NEWS & EVENTS

Predictors of Obesity, Weight Gain, Diet, and Physical Activity Workshop, 2004

August 4 - 5, 2004
Bethesda, MD

Description

Rationale and Objectives for the Workshop

National data have shown continuing increases in overweight among adults and children over the past 30 years (Flegal et al., 2002, Ogden et al., 2002). Obesity among adults 20 years and older has nearly doubled during that time. By 1999-2002, approximately two-thirds (65%) of adults were overweight or obese while nearly one-third (31%) of children ages 6-19 were overweight or at risk of being overweight (Hedley et al., 2004). These unabating increases in overweight and obesity clearly indicate the need for more effective interventions that reach all segments of the population. Efforts are needed to augment those currently in place, and will need to include multi-factorial approaches at the individual, community, and national level.

Recap

Summary of findings

Longitudinal cohort studies have documented increases in weight and obesity prevalence, declines in physical activity, and increases in caloric intake over the past twenty or more years. These trends were found in all segments of the populations examined although some segments were identified as at higher risk than others, particularly African-American women, American Indians, and Mexican-American women. Such data provide an empirical rationale for targeting weight control efforts in specific subgroups.

Diet, Physical Activity, and Sedentary Behaviors as Predictors of Weight Outcomes

Most of the prospective studies measured diet at least once and physical activity multiple times and thus were able to examine relationships of diet and physical activity to weight outcomes. Prospective analyses clearly established a link between these behaviors and adverse weight outcomes (e.g., weight gain, incident overweight or obesity). In addition, they generally identified more consistent and stronger predictors of weight outcomes than cross-sectional analyses.

Several dietary components and behaviors consistently predicted adverse weight outcomes including greater fat intake (total or saturated), higher consumption of fast foods, and lower intake of fiber and/or whole grain foods. Other dietary components found to be predictive of weight gain but not examined in multiple studies included greater trans-fatty acid intake, higher consumption of sodas, a dietary pattern characterized as ?empty calorie?, and snacking between meals.

Weight gain was consistently associated with declines in physical activity and fitness. Obesity was lowest in participants who maintained their physical activity levels over time. Although the type of activity was not routinely examined, one study found that weight training was associated with less subsequent gain in waist circumference among adult men.

Television watching was the most often measured sedentary behavior and was directly related to weight gain in most studies. Television watching was inversely associated with parental income and education in white but not black girls. In young adults, television watching was positively associated with smoking, alcohol consumption, low physical activity, hostility, and depression but inversely associated with age, education, income, and employment in cross-sectional analyses. One study that examined other sedentary behaviors found that adult women with more frequent television watching had the highest risk of developing obesity.

Other Predictors of Weight Outcomes

Initial weight at study entry was an important predictor of later weight gain or status in almost all studies--a finding that highlights the importance of preventing weight gain as early in life as possible. Data from white and black girls suggested that weight gain markedly increases during adolescence. In a study of young adults in their twenties, many were already overweight at study entry. Taken together these findings strongly indicate that prevention efforts must begin during childhood.

White girls were less likely to be satisfied with physical appearance and social acceptance with increasing adiposity, whereas black girls were more satisfied and felt socially accepted even with increasing adiposity. Black girls were more likely to engage in eating practices that could lead to overweight, such as eating big helpings and eating in the bedroom.

Among Latino and Asian adolescents, overweight was more prevalent for those who speak English at home. First generation immigrant adolescents consumed more fruit and less fast food than their US?born counterparts. Among Mexican-American adult men but not women, greater education, income, and functional integration into the broader society (a measure of acculturation and assimilation) were associated with weight gain. These findings suggest the need for interventions among Latinos and other immigrants that are not only culturally-appropriate but also tailored to acculturation status.

Excess weight gain was higher in women following their first pregnancy but there was no association with further pregnancies. Weight gain was greater if new mothers were already overweight at study entry. Additionally, becoming a parent was associated with greater declines in physical activity among women, especially white women. These findings suggest that new mothers might be a possible target for intervention. For example, interventions with counseling and/or materials aimed at preventing weight gain and increasing physical activity could be provided to new mothers attending healthy baby clinics or pediatric visits.

A consistent but paradoxical finding in both cohort and intervention studies was that more frequent attempts to lose weight through dieting (either on their own or through a formal weight loss program) were associated with adverse weight outcomes. Because this finding may be confounded by pre-existing overweight in cohort studies, further research should attempt to clarify the temporal relationship. As a first step, further data analyses should stratify on overweight status at baseline.

Although somewhat inconsistent across studies, participants with better perceived health status were more likely to lose weight than those with poorer perceived health. Among persons who successfully lost weight, those who reported a medical condition were more likely to maintain their weight loss after 1-2 years. Findings related to perceived stress and weight outcomes were also inconsistent but suggest that participants with higher baseline stress levels gained more weight.

Few cohort studies have examined the influence of environmental factors on diet behaviors, physical activity, and weight outcomes. However, plans were presented for research underway in several cohort studies that will link physical environment measures to participant's residential addresses at multiple study visits using Geographic Information Systems (GIS). Such data will allow individual-level, longitudinal analyses that examine the impact of environmental shifts on physical activity, diet behaviors, and weight outcomes. Environmental factors include community design; transportation; availability of restaurants, grocery stores, and physical activity facilities; and socio-economic factors such as crime and community demographics. These studies should improve our understanding of the role of the physical environment in the development of the obesity epidemic as well as yield testable hypotheses for intervention studies.

Predictors of Dietary Behavior and Physical Activity

Higher education, of either the participant or participant's parents, was consistently associated with healthier dietary outcomes. Initial dietary intake was highly correlated with intake 7 years later in young adults. Poorer diets among adults were associated with living in lower income neighborhoods. For African American adults, having at least one large supermarket nearby rather than small grocers or convenience stores was associated with a healthier diet.

Substantial declines in physical activity occurred during adolescence in girls and were greater in black girls than in white girls. Declines in physical activity in this age group were associated with lower levels of parental education, more cigarette smoking, higher BMI, and pregnancy in white and black girls although not consistently in both race groups. Change in fitness in young adults was related to 7-year change in physical activity and BMI but not baseline levels of these variables. Smoking status at baseline and younger age were related to declines in fitness yet education was not.

Higher compared to lower socio-economic neighborhoods had more varied physical activity resources that might influence activity in adolescents, such as number of weekly physical education classes and availability of community recreation centers.

Predictors of Weight Loss in Trials or Weight Regain

Adults who successfully maintained a weight loss of greater than or equal to 30 lbs were followed for 1-2 years. Those who were able to avoid regaining weight were characterized by less depression, and more years at maintaining weight loss. Weight regain was more likely in persons with decreased dietary restraint, increased susceptibility to overeating, small relapses, and inconsistent dietary habits (e.g., weekdays vs. weekends, holidays vs. non-holidays).

In trials with weight loss interventions, participants with more confidence in their dietary choices, especially with respect to fat intake, and who showed greater and improved dietary restraint were more likely to lose weight or have greater weight loss. Thus new skills for coping with an environment that has increasingly more convenient ready-to-eat food with larger portions at lower cost may be critical in weight loss and prevention of weight gain. Intervention studies also showed that reduced consumption of high-fat foods and increased consumption of fruits and vegetables were associated with greater weight loss. Increased exercise and support from friends regarding exercise were also predictive of greater weight loss. Improvements in mental and physical health scores were predictive of greater weight loss. Self-monitoring of weight was also associated with greater weight loss. The effects of previous weight loss attempts on current weight loss were counterintuitive. More frequent previous attempts were associated with less weight loss, suggesting that frequent failed attempts may impede future success at weight loss.

Methodological Issues

The strengths and limitations of several statistical approaches to modeling predictors, mediators, and moderators were discussed. One approach, multilevel modeling, has the key advantage of simultaneously examining the effects of variables at both the individual and group level, as well as possible cross-level interactions. Data from longitudinal studies as well as those from trials with weight loss interventions could be used to test specific pathways in behavioral change models using this method.

Translation of Research to Medical Care Settings

Several trials involving weight loss interventions have developed successful strategies that are multifaceted, intensive, and costly. However, concerns have been raised about implementing such programs in medical care settings given the seemingly high costs. One approach presented at the workshop is to use a stepped care approach to behavioral treatment in medical care settings, with screening and advice for all patients, less intensive programs for moderate risk patients, and more intensive treatment programs for high risk patients. Some studies have shown that changes in diet or weight resulted from relatively few intervention contacts; these studies could be used as models for less intensive programs. Electronic medical records could incorporate prompts for physician advice and could also be used to evaluate screening and treatment programs.

Implications for Further Research and Prevention Efforts

Workshop participants noted that the availability of obesity-related predictor data was limited in some cohort studies largely because these studies were designed to examine cardiovascular disease outcomes and a variety of risk factors, not just obesity. If available in cohort studies, psychosocial, neighborhood environment, and social support questionnaires tend to be more general than those used in recent lifestyle intervention studies aimed at weight loss. In the latter studies, such questionnaires specifically target diet, physical activity, and sedentary behaviors as they related to weight outcomes. Thus design differences may account for the relatively few analyses of correlates and predictors of obesity-related behaviors such as diet and physical activity beyond basic demographic factors in most cohort studies. One consequence is that even if data are available in one cohort study, the findings generally can not be confirmed using other cohorts.

On the other hand, some longitudinal studies have begun to retrospectively add data on the physical environment. These data could not only improve our understanding of the influences of the physical environment on diet and physical activity but also may be used to document the development of the obesity epidemic. The addition of weight behavior questionnaires that allow examination of individual and more proximal social influences would greatly increase the value of these data to help us understand how our current environment interacts with individual and social factors to influence weight outcomes.

The longitudinal nature of these studies was recognized as an extremely valuable feature that should be exploited to its fullest potential by utilizing all available data points whenever possible. Data from these studies as well as those from trials with weight loss interventions could be used to test specific pathways in behavioral change models. These analyses could lead to hypotheses to be examined further in behaviorally-focused obesity research, either observational or clinical trials.

Finally, the workshop offered the opportunity for dialogue between investigators involved in longitudinal and intervention studies. Efforts to encourage further opportunities for collaboration and sharing of questionnaires should continue to be facilitated through the NHLBI and other websites (see links below).

