Body Mass Index Z-score In Adolescents Research Articles

Mobile health (m-health) smartphone interventions for adolescents and adults with overweight or obesity.

Mobile health (m-health) smartphone interventions for adolescents and adults with overweight or obesity.

Hedonic hunger, food addiction, and night eating syndrome triangle in adolescents and ıts relationship with body mass ındex

BackgroundThe relationship between adolescent obesity and eating disorders is an issue that needs urgent attention. Screening for eating disorders is as important as dietary interventions to treat obesity. This study aimed to determine the relationship between hedonic hunger, food addiction, and night eating syndrome, which are considered potential risk factors for obesity, and body mass index (BMI) in adolescents.MethodsThe data were collected through an online questionnaire. The “Power of Food Scale (PFS)” was used to assess hedonic hunger; the “Yale Food Addiction Scale for Children 2.0 (dYFAS-C 2.0)” was used to assess food addiction; and the “Night Eating Questionnaire (NEQ)” was used to assess night eating syndrome (NES)”. BMI was calculated using self-reported height and weight values of adolescents. The mediated structural model analysis was performed to evaluate the effect of hedonic hunger on BMI z score via food addiction and NES.ResultsThe study was conducted with 614 voluntary adolescents aged between 11 and 18 years. The dYFAS-C 2.0 and NEQ scores were found to be higher in adolescents with overweight and obesity, and the BMI z-score of the adolescents had significant positive correlation with their PFS scores (p < .001). Hedonic hunger has no direct effect on BMI independent of food addiction and NES (β = − 0.051, p = .468), but when the total indirect effect is evaluated with the bootstrap analysis, it was found that one-unit increase in hedonic hunger score increases BMI z-score by approximately 0.22 units (β = 0.223, SE = 0.046, 95% CI 0.131–0.313). Hedonic hunger, food addiction, and NES together explained 5.2% of the total variance in BMI z score.ConclusionThis study showed that hedonic hunger significantly predicted BMI z-score in adolescents through food addiction and NES. This emphasizes the critical importance of evaluating adolescents in terms of hedonic hunger, food addiction, and NES in the prevention, diagnosis, and treatment of obesity.

Does weight impact adolescent mental health? Evidence from China.

In this paper, we examine the causal impact of weight on adolescent mental health. Using the China Family Panel Studies, we find significant negative effects of adolescent weight, instrumented by cohort-level parental body mass index (BMI), on mental illness. In particular, a one standard deviation increase in adolescent BMI z-score decreases the K6 score by 0.766 (or 0.232 standard deviations). This finding is contrary to recent evidence from adults. We find this contrast can partly be explained by the different impacts of adolescent weight on self-image and social relationships. Unlike adults, heavy adolescents feel that they are popular among peers in China.

Effects of Parenting and Perceived Stress on BMI in African American Adolescents.

This study set out to examine the role of parenting practices in protecting or exacerbating the negative effects of parent and adolescent stress on adolescent body mass index (BMI) over time. Separate longitudinal models were conducted to evaluate how parenting practices interacted with parental perceived stress and adolescent perceived stress in predicting adolescent BMI. Baseline data were collected from 148 African American adolescents (Mage = 12.93, SD = 1.75; Mz-BMI = 0.78, SD = 0.50; MBMI%-ile = 96.7, SD = 3.90) and their caregivers (Mage = 44.45, SD = 8.65; MBMI = 37.63, SD = 8.21) enrolled in the Families Improving Together for Weight Loss trial. Adolescents self-reported their perceptions of caregiver parenting style and feeding practices. Both caregivers and adolescents self-reported their perceptions of chronic stress. BMI for parents and adolescents was assessed objectively at baseline and 16 weeks post-intervention. Hierarchical regression models predicting adolescent BMI z-score (z-BMI) indicated a significant interaction between parental perceived stress and parental pressure to eat. Simple slopes analyses demonstrated that for those parents that exhibit higher pressure to eat, parent stress was positively associated with adolescent z-BMI. These findings provide preliminary support suggesting that certain parenting practices interact with chronic stress on adolescent weight-related outcomes and that future interventions may consider integrating these factors.

Effects of intergenerational exposure interventions on adolescent outcomes: An application of inverse probability weighting to longitudinal pre-birth cohort data.

There is great interest in understanding whether interventions on sugar-sweetened beverage (SSB) consumption through pregnancy and early childhood affect adolescent body mass index (BMI). Without data from randomised trials, unbiased estimation of such effects might be achieved with observational data given sufficient and appropriate adjustment for both baseline and time-varying confounders. To illustrate the use of inverse probability (IP) weighting of marginal structural models (MSM) for estimating the effects of SSB consumption through pregnancy and early childhood on the mean early adolescent BMI z-score. Our baseline sample consisted of 1584 pregnant women from a pre-birth cohort. We defined 6 intervention intervals: early pregnancy, late pregnancy, 3, 4, 5, and 6years. We fitted a MSM via a weighted linear regression with IP exposure and censoring weights to estimate the mean difference in BMI z-score under interventions: "maintain SSB consumption below (vs above) 0.5 servings/day in all intervals." The estimated difference in mean BMI z-score under interventions maintaining SSB consumption at or below (vs above) 0.5 servings/day from pregnancy to 6years was -0.94 (95% confidence interval [CI] -1.52, -0.08). The effect estimate in pregnancy, while fixing the exposure range in childhood, was -0.05 (95% CI -0.34, 0.23), and in early childhood, while fixing the range in pregnancy was -0.89 (95% CI -1.46, -0.11). The effect estimates were largely unchanged under sensitivity analyses to different implementation choices except for the choice of time interval length. Under assumptions that include no unmeasured confounding and selection bias, and no model misspecification, results of thisIP weighting application are in line with a lower mean BMI z-score in early adolescence under interventions ensuring lower, vs greater, SSB consumption in early life. This application provides a resource for researchers working with longitudinal birth cohort studies and interested in similar causal questions.

