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Bruce G. Simons-Morton, Ed.D., M.P.H.

Bruce G. Simons-Morton, Ed.D., M.P.H.

Research on Young Drivers
Adolescent Health Behaviors
Behavioral Intervention in Health Care

Branch Chief:  Bruce G. Simons-Morton, Ed.D., M.P.H.

The mission of the Prevention Research Branch includes the following: 1) conduct research on child and adolescent health and health behavior; 2) provide service to the Division, Institute, and the scientific community through consultation, collaboration and assistance to advance the goals of science and public health; and 3) train young researchers.  The Prevention Research Branch's research identifies determinants of health and behavior and tests the effectiveness of behavioral and environmental strategies to improve or protect child and adolescent health.  The research is conducted within a developmental framework and emphasizes family context, characteristics of the individual and the social and physical environment.  Our studies are guided by social cognitive and social norms theories and draw on concepts of adolescent development and authoritative parenting.  Social influence is a common theme across the areas of research.  The Branch's research is organized according to three themes:  1) young drivers; 2) adolescent health and 3) behavioral interventions in health care. 

Our program of research on young drivers, headed by Dr. Bruce Simons-Morton, includes studies employing naturalistic, observational, and experimental study designs.  This research has examined the prevalence and patterns of risky driving, the effects of corrective feedback and, separately, teenage passengers on risky driving, and the effects of distraction on crash outcomes. 

Our research on adolescent health behavior, directed by Drs. Bruce Simons-Morton and Ronald Iannotti, focuses on longitudinal trajectories and determinants of substance use, diet, obesity, physical activity, and risky driving through the transition from high school to young adulthood. 

Our research on behavioral interventions in health care, headed by Dr. Tonja Nansel, utilizes our understanding of the determinants of health behaviors and health behavioral change to develop and test theory-based interventions for sustained health behavior change among patients in clinical care.  The current focus is on youth with type 1 diabetes and their families including diabetes management and dietary intake.


  • Bruce G. Simons-Morton, Ed.D, M.P.H., Senior Investigator and Chief
  • Tonja Nansel, Ph.D., Senior Investigator
  • Denise Haynie, Ph.D., M.P.H., Staff Scientist
  • Ronald Iannotti, Ph.D., Staff Scientist
  • Leah Lipsky,Ph.D., Staff Scientist
  • Kaigang Li, Ph.D., Research Fellow
  • Johnathan Ehsani, Ph.D., Postdoctoral Fellow
  • Anuj Pradhan, Ph.D., Postdoctoral Fellow
  • Ashley Russell, Ph.D., M.P.H., Postdoctoral Fellow
  • Virginia Quick,Ph.D., Postdoctoral Fellow
  • Brittney Barbieri, B.S., Postbaccalaureate Fellow
  • Faith Summersett-Ringgold, B.S., Postbaccalaureate Fellow
  • Jessamyn Perlus, B.A., Postbaccalaureate Fellow

Research on Young Drivers

Bruce G. Simons-Morton, Ed.D., M.P.H.

Bruce G. Simons-Morton, Ed.D., M.P.H.

Principal Investigator: Bruce G. Simons-Morton, Ed.D., M.P.H.
Division Collaborators:

  • Paul S. Albert, Ph.D., Senior Investigator
  • Kaigang Li, Ph.D., Research Fellow
  • Johnathan Ehsani, Ph.D., Postdoctoral Fellow
  • Anuj Pradhan, Ph.D., Postdoctoral Fellow
  • Bruce Simons-Morton, Ed.D., M.P.H.
  • Ashley Russell, Ph.D., M.P.H., Postdoctoral Fellow
  • Brittney Barbieri, B.S., Postbaccalaureate Fellow

Crash risk is highly elevated early in licensure, declines rapidly for a period of months and then slowly over a period of years, reaching adult levels in the mid-twenties.  Compared with older drivers, teenage and young adult drivers drive more often late at night, with multiple passengers, and possibly after drinking alcohol, which contribute to their relatively higher crash rates.  Additionally, the presence of teenage passengers has been shown to increase crash risk.  However, little is known about how driving behavior varies over time.

