanner stage, there were no differences between groups for height, weight and BMI (data not shown). Table 2 presents the changes obtained in body composition and metabolic variables as well as for PA, after adjustments for age, maturation and relative variation of height, after the 8-month intervention. In general, favourable changes were observed in all variables for the two groups. Differences were observed between the two groups: at baseline, %BF, %TF, TC, light PA and kcal/day were significantly higher in IDC (p50.05), but glucose and sedentary time were significantly lower (p50.05). After intervention, WC, %TF, DBP and MVPA were higher in IDC compared with WIDC, while SBP and sedentary time were significantly lower (p50.05). Over time, favourable changes were found for glucose, time spent in LPA, MVPA and Kcal per day in WIDC group, while for the IDC group, favourable changes were observed for %TF, SBP, TC, Sedentary time, MVPA and Kcal/day (p50.05). In contrast, WC tended to increase over time, although without significant differences in both groups. Regarding the relative variation over time (%D), significant differences were observed for SBP, TC and LDLc, showing favourable changes for the IDC group vs the WIDC group. An increase of 40% was observed for MVPA in both groups


DOI: 10.3109/03014460.2015.1059889

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Table 1. Descriptive analysis for age, height, weight and maturation status according to group and time point of evaluation. IDC

Age (years) Height (cm) Weight (kg) Tanner 1 (%) Tanner 2 (%) Tanner 3 (%) Tanner 4 (%)

WIDC

TP0

TP1

TP0

TP1

11.8 (3.1) 148.3 (15.6) 63.7 (23.2) 30.4 4.3 21.7 34.8

11.8 (3.1) 150.7 (14.7) 65.2 (22.4) 13.0 21.7 21.7 34.8

8.8 (1.2)* 134.7 (6.7)* 41.9 (8.1)* 60.9* 26.1 4.3 0

8.8 (1.2) 138.0 (7.5) 43.6 (8.5) 52.2 26.1 13.0 4.3

IDC, with individualised dietary counselling; WIDC, without individualised dietary counselling; TP, time point; Values are mean and standard deviations (SD). *Differences (p50.05) between ‘‘IDC’’ and ‘‘WIDC’’ groups in TP0.


Table 2. Estimated body composition, metabolic measurements and physical activity with adjustments for covariates. IDC TP0 2

Body Mass Index (kg/m ) Waist Circumference (cm) % Body Fat (DXA) % Trunk Fat (DXA) % Body Fat (Tanita) Systolic Blood Pressure (mmHg) Diastolic Blood Pressure (mmHg) Glucose (mg/dl) Insulin (mU/mL) TG (mg/dl) TC (mg/dl) HDLc (mg/dl) LDLc (mg/dl) VLDLc (mg/dl) Sedentary Time (min/day) Light PA (min/day) MVPA (min/day) Steps/day (number) Kcal/day (spent in PA)

26.2 87.95 44.5 43.7 35.4 117.3 59.2 78.8 12.1 94.3 188.0 53.9 94.7 18.6 569.05 380.0 48.5 9071.0 445.9

(0.9) (2.7) (1.0) (1.3) (1.9) (2.4) (1.6) (1.6) (1.4) (8.9) (4.1) (2.3) (4.1) (1.8) (15.3) (12.5) (3.8) (351.4) (23.3)

TP1 26.0 91.12 42.8 40.8 35.7 104.6 58.1 78.1 11.4 83.3 162.9 54.6 87.6 18.8 501.4 394.9 65.0 10 798.5 524.4

(0.9) (2.3) (1.0) (1.3)c (1.5) (2.1)c (1.3) (2.2) (1.2) (9.7) (5.7)c (2.5) (4.9)c (3.3) (16.3)c ((12.8) (4.7)c (436.7)c (26.7)c

WIDC %D 0.2 4.6 3.5 6.4 5.1 10.3 0.3 2.8 19.1 6.8 13.7 2.9 7.0 4.7 11.3 5.3 40.0 20.3 20.2

(1.2) (2.5) (2.3) (3.1) (4.4) (2.2) (3.0) (3.2) (10.9) (8.2) (2.6) (6.1) (3.6) (14.5) (3.4) (4.5) (14.4) (5.5) (6.6)

