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Predicting success: Factors associated with weight change in obese youth undertaking a weight management program
Obesity Research & Clinical Practice (2013) 7, e147—e154
ORIGINAL ARTICLE
Predicting success: Factors associated with weight change in obese youth undertaking a weight management program Kimberley A. Baxter a,∗, Robert S. Ware b, Jennifer A. Batch c, Helen Truby d a
Children’s Nutrition Research Centre, The University of Queensland, Discipline of Paediatrics & Child Health, The Royal Children’s Hospital, Herston, QLD 4029, Australia b School of Population Health, The University of Queensland, Australia c Department of Endocrinology and Diabetes, The Royal Children’s Hospital, Australia d Department of Nutrition and Dietetics, Monash University, Australia Received 2 June 2011 ; received in revised form 27 September 2011; accepted 29 September 2011
KEYWORDS Adolescent; Obesity; Weight loss predictors; Body mass index
∗
Summary Objective: To explore which baseline physiological and psychosocial variables predict change in body mass index (BMI) z-score in obese youth after 12 weeks of a dietary weight management study. Methods: Participants were obese young people participating in a dietary intervention trial in Brisbane Australia. The outcome variable was change in BMI z-score. Potential predictors considered included demographic, physiological and psychosocial parameters of the young person, and demographic characteristics of their parents. A multivariable regression model was constructed to examine the effect of potential predictive variables. Results: Participants (n = 88) were predominantly female (69.3%), and had a mean(standard deviation) age of 13.1(1.9) years and BMI z-score of 2.2(0.4) on presentation. Lower BMI z-score (p < 0.001) and insulin resistance (p = 0.04) at baseline, referral from a paediatrician (p = 0.02) and being more socially advantaged (p = 0.046) were significantly associated with weight loss. Macronutrient distribution of diet and physical activity level did not contribute. Conclusions: Early intervention in obesity treatment in young people improves likelihood of success. Other factors such as degree of insulin resistance, social advantage and referral source also appear to play a role. Assessing presenting characteristics
Corresponding author. Tel.: +61 07 3365 5476; fax: +61 07 3636 4684. E-mail address: [email protected] (K.A. Baxter).
1871-403X/$ — see front matter © 2011 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
doi:10.1016/j.orcp.2011.09.004
e148
K.A. Baxter et al. and factors associated with treatment outcome may allow practicing clinicians to individualise a weight management program or determine the ‘best-fit’ treatment for an obese adolescent. © 2011 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
Introduction Obesity in children and adolescents has been an issue of public health concern for some years, and is known to track into adulthood [1]. The latest Cochrane Review on childhood obesity treatment, has identified that there is a lack of quality research to inform best practice in obesity treatment for young people and in those studies that exist, combined lifestyle and behavioural interventions are the most likely to produce a significant benefit [2]. To date, interventions to address childhood obesity have largely lacked efficacy and results have been modest, with reviews of randomised controlled trials in free living adolescents reporting weight losses in the range of 1—4 kg [3]. It is also understood that in such weight management programs, not all children respond equally and evidence shows that benefits are present for only 50% of young participants [4]. In order to design more effective and targeted weight management programs, it may be helpful to identify factors associated with a successful treatment outcome. The meaning of ‘‘success’’ in obese young people taking part in an intervention is in itself a contentious issue and various studies have utilised different definitions including weight stabilisation, absolute weight loss, absolute BMI change, percentage weight change, percentage BMI change, and BMI z-score change. For the purpose of our study, success is defined as the magnitude of decrease in BMI standard deviation score (BMI z-score). Previous research has predominantly considered factors associated with weight loss success in adults. The limited research focussing on young people has found body weight is typically positively correlated with weight loss [5—8], however another study has found no association [9]. Age is often reported as a predictor of success, with older children being more successful [5]. Other factors identified as positively influencing weight loss are male gender, frequent self-monitoring of weight, a higher number of meals eaten at home and stronger family/friend support [6]. There is a higher prevalence of obesity in lower socio-economic status (SES) groups [5], however the role of SES as a predictor of weight loss in the overweight child remains unclear, with many
finding no relationship [5,6,9,10]. Ethnicity of the participant has also been identified as a key factor, with Caucasian origin being associated with more successful outcomes [3]. The aim of this study was to ascertain which physiological and psychosocial factors were most associated with successful weight loss, as measured by decrease in BMI z-score, in a prospective cohort of obese young people after 12 weeks of an intensive weight loss intervention.
Methods Participants and study design This prospective cohort study was conducted at a tertiary paediatric hospital in Queensland, Australia. Cohort members were sourced from the Eat Smart Studies, which included a pilot (2007) and a randomised controlled trial (RCT) run over 2008-2010. The Eat Smart studies were designed to determine the cardiovascular and body composition effects of a low fat versus a moderate carbohydrate diet in obese young people. In both the pilot and RCT phases of the study the dietary interventions delivered and the measures utilised were identical. The RCT protocol has been previously described [11]. To be eligible for inclusion, young people had to be aged 10—17 years and have simple obesity as measured by BMI > 90th percentile for sex and age, based on the Centres for Disease Control and Prevention (CDC) 2000 growth charts [12]. A referral from a health professional (medical officer, dietitian or psychologist) was required so follow up after study completion could be arranged. Participants were required to be motivated to lose weight (by self-report) and to not be prescribed medications known to alter body composition, insulin sensitivity or metabolism. Prior to commencing the study all participants attended an outpatient appointment with a Paediatric Endocrinologist which included a physical examination at which time the presence of acanthosis nigricans was determined and pubertal staging and medical history were recorded. Energy prescription was individualised and included an energy deficit that was equivalent between diet groups. Physical activity was
Baxter: Factors associated with weight change in obese youth encouraged in-line with the age-appropriate national recommendations [13,14]. The low fat and moderate carbohydrate dietary plans differed in macronutrient composition; the low fat diet provided 25% energy from fat, 55% from carbohydrate and 20% from protein, whilst the moderate carbohydrate provided 35% from carbohydrate, 30% protein and 35% from fat. Families were given meal and snack plans and were given advice to assist with compliance to the allocated diet. Participants were required to attend 5 appointments over the course of 12 weeks at which time dietary advice was provided and study assessments and measures conducted. This period of time was chosen as it was an intensive treatment phase and it was expected that the greatest shifts in BMI were likely to occur in this period of time and was therefore an opportune time to examine predictors of success.
