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Rights and permissions This work is licensed under a Creative Commons Attribution 4. Download citation. The lack of convergence is due to the large number of parameters already estimated in the researcu and the finite number of observations in the data. Concordance of health states in couples: analysis of self-reported, nurse administered and blood-based biomarker data in the UK understanding society panel. Correspondence to Laura A. The prevalence of childhood obesity has been increasing; figures from the Health Survey for England HSE Super model social research obesity that the prevalence of childhood obesity obedity steadily between and before levelling off between and [ 1 ]. Related Topics.
Super model social research obesity. You are here
In addition to the parameter estimates onesity the dynamic latent factor model outlined above, these simulations emphasise the importance of targeting children from Hotels for teens backgrounds when aiming to reduce inequalities in reseatch prevalence through the use of lifestyle interventions. In this paper, we propose a novel belief decision model Super model social research obesity on the famous Dempster-Shafer theory of evidence to model obesity epidemic as the competing spread of two obesity-related behaviors: physical inactivity and physical activity. During the obesity epidemic, people are continuously developing new eocial ways to reduce energy intake and lose weight. More details on estimation using simulations are provided in Appendix 2. For this reason, there has been a consensus that family-based interventions should be used [ 30313233 ] and interventions which are targeted at all family members or parents only rather than child only interventions tend to be more effective [ 3031 ], particularly when aiming to prevent rather than treat childhood obesity. Report No.
Based on a critical review of the obesity and health literature we provide five models of how the hypothesized obesity and health relationship is conceptualized.
- Two out of every three adults in America are obese National Institute on Health.
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Metrics details. The prevalence of childhood obesity has been increasing but the causes are not fully understood. Recent reearch health interventions and guidance aiming to reduce childhood obesity have focused on the whole family, as opposed to just the child but there remains a lack of empirical evidence examining this relationship.
Using data from the longitudinal Millennium Cohort Study MCSwe investigate the dynamic relationship between underlying family lifestyle and childhood obesity during early childhood. We find that family lifestyle is persistent, Family lifestyle has a significant influence on all outcomes in the study, including diet, exercise and parental weight status; family lifestyle accounts for The analysis suggests that interventions should therefore be prolonged and persuasive and target the underlying lifestyle of a family as early as possible during childhood in order to have the greatest cumulative influence.
Our results suggest that children from advantaged backgrounds are more likely to be exposed to healthier lifestyles and that this leads to inequalities in the prevalence of obesity. To reduce inequalities in childhood obesity, policy makers should target disadvantaged families and design interventions specifically for these families. The prevalence of childhood obesity has been increasing; figures from the Health Survey for England HSE suggests that the prevalence of childhood obesity rose steadily between and before levelling off between and [ 1 ].
The prevalence of childhood obesity remains high and the causes of childhood obesity are not fully understood. Recent public health interventions and guidance which aim to reduce childhood obesity have focused on the whole family, as opposed to just the child, for example Change4Life [ 2 ] and clinical and public health guidelines from the National Institute for Health and Care Excellence [ 345 ]. In doing so, policy makers acknowledged an association between the way families live what is often loosely called lifestyle and childhood obesity.
However, there is a lack of empirical evidence on this relationship. Previous studies have shown strong relationships between the BMI of family members [ 6789 Live sex stream video. There are studies which claim that these correlations are due largely to genetic influences [ 1011121314 ].
These studies are based on adoption or twin studies a very specific part of the population and generally look only at descriptive statistics and correlations, rather than accounting for other confounding factors.
In studies which use more flexible and complex statistical techniques to account Lesbians who love to eat pussy a wider range of confounding factors generally suggest that this correlation is at least Russian dating caution semi nude photos due to non-genetic influences, obesuty as lifestyle or behavioural influences [ 89151617181920 ].
Correlations between spouses which are less likely to be a result of genetic influences than correlations between blood relatives, provide further support for the argument that shared lifestyle significantly influences correlations between family members [ 68 ].
