Temporal pathways of known associations between overweight and poor health-related quality of life (HRQoL) in adolescents remain poorly documented. This study aims to (1) examine timing and strength of the association between HRQoL and body mass index (BMI) in childhood, and (2) investigate directionality and impact of cumulative burden in any observed HRQoL–BMI associations.
Design, setting and participants:
Participants were 3898 children in the population-based Longitudinal Study of Australian Children (LSAC) assessed at four biennial waves from ages 4–5 (2004) to 10–11 years (2010).
At every wave, parents completed the Pediatric Quality of Life Inventory, and measured BMI (kg m−2) was converted into BMIz and overweight using international norms.
Linear and logistic regressions.
Overweight first became cross-sectionally associated with HRQoL at 6–7 years of age, with linear associations between poorer HRQoL (physical and psychosocial health) and higher BMI developing by 8–9 years and strengthening by 10–11 years. Longitudinal analyses revealed cumulative relationships such that the number of times a child was overweight between the ages 4 and 11 years predicted progressively poorer scores on both physical and psychosocial health at 10–11 years (P-values for trend <0.001). In the weaker reverse associations, children with poor (vs persistently good) physical health at any wave had slightly higher mean BMIz at age 10–11 years, but this difference was small (0.14, 95% confidence interval (CI): 0.04, 0.24) and not cumulative; results for psychosocial health were even weaker, with mixed subscale findings.
Middle childhood appears to be the critical period in which HRQoL–BMI comorbidities emerge and strengthen, first among children with clinically relevant conditions, that is, overweight or poor HRQoL, and then more generally across the whole range of BMI. Poorer HRQoL seemed predominantly a consequence of higher BMI, rather than a cause, suggesting that effective promotion of healthy weight could benefit multiple aspects of children’s well-being.
Childhood overweight is a worldwide public health concern,1 given its high prevalence2, 3 and associated morbidities. Besides its immediate and long-term adverse medical consequences,4 obesity has a negative impact on subjective functional health, or what is commonly referred to as health-related quality of life (HRQoL).5–10 In adults, impairment in daily functioning and engagement in activities is observed even among obese persons without chronic illness.8, 9
Besides actual disease status, the individual’s perception of functional health and well-being is a key aspect of health,11 with poorer HRQoL predicting mortality and greater use of health-care services.12, 13, 14 In children and adolescents, a higher body mass index (BMI) is adversely related to multiple dimensions of daily functioning, with physical and social functioning most affected,10 mixed findings for Emotional Health and School Functioning apparently largely unaffected. In a comprehensive review summarizing cross-sectional BMI–HRQoL associations in studies involving children and adolescents, Tsiros et al.10 pinpointed adolescence as a particularly vulnerable period for decline in HRQoL among overweight youth. Other studies have suggested that the BMI–HRQoL association is already evident before adolescence.15, 16 However, the existing literature is flawed because conclusions rest on combining fragmented samples of children of different ages,15 rather than assessing the same children repeatedly, and there are insufficient studies with repeated assessments spanning the pre- and primary school years.16 It therefore remains difficult to conclude when cross-sectional associations first emerge and how these develop over time.
Furthermore, questions remain about causality and direction of the association between high BMI and poor HRQoL. Although it is generally assumed that overweight influences functioning, the majority of previous studies had a cross-sectional design and could not thus discard the possibility that a poor HRQoL caused excessive weight gain.10 There are plausible pathways for this to occur, such as eating in response to emotional problems, or lower levels of physical activity and increased time spend sedentary due to a poor general health.17, 18 Some evidence for a causal relation comes from intervention studies among obese youth, showing that HRQoL improved after weight loss.10, 19, 20 However, as these intervention programs often had a multidisciplinary nature, HRQoL might also have improved due to other influences, such as increased physical activity or therapy aimed at psychosocial problems.10, 19, 20
Longitudinal population-based research may offer further insight into directionality and causality of the BMI–HRQoL association, but this requires large cohort studies with repeated measurements of BMI and HRQoL. Very recently, several such studies were published, all using data from population-based cohorts in Australia. Cameron et al.6 observed a bidirectional association over a 5-year period in a sample of nearly 6000 adults, with obesity predicting deterioration in physical and mental HRQoL, and poorer HRQoL also predicting weight gain. However, the picture for children was somewhat different. Using data from the Longitudinal Study of Australian Children (LSAC), Sawyer et al.21 showed that higher BMI at the age of 4–5 years predicted poorer physical and social health 4 years later, but early HRQoL did not predict later BMI. In a study among 851 Australian adolescents, Williams et al.16 did not find convincing evidence that BMI was prospectively associated with falling HRQoL or vice versa.