Our program of research on young drivers is varied.  We have studied aspects of driving risk and prevention.  Our research has included surveys, observation, naturalistic driving, test track, and simulation.  Notably, we conducted one of the first naturalistic driving studies with teenage drivers using highly sophisticated data acquisition systems installed in teenagers vehicles.  Currently we are conducting a unique series of experimental studies using driving simulation to evaluate the effects of teenage passengers on teenage driving performance. We have integrated assessments of fMRI and executive functioning into this research.  Thus, we employ the best methodology available to answer key research questions about teenage driving (Teen Driving Risk Studies).

The Naturalistic Teenage Driving Study (NTDS): The Effect of Driving Experience on the Driving Performance of Newly Licensed Teens

The NTDS is one of the first studies to assess driving risk objectively among teenage drivers.  The purpose was to assess the prevalence and determinants of crash/near crash and risky driving rates.  The sample included 42 newly licensed teenage drivers and their parents recruited.  The teen's primary vehicle was instrumented with a data acquisition system that included an accelerometer, GPS, and cameras mounted near the rear view mirror that looked forward and rearward and at the driver's face.  A blurred still photo was taken of the vehicle occupants using a fisheye lens to enable identification of occupants by age and sex.  Data were continually recorded and stored on a CPU in the vehicles' trunks with removable hard drives. 

Data were successfully collected from 41 of 42 study participants.  We have published papers on methods, driving exposure, crash risk, and risky driving.  Elevated g-force event rates predict the likelihood of a crash or near crash (CNC) in the following month (Simons-Morton et al., 2012).  This is important because it established elevated g-force rates as an objective measure of risky driving.  With investigators in the Biostatics and Bioinformatics Branch, we published several papers evaluating methods for analyzing the unique data structure of this study, with large numbers of counts (kinematic and crash/near crash events) and data on few subjects (Zhang, Albert, Simons-Morton, 2012; Jackson, Albert, Zhang, Simons-Morton., in press). Regarding driving risk, we found that crash/near crash risk was 3.91 times higher and elevated g-force event rates were 5.08 times higher among teenagers compared with adults (Simons-Morton et al., 2011).  Curiously, CNC rates among teen drivers declined over time, but risky driving did not.  Furthermore, we reported that CNC rates were 75% lower and risky driving was 67% lower among teenage drivers in the presence of adult passengers; and risky driving was 18% lower in the presence of teen passengers, suggesting that the presence of teen passengers does not always increase risk.  However, having risky friends (those who smoked, drank alcohol, used marijuana, engaged in risky driving) increased CNC rates by 96% and risky driving by 109% (Simons-Morton et al., 2011).  The publications from this study provide some of the best information available on key aspects of teenage driving.

The Supervised Practice Driving Study (SPD): The Effect of Supervised Practice Driving on Independent Driving Performance 

It is logical that more supervised practice driving leads to improved independent driving outcomes.  It may be that at least some adolescents who quickly learn to manage the vehicle receive little supervised practice driving prior to licensure while other adolescents for whom managing the vehicle is more difficult receive a great deal of supervise practice driving prior to licensure.  Only one previous naturalistic study of supervised practice driving has been conducted (Goodwin, Margolis, Waller, 2010), but has not reported effects of supervised practice driving on independent driving.  In collaboration with the Virginia Transportation Technology Institute (VTTI) we recruited a sample (n=90) of adolescents soon after they obtained their learner's permit, instrumented their vehicles with a data acquisition system, and are following them for 12 months after licensure.  Data collection is expected to be completed by December 2013.  We have developed data reduction protocols, including procedures for evaluating audio recordings of teen-parent verbal communications during instructional drives.

The Effect of Teenage Passengers on Teenage Simulated Driving Performance

The presence of teenage passengers has been shown to increase crash risk.  Notably, Ouimet et al. (2010) reported that male teenage passengers increased fatal crash risk not only among teenage but also among young adult drivers, particularly male drivers. In previous research we observed vehicles exiting high school parking lots and found that teenage drivers with male teenage passengers drove faster and closer to the lead vehicle than other drivers (Simons-Morton, Lerner, Singer, 2005).  However, in the NTDS we found that teen passengers provided a slightly protective effect on crash/near crash and risky driving compared to the no passenger condition.  A series of simulation studies is being conducted to learn more about the nature of teen passenger influences in collaboration with the University of Michigan Transportation Research Institute (UMTRI; Ray Bingham, PI).  One study will be completed each year over a 4-5 year period, incorporating what is learned from each study into the next study.

