Like Father, Like Son: Familial Infuences on Obesity in Adolescence
Nichola Birch, Alice Jih, Rachel Sluder, Eline Verbon
University College Utrecht
UCSSCPSY22: Lifespan Developmental Psychology
1 December 2011
Like Father, Like Son: Familial Infuences on Obesity in Adolescence
“This might be the first generation where kids are dying at a younger age than their parents and it’s related primarily to the obesity problem.” Judy Davis
Obesity is commonly defined as having a body mass index (BMI) of the 95th percentile or higher (Ogden, 2010a). According to several research institutes, the prevalence of obesity both in the USA and Western Europe has reached epidemic proportions in the last few decades (Berghöfer, 2008), with 33% of all individuals in American society and more than 20% of all individuals in many European countries being diagnosed as obese in 2010 (Flegal, 2010; Crawford, 2010). This high prevalence of obesity is especially relevant because of obesity’s association with increased risk for many diseases (Table 1) (Aronne, 2002). Therefore, the increased prevalence of obesity has led to a large increase in obesity related healthcare costs in the past decades; in the USA, for example, these costs had risen to 10% of all medical costs by 2008 (Finkelstein, 2009). Because of the health risks associated with obesity, a large amount of research has been conducted on this topic.
A considerable part of this research has focused on adolescents. There are several reasons for this; one being the high risk of adult obesity that is correlated with adolescent obesity. Viner and colleagues (2006) studied the data collected from a survey in the UK in which 11,261 individuals filled in questionnaires when they were 16 and again when they were 29-30. From the obtained data, the BMI of the subjects could be calculated. Of individuals found to be obese when they were 16 years old, 60.7% were found to still be obese fourteen years later. Other research groups have found similar percentages, i.e. more than 60%, for the proportion of obese adolescents that become obese adults when they reach later life (Bray, 2002; Engeland, 2004).
Like in the general population, obesity prevalence among adolescents has also been on the rise. Research conducted by Ogden et al (2002) in the USA showed that the amount of adolescents with a BMI in the 95th percentile or higher increased from 5% in the 1960s to 17.4% in 2003-4 (Ogden, 2002; Ogden, 2006). Similar increases in the prevalence of obesity have been found in Europe, for example in Belgium (Hulens, 2001), Finland (Kautiainen, 2002) and the UK (Stamatakis, 2010). The trend has been found to level off during the 2000s (Ogden, 2010b; Ruesten, 2011; Flegal, 2010; Stamatakis, 2010). However, this does not mean that obesity rates are decreasing. Thus, a lot of research is still conducted on obesity in adolescence to attempt to reverse the trend and lower the prevalence of obesity among adolescents.
One interesting fact of obesity is that it runs in families. Children with obese parents have been shown to possess a higher chance of becoming obese themselves (Bouchard, 1997). Furthermore, it has been found that family environment influences the success of weight reduction programs directed to adolescents. For example, Pott and colleagues (2009) did a study on 111 obese children and adolescents, who were referred to a weight reduction program in which the children received behavioral therapy to improve their eating and physical activity behavior. In addition, they followed lessons on physical activity and diet. Before and after the therapy, body weight and height of the children, plus all the other household members, were taken. In addition, the parents had to fill in questionnaires regarding their educational level and socioeconomic status. Furthermore, measures were taken regarding depression levels of the mother and attachment between child and caregivers. The researchers found that maternal depression, obese siblings and the absence of attachment between caregiver and child were all correlated with a lower degree of success of the intervention program.
Thus, family environment influences both the chance of adolescents to become obese and the ability of the obese adolescents to successfully lose weight. In other words, an adolescent’s family influences the chance of the adolescent to become obese. As with all traits that run in families, the influence of the family can be due to the genes that the parents pass on to their children, to the family environment, which is shared by both the parents and siblings or a combination of both (Mirmiran, 2002). In this review we will attempt to investigate both the hereditary component of obesity and the influence of the home environment on the development of obesity. The family environment is believed to be influenced by the sociodemographic factors of the family. Therefore, we will also explore these factors. At the end, we hope to be able to answer our research question: in what ways does an adolescent’s direct family influence have an impact on the prevalence of obesity in adolescents in Western society?
