Age and the Social Stratification of Long-Term Trajectories of Physical Activity
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Age and the Social Stratification of Long-Term Trajectories of Physical Activity ACKNOWLEDGEMENTS This research was supported by the grant R01 AG031109-02 (Benjamin Shaw, PI) from the National Institute on Aging. Benjamin A. Shaw 1,2 Jersey Liang 3 , Neal Krause 3 , Mary Gallant 1,2 , and Kelly McGeever 2 1 University at Albany, School of Public Health; 2 University at Albany, Center for Social and Demographic Analysis 3 University of Michigan, School of Public Health BACKGROUND • Males, whites, highly educated, and young adults were more active, on average. • Gender and education differences were larger, and race differences smaller, among older cohorts. • On average, levels of physical activity decreased within individuals during the follow-up period • However, significant age differences indicated that younger adults increased, while older adults decreased. • Elevated levels of activity among whites diminished over time, especially among older adults (see figure). • Gender differences widened over time among older adults (see figure). • Gender-based differences did not remain after accounting for time-varying covariates; however, race-based differences did remain. METHODS • Data source •Americans’ Changing Lives study (4 waves: 1986-2002) •3,360 individuals; 9,757 observations •Mean age at baseline: 54.18 (SD = 17.60) • Key measure •Physical activity : “How often do you work in the garden or yard?” “Engage in active sports or exercise?” “Take walks?” •Data analysis •Hierarchical Linear Modeling, with occasions of measurement nested within individuals Level 1 Model: Activity ij = π 0i + π 1i Time + π 2i Z + ij Level 2 Model: π 0i = 00 + 01 Baseline Age i + 02 X i + 03 Baseline Age i *X i + u 0i π 1i = 10 + 11 Baseline Age i + 12 X i + 13 Baseline Age i *X i + u 1i π 2i = 20 Where Z equals time-varying predictors (health, social and psychological resources; and X equals time constant predictors (race:1=white, 0=black; gender: 1=male, 0=female; and education level); models also control for attrition, and time-varying occupational type (blue collar vs. other). RESULTS • Physical activity has well-documented health benefits and is considered one of the most effective measures for preventing and controlling chronic illnesses, enhancing psychological well-being, and preventing premature death. • However, current data from the Behavioral Risk Factor Surveillance Study (BRFSS) indicate that approximately 50% of all adults do not meet recommended levels of regular physical activity (CDC 2007); these data also show substantial and persistent social stratification in rates of physical activity. • Although these data are useful in charting our nation’s progress towards its public health goals, repeated cross-sectional assessments of physical activity are insufficient in that they reveal little about within- persons changes in physical activity over time. • Assessing within-persons changes in physical activity over extended periods of time (i.e., trajectories) can add to our understanding of the stratification of physical activity by revealing how, and why, various forms of physical activity stratification might change as adults at different points in the life course, and from different birth cohorts, grow older. •This study suggests that with increasing age, adults may be spending less of their discretionary time – which itself may actually be expanding with age – participating in physical activities. •On average, stable or increasing levels of activity over time were evident in adults up to the baseline age of approximately 33 years, with adults older than age 33 at baseline exhibiting trajectories that were increasingly negative. •This transition is perhaps earlier in the life course than would be expected if declines in physical activity were due only to the onset of health and functional problems. •We also recognize that some of our observed age differences may also be due to cohort differences in leisure-time physical activity. •Our findings suggest that excess decline in leisure-time physical activity among women is primarily due to gender differences in time-varying health factors. •Furthermore, the observed convergence of race differences in activity appears to be largely the result of declines in rates of physical activity among older whites, while rates among blacks remain fairly stable. •This may be a case of old age “leveling the playing field” with respect to activity, a healthy survivor CONCLUSIONS 1.00 1.50 2.00 2.50 3.00 Age A ctivity Male Female 1.00 1.50 2.00 2.50 3.00 Age A ctivity White Black Coefficient p Coefficient p FIXED EFFECTS For intercept (π 0i ) Intercept 2.766 .000 1.992 .000 Baseline age -.100 .000 -.092 .000 Education .114 .000 .033 .011 Race (White) .054 .000 .043 .001 Gender (Male) .136 .000 .125 .000 Age*Education .029 .031 .027 .028 Age*Race -.026 .036 -.018 .105 Age*Gender .051 .000 .035 .001 For time slope (π 1i ) Intercept -.037 .000 -.010 .298 Baseline age -.034 .001 -.027 .006 Education .001 .955 -.003 .722 Race (White) -.046 .000 -.046 .000 Gender (Male) .016 .050 .008 .311 Age*Education .006 .553 .003 .741 Age*Race -.030 .003 -.029 .002 Age*Gender .014 .085 .008 .342 Time-varying preds SRH Functional Lims Underweight Overweight Married Support Integration Mastery Self-esteem .066 -.170 -.030 -.085 .094 .052 .113 .014 .011 .000 .000 .421 .000 .000 .000 .000 .148 .339 RANDOM EFFECTS Intercept (u ) .288 .000 .219 .000 Americans’ Changing Lives, 1986-2002; Physical activity over time
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Table 2. Americans’ Changing Lives, 1986-2002; Physical activity over time. Age and the Social Stratification of Long-Term Trajectories of Physical Activity. Benjamin A. Shaw 1,2 Jersey Liang 3 , Neal Krause 3 , Mary Gallant 1,2 , and Kelly McGeever 2 - PowerPoint PPT Presentation
Transcript of Age and the Social Stratification of Long-Term Trajectories of Physical Activity
Age and the Social Stratification of Long-Term Trajectories of Physical Activity
ACKNOWLEDGEMENTSThis research was supported by the grant R01 AG031109-02 (Benjamin Shaw, PI) from the National Institute on Aging.