TP0 24.6 80.9 39.8 37.7 33.4 116.4 61.4 88.3 13.1 84.2 171.8 52.6 95.0 16.9 618.1 326.3 37.7 8807.9 334.3

(0.9) (2.7) (1.0)a (1.3)a (1.9) (2.4) (1.6) (1.6)a (1.4) (8.9) (4.1)a (2.3) (4.1) (1.8) (15.3)a (12.5)a (3.8) (351.4) (23.3)a

TP1 24.6 81.0 40.1 37.5 6.7 113.2 63.7 80.8 12.2 81.8 169.1 51.3 99.0 16.9 587.0 366.2 47.1 9443.8 427.5

(0.9) (2.3)b (1.0) (1.5) (4.4) (2.1)b (1.3)b (2.2)c (1.2) (9.7) (5.7) (2.5) (4.9) (3.3) (16.3)b (12.8)c (4.7)b,c (436.7) (26.7)b,c

%D 0.6 0.6 1.3 0.2 3.7 1.9 4.6 8.4 5.5 1.3 1.3 0.4 4.9 0.7 4.0 13.1 41.6 8.8 36.4

(1.2) (2.5) (2.3) (3.1) (1.7) (2.2)d (3.0) (3.2) (10.9) (8.2) (2.6)d (6.1) (3.6)d (14.5) (3.4) (4.5) (14.4) (5.5) (6.6)

Values are mean and standard error (SE); IDC, with individualised dietary counselling; WIDC, without individualised dietary counselling; TP, time point; TG, triglycerides; TC, total cholesterol; HDLc, high-density lipoprotein cholesterol; LDLc, low-density lipoprotein cholesterol; VLDLc, very low-density lipoprotein cholesterol; Covariates in the model set at the following values: Age ¼ 10.3, Tanner Stage ¼ 2.09 and %DHeight ¼ 2.08. a Differences (p50.05) between ‘‘IDC’’ and ‘‘WIDC’’ groups in TP0; bdifferences (p50.05) between ‘‘IDC’’ and ‘‘WIDC’’ groups in TP1; c differences (p50.05) between TP0 and TP1 within the group; ddifferences (p50.05) between ‘‘IDC’’ and ‘‘WIDC’’ groups in %D.

(40% and 41.6%, respectively, in IDC and WIDC), corresponding to a mean difference of 1.59 minutes (±19.21 minutes) for IDC and 14.48 minutes (±18.03 minutes) for the WIDC group. Although pairwise comparisons indicated significant differences between groups and changes over time, time*group interactions were significant only for SBP [F(1,41) ¼ 4.835; p50.05; Partial 2 ¼ 0.105], TC [F(1,41) ¼ 10.058; p50.05; Partial 2 ¼ 0.197] and LDL [F(1,41) ¼ 4.559; p50.05; Partial 2 ¼ 0.100].

Discussion Our results showed that the ACORDA Project significantly increased habitual PA over time in both groups. However, the group IDC showed better results for body fat and metabolic variables with favourable changes observed for %TF (DEXA),

SBP, TC and LDLc. These favourable results for the IDC group can be related to the increased MVPA levels. Despite the success in increasing habitual PA, it did not yield a significant decrease in total BF (only in %TF). The lack of effect or the modest effects for BMI or other body fat outcome measures are much in line with results from most school-based PA and dietary interventions (Dobbins et al., 2009; Janssen & Leblanc, 2010; Magnusson et al., 2012; van Hoek et al., 2014). However, the referred studies used only BMI, WC or skin-folds as outcome. Those using DEXA to analyse body composition, as we did, are scarce. One study including PA, supplemented with nutrition and individual sessions, did not lead to significant changes in %BF or BMI (Neumark-Sztainer et al., 2010). Further multidisciplinary programmes showed inconclusive results for %BF (Magnusson et al., 2012), while others found favourable changes in fat mass (Gutin et al., 2008; Lubans et al., 2012).