Although these results suggest that the BMI–HRQoL association might depend on age, the lack of findings in childhood and adolescence could also be due to the fact that the duration of overweight or poor HRQoL was not quantified.16, 21 As children and adolescents are rapidly growing and developing, they show considerable variability in both HRQoL22 and BMI23 over time, probably more so than adults.24, 25, 26 Furthermore, differences in duration may also reflect different levels of severity: children with a score just above the cut point to define overweight or poor HRQoL are probably more likely to change to a healthy status as opposed to children far above the cut point. Thus, it is plausible to hypothesize that the risk of poor HRQoL would be different in children who were overweight for 1 year from those overweight for several years or throughout childhood.
With a fourth wave of data recently released, the LSAC21 offers an ideal opportunity to address these evidence gaps regarding timing, strength and directionality of the association between HRQoL and BMI. This multiwave population-based study spans a large age window during which the HRQoL–weight relationship is suggested to be established.
Therefore, in this study we specifically aimed to examine the HRQoL–BMI association at multiple time points spanning 4–11 years of age to determine when the association emerges and whether it changes with age. We hypothesized the cross-sectional HRQoL–BMI association reported to be absent at the age of 4–5 years in these same Australian children27 would emerge across the childhood years. Our second aim was to investigate longitudinal associations between cumulative burden of poor HRQoL and future BMI, and the reverse. We expected bidirectional and cumulative relationships, such that persistent or repeated episodes of poor HRQoL and overweight would be associated with higher BMI and lower levels of HRQoL, respectively.
Design and study population
This study was conducted using all four waves of data from the Kindergarten (K) cohort of the LSAC, a large nationally representative cohort of children living in Australia. The LSAC sampling design and field methods are detailed elsewhere.28 Briefly, participants (n=4983) were aged 4–5 years when recruited in 2004, and aged 10–11 years (n=4169, 84% retention) at wave 4 in 2010. Data were collected every 2 years with face-to-face interviews, questionnaires and direct anthropometric measurements. Study informants included the children, parents and teachers. Written informed consent was obtained for each child, and the Australian Institute of Family Studies Ethics Committee approved the study.
The first aim of the study was addressed in repeated cross-sectional analyses involving children with data available on BMI and HRQoL within each wave. Owing to varying numbers of missing data, the number of children included in the analyses varied per wave (minimum n=3455, maximum n=4171). The second aim of the study was addressed in longitudinal analyses that were conducted in children with outcome data available, that is BMI (n=3800) or HRQoL (n=3898) in wave 4. Missing values in the determinants of these analyses (cumulative burden of poor HRQoL or overweight from waves 1–4) were imputed (see statistical analyses for further details).