The Teen Passenger Study 1 (TPS1), completed in the spring of 2012, was designed to ascertain the effect of a risk-accepting or risk-averse teenage passenger on teenage risky driving.  We recruited 66 newly licensed male teenage drivers and randomized them to risk-accepting or risk-averse passenger conditions.  The passenger was a trained, male confederate.  We were interested in the effect of social norms on driving behavior, so we employed a pre-drive priming task  in which the participant and confederate passenger watched a video of risky driving and the confederate passenger verbalized that he would or would not, depending on the role he was playing, ever ride with that driver.  We used a randomized block design with 2 conditions (passenger: risk-accepting vs. risk-averse) X 2 drive orders (driving alone first vs. driving with the passenger first).  T-test comparisons of difference scores (passenger minus solo) were in the expected direction favoring greater driving risk in the risk-accepting passenger group.  We concluded that teenage drivers exposed to a risk-accepting teenage passenger were less likely to stop at red lights (p=0.04) while driving in a simulator. 

Further analyses are planned, including the fMRI, executive function, and psycho-social tasks such as Cyberball, an exclusion task, and Go No, a risk tolerance task.  The TPS2, now underway, tests the effect of teenage peer pressure on teenage risky driving performance.  The study design is similar to TPS1, except we put the drivers under pressure by instructing them to reach a particular destination within a limited time without error.  The confederate passenger serves as the navigator and at key points in the drive verbally encourages the driver to hurry (in the role of a risk-accepting teen) or make no errors (in the role of a risk-averse teen).  Assessment of fMRI and psycho-social tasks will also be conducted. TPS3, planned for 2012-13, will involve actual peers rather than confederates.

The Effect of Feedback About Elevated G-Force Events on Risky Driving Among Novice Teenage Drivers

Elevated g-force event rates are associated with motor vehicle crashes (Simons-Morton, Zhang, Albert, 2012) and novice teenage drivers have high rates that persist at least through 18 months after licensure (Simons-Morton et al., 2011).  Feedback about elevated g-force events has been shown to reduce event rates among young drivers in several pre-post, non-randomized studies using the DriveCam, a commercial event recorder marketed to the families of young drivers.  The DriveCam includes an accelerometer connected to small cameras directed at the vehicle occupants and forward roadway.  Data are continually recorded, but only the few seconds before and after an event were stored for retrieval.  The study evaluated the effect of two forms of feedback from the DriveCam device on risky driving behavior.  A sample of 90 novice teenage drivers was recruited and randomized to one of two treatment conditions.  The DriveCam device was installed (near the rear view mirror) of the study participants' vehicles.  For the first two weeks the devices provided no feedback to the participants.  Thereafter, for those assigned to the Lights Only condition, the device was set to provide immediate feedback in the form of a blinking red and green light each time the threshold of 0.5 g was exceeded.  Similarly, for those assigned to the Lights Plus condition, the device was set to provide immediate feedback in the form of the blinking light and delayed feedback in the form of a weekly report card and access to video footage of each event on a secure website by teens and parents.  DriveCam, Inc. coders blinded to the study evaluated each event according to standard procedures to derive the risk scores.  The findings indicate that immediate feedback only had no effect on event rates.  However, event rates among those who received immediate plus family feedback declined immediately and substantially.  The effect size of 1.67 favored the Lights Plus group. 

NEXT Naturalistic Driving Study

Little is known about how driving behavior varies over time, particularly among young drivers.  Naturalistic driving methods lend themselves to longitudinal assessment, but to date most studies have included few study participants and have been of short duration.  This study assesses the driving performance of a sample of 150 young drivers starting in the 12th grade (ages 17-18) and ending when the participants are ages 21-22.  Assessment is done using the DriveCam driving assessment device.  The sample is drawn from the NEXT Study, and will also have completed 7-years of annual assessments on their health behaviors.  The research questions of interest include:  1) What is the variability within the sample and over time in driving performance (elevated g-force events and crash/near crash)?  and 2) What individual and driving condition factors are associated with risky driving and crashes/near crashes?  Recruitment and installation of the data acquisition systems are underway.  Study participants will be followed for a period of 4 years.

