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. 2012 Jul;22(7):487–498. doi: 10.1016/j.annepidem.2012.03.002

Is Intergenerational Social Mobility Related to the Type and Amount of Physical Activity in Mid-Adulthood? Results from the 1946 British Birth Cohort Study

Richard J Silverwood a,, Mary Pierce b, Dorothea Nitsch a, Gita D Mishra c, Diana Kuh b
PMCID: PMC3383988  PMID: 22534178

Abstract

Purpose

Greater levels of leisure-time or moderate-vigorous physical activity have consistently been found in those with greater socioeconomic position (SEP). Less is known about the effects of intergenerational social mobility.

Methods

We examined the influence of SEP and social mobility on mid-adulthood physical activity in the Medical Research Council National Survey of Health and Development. Two sub-domains of SEP were used: occupational class and educational attainment. Latent classes for walking, cycling, and leisure-time physical activity (LTPA) were used, plus sedentary behavior at age 36. Associations between types of physical activity and SEP were examined with the use of logistic or multinomial logistic regression.

Results

Being a manual worker oneself or having a father who was a manual worker was, relative to nonmanual work, associated with lower levels of sedentary behavior and greater walking activity, but also with lower LTPA. Compared with those who remained in a manual occupational class, upward occupational mobility was associated with more sedentary behavior, less walking, and increased LTPA. Associations with downward mobility were in the opposite directions. Similar results were obtained for educational attainment.

Conclusions

This study found clear evidence of social differences in physical activity. Persistently high SEP and upward social mobility were associated with greater levels of LTPA but also increased sedentary behavior and less walking.

Key Words: Cohort Study, Education, Exercise, Leisure Activities, Occupation, Physical Activity, Social Mobility, Socioeconomic Position

Selected Abbreviations and Acronyms: CVD, cardiovascular disease; LCA, latent class analysis; LTPA, leisure-time physical activity; SEP, socioeconomic position

Introduction

Epidemiological evidence has confirmed the benefits of regular physical activity on health and well-being (1). Promoting a physically active lifestyle is now considered a major element of public health policies, and increases in leisure-time physical activity (LTPA) have been reported in some countries (2, 3).

Recently, time spent in sedentary behavior, as defined by prolonged sitting or reclining characterized by low energy expenditure, has been shown to be associated with obesity (4–6), metabolic syndrome (7, 8), type 2 diabetes mellitus (5, 9), markers of cardiovascular disease (CVD) risk (4), and all-cause and CVD mortality (10), independently of levels of physical activity.

Greater levels of leisure-time or moderate-vigorous activity have consistently been found in those with greater socioeconomic position (SEP) (11). However, most studies to date have focused on LTPA only (12). In addition, many studies of physical activity and SEP use only a single subdomain of SEP (11), reducing the robustness of their conclusions.

Little is known about the effects of social mobility on levels of physical activity. One recent study of more than 2000 Australian adults age 26 to 36 years found that persistently high SEP and upward social mobility (indicated by educational level) from childhood to adulthood were associated with increased physical activity (13). Upward social mobility has also been found to be associated with decreased prevalence of physical inactivity in studies of health behaviors among Finnish adolescents (14) and older women in the UK (15). Better understanding the relationship between social mobility and physical activity may provide important insights into how social inequalities lead to poorer health.

The Medical Research Council National Survey of Health and Development is a nationally representative population-based birth cohort study that provides an opportunity to study the patterns of physical activity in a sample of more than 3800 men and women between age 31 and 53 years and relate them to SEP and inter-generational social mobility.

The aim of this work is to examine whether intergenerational change in SEP (as indicated by occupational class and educational attainment) was associated with differences in the types and patterns of physical activity, and if so, how.

Methods

Participants

The sample comprised National Survey of Health and Development participants, an initial sample of 2815 men and 2547 women followed since their birth in March 1946 (16). Medical and social data have been collected 23 times by home visits, medical examinations, and postal questionnaires.

Measures

Self-reported information about physical activity was collected to differing extents at several sweeps of data collection. At ages 31, 36, and 43, a number of questions were asked about specific types of physical activity, and at age 53 a more general question was asked regarding sports, vigorous leisure activities, or exercises. In addition, at 36 years, more detailed information was collected, with study participants asked about the frequency and duration of participation in many different leisure time activities in the preceding month on the basis of the Minnesota leisure time physical activity questionnaire (17).

