PEDIATRICS Vol. 118 No. 6 December 2006, pp. e1627-e1634 (doi:10.1542/peds.2006-0926)
ARTICLE |
Longitudinal and Secular Trends in Physical Activity and Sedentary Behavior During Adolescence
Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota
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ABSTRACT |
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 TOP ABSTRACT METHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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OBJECTIVE. There is little epidemiologic research on longitudinal and secular trends in weight-related health behaviors throughout the stages of adolescence. In particular, few data are available to assess secular trends in various sedentary behaviors. The objective of this research was to investigate longitudinal and secular trends in physical activity and sedentary behavior in a large, diverse cohort of adolescents.
METHODS. Project EAT-II is a 5-year longitudinal study (N = 2516) that includes 2 cohorts that allow for the observation of longitudinal changes from early to midadolescence (junior high to high school; n = 806; mean baseline age: 12.8 ± 0.8 years) and mid- to late adolescence (high school to post–high school; n = 1710; mean baseline age: 15.8 ± 0.8 years). EAT-II also examined secular trends in health behavior from 1999 to 2004 in midadolescence. The main outcome measures of the mixed-model regression analyses used in this research were self-reported weekly hours of moderate to vigorous physical activity, television/video viewing, and leisure-time computer use.
RESULTS. Our findings indicate substantial longitudinal changes in moderate to vigorous physical activity, particularly among girls (decreasing 5.9–4.9 hours/week from early to midadolescence and 5.1–3.5 hours/week from mid- to late adolescence), and leisure-time computer use, particularly among boys (increasing 11.4–15.2 hours/week from early to midadolescence and 10.4–14.2 hours/week from mid- to late adolescence). Secular trends further indicate dramatic increases in midadolescent computer use from 1999 to 2004; girls increased from 8.8 to 11.1 hours/week, and boys increased from 10.4 to 15.2 hours/week.
CONCLUSIONS. These adolescents experienced unfavorable shifts in activity patterns, such as longitudinal decreases in moderate to vigorous physical activity, coupled with longitudinal and secular increases in leisure-time computer use. Developing effective health promotion strategies that address a wide array of changing behavioral patterns will be important in promoting long-term health and active lifestyles among adolescents and young adults.
Key Words: adolescent • exercise • health behavior • longitudinal studies • television • physical activity
Abbreviations: MVPA—moderate to vigorous physical activity • SES—socioeconomic status • YRBSS—Youth Risk Behavior Surveillance System
Low levels of moderate to vigorous physical activity (MVPA) and high levels of sedentary behavior (eg, television viewing) have been shown to be associated with obesity, although epidemiologic evidence in this area is not entirely consistent.1–3 Furthermore, MVPA and sedentary behaviors have been identified as major targets for health promotion strategies in reducing excess weight gain.4 Given dramatic increases in the national prevalence of obesity, a popular belief is that overall levels of MVPA have declined dramatically and that levels of sedentary behavior have increased in recent years. However, although national survey data are limited, previous research has suggested relatively stable secular trends in leisure-time MVPA in the past decade,5 with potential declines in occupation- and transportation-related activity.6 In addition, it appears as though secular trends in television viewing may have remained stable over time,7,8 although the available data in this area are even more limited and findings also are inconsistent.6 Little scientific research has assessed secular trends in other sedentary pursuits (eg, leisure-time computer use), despite recent dramatic changes in availability and accessibility.9
Whereas few epidemiologic data are available to assess purely time-dependent trends (ie, secular trends), cohort data are more readily available and allow us to quantify concurrent age- and time-dependent trends. Striking longitudinal declines in MVPA have been demonstrated in various cohorts,10–14 particularly those that are making the transition between childhood and adulthood. In addition, television viewing and video gaming seem to remain consistently high through this adolescent period.5,15 However, little is known about how other important sedentary behaviors, such as leisure-time computer use, change with age and time.9 Overall, however, these kinds of longitudinal findings are derived from cohorts that are followed as participants age and generally lack a concurrent comparison of secular changes over time in these populations. Therefore, given these data, it is difficult to tease apart longitudinal trends from secular trends and to estimate the true extent to which these changes are attributable to age versus time.