2012 Publications

  1. Jackson J, Albert PS, Zhang Z, Simons-Morton B.  Ordinal latent variable models and their application in the study of newly licensed teenage drivers. Journal of the Royal Statistical Society-Series C, Applied Statistics 62, Part 3, 2012.
  2. Simons-Morton BG, Ouimet MC, Wang J, Chen R, Klauer SG, Lee SE, Dingus T.  Psychosocial factors associated with speeding among teenage drivers. Journal of Safety Research 2012; Nov 13 online. 
  3. Simons-Morton BG, Zhang Z, Jackson JC, Albert PS.  Do elevated gravitational-force events while driving predict crashes and near crashes?  American Journal of Epidemiology 175(10):1075-1079, 2012.
  4. Simons-Morton BG, Cheon K, Guo F, Albert P. Trajectories of kinematic risky driving among novice teenagers. Accident Analysis & Prevention 51C:27-32, 2012.
  5. Simons-Morton BG, Bingham R, Ouimet MC, Pradhan A, Chen R, Wang J. The effect on teenage risky driving of feedback from a safety monitoring system:  A randomized controlled trial.  Journal of Adolescent Health (In press).
  6. Zhang Z, Albert PS, Simons-Morton BG.  Marginal analysis of longitudinal count data in long sequences: Methods and application to a driving study.  Annals of Applied Statistics 6 (1):27–54, 2012.

Adolescent Health Behavior

Ronald J. Iannotti, Ph.D.

Ronald J. Iannotti, Ph.D.

Bruce G. Simons-Morton,  Ed.D., M.P.H.

Bruce G. Simons-Morton, Ed.D., M.P.H.

Principal Investigator: Bruce G. Simons-Morton, Ed.D., M.P.H.
Study Director: Ronald J. Iannotti, Ph.D., Staff Scientist

Division Collaborators

  • Tonja R. Nansel, Ph.D., Senior Investigator
  • Paul S. Albert, Ph.D., Senior Investigator
  • Ruzong Fan, Ph.D., Investigator
  • Bruce Simons-Morton, Ed.D., M.P.H.
  • Danping Liu, Ph.D., Investigator
  • Denise L. Haynie, Ph.D., M.P.H., Staff Scientist
  • Kaigang Li, Ph.D., Research Fellow
  • Leah Lipsky, Ph.D., Staff Scientist
  • Johnathan Ehsani, Ph.D., Postdoctoral Fellow
  • Anuj Pradhan, Ph.D., Postdoctoral Fellow
  • Ashley Russell, Ph.D., M.P.H., Postdoctoral Fellow
  • Brittney Barbieri, B.S., Postbaccalaureate Fellow
  • Jessamyn Perlus, B.A., Postbaccalaureate Fellow

Adolescence is a critical period for the development of unhealthy behavioral patterns that may be associated with subsequent adolescent and adult morbidity and mortality.  Adolescence is also a critical period for physiological and behavioral changes and for the onset of obesity and substance use. The influence of peers and physical environment (e.g., community programs, policies, and resources) increase during this period as adolescents spend more time outside the family.  As adolescents move from high school to post-secondary education or new places of work, their personal, social and physical environments change.  These transitions impact their health and behavior.  Currently, we are conducting the NEXT Longitudinal Study of Adolescent Health Behavior (NEXT), which follows a nationally representative sample during the transition from high school to early adulthood.  The NEXT Study captures assessments of cardiovascular risk factors, adolescent problem behaviors (substance use and dating violence) and novice driving.  With funding from outside groups, the study has a number of subsamples on which in-depth behavioral and biomedical data are collected.  The NEST Study promises to be among the most important longitudinal studies since the Add Health.

Health Behavior in School Children Survey (HBSC)

The HBSC is a national probability survey of adolescent health behavior in the U.S, which has been conducted every 4 years since 1998.  The U.S. HBSC complements the Youth Risk Behavior Survey (YRBS) in that it is the only survey simultaneously conducted in approximately 40 European countries.  As a result, it is the only survey that permits comparisons of U.S. adolescents with adolescents throughout Europe.  The aims of the survey are to assess the prevalence of health behaviors and identify contextual factors associated with them in a national probability sample of 6th to 10th grade students, allowing for trend analyses and cross-national comparisons among participating countries involved in the quadrennial international HBSC surveys.  Because core survey items have remained consistent both nationally and internationally since 2001, HBSC surveys provide essential data for examining and comparing national and international trends.  Participating countries may also collect data on approved optional measures that are unique topics, for example, injury, or more in depth assessments, for example, substance use.  The US HBSC has concentrated assessments on diet, physical activity, bullying and substance use.  It also includes measures of medication taking, mental health, school functioning, social relations, and socio-economic status.  Public access HBSC datasets are made available three years after they are available to DESPR scientists.