Proportion of Disease Prevalence Attributable to Obesity (Aronne, 2002)
Disease Prevalence (%)
Type 2 diabetes 61
Uterine cancer 34
Gallbladder disease 30
Coronary heart disease 17
Breast cancer 11
Colon cancer 11
Biological influences: Genes transmitted from parent to child
Many studies conducted to discover biological risk factors for obesity acknowledge that obesity has a heritable component. The three studies described here all studied the heritability of obesity and did indeed find indications for this heritable component. The first study focuses on leptin resistance, which is associated with a higher susceptibility to obesity, and its aggregation in families (Lee, 2001). The aggregation in families indicates that there may be a genetic aspect to leptin resistance. The second study focuses on the heritability of anthropometric traits that are often associated with obesity (den Hoed, 2010). Last of all, the third study focuses on the heritability of eating behavior associated with obesity (Sung, 2010).
Lee et al (2001) studied leptin resistance in families which consisted of both extremely obese and average weight family members. Leptin is a hormone secreted by adipose tissue. It is part of a negative feedback system in the brain and indicates satiety. Thus, leptin resistance can lead to the absence of a satiety signal and subsequent overeating. Apart from regulating appetite, leptin also regulates both metabolism and sexual maturation (Lee, 2001). In 1998, a study by Friedman et al showed that extremely obese individuals have high amounts of leptin. The combination of high leptin levels and overeating, indicates a possible resistance to leptin (as cited in Lee, 2001). In addition, meta-analyses performed in 1998 by Heo et al demonstrated correlations between obesity and the region of the genome containing the leptin gene (as cited in Lee, 2001). Lee et al (2001), obtained plasma samples from 968 individuals from families with at least one extremely obese
Risk of Leptin Resistance [LEP]/BMI for Family Members (Lee, 2001)
member, and measured the amounts of leptin in the samples. The data were used to create a ratio of leptin concentration to BMI. The high odds ratio for family members of people with a BMI over 40 for the risk of leptin resistance (Table 2) provides support for the heritability of leptin resistance. Moreover, using all the data obtained in the studies the researchers conclude that BMI and sex accounted for 63% of the variance in plasma leptin levels. Ultimately, by comparing the data from different families, the authors conclude that both plasma leptin concentration and leptin resistance are indeed heritable (Lee 2001).
den Hoed and colleagues (2010) did not study the effect of genetics on hormone levels but on the level of bodily changes. Using data from previous genome-wide association studies, they identified 17 “effect alleles” that had been indicated as having a potential effect on obesity. Their goal was to determine whether or not these loci were not only correlated with obesity but also with anthropometric traits. In addition, they studied whether physical activity had an effect on this correlation. The researchers studied children, with a mean age of 9.6 years, and adolescents, with a mean age of 15.5 years, in Denmark, Estonia, Norway, and Portugal. The authors measured body mass, height, waist circumference, skin folds, sexual maturity, and physical activity levels of all these individuals. Ultimately, they found that nine variants in the obesity susceptibility loci had significant associations with BMI as well. Each additional allele present out of the 17 ‘effect alleles’ was significantly correlated with an increase in BMI by 0.034 standard deviations, sum of skinfolds by 0.039 SD, and waist circumference by 0.022 SD (Figure 1).
Thus, both the heritability of leptin resistance and of certain anthropometric traits has been determined. Since both of these parameters are associated with obesity, the next step is to consider the heritability of obesity in general. Sung (2010) studied a Korean population and demonstrated this heritability as it relates to eating behavior. Even though this paper is not on Western society, this study is relevant since it demonstrates the direct heritability of obesity and it is purported to use a more reliable questionnaire than most studies on Western populations.
The questionnaire in this study is the Dutch Eating Behavior Questionnaire (DEBQ). It is a 33-unit self-assessment in which restraint, emotional eating, and external eating are evaluated. Restraint is defined as the conscious effort to reduce or modify energy intake in order to reduce weight or prevent weight gain, emotional eating is defined as a response to negative emotions and external eating is identified as overeating in response to external stimuli, such as enjoyable taste. The alternative questionnaire that is used in many studies on Western society is known as the Three-Factor Eating Questionnaire, or TEFQ. This paper cites Allison et al (1992) which states that the DEBQ had higher internal consistency and more stable factor structure across sexes and weight categories than TEFQ (as cited in Sung, 2010).