Ethical approvals The study protocol was approved by the Royal Children’s Hospital & Health Service District Ethics Committee (05/02/2008); The University of Queensland Medical Research Review Committee (14/02/2008); West Moreton South Burnett Health Service District Health Research Ethics Committee (25/08/2008) and The Mater Health Services Human Research Ethics Committee (02/02/2009). Informed consent and assent were required from the parent and young person respectively. The RCT was registered with the International Clinical Trials Registry (ISRCTN49438757).
Outcome measure Change in BMI z-score from baseline to week 12 of the intervention was the outcome measure. BMI z-score reflects weight status in children whilst adjusting for age, sex and height [2]. Anthropometric measures were performed in a fasting state. Height was measured using a wall-mounted stadiometer (Holtain Instruments Limited Crymmch UK). Weight was measured to the nearest 0.05 kg using calibrated electronic scales (Tanita BWB-600 Wedderburn Scales Australia). BMI z-score was calculated using Epi InfoTM 3.5.1 with reference to the CDC 2000 dataset.
Predictive variable measures Demographic characteristics of participants recorded included age, sex, and ethnicity. Ethnicity of the participant was collected via questionnaire administered to the parent, which offered the following categories; Caucasian, Asian, South Sea
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Islander, Aboriginal, Torres Strait Islander, Chinese or any other ethnic group, where the parent was asked to specify. Due to the limited number of those in ethnic minority groups Caucasian individuals were compared to those from other ethnic minorities for analysis purposes. Social disadvantage was determined using the Socio-Economic Index for Areas (SEIFA) Index of Advantage/Disadvantage. SEIFA scores allow for the ranking of geographic areas based on the level of economic and social wellbeing in that area [15]. Each area represented by a postcode is assigned a decile value from 1 (most disadvantaged) to 10 (most advantaged). For our analyses we dichotomised SEIFA score by splitting at the 70th percentile. Demographic and social information regarding the participants’ parents was obtained from the presenting parent at the baseline assessment. Fasting glucose and insulin were measured using an automated Clinical Chemistry Analyser. Insulin resistance was determined using the homeostatic model assessment for insulin resistance (HOMA-IR). HOMA-IR was calculated with the following formula HOMA-IR = [fasting insulin (U/mL) × fasting glucose (mmol/L)]/22.5, which indexes normal young subjects at the value 1 [16]. Physical Activity Level (PAL) was calculated via a 4 day physical activity diary completed prior to the baseline assessment appointment. Young people were required to record their activities over the 4 days (3 weekdays and 1 weekend day) accounting for each 24 h period in 15 min blocks with the use of codes to represent their relative activities. Parents were asked to oversee this. These activity codes were assigned metabolic equivalent (MET) values [17,18]. These values were then summed and averaged over the 4 days to produce a PAL value [17,18]. Dietary restraint was measured with the Dutch Eating Behaviour Questionnaire — restraint subscale, child version (DEBQ-R) [19]. This is a validated questionnaire for young people which measures restrained eating behaviours. Self-rated confidence and motivation towards dietary change was measured at baseline on a scale of 1—10. Young people were asked to select a number which represented how motivated/confident they felt towards being able to change their food habits in the subsequent 3 months (1 = not at all; 10 = extremely). The type of dietary program that the young person was allocated to was also used as a predictive measure to determine the effect of diet type on BMI z-score change. Referral source was recorded for each participant; referrals were categorised into two groups: those referred from a specialist (paediatrician or paediatric sub specialist) and those
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K.A. Baxter et al.
Results Characteristics of participants
Sample size calculations from the Eat Smart RCT indicated the cohort investigated for this component of the study would contain approximately 90 adolescents. Given this number of participants, and the assumption the standard deviation of BMI zscore change was 0.12, the detectable difference for a binary variable which has participants evenly distributed across categories, with 80% power and alpha = 0.05, is BMI z-score change of 0.072 or greater. For example, for a 10 year old boy with a weight of 70 kg and a height of 150 cm (BMI zscore = 2.48), a reduction in BMI z-score of 0.072 equates to a weight loss of 3 kg. This reflects the effect size of comparable lifestyle intervention studies in children; the Cochrane Review with 6 months follow up reported a pooled effect size of −0.06 (−0.12, −0.01) BMI z-score change in children <12 years and −0.14 (−0.17, −0.12) for >12 years. Baseline variables investigated as potential predictors of change in BMI z-score were the young person’s age, sex, Tanner Stage, SEIFA category, ethnicity, weight, insulin status (fasting insulin and HOMA-IR), presence of acanthosis nigricans, physical activity measure, dietary restraint (DEBQR), motivation and confidence to change, referral source, and diet group, and the parental factors of age, sex, family structure, number of children in household, employment status and highest education level. Differences between individuals who completed 12 weeks of the program and between individuals who did not were compared using logistic regression. Descriptive statistics were calculated as mean (standard deviation) for continuous variables and frequency (percentage) for categorical variables. Univariate analysis considered the association between change in BMI z-score and each predictive variable of interest. All variables significantly associated with change in BMI z-score at the p < 0.2 level were investigated further in a multiple logistic regression model. Initially the variable most significantly associated with the outcome was included in the model. Variables were successively added to the model, and retained if they added significant information at the p < 0.05 level as assessed by the likelihood ratio test. Analyses were performed using the Statistical Package for Social Sciences Version 18 (SPSS 18.0).