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This inconsistency of results reinforces the awareness of more demanding and controlled intervention programmes. Another relevant health problem in children and adolescents is hypertension (Monyeki & Kemper, 2008), as they are at increased risk of being hypertensive later in life (Sun et al., 2007). Time spent in MVPA and the reduction of sedentary behaviours can be beneficial to decrease BP, in particular SBP (Gaya et al., 2009). Similar to our results, other studies have found a beneficial influence of intervention on SBP and DBP (Burguera et al., 2011; Cesa et al., 2014; Eagle et al., 2013; Janssen & Leblanc, 2010; Rush et al., 2012) and those targeting both diet and PA seem to be more effective than diet or PA-alone (Cai et al., 2014b). Regarding other metabolic risk factors such as lipid profile, inconsistent results are frequently reported: either with no significant improvements in blood lipids (Ardoy et al., 2013; Jago et al., 2011; Yin et al., 2005) or the opposite, showing positive effects on TG, TC and LDLc (Janssen & Leblanc, 2010; Kleber et al., 2009; van Hoek et al., 2014). Cai et al. (2014a) demonstrated that childhood obesity prevention programmes had a significant desirable effect on LDLc and HDLc, but only 15% of interventions improved other lipids outcomes and 55% had no significant effects. A long-term biannual dietary intervention carried out by Kaitosaari et al. (2006) with individualised counselling sessions aimed to minimise children’s exposure to environmental atherosclerosis risk factors, showed improvements in insulin resistance (HOMA-IR) and TG. It is difficult to compare results when characteristics differ highly between studies. However, reinforcing what was previously mentioned, results can be more efficient in the long-term if interventions combine other components besides diet counselling alone. A review including multicomponent programmes showed that 26 hours of moderateto-high intensity PA intervention combining diet and PA education and behavioural therapy appeared to be most effective in treating overweight young children (van Hoek et al., 2014). One of the best outcomes of the ACORDA Project was the improvement in PA habits. Since PA tracks moderately from childhood to adolescence and to adulthood (Kristensen et al., 2008; Telama, 2009) it is of most relevance to contribute to this behaviour change. The differences in PA over time for both groups (IDC and WIDC) were promising showing a decrease in sedentary time (Neumark-Sztainer et al., 2010) and an increase in light and moderate intensities, which was consistent with other school-based interventions (Demetriou & Honer, 2012; Li et al., 2014), confirming school as an important setting to promote PA with a central role in health programmes. Considering that Portuguese children have a high prevalence of obesity and low PA levels, this study presents an effective and successful way of implementing a school-based PA intervention programme to reduce childhood obesity and improve obesity-related health outcomes.


However, most reviews alert to the methodological limitations in the analysed studies, such as heterogeneity of study designs, different types of outcomes (BMI, body fat, WC), weakness of measurements and different reviews also confer different results, thus making it a challenge to make comparisons between studies. Several methodological limitations of the present study must be acknowledged: first, due to practical reasons, our study was not a randomised controlled trial; second, there is a lack of information about energy intake and control group; and lastly the differences observed between groups in respect to BMI categories. A larger sample size could be more sensitive to detect other changes. Not surprisingly, the interventions that produced more significant changes were also based on the studies that employed the largest sample sizes (Janssen & Leblanc, 2010). Nonetheless this is a study with a long-term effect that presents robust and objective measures to assess all variables, emphasising the use of DEXA and accelerometers. In fact, few school-based intervention studies have gathered so many metabolic variables as this study did and much less used DEXA for assessing body composition. It is now a consensus among several authors (Kipping et al., 2014; Magnusson et al., 2012) that future intervention programmes might need to be stricter when it comes to promoting intense PA in school and that those types of actions may require more intensive behavioural interventions with children or upstream interventions at family, social or even school environmental levels, in order to achieve a positive impact in body composition and other weight-related problems. Nevertheless, the link between schools and health centres should be prioritised and include individualised counselling to consolidate changes in healthy behaviours.

Conclusion This study provides evidence that school-based intervention programmes can be successful in promoting healthy behaviours increasing habitual PA and that an individualised support for children can change habits and trigger important physiologic changes to reduce risk factors for atherosclerosis later in life.

Acknowledgements This study was supported by National Funds through FCT – Foundation for Science and Technology Ref: PEst-OE/SAU/UI0617/2014 FEDER funds through the Operational Competitiveness Programme – COMPETE and by the project FCOMP-01-0124-FEDER-028613 (PTDC/DTP-DES/0393/2012).

Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.

References Methodological considerations There is a long list of systematic reviews and meta-analyses, comparing a larger list of school-based intervention studies showing that plenty of work has been done in this field.

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