The NEXT Generation Health Study

The NEXT Generation Health Study is a longitudinal survey of adolescent health and behavior.  A nationally representative cohort of 2770 adolescents, approximately 16 years of age, was recruited in 2010 and is assessed annually up to age 22. The primary goals of the study are to examine trajectories of adolescent health status and behaviors from mid-adolescence through the post high school years. The study focuses on the following areas of adolescent risk: substance use, driving, and cardiovascular disease risk factors and biomarkers.  At the end of the recently completed Wave 3 survey, we have a retention rate of 86% of the original 2,770 10th graders recruited at Wave 1.  In addition to annual surveys conducted with the entire sample, a subsample of 540 (NEXT Plus) of the 2770 provide additional data, including diet and physical activity recalls, accelerometers to measure activity and sleep, biospecimens to assess cardiovascular risk, saliva for genetic analyses, peer networks, and driving.  Retention among the NEXT Plus subsample at the Wave 3 assessments is 92%.

In collaboration with colleagues at NIDA (which co-sponsors NEXT), we have under review a paper on the prevalence of substance use, including poly-drug users (tobacco, alcohol, marijuana, medication misuse, and other illicit drugs).  Using a Latent Class Analysis approach, we found approximately 8% of 10th grade students were characterized as poly-substance users.  An analysis of peer influences on alcohol use using the first two waves of data revealed that descriptive norms regarding alcohol use mediated the relationship between adolescent exposure to peer drinking at Wave 1 and adolescent drinking at Wave 2.  Furthermore, the NEXT study provides unique data on alcohol/drug impaired driving and riding with an impaired driver.  In the first of these analyses, we found that approximately 12.5 % of licensed 11th grade students reported driving while alcohol/drug impaired at least once; and 23.9% of all 11th graders reported riding with an impaired driver.  

2012 Publications

  1. Bjarnason T, Bendtsen P, Arnarsson AM, Borup I, Iannotti RJ, Löfstedt P, Haapasalo I, Niclasen B.  Life satisfaction among children in different family structures: A comparative study of 36 western societies. Children & Society 26:51-62, 2012.
  2. Bogt TF, Gabhainn SN, Simons-Morton BG, Ferreira M, Hublet A, Godeau E, Kuntsche E, Richter M.  Dance is the new metal: adolescent music preferences and substance use across Europe. Substance Use & Misuse 47(2):130-42, 2012.
  3. Caccavale LJ, Farhat T, Iannotti R J. Social engagement in adolescence moderates the association between weight status and body image. Body Image 9(2):221-226, 2012.
  4. Divekar G, Pradhan AK, Pollatsek A, Fisher DL. External distractions: Evaluation of their effect on younger novice and experienced drivers' behavior and vehicle control.  Transportation Research Record (In press).
  5. Farhat T, Simons-Morton BG, Kokkevi A, van der Sluijset W, Fotiou A, Kuntsche E. Early adolescent and peer drinking homogeneity: Similarities and differences among European and North American countries. Journal of Early Adolescence 32(1): 81-103, 2012.
  6. Hingson R, Zha W, Iannotti RJ, Simons-Morton BG. Physician advice to adolescents about drinking and other health behaviors. Pediatrics (In press).
  7. Iannotti R J, Chen R, Kololo H, Petronyte G, Haug E, Roberts C. Motivations for adolescent participation in leisure-time physical activity: international differences. Journal of Physical Activity and Health (In press)
  8. Kuntsche E, Rossow I, Simons-Morton BG, ter Bogt T, Kokkevi A, Godeau E. Not early drinking but early drunkenness is a risk factor for problem behaviors among adolescents from 38 European and North American countries. Alcoholism: Clinical and Experimental Research (In press)
  9. Lipsky LM, Iannotti RJ.  Associations of television viewing with eating behaviors in the 2009 Health Behavior in School-Aged Children study (HBSC). Archives of Pediatrics & Adolescent Medicine 166:465-472, 2012. 
  10. Luk JW, Farhat T, Iannotti RJ, Simons-Morton BG.  Parent-child communication and substance use among adolescents: Do father and mother communication play a different role for sons and daughters? Addictive Behaviors (In press)
  11. Luk JW, Wang J, Simons-Morton BG.  A latent class analysis of bullying victimization among U.S. adolescents: Associations with demographic characteristics and psychosomatic symptoms. Journal of Adolescence (In press)
  12. Mikolajczyk RT, Iannotti RJ, Farhat TM, Thomas V. Ethnic differences in perceptions of body satisfaction and body appearance among U.S. schoolchildren: A cross-sectional study. BMC Public Health 12(1):425, 2012
  13. Ogbagaber S, Albert PS, Lewin D, Iannotti RJ. Summer activity patterns among teenage girls: harmonic shape invariant modeling to estimate circadian cycles. Journal of Circadian Rhythms (In press)
  14. Simoes C, Gaspar Matos M, Moreno C, Rivera F, Bastiste-Foguet J, Simons-Morton BG.  Substance use in Portuguese and Spanish adolescents: highlights from differences and similarities and moderate effects.  Spanish Journal of Psychology 15(3):1024-37, 2012.
  15. Simons-Morton BG, Kuntsche E.  Adolescent estimation of peer substance use: Why it matters (Commentary).  Addiction 107:885-891, 2012.
  16. Wang J, Iannotti RJ, Luke JW. Patterns of adolescent bullying behaviors:  Physical, verbal, exclusion, rumor, and cyber. Journal of School Psychology (In press).