Health-related quality of life
Children’s HRQoL was assessed at all four waves of data collection using the Pediatric Quality of Life Inventory (PedsQL), a 23-item questionnaire assessing health-related functioning of children between 2–16 years of age.29 The PedsQL has good psychometric properties29 and has been shown to differentiate between healthy children and pediatric patients with diverse chronic conditions.30
The questionnaire was filled out by the child’s primary caregiver, who was the mother for 97% of the children. Primary caregivers rated the occurrence of each item during the past month on a 5-point scale, ranging from 0=‘never a problem’ to 4=‘almost always a problem’. Example items are how often a child has had problems ‘with running, aches, low energy levels, trouble sleeping, keeping up when playing with other children and missing school days because of not feeling well’. The questionnaire yields a Total score and summary scores of Physical (8 items) and Psychosocial Health (15 items). The psychosocial health scale consists of three separate five-item subdomains assessing health-related Emotional Functioning, Social Functioning and School Functioning (in preschoolers, that is, wave 1 in this study, the latter scale comprises only three items). Items were reverse scored and linearly transformed to a 0–100 scale (0=100, 1=75, 2=50, 3=25 and 4=0), so that lower scores indicate a poorer quality of life. Scale scores are computed as the sum of the items divided by the number of items answered, then converted into z-scores using the mean and s.d. of each scale within our sample, to be able to compare findings for different scales. We applied internal cut points of one s.d. below the mean to identify children with a poor HRQoL relative to their peers. Our cut points were comparable to, though in general slightly higher than, those derived using the same internal approach in the relevant population-based PedsQL validation paper, differing at most five points on a 0–100 scale;29 these small differences possibly reflect the relatively disadvantaged nature of the US population on which this validation was undertaken.
BMI and weight status
During home visits at every wave, interviewers measured children’s weight and height using standardized portable equipment and procedures. After converting BMI (kg m−2) into age- and gender-specific z-scores31 (BMIz) children were dichotomised into ‘normal weight’ (including underweight) or ‘overweight’ (including obesity) by International Obesity Task Force criteria.32
Several sociodemographic variables were considered as possible confounders, as they were previously linked with children’s weight status in LSAC.33 In wave 1, parents reported their child’s sex, Indigenous status and whether a main language other than English is spoken at home. At each wave, LSAC releases a composite indicator of family socioeconomic position, based on parents’ reports of annual family income, and parental occupation and education.34 Children’s age (in months) was also available for every wave of data collection.
Statistical analyses were performed using STATA version 12 (Stata Corporation, College Station, TX, USA). Survey weights taking account of differential non-response at wave 1 (all analyses) and sample attrition (only longitudinal analyses) were applied to ensure that the study continued to be as representative as possible of the Australian population of children, as per the original survey design.35 To address the first aim, cross-sectional relationships of PedsQL subscales with child weight were analyzed using linear (for BMIz) and logistic (for overweight status) regression analyses. Tests of interaction between wave and PedsQL subscales were conducted to assess age trends in the association between PedsQL and child BMIz or overweight status.
Next, we ran two sets of longitudinal analyses to examine the effect of cumulative burden of poor HRQoL and overweight (aim 2). First, we examined cumulative burden of poor HRQoL as a predictor of BMI. Cumulative burden was defined by the number of waves at which children had a poor HRQoL. Separate burden scores were calculated for each PedsQL subscale, and ranged from 0 (good HRQoL at any wave) to 4 (poor HRQoL at all four waves). Six separate linear regression analyses were conducted to estimate the association between the HRQoL burden scores (Total Problems, physical Functioning, psychosocial health and three Psychosocial Health subscales) and the single outcome of mean BMIz at the age of 10–11 years (wave 4). Children with a good HRQoL at all waves were the reference group.
Second, we examined the cumulative burden of overweight as a predictor of HRQoL. Cumulative burden of overweight was calculated similarly, with possible scores ranging from 0 (no overweight at any wave) to 4 (overweight/obese at all four waves). Again, six linear regression analyses then summarized the associations between the overweight burden score and the six PedsQL scales (in z-scores) at 10–11 years of age as separate outcomes. Children with normal weight at all waves were the reference group.
In the longitudinal analyses, we included the burden scores as categorical variables to examine differences in HRQoL or BMI z-scores between categories of cumulative burden, then repeated the analyses using the burden score as a continuous variable to obtain P-values for trend. We tested the robustness of our findings with sensitivity analyses. All analyses were adjusted for covariates listed above.