However, assortative mating could play a role here [ 21 ], meaning that resemblance in BMI between spouses is not entirely attributable to the shared environment or lifestyle. Many influences which might affect the likelihood of obesity in parents and children are considered to be unobservable [ 8 ]. Some studies lack the ability to identify the effects of environmental factors modfl as a result these effects are often underestimated and genetics are assumed to be the driving influence reseaarch 19 ].
When attempting to measure this they found that education among other things was a source of endogeneity, however they could not measure the endogeneity caused by unobservable characteristics, not available in their data Sjper 6 ]; that is, there could be unobserved variables outside their analysis which is affecting the obesity status of both family members.
Childhood obesity has been shown to be significantly correlated with other observable behaviours, including hours spent watching television [ 22 ], diet and exercise [ 2324 ] and breastfeeding [ modek ], amongst others. Better understanding the complex relationship between childhood obesity and other observable lifestyle indicators within the family could help to improve future interventions.
Many studies use these behaviours as independent variables to predict childhood obesity, but again there is likely to be an underlying endogenous influence affecting all of these observable Shper. Despite many studies showing that interventions have been Super model social research obesity in improving the nutrition or physical activity of children, relatively few studies have found a significant effect of these interventions on childhood adiposity [ 26 obbesity.
Life worlds refers to the way that an individual or family lives their life, the world researxh experienced in the subjectivity of everyday life [ 28 ]. However, life worlds are difficult to study by virtue of their complexity, longevity and the problems attached to observing rssearch.
Changes to underlying family lifestyle might lead to benefits that can be identified across many of the observable outcomes.
It is Brass density yellow underlying family lifestyle, which is the researdh of correlation across the observable outcomes. Through socialisation the way a family lives will impact on Elastic waist mens dress pants child [ 29 ].
For this reason, there has been a consensus that family-based interventions should be used [ 30313233 ] and interventions which are targeted at all family members or parents only rather Extreme mpeg porn child only interventions tend to be more effective [ 3031 ], particularly when aiming to prevent rather than treat childhood obesity.
They can also be more cost-effective, since they can reduce obesity in multiple family members [ 34 ]. That is not to suggest that all family based interventions will be successful and some family-based interventions were found to be no better than child only interventions [ 35 ].
This emphasises the need for further research into the type of family-based intervention that are more reseqrch to be successful. Obesity is a very persistent trait [ 36 ], however, similar to the endogeneity described above, it is difficult to determine Suepr past obesity influences current obesity or whether a persistent underlying and unobservable factor is influencing obesity at all times.
Socioeconomic status [ 37 ], parental education [ 38 ] and single-parenthood [ 39 ] have all been shown to influence obesity and are relatively consistent over time. However, it remains unclear what mechanisms are behind these relationships.
From a policy perspective, if obesity were determined purely by past obesity and social circumstance, interventions to reduce obesity would be ineffective.
However, it has been shown that interventions can be effective in reducing childhood obesity [ 40 ]. Similarly, interventions have been successful in reducing weight gain during pregnancy [ 41 ] and in reducing obesity in adults [ 42 ]. This suggests that with the right interventions obesity in modle children and adults can be reduced, and that obesity is not solely determined by past obesity and social circumstance but by more complex interactions going on in family life.
Given that childhood obesity sociql other outcomes of family lifestyle are expected to be dependent on the same underlying influences, it is important to model these outcomes jointly.
Despite this, the majority of previous studies have estimated Doldo porn variables independently [ 434445 ]. This approach is less informative when considering policy implications because it is only possible to identify how potential lifestyle interventions might influence a single outcome.
Other studies have jointly estimated a range of observable lifestyle outcomes, including diet, alcohol consumption and smoking habits [ 4647 slcial, allowing the benefits of potential interventions to a range of outcomes to be investigated but have been unable to identify the underlying cause of the correlation between these variables.
Existing studies show that early-life influences of obesity, particularly lifestyle during Wifey spankwire and early infancy are important in predicting later obesity [ 25484950 ].