Behavioral Intervention in Health Care

Tonja R. Nansel, Ph.D.

Tonja R. Nansel, Ph.D.

Principal Investigator: Tonja R. Nansel, Ph.D.

Division Collaborators:

  • Aiyi Liu, Ph.D., Senior Investigator
  • Leah Lipsky, Ph.D., Staff Scientist
  • Virginia Quick, Ph.D., R.D., Postdoctoral Fellow
  • Sabrina Mathenia, B.S., Undergraduate Scholars Program
  • Tonja Nansel, Ph.D.
  • Faith Summersett-Ringgold, B.S., Postbaccalaureate Fellow

Chronic disease and other behavior-related or behavior-managed conditions account for the majority of morbidity, mortality, and health care costs; yet the health care system is based on an acute care model that cannot adequately assist individuals to engage in the health behaviors required to prevent or manage these conditions.  The behavioral sciences offer a substantial knowledge base in mechanisms of promoting behavior change; thus integration of the behavioral and medical sciences in clinical practice offers great potential for improving health and decreasing the burden of illness.

Our research in this area includes a series of studies involving children and adolescents with type 1 diabetes, including the recently completed Family Management of Childhood Diabetes and the ongoing Cultivating Healthy Eating in Families of Youth with Type 1 Diabetes, and a forthcoming intervention trial on diet among pregnant women.  The future course of this area of research will be:  1) to determine the effect of a clinic-linked intervention on the diet and disease management practices of families with children with type 1 diabetes mellitus and 2) to examine diet and weight gain among pregnant women and test an intervention to foster healthy eating and weight gain.

Family Management of Type 1 Diabetes in Youth

Management of type 1 diabetes is a complex, intensive task, including multiple daily insulin injections or use of an insulin pump, multiple daily blood glucose testing, regulation of carbohydrate intake, regular physical exercise, and problem-solving to correct excessive blood glucose fluctuations.  Careful management is important to prevent short- and long-term complications, including diabetic ketoacidosis and damage to the heart, kidneys, nerves, eyes, blood vessels, and other organs.  Successful management of diabetes in youth is heavily dependent upon family adaptation to the affective, behavioral, and cognitive demands imposed by the disease.  The transfer of diabetes management responsibilities from parents to adolescents is a particularly important process.  Although many youths and parents negotiate this transition effectively, it is also a period when many other youths take costly, self-destructive paths resulting in preventable health care costs and psychological suffering in the short-term and accelerated onset and progression of long-term complications of the disease.  Poor adaptation to diabetes during adolescence is likely to persist into early adulthood, accelerating the risks of both long-term medical complications and psychiatric sequelae.  Research to date on behavioral interventions for youth with type 1 diabetes suggest that adherence, quality of life, and glycemic control could be enhanced if behavioral interventions were routinely implemented as part of standard care.  Yet there are many barriers to the translation of these interventions into routine clinical practice, including cost, access, third party coverage, availability of qualified clinicians, convenience and social stigma.  An optimal chronic illness model for health care would involve the integration of behavioral management principles into routine clinical care, including assessment and specification of target behaviors, identification of barriers and motivators, collaborative setting of goals, facilitation of problem-solving and coping skills, and provision of follow-up and support.  A multi-component behavioral intervention that integrates behavioral principles into medical management of diabetes is likely to enhance family management of diabetes during early adolescence in a practical, cost-effective and lasting manner.