Recommendations

Analyses of Existing Data From Longitudinal Studies

  • Examine predictors of long-term weight loss and maintenance taking into account involuntary vs. voluntary weight loss and baseline weight status. appropriate to overcome the small number of participants in these categories.
  • Use detailed dietary data to identify and develop simpler dietary measures, such as key marker foods and behaviors, which strongly predict healthy or unhealthy dietary patterns or weight outcomes.
  • Examine sedentary behaviors and their changes over time as predictors of weight and whether these associations are mediated through physical activity and diet.
  • Analyze changes in diet and physical activity jointly to examine their combined effects on energy balance/imbalance and weight outcomes.
  • Examine the effects on weight outcomes of portion size and their changes over time.
  • Examine predictors of diet patterns and behaviors and physical activity, particularly the relative contributions and potential interactions among personal, social, and physical environmental factors.
  • Exploit the longitudinal study design by utilizing all available time points.
  • Test theory-based behavioral change models, including the use of multi-level analyses that examine predictors, mediators, and moderators of outcomes.
  • Analyze change in predictor variables as they relate to change in weight outcomes and, if possible, model and test specific pathways.

Measures To Add To Longitudinal Studies

  • Objective measures of physical activity and fitness (e.g., accelerometers, treadmill tests), which use a protocol common to other studies
  • Questionnaires which measure psychosocial, neighborhood environment, social support, and other factors which specifically target diet, physical activity, and sedentary behaviors as they relate to weight outcomes (e.g., social support specific to weight loss attempts, dietary restraint, frequency of self-weighing, and weight loss history)
  • Questionnaires and/or community-level environmental data (e.g., through GIS linkage) to assess the impact of changes in the physical environment on changes in diet, physical activity, and weight
  • Short questionnaires that identify key indicator foods (e.g., soda) and diet behaviors (e.g. meal frequency) at every data collection period
  • Weight and dieting history questionnaires
  • Questions about portion sizes usually consumed
  • Questions about the role of family influences on diet and physical activity

Measures To Add To Intervention Studies

  • Objective measures of physical activity and/or fitness
  • Indicators of the physical environment, either self-perceived or GIS-based with associated data
  • Target the following behaviors in weight loss interventions: reduced consumption of fast foods and sodas, increased consumption of low fat and whole grain foods, decreased television viewing

Methodological Research To Develop New Measures

  • Develop short questionnaires containing key indicator foods and diet behaviors that strongly predict healthy or unhealthy dietary intake or patterns.
  • Develop questions related to portion size as an indicator of dietary behavior.
  • Develop easier methods or more focused questionnaires for self?monitoring of important diet behaviors, physical activity, and weight.
  • Develop questions assessing the role of media and advertising on dietary choices.
  • Use a qualitative approach to adapt and develop questions that are culturally-appropriate.

Further Studies Needed

  • Longitudinal studies are needed that focus on determinants of weight outcomes and related lifestyle behaviors. These studies should utilize contemporary questionnaires that measure psychosocial, neighborhood environment, social support, and other factors which specifically target diet, physical activity, and sedentary behaviors as they relate to weight outcomes. Study sites should be selected in areas with good, existing environmental databases or, if unavailable, in areas where collection of local physical environment data is feasible.
  • Longitudinal studies in young children are needed to reduce the potential confounding of behavioral influences by pre-existing overweight.
  • Intervention studies are needed in new mothers aimed at returning to a healthy weight after pregnancy.
  • Studies are needed in immigrants to better understand the process and impact of acculturation and assimilation on weight outcomes

Links to Study Resources on the Web

Citations

Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999-2000. JAMA. 2002 Oct 9;288(14)1723?7.

Ogden CL, Flegal KM, Carroll MD, Johnson CL. Prevalence and trends in overweight among US children and adolescents, 1999-2000. JAMA. 2002 Oct 9;288(14)1728?32.

Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR, Flegal KM.  Prevalence of overweight and obesity among US children, adolescents, and adults, 1999-2002. JAMA. 2004 Jun 16;291(23)2847?50.

Sallis JF, Owen N, Fotheringham MJ. Behavioral epidemiology: a systematic framework to classify phases of research on health promotion and disease prevention. Ann Behav Med. 2000;22(4)294-8.

An Ecological Model of Diet, Physical Activity, and Obesity

There are a variety of influences that have an effect on behaviors which, in turn, affect energy balance, and may alter health outcomes.

The potential influences are:

  • Biological & Demographic (e.g. Age, sex, race/ethnicity, SES, genes)
  • Psychological (e.g. Beliefs, preferences, emotions, self-efficacy, intentions, pros, cons, behavior change skills, body image, motivation, knowledge)
  • Social/Cultural (e.g. Social support, modeling, family factors, social norms, cultural beliefs, acculturation)
  • Organizational (e.g. Practices, programs, norms, & policies in schools, worksite, health care settings, businesses, community organizations)
  • Physical Environment (e.g. Access to & quality of foods, recreational facilities, cars, sedentary entertainment; urban design, transportation infrastructure, information environment)
  • Policies/Incentives (e.g. Cost of foods, physical activities, & sedentary behaviors; incentives for behaviors; regulation of environments)

The behaviors affecting energy balance:

  • Eating (e.g. Dietary patterns, nutrient intake)
  • Sedentary Behaviors (e.g. TV, computer use, driving)
  • Physical Activity (e.g. Recreation, transportation, occupation, domestic)

The behaviors affect:

  • Body Weight, Fat, & Distribution

which, in turn, affect the following health outcomes:

  • Risk Factors, CVD, Diabetes, Cancers

Speaker Roster

Phillip J. Brantley, Ph.D. 
Professor and Director 
Division of Education 
Chief, Behavioral Medicine Laboratory 
Pennington Biomedical Research Center 
Louisiana State University 
6400 Perkins Road 
Baton Rouge, LA 70808-4124 
Phone: (225) 763-3046 
Fax:(225) 763-3045 
E-Mail: Brantley

Diane J. Catellier, Dr.P.H. 
University of North Carolina at Chapel Hill 
Department of Biostatistics 
Bank of America Center 
137 E. Franklin Street, Suite 400 
CB #8030, Room 4 
Chapel Hill, NC 27514-4145 
Phone: (919) 966-1895 
Fax: (919) 962-3265 
E-Mail: Catellier

Linda Delahanty, M.S., R.D. 
Chief Dietitian and Director of 
Nutrition and Behavioral Research 
Massachusetts General Hospital 
Diabetes Center 
50 Staniford Street, Suite 340 
Boston, MA 02114 
Phone: (617) 724-9727 
Fax: (617) 726-1871 
E-Mail: Delahanty

Karen Glanz, Ph.D., M.P.H. 
Professor, Behavioral Sciences and 
Health Education 
Georgia Cancer Coalition 
Distinguished Research Scholar 
Rollins School of Public Health 
Emory University 
1518 Clifton Road, N.E., Room 526 
Atlanta, GA 30322 
Phone: (404) 727-7536 
Fax: (404) 727-1369 
E-Mail: Glanz

Penny Gordon-Larsen, Ph.D. 
Assistant Professor of Nutrition 
School of Public Health 
Carolina Population Center (CPC) 
CB # 8120 University Square 
University of North Carolina at Chapel Hill 
Chapel Hill, NC 27516-3997 
CPC Phone: (919) 843-9966 
Nutrition Phone:(919) 843-3640 
Fax: (919) 966-9159 
E-Mail: Gordon-Larsen

Helen P. Hazuda, Ph.D. 
Professor of Medicine 
Department of Medicine 
Division of Clinical Epidemiology 
University of Texas at San Antonio 
Health Science Center 
7703 Floyd Curl Drive - Mail Code 7873 
San Antonio, TX 78229-3900 
Phone: 210-567-6678 
Fax: 210-567-1990 
E-Mail: Hazuda

Barbara Howard, Ph.D. 
President 
MedStar Research Institute 
6495 New Hampshire Avenue 
Suite 201 
Hyattsville, MD 20783 
Phone: (301) 560-7302 
Fax: (301) 560-7307 
E-Mail: Howard

Sue Y.S. Kimm, M.D., M.P.H., S.M. 
Professor 
Division of Epidemiology, Department of Internal Medicine 
University of New Mexico Health Sciences Center 
14 Brahma Lane 
Santa Fe, NM 87506 
Phone: 505-820-2611 
412-648-1968 
Fax: 412-383-8598 
E-mail: Kimm

Cora Elizabeth Lewis, M.D., M.S.P.H 
Professor of Medicine 
Division of Preventive Medicine 
University of Alabama at Birmingham 
1717 - 11th Avenue South, Suite 14 
Birmingham, AL 35205 
Phone: (205) 934-6383 
Fax: (205) 934-7959 
E-Mail: Lewis

Barbara E. Millen, D.P.H., R.D., FADA 
Professor of Social & Behavioral Sciences 
Professor of Socio-Medical Sciences and Community Medicine 
Boston University 
Schools of Medicine Public Health 
Chair, MA and PhD Programs in Medical Nutrition Sciences 
Director of Nutrition Research 
The Framingham Study 
715 Albany Street, T2W 
Boston, MA 02118 
Phone: (617) 638-5160 
Fax: (617) 638-4433 
E-Mail: Millen

Nico Pronk, Ph.D., FACSM FAWHP 
Vice President 
Center for Health Promotion 
Research Investigator 
Health Partners Research Foundation 
8100 ? 34th Avenue South 
P. O. Box 1309 
Minneapolis, MN 55440-1309 
Phone: (952) 967-6729 
Fax: (952) 967-6710 
E-Mail: Pronk

Eric B. Rimm, Sc.D. 
Associate Professor of Epidemiology and Nutrition 
Harvard School of Public Health 
677 Huntington Avenue 
Boston, MA 02115 
Phone: (617) 432-1843 
Fax: (617) 432-2435 
E-Mail: Rimm

James F. Sallis, Jr., Ph.D. 
San Diego State University Foundation 
5250 Campanlie Drive 
Phone: (619) 260-5535 
Fax: 619) 260-1510 
E-Mail: Sallis

June Stevens, M.S., Ph.D. 
Professor of Nutrition and Epidemiology 
Department of Nutrition 
University of North Carolina 
CB 7461 
Chapel Hill, NC 27599 
Phone: (919) 966-1065 
FAX: (919) 962-3265 
E-mail: J. Stevens

Victor J. Stevens, Ph.D. 
Assistant Director for Epidemiology & Disease Prevention 
Kaiser Permanente Center for Health Research 
3800 North Interstate Avenue 
Portland, OR 97227 
Phone: (503) 335-6751 
Fax: (503) 335-2428 
E-Mail: V. Stevens

Stewart G. Trost, Ph.D. 
Assistant Professor 
Department of Kinesiology and 
Community Health Institute 
Kansas State University 
1A Natatorium 
Manhattan, KS 66506-0302 
Phone: (785) 532-3365 
Fax: (785) 532-6486 
E-Mail: Trost

Rena R. Wing, Ph.D. 
The Miriam Hospital/Brown Medical School 
Weight Control & Diabetes Research Center 
196 Richmond Street 
Providence, RI 02903 
Phone: 401-793-8959 
Fax: 401-793-8944 
Email: Wing

Agenda

8:30 AM
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Registration and light refreshments

9:00 AM
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Welcome and Introductions

Cay Loria
Denise Simons-Morton

9:15 AM
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Goals of the workshop

Cora E. Lewis

9:25 AM
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An Ecological Model of Diet, Physical Activity, and Obesity

James Sallis

9:45 AM
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Diet and Physical Activity as Predictors of Weight Outcomes
What We Know about Obesity Development During Adolescence: Findings from the NHLBI Growth and Health Study - The Role of Diet and Activity in Obesity Development During Adolescence

Moderator: Karen Glanz

Sue Y. S. Kimm, M.D., M.P.H., Nancy W. Glynn, Ph.D.
University of Pittsburgh

The Role of Diet and Activity in Obesity Development During Adolescence

The NHLBI Growth and Health Study (NGHS) is a longitudinal study designed to assess factors associated with progressive weight gain and development of obesity in black and white girls during the transition between childhood and young adulthood. NGHS was initiated in 1985 because of the notion that the higher mortality rate from cardiovascular disease was in part explicated by the high prevalence of obesity among African American women. Cross-sectional data indicated that prepubertal black girls were leaner than white girls, but it was during adolescence when the racial divergence in obesity development took place. Thus, the NGHS is a 10-year cohort study of 1213 black and 1166 white girls, aged 9 or 10 at entry, who were followed annually until ages 18 or 19. The follow up rate was 91% for black girls and 88% for white girls.