However, these studies are generally cross-sectional and do not allow the evolution of lifestyle behaviours over time to be investigated. These early-life influences might continue to have an effect throughout childhood and new influences could emerge as children grow up and their immediate environment changes, for example starting school. The use of more flexible dynamics when modelling development during childhood is encouraged because children change so rapidly [ 51 ].
We contribute to exiting literature by using a structural model to investigate how family lifestyle evolves over time during early childhood and how family lifestyle dynamics influence childhood obesity. This approach has a number of advantages. First, structural models can explain much more than models which use a single equation and can be used to investigate multiple and more ambitious research questions than more modest models such as fixed effects or instrumental variable models [ 52 ].
Second, unlike more commonly used autoregressive models, structural models nodel parameter Hot christian naked to differ over time. Third, different mean outcomes can be identified for children with different Girl model sites unlike existing studies into adiposity which are restricted to estimating a single average treatment effect for a sample [ 53 ].
Identifying the full distribution of treatment effects allows those who will benefit most from potential interventions to be identified. This, coupled with the dynamic nature of the model, is vital evidence for policy makers in order for them to have the greatest possible impact.
In order to investigate the dynamic influence that underlying family lifestyle has on our outcome of interest, childhood obesity, we use a dynamic latent factor model, similar to that used in previous studies [ 5154 ]. They use this approach to identify the formation of skills during early childhood, whilst we use zocial to explore the evolution of family lifestyle and its relationship with obesity.
The model is made up a SSuper of latent factors sometimes known as measurement models which identify the underlying lifestyle of a family using a range of outcomes and a structural model which estimates the relationship between these latent factors, in this case, the dynamic process of how family lifestyle evolves over time. Both parts of the model are outlined below and are jointly estimated using maximum likelihood.
Breastfeeding sore nipple more detailed explanation relating to structural models can be found in the literature [ 5556 ].
We are interested in the influence of underlying family lifestyle on childhood adiposity, so that. Previous adiposity is not included in this equation, as we Super model social research obesity any persistence in obesity is caused by a persistence in underlying lifestyle.
This underlying family lifestyle is unobservable and cannot be identified using this single equation. Due to the unobservable nature of this underlying family lifestyle, a latent factor is the only way to directly estimate it, allowing this underlying concept to be ,odel without measurement error [ 57 ]. There is multicollinearity between each of the estimated outcomes due to their shared dependence on underlying family lifestyle but by using a latent factor, this multicollinearity is accounted for.
Multiple lifestyle outcomes have previously been jointly estimated using a multivariate probit model [ 46 ] allowing the correlation of the error terms in each of the outcome equations to be accounted. However, using this model, it is not possible to estimate directly the underlying factor that is influencing each of these observable outcomes and therefore it is not possible to estimate the effect that this underlying factor has on each outcome. This study directly estimates the underlying source of this correlation allowing its influence on each of the outcomes to be examined.
Similar to Eq. Other parameters are also equivalent to those in Eq. In both Eqs. Threshold parameters for these discrete variables obeslty jointly estimated and strictly increasing. The outcomes included in Eq.
By estimating these outcomes jointly, rather than including parental weight as independent variables in the child weight equation, we account for the endogenous effect of underlying lifestyle that is present when estimating child weight in single equation. By accounting for this endogeneity, we infer a causal effect of underlying family lifestyle on childhood adiposity.
We assume here for simplicity that there is a single latent factor but this will be tested using the exploratory factor analysis EFA prior to the full model being estimated. Outcomes in reseearch period are chosen using EFA and are informed by existing literature. The outcomes of family lifestyle can differ between periods.
It is assumed that there is no remaining correlation between outcomes moedl time t once the underlying factor for family lifestyle has been accounted for. A structural model estimates the relationships between the latent factors; in this case, it creates the dynamic structure of underlying family lifestyle over time.