In the present analysis we focused on four different types of self-reported physical activity: (1) sedentary behavior during the working day; (2) walking during the working day and for pleasure; (3) cycling during the working day and for pleasure; and (4) LTPA. Sedentary behavior was examined at age 36 years; walking at 36 and 43 years; cycling at 31, 36, and 43 years; and LTPA at ages 36, 43 and 53. To summarize, categorical variables in each of these dimensions were derived at available ages on the basis of self-reported questionnaire information. A total of 16 response variables were used across the four types of physical activity, and 3847 study participants (71.7% of the original cohort) were included on at least one of these measures. By the beginning of the period considered in the present analysis, 6.0% of the original cohort had died, 9.7% had permanently refused, and 12.0% were living abroad (16).

Two different subdomains of SEP were examined: occupational class and educational attainment. Prospectively collected data were used to classify study members according to the occupational class of the head of the household at age 36 years and the occupational class of their father in 1950 (i.e., at age 4 years) on the basis of the British Registrar General's Social Classification (18): ‘I and II’, ‘III non-manual’, ‘IV manual’, or ‘IV and V’. Intergenerational occupational mobility was defined by combining ‘I and II’ with ‘III nonmanual’ (“nonmanual”) and ‘IV manual’ with ‘IV and V’ (“manual”) then defining the following four groups: ‘manual/manual’, ‘manual/nonmanual (upward)’, ‘nonmanual/manual (downward)’, and ‘nonmanual/nonmanual’.

Prospectively collected information on study members’ educational qualifications achieved by age 26 years were grouped into ‘no qualifications’, ‘lower secondary’ (‘O’-levels or equivalent, usually attained at 16 years), ‘advanced secondary’ (‘A’-levels or equivalent, usually attained at 18 years), and ‘degree-level or equivalent’. Father’s educational level, reported in 1952, was classified as ‘primary only’, ‘primary and further education (no qualifications attained)’, ‘secondary only or primary and further education or higher’, or ‘secondary and higher’. Intergenerational educational mobility was defined by combining the lower two classes in each education variable (‘lower’) and the more advance two classes (‘advanced’) then defining the following four groups: ‘lower/lower’, ‘lower/advanced (upward)’, ‘advanced/lower (downward)’, and ‘advanced/advanced’. Of the 3847 study members with at least one measure of physical activity, 77.7% had information on intergenerational occupational mobility and 84.5% had information on inter-generational educational mobility.

Statistical Analyses

Different self-reported measures of physical activity were obtained at different time points, leading to complex, correlated data. Latent class analysis (LCA) was used to reduce the many derived measures of physical activity to a more useable form. LCA models identify a categorical latent (i.e., unobserved) class variable which is measured by a number of observed response variables. The objective is to identify the response variables that best distinguish between classes and to categorize people into their most likely classes given their observed responses (19).

A more detailed account of how the LCA was performed is available elsewhere (20). The purpose of the present paper is to use the previously derived latent classes, so only a brief description is given here. LCA was conducted separately for each type of physical activity (apart from sedentary behavior, for which data reduction was not required), and all participants with at least one measure of a given type of physical activity were included. Separate LCA models for males and females were used because these were found to give the best fit to the data. The most appropriate number of latent classes for each type of physical activity was determined with the use of several different measures of model fit (20). Posterior probabilities were derived by the LCA to quantify the probability with which an individual with given values for the response variables belonged to each latent class.

Logistic or multinomial logistic regression of one latent variable on another was used to examine pairwise associations between the latent variables for each type of physical activity. These analyses used robust standard errors and were weighted by LCA posterior probabilities to account for the uncertainty in class membership where appropriate. Associations between the latent variable for each type of physical activity and each measure of SEP were examined in the same manner. Analyses were repeated by use of the most likely latent class in unweighted logistic regressions for comparison.

Analyses were repeated by use of the study member's own occupational class at age 36 years (rather than the head of household's) and mother's educational level (rather than father's) as comparisons. Models were also fitted with adjustment for the season of data collection. Latent class analysis was performed with Mplus 6 (21), whereas (multinomial) logistic regression was conducted using Stata 11 (22).