The study was performed on same-sex twins from 20 years up to age 81 and their families. They were asked to report on their weight at age 20 and two years before the survey, in addition to completing the DEBQ (Table 3). Authors also performed genotyping on all sets of twins to determine whether they were monozygotic or dizygotic
Heritability Estimates for the DEBQ Subscales and Body Weight-related Traits
In statistical analysis, DEBQ scores were handled as a constant variable, and they ran the data through the Sequential Oligogenic Linkage Analysis Routines software, which allows an estimate to be made regarding the likelihood of a given trait to be genetic or not. They then calculated heritability as the proportion of the “total phenotypic variance explained by additive genetic effects” (Sung, 2010). They controlled for all age and sex interactions. Overall, the authors concluded that there was significant heritability in each of the three measurements on the DEBQ. They claimed that “unmeasured household and sibling effects…[were] negligible”, and that biological heritability of eating behavior was a much stronger factor than social heritability.
Environmental Influences: What parents say and do
Although genetic factors seem to play a large role in the development of obesity, its etiology can only be fully understood when considering the interaction of both genes and environment. Research into the family home environment is abundant. For example, encouragement by parents to choose healthy food options has been associated with many positive dietary habits including higher intake of fruit and vegetables (Pearson, Biddle & Gorley, 2009) and protecting against increase in fast food consumption (Bauer, Larson & Nelson, 2009). Similarly, encouragement of physical activity participation is associated with increased healthy physical activity habits (Heitzler, Martin, Duke & Huhman, 2006). Adolescent involvement in these behaviors can play a critical role in the development of obesity and can contribute to healthy weight maintenance into adult life (Menschik, Ahmed, Alexander and Bum, 2008). In contrast, encouragement by parents to diet has been shown to have high risks of negative effects such as depression and lower self esteem (Fulkerson, McGuire & Neumark-Sztainer, 2002). Furthermore these themselves are related to obesity (McElroy et al, 2004; French, Perry, Leon & Fulkerson, 1996).
Project EAT is a continuing scheme which aims to study eating patterns and weight issues among a large and ethnically diverse adolescent population in America (Larson, 2007). From project EAT I (1998-1999) and EAT II (2003-2004), Larson and colleagues (2007) studied fruit and vegetable intake. They found that between 1999 and 2004, the years in which the obesity trend still increased, fruit and vegetable intake decreased by 0.7 servings per day for girls and 0.4 among boys, as well as decreasing by 0.7 servings per day from early to middle adolescence and by 0.6 from middle to late adolescence (Larson, Neumark-Sztainer, Hannan & Story, 2007). Bauer, Laska, Fulkerson and Neumark-Sztainer (2011) have shown that during the same age period perception of parental encouragement of fruit and vegetable intake decreases. Suggested reasons for the perceived changes during these years may be the increasing independence and autonomy of the adolescents in making behavior decisions (Story, Neumark-Sztainer & French, 2002). As a response, parents could also think that as their adolescents are becoming more independent they are making more decisions on their own and are less receptive to their parents’ options. Hence to avoid possible negative outcomes of direct encouragement parents might try to be more subtle. For example, by parents making healthy food options more accessible at home and providing transport and financial means, so adolescents can participate in various healthy activities (Bauer, Laska, Fulkerson and Neumark-Sztainer, 2011).
Similarly, what parents do, as well as what they say, has been found to be important. For example, modeling and intake of food by parents has been shown to correlate positively with children’s consumption of fruit and FJV (fruit, juice, vegetables) (Pearson, Biddle & Gorley, 2008). Furthermore the number of adolescents having five or more meals with their family a week decreases from 74% for those aged 12-14 to 42% for those aged 17-19 (Story, Neumark-Sztainer & French, 2002). This might mean that as adolescents are not there to see their parents participate in healthy habits, i.e. see their parents model the behavior, it may lead to the adolescents not participating in or reducing the same habits.
Environmental Influences: Entertainment provided by the parents
Adolescents who are not at the dinner table must be doing something else and this could involve being engaged in some type of media. For example, in the US adolescents spend 1.5-2.5 hours per day watching television over an average week, which is equal to half the time they spend at school, 3.0-4.5 hours (Larson, 2001).
As so much time is devoted to this type of entertainment, which involves lack of physical activity and food choice persuasion via adverts, it is likely that television has an influence on obesity rates.
This review chooses to focus on television since it has both been in use for many years and is still in high use today by the average family. This is in contrast to, for example, the internet which has only been in high use for a relatively short time. Hence we will be able to draw secular trends and apply them to today’s adolescence.