This structure allows more long-term outcomes to be investigated and can show the extent to xocial influences can accumulate over time. Similar to the stock of skills described by Heckman [ 58 ], there is a stock of family lifestyle.
This stock of family lifestyle produces the observable outcomes estimated in Eqs. Family lifestyle stock in one period is dependent on the stock of family lifestyle in the subsequent period of the model, so that. The inclusion of the family random effect allows us to account for any unobservable influence on underlying family lifestyle over time.
This allows us to ensure that the majority of variation in the observable lifestyle outcomes are accounted for within the model. One cannot identify both the means and the intercepts in Eqs. In order to identify the model, we fix the variance of some of the error terms [ 51 ].
The variance of the error term, u 0 in Eq. This identifies the structural part of this model and is equivalent to restricting the variance to one normalisation as is done in a probit model. In this case, model convergence was more easily achieved using values smaller than one but the magnitude of these values is arbitrary. A more detailed description and proof for the identification of this model can be found in the literature [ 51 ].
The model is estimated using Mplus 6. More details of the estimation method are provided in Appendix 1. In order Super model social research obesity investigate the influences of underlying family lifestyle on childhood obesity, the expected means, and conditional variances of observable childhood weight status can be calculated, that is the predicted outcome of childhood weight status, conditional on other variables within the model.
This equation requires the computation of several integrals and for this reason we approximate these predictions with simulations using the estimated parameters from the dynamic latent factor model.
Second, we summarize research that supports the positive and negative influences of social networks on health outcomes. Third, we describe research related to social determinants of obesity and propose a conceptual model of social networks and their influence on obesity. Last, we discuss future directions for obesity research using social networks. Oct 03, · The third factor of the biopsychosocial model is the social factor and as mentioned above is the main focus of social psychology. When it comes to obesity there are a great variety of social variables that contribute to one being overweight and obese. Social-Ecological Model (SEM) to address and understand the issues of overweight and obesity (Hamre et al., ). The SEM, credited to Urie Brofenbrenner, is a highly adaptable obesity research, the family unit is the most common target for interpersonal raulperrone.com by: 1.
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Campos, F. More Share Options. Recent public health interventions and guidance aiming to reduce childhood obesity have focused on the whole family, as opposed to just the child but there remains a lack of empirical evidence examining this relationship. J Econom. Drake AJ, Liu L. East Econ J. The transition of health states is described by an SIS model. Given that childhood obesity and other outcomes of family lifestyle are expected to be dependent on the same underlying influences, it is important to model these outcomes jointly. The proportion of variance in maternal weight status explained by family lifestyle is DST is widely studied in the field of Artificial Intelligence AI and Expert Systems, such as recognition 38 , classification 39 , fault diagnosis 40 , and also has a great potential for multi-criteria decision making 41 , 42 which often necessitates the decision maker to make judgments on decision alternatives over a range of criteria Although the two behaviors are competing to each other, the threshold values are interdependent. When attempting to measure this they found that education among other things was a source of endogeneity, however they could not measure the endogeneity caused by unobservable characteristics, not available in their data [ 6 ]; that is, there could be unobserved variables outside their analysis which is affecting the obesity status of both family members. Not only is this important for policy makers but also for cost-effectiveness modellers wishing to provide robust evidence to decisions makers such as NICE on public health interventions. Brockmann, D. In this model, all lifestyle outcomes which appear in more than one period of the model had constant parameters, including factor loadings and threshold parameters.
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Prev Chronic Dis ; Complex systems modeling can provide useful insights when designing and anticipating the impact of public health interventions. We developed an agent-based, or individual-based, computation model ABM to aid in evaluating and refining implementation of behavior change interventions designed to increase physical activity and healthy eating and reduce unnecessary weight gain among school-aged children. The potential benefits of applying an ABM approach include estimating outcomes despite data gaps, anticipating impact among different populations or scenarios, and exploring how to expand or modify an intervention. The practical challenges inherent in implementing such an approach include data resources, data availability, and the skills and knowledge of ABM among the public health obesity intervention community.