The primary dataset contained 116 individuals. Those who did not complete the intervention in its entirety to the week 12 assessment appointment (n = 14) and those in the untreated control group (n = 14) were removed. This provided a completer’s dataset of 88 individuals, with 45 in the modified carbohydrate group and 43 in the low fat dietary group. Young people who took part in this study were predominantly female (69.3%), had a mean(standard deviation) age of 13.1(1.9) years, weight at presentation of 87.6(23.0) kg, BMI of 32.7(5.7) kg/m2 and BMI z-score of 2.2(0.4). There were 80(91%) children of Caucasian ethnicity, 2(2%) of Asian ethnicity and 6(7%) of Indigenous or Pacific Islander ethnicity. There were no statistically significant differences between individuals who completed, and did not complete, the program in terms of age, sex, pubertal stage, ethnicity, baseline BMI z-score and SEIFA (minimum p-value = 0.48 across the 6 comparisons).
Outcome results At the end of the 12 weeks the mean(standard deviation) weight was 85.7(22.9) kg, BMI was 31.5(5.8) kg/m2 and BMI z-score was 2.08(0.42). Participants had grown 1.2(0.9) cm. There was considerable variability in the primary outcome (Fig. 1), with change in BMI z-score ranging from −0.45 to +0.01, with mean(standard deviation) change of −0.13(0.11). This resulted in a 6.3(6.2)% reduction in BMI z-score. This reflects the pooled effect size seen for lifestyle intervention reported in the Cochrane Review of −0.14 (−0.17, −0.12) BMI z-score change for children >12years. There was no interaction between BMI z-score change and diet 0.1
Individual change in BMI z score (n=88)
Statistical analysis
0 -0.1 -0.2 -0.3 -0.4 -0.5
Figure 1 Variability in BMI z-score change among participants over the 12 week intervention.
Baxter: Factors associated with weight change in obese youth type (p = 0.33), consequently both diet groups were combined for the regression analysis. Summary statistics and univariate regression results are displayed in Table 1. Baseline BMI z-score accounted for the most significant univariate association with change in BMI z-score (p < 0.001, and R2 = 22%). Baseline variables which were univariately significant at p-value < 0.2 were BMI z-score, insulin resistance, social advantage, referral source and ethnicity. Complete data was available for all of these variables, except insulin resistance, where data was unavailable for 6 individuals, due to incomplete blood results for insulin and/or glucose. These variables were added consecutively, until the final model was selected. Variables included were BMI z-score, HOMA-IR, SEIFA category and source of referral. This model accounted for 36% of explained variance for change in BMI z-score among this group (Table 2). BMI z-score at baseline in the adjusted model was clearly the most important factor (p < 0.001). For each 1 unit increase in BMI z-score at baseline the model predicts a change in BMI z-score over 12 weeks of +0.112. Considering the mean change in BMI z-score across this cohort of young people was −0.13, this is a substantial amount. The other included variables contributed less significantly to the model. For insulin resistance each 1 unit increase in HOMA-IR at baseline young people could be predicted to experience a +0.013 change in the BMI z-score. In terms of social advantage and referral source it appeared that those in the higher group of social advantage (≥70th percentile) and those referred from a specialist doctor experienced a greater change in BMI z-score but this effect was modest.
Discussion The characteristic most strongly associated with BMI z-score change was degree of overweight at baseline (BMI z-score), with individuals who had lower BMI z-score more likely to reduce their BMI z-score over the course of treatment. Other influential characteristics were insulin resistance, referral source and SEIFA category. The directional nature of the BMI z-score change association contradicts other studies which have reported that those who are heavier are more likely to experience a greater amount of weight loss [5—8]. These results highlight the importance of early intervention in obese young people. It may also suggest that the Eat Smart program was most effective for children with less severe obesity and that those
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young people with severe obesity may require an alternative approach. Similarly for insulin resistance, those participants who had lower levels of insulin resistance were more likely to reduce their BMI z-score. Individuals in the upper SEIFA category, representing higher social advantage for their area of residence were more likely to do well. Assuming that those living in a more economically advantaged area may have greater access to services and support these results make intuitive sense. Referral source also predicted outcome success such that those with a specialist referral (e.g., paediatrician or paediatric specialist) experienced greater reductions in BMI z-score change than those from other referral sources (primary health care provider, dietitian, psychologist). This association is interesting and the authors speculate that those families referred from a specialist may be more motivated to address obesity, having undertaken what is often a lengthy and complicated process to see such specialist doctors. Although ethnicity of participants was considered for inclusion in the multivariable model based on its univariate significance (p = 0.06), it was not included in the final model. This may be due to the low number of non-Caucasians in the sample, which has restricted the power of the test to achieve significance. Other research has commonly identified ethnicity as a contributing factor towards outcome success in obesity treatment [3]. These results lead us to speculate that this intervention may be most suited to Caucasian participants although the study protocol was not designed with reference to any ethnic or cultural group. Physical activity factors measured at baseline were not found to predict outcome. This is interesting as physical activity habits are often thought to be associated with obesity and weight status. This may be because the method used (4 day diary) was not an accurate indication of actual physical activity over the period measured, or it may be that change in physical activity over the period of the intervention (not formally explored in this analysis), is a more important predictor of weight change. Self-rated motivation and confidence towards dietary change at baseline was not found to be associated with the outcome, this was unexpected and may reflect the way these constructs were measured. A review by Teixeira et al. found mixed results for self-efficacy, and that the outcome was dependent on the method used to determine the self-efficacy [20]. Further investigation on these characteristics is worthwhile. The size of the cohort (n = 88) investigated was large enough to allow the investigation of a range of predictive variables, including physical activity
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Table 1
Baseline characteristics of study participants with effect size on primary outcome measure: change in BMI z-score.