Racial divergence in adiposity

Puberty was associated with a significant gain in adiposity for both racial groups with the largest gain in adiposity seen at the time of pubescence for both groups, an approximate increase of 8.0 mm of SSF for white girls and 10.8 mm for black girls. At age 9, there were no racial differences in adiposity as measured by the sum of skinfolds (SSF) at the triceps, subscapular and suprailiac sites. Longitudinal regression analysis showed that adiposity for black girls became significantly greater at age 12, after adjusting for pubertal maturation stages. Although the effect of puberty on the gain in adiposity was similar for both races, for each chronological age, there was a greater accrual of adiposity in black girls, because they matured earlier than white girls.

Effect of Dietary Intake and Patterns

Energy intake was significantly higher for black girls at every age, except at age 9 years, with racial differences in the average daily energy intake ranging from 127 kcal at age 10 to 216 kcal at age 17.

At baseline (ages 9 or 10), there was no significant relationship between daily energy intake and adiposity or BMI. However, there was a significant (p<0.01) direct relationship between saturated fat intake and BMI for black girls. For white girls, there was a significant (p=0.002) direct relationship between total fat intake and BMI.

Longitudinal regression analysis revealed that daily caloric intake was inversely associated with adiposity.

Effect of Levels of Physical Activity

Levels of habitual activity (HAQ) were significantly lower in black girls even at ages 9 or 10 (p=0.008). Scores for HAQ declined steadily from baseline until the end of the study, ages 18 or 19. The decline was more precipitous between ages 9 or 10 to 15 or 16. The overall decline in HAQ was 83% for the cohort, but there was a significant racial difference with the decline greater in black girls. Their median HAQ score declined by 100% while that for white girls declined by 64%. The median score for the daily activity scores declined, but not as precipitously as that of HAQ during the same time by 35% for the cohort. At baseline, neither the AD nor HAQ scores were significantly related to adiposity. However, television watching was significantly associated with adiposity in 9 or 10 year old girls when adjusted for energy intake. When the multivariate model included parental education, household income, the number of parents in the household and caloric intake as adjustment variables, television watching was a significant risk factor for obesity only for black and not for white girls. Longitudinal regression analysis revealed a significant (p<0.0001) relationship between BMI and the decline in HAQ after adjusting for race, energy intake, cigarette smoking, age at menarche and childbirth.

References

  1. The National Heart, Lung and Blood Institute Growth and Health Study Research Group. Obesity and cardiovascular disease risk factors in black and white girls: The NHLBI Growth and Health Study. Am J Public Health 1992; 82:1613-1620.
  2. Kimm SYS, Barton BA, Obarzanek E, et al. Racial divergence in adiposity during adolescence: the NHLBI Growth and Health Study. Pediatrics 2001:107 http://www.pediatrics.org/cgi/content/full/107/3/e34.
  3. Kimm SYS, Barton BA, Obarzanek E, et al. Obesity development during adolescence in a biracial cohort: The NHLBI Growth and Health Study. Pediatrics 2002:110 http://www.pediatrics.org/cgi/content/full/110/5/e54.
  4. Kronsberg SS, Obarzanek E, Affenito SG, et al. Macronutrient intake of black and white adolescent girls over ten years: The NHLBI Growth and Health Study. J Am Diet Assoc 2003; 103:852-60.
  5. Obarzanek E, Schreiber GB, Crawford PB, et al. Energy intake and physical activity in relation to indexes of body fat: the National Heart, Lung, and Blood Institute Growth and Health Study. Am J Clin Nutr 1994; 60:15-22.
  6. Kimm SYS, Glynn NW, Kriska AM, et al. Longitudinal changes in physical activity in a biracial cohort during adolescence. Med Sci Sports Exerc 2000; 32:1445-1454.
  7. Kimm SYS, Glynn NW, Kriska AM, et al. Decline in physical activity in black girls and white girls during adolescence. N Engl J Med 2002; 347:718-24.
  8. Kimm SYS, Obarzanek E, Barton BA, et al. Race, socioeconomic status, and obesity in 9- to 10-year-old girls: The NHLBI Growth and Health Study. Ann Epidemiol 1996; 6:266-75.
  9. Kimm SYS, Glynn NW, Barton BA, et al. The association of physical activity with obesity development during adolescence: NHLBI Growth and Health Study. Circulation 2001; 103:1345.
  10. Kimm SYS, Obarzanek EO. Childhood obesity: A new pandemic of the new millennium. Pediatrics 2002; 110:1003-1007.
  11. Kimm SYS, Glynn NW, Aston CE, et al. Effects of race, cigarette smoking, and use of contraceptive medications on resting energy expenditure in young women. Am J Epidemiol 2001; 154:718-24.
  12. Kimm SYS, Glynn NW, Aston CE, et al. Racial differences in the relation between uncoupling protein genes and resting energy expenditure. Am J Clin Nutr 2002;75:714-9.
  13. Kimm SYS, Glynn NW, Obarzanek E et al. Demographic and psychosocial correlates of misreporting of energy intake in a biracial cohort of young women. Am J Clin Nutr 2004 (under revision)

10:00 AM
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Diet and Physical Activity as Predictors of Weight Outcomes
Predictors of Weight Outcomes, Dietary Behaviors, and Physical Activity in Coronary Artery Risk Development in Young Adults Study (CARDIA)

Moderator: Karen Glanz

Cora E. Lewis, MD, MSPH for the CARDIA Investigators
University of Alabama at Birmingham

CARDIA study is a prospective, epidemiologic investigation of the determinants and evolution of cardiovascular risk factors among 5,115 African American and white young adults 18-30 years of age at baseline in 1985-86. Participants were recruited from the populations of four geographic locations (Birmingham, AL; Chicago, IL; Minneapolis, MN; and Oakland, CA). The study population was approximately balanced according to sex (54% women), ethnicity (52% African American), and education (40 % with less than equal to 12 years of education) at each center. Additional examinations were undertaken at years 2 (1987-88), 5 (1990-91), 7 (1992-93), 10 (1995-96), and 15 (2000-01). Overall retention rates for follow-up examinations were: 90 percent, 86 percent, 81, 79 percent, and 74 percent of surviving participants, respectively (approximately 2.5 percent were deceased as of the year 15 examination) over the 15 year follow-up.

Weight and height were measured at each exam using the same equipment and protocols; baseline height was used to determine BMI. A quantitative food frequency questionnaire was interviewer-administered at baseline and year 7, as was a symptom-limited maximum treadmill exercise test. Physical activity, and a variety of behavioral, psychosocial, attitudinal, and socioeconomic factors were assessed by self-report.

As of the 15 year examination, the average participant had gained: 16 kg for BW, 10.1 kg for WW, 14.1 kg for BM and 11.1 kg for WM, and of these groups 48%, 26%, 41%, 29%, respectively, gained greater than equal to 15 kg. Large increases in obesity prevalence had occurred in all groups.

In general, lifestyle variables potentially related to overweight were associated with each other. Frequent fast food intake was related to a higher intake of refined grains and soft drinks and lower intake of fruits and vegetables at baseline. At year 5, hours per day of TV viewing was inversely associated with physical activity, education, income, and with hostility and depression (in whites) scores.

Over the first 7 years of follow-up, overall treadmill duration decreased 10 percent, ranging from a 15 percent decrease in African-American men to a 7 percent decrease in white women. Dietary intake improved in terms of cholesterol and saturated fat intake and Keys score. There was some evidence for tracking for both physical activity and diet with many participants maintaining their relative ranking on these two variables.

Findings of relationships of lifestyle and other factors with weight change vary depending on the time period evaluated and/or the specific dependent variable examined. Between baseline and year 2, weight gain of at least 5% was associated with greater baseline BMI, previous dieting, considering oneself too fat, and the report of previous loss of 10 lb or more, compared to those whose weight remained stable. Over the first 7 years, change in physical fitness explained the greatest amount of the variance in weight change; each 60 second decline in exercise duration was associated with an average weight gain of 2.1 kg in women and 1.5 kg in men. Other factors significantly associated with greater 7-year weight gain in both sexes were ex-smoker status, younger baseline age, greater baseline percent energy from fat, lower baseline fitness, and ethnicity (men).

Over the first 10 years, dietary fiber was inversely related to weight gain in both African Americans and whites. The 10-year incidence of obesity was inversely related to dairy intake in those overweight at baseline but not in those initially normal weight.

Over 15 years, baseline fast food consumption (frequency per week) was independently related to weight increase in both African Americans and whites, and increased fast food consumption from baseline to year 15 was related to weight increase in whites. There was a particularly strong relationship among those least physically active at baseline. When 18 baseline behavioral, psychosocial, attitudinal, and socioeconomic factors were examined for associations with 15-year weight gain of greater than equal to 15 kg, comparing to those participants who maintained a stable weight (within 5 kg of baseline), only 8 variables (age, overweight at baseline, alcohol intake, considering oneself too fat, job demands, report of previous dieting, physical activity and physical fitness) were significant predictors of large weight gain, and none was significant in more than 2 of the 4 race-sex groups. There were no associations for fast food, fat, or carbohydrate intake, or TV watching.

Large increases in weight and obesity have occurred during follow-up in CARDIA. These increases have affected all four major demographic groups. Associations with potential predictors have been inconsistent across analyses.