Benjamin A. Shaw1,2 Jersey Liang3, Neal Krause3, Mary Gallant1,2, and Kelly
McGeever2
1University at Albany, School of Public Health; 2 University at Albany, Center for Social and Demographic Analysis3University of Michigan, School of Public Health
BACKGROUND
• Males, whites, highly educated, and young adults were more active, on average.• Gender and education differences were larger, and race differences smaller, among older cohorts.
• On average, levels of physical activity decreased within individuals during the follow-up period• However, significant age differences indicated that younger adults increased, while older adults
decreased.• Elevated levels of activity among whites diminished over time, especially among older adults (see
figure).• Gender differences widened over time among older adults (see figure).• Gender-based differences did not remain after accounting for time-varying covariates; however, race-
based differences did remain.
METHODS
• Data source•Americans’ Changing Lives study (4 waves: 1986-2002)•3,360 individuals; 9,757 observations•Mean age at baseline: 54.18 (SD = 17.60)
• Key measure•Physical activity: “How often do you work in the garden or yard?” “Engage in active sports or exercise?” “Take walks?”
•Data analysis•Hierarchical Linear Modeling, with occasions of measurement nested within individuals
Level 1 Model: Activityij = π0i + π1iTime + π2iZ + ij
Level 2 Model: π0i = 00 + 01Baseline Agei + 02Xi + 03Baseline Agei*Xi + u0i
π1i = 10 + 11Baseline Agei + 12Xi + 13Baseline Agei*Xi + u1i
π2i = 20
Where Z equals time-varying predictors (health, social and psychological resources; and X equals time constant predictors (race:1=white, 0=black; gender: 1=male, 0=female; and education level); models also control for attrition, and time-varying occupational type (blue collar vs. other).
RESULTS• Physical activity has well-documented health
benefits and is considered one of the most effective measures for preventing and controlling chronic illnesses, enhancing psychological well-being, and preventing premature death.
• However, current data from the Behavioral Risk Factor Surveillance Study (BRFSS) indicate that approximately 50% of all adults do not meet recommended levels of regular physical activity (CDC 2007); these data also show substantial and persistent social stratification in rates of physical activity.
• Although these data are useful in charting our nation’s progress towards its public health goals, repeated cross-sectional assessments of physical activity are insufficient in that they reveal little about within-persons changes in physical activity over time.
• Assessing within-persons changes in physical activity over extended periods of time (i.e., trajectories) can add to our understanding of the stratification of physical activity by revealing how, and why, various forms of physical activity stratification might change as adults at different points in the life course, and from different birth cohorts, grow older.
•This study suggests that with increasing age, adults may be spending less of their discretionary time – which itself may actually be expanding with age – participating in physical activities.
• On average, stable or increasing levels of activity over time were evident in adults up to the baseline age of approximately 33 years, with adults older than age 33 at baseline exhibiting trajectories that were increasingly negative.
• This transition is perhaps earlier in the life course than would be expected if declines in physical activity were due only to the onset of health and functional problems.
• We also recognize that some of our observed age differences may also be due to cohort differences in leisure-time physical activity.
•Our findings suggest that excess decline in leisure-time physical activity among women is primarily due to gender differences in time-varying health factors.
•Furthermore, the observed convergence of race differences in activity appears to be largely the result of declines in rates of physical activity among older whites, while rates among blacks remain fairly stable.
• This may be a case of old age “leveling the playing field” with respect to activity, a healthy survivor effect among blacks, or perhaps the results of cultural influences
CONCLUSIONS
1.00
1.50
2.00
2.50
3.00
Age
Act
ivity
MaleFemale
1.00
1.50
2.00
2.50
3.00
Age
Act
ivity
WhiteBlack
Coefficient p Coefficient p
FIXED EFFECTS
For intercept (π0i)
Intercept 2.766 .000 1.992 .000
Baseline age -.100 .000 -.092 .000
Education .114 .000 .033 .011
Race (White) .054 .000 .043 .001
Gender (Male) .136 .000 .125 .000
Age*Education .029 .031 .027 .028
Age*Race -.026 .036 -.018 .105
Age*Gender .051 .000 .035 .001
For time slope (π1i)
Intercept -.037 .000 -.010 .298
Baseline age -.034 .001 -.027 .006
Education .001 .955 -.003 .722
Race (White) -.046 .000 -.046 .000
Gender (Male) .016 .050 .008 .311
Age*Education .006 .553 .003 .741
Age*Race -.030 .003 -.029 .002
Age*Gender .014 .085 .008 .342
Time-varying preds
SRH
Functional Lims
Underweight
Overweight
Married
Support
Integration
Mastery
Self-esteem
.066
-.170
-.030
-.085
.094
.052
.113
.014
.011
.000
.000
.421
.000
.000
.000
.000
.148
.339
RANDOM EFFECTS
Intercept (u0i)
Time slope (u1i)
.288
.028
.000
.000
.219
.027
.000
.000
Level-1 (ij) .263 .251
Americans’ Changing Lives, 1986-2002; Physical activity over time