Characteristics Age (years) Gender (female) n/% BMI z-score Pubertal stage (tanner stage) (n/%)
Fasting insulin (U/L) HOMA-IR Acanthosis nigricans present (n/%) Physical activity measures
SEIFA deciles n/% Ethnicity n/% DEBQ-R (range 1—3) Motivation to change (range 1—10) Confidence to change (range 1—10) Referral source (n/%) Parental factors: (n/%) Gender Age Family structure Number of children Employment status Highest education
Effect estimate
95% CI
p-Value
1.9 69.3% 0.4 11.4% 19.3% 20.5% 15.9% 33.0% 7.9 1.7 .055 0.2 2.2 2.2 51.1% 90.9% 0.5 0.001 −0.005 41%
0.004 0.02 0.14 0.002
−0.007 to 0.017 −0.03 to 0.07 0.09—0.20 −0.014 to 0.017
0.41 0.53 <0.001a 0.84
0.004 0.019 −0.001 to 0.111 −0.065 −0.004 0.006 −0.055 0.076 −0.012 −0.015 to 0.017 −0.022 to 0.012 0.051
0.001—0.007 0.006—0.031 0.05 −0.22 to 0.090 −0.015 to 0.007 −0.005 to 0.017 −0.101 to −0.01 −0.004 to 0.155 −0.079 to 0.054 0.92 0.56 0.005—0.097
0.004a 0.004a
7.3 6.6 Specialist referral
13.2 61 2.2 10 17 18 14 29 14.5 3.1 21.6% 1.4 9.5 3.5 45 80 1.8 1.5 1.4 36
Female 25—44 years One parent family 1—2 Employed PT/FT School
77 55 24 45 37/38 34
87.5% 62.5% 27.3% 51.1% 42%/43.2% 38.6%
−0.019 0.011 −0.019 0.002 0.011 −0.002
−0.090 to 0.052 −0.02 to 0.043 −0.071 to 0.034 −0.047 to 0.05 −0.022 to 0.044 −0.035 to 0.03
0.60 0.47 0.48 0.95 0.51 0.89
Female Stage 1 2 3 4 5
19 PAL Daily sedentary (h) Daily screen time (h) ≥70th percentile Caucasian
0.41 0.49 0.30 0.02a 0.06a 0.71
0.03a
K.A. Baxter et al.
Data are presented as mean and standard deviation unless otherwise specified. HOMA-IR = homeostatic model assessment of insulin resistance; PAL = physical activity level; SEIFA = socioeconomic index for areas, index for social advantage and disadvantage; DEBQ-R = Dutch eating behaviour questionnaire — restraint subscale; PT = part time employment; FT = full time employment. HOMA-IR n = 82; Fasting Insulin n = 84; Physical activity measures n = 79; all other variables included the full dataset of n = 88. a Selected for potential inclusion in multivariate model. For continuous variables the effect estimate reflects the mean difference in change in BMI z-score for each one unit increase in the predictor variable. For categorical variables the effect estimate reflects the difference in change in BMI z-score for the comparison category compared to the reference category.
Baxter: Factors associated with weight change in obese youth Table 2
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Results of multivariate logistic regression model.
Variable
Change (95% CI)
p-Value
Baseline BMI z-score Baseline insulin resistance (HOMA-IR) Social advantage (SEIFA) Referral source
0.112 (0.059—0.165) 0.013 (0.002, 0.025) −0.041 (−0.078, −0.004) 0.047 (0.010, 0.085)
0.000 0.023 0.030 0.015
All variables included the full dataset of n = 88, except for HOMA-IR where n = 82. ‘Change’ is a measure of how a one unit increase in the explanatory variable affects BMI z-score at 12-weeks. A negative coefficient indicates that as the explanatory variable increases, BMI z-score decreases, whereas a positive coefficient indicates a decrease in the explanatory variable is associated with greater decrease in the outcome.
habits, eating behaviour, parental factors and selfperceived motivation and confidence to change. In any investigation related to weight loss, length of time is an important feature of the study to consider. The relatively short 12 week period in this study was chosen as this initial period is often when the greatest shifts in weight occur but clearly it would be of interest to include a longer follow up. The use of different variables and measurement methods in predictive analyses limits the comparability between studies and hinders the usefulness of these results in clinical practice. Measures utilised here such as SEIFA category, physical activity factors, insulin resistance, dietary restraint and motivation and confidence to change, can be directly compared to other studies only if the same methodology is used in both studies. However, other factors such as BMI z-score, ethnicity and characteristics of the presenting parent may be more easily compared between studies. Given the evident variation in outcome response to the same intervention, another variable of interest is compliance. However this analysis included only baseline predictive variables, thus compliance during the intervention was not explored. Compliance can be difficult to quantify but it an important factor to consider. Some studies have found a ‘dose response’ relationship between compliance to an intervention and success [21], and between participation in exercise and success [9]. It is important to recognise that there are likely many factors related to whether an individual is successful in losing weight. The model presented in this study was able to explain 36% of the variance in change in BMI z-score in this cohort; therefore, there are other factors at play that were not captured. Obesity is a difficult and frustrating condition to treat, both for the clinician and the individual. Learning more about those factors which are associated with a desirable outcome may help to design better obesity treatment programs which target these characteristics. Additionally, it may assist clinicians in taking a more individual approach based on an assessment of their patient’s
characteristics. This may allow for clinicians in practice to assess young people on entering a weight management program to determine the ‘best-fit’ treatment for that individual.
Funding The study was funded in part by a National Heart Foundation Grant-in-Aid, the Royal Children’s Hospital Foundation, ANZ Trustees and the University of Queensland. K.A. Baxter was supported by an Australian Post Graduate Award Scholarship. Template systems were kindly donated by Pharmacy Health Solutions Pty Ltd.
Conflict of interest The authors have no conflicts of interest to declare in relation to this manuscript or research study.
Acknowledgements Modification of the TemplateTM system to a reduced carbohydrate model was allowed by the patent holders Dr. Rhys Collins and Dr. Trent Watson. We would like to thank Janet Warren and Hannah Evans for assistance with the study and all the families who took part.