References

  1. Anderssen N, Jacobs DR, Jr, Sidney S, Bild DE, Sternfeld B, Slattery ML, Hannan P. Change and secular trends in physical activity patterns in young adults: A seven-year longitudinal follow-up in the Coronary Artery Risk Development in Young Adults Study (CARDIA). American Journal of Epidemiology. 1996; 143(4):351-362
  2. Bild DE, Sholinsky P, Smith DE, Lewis CE, Hardin JM, Burke GL. Correlates and predictors of weight loss in young adults: The CARDIA Study. International Journal of Obesity. 1996; 20:47-55
  3. Dunn JE, Liu K, Greenland P, Hilner JE, Jacobs DR, Jr. Seven year tracking of dietary factors in young adults: The CARDIA Study. American Journal of Preventive Medicine. 2000; 18(1):38-45
  4. Lewis CE, McCreath H, West DS, Loria C, Kiefe CI, Hulley SB. Factors associated with 15-year weight gain in a bi-racial cohort of young adults: CARDIA. 43rd Annual Conference on Cardiovascular Disease Epidemiology and Prevention in Association with the Council on Nutrition, Physical Activity and Metabolism, 2003.
  5. Lewis CE, McCreath H, West DE, Loria CE, Kiefe CI, Hulley SB. The obesity epidemic rolls on: 15 years in CARDIA. American Heart Association Meeting, Scientific Sessions 2001.
  6. Lewis CE, Smith DE, Wallace DD, Williams OD, Bild DE, Jacobs DR, Jr. Seven year trends in body weight and associations of weight change with lifestyle and behavioral characteristics in black and white young adults: The CARDIA Study. American Journal of Public Health. 1997; 87:635-642
  7. Loria C, Yan LL, Lewis CE, Hilner JE, Liu K. Adoption of lower fat diet not associated with obesity rise: the CARDIA study. 43rd Annual Conference on Cardiovascular Disease Epidemiology and Prevention, 2003.
  8. Ludwig DS, Pereira MA, Kroenke CH, Hilner JE, Van Horn L, Slattery ML, Jacobs DR, Jr. Dietary fiber, weight gain and cardiovascular disease risk factors in young adults: The CARDIA Study. Journal of the American Medical Association. 1999; 282(16):1539-1546
  9. Pereira M, Jacobs DR, Jr, Van Horn L, Hilner JE, Slattery M, Kartashov AI, Ludwig DS. Dairy intake, insulin resistance, and cardiovascular disease risk factors in young adults. Journal of the American Medical Association. 2002; 287(16): 2081-2089
  10. Pereira et al. Fast food habits, weight gain, and insulin resistance in a 15-year prospective analysis of the CARDIA study. Lancet, submitted
  11. Pereira MA, Kartashov AI, Ebbeling CE, Hilner JE, Van Horn L, Slattery ML, Jacobs,DR,Jr., Ludwig DS. Fast food consumption and the incidence of obesity and glucose intolerance in young black and white adults: The CARDIA Study. 43rd Annual Conference on Cardiovascular Disease Epidemiology and Prevention in Association with the Council on Nutrition, Physical Activity and Metabolism, 2003.
  12. Sidney S, Sternfeld B, Haskell WL, Jacobs DR, Jr, Chesney MA, Hulley SB. Television viewing and cardiovascular risk factors in young adults: The CARDIA Study. Annals of Epidemiology. 1996; 6:154-159
  13. Sidney S, Sternfeld B, Haskell WL, Quesenberry CP, Jr, Crow RS, Thomas RJ. Seven-year change in treadmill test performance in young adults in the CARDIA Study. Medicine and Science in Sports and Exercise. 1998; 30:427-433

10:25 AM
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Break

10:40 AM
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Diet and Physical Activity as Predictors of Weight Outcomes
The Framingham Nutrition Studies Insights into Weight History, Dietary Patterns, Obesity Prevention, and Risk Reduction

Barbara E. Millen, Michael Pencina, Ruth Kimokoti, Ralph B. D'Agostino 
Boston University School of Medicine

The Framingham Nutrition Studies (FNS) were initiated in 1984-88 with a comprehensive assessment of population nutritional risk at the third examination of the Framingham Offspring-Spouse (FOS) cohort (n=5135 adult men and women). The major goals of FNS were to develop innovative nutritional risk assessment methods, to quantify trends in food and nutrient intake in relation to expert health and nutrition guidelines, to examine relationships between diet and CVD-related disease risk and outcomes (controlling for genetic, biological, and behavioral factors), and to identify opportunities for preventive nutrition interventions.

FNS utilized historical nutrition data sets from the original Framingham cohort (n=5209) to initiate studies of population dietary trends and to explore selected diet:disease hypotheses. The experiences of the FOS cohort members, 20-64 years of age at baseline and who participated in the FNS, were examined to assess the adult cohort?s experience with overweight and obesity over 28 years of follow-up (1971+). At baseline, these FOS men were more overweight (52%) and obese (17%) than women (20% and 9%, respectively), indicating an earlier onset of excess weight in men. Over follow-up, FOS men gained a mean level of 10-16 pounds and women added 8-21 pounds of body weight. Nearly 60% of men and 50% of women who were normal weight at baseline became overweight or obese. In addition, one-third of overweight men and half of overweight women became obese over follow-up. Over 28 years, only 14-15% of men or women remained ?weight stable? (within 5 pounds of baseline weight). Weight gain persisted into the 5th decade in FOS men and the 6th decade in FOS women. At 28 years, 18.5% of FOS men and 38% of FOS women were within normal weight range; 48% of men and 35% of women were overweight; and 33% of FOS men and 27% of women were obese.

In addition to exploring the FOS cohort experience with the development of overweight and obesity, relationships were examined between nutrient intake, weight change, and the development of overweight and obesity or obesity-related outcomes. These models suggested the potential role of the following nutrients in the development of excess body weight: higher energy, total and saturated fat, and alcohol intakes and lower total carbohydrate intakes. A composite nutrient risk score (based upon the intakes of 19 protective and disease risk-related nutrients) was predictive of weight change over time.

The Framingham Nutrition Studies also applied cluster analysis to food frequency data collected at FOS cohort Exam 3 (1984-88), five non-overlapping and unique dietary patterns of FOS women were identified (Heart Healthy, Lighter Eating, Wine and Moderate Eating, High Fat, and Empty Calories) and five in FOS men (Transition to Heart Healthy, Higher Starch, Average Male, Lower Variety, and Empty Calories). The dietary patterns of both men and women varied in terms of food and nutrient intake, levels of compliance with expert nutrition guidelines, and the composite nutritional risk score. The biological risk factor profile of FOS men and women varied by dietary pattern at baseline and follow-up; prevalence rates of overweight and obesity were particularly high. The nutritional risk profile associated with the dietary patterns of both men and women was stable over eight years of follow-up. Weight gain over follow-up in FOS female subjects who were at or near ideal body weight at Exam 3 baseline varied by dietary pattern. In multivariate analyses controlling for genetic, biological, and behavioral risk factors, FOS male and female subjects who had differing dietary patterns and were free of the Metabolic Syndrome (MetS) risk factors, CHD, and diabetes at baseline had varying risks for the development of MetS outcomes at follow-up. Discriminant analysis was employed to cross-validate the clustering methodology and confirmed substantial agreement between the predicted and actual dietary pattern classification in men and women.

The evidence that the dietary patterns of adult males and females were associated with varying nutritional risk, levels of compliance with expert nutrition guidelines, biological risk factor profiles, and an array of health outcomes (including overweight, obesity, and CVD and MetS risk) indicates their importance in epidemiological investigations and suggests their potential for application to the design of clinical nutrition interventions. The identification of unique dietary patterns of adult men and women provides a mechanism for evaluating key aspects of compliance and non-compliance with expert nutrition guidelines and for targeting specific foods and nutrients in behavioral interventions aimed at obesity risk reduction as well as other health outcomes. Furthermore, the dietary pattern approach provides a framework for considering the unique nutritional and health risk profile of population subgroups while formulating nutrition intervention strategies. Dietary patterns also enable the articulation of specific health and nutrition messages for use in targeting population subgroups with direct communications in health promotion programs and campaigns.