Estimating Age- and Sex-Specific Utility Values from the CHU9D Associated with Child and Adolescent BMI z-Score.

Our objective was to identify age- and sex-specific utilities for children and adolescents by body mass index (BMI) z-score. We used data from 6822 participants and 12,094 observations from two cohorts and two waves of interviews from the Longitudinal Study of Australian Children. We fit linear models using generalised estimating equations to investigate associations between Child Health Utility 9D and BMI z-score in girls and boys aged 10-17years. We initially fit models for each sex, fully adjusted for known predictors of health-related quality of life, including socioeconomic position, long-term medical condition and maternal smoking status and also included an interaction between age and BMI z-score to examine age-specific effects. Finally, we derived a minimal model for each sex by eliminating interaction terms with P > 0.01 and predictors with P > 0.05. Our adjusted results show different utility patterns in girls and boys. In girls, utility decrements for each unit increase in BMI z-score changed with age (P < 0.01 for interaction between age and BMI z-score). At age 10years, the mean utility decrement for each unit increase in BMI z-score was 0.002 (95% confidence interval [CI] 0.011 decrement to 0.006 increment), but, by age 17years, this utility decrement was 0.023 (95% CI 0.013 to0.032). In boys, small non-significant decrements were found in utility for each unit increase in BMI z-score, with no observable change with age. Our analyses demonstrated that age and sex should be considered when attributing utility values and decrements to BMI z-scores.

A low resting metabolic rate in late childhood is associated with weight gain in adolescence

Background and objectivesLower total energy expenditure (TEE) and resting metabolic rate (RMR) are associated with greater weight gain in Native American adults. Whether these effects exist in childhood is unclear. We hypothesized that lower energy expenditure measured in childhood would predict greater relative change in body mass index (BMI) during adolescence. MethodsMeasurements of height, weight, body composition, RMR and TEE were completed in 181 Native American children at exams done at age 5 and 10years, with 126 children having biennial follow-up assessments of weight and height after age 10years until age 20years. TEE and RMR were adjusted for age, sex, height, fat mass and fat free mass. BMI-change was assessed using population specific and Center for Disease Control (CDC) BMI z-scores and change in the relative difference to the 95th BMI-centile. ResultsLower adjusted RMR at age 10years was associated with greater increase in population-specific and CDC BMI z-scores, greater increase in the relative difference to the 95th BMI-centile and greater weight gain (all r≤−0.22, p≤0.01). However, no association was found with adjusted RMR at age 5years and with adjusted TEE and physical activity level assessed at age 5 or 10years. ConclusionsLower adjusted RMR at age 10years predicted greater change in adolescent BMI z-score indicating that the effects of relatively low metabolic rate on future weight gain in this population may begin in late childhood.

Mesolimbic connectivity signatures of impulsivity and BMI in early adolescence

Across age groups, differences in connectivity of the mesolimbic and the prefrontal cortex co-vary with trait impulsivity and sensation-seeking. Impulsivity and sensation-seeking are also known to increase during early adolescence as maturation of subcortical structures outpaces that of the prefrontal cortex. While an imbalance between the striatum and prefrontal cortex is considered a normal developmental process, higher levels of adolescent impulsivity and sensation-seeking are associated with an increased risk for diverse problems, including obesity.To determine how the relationship between sensation-seeking, impulsivity and body mass index (BMI) is related to shared neural correlates we measured their relationships with the connectivity of nuclei in the striatum and dopaminergic midbrain in young adolescents. Data were collected from 116 children between the ages of 12 and 14, and included resting state functional magnetic resonance imaging, personality measures from the Substance Use Risk Profile Scale, and BMI Z-score for age. The shared variance for the connectivity of regions of interest in the substantia nigra, ventral tegmental area, ventral striatum and sub-thalamic nucleus, personality measures and BMI Z-score for age, were analyzed using partial least squares correlation.This analysis identified a single significant striato-limbic network that was connected with the substantia nigra, ventral tegmental area and sub-thalamic nuclei (p = 0.002). Connectivity within this network which included the hippocampi, amygdalae, parahippocampal gyri and the regions of interest, correlated positively with impulsivity and BMI Z-score for age and negatively with sensation-seeking. Together, these findings emphasize that, in addition to the well-established role that frontostriatal circuits play in the development of adolescent personality traits, connectivity of limbic regions with the striatum and midbrain also impact impulsivity, sensation-seeking and BMI Z-score in adolescents.