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Available online at www.sciencedirect.com
ORIGINAL ARTICLE
Predicting success: Factors associated with weight change in obese youth undertaking a weight management program Kimberley A. Baxter a,∗, Robert S. Ware b, Jennifer A. Batch c, Helen Truby d a
Children’s Nutrition Research Centre, The University of Queensland, Discipline of Paediatrics & Child Health, The Royal Children’s Hospital, Herston, QLD 4029, Australia b School of Population Health, The University of Queensland, Australia c Department of Endocrinology and Diabetes, The Royal Children’s Hospital, Australia d Department of Nutrition and Dietetics, Monash University, Australia Received 2 June 2011 ; received in revised form 27 September 2011; accepted 29 September 2011
KEYWORDS Adolescent; Obesity; Weight loss predictors; Body mass index
∗
Summary Objective: To explore which baseline physiological and psychosocial variables predict change in body mass index (BMI) z-score in obese youth after 12 weeks of a dietary weight management study. Methods: Participants were obese young people participating in a dietary intervention trial in Brisbane Australia. The outcome variable was change in BMI z-score. Potential predictors considered included demographic, physiological and psychosocial parameters of the young person, and demographic characteristics of their parents. A multivariable regression model was constructed to examine the effect of potential predictive variables. Results: Participants (n = 88) were predominantly female (69.3%), and had a mean(standard deviation) age of 13.1(1.9) years and BMI z-score of 2.2(0.4) on presentation. Lower BMI z-score (p < 0.001) and insulin resistance (p = 0.04) at baseline, referral from a paediatrician (p = 0.02) and being more socially advantaged (p = 0.046) were significantly associated with weight loss. Macronutrient distribution of diet and physical activity level did not contribute. Conclusions: Early intervention in obesity treatment in young people improves likelihood of success. Other factors such as degree of insulin resistance, social advantage and referral source also appear to play a role. Assessing presenting characteristics
Corresponding author. Tel.: +61 07 3365 5476; fax: +61 07 3636 4684. E-mail address: [email protected] (K.A. Baxter).
1871-403X/$ — see front matter © 2011 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
doi:10.1016/j.orcp.2011.09.004
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K.A. Baxter et al. and factors associated with treatment outcome may allow practicing clinicians to individualise a weight management program or determine the ‘best-fit’ treatment for an obese adolescent. © 2011 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
Introduction Obesity in children and adolescents has been an issue of public health concern for some years, and is known to track into adulthood [1]. The latest Cochrane Review on childhood obesity treatment, has identified that there is a lack of quality research to inform best practice in obesity treatment for young people and in those studies that exist, combined lifestyle and behavioural interventions are the most likely to produce a significant benefit [2]. To date, interventions to address childhood obesity have largely lacked efficacy and results have been modest, with reviews of randomised controlled trials in free living adolescents reporting weight losses in the range of 1—4 kg [3]. It is also understood that in such weight management programs, not all children respond equally and evidence shows that benefits are present for only 50% of young participants [4]. In order to design more effective and targeted weight management programs, it may be helpful to identify factors associated with a successful treatment outcome. The meaning of ‘‘success’’ in obese young people taking part in an intervention is in itself a contentious issue and various studies have utilised different definitions including weight stabilisation, absolute weight loss, absolute BMI change, percentage weight change, percentage BMI change, and BMI z-score change. For the purpose of our study, success is defined as the magnitude of decrease in BMI standard deviation score (BMI z-score). Previous research has predominantly considered factors associated with weight loss success in adults. The limited research focussing on young people has found body weight is typically positively correlated with weight loss [5—8], however another study has found no association [9]. Age is often reported as a predictor of success, with older children being more successful [5]. Other factors identified as positively influencing weight loss are male gender, frequent self-monitoring of weight, a higher number of meals eaten at home and stronger family/friend support [6]. There is a higher prevalence of obesity in lower socio-economic status (SES) groups [5], however the role of SES as a predictor of weight loss in the overweight child remains unclear, with many
finding no relationship [5,6,9,10]. Ethnicity of the participant has also been identified as a key factor, with Caucasian origin being associated with more successful outcomes [3]. The aim of this study was to ascertain which physiological and psychosocial factors were most associated with successful weight loss, as measured by decrease in BMI z-score, in a prospective cohort of obese young people after 12 weeks of an intensive weight loss intervention.
Methods Participants and study design This prospective cohort study was conducted at a tertiary paediatric hospital in Queensland, Australia. Cohort members were sourced from the Eat Smart Studies, which included a pilot (2007) and a randomised controlled trial (RCT) run over 2008-2010. The Eat Smart studies were designed to determine the cardiovascular and body composition effects of a low fat versus a moderate carbohydrate diet in obese young people. In both the pilot and RCT phases of the study the dietary interventions delivered and the measures utilised were identical. The RCT protocol has been previously described [11]. To be eligible for inclusion, young people had to be aged 10—17 years and have simple obesity as measured by BMI > 90th percentile for sex and age, based on the Centres for Disease Control and Prevention (CDC) 2000 growth charts [12]. A referral from a health professional (medical officer, dietitian or psychologist) was required so follow up after study completion could be arranged. Participants were required to be motivated to lose weight (by self-report) and to not be prescribed medications known to alter body composition, insulin sensitivity or metabolism. Prior to commencing the study all participants attended an outpatient appointment with a Paediatric Endocrinologist which included a physical examination at which time the presence of acanthosis nigricans was determined and pubertal staging and medical history were recorded. Energy prescription was individualised and included an energy deficit that was equivalent between diet groups. Physical activity was
Baxter: Factors associated with weight change in obese youth encouraged in-line with the age-appropriate national recommendations [13,14]. The low fat and moderate carbohydrate dietary plans differed in macronutrient composition; the low fat diet provided 25% energy from fat, 55% from carbohydrate and 20% from protein, whilst the moderate carbohydrate provided 35% from carbohydrate, 30% protein and 35% from fat. Families were given meal and snack plans and were given advice to assist with compliance to the allocated diet. Participants were required to attend 5 appointments over the course of 12 weeks at which time dietary advice was provided and study assessments and measures conducted. This period of time was chosen as it was an intensive treatment phase and it was expected that the greatest shifts in BMI were likely to occur in this period of time and was therefore an opportune time to examine predictors of success.