Missing values in covariates (two participants missing Indigenous status, and eight participants missing socioeconomic position) and in variables used to calculate the HRQoL and BMI burden scores were imputed with chained regression multiple imputation techniques in STATA. Missing data in PedsQL Total scores ranged from 440 to 728 (11.2–18.5%) in waves 1–3 falling to 40 (1.0%) in wave 4, reflecting its move from a leave-behind to in-home questionnaire. Across all waves, data on PedsQL Total scores were complete for 2816 children (71.5%); 696 children (17.7%) had data missing in one wave, 318 (8.1%) in two waves, 103 (2.6%) in three waves and 5 (0.1%) in four waves. In total, 3740 children (95.0%) had complete BMI data across all four waves, and 175 (4.4%), 18 (0.5%), 4 (0.1%) and 1 (0.03%) child had missing data on overweight in one, two, three or four occasions, respectively. All variables included in the longitudinal analyses as well as available information on a variety of indicators of child physical conditions and mental well-being were used to impute missing values.36 Longitudinal regression analyses were performed on the imputed data sets, and reported effect estimates are the pooled results of 40 imputed data sets. All CIs were based on the combined within- and between-imputation variance.37
The study population comprised 2021 boys and 1917 girls (see Table 1). There were no strong gender differences in poor HRQoL or overweight status (P-values=0.09 and 0.74, respectively). Prevalence of poor HRQoL and overweight were relatively higher among children of non-English-speaking and socially disadvantaged backgrounds (P-values <0.001).
Table 2, addressing the first aim, shows that cross-sectional linear associations of poorer HRQoL—as indicated by lower scores on Physical and psychosocial health—with higher BMI z-score first emerged at 8–9 years of age and this relationship became stronger at 10–11 years (P-value for age trends 0.001 and 0.01, respectively). The association between psychosocial health and BMI was largely driven by poorer Social Functioning, which was already evident by 4–5 years of age and strengthened with age(P-value=0.006), and to a lesser extent by later-developing poorer Emotional Functioning. Little evidence was found for an association with School Functioning. Using logistic regression analyses, we also examined this relationship with data expressed as the clinically relevant categories of overweight and poor HRQoL. Analyses indicated that poor physical and psychosocial health were already associated with a higher risk of overweight by 6–7 years of age (odds ratio=1.32, 95% confidence interval (CI): 1.03, 1.69; odds ratio=1.39, 95% CI: 1.10, 1.76, respectively).
In longitudinal analyses addressing the second aim, we first examined the cumulative burden of poor HRQoL as a predictor of BMI. In total, 62% of the participants had a good PedsQL Total score at all four waves of data collection, whereas 20, 9, 6 and 3% of the children had a poor PedsQL Total score at one, two, three or all four waves, respectively. The latter two categories were collapsed into one ‘persistently poor HRQoL’ category due to small numbers. Figure 1 shows that children with poor physical health at any one of the waves had a 0.14 higher mean BMIz at 10–11 years of age (95% CI: 0.04, 0.24) than children with consistently good physical health. Overall psychosocial health was not significantly associated with subsequent BMI, although at subscale level it became apparent that children with poor Social Functioning at any one wave had a higher mean BMI in wave 4 (difference in mean BMIz=0.11, 95% CI: 0.01, 0.22). There was little additional change in BMIz if HRQoL (physical health or Social Functioning) was poor at two or more waves.
In sensitivity analyses, we repeated the analyses with cumulative burden scores based on the occurrence of poor HRQoL in the first three waves only. The initial relationship of cumulative burden of poor physical health and social functioning predicting BMIz did not change substantially (P-values for trend 0.03 and 0.02, respectively), although the effect on BMIz of having a poor Social Functioning at one wave no longer reached statistical significance (difference in mean BMIz=0.10, 95% CI: −0.01, 0.20). These results suggest that reported associations were partly due to wave 4 cross-sectional associations.