Family Management of Diabetes Multi-site Trial

Families receiving care at one of four geographically disperse clinical sites were randomized to receive either standard care or a clinic-integrated behavioral intervention, in which a trained non-professionals delivered the semi-structured approach based on applied problem-solving at each routine clinic visit.  A sample of 390 families was followed for two years.  Biomedical and self-report data were collected during clinic visits, as well as in-home and by telephone.  The intervention tested in this study was based on both individual and family system theoretical perspectives, including social cognitive theory, self-regulation, and authoritative parenting.  It was designed to provide experiential training for families in the use of a problem solving approach (represented by the acronym "WE*CAN") to promote improved parent-child teamwork and more effective problem-solving skills for diabetes management.  The intervention was designed to be applicable to the broad population of youth with diabetes and their families, flexibly implemented and tailored to the varying needs of families, and delivered at a low intensity over time to meet the changing families' needs and roles during the period in which responsibility for diabetes management typically undergoes transition.  Intervention components included a preparation telephone contact prior to clinic visits, an action session during clinic visits designed to assist the family in setting specific goals for diabetes management and problem-solving to facilitate goal attainment, and follow-up telephone contacts to reinforce effort and further assist progress.  Findings from the Family Management of Diabetes Multisite Trial demonstrated an intervention effect on glycemic control at two-year follow-up.  This intervention effect was observed only among adolescents.  Given the well-documented deterioration in glycemic control that occurs during adolescence, the development of an effective approach for this age group is of particular clinical significance.  Analysis of hypothesized behavioral mediators of the intervention effect on glycemic control, however, indicated no significant differences between groups.  These findings are highly informative for guiding future research.  Previous research has focused on family conflict and responsibility-sharing as key family behaviors impacting diabetes management.  Our findings suggest an additional unmeasured behavioral pathway for successfully impacting glycemic control.

Dietary Intake in Youth with Type 1 Diabetes

Nutrition guidelines for children with type 1 diabetes are similar to those for the general population, and nutrition education for families of children with type 1 diabetes includes recommendations for general healthful eating and efforts to achieve and maintain optimal weight for height.  A primary component of medical nutrition therapy in type 1 diabetes is carbohydrate estimation, especially in the current era emphasizing physiologic insulin replacement, as carbohydrates are the principal macronutrient affecting glycemic excursions.   As such, a major focus is on integrating the insulin regimen and carbohydrate estimation into the family's lifestyle, conforming to preferred meal routines, food choices, and physical activity patterns.  Concurrently, children with type 1 diabetes are consuming diets low in fruits, vegetables, and whole grains, and high in saturated fat.  Poor diet quality is particularly concerning due to the increased risk of dyslipidemia and cardiovascular disease and the high prevalence of cardiovascular risk factors recently observed in youth with diabetes.  Limited research has examined relations between usual dietary intake and diabetes management in type 1 diabetes. However, there is a growing body of evidence indicating that dietary intake, particularly carbohydrate quality, may affect blood sugar control, insulin demand, and weight management.  To date, research has not examined individual and family determinants of dietary intake in youth with type 1 diabetes.  Further, excepting one study using only a nutrition education approach, no intervention studies to improve dietary quality among this population have been conducted.  Research within the general population indicates a complex interplay of socio-environmental and personal factors impacting children's dietary intake.  The family plays a critical role in influencing children's eating habits, both through regulating food availability as well as by shaping food attitudes, preferences and values through modeling and food-related parenting practices.  Challenges to healthful eating faced by families include perceived time constraints, perceived cost of healthful eating, increase in food consumed away from home, and a food environment characterized by an abundance of unhealthful options.  Intervention studies in other clinical populations demonstrate substantial challenges in promoting healthful eating, and suggest the importance of family-based approaches that enhance motivation, facilitate skills, and assist families in overcoming the many barriers to healthful eating.