References

  1. Posner BM, Cobb JL, Belanger AJ, D'Agostino RB, Stokes III J: Dietary Lipid Predictors of Coronary Heart Disease in Men: The Framingham Study. Arch Intern Med 1991;151:1181-7
  2. Sonnenberg LM, Posner BM, Belanger AJ, Cupples LA, D'Agostino RB. Dietary Predictors of Serum Cholesterol in Men: The Framingham Cohort Population. J Clin Epidemiol 1992;45:413-8.
  3. Posner BM, Martin-Munley SS, Smigelski C, Cupples LA, Cobb JL, Schaefer E Miller DR, D'Agostino RB. Comparison of Techniques for estimating Nutrient Intake: The Framingham Study. Epidemiology 1992;3:171-7.
  4. Posner BM, Cupples LA, Miller DR, Cobb JL, Lutz KJ, D'Agostino RB. Diet, Menopause, and Serum Cholesterol Levels in Women: The Framingham Study. Am Heart J. 1993;125(2 Pt 1):483-9.
  5. Posner BM, Cupples LA, Gagnon D, Wilson PW, Chetwynd K, Felix D. Healthy People 2000 The Rationale and Potential Efficacy of Preventive Nutrition in Heart Disease: The Framingham Offspring-Spouse Study. Arch Intern Med 1993;153:1549-56.
  6. Posner BM, Cupples LA, Franz MM, Gagnon DR. Diet and Heart Disease Risk Factors in Adult American Men and Women: The Framingham Offspring-Spouse Nutrition Studies. Int J Epidemiol 1993;22:1014-25.
  7. Posner BM, Franz MM, Quatromoni PA, Gagnon DR, Sytkowski PA, D'Agostino RB, Cupples LA. Secular Trends in Diet and Risk Factors for Cardiovascular Disease: The Framingham Study. J Am Diet Assoc 1995;95(2):171-9.
  8. Gillman MW, Cupples LA, Gagnon DR, Posner BM, Ellison RC, Castelli WP, Wolf PA. Protective Effects of Fruits and Vegetables on Development of Stroke in Men. JAMA 1995;273:1113-1117.
  9. Millen BE, Franz MM, Quatromoni PA, Gagnon DR, Sonnenberg LM, Schaefer EJ, Cupples LA. Diet and Plasma Lipids in Women. I. Macronutrients and Plasma Total and Low-density Lipoprotein Cholesterol in Women: The Framingham Nutrition Studies. J Clin Epidemiol 1996;49:657-63.
  10. Sonnenberg LM, Quatromoni PA, Gagnon DR, Cupples LA, Franz MM, Ordovas JM, Wilson PWF, Schaefer EJ, Millen BE. Diet and Plasma Lipids in Women. II. Macronutrients and Plasma Triglycerides, High-Density Lipoprotein, and the Ratio of Total to High-Density Lipoprotein Cholesterol in Women: The Framingham Nutrition Studies. J Clin Epidemiol 1996;49:665-72.
  11. Millen BE, Quatromoni PA, Gagnon DR, Cupples LA, Franz MM, D'Agostino RB. Dietary Patterns of Men and Women Suggest Targets for Health Promotion: The Framingham Nutrition Studies. Am J Health Promotion 1996;11:42-52.
  12. Millen BE, Quatromoni PA, Franz MM, Epstein BE, Cupples LA, Copenhafer DL. Population Nutrient Intake Approaches Dietary Recommendations: 1991 to 1995 Framingham Nutrition Studies. J Am Diet Assoc 1997;97:742-9.
  13. Gillman MW, Cupples LA, Gagnon DR, Millen BE, Ellison RC, Castelli WP. Margarine Intake and Subsequent Coronary Heart Disease in Men. Epidemiology 1997;8:144-9.
  14. Gillman MW, Cupples LA, Millen BE, Ellison RC, Wolf PA. Inverse Association of Dietary Fat With Development of Ischemic Stroke in Men. JAMA 1997 Dec 24-31;278(24):2145-50.
  15. Millen BE, Quatromoni PA, Copenhafer DL, Demissie S, O'Horo CE, D'Agostino RB. Validation of a Dietary Approach Pattern for Evaluating Nutritional Risk: The Framingham Nutrition Studies. J Am Diet Assoc 2001;101:187-94.
  16. Quatromoni PA, Copenhafer DL, Demissie S, D'Agostino RB, O'Horo CE, Nam BE, Millen BE. The Internal Validity of a Dietary Pattern Analysis: The Framingham Nutrition Studies. J Epidemiol Comm Health 2002;56:381-8.
  17. Quatromoni PA, Copenhafer DL, D'Agostino RB, Millen BE. Dietary Patterns Predict the Development of Overweight in Women: The Framingham Nutrition Studies. J Am Diet Assoc 2002;102:1240-6.
  18. Millen BE, Quatromoni PA, Nam BH, O'Horo CE, Polak JF, D'Agostino RB. Dietary Patterns and the Odds of Carotid Atherosclerosis in Women: The Framingham Nutrition Studies. Prev Med 2002 Dec; 35(6):540-7.
  19. Millen BE, Quatromoni PA, Nam BH, O'Horo CE, Polak JF, Wolf PA, D'Agostino RB. Dietary Patterns, Smoking, and Sub-clinical Heart Disease in Women: Opportunities for Primary Prevention from the Framingham Nutrition Studies. J Am Diet Assoc 2004;104:208-214.
  20. Sonnenberg LM, Pencina MJ, Kimokoti RW, Quatromoni PA, Nam BH, D'Agostino RB, Meigs JB, Ordovas JM, Cobain MR, Millen BE. Dietary Patterns of Women and the Metabolic Syndrome in Obese and Non-Obese Women: Opportunities for Preventive Intervention on Obesity from the Framingham Nutrition Studies. Obes Res In press.

11:00 AM
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Diet and Physical Activity as Predictors of Weight Outcomes
Strong Heart Study

Barbara Howard, Ph.D.
MedStar Research Institute

The Strong Heart Study includes longitudinal cohort and family/genetic studies of cardiovascular disease (CVD) among American Indian men and women. This report focuses on longitudinal data from the cohort study, whose population consists of 13 Tribes in three geographic areas (Arizona, Oklahoma and North and South Dakota). Employing standardized methods, the cohort study is designed to estimate CVD mortality and morbidity and the prevalence of known and suspected CVD risk factors and target organ damage among American Indians and to assess the significance of these risk factors over time. It is the largest cohort of individuals with diabetes under continuous CVD surveillance in the U.S. During the 1989?1991 baseline examination, 4,549 tribal members (62% of the total population aged 45-74 years) were examined. A second examination, including 89% of surviving original cohort members, was conducted between 1993 and 1995. A third and final exam in 1998-1999 included 88% of the surviving cohort (3,197 participants).

Obesity is an important health problem in American Indians, with respect to both diabetes and CVD. Using NHLBI guidelines, Mmore than 75% of SHS were overweight at baseline, and 44% and 55% of men and women, respectively, were obese. Obesity is more prevalent in younger SHS participants, and in those with diabetes. With the exception of insulin concentration, metabolic measures such as lipoproteins and blood pressure do not change substantially with increasing BMI in cross-sectional analyses.

To examine determinants of weight change over time in SHS, non-diabetic individuals who did not develop either cancer or diabetes during follow-up period were identified. Mean BMI, BMI categories, and weight change over an average of 7.9 years of follow-up are summarized in the following table:

 

Men (N = 437)

Women (N = 624)

Baseline BMI

28.0

30.0

% Normal (18.5 to <25)

18.5

17.8

% over weight (25 to <30)

45.8

34.6

% obese (>=30)

35.7

46.8

Mean wt change (kg)

0.76 range (-27 to 28)

1.62 range (-32 to 29)

 

44.76% of the men and 62.9% of the women reported spending zero hours per week performing physical activity and few performed more than 5 hours per week. In mLinear models predicting weight change between the baseline and 3rd examinations were fitted separately for men and women. In men, age was negatively related and education positively to weight change (r squared =.0583). In women, age and baseline BMI were negatively related and education was positively related to weight change (r squared =.1265). Weight change was unrelated to measures of baseline income or physical activity in these adjusted models.

In this population with high rates of diabetes and CVD, it is clear that by the later adult years there are no easily remediable determinants of wt gain. Current emphasis in Indian communities is being placed on nutrition and activity programs in schools and young adults. However, the low levels of physical activity suggest that community programs designed to increase activity levels in middle aged and older adults are needed.

References

  1. Gray, R.S., Fabsitz, R.R., Cowan, L.D., Lee, E.T., Welty, T.K., Jablonski, K.A., and Howard, B.V. Relation of generalized and central obesity to cardiovascular risk factors and prevalent coronary heart disease in a sample of American Indians: the Strong Heart Study. Int. J. Obesity 24:849-860, 2000.
  2. Howard, B.V., Welty, T.K., Fabsitz, R.R., Cowan, L.D., Oopik, A.J., Lee, E.T., Yeh, J., Savage, P.J., and Lee, E.T. Risk Factors for Coronary Heart Disease in Diabetic and Non-Diabetic American Indians: The Strong Heart Study. Diabetes, 41;(Suppl. 2):4-11, 1992.
  3. Lee, E.T., Welty, T.K., Fabsitz, R., Cowan, L.D., Le, N.-A., Oopik, A.J., Cucchiara, A.J., Savage, P.J. and Howard, B.V. The Strong Heart Study - A Study of Cardiovascular Disease in American Indians: Design and Methods. Am J Epidemiology 132:1141-1155, 1990.
  4. Welty, T.K., Lee, E.T., Yeh, J. Cowan, L.D., Go, O., Fabsitz, R.R., Le, N.A., Oopik, A.J., Robbins, D.C., Howard, B.V. Cardiovascular Disease Risk Factors Among American Indians: The Strong Heart Study. American Journal of Epidemiology, 142(3):269-287, 1995.

11:20 AM
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Diet and Physical Activity as Predictors of Weight Outcomes
Predictors of obesity and weight gain in the Health Professionals Follow-up Study and the Nurses' Health Studies

Eric Rimm, ScD
Associate Professor of Epidemiology and Nutrition
Harvard School of Public Health

The etiology of obesity is made up of a complex set of behavioral and lifestyle factors which interact to create a environment that leads to insidious weight gain. To determine the root causes of obesity and weight gain is challenging from an epidemiologic perspective because the development of the outcome (weight gain and obesity) is almost always associated with changes in the exposures under study. For example, in some cross-sectional epidemiologic studies, obesity is associated with more diet soda consumption. This irretractable confounding by indication or reverse causality, can make research on obesity complicated and make the interpretation of results particularly challenging.

In the Health Professionals Follow-up Study, the Nurses Health Study and the Nurses? Health Study 2, we have been following over 280,000 men and women with data collected biennially to study prospectively changes in risk factors as they relate to changes in weight gain. The main areas of research can be broken down into three domains: 1) diet, 2) physical activity and inactivity, and 3) biological markers or mediators of these exposures.

In the Health Professionals Follow-up Study, a prospective study of 51,529 male dentists, veterinarians, and pharmacists, we have conducted several sets of analyses to examine new onset obesity or to look at changes in weight or waist girth over time. In our initial work (Coakley, 1998), we looked at predictors of 4-year weight change. Over this follow-up period, middle-aged men who increased their exercise, decreased TV viewing and stopped eating between meals, lost an average weight of -1.4 kg (95% confidence interval (CI) -1.6 - -1.1 kg), compared to a weight gain of 1.4 kg among the overall population. The prevalence of obesity among middle-aged men was lowest among those who maintained a relatively high level of vigorous physical activity, compared to those who were relatively sedentary. With further follow-up, we found that whole grain intake and specifically bran intake were important predictors of a slower rate of weight gain (Koh-Banerjee, 2004).

In an interesting and novel twist we also looked at waist gain (assessed in 1987 and 1996) as a potential better marker of atherogenic weight gain. In multivariate analyses, a 2% increment in energy intake from trans fats that were isocalorically substituted for either polyunsaturated fats or carbohydrates was significantly associated with a 0.77-cm waist gain over 9 y (P < 0.001 for each comparison). An increase of 12 g total fiber/d was associated with a 0.63-cm decrease in waist circumference (P < 0.001), whereas smoking cessation and a 20-h/wk increase in television watching were associated with a 1.98-cm and 0.59-cm waist gain, respectively (P < 0.001). Increases of 25 (METs) · h/wk in vigorous physical activity and of >=0.5 h/wk in weight training were associated with 0.3cm and 0.91-cm decreases in waist circumference, respectively (P < 0.001 for each comparison). These associations remained significant after further adjustment for concurrent change in body mass index. (Koh-Banerjee, 2003). The findings for trans fat intake, television watching, and physical activity on waist gain are further supported by our recent work (Mozaffarian, 2003; Fung, 2000) which suggests associations between these exposures and inflammatory markers - potential precursors of insulin resistance and obesity.