Next, we examined the cumulative burden of overweight as a predictor of HRQoL. Of the children included in the analyses, the majority (62%) had a persistently normal weight at all four waves, whereas 11, 9, 6 and 12% were overweight at one, two, three or all four waves, respectively. Figures 2a and b indicate a clear dose–response pattern with physical and psychosocial health at 10–11 years of age, falling progressively with increasing number of overweight episodes. These associations were more striking than for the reverse association, reaching around 0.3 cumulative difference in HRQoL z-scores, compared with 0.1 cumulative difference for poor HRQoL predicting BMIz. At PedsQL subscale level (Figure 2c), the cumulative burden of overweight was mainly associated with problems in Social and Emotional Functioning(P-values <0.001 and 0.003, respectively), rather than with School Functioning. In sensitivity analyses with cumulative burden based on the occurrence of HRQoL in the first three waves only, the effect estimates for overweight burden on subsequent HRQoL did not change substantially, suggesting the reported associations are not only due to children with a high BMI at wave 4 having concurrent poor HRQoL.
We repeated the analyses presented in Figures 1 and 2 among children with complete data on HRQoL (n=2728) and BMI (n=3740) to check the influence of missing data. Effect sizes were comparable but, as fewer children were included, the estimates were less precise (that is, wider CIs) such that some ceased to be statistically significant in the complete case analysis.
In this large population-based study spanning the pre- and primary-school years, comorbidities between high BMI and poor physical and psychosocial health first emerge then strengthen from around 6–7 years of age. In this age group, poor HRQoL appeared to be predominantly a consequence of high BMI, rather than the reverse. Associations were strongest for physical health and within the psychosocial health subdomain for Social Functioning, suggesting that effective promotion of healthy weight could benefit multiple aspects of children’s well-being.
Our study confirms previous findings that a high BMI is related to poorer HRQoL in late childhood,15, 16 but is unique in showing when these associations emerge. Overweight and poor physical and psychosocial health do not profoundly have an impact on each other in early childhood, but comorbidity between these conditions becomes apparent in middle childhood, first among children with clinically relevant conditions, that is, overweight or poor HRQoL, and then more generally across the whole BMI range. The only exception was that poorer social functioning, a subdomain of psychosocial health, was already associated witha higher BMI at 4–5 years of age. This finding seemingly contrasts with our earlier study, in which we showed the relationship between BMI and Peer Problems—a subscale of the Strengths and Difficulties Questionnaire—first emerged around 8–9 years of age.38 It may be that the physical conditions essential for social interactions—like being able to keep up with other children—are already associated with a high BMI early in childhood, but influences on the content and quality of peer relations (as measured with the Strengths and Difficulties Questionnaire) do not begin until somewhat later.
Mirroring population-based longitudinal research among adults,6 we found that overweight has an impact on several domains of functional health in children in the general population. This finding suggests that previously reported improved HRQoL among children and adolescents participating in intervention studies10, 19, 20 was indeed—at least partially—due to weight loss, rather than solely to other influences of the treatment program. Our report of a cumulative effect as well as the largely unchanged findings after eliminating wave 4 cross-sectional influences supports the existence of a causal relationship, although further research is necessary to ascertain this; such studies could also clarify the underlying causal mechanisms. An adult study showed that obesity-related health problems such as diabetes and musculoskeletal pain explained part of the overweight–HRQoL association.39 However, the impact of obesity-related comorbidities on HRQoL in overweight children is largely unknown, as comparable studies on mediating mechanisms are lacking in pediatric research.10
We also showed that poor physical and social functioning may have a small role in weight gain at the population level in middle childhood. This finding contrasts with earlier studies in population-based and clinical samples of children and adolescents.16, 21, 40 This includes our own earlier report21 drawing on the first three waves of this same cohort, with differences in findings possibly reflecting both the younger ages of the children and the analytic methods. Thus, while in the earlier report by Sawyer et al.21 we related change in HRQoL from 4–5 till 8–9 years of age to BMI at 8–9 years of age, we neither accounted for duration of poor HRQoL nor captured children who had poor HRQoL for the period between baseline and outcome. On reanalyzing the first three waves using the approach reported here, we find the same patterns evident as we now report for four waves (results not shown), suggesting that duration and severity are indeed important even by 8–9 years of age.