65% of parents choose to let their children have a television in their room (Story, Neumark-Sztainer & French, 2002) and watching television has been found to be a significant risk factor for obesity. Adolescents watching 2+ hours of TV/day are nearly 50% more likely to be obese, independent of their reported activity levels and fruit/vegetable intake, as found in a study by Fleming-Moran and Thiagarajah (2005). This can be due to the fact that adolescents who watch more television tend to consume more calories or eat higher-fat diets, drink more sodas and eat fewer fruit and vegetables (Strasburger, 2011) all of which can attribute to obesity.
Strasburger (2011) suggests that eating and watching television suppresses cues of satiety, which leads to over eating. However it seems that television viewing, in the US, has remained stable between 1999-2003 (Nelson, Nuemark-Sztainer, Hannan, Sirad & Sotry, 2006) and so cannot account for secular trends in obesity during that timeframe.
Hence the content of television might be related to the secular increase. Nowadays, 90% of food adverts for teenagers are for products that are high in fat, sugar, and/or sodium and are low in nutritional content. The amount of food adverts aimed at teenagers, is also rising (Powell, Szczypka & Chaloupka, 2007). Presently there are very few studies relating food advertisement and adolescent obesity. Most are focused on childhood obesity which has meant a decline in food adverts for children (Kunkel, McKinley & Stitt, 2010), which contrasts the rise for adolescents. However Bronwell, Scwartz, Puhl, Henderson and Harris (2009) suggest that since adolescents are more susceptible to influence by tobacco and alcohol advertising than adults, they might be vulnerable to food advertising as well. They also suggest these adverts are being shown at a time when the teenagers have reduced ability to inhibit impulsive behaviors and at a time when they are establishing lifelong consumer and dietary habits.
Last of all, if advertisement would not influence adolescents at all, advertisers would not spend over $1 billion marketing to adolescents in the US.
Another factor that indirectly links television viewing to overeating is the sleep disruption caused by participation in this behavior. A longitudinal study of New York adolescents showed that adolescents viewing TV for more than 3 hours/day doubled the risk of difficulty falling asleep in the future, compared to adolescents who watch less than one hour/day (Johnson, Cohen, Kasen, First & Brook, 2004). Less sleep may be associated with a greater risk of obesity as it can lead to increased snacking and consumption of less healthy food to maintain energy (Strasburger, 2011).
Environmental Influences: Sociodemographic factors of the family
The home environment is influenced by sociodemographic factors that characterize the environment the family finds itself in. Studies have shown that some sociodemographic factors have a clear association with obesity rates. Examples of sociodemographic factors are gender, race/ethnicity and socioeconomic status (SES), all of which interact. Few papers look into the effect of a combination of multiple sociodemographic factors on the prevalence of obesity in adolescents (Gopal, 2008; Miech, 2011). Studies that do attempt to take a multiperspective view are inconclusive concerning differences between different sociodemographic groups (Kautianen, 2011; Miech, 2006).
Many studies that focus on only gender or race/ethnicity have found that there are no differences in secular trends between different groups. For example, both Kimm (2002) and Miech (2011), which analyze obesity trends in relation to race and ethnicity, conclude that secular changes in rates of obesity are similar between different subgroups. Although ethnic minority groups maintain significantly higher prevalence of obesity, relative racial differences in the prevalence of obesity have remained stable throughout the years.
One factor that does have strong support for its role on secular trends of obesity, however, is socioeconomic status (SES).
Social Economic Status
The marked increase in the prevalence of adolescent obesity is not just confined to a single demographic group (Starfield et al, 2011). Consideration of SES is important when analyzing familial factors because SES might play a role in the dynamics and conditions of the family environment. Additionally, research shows that SES has strong correlation with adolescents’ health outcomes (Escarce, 2003).
Because SES is multifaceted, studying population-level effects of SES requires researchers to explore several factors. Therefore, results vary slightly depending on definition, but in general results are quite similar. Most studies only focus on income level or education. In addition, looking at SES factors independently helps to identify individuals at risk.