Results

For the majority of physical activity and SEP variables, there was strong (p < .001) evidence of a gender difference (Tables 1 and 2). The LCAs for walking, cycling, and LTPA included 3587, 3776, and 3671 study participants, respectively. The most appropriate number of latent classes was found to be two for walking (both males and females), two for cycling (both males and females), and three for LTPA (both males and females) (20).

Table 1.

Physical activity variables in the Medical Research Council National Survey of Health and Development

Physical activity variable Males
Females
Total
n % n % n %
Sedentary behavior
 Time sitting down during day at age 36 years
 More than half to practically all the time 534 32.6 333 20.3 867 26.4
 Less than to about half the time 586 35.7 545 33.2 1131 34.5
 Almost none of the time 520 31.7 765 46.6 1285 39.1
 Total 1640 1643 3283
Walking
 Age 36 years
 Time spent walking during day
 Less than half the time 747 46.1 497 30.5 1244 38.3
 At least half the time 512 31.6 594 36.5 1106 34.0
 Practically all the time 363 22.4 538 33.0 901 27.7
 Total 1622 1629 3251
 Time spent walking to work
 <5 minutes 1219 80.2 679 67.4 1898 75.1
 5–15 minutes 224 14.7 249 24.7 473 18.7
 16+ minutes 77 5.1 79 7.9 156 6.2
 Total 1520 1007 2527
 Time spent walking for pleasure in last month
 0 hours 583 35.9 492 30.0 1075 33.0
 1–6 hours 538 33.2 583 35.6 1121 34.4
 > 6 hours 502 30.9 564 34.4 1066 32.7
 Total 1623 1639 3262
 Age 43 years
 Distance walked on average weekday
 ≤0.5 miles 493 30.8 611 38.2 1104 34.5
 0.5–2.5 miles 709 44.2 785 49.0 1494 46.6
 >2.5 miles 401 25.0 205 12.8 606 18.9
 Total 1603 1601 3204
Cycling
 Age 31 years
 Frequency of cycling
 Seldom or never 780 79.0 830 82.2 1610 80.6
 Less than once a week 106 10.7 105 10.4 211 10.6
 At least once a week 102 10.3 75 7.4 177 8.9
 Total 988 1010 1998
 Age 36 years
 Time spent cycling per week
 0 minutes 1334 80.8 1392 83.6 2726 82.2
 1–99 minutes to work or 1–59 minutes outside work 134 8.1 102 6.1 236 7.1
 100+ minutes to work or 60+ minutes outside work 184 11.1 171 10.3 355 10.7
 Total 1652 1665 3317
 Age 43 years
 Distance cycled on average weekday
 0 miles 1390 87.1 1384 86.6 2774 86.9
 0.1–1.5 miles 67 4.2 120 7.5 187 5.9
 >1.5 miles 139 8.7 94 5.9 233 7.3
 Total 1596 1598 3194
Leisure time physical activity
 Age 36 years
 Gardening
 Inactive 334 20.9 432 26.5 766 23.7
 Less active 538 33.7 684 42.0 1222 37.9
 Most active 724 45.4 514 31.5 1238 38.4
 Total 1596 1630 3226
 DIY
 Inactive 506 31.7 987 59.9 1493 46.0
 Less active 458 28.7 402 24.4 860 26.5
 Most active 631 39.6 259 15.7 890 27.4
 Total 1595 1648 3243
 Sport or leisure activities
 Inactive 512 32.2 706 43.8 1218 38.0
 Less active 466 29.3 525 32.5 991 30.9
 Most active 614 38.6 382 23.7 996 31.1
 Total 1592 1613 3205
 Age 43 years
 Vigorous housework or cleaning
 Inactive 1146 71.3 429 26.7 1575 49.0
 Less active 356 22.2 537 33.4 893 27.8
 Most active 105 6.5 642 39.9 747 23.2
 Total 1607 1608 3215
 Heavy gardening
 Inactive 860 54.3 1101 68.9 1961 61.6
 Less active 412 26.0 302 18.9 714 22.4
 Most active 311 19.6 196 12.3 507 15.9
 Total 1583 1599 3182
 Heavy building or DIY
 Inactive 1153 75.0 1522 95.4 2675 85.4
 Less active 203 13.2 47 2.9 250 8.0
 Most active 182 11.8 26 1.6 208 6.6
 Total 1538 1595 3133
 Sports or vigorous leisure activities
 Inactive 774 48.6 888 55.4 1662 52.0
 Less active 315 19.8 455 28.4 770 24.1
 Most active 503 31.6 259 16.2 762 23.9
 Total 1592 1602 3194
 Age 53 years
 Regular vigorous physical activity
 Inactive 705 48.1 772 50.9 1477 49.5
 Less active 434 29.6 397 26.2 831 27.9
 Most active 326 22.3 349 23.0 675 22.6
 Total 1465 1518 2983

DIY = do-it-yourself.