Because of the important links between physical activity, inactivity, and health-related outcomes,16–19 it is critical that we gain a comprehensive understanding of both secular and longitudinal trends in physical activity and various sedentary behaviors (extending beyond television viewing), particularly during important stages of the life course, such as adolescence, when long-term behavior patterns are being established. By quantifying dynamic changes and emerging trends in health behavior, we may be able to inform better health promotion strategies and appropriately tailor these efforts to the ever-changing environment in which we live.
Project EAT-I (Eating Among Teens) and Project EAT-II (a follow-up study) follow a large cohort of individuals longitudinally through various stages of the adolescent transition to young adulthood. These studies have allowed us the unique opportunity to examine important health behavior trends that are occurring concurrently as a result of age (longitudinal trends from early to midadolescence and from mid- to late adolescence) and time (secular trends in midadolescence, from 1999 to 2004). The objective of this research is to evaluate these 5-year longitudinal and secular trends in MVPA, television viewing, and leisure-time computer use in a large, diverse cohort of teens.
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METHODS |
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Study Design and Population
Project EAT-II is a follow-up study of Project EAT-I, a study of the socioenvironmental, personal, and behavioral determinants of dietary intake and weight status among a large and ethnically diverse adolescent population.20 For the initial EAT-I study, 3 school districts in a metropolitan area that served socioeconomically and ethnically diverse communities were identified and invited to participate. Within the 3 districts, 53 public junior high and high schools were contacted on the basis of criteria of large size and current nonparticipation in other research studies. Of those, 31 schools agreed to take part in the study. Within schools, all students who were enrolled in health, physical education, or science classes were invited to take part in the study. These classes were chosen in an effort to reach a census of students in each grade served by the school.
In Project EAT-I, 4746 junior and senior high school students in these 31 Minnesota schools completed in-class surveys and anthropometric measures during the 1998–1999 academic year. The goal of Project EAT-II was to resurvey all original participants to examine changes in their eating patterns and weight status 5 years later (2003–2004) as the younger cohort progressed from early adolescence (junior high school; age range: 11–15 years) to midadolescence (high school; age range: 15–18 years) and the older cohort progressed from midadolescence (high school; age range: 14–18 years) to late adolescence/young adulthood (post–high school; age range: 18–23 years). Of the original study population, 1074 (22.6%) were lost to follow-up for various reasons, primarily missing contact information at Project EAT-I (n = 411) and no address found at follow-up (n = 591). Of the remaining 3672 participants who were contacted by mail, 2516 completed surveys, representing 53.0% of the original cohort and 68.4% of participants who could be contacted for Project EAT-II.
Data Collection
Project EAT-II surveys were sent by mail to the address provided by the participant during Project EAT-I. Internet tracking services were used to identify correct addresses when mail was returned as a result of an incorrect address. To enhance participant response, nonresponders were sent 2 reminder postcards and 3 additional survey packets. Data collection ran from April 2003 to June 2004 and was conducted by the Data Collection and Support Services in the Division of Epidemiology and Community Health at the University of Minnesota. The University of Minnesota’s Institutional Review Board Human Subjects Committee approved all study protocols.
Physical Activity and Sedentary Behavior
Projects EAT-I and EAT-II surveys included several questions to assess physical activity and sedentary behavior, developed from survey items that were validated previously and are similar to those that are used in national surveillance systems.8 Questions related to physical activity were adapted specifically from the widely used Godin Leisure-Time Exercise Questionnaire21,22 and Planet Health surveys.23 These 2 survey items individually assessed moderate and vigorous activity, asking, "In a usual week, how many hours do you spend doing the following activities... ." Vigorous activity was described as strenuous, during which the heart beats rapidly, whereas moderate activity was described as not exhausting. More than 10 examples of specific activities were given after each question. Possible responses ranged from 0 to 
6 hours per week.
In addition, survey items that were adapted from Planet Health23 were included to assess usual time spent (1) "watching TV & videos" and (2) "using a computer (not for homework)." Participants reported average hours per weekday spent engaging in these behaviors, as well as average hours per weekend day (Saturday or Sunday). Possible categorical responses ranged from 0 to 5 hours per day. Test–retest correlations for these survey items range from 0.66 to 0.80,24 and validation of similar questions has been described in detail elsewhere.25 Project EAT survey questions that assessed physical activity and sedentary behaviors were identical in 1999 and 2004.