Social Patterning in Adiposity in Adolescence: Prospective Observations from the Chinese Birth Cohort ''Children of 1997''.

IntroductionLow early life socio-economic position is more strongly associated with adiposity among women than men. We examined whether the sex difference of social patterning in general and central adiposity exists before adulthood.MethodsIn Hong Kong’s “Children of 1997” birth cohort, we used multivariable regression to examine the association of parental education, a marker of early life socio-economic position, with body mass index (BMI) (n = 7252, 88% follow-up) and waist-height ratio (n = 5636, 68% follow-up), at 14 years.ResultsParental education of Grade 9 or below, compared to Grade 12 or above, was associated with higher waist-height ratio z-score particularly in girls (0.30, 95% confidence interval (CI) 0.19, 0.41) compared to boys (0.12, 95% CI 0.02, 0.22) (p for sex interaction = 0.02). Lower parental education was associated with greater BMI z-score in adolescents of locally born mothers, but not adolescents of migrant mothers, with no difference by sex.ConclusionsDifferent social patterning in different markers of adiposity may imply different sociological and biological mediating pathways. A stronger association between low early life socio-economic position and waist-height ratio in adolescent girls may indicate sex-specific influences of SEP related early life exposures on central adiposity.

Racial differences in the built environment--body mass index relationship? A geospatial analysis of adolescents in urban neighborhoods.

BackgroundBuilt environment features of neighborhoods may be related to obesity among adolescents and potentially related to obesity-related health disparities. The purpose of this study was to investigate spatial relationships between various built environment features and body mass index (BMI) z-score among adolescents, and to investigate if race/ethnicity modifies these relationships. A secondary objective was to evaluate the sensitivity of findings to the spatial scale of analysis (i.e. 400- and 800-meter street network buffers).MethodsData come from the 2008 Boston Youth Survey, a school-based sample of public high school students in Boston, MA. Analyses include data collected from students who had georeferenced residential information and complete and valid data to compute BMI z-score (n = 1,034). We built a spatial database using GIS with various features related to access to walking destinations and to community design. Spatial autocorrelation in key study variables was calculated with the Global Moran’s I statistic. We fit conventional ordinary least squares (OLS) regression and spatial simultaneous autoregressive error models that control for the spatial autocorrelation in the data as appropriate. Models were conducted using the total sample of adolescents as well as including an interaction term for race/ethnicity, adjusting for several potential individual- and neighborhood-level confounders and clustering of students within schools.ResultsWe found significant positive spatial autocorrelation in the built environment features examined (Global Moran’s I most ≥ 0.60; all p = 0.001) but not in BMI z-score (Global Moran’s I = 0.07, p = 0.28). Because we found significant spatial autocorrelation in our OLS regression residuals, we fit spatial autoregressive models. Most built environment features were not associated with BMI z-score. Density of bus stops was associated with a higher BMI z-score among Whites (Coefficient: 0.029, p < 0.05). The interaction term for Asians in the association between retail destinations and BMI z-score was statistically significant and indicated an inverse association. Sidewalk completeness was significantly associated with a higher BMI z-score for the total sample (Coefficient: 0.010, p < 0.05). These significant associations were found for the 800-meter buffer.ConclusionSome relationships between the built environment and adolescent BMI z-score were in the unexpected direction. Our findings overall suggest that the built environment does not explain a large proportion of the variation in adolescent BMI z-score or racial disparities in adolescent obesity. However, there are some differences by race/ethnicity that require further research among adolescents.

Health-Related Quality of Life in Adolescents with or at Risk for Type 2 Diabetes Mellitus

To evaluate how adolescents with or at risk for type 2 diabetes mellitus (T2DM) and their parent/guardians (parents) perceive adolescents' health-related quality of life. We interviewed overweight/obese, 12- to 18-year-old youth with T2DM, prediabetes, or insulin resistance and one parent from 5 US sites. Assessments included Pediatric Quality of Life Inventory (PedsQL), Health Utilities Index, family conflict, and diabetes burden. In 108 adolescents, diagnoses included 40.7% with T2DM, 25.0% with prediabetes, and 34.3% with insulin resistance. PedsQL summary score (SS) was higher in adolescents than parents (P=.02). Parents rated physical functioning lower than adolescents (P<.0001), but there were no differences in psychosocial health. Adolescent PedsQL SS did not differ with diagnosis, but was inversely associated with adolescent body mass index z-score (P=.0004) and family conflict (P<.0001) and associated with race/ethnicity (P<.0001). Number of adolescent co-morbidities (P=.007) and burden of diabetes care (P<.05) were inversely associated with parent PedsQL SS. There were no differences in the Health Utilities Index-Mark 3 multi-attribute utility score. Parents perceive their adolescents' physical functioning as more impaired than adolescents themselves. Contextual factors including severity of obesity, race/ethnicity, family conflict, and burden of diabetes care influence health-related quality of life. Family-based approaches to treatment and prevention of T2DM may benefit from increased attention to the biopsychosocial context.