All % are column percentages.

χ2 test for difference between males and females: p < .001.

χ2 test for difference between males and females: 0.001 ≤ p < .05.

Table 2.

Socioeconomic position variables in subjects who have data for at least one dimension of physical activity in the Medical Research Council National Survey of Health and Development

Socioeconomic position variable Males (n = 1940)
Females (n = 1907)
Total (n = 3847)
n % n % n %
Head of household’s occupational class at age 36
 I and II 729 44.9 623 39.6 1352 42.3
 III nonmanual 166 10.2 257 16.3 423 13.2
 III manual 523 32.2 429 27.3 952 29.8
 IV and V 204 12.6 265 16.8 469 14.7
 Total 1622 1574 3196
Father’s occupational class in 1950
 I and II 405 22.8 392 22.7 797 22.7
 III nonmanual 329 18.5 328 19.0 657 18.7
 III manual 540 30.4 531 30.7 1071 30.5
 IV and V 504 28.3 478 27.6 982 28.0
 Total 1778 1729 3507
Intergenerational occupational mobility
 Manual/manual 519 34.7 474 32.6 993 33.7
 Manual/nonmanual (upward) 359 24.0 368 25.3 727 24.7
 Nonmanual/manual (downward) 144 9.6 174 12.0 318 10.8
 Nonmanual/nonmanual 475 31.7 436 30.0 911 30.9
 Total 1497 1452 2949
Educational qualifications achieved by age 26
 No qualifications 712 39.0 694 38.7 1406 38.9
 Lower secondary 370 20.3 616 34.3 986 27.3
 Advanced secondary 486 26.6 395 22.0 881 24.4
 Degree level 256 14.0 89 5.0 345 9.5
 Total 1824 1794 3618
Father’s education
 Primary only 959 56.4 979 58.2 1938 57.3
 Primary and further education (no qualifications attained) 242 14.2 212 12.6 454 13.4
 Secondary only or primary and further education or higher 235 13.8 225 13.4 460 13.6
 Secondary and greater 265 15.6 267 15.9 532 15.7
 Total 1701 1683 3384
Inter-generational educational mobility
 Lower/lower 804 49.4 979 60.4 1783 54.9
 Lower/advanced (upward) 343 21.1 170 10.5 513 15.8
 Advanced/lower (downward) 169 10.4 210 12.9 379 11.7
 Advanced/advanced 312 19.2 262 16.2 574 17.7
 Total 1628 1621 3249

All % are column percentages.

χ2 test for difference between males and females: p < .001.

More details regarding the interpretation of the latent classes are available elsewhere (20). To summarize, the two walking latent classes can be considered as ‘low’ (males 52.8% using estimated posterior class membership probabilities, females 33.5%) and ‘high’ (males 47.2%, females 66.5%) levels of activity; the two cycling classes as ‘low’ (males 91.4%, females 82.1%) and ‘high’ (males, 8.6%; females, 17.9%) levels of activity; and the three LTPA classes as ‘low’ activity (males 46.2%, females 48.2%), ‘gardening and do-it-yourself’ (males 22.8%, females 16.5%), and ‘sport and leisure’ (males 31.0%, females 35.3%).

In LCA the separation of the classes is often quantified in terms of entropy, which takes values between 0 and 1, with scores close to 1 indicating clearer classifications (23). The male walking classes (0.66) and cycling classes (0.87 and 0.64 for males and females, respectively) were clearly separated and the LTPA classes reasonably so (0.56 and 0.57), although entropy for female walking was low (0.37).

The three latent variables (walking, cycling, LTPA) and sedentary behavior at age 36 were associated with each other (Tables 3 and 4). Male respondents who reported being most sedentary during the working day at age 36 were much less likely to be in the high walking and cycling latent classes compared with those in the least sedentary group but more likely to be in the sport and leisure LTPA latent class. In females, only the association with walking latent class was observed. Males in the high walking latent class were less likely to be in the sport and leisure LTPA latent class compared with those in the low walking latent class. Both males and females in the high cycling latent class were more likely to be in the sport and leisure LTPA latent class.