Sociodemographic Characteristics
Gender, age, ethnicity/race, and socioeconomic status (SES) were based on self-report in Project EAT-1. The primary determinant of SES was parental educational level, defined by the highest level of educational attainment of either parent. In addition, an algorithm26 was developed to take into account family eligibility for public assistance, eligibility for free or reduced-coast school meals, and employment status of the mother and the father.
Statistical Analysis
Longitudinal and secular trends were estimated and tested for all outcomes using individuals who had nonmissing data at both time points for the particular outcome being examined (physical activity, sedentary behavior). Missingness ranged from 1% to 4% of the longitudinal sample. Mixed-model regressions27 including a main effect for year (1999 or 2004), cohort (younger or older), and a year by cohort interaction along with a random effect for individuals to account for longitudinal correlation were used to estimate and test differences in means (hours per week of MVPA, television viewing, and computer use) across time, both within and across cohorts. These models used data from Projects EAT-I and EAT-II to estimate the extent to which behavior change is associated with age (longitudinal trends from early to midadolescence and from mid- to late adolescence) and time (secular trends in midadolescence, from 1999 to 2004). All mixed-model regression analyses were stratified by gender and adjusted for baseline SES and race to balance observed demographic differences across the cohorts. In addition, adjustment for age (in years) was made in the mixed-model regression so that estimates and tests for secular changes in high school adolescents from 1999 to 2004 would compare high school adolescents with the same mean age. SAS 8.2 statistical software (SAS Institute, Cary, NC) was used for all analyses.
Because attrition in the study population during the 5-year study period did not occur completely at random, the data were weighted to adjust for differential response rates in Project EAT-II using a response propensity method.28 The use of this method with Project EAT data are described in detail elsewhere, where it has been evaluated as a means of correcting potential response bias.29 The weighted Project EAT-II sample has a similar demographic makeup of the original Project EAT-I sample. The weighted ethnic/racial proportions are as follows: 48.3% white, 18.9% black, 5.8% Hispanic, 19.6% Asian, 3.6% Native American, and 3.8% mixed or other race. The weighted SES proportions are as follows: 17.8% low, 18.9% middle-low, 26.7% middle, 23.3% middle-high, and 13.3% high.
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RESULTS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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The longitudinal Project EAT sample (n = 2516) included 806 adolescents in the younger cohort (440 female and 366 male junior high school students) and 1710 adolescents in the older cohort (946 female and 764 male high school students). Therefore, one third (32.0%) of the participants were in the younger cohort; in Project EAT-I (1999), their mean age was 12.8 years (SD: 0.8), and in EAT-II (2004), their mean age was 17.2 years (SD: 0.6). Two thirds (68.0%) of the participants were in the older cohort; in EAT-I (1999), their mean age was 15.8 years (SD: 0.8), and in EAT-II (2004), their mean age was 20.4 years (SD: 0.8).
Longitudinal Trends
Across this adolescent transition period, there were substantial longitudinal decreases in MVPA and increases in leisure-time computer use (Table 1). MVPA among girls dramatically declined from 5.9 to 4.9 hours/week during the transition from early to midadolescence and from 5.1 to 3.5 hours/week during mid- to late adolescence. Among girls who were making the transition from early to midadolescence, television/video viewing time decreased significantly (by 2.2 hours/week), and leisure-time computer use showed a nonsignificant trend toward increasing. Computer use significantly increased among older girls during the transition from mid- to late adolescence.
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In contrast, boys showed a more delayed decline in physical activity, such that MVPA did not decline from early to midadolescence but did decline significantly from mid- to late adolescence (from 6.5 to 5.1 hours/week). Leisure-time computer use increased substantially from both early to midadolescence (from 11.4 to 15.2 hours/week) and mid- to late adolescence (from 10.4 to 14.2 hours/week). However, television/video viewing did not show longitudinal changes among boys of either age group.