Eating Behaviors Among Youth with Type 1 Diabetes

This study enrolled 291 families (parent-youth dyads) in a cross-sectional study of psychosocial factors related to eating behaviors in families with youth with type 1 diabetes.  Data were obtained using medical record abstraction, parent-youth interview, youth self-report surveys, parent self-report surveys, youth 3-day diet records, parent food frequency questionnaire.  Two-week retest data were also obtained from youth and parents on select self-report survey items developed by the investigators. Key findings include the poor dietary quality of youth with type 1 diabetes and associations with BMI, the direct association of parental modeling and attitudes on healthy eating with youth diet quality, the inverse association of disordered eating behaviors with diet quality, and significant associations of youth food preferences and the family food environment with dietary intake.  We have developed measures for assessing nutrition knowledge, diabetes management adherence, and intake of whole plant foods.  We have demonstrated the utility of a random effects model for examining dietary intake by weekends and weekdays, providing a method for improved data analysis when differential constellations of weekdays and weekend days are obtained across subjects or over time.  We have also developed an extensive food cost database, providing estimated costs of all foods reported by study subjects in the 3-day diet records.  Our examination of the association of food cost with diet quality indicates very modest relations, and suggests that cost need not be a barrier to healthful eating. 

Cultivating Healthful Eating in Families of Children with Type 1
Diabetes (CHEF)

This 18-month study, which is currently in the field, tests the efficacy a family-based behavioral intervention designed to improve diet quality by promoting intake of fruit, vegetables, whole grains, legumes, nuts, and seeds.  A sample of 139 families was randomized to the behavioral nutrition intervention including continuous glucose monitoring feedback or to continuous glucose monitoring feedback only.  The intervention approach, which is grounded in social cognitive theory, self-regulation, and self-determination theory, integrates motivational interviewing, active learning, and applied problem-solving to target increased dietary intake of fruits, vegetables, whole grains, legumes, nuts, and seeds.  The intervention sessions, which are delivered by trained non-professionals, are structured such that concepts and activities are subsequently applied to each meal of the day, providing for cyclical learning and behavior change.  The semi-structured approach allows for flexibility in delivery to accommodate differences in youth age as well as family cultural and socioeconomic differences.  Data collected include medical record abstraction, parent-youth interview, youth self-report surveys, parent self-report surveys, youth 3-day diet records, parent 3-day diet records, youth continuous glucose monitoring, youth body composition (DXA), and youth biomarkers including lipids, carotenoids, and markers of inflammation and oxidative stress.  Primary outcomes include glycemic control and dietary intake.  Conduct of the CHEF efficacy trial is underway; 96% of participants who completed baseline assessment have been retained through the 6-month assessment, encompassing the majority of intervention visits.

2012 Publications

  1. Lipsky LM, Nansel TR, Haynie DL, Laffel L, Mehta S.  Associations of food preferences and household food availability to dietary intake and quality in youth with type 1 diabetes. Appetite 59(2):218-223, 2012.
  2. Lipsky LM, Cheon K, Albert P, Nansel TR. Candidate measures of whole plant food intake are related to biomarkers of nutrition and health in the US population (NHANES 1999-2002). Nutrition Research 32(4):251-259, 2012.
  3. Nansel TR, Iannotti RJ, Liu A. Clinic-integrated behavioral intervention for families of youth with type 1 diabetes: RCT.  Pediatrics 129:e866-e873, 2012.
  4. Nansel TR, Haynie DL, Lipsky LM, Laffel L, Mehta SN. Multiple indicators of poor diet quality in youth with type 1 diabetes are associated with higher weight status but not glycemic control. Journal of the Academy of Nutrition and Dietetics 112(11):1728-1735, 2012.
  5. Rovner AJ, Nansel TR, Mehta SN, Higgins LA, Haynie DL, Laffel LMB. Development and validation of the type 1 diabetes nutrition knowledge survey. Diabetes Care 35:1643-1647, 2012.
  6. Tse J, Nansel TR, Haynie D, Laffel L, Mehta S. The relationship of diet quality and disordered eating behaviors in adolescents with type 1 diabetes. Journal of the Academy of Nutrition and Dietetics 112(11):1810-1814, 2012.
  7. Williams J, Nansel TR, Weaver NL, Tse J. Safe N' Sound: an evidenced based tool to prioritize injury messages for pediatric healthcare. Family and Community Health 2012, 35(3):212-224, 2012.
  8. Weaver NL, Brixey S, Williams S, Nansel TR. Promoting correct car seat use in parents of young children: challenges and recommendations. Health Promotion Practice, 2012; Sept 17 online.
Last Reviewed: 02/05/2013

Contact Information

Name: Dr Stephen Gilman
Acting Branch Chief
Health Behavior Branch
Phone: 301-435-8395

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