Ethical approvals The study protocol was approved by the Royal Children’s Hospital & Health Service District Ethics Committee (05/02/2008); The University of Queensland Medical Research Review Committee (14/02/2008); West Moreton South Burnett Health Service District Health Research Ethics Committee (25/08/2008) and The Mater Health Services Human Research Ethics Committee (02/02/2009). Informed consent and assent were required from the parent and young person respectively. The RCT was registered with the International Clinical Trials Registry (ISRCTN49438757).
Outcome measure Change in BMI z-score from baseline to week 12 of the intervention was the outcome measure. BMI z-score reflects weight status in children whilst adjusting for age, sex and height [2]. Anthropometric measures were performed in a fasting state. Height was measured using a wall-mounted stadiometer (Holtain Instruments Limited Crymmch UK). Weight was measured to the nearest 0.05 kg using calibrated electronic scales (Tanita BWB-600 Wedderburn Scales Australia). BMI z-score was calculated using Epi InfoTM 3.5.1 with reference to the CDC 2000 dataset.
Predictive variable measures Demographic characteristics of participants recorded included age, sex, and ethnicity. Ethnicity of the participant was collected via questionnaire administered to the parent, which offered the following categories; Caucasian, Asian, South Sea
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Islander, Aboriginal, Torres Strait Islander, Chinese or any other ethnic group, where the parent was asked to specify. Due to the limited number of those in ethnic minority groups Caucasian individuals were compared to those from other ethnic minorities for analysis purposes. Social disadvantage was determined using the Socio-Economic Index for Areas (SEIFA) Index of Advantage/Disadvantage. SEIFA scores allow for the ranking of geographic areas based on the level of economic and social wellbeing in that area [15]. Each area represented by a postcode is assigned a decile value from 1 (most disadvantaged) to 10 (most advantaged). For our analyses we dichotomised SEIFA score by splitting at the 70th percentile. Demographic and social information regarding the participants’ parents was obtained from the presenting parent at the baseline assessment. Fasting glucose and insulin were measured using an automated Clinical Chemistry Analyser. Insulin resistance was determined using the homeostatic model assessment for insulin resistance (HOMA-IR). HOMA-IR was calculated with the following formula HOMA-IR = [fasting insulin (U/mL) × fasting glucose (mmol/L)]/22.5, which indexes normal young subjects at the value 1 [16]. Physical Activity Level (PAL) was calculated via a 4 day physical activity diary completed prior to the baseline assessment appointment. Young people were required to record their activities over the 4 days (3 weekdays and 1 weekend day) accounting for each 24 h period in 15 min blocks with the use of codes to represent their relative activities. Parents were asked to oversee this. These activity codes were assigned metabolic equivalent (MET) values [17,18]. These values were then summed and averaged over the 4 days to produce a PAL value [17,18]. Dietary restraint was measured with the Dutch Eating Behaviour Questionnaire — restraint subscale, child version (DEBQ-R) [19]. This is a validated questionnaire for young people which measures restrained eating behaviours. Self-rated confidence and motivation towards dietary change was measured at baseline on a scale of 1—10. Young people were asked to select a number which represented how motivated/confident they felt towards being able to change their food habits in the subsequent 3 months (1 = not at all; 10 = extremely). The type of dietary program that the young person was allocated to was also used as a predictive measure to determine the effect of diet type on BMI z-score change. Referral source was recorded for each participant; referrals were categorised into two groups: those referred from a specialist (paediatrician or paediatric sub specialist) and those
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Results Characteristics of participants
Sample size calculations from the Eat Smart RCT indicated the cohort investigated for this component of the study would contain approximately 90 adolescents. Given this number of participants, and the assumption the standard deviation of BMI zscore change was 0.12, the detectable difference for a binary variable which has participants evenly distributed across categories, with 80% power and alpha = 0.05, is BMI z-score change of 0.072 or greater. For example, for a 10 year old boy with a weight of 70 kg and a height of 150 cm (BMI zscore = 2.48), a reduction in BMI z-score of 0.072 equates to a weight loss of 3 kg. This reflects the effect size of comparable lifestyle intervention studies in children; the Cochrane Review with 6 months follow up reported a pooled effect size of −0.06 (−0.12, −0.01) BMI z-score change in children <12 years and −0.14 (−0.17, −0.12) for >12 years. Baseline variables investigated as potential predictors of change in BMI z-score were the young person’s age, sex, Tanner Stage, SEIFA category, ethnicity, weight, insulin status (fasting insulin and HOMA-IR), presence of acanthosis nigricans, physical activity measure, dietary restraint (DEBQR), motivation and confidence to change, referral source, and diet group, and the parental factors of age, sex, family structure, number of children in household, employment status and highest education level. Differences between individuals who completed 12 weeks of the program and between individuals who did not were compared using logistic regression. Descriptive statistics were calculated as mean (standard deviation) for continuous variables and frequency (percentage) for categorical variables. Univariate analysis considered the association between change in BMI z-score and each predictive variable of interest. All variables significantly associated with change in BMI z-score at the p < 0.2 level were investigated further in a multiple logistic regression model. Initially the variable most significantly associated with the outcome was included in the model. Variables were successively added to the model, and retained if they added significant information at the p < 0.05 level as assessed by the likelihood ratio test. Analyses were performed using the Statistical Package for Social Sciences Version 18 (SPSS 18.0).