Goodman et al. (2003) examined the impact of the SES gradient on adolescents’ physical health using obesity as an indicator of physical health. In this study, population attributable risk (PAR) was calculated for household income and parental education relative to obesity among nationally representative samples of 15,112 adolescents. PAR represents the proportion of disease that would be prevented if the exposure were removed. SES gradient defined the exposure pattern. Results show that the attributable risk among the exposed – those at the bottom of the SES gradient – was much greater. Specifically, the PAR for income was 32% and the PAR for education level was 39%. Thus, lower household income and lower education could both explain approximately one third of obesity in this national sample. In other words, both lower income level and lower parental education level are associated with an increase in prevalence of the adolescent’s obesity.
In terms of secular trends, the gap in prevalence of obesity has widened between poor versus non-poor families (Miech 2011; Miech et al 2006). The gap in prevalence of obesity has also widened between conscripts with low- and high-educated mothers over time (Kark & Rasmussen, 2005).
Kark and Rasmussen (2005) investigated time trends and social inequalities in 1,500,499 overweight 18-year-old men in Sweden from 1970 to 2000. Results show an increasing absolute difference in prevalence of obesity between young men with low-educated versus high-educated mothers (Figure 2).
Figure 2. Absolute differences in prevalence of overweight and obesity at age 18 between Swedish men with low-educated versus highly-educated mothers by year-of-birth category (Kark & Rasmussen, 2005).
In a review of existing data on social class gradients in US adolescent health, Starfield et al (2001) showed parallel findings. An increase in obesity rate was found to be associated with social class, regardless of how social class was characterized whether it was parental education or income. This held true even after taking into account other factors such as self esteem, access to medical care, and other mediating factors. Many other studies found the same association between an increase in obesity with decrease in socioeconomic status based on parental income and education level (Gopal et al, 2008; Babey et al; Wang and Beydoun, 2007; Kautianen, 2011).
Over the past few decades, there has been a clear increase in obesity rates in adolescents (Ogden, 2002; Ogden, 2006). Researchers have responded by looking into several different factors that could have influenced this trend. It has been found that children with obese parents have a greater chance of becoming obese themselves. This review investigated this phenomenon and found several influencing factors. Looking at both genetic and environmental factors, it is clear that both are associated with obesity. There are several ways that parents influence their child’s weight status: genetics, the home environment, and sociodemographic factors, which are all at least partly provided by the parents. In other words, it is an example of a biocultural co-construction (Baltes, 2006).
Research in the biological domain shows that obesity might be transmitted to children via genes. For example, Leptin intolerance which controls satiety is more prevalent in obese families, indicating that it may have a heritable aspect (Lee, 2001). In addition, alleles that have been associated with obesity are also associated with anthropometric traits that often go with obesity, such as waist circumference and skin folds. Since some of those characteristics pose additional health risks, it is important to note their heritable aspect (den Hoed, 2010). Finally, behavioral patterns appear to be genetic, since eating behaviors hold similar patterns in families, as shown by a twin study. The authors concluded that biological factors had a much stronger influence over the correlation than social influences (Sung, 2010). Nonetheless, it is still very important to consider behavioral influences on the development of adolescent obesity.
Modeling and encouragement are some of these parental influences. Both are good predictors of how much healthy behavior an adolescent participates in, such as amount of fruit eaten having a positive correlation with parental encouragement (Larson, Neumark-Sztainer, Hannan & Story, 2007). In addition what they decide to provide as entertainment, including what they put in their adolescent’s room, is also associated with obesity. The content of television, i.e. the amount of unhealthy food adverts has increased (Powell, Szczypka & Chaloupka, 2007) and sleep deprivation from watching television could increase snacking.(Strasburger, 2011). All these factors could help account for the previously increasing trend in adolescent obesity.
Sociodemographic factors that are part of the environment provided by the parents also come into play, as is shown by studies that look at differences between sociodemographic groups. One of the most important factors is socioeconomic status encompassing especially income level and education. More specifically, there has been a decrease in income level and a decrease in parent’s education (Kark & Rasmussen, 2005; Starfield et al., 2001). This widening gap between sociodemographic groups of obesity prevalence demonstrates that sociodemographic factors also have an impact on the prevalence of obesity in adolescents.
Therefore, literature on hereditary components on obesity and the influence of the home environment on the development of obesity confirms a biocultural co-construction described by Baltes (2006). That is, family influences impact the prevalence of obesity in adolescents in Western society through dynamic exchanges of nature and nurture.
Adachi-Mejia, A.M., Longacre, M.R., Gibson, J.J., Beach, M.L., Titus-Ernstoff, L.T., & Dalton, M.A. (2007). Children with a TV set in their bedroom at higher risk for being overweight. Interantional Journal of Obesity, 31,644–651.