Table 3.

Associations between physical activity (latent) variables in the Medical Research Council National Survey of Health and Development (males)

Walking latent class
Cycling latent class
Leisure time physical activity latent class
n Low (%) High (%) LRT p n Low (%) High (%) LRT p n Low (%) Gardening and DIY (%) Sport and leisure (%) LRT p
Sedentary behavior at age 36 years
 Much sitting 1640 98.3 1.7 <.001 1639 94.3 5.7 .003 1638 38.7 22.1 39.2 <.001
 Average sitting 51.5 48.5 91.1 8.9 43.1 23.7 33.2
 Little sitting 18.5 81.5 89.5 10.5 50.1 23.8 26.1
Walking latent class
 Low 1794 92.3 7.7 .04 1795 40.4 23.0 36.7 .001
 High 90.2 9.8 49.5 23.0 27.5
Cycling latent class
 Low 1807 45.3 22.6 32.1 <.001
 High 33.8 27.5 38.7

DIY = do-it-yourself; LRT = likelihood ratio test.

All % are row percentages.

Table 4.

Associations between physical activity (latent) variables in the Medical Research Council National Survey of Health and Development (females)

Walking latent class
Cycling latent class
Leisure time physical activity latent class
n Low (%) High (%) LRT p n Low (%) High (%) LRT p n Low (%) Gardening and DIY (%) Sport and leisure (%) LRT p
Sedentary behavior at age 36 years
 Much sitting 1643 59.3 40.7 <.001 1643 81.1 18.9 .38 1643 48.6 13.8 37.5 .02
 Average sitting 42.3 57.7 82.1 17.9 47.3 14.5 38.2
 Little sitting 18.7 81.3 79.6 20.4 44.5 19.3 36.2
Walking latent class
 Low 1788 81.8 18.2 .23 1790 46.6 14.7 38.8 .03
 High 80.3 19.7 45.6 17.7 36.7
Cycling latent class
 Low 1793 48.3 15.9 35.8 <.001
 High 36.2 19.6 44.2

DIY = do-it-yourself; LRT = likelihood ratio test.

All % are row percentages.

Tables 5 and 6 show cross-tabulations of the physical activity latent variables with the SEP variables for males and females, respectively. For males, being a manual worker was relative to being a nonmanual worker, associated with lower levels of adult sedentary behavior during the working day (13.4% much sitting in classes IV and V compared with 43.6% in classes I and II), greater levels of walking (66.1% high compared with 32.8%), but also lower LTPA (24.3% sport and leisure compared with 39.4%). For female respondents, LTPA showed a similarly strong association, walking was somewhat less marked, and sedentary behavior showed a nonlinear association, with the III nonmanual class corresponding to the greatest level of adult sedentary behavior.

Table 5.

Associations between physical activity (latent) variables and socioeconomic position in the Medical Research Council National Survey of Health and Development (males)