Secular Trends
Figures 1 and 2 allow us to compare graphically the magnitude of these longitudinal changes with secular changes in midadolescence between 1999 and 2004. During this 5-year period, secular trends during midadolescence also indicate further striking increases in computer use between 1999 and 2004. Midadolescent boys engaged in 10.4 hours/week of leisure-time computer use in 1999, compared with 15.2 hours/week in 2004, marking a nearly 50% increase in this sedentary behavior (Table 1). Midadolescent girls also engaged in 2.3 more hours of computer use in 2004, compared with 1999. There was no evidence of a secular decline in MVPA between 1999 and 2004 for either boys or girls.
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DISCUSSION |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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This study examined concurrent longitudinal and secular trends in MVPA, television viewing, and leisure-time computer use in a large, diverse cohort of early, mid-, and late adolescents. Our findings indicate substantial longitudinal decreases in MVPA and increases in computer use as these youth progressed through adolescence. Striking secular trends further highlight the increasing leisure-time computer use among midadolescents between 1999 and 2004. To our knowledge, this is one of the first studies of its kind to examine secular and longitudinal trends in various sedentary behaviors (beyond simply television viewing), as well as MVPA, across this critical age when adolescents are making the transition into adulthood.
These longitudinal findings are consistent with previous literature that indicated a substantial age-related decline in physical activity.10–14 Also echoing previous work,10 our findings illustrate that although MVPA declines among both adolescent boys and girls, the girls seem to begin this downward trend at a particularly young age. It is interesting that our data suggest that gender differences in longitudinal computer use follow an opposing trend, beginning to rise only among older girls but increasing across boys of all ages, whereas television viewing seems to remain more consistent as adolescents age.
Our findings also confirm the limited previous research that has been available to assess secular trends over time. For example, our finding of no significant MVPA decline across this 5-year period is consistent with that of the Youth Risk Behavior Surveillance System (YRBSS), which currently is one of the few epidemiologic surveys that has been used to assess secular trends in adolescent MVPA and television in the United States.8 The YRBSS showed that between 1993 and 2003, there was only a 3% decline in the percentage of youth who achieved sufficient levels of vigorous activity, and from 1999 to 2003, there was a 2% decline in those who achieved sufficient levels of moderate activity.8 YRBSS data also indicate that the percentage of adolescents who watch an average of 3 hours of television on school days declined from 43% in 1999 to 38% in 2003. However, although our findings of relatively stable secular (time-dependent) trends in MVPA and television viewing are supported by other research, they continue to be somewhat surprising given the striking increase in obesity over time.
Important societal shifts have occurred during the past decade in the United States and beyond,30 many of which have been influenced seemingly by the increasing accessibility of various technologic advances. The price of personal computers has declined dramatically during the past 5 years in particular, and access to related capabilities, such as the Internet, has increased, marking a now unprecedented level of computer availability in the average American household.9 It is reasonable to suspect that societal changes of this magnitude have had an important influence on individual-level activity patterns. Data included in a recent report from the Kaiser Family Foundation9 also suggested that computer use among 8- to 18-year-olds more than doubled from 1999 to 2004, with substantial increases specifically in computer gaming and Internet "surfing."
Although technologic advances long have been credited for declines in occupation-related physical activity,6 these are among the first data-driven findings to suggest that such changes have an important impact on leisure-time activity, particularly among youth. Although the time that individuals spend watching television, for example, may be remaining stable, it appears that total sedentary time may be increasing via increases in other sedentary behaviors (eg, leisure-time computer use), particularly among boys. Therefore, it is important that our future research and surveillance systems reflect these population changes by assessing various sedentary behaviors (beyond television viewing) to understand better the current activity patterns and effectively inform large-scale health promotion efforts.
Our data and findings have several limitations that should be noted, such as the use of self-reported measures of MVPA and sedentary behaviors. Although Project EAT uses standardized 7-day recall survey items that have been deemed relevant and practical for epidemiologic studies31 and are similar to those that are used in the YRBSS, self-reported measures contain biases and error that we cannot address. In addition, although our sample is large and diverse, it is drawn from one geographic region in the Midwestern United States. Finally, Project EAT surveyed more participants in the older cohort (older cohort: n = 1710; younger cohort: n = 806), thereby yielding additional statistical power to detect significant associations during the transition from mid- to late adolescence, compared with those from early to midadolescence.