In the Nurses Health Study (n=121,600) and the Nurses? Health Study 2 (n=116,000) covering a 45-year age range, we also found strong associations between lifestyle factors and risk of obesity. Among the younger women in the NHS2 study, two of the more novel factors studied were related to beverage consumption. For alcohol consumption we found a U-shaped association between alcohol and weight gain (Wannamathee, 2004). Compared to non-drinkers the adjusted relative odds (95% CI) of 8-year weight gain >=5kg according to grams/day were 0.93 (0.89,0.97) for those consuming 0.1-4.9 g/day, 0.91 (0.86,0.96) for 5-14.9g/day, 0.85 (0.76,0.95) for 15-29.9g/day and 1.06 (0.89,1.26) for those consuming 30+g/day (p<0.0001 for quadratic trend). For soft drinks we studied weight change over 2 separate 4-year periods (Schulze, 2004). Those with stable consumption patterns had no difference in weight gain, but weight gain over a 4-year period was highest among women who increased their sugar-sweetened soft drink consumption from <=1/week to >=1/day (multivariate adjusted means: 4.69 kg for 1991-95, 4.20 kg for 1995-99), and smallest among women who decreased their intake (1.34 and 0.15 kg for the two time periods), after adjusting for lifestyle and dietary confounders. An increased consumption of fruit punch was also associated with greater weight gain compared to those who decreased their consumption.

In the NHS, we also have documented the separate domains of television/sedentary activity and vigorous activity on risk of obesity. In the multivariate analyses adjusting for age, smoking, exercise levels, dietary factors, and other covariates, each 2-h/d increment in TV watching and sitting at work was associated with a 23% (95% CI, 17%-30%) and 5% (95% CI, 0%-10%) increase in obesity respectively. In contrast, standing or walking around at home (2 h/d) was associated with a 9% (95% CI, 6%-12%) reduction in obesity. Each 1 hour per day of brisk walking was associated with a 24% (95% CI, 19%-29%) reduction in obesity. Even small differences in energy expenditure can have important long term implications for obesity if patterns of behavior are sustained.

References

  1. Chu N-F, Stampfer MJ, Spiegelman D, Rifai N, Hotamisligil GA, Rimm EB. Dietary and lifestyle factors in relation to plasma leptin concentrations among normal weight and overweight men. Int J Obesity 2001;25:106-114.
  2. Chu N-F, Spiegelman D, Yu J, Rifai N, Hotamisligil GA, Rimm EB. Plasma leptin concentrations and 4-year weight gain among US men. Int J Obesity 2001;25:346-353.
  3. Coakley EH, Rimm EB, Colditz GA, Kawachi I, Willett WC. Predictors of weight change in men: Results from the Health Professionals Follow-up Study. Int J Obesity 1998;22:89-96.
  4. Fung TT, Hu FB, Yu J, Chu N-F, Spiegelman D, Tofler GH, Willett WC, Rimm EB. Leisure-time physical activity, television watching, and plasma biomarkers of obesity and cardiovascular disease risk. Am J Epidemiol 2000;152:1171-1178.
  5. Hu FB, Li TY, Colditz GA, Willett WC, Manson JE. Television Watching and Other Sedentary Behaviors in Relation to Risk of Obesity and Type 2 Diabetes Mellitus in Women. JAMA. 289(14):1785-1791, April 9, 2003.
  6. Koh-Banerjee P, Franz M, Sampson L, Liu S, Jacobs D, Spiegelman D, Willett WC, Rimm EB. Changes in whole grain, bran, and cereal fiber consumption in relation to 8-year weight gain among men. Am J Clin Nutr (In Press)
  7. Koh-Banerjee P, Chu N-F, Spiegelman D, Rosner B, Colditz G, Willett WC, Rimm EB. Prospective study of the association of changes in dietary intake, physical activity, alcohol consumption and smoking with 9-y gain in waist circumference among 16,587 US men. Am J Clin Nutr 2003;78:719-27.
  8. Mozaffarian D, Pischon T, Hankinson SE, Rifai N, Joshipura K, Willett WC, Rimm EB. Dietary intake of trans fatty acids and systemic inflammation in women. Am J Clin Nutr. 2004 Apr;79(4):606-12.
  9. Schulze MB, Manson JE, Ludwig DS, Colditz GA, Stampfer MJ, Willett WC, Hu FB. Sugar-sweetened beverages, weight gain, and incidence of type 2 diabetes in young and middle-aged women. JAMA (in press)
  10. Wannamethee SG , Field AE, Colditz GA, Rimm EB. Alcohol intake and 8 year weight gain in women: a prospective study. Obesity Research (in press)

11:40 AM
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Discussion

12:10 PM
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Lunch (on your own)

1:20 PM
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Behavioral, Social, and Environmental Predictors of Weight Outcomes, Dietary Behaviors, and Physical Activity
What We Know about Obesity Development During Adolescence: Findings from the NHLBI Growth and Health Study - The Role of Sociodemographic, Psychologic and Behavioral Predictors of Weight Outcomes, Dietary Behaviors and Physical Activity

Moderator: Phil Brantley

Sue Y. S. Kimm, M.D., M.P.H., Nancy W. Glynn, Ph.D.
University of Pittsburgh

The Role of Sociodemographic, Psychologic and Behavioral Predictors of Weight Outcomes, Dietary Behaviors and Physical Activity

Socioeconomic Factors

Although poverty has often been viewed as a major contributor to the high prevalence of obesity in African American women, the findings from NGHS indicate racial differences in the association between socioeconomic status and obesity. For 9 or 10 year old NGHS black girls, obesity risk did not vary across household income or parental education. In a logistic regression model, when income, parental education, and the number of parents in the household were included, only greater television viewing was associated with higher likelihood of being obese in black girls, but not in white girls. For white girls, television viewing was inversely associated with household income and parental education. However, when TV viewing was included in the model with both SES and energy intake, parental education and the number of parents in the household, but not television viewing, were significantly associated with obesity risk for white girls. This association became somewhat attenuated with age. For black girls, from age 14 and onward, household income, but not parental education, became significantly and inversely associated with adiposity.

Demographic Factors

Several demographic factors were associated with obesity in 9 or 10 year old girls with some racial differences. For black girls, the prevalence of obesity was not related to parental employment. For white girls, the odds of obesity were higher for girls with an unemployed parent/guardian in the household. The likelihood of being obese increased by 14% for each 5-year increase in mothers/female guardians' age. Obesity was less common for girls with more siblings, with the odds for obesity decreasing by 14% for each additional sibling in the household.

Psychosocial and Cultural Factors

Measures of self-perception with the Harter scale were assessed longitudinally in the NGHS cohort. From inception, there were racial differences in the association between adiposity and the scores of "physical appearance", "social acceptance" and "global self worth" domains. Adiposity in general impacted negatively on the scores of these 3 domains for white girls. The magnitude of the effect was somewhat less in black girls. Perceived physical attractiveness began to decrease for 9 or 10 year old white girls, even at extreme thinness, and declined linearly across the entire spectrum of adiposity. This finding suggests the presence of an underlying drive for thinness even among prepubertal white girls. For black girls, their perceived attractiveness decreased with adiposity levels around the 70th percentile of the sum of skinfolds (SSF) at the triceps, subscapular and suprailiac sites. The slope for the decline in appearance scores was more precipitous at the higher end of SSF distribution for white girls, whereas the rate of decline in the score for black girls did not vary at the higher end of the SSF distribution. The racial difference was most striking for scores of perceived "social acceptance" for black girls whose scores remained unchanged across the entire spectrum of SSF. For white girls, there was a significant inverse association between perceived social acceptance and adiposity. These findings suggest that in contrast to white girls, black girls did not feel socially rejected even at very high levels of adiposity. Perhaps, this is a manifestation of greater tolerance for obesity in African American culture.

While global self-worth scores showed little change in black girls ages 9-14, they decreased in white girls. Physical appearance scores for both races declined between ages 9 and 14. Social acceptance scores increased for both races between ages 9 and 14. The scores for these 3 subscales decreased with increasing BMI during early adolescence. However, these trends in physical appearance and social acceptance were less pronounced in black girls than in white girls.

Approximately 600 girls were surveyed at one of the NGHS sites at baseline with the "drive for thinness" subscales of the Eating Disorder Inventory. Black girls reported significantly (p=0.0001) higher scores of "drive for thinness" than white girls. Their mean score was 5.19 as compared to 3.24 of white girls. The score of this domain was significantly associated with adiposity for both black and white girls after adjustment for maturation, education, income, physical appearance (Harter scale), self-esteem (Harter scale) and criticism (beta =2.14 p=0.023 for black; beta =4.06 p=0.003 for white). While these findings were limited only to a subsample and the subscales were reworded to be more age-appropriate, nevertheless, they indicate that "drive for thinness" and adiposity are directly associated. These findings seem somewhat counter intuitive. Perhaps, this relationship may reflect a psychological consequence of weight gain rather than a driving force for the maintenance of thinness. No longitudinal information from NGHS is currently available.

At baseline, emotion-induced eating behavior was assessed using selected items from the Nutrition Pattern Questionnaire developed for the NGHS protocol. This "scale" consists of 7 items concerning emotion-induced eating. Black girls scored significantly (p<0.0001) higher on emotion-induced eating than did white girls. There was an inverse (p=0.0001) relationship between emotion-induced eating and BMI. In white girls, this scale was significantly associated with increased sucrose intake, but this was not present in black girls.

Body Dissatisfaction

There were racial differences in maternal influences on body satisfaction. Black mothers were less tolerant than white mothers of body build among moderately heavy 9 or 10 year old daughters. However, black mothers were more tolerant than white mothers of body build for their heaviest daughters.

Black girls had higher body satisfaction scores than did white girls (P<0.01). However, maternal disapproval of their build and habitus had little effect on daughters( body satisfaction (2.2% of variation explained). Body satisfaction scores decreased with increasing BMI, yet, they decreased less for black girls than for white girls. The largest proportion of variation in daughters? body satisfaction (21%) was explained by race, BMI, household income and race-BMI interaction.

Dietary Intake

Higher levels of parental education tended to be consistently related to more favorable nutrient intakes for both racial groups. Girls with parents with higher levels of education had lower fat intake and higher intakes of vitamin C, calcium and potassium.

Dietary Patterns and Dieting Practices

Attempts at weight reduction were seen even among 9 or 10 year old girls, but with racial differences. At ages 9 and 10, almost one half of NGHS girls were trying to lose weight. More of the thin black girls than white girls (16-19% vs. 5-7% in the 1st quartile of BMI) reported trying to gain weight, suggesting a lower tolerance by black girls for being too thin. Logistic regression analysis identified high BMI, the mother telling her child she was too fat and body dissatisfaction as the major factors associated with "trying to lose weight". Chronic dieting, however, was only associated with a high BMI and the mother telling the daughter "she was too fat".

The NGHS examined "weight-related" eating practices (11 items) which are defined as those traditionally targeted for behavior modification in weight reducing programs. Black girls were more than twice as likely as white girls to engage in these weight-related eating practices. Although there was an inverse association between SES and these practices, even after adjustment, black girls remained more likely to engage in these eating practices than white girls. In general, girls who frequently practiced one of these behaviors had higher energy intake than those who practiced such behaviors infrequently.