Though these effects are small, they are plausible. Physical health problems could reduce physical activity and increase sedentariness.17 Poorer social functioning might lead to comfort eating of unhealthier snacks,18 exclusion from games and/or directly drive changes in BMI through neuroendocrine disturbances, like dysregulation of the hypothalamic–pituitary–adrenal axis heightening cortisol and adrenocorticotropic hormone levels.41 On occasion, poor health and overweight could both directly result from the same physical illness like hypothyroidism.42
Strengths of the present study include the use of a large population-based sample, repeated standardized assessments of both key constructs and objectively measured BMI. We also note a number of limitations. Using BMI as a proxy measure of adiposity may misclassify certain children, particularly those with less severe overweight.43 Nevertheless, even these children appear to have a greater risk of adverse health outcomes than those with lower BMI.43 Furthermore, substantial data were missing for the PedsQL, reflecting its positioning in the leave-behind questionnaire in the earlier waves. As complete case analyses reduced power, we followed current recommendations36 and handled missing values using multiple imputation procedures. Finally, for the cross-sectional age trends in the HRQoL–BMI association (Table 2) it was not possible to simultaneously account for the complex survey design and for correlations arising from repeated measurements within the same participants. Analyses accounting for each of these influences were compared and the most conservative results (the complex survey design) are presented.
Based on our findings, we have some final points for future directions. Firstly, accepting that it is difficult to conceive an experimental design that could establish cause and effect, our longitudinal design and demonstrated cumulative associations support some causal inferences. If causal, then it is also possible that some of these impacts may be reversible. Thus, promoting normal body weight has not only the potential to affect risks for later life disease, but appears central to improving health and well-being in childhood, and therefore we strongly urge continuing intervention research, including a careful evaluation of costs, benefits and harms. At a population-based level, we will continue to study the evolution—and any ensuing opportunities to avert morbidity—of the HRQoL–BMI association throughout adolescence, as we presented an incomplete picture truncated at the age of 11 years. Lastly, we advocate the development of accurate algorithms for children to predict the risk of future physical and psychological morbidity, based on firm longitudinal population data, as are already available for adults.44, 45
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The Longitudinal Study of Australian Children is conducted in partnership between the Department of Families, Housing, Community Services and Indigenous Affairs (FaHCSIA), the Australian Institute of Family Studies (AIFS) and the Australian Bureau of Statistics (ABS). The findings and views reported are those of the authors and should not be attributed to FaHCSIA, AIFS or the ABS. We thank all the parents and children for their continuing support and participation in the LSAC. PWJ was supported by the Netherlands Organization for Scientific Research (NWO) and the Marie Cofund Action (Rubicon grant 446-11-010), and by the Ter Meulen Fund of the Royal Netherlands Academy of Arts and Sciences (KNAW). MW was supported by NHMRC Population Health Career Development Award 546405, and FKM by NHMRC Public Health Capacity Building Grant 436914 and NHMRC Early Career Fellowship 1037449. All research at the Murdoch Childrens Research Institute is supported by the Victorian Government’s Operational Infrastructure Program. This work also received support from the ‘Parenting Australia’s Children’ research group at the Parenting Research Centre.
The authors declare no conflict of interest.
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Jansen, P., Mensah, F., Clifford, S. et al. Bidirectional associations between overweight and health-related quality of life from 4–11 years: Longitudinal Study of Australian Children. Int J Obes 37, 1307–1313 (2013). https://doi.org/10.1038/ijo.2013.71
- quality of life
- Longitudinal Study of Australian Children
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