The primary dataset contained 116 individuals. Those who did not complete the intervention in its entirety to the week 12 assessment appointment (n = 14) and those in the untreated control group (n = 14) were removed. This provided a completer’s dataset of 88 individuals, with 45 in the modified carbohydrate group and 43 in the low fat dietary group. Young people who took part in this study were predominantly female (69.3%), had a mean(standard deviation) age of 13.1(1.9) years, weight at presentation of 87.6(23.0) kg, BMI of 32.7(5.7) kg/m2 and BMI z-score of 2.2(0.4). There were 80(91%) children of Caucasian ethnicity, 2(2%) of Asian ethnicity and 6(7%) of Indigenous or Pacific Islander ethnicity. There were no statistically significant differences between individuals who completed, and did not complete, the program in terms of age, sex, pubertal stage, ethnicity, baseline BMI z-score and SEIFA (minimum p-value = 0.48 across the 6 comparisons).
Outcome results At the end of the 12 weeks the mean(standard deviation) weight was 85.7(22.9) kg, BMI was 31.5(5.8) kg/m2 and BMI z-score was 2.08(0.42). Participants had grown 1.2(0.9) cm. There was considerable variability in the primary outcome (Fig. 1), with change in BMI z-score ranging from −0.45 to +0.01, with mean(standard deviation) change of −0.13(0.11). This resulted in a 6.3(6.2)% reduction in BMI z-score. This reflects the pooled effect size seen for lifestyle intervention reported in the Cochrane Review of −0.14 (−0.17, −0.12) BMI z-score change for children >12years. There was no interaction between BMI z-score change and diet 0.1
Individual change in BMI z score (n=88)
Statistical analysis
0 -0.1 -0.2 -0.3 -0.4 -0.5
Figure 1 Variability in BMI z-score change among participants over the 12 week intervention.
Baxter: Factors associated with weight change in obese youth type (p = 0.33), consequently both diet groups were combined for the regression analysis. Summary statistics and univariate regression results are displayed in Table 1. Baseline BMI z-score accounted for the most significant univariate association with change in BMI z-score (p < 0.001, and R2 = 22%). Baseline variables which were univariately significant at p-value < 0.2 were BMI z-score, insulin resistance, social advantage, referral source and ethnicity. Complete data was available for all of these variables, except insulin resistance, where data was unavailable for 6 individuals, due to incomplete blood results for insulin and/or glucose. These variables were added consecutively, until the final model was selected. Variables included were BMI z-score, HOMA-IR, SEIFA category and source of referral. This model accounted for 36% of explained variance for change in BMI z-score among this group (Table 2). BMI z-score at baseline in the adjusted model was clearly the most important factor (p < 0.001). For each 1 unit increase in BMI z-score at baseline the model predicts a change in BMI z-score over 12 weeks of +0.112. Considering the mean change in BMI z-score across this cohort of young people was −0.13, this is a substantial amount. The other included variables contributed less significantly to the model. For insulin resistance each 1 unit increase in HOMA-IR at baseline young people could be predicted to experience a +0.013 change in the BMI z-score. In terms of social advantage and referral source it appeared that those in the higher group of social advantage (≥70th percentile) and those referred from a specialist doctor experienced a greater change in BMI z-score but this effect was modest.
Discussion The characteristic most strongly associated with BMI z-score change was degree of overweight at baseline (BMI z-score), with individuals who had lower BMI z-score more likely to reduce their BMI z-score over the course of treatment. Other influential characteristics were insulin resistance, referral source and SEIFA category. The directional nature of the BMI z-score change association contradicts other studies which have reported that those who are heavier are more likely to experience a greater amount of weight loss [5—8]. These results highlight the importance of early intervention in obese young people. It may also suggest that the Eat Smart program was most effective for children with less severe obesity and that those
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young people with severe obesity may require an alternative approach. Similarly for insulin resistance, those participants who had lower levels of insulin resistance were more likely to reduce their BMI z-score. Individuals in the upper SEIFA category, representing higher social advantage for their area of residence were more likely to do well. Assuming that those living in a more economically advantaged area may have greater access to services and support these results make intuitive sense. Referral source also predicted outcome success such that those with a specialist referral (e.g., paediatrician or paediatric specialist) experienced greater reductions in BMI z-score change than those from other referral sources (primary health care provider, dietitian, psychologist). This association is interesting and the authors speculate that those families referred from a specialist may be more motivated to address obesity, having undertaken what is often a lengthy and complicated process to see such specialist doctors. Although ethnicity of participants was considered for inclusion in the multivariable model based on its univariate significance (p = 0.06), it was not included in the final model. This may be due to the low number of non-Caucasians in the sample, which has restricted the power of the test to achieve significance. Other research has commonly identified ethnicity as a contributing factor towards outcome success in obesity treatment [3]. These results lead us to speculate that this intervention may be most suited to Caucasian participants although the study protocol was not designed with reference to any ethnic or cultural group. Physical activity factors measured at baseline were not found to predict outcome. This is interesting as physical activity habits are often thought to be associated with obesity and weight status. This may be because the method used (4 day diary) was not an accurate indication of actual physical activity over the period measured, or it may be that change in physical activity over the period of the intervention (not formally explored in this analysis), is a more important predictor of weight change. Self-rated motivation and confidence towards dietary change at baseline was not found to be associated with the outcome, this was unexpected and may reflect the way these constructs were measured. A review by Teixeira et al. found mixed results for self-efficacy, and that the outcome was dependent on the method used to determine the self-efficacy [20]. Further investigation on these characteristics is worthwhile. The size of the cohort (n = 88) investigated was large enough to allow the investigation of a range of predictive variables, including physical activity
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Table 1
Baseline characteristics of study participants with effect size on primary outcome measure: change in BMI z-score.