Physical Activity Levels

Habitual physical activity levels declined by a dramatic 83% during adolescence in the NGHS cohort and was found to be inversely associated with parental education, but primarily in white girls whose parents had attended high school only. White girls whose parent/parents had some college had a greater decline in activity than those whose parent/parents had 4 or more years of college. This effect became attenuated at older ages. Self-reported obstacles to participating in physical activity were queried annually for 3 consecutive years among those girls who exercised "sometimes" or "rarely" (i.e., </=1-2 times a week) from NGHS Study Years 8-10.

The perceived barrier scores as well as the barrier items remained remarkably stable without racial differences among 16 or 17 to 18 or 19 year old sedentary NGHS girls. Despite the greater decline in activity and greater prevalence of inactivity, black girls reported fewer barriers than did white girls (2.2 for black and 2.7 for white girls, P<0.001). The scores remained unchanged for all 3 years and the items did not vary across the years, nor across socioeconomic status. Lack of time was the most frequently cited barrier to activity participation (65% of black and 81% of white girls). Fatigue was the second most frequently cited item for 48% of black and 59% of white girls. Lack of interest was another barrier for both black (37%) and white (35%) girls. The first two barrier items were not corroborated by the amount of house work or household chores done, as there were no differences between those who cited this barrier and those who did not. Similarly, there were no differences in the average daily hours of sleep between those who cited "fatigue" and those who did not. However, those answering "yes" to lack of interest in exercising were more likely to admit to "rather be doing other things than exercise".

In conclusion, the composite findings from NGHS show that while there is a consistent inverse relationship between obesity and SES, this relationship is present primarily in white girls. For black girls, at younger ages, SES appeared to have less influence on the likelihood of being obese. Additionally, there were also racial differences in some of the behaviors associated with obesity development. For instance, despite the far greater decline in physical activity among black girls, SES had almost no impact on this decline, whereas it was inversely associated with the decline in white girls. Psychosocial parameters indicate that although black girls manifest the stigma of obesity in their perception of physical attractiveness and self-worth, they appeared remarkably resistant to the stigma of social rejection. Since some of these environment factors traditionally associated with obesity risk indicate racial differences, the ethnic differences seen may signal cultural differences. As culture is a concept which can be neither readily categorized nor measured, the challenge remains for all of us to gain further mechanistic insights into the complex interplay between ethnicity and disease risk and its associated risk factors.

References

  1. Kimm SYS, Obarzanek E, Barton BA, et al. Race, socioeconomic status, and obesity in 9- and 10-year old girls: the NHLBI Growth and Health Study. Ann Epidemiol 1996;6:266-275.
  2. Kimm SYS, Barton BA, Similo S, et al. Racial differences in the association of socioeconomic status with adiposity. L.J. Filer, Jr. Third International Conference on Atherosclerosis in the Young, March 2000, San Diego, CA.
  3. Patterson ML, Stern S, Crawford PB, et al. Sociodemographic factors and obesity in preadolescent black and white girls: NHLBI(s Growth and Health Study. N Nat Med Assoc 1997; 89:594-600.
  4. Kimm SYS, Barton BA, Berhane K, et al. Self-esteem and adiposity in black and white girls: The NHLBI Growth and Health Study Ann Epidemiol 1997; 7:550-560.
  5. Brown KM, McMahon RP, Biro FM, et al. Changes in self-esteem in black and white girls between the ages of 9 and 14 years. J Adolesc Health 1998; 23:7-19.
  6. Striegel-Moore RH, Schreiber GB, Pike KM, et al. Drive for thinness in black and white preadolescent girls. Int J Eating Disord 1995; 18:59-69.
  7. Striegel-Moore RH, Morrison JA, Schreiber GM, et al. Emotion-induced eating and sucrose intake in children: The NHLBI Growth and Health Study. Int J Eat Disord 1999; 25:389-98.
  8. Brown KM, Schreiber GB, McMahon RP, et al. Maternal influences on body satisfaction in black and white girls aged 9 and 10: the NHBLI Growth and Health Study. Ann Behav Med 1995; 17:213-220.
  9. Crawford PB, Obarzanek E, Schreiber GB, et al. The effects of race, household income, and parental education on nutrient intake of 9 and 10 year old girls. Ann Epidemiol 1995; 5:360-368.
  10. Schreiber GB, Robins M, Striegel-Moore R, et al. Weight modification efforts reported by black and white preadolescent girls: the National Heart, Lung, and Blood Institute Growth and Health Study. Pediatrics 1996; 98:63-70.
  11. McNutt SW, Hu Y, Schreiber GB, et al. A longitudinal study of the dietary practices of black and white girls 9 and 10 years old at enrollment: The NHLBI Growth and Health. Study J Adolesc Health 1997; 20:27-37.
  12. Kimm SYS, Glynn NW, Kriska AM, et al. Decline in physical activity in black girls and white girls during adolescence. N Engl J Med 2002; 347:709-15.
  13. Kimm SYS, Glynn NW, McMahon RP, Streigel-Moore R, Voorhees C. Barriers to physical activity in a biracial cohort of adolescent girls: Perception or reality? Annual Meeting of the Behavioral Medicine Society. March, 1999.
  14. Morrison JA, Payne G, Barton BA, et al. Mother-daughter correlations of obesity and cardiovascular risk factors in black and white households: The NHLBI Growth and Health Study. Am J Publ Health 1994; 84:1761-1767.

1:40 PM
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Behavioral, Social, and Environmental Predictors of Weight Outcomes, Dietary Behaviors, and Physical Activity
Obesity and the Environment: The National Longitudinal Study of Adolescent Health (Add Health)

Moderator: Phil Brantley

Penny Gordon-Larsen PhD for the Add Health Obesity and Environment Investigators

There are substantial racial and ethnic differences in patterns of obesity, chronic disease risk factors, morbidity and mortality that are related to disparities in socio-demographic and environmental stressors. The Obesity and Environment project focuses on disparity in environmental and socio-demographic resources, lifecycle changes in these contexts and resources, and how this disparity impacts obesity and obesity-related behaviors. This work provides a national perspective on the complex environmental, biological, behavioral, and social relationships underlying the obesity epidemic.

The Obesity and Environment project has produced several papers on obesity and its biological, sociodemographic, behavioral, and environmental correlates in the National Longitudinal Study of Adolescent Health (Add Health). Add Health is a nationally representative, school-based, prospective longitudinal study of adolescent health. The Add Health cohort includes 20,747 ethnically diverse US youths (grades 7-12), followed with multiple interview waves (1995, 1996, and 2001) into young adulthood.

Longitudinal changes in overweight. Longitudinal multivariate models were used to assess the association of overweight with initial (and one-year change in) physical activity, controlling for age, ethnicity, SES, urbanicity, smoking, and region (Gordon-Larsen, et al., 2002). Findings suggest differential sex and ethnicity effects, with greater impact of physical activity on overweight among white versus minority adolescents. A further analysis explored obesity incidence in the transition to adulthood. During a five-year period, obesity incidence was 12.7%, with 9.4% remaining obese, and 1.6% shifting from obese to non-obese. Incidence was highest among non-Hispanic black and Hispanic females (Gordon-Larsen, et al., 2004a). The Add Health analysis sample represents about 15.6 million 13-20 year old students at public and private schools in the US and indicates that more than 1.9 million adolescents became obese and an additional 1.5 million adolescents remained obese during the five-year study period.

Add Health physical activity/inactivity data. One descriptive paper (Gordon-Larsen et al. 1999) on physical activity and inactivity patterns in waves I and II of Add Health shows higher inactivity among minority adolescents, with the exception of Asian females. Another paper (Gordon-Larsen et al., 2004b) explored the longitudinal shifts in activity and inactivity between Waves I, II, and III. Findings indicate that a majority of individuals who were physically active as adolescents became relatively inactive as young adults. Declines in physical activity were greatest among females, particularly non-Hispanic black females (Gordon-Larsen, et al., 2004b).

SES and overweight. Gordon-Larsen, et al. (2003a) examined the extent to which ethnic differences in income and education account for disparities in overweight prevalence. Analyses indicated that changing only family income and parental education, without manipulating environmental factors, had a limited effect on overweight disparities. Ethnicity-SES-overweight differences were greater among females. Given that overweight prevalence decreased with increasing SES among white females and remained elevated and even increased among high SES black females, black-white disparity in overweight increased at highest SES. Conversely, disparity was smallest at highest SES for white, Hispanic, and Asian females. Among males, disparity was lowest at average SES. The benefits of increased SES, in terms of reduced overweight, shown among white adults was not present in other gender-age-ethnic groups.

Acculturation. Popkin and Udry (1998) investigated patterns of overweight by immigrant status and found large and statistically significant changes in overweight prevalence for foreign-born (relative to US-born) Hispanic and Asian children. A more detailed paper (Gordon-Larsen, et al., 2003b) on determinants of overweight, including neighborhood context, explored the differences in overweight in foreign-born (relative to US-born) Hispanic adolescents. Findings include important changes in dietary patterns as adolescents immigrate to the US, with healthier diets in more recent immigrants. In sum, these findings suggest a rapid assimilation to an increase in overweight-related behaviors from first to subsequent generations of US residence (Gordon-Larsen et al., 2003b).

Maturation and Breastfeeding. An analysis of the timing of sexual maturation in relation to overweight found early (relative to average) maturation nearly doubled the odds of overweight (Adair and Gordon-Larsen, 2001). Nelson et al. (in press) investigated the relationship between breastfeeding and overweight in the Add Health cohort and in the Add Health sibling pair sample (controlling for unmeasured genetic and/or environmental factors). Findings from the cohort data suggest that the odds of overweight decrease significantly (females only) as breastfeeding duration increases. However, sibling analyses indicate that this relationship may be attributable to unmeasured confounding related to mothers? choice to breastfeed or other childhood risk factors for overweight.

Environmental correlates of physical activity. Gordon-Larsen et al. (2000) used Wave I contextual data and self-reported use of community facilities to illustrate large and significant influence of contextual factors on physical activity and inactivity. These findings show a major impact of environmental factors, such as PE, community recreation center use, and neighborhood crime on being physically active. Importantly, these findings indicate that physical activity is more influenced by environmental factors, while sociodemographic factors had greater impact on inactivity. Another analysis investigated the inequitable distribution of physical activity-related resources and facilities in Add Health neighborhoods varying in SES and ethnicity (Gordon-Larsen et al., submitted). Physical activity resources and facilities in each of the >45,000 block groups within 5 miles of the participants? residences (representing approximately 20% of the US census block groups) were surveyed. Findings show that high SES, low minority communities were significantly more likely to have various types of activity-related resources compared to low SES, high minority communities.