Aronne, L.J. (2002) Classification of Obesity and Assessment of Obesity-Related Health Risks. Obesity Research, 10, 105-115.
Auerbach J, Krimgold B, Lefkowitz B. Improving Health; It Doesn’t Take A Revolution. National Policy Association, 2000, 1–30.
Babey S, Hastert TA, Wolstein J, Diamant AL. (2010) Income disparities in obesity trends among California adolescents. American Journal of Public Health,100, 2149-2155.
Baltes, P.B.,Reuter-Lorenz, P.A., Rösler, F. (2006). Lifespan Development and the Brain. Cambridge: Cambrisge University Press
Bauer, K.W., Larson, N.I., & Nelson, M.C. (2009). Socio-environmental, personal and behavioural predictors of fast-food intake among adolescents. Public Health and Nutrition, 12, 1767-1774.
Bauer, K.W., Laska, M.N., Fulkerson, J.A., & Neumark-Sztainer, D. (2011). Longitudinal and Secular Trends in Parental Encouragement for Healthy Eating, Physical Activity, and Dieting Throughout the Adolescent Years. Journal of Adolescent Health, 49, 306-311.
Berghöfer, A., Pischon, T., Reinhold, T., Apovian, C.M., Sharma, A.M., Willich, S.N. (2008) Obesity prevalence from a European perspective: a systematic review. BMC Public Health, 8, 200.
Bouchard, C. (1997). Obesity in adulthood: The importance of childhood and parental Obesity. New England Journal of Medicine, 337, 926-927.
Bray, G.A. (2002). Predicting obesity in adults from childhood and adolescent weight. American Journal of Clinical Nutrition, 76, 497–8.
Bronwell, K.D., Schwartx, M.D., Puhl, R.M., Henderson K.E., & Harris, J.L. (2009). The need for bold action to prevent adolescent obesity. Journal of Adolescent Health, 45, 8-17.
Crawford, D., Jeffery, R.W., Ball, K., Brug, J. (2010). Obesity Epidemiology: From Aetiology to Public Health. Oxford University Press, 2, 78-85.
Doll, H.A., Petersen, S.E.K., & Stewart-Brown, S.L. (2000). Obesity and Physical and Emotional Well-Being: Associations between body mass index, chronic illness, and the physical and mental components of the SF-36 questionnaire. Obesity Research, 8, 160-170.
Engeland, A., Bjørge T., Tverdal A., Søgaard A.J. (2004). Obesity in adolescence and adulthood and the risk of adult mortality. Epidemiology, 15, 79-85.
Escarce, Jose J. (2003) Socioeconomic Status and the Fates of Adolescents. Health Services Research, 38, 1229-1234.
Finkelstein, E.A., Trogdon, J.G., Cohen, J.W., Dietz, W. (2009). Annual Medical Spending Attributable To Obesity: Payer-And Service-Specific Estimates. Health Affairs, 28, w822–w831.
Flegal, K.M., Caroll, M.D., Ogden, C.K., Curtin, L.R. (2010). Prevalence and Trends in Obesity Among US Adults, 1999-2008. JAMA, 303, 235-241.
Fleming-Moran, M., Thiagarajah, K. (2005). Behavioral interventions and the role of television in the growing epidemic of adolescent obesity—data from the 2001 youth risk behavioral survey. Methods Inf Med, 44, 303—309.
French, S.A., Perry, C.L., Leon, G.R., & Fulerson, J.A. (1996). Self-esteem and change in body mass index over 3 years in a cohort of adolescents. Obesity Research, 4, 27-33.
Fulkerson, J.A., McGuire M.T., & Neumark-Sztainer, D. (2002). Weight-related attitudes and behaviors of adolescent boys and girls who are encouraged to diet by their mothers. International Journal Obesity Related Metabolic Disorder, 26, 1579–1587.
Goodman E, Slap GB, Huang B (2003). The public health impact of socioeconomic status on adolescent depression and obesity. American Journal of Public Health, 93, 1844-50.
Heitzler, C.D., Martin, S.L., Duke, J., & Huhman, M. (2006). Correlates of physical activity in national sample of children aged 9-13 years. Preventative Medicine, 42, 254-260.
den Hoed, M., Ekelund, U., Brage, S., & Grontved, A. (2010). Genetic susceptibility to obesity and related traits in childhood and adolescence. Diabetes, 59, 2980-2988.