Characteristics Age (years) Gender (female) n/% BMI z-score Pubertal stage (tanner stage) (n/%)
Fasting insulin (U/L) HOMA-IR Acanthosis nigricans present (n/%) Physical activity measures
SEIFA deciles n/% Ethnicity n/% DEBQ-R (range 1—3) Motivation to change (range 1—10) Confidence to change (range 1—10) Referral source (n/%) Parental factors: (n/%) Gender Age Family structure Number of children Employment status Highest education
Effect estimate
95% CI
p-Value
1.9 69.3% 0.4 11.4% 19.3% 20.5% 15.9% 33.0% 7.9 1.7 .055 0.2 2.2 2.2 51.1% 90.9% 0.5 0.001 −0.005 41%
0.004 0.02 0.14 0.002
−0.007 to 0.017 −0.03 to 0.07 0.09—0.20 −0.014 to 0.017
0.41 0.53 <0.001a 0.84
0.004 0.019 −0.001 to 0.111 −0.065 −0.004 0.006 −0.055 0.076 −0.012 −0.015 to 0.017 −0.022 to 0.012 0.051
0.001—0.007 0.006—0.031 0.05 −0.22 to 0.090 −0.015 to 0.007 −0.005 to 0.017 −0.101 to −0.01 −0.004 to 0.155 −0.079 to 0.054 0.92 0.56 0.005—0.097
0.004a 0.004a
7.3 6.6 Specialist referral
13.2 61 2.2 10 17 18 14 29 14.5 3.1 21.6% 1.4 9.5 3.5 45 80 1.8 1.5 1.4 36
Female 25—44 years One parent family 1—2 Employed PT/FT School
77 55 24 45 37/38 34
87.5% 62.5% 27.3% 51.1% 42%/43.2% 38.6%
−0.019 0.011 −0.019 0.002 0.011 −0.002
−0.090 to 0.052 −0.02 to 0.043 −0.071 to 0.034 −0.047 to 0.05 −0.022 to 0.044 −0.035 to 0.03
0.60 0.47 0.48 0.95 0.51 0.89
Female Stage 1 2 3 4 5
19 PAL Daily sedentary (h) Daily screen time (h) ≥70th percentile Caucasian
0.41 0.49 0.30 0.02a 0.06a 0.71
0.03a
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Data are presented as mean and standard deviation unless otherwise specified. HOMA-IR = homeostatic model assessment of insulin resistance; PAL = physical activity level; SEIFA = socioeconomic index for areas, index for social advantage and disadvantage; DEBQ-R = Dutch eating behaviour questionnaire — restraint subscale; PT = part time employment; FT = full time employment. HOMA-IR n = 82; Fasting Insulin n = 84; Physical activity measures n = 79; all other variables included the full dataset of n = 88. a Selected for potential inclusion in multivariate model. For continuous variables the effect estimate reflects the mean difference in change in BMI z-score for each one unit increase in the predictor variable. For categorical variables the effect estimate reflects the difference in change in BMI z-score for the comparison category compared to the reference category.
Baxter: Factors associated with weight change in obese youth Table 2
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Results of multivariate logistic regression model.
Variable
Change (95% CI)
p-Value
Baseline BMI z-score Baseline insulin resistance (HOMA-IR) Social advantage (SEIFA) Referral source
0.112 (0.059—0.165) 0.013 (0.002, 0.025) −0.041 (−0.078, −0.004) 0.047 (0.010, 0.085)
0.000 0.023 0.030 0.015
All variables included the full dataset of n = 88, except for HOMA-IR where n = 82. ‘Change’ is a measure of how a one unit increase in the explanatory variable affects BMI z-score at 12-weeks. A negative coefficient indicates that as the explanatory variable increases, BMI z-score decreases, whereas a positive coefficient indicates a decrease in the explanatory variable is associated with greater decrease in the outcome.
habits, eating behaviour, parental factors and selfperceived motivation and confidence to change. In any investigation related to weight loss, length of time is an important feature of the study to consider. The relatively short 12 week period in this study was chosen as this initial period is often when the greatest shifts in weight occur but clearly it would be of interest to include a longer follow up. The use of different variables and measurement methods in predictive analyses limits the comparability between studies and hinders the usefulness of these results in clinical practice. Measures utilised here such as SEIFA category, physical activity factors, insulin resistance, dietary restraint and motivation and confidence to change, can be directly compared to other studies only if the same methodology is used in both studies. However, other factors such as BMI z-score, ethnicity and characteristics of the presenting parent may be more easily compared between studies. Given the evident variation in outcome response to the same intervention, another variable of interest is compliance. However this analysis included only baseline predictive variables, thus compliance during the intervention was not explored. Compliance can be difficult to quantify but it an important factor to consider. Some studies have found a ‘dose response’ relationship between compliance to an intervention and success [21], and between participation in exercise and success [9]. It is important to recognise that there are likely many factors related to whether an individual is successful in losing weight. The model presented in this study was able to explain 36% of the variance in change in BMI z-score in this cohort; therefore, there are other factors at play that were not captured. Obesity is a difficult and frustrating condition to treat, both for the clinician and the individual. Learning more about those factors which are associated with a desirable outcome may help to design better obesity treatment programs which target these characteristics. Additionally, it may assist clinicians in taking a more individual approach based on an assessment of their patient’s
characteristics. This may allow for clinicians in practice to assess young people on entering a weight management program to determine the ‘best-fit’ treatment for that individual.
Funding The study was funded in part by a National Heart Foundation Grant-in-Aid, the Royal Children’s Hospital Foundation, ANZ Trustees and the University of Queensland. K.A. Baxter was supported by an Australian Post Graduate Award Scholarship. Template systems were kindly donated by Pharmacy Health Solutions Pty Ltd.
Conflict of interest The authors have no conflicts of interest to declare in relation to this manuscript or research study.
Acknowledgements Modification of the TemplateTM system to a reduced carbohydrate model was allowed by the patent holders Dr. Rhys Collins and Dr. Trent Watson. We would like to thank Janet Warren and Hannah Evans for assistance with the study and all the families who took part.
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