Future Directions. A major push for the Obesity and Environment project is the linkage of physical environment measures from existing databases to Add Health respondents? geographic locations. A central task is the development of a diverse and detailed database comprised of time-varying modifiable environmental factors for waves I, II, and III using data sources, such as the US Geologic Survey, Census, and Department of Labor Statistics. Measures of access to recreation facilities, transportation options, and other data (e.g., crime, cost of living, land use, walkability, climate) will be added to the Add Health dataset. The ultimate aim of this work is to assess the impact of density and proximity of these resources on physical activity and obesity patterns.

References

  1. Adair LS, Gordon-Larsen P. 2001. Maturational timing and overweight prevalence in US adolescent females. American Journal of Public Health 91:642-4.
  2. Gordon-Larsen P, McMurray RG, Popkin BM. 1999. Adolescent physical activity and inactivity vary by ethnicity: the National Longitudinal Study of Adolescent Health. Journal of Pediatrics 135:301-6.
  3. Gordon-Larsen P, McMurray RG, Popkin BM. 2000. Determinants of adolescent physical activity and inactivity patterns. Pediatrics 105:1-8.
  4. Gordon-Larsen P, Adair L, Popkin BM. 2002. US adolescent physical activity and inactivity patterns are associated with overweight: The National Longitudinal Study of Adolescent Health. Obesity Research 10: 141-149.
  5. Gordon-Larsen P, Adair LS, Popkin BM. 2003a. The relationship between ethnicity, socioeconomic factors and overweight in US adolescents. Obesity Research 11:121-9.
  6. Gordon-Larsen P, Harris KM, Ward, DS, Popkin BM 2003b. Exploring increasing overweight and its determinants among Hispanic and Asian immigrants to the US: The National Longitudinal Study of Adolescent Health. Social Science & Medicine 57:2023-34.
  7. Gordon-Larsen P, Adair LS, Nelson MC, Popkin BM. 2004a. Five-year obesity incidence in the transition period between adolescence and adulthood: The National Longitudinal Study of Adolescent Health. American Journal of Clinical Nutrition 80:569-75.
  8. Gordon-Larsen P, Nelson MC, Popkin BM. 2004b. Longitudinal Physical Activity and Sedentary Behavior Trends: Adolescence to Adulthood. American Journal of Preventive Medicine (in press for November).
  9. Gordon-Larsen P, Nelson MC, Popkin BM.. Submitted. Socioeconomic inequality is linked with disparity in type and availability of physical activity facilities.
  10. Nelson MC, Gordon-Larsen P, Adair LS. In press. Are adolescents who were breast fed less likely to be overweight? Evaluation using traditional cohort analysis and matched sibling controls. Epidemiology.
  11. Popkin BM, Udry JR. 1998. Adolescent obesity increases significantly for second and third generation U.S. immigrants: the National Longitudinal Study of Adolescent Health. Journal of Nutrition 128:701-6.

2:00 PM
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Behavioral, Social, and Environmental Predictors of Weight Outcomes, Dietary Behaviors, and Physical Activity
Predictors of Weight Outcomes, Dietary Behaviors, and Physical Activity in Coronary Artery Risk Development in Young Adults Study (CARDIA)

Moderator: Phil Brantley

Cora E. Lewis, MD, MSPH for the CARDIA Investigators
University of Alabama at Birmingham

CARDIA study is a prospective, epidemiologic investigation of the determinants and evolution of cardiovascular risk factors among 5,115 African American and white young adults 18-30 years of age at baseline in 1985-86. Participants were recruited from the populations of four geographic locations (Birmingham, AL; Chicago, IL; Minneapolis, MN; and Oakland, CA). The study population was approximately balanced according to sex (54% women), ethnicity (52% African American), and education (40 % with less than equal to 12 years of education) at each center. Additional examinations were undertaken at years 2 (1987-88), 5 (1990-91), 7 (1992-93), 10 (1995-96), and 15 (2000-01). Overall retention rates for follow-up examinations were: 90 percent, 86 percent, 81, 79 percent, and 74 percent of surviving participants, respectively (approximately 2.5 percent were deceased as of the year 15 examination) over the 15 year follow-up.

Weight and height were measured at each exam using the same equipment and protocols; baseline height was used to determine BMI. A quantitative food frequency questionnaire was interviewer-administered at baseline and year 7, as was a symptom-limited maximum treadmill exercise test. Physical activity, and a variety of behavioral, psychosocial, attitudinal, and socioeconomic factors were assessed by self-report.

As of the 15 year examination, the average participant had gained: 16 kg for BW, 10.1 kg for WW, 14.1 kg for BM and 11.1 kg for WM, and of these groups 48%, 26%, 41%, 29%, respectively, gained greater than equal to 15 kg. Large increases in obesity prevalence had occurred in all groups.

In general, lifestyle variables potentially related to overweight were associated with each other. Frequent fast food intake was related to a higher intake of refined grains and soft drinks and lower intake of fruits and vegetables at baseline. At year 5, hours per day of TV viewing was inversely associated with physical activity, education, income, and with hostility and depression (in whites) scores.

Over the first 7 years of follow-up, overall treadmill duration decreased 10 percent, ranging from a 15 percent decrease in African-American men to a 7 percent decrease in white women. Dietary intake improved in terms of cholesterol and saturated fat intake and Keys score. There was some evidence for tracking for both physical activity and diet with many participants maintaining their relative ranking on these two variables.

Findings of relationships of lifestyle and other factors with weight change vary depending on the time period evaluated and/or the specific dependent variable examined. Between baseline and year 2, weight gain of at least 5% was associated with greater baseline BMI, previous dieting, considering oneself too fat, and the report of previous loss of 10 lb or more, compared to those whose weight remained stable. Over the first 7 years, change in physical fitness explained the greatest amount of the variance in weight change; each 60 second decline in exercise duration was associated with an average weight gain of 2.1 kg in women and 1.5 kg in men. Other factors significantly associated with greater 7-year weight gain in both sexes were ex-smoker status, younger baseline age, greater baseline percent energy from fat, lower baseline fitness, and ethnicity (men).

Over the first 10 years, dietary fiber was inversely related to weight gain in both African Americans and whites. The 10-year incidence of obesity was inversely related to dairy intake in those overweight at baseline but not in those initially normal weight.

Over 15 years, baseline fast food consumption (frequency per week) was independently related to weight increase in both African Americans and whites, and increased fast food consumption from baseline to year 15 was related to weight increase in whites. There was a particularly strong relationship among those least physically active at baseline. When 18 baseline behavioral, psychosocial, attitudinal, and socioeconomic factors were examined for associations with 15-year weight gain of greater than equal to 15 kg, comparing to those participants who maintained a stable weight (within 5 kg of baseline), only 8 variables (age, overweight at baseline, alcohol intake, considering oneself too fat, job demands, report of previous dieting, physical activity and physical fitness) were significant predictors of large weight gain, and none was significant in more than 2 of the 4 race-sex groups. There were no associations for fast food, fat, or carbohydrate intake, or TV watching.

Large increases in weight and obesity have occurred during follow-up in CARDIA. These increases have affected all four major demographic groups. Associations with potential predictors have been inconsistent across analyses.

References

  1. Anderssen N, Jacobs DR, Jr, Sidney S, Bild DE, Sternfeld B, Slattery ML, Hannan P. Change and secular trends in physical activity patterns in young adults: A seven-year longitudinal follow-up in the Coronary Artery Risk Development in Young Adults Study (CARDIA). American Journal of Epidemiology. 1996; 143(4):351-362
  2. Bild DE, Sholinsky P, Smith DE, Lewis CE, Hardin JM, Burke GL. Correlates and predictors of weight loss in young adults: The CARDIA Study. International Journal of Obesity. 1996; 20:47-55
  3. Dunn JE, Liu K, Greenland P, Hilner JE, Jacobs DR, Jr. Seven year tracking of dietary factors in young adults: The CARDIA Study. American Journal of Preventive Medicine. 2000; 18(1):38-45
  4. Lewis CE, McCreath H, West DS, Loria C, Kiefe CI, Hulley SB. Factors associated with 15-year weight gain in a bi-racial cohort of young adults: CARDIA. 43rd Annual Conference on Cardiovascular Disease Epidemiology and Prevention in Association with the Council on Nutrition, Physical Activity and Metabolism, 2003.
  5. Lewis CE, McCreath H, West DE, Loria CE, Kiefe CI, Hulley SB. The obesity epidemic rolls on: 15 years in CARDIA. American Heart Association Meeting, Scientific Sessions 2001.
  6. Lewis CE, Smith DE, Wallace DD, Williams OD, Bild DE, Jacobs DR, Jr. Seven year trends in body weight and associations of weight change with lifestyle and behavioral characteristics in black and white young adults: The CARDIA Study. American Journal of Public Health. 1997; 87:635-642
  7. Loria C, Yan LL, Lewis CE, Hilner JE, Liu K. Adoption of lower fat diet not associated with obesity rise: the CARDIA study. 43rd Annual Conference on Cardiovascular Disease Epidemiology and Prevention, 2003.
  8. Ludwig DS, Pereira MA, Kroenke CH, Hilner JE, Van Horn L, Slattery ML, Jacobs DR, Jr. Dietary fiber, weight gain and cardiovascular disease risk factors in young adults: The CARDIA Study. Journal of the American Medical Association. 1999; 282(16):1539-1546
  9. Pereira M, Jacobs DR, Jr, Van Horn L, Hilner JE, Slattery M, Kartashov AI, Ludwig DS. Dairy intake, insulin resistance, and cardiovascular disease risk factors in young adults. Journal of the American Medical Association. 2002; 287(16): 2081-2089
  10. Pereira et al. Fast food habits, weight gain, and insulin resistance in a 15-year prospective analysis of the CARDIA study. Lancet, submitted
  11. Pereira MA, Kartashov AI, Ebbeling CE, Hilner JE, Van Horn L, Slattery ML, Jacobs,DR,Jr., Ludwig DS. Fast food consumption and the incidence of obesity and glucose intolerance in young black and white adults: The CARDIA Study. 43rd Annual Conference on Cardiovascular Disease Epidemiology and Prevention in Association with the Council on Nutrition, Physical Activity and Metabolism, 2003.
  12. Sidney S, Sternfeld B, Haskell WL, Jacobs DR, Jr, Chesney MA, Hulley SB. Television viewing and cardiovascular risk factors in young adults: The CARDIA Study. Annals of Epidemiology. 1996; 6:154-159
  13. Sidney S, Sternfeld B, Haskell WL, Quesenberry CP, Jr, Crow RS, Thomas RJ. Seven-year change in treadmill test performance in young adults in the CARDIA Study. Medicine and Science in Sports and Exercise. 1998; 30:427-433