Hulens, M., Beunen, G., Claessens, A.L., Lefevre, J., Thomis, M., Philippaerts, R., Borms, J., Vrijens, J., Lysens, R., Vansant, G. (2001). Trends in BMI among Belgian children, adolescents and adults from 1969 to 1996. International Journal of Obesity, 25, 395-399.
Johnson, J.G., Cohen, P., KAsen, S., First, M.B., & Brook, J.S. (2004). Association between television viewing and sleep problems during adolescence and early adulthood. Archive of Paediatric and Adolescent Medicine, 158, 562-568.
Kark M, Rasmussen F. (2005). Growing social inequalities in the occurrence of overweight and obesity among young men in Sweden. Scand J Public Health, 33, 472–477
Kautiainen, S. (2008). Overweight and obesity in adolescence. Tampere University Press, 16-25.
Kautiainen, S., Rimpelä, A., Vikat, A., Virtanen, S.M. (2002). Secular trends in overweight and obesity among Finnish adolescents in 1977-1999. International Journal of Obesity, 26, 544-552.
Kimm, S.Y.S., Barton, B.A., Obazarnek, E., & McMahon, R.P. (2002). Obesity Development During Adolescence in a Biracial Cohort. Pediatrics, 110, 1-5.
Kinra S, Baumer JH, Davey Smith G. (2005), Early growth and childhood obesity: a historical cohort study. Archives of Disease in Childhood, 90, 1122–7.
Kunkel, D., McKinley, C., & Stitt, C. (2010). Food Advertising During Children’s Programming: A Two-Year Comparison. Tucson, AZ: University of Arizona.
Larson, N.I., Neumark-Sztainer, D., Hannan, P.J., & Story,M. (2007). Trends in adolescent fruit and vegetable consumption, 1999-2004: project EAT. American Journal of Preventative Medicine, 32, 147-150.
Larson, R.W. (2001). How US children and adolescents spend time: What it does (and doesn’t) tell us about their development. Current Directions in Psychological Science, 10, 160-164.
Lee, J.H., Reed, D.R., & Price, R.A. (2001). Leptin resistance is associated with extreme obesity and aggregates in families. International Journal of Obesity. 25, 1471-1473.
McElroy, S.L., Kotwal, R., Malhorta, S., Nelson, E.B., Keck, P.E., & Nemeroff, C.B. (2004). Are mood disorders and obesity related? A review for the mental health professional. Journal of Clinical Psychiatry, 65, 634-651.
Menschik, D., Ahmed, S., Alexander, M.H., & Blum, R.W. (2008). Adolescent physical activities as predictors of young adult weight, Archive of Adolescent Medicine, 162, 29-33.
Miech RA, Kumanyika SK, Stettler N et al (2006) Trends in the association of poverty with overweight among US adolescents, 1971-2004. JAMA. 295, 2385-93.
Mirmiran, P., Mirbolooki, M., Azizi, F. (2002) Familial clustering of obesity and the role of nutrition: Tehran Lipid and Glucose Study. International Journal of Obesity, 26, 1617–1622.
Mustillo, S., Worthman, C., Erkanli, A., Keeler, G., Angold, A., & Costello, E.J.(2003). Obesity and Psychiatric Disorder: Developmental Trajectories. Pediatrics, 111, 851-859.
Nelson, M.C., Neumark-Stzainer, D., Hannan, P.J., Sirad, J.R., & Story, M. (2006). Longitudinal and Secular Trends in Physical Activity and Sedentary Behavior During Adolescence. Pediatrics, 118, 1627-1634.
Neumark-Sztainer, D., Hannan, P.J., Story, M., Croll, J., Perry, C. (2003). Family meal patterns: Associations with sociodemographic characteristics and improved dietary intake among adolescents. Journal of the American Dietetic Association. 103, 317-322.
Nuemark-Sztainer, D., Wall, M., Story, M., & van den Berg, P. (2008). Accurate parental classification of overweight adolescents’ weight status: Does it matter? Pediatrics, 121, 1495-1502.
Ogden, C.L., Carroll, M.D., Curtin, L.R., Lamb, M.M., Flegal, K.M. (2010b). Prevalence of High Body Mass Index in US Children and Adolescents, 2007-2008. JAMA, 303, 242-249.