This research has numerous strengths. For example, through the use of this large and diverse study population, we have the ability to capture robust trends in activity-related behavior that occur over a substantial period of time (5 years) during these key adolescent transition periods. Although many scholars contend that adolescence is a critical period for the foundation of long-term behavior patterns, few large, epidemiologic studies are available to assess changes in weight-related health behavior across these ages. More important, however, the unique study design of Project EAT allowed us to assess changes in MVPA and television and leisure-time computer use both longitudinally and secularly, which provides the important concurrent comparison of age versus time effects.
Our findings clearly indicate that adolescents’ behavior patterns are shifting and that sedentary behaviors such as computer use are increasing. However, many of the implications of these longitudinal and secular trends currently are unknown, and our findings raise a number of important questions for discussion in terms of public health impact and future research. For example, in this research we have not been able to "decompose" leisure-time computer use, estimating the time that adolescents spend in specific computer-based activities (eg, gaming, instant messaging, Internet searching, information gathering). More detailed time-use data are needed to assess these issues, as well as the possibility that computer use may be replacing time spent in other activities, including both sedentary and active pursuits. In addition, future work is needed to explore the extent to which computer use resembles television viewing as a characteristic of sedentary behavior patterns. In previous research, decreases in television viewing have not been associated consistently with gains in MVPA,32–36 but is the same true of computer use? Future research also is needed to assess the types of health-related messages (both positive and negative) that are being disseminated to adolescents via the computer (eg, through online advertising and other exposures). Furthermore, it may be worthwhile to explore mechanisms through which youth-oriented health promotion efforts can capitalize on these trends in adolescent computer use, as a means of information dissemination, behavior monitoring, and/or interventions that focus on characteristics that are appealing specifically to this age group (eg, periodically instant messaging "get up and move" reminders to teens; promoting active computer gaming, such as Konami’s Dance Dance Revolution for the Microsoft Xbox).37
Ultimately, there is a need to gain a better understanding of how health promotion strategies can address these population-wide trends. Our findings indicate that youth spend a substantial amount of time engaging in leisure-time computer use, suggesting that public health initiatives that target reductions in television viewing may need to be broadened to address a wider range of sedentary activities. Recommendations from clinicians and public health practitioners also may need to address such a range of activities. The American Academy of Pediatrics has voiced long-standing concerns regarding excess media exposure on the physical and mental health of children and adolescents, and current American Academy of Pediatrics policy statements recommend limiting screen time (which encompasses multiple forms of entertainment media, including television, videos, computer, and video games)38 to no more than 1 to 2 hours per day.38,39 Previous research has shown that a majority of pediatricians are aware of these recommendations, agree with the recommendations, and believe that they are at least "a little effective" when they are given to parents/children; however, the implementation of these recommendations in clinical settings could be improved substantially.40 Clinicians also may be able to provide additional recommendations to parents, such as to encourage alternative nonmedia activities and emphasize opportunities for physical activity, to establish an "electronic media-free" zone in children’s and adolescents’ bedrooms, and to serve as positive role models for their children by engaging in regular physical activity and limiting their own media choices.41
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CONCLUSIONS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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Clearly the culture of adolescence is changing, including adolescent behavior patterns. Our findings indicate that adolescents are experiencing unfavorable shifts in activity patterns, such as longitudinal decreases in MVPA, coupled with dramatic longitudinal and secular increases in sedentary behaviors that are attributable specifically to computer use. As the prevalence of obesity continues to rise in this and other age groups, we need to continue to advance our understanding of dynamic population-wide trends in behavior patterns, so as to inform effectively a broad array of health promotion strategies and public policies that aim to prevent obesity.
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ACKNOWLEDGMENTS |
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This study was supported by grant R40 MC 00319 from the Maternal and Child Health Bureau (Title V, Social Security Act), Health Resources and Services Administration, Department of Health and Human Services. Additional salary support was provided by the Obesity Prevention Center at the University of Minnesota.
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FOOTNOTES |
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Accepted Jun 23, 2006.
Address correspondence to Melissa C. Nelson, PhD, RD, Division of Epidemiology and Community Health, University of Minnesota, 1300 S 2nd St, WBOB Suite 300, Minneapolis, MN 55454-1015. E-mail: nelson{at}epi.umn.edu
The authors have indicated they have no financial relationships relevant to this article to disclose.
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