Ogden, C.L., Carroll, M.D., Curtin, L.R., McDowell, M.A., Tabak, C.J., Flegal, K.M. (2006) Prevalence of Overweight and Obesity in the United States, 1999-2004. JAMA, 295, 1549-1555.
Ogden, C.L., Flegal, K.M. (2010a). Changes in terminology for childhood overweight and obesity. Hyattsville, MD: National Center for Health and Statistics.
Padez C,Fernandes T, Mourão I, Moreira P, Rosado V. (2004). Prevalence of overweight and obesity in 7-9-year-old Portuguese children: trends in body mass index from 1970-2002. American Journal of Human Biology, 16, 670–678.
Patrick, K., Norman, G.J., Calfas, K.J., Sallis, J.F., Zabinski M.F., Rupp, J., Cella, J. (2004) Diet, physical activity, and sedentary behaviors as risk factors for overweight in adolescence. Archive Pediatrics and Adolescents Medicine, 158, 385-90
Pearson, N., Biddle, S.J., & Gorley, T. (2008). Family correlates of fruit and vegetable consumption in children and adolescents: A systematic review. Public Health and Nutrition, 12, 267-283.
Pott, W., Albayrak, Ö., Hebebrand, J., Pauli-Pott, U. (2009). Treating Childhood Obesity: Family Background Variables and the Child’s Success in a Weight-Control Intervention. International Journal of Eating Disorders, 42, 284–289.
Powell L.M., Szczypka G., & Chaloupka F.J. (2007). Exposure to food advertising on television among US children. Archive of Pediatric and Adolescent Medicine,161 ,553–560.
Rasmussen F., Johansson M., Hansen HO (1999) Trends in overweight and obesity among 18-year-old males in Sweden between 1971 and 1995. Actademic Paediatrics, 88, 431—7.
Ruesten, A., Steffen, A., Floegel, A., Daphne .L. van der A., Masala, G., Tjønneland, A., Halkjaer, J., Palli, D., Wareham, N.J., Loos, R.J.F., Sørenson, A., Boeing, H. (2011). Trend in Obesity Prevalence in European Adult Cohort Populations during Follow-up since 1996 and Their Predictions to 2015. PLoS ONE, 6, e27455. doi:10.1371/journal.pone.0027455
Singh, G. K., Kogan, M. D., Van Dyck, P. C., & Siahpush, M. (2008). Racial/ethnic, socioeconomic, and behavioral determinants of childhood and adolescent obesity in the United States: analyzing independent and joint associations. Annals of Epidemiology, 18, 682-695.
Stamatakis, E., Zaninotto, P., Falaschetti, E., Mindell, J., Head, J. (2010). Time trends in childhood and adolescent obesity in England from 1995 to 2007 and projections of prevalence to 2015. Journal of Epidemiology and Community Health, 64, 167-174.
Starfield B, Riley AW, Witt WP, et al. (2002) Social class gradients in health during adolescence. Journal of Epidemiology and Community Health. 56, 354-361
Story, M., Neumark-Sztainer, D., & French, S. (2002). Individual and environmental influences on adolescent eating behaviours. Journal of the American Diet Association, 102, 40-51.
Strasburger, V.C. (2011). Children, Adolescents, Obesity and the Media. Pediatrics, 128, 201-208.
Sung, J., Lee, K., Song, Y., Lee, M.K., & Lee, D. (2010) Heritability of eating behaviour assessed using the DEBQ (Dutch Eating Behavior Questionnaire) and weight related traits: the healthy twin study. Obesity, 18, 1000-1005.
Timlin, M.T., Pereira, M.A., Story, M., Neumark-Sztainer, D. (2008) Breakfast Eating and Weight Change in a 5-Year Prospective Analysis of Adolescents: Project EAT (Eating Among Teens). Pediatrics, 121, 638-645.
Viner, R.M., Cole, T.J. (2006) Who changes body mass between adolescence and adulthood? Factors predicting change in BMI between 16 year and 30 years in the 1970 British Birth Cohort. International Journal of Obesity. 30, 1368–1374.
Wang Y, Beydoun MA. (2007). The obesity epidemic in the United States—gender, age, socioeconomic, racial/ethnic, and geographic characteristics: a systematic review and meta-regression analysis. Epidemiological Review, 296–28.