Sedentary behavior (age 36 years)
Walking (ages 36 and 43 years)
Cycling (ages 31, 36, and 43 years)
LTPA (age 36, 43, and 53 years)
n Much sitting % Average sitting % Little sitting % n Low % High % n Low % High % n Low % Gardening and DIY % Sport and leisure %
Head of household’s occupational class at age 36 years
 I and II 727 43.6 39.9 16.5 729 67.2 32.8 729 92.0 8.0 727 35.9 24.7 39.4
 III nonmanual 164 50.6 39.6 9.8 166 72.5 27.5 166 93.2 6.8 166 38.7 23.4 37.9
 III manual 521 19.2 28.8 52.0 523 45.0 55.0 523 91.6 8.4 523 49.4 24.3 26.3
 IV and V 201 13.4 34.8 51.7 204 33.9 66.1 204 88.1 11.9 204 58.8 16.9 24.3
 N 1613 1622 1622 1620
 LRT p <.001 <.001 .17 <.001
 LRT p (trend) <.001 <.001 .14 <.001
Father’s occupational class in 1950
 I and II 346 41.3 35.3 23.4 375 65.1 34.9 399 90.3 9.7 384 39.9 22.4 37.7
 III nonmanual 277 45.1 35.7 19.1 309 67.0 33.0 322 94.1 5.9 317 37.7 25.0 37.3
 III manual 465 26.9 35.9 37.2 510 52.4 47.6 534 92.8 7.2 516 45.1 23.7 31.2
 IV and V 427 24.6 36.3 39.1 453 47.5 52.5 489 89.3 10.7 476 51.9 22.6 25.5
 N 1515 1647 1744 1693
 LRT p <.001 <.001 .01 <.001
 LRT p (trend) <.001 <.001 .41 <.001
Intergenerational occupational social mobility
 Manual/Manual 516 17.1 29.7 53.3 519 40.7 59.3 519 90.9 9.1 519 53.4 21.8 24.9
 Manual/nonmanual (upward) 359 38.4 45.1 16.4 359 63.5 36.5 359 91.7 8.3 358 38.0 26.8 35.2
 Nonmanual/manual (downward) 143 20.3 35.0 44.8 144 49.4 50.6 144 90.0 10.0 144 46.3 25.9 27.7
 Nonmanual/nonmanual 471 50.3 35.5 14.2 475 72.6 27.4 475 93.1 6.9 474 35.1 23.4 41.5
 N 1489 1497 1497 1495
 LRT p <.001 <.001 .40 <.001
Educational qualifications achieved by age 26 years
 No qualifications 585 18.5 33.3 48.2 646 40.6 59.4 698 91.0 9.0 668 54.8 20.7 24.5
 Lower secondary 332 31.6 38.9 29.5 352 58.5 41.5 363 91.0 9.0 360 44.1 21.8 34.1
 Advanced secondary 427 38.2 37.7 24.1 463 62.9 37.1 480 92.7 7.3 469 37.3 26.5 36.2
 Degree level 219 58.9 35.2 5.9 236 80.0 20.0 252 88.6 11.4 241 29.4 27.1 43.5
 N 1563 1697 1793 1738
 LRT p <.001 <.001 .18 <.001
 LRT p (trend) <.001 <.001 .68 <.001
Father’s educational level
 Primary only 813 25.0 36.2 38.9 884 49.4 50.6 937 91.8 8.2 913 48.7 23.0 28.3
 Primary and further education (no qualifications attained) 219 34.2 35.6 30.1 229 59.1 40.9 238 90.9 9.1 233 40.9 24.9 34.2
 Secondary only or primary and further education or greater 204 39.2 40.2 20.6 221 62.9 37.1 233 90.7 9.3 225 41.1 24.9 34.0
 Secondary and greater 219 48.9 38.4 12.8 244 71.0 29.0 260 91.9 8.1 250 35.4 21.9 42.8
 N 1455 1578 1668 1621
 LRT p <.001 <.001 .90 <.001
 LRT p (trend) <.001 <.001 .81 <.001
Intergenerational educational social mobility
 Lower/lower 688 21.8 34.3 43.9 739 45.7 54.3 788 91.1 8.9 763 51.9 21.5 26.6
 Lower/advanced (upward) 306 37.3 40.2 22.5 328 63.5 36.5 338 92.4 7.6 332 36.3 28.6 35.1
 Advanced/lower (downward) 145 29.7 44.1 26.2 159 54.4 45.6 166 92.9 7.1 163 45.3 20.6 34.1
 Advanced/advanced 264 52.3 36.7 11.0 287 74.6 25.4 308 90.4 9.6 293 33.1 25.1 41.8
 N 1403 1513 1600 1551
 LRT p <.001 <.001 .61 <.001

LRT = likelihood ratio test; LTPA = leisure-time physical activity.

All % are row percentages.

Table 6.

Associations between physical activity (latent) variables and socioeconomic position in the Medical Research Council National Survey of Health and Development (females)

Sedentary behaviour (age 36 years)
Walking (ages 36 and 43 years)
Cycling (ages 31, 36, and 43 years)
LTPA (age 36, 43, and 53 years)
n Much sitting (%) Average sitting (%) Little sitting (%) n Low (%) High (%) n Low (%) High (%) n Low (%) Gardening and DIY (%) Sport and leisure (%)
Head of household’s occupational class at age 36 years
 I and II 613 18.1 36.4 45.5 623 38.4 61.6 623 80.6 19.4 623 36.6 17.0 46.4
 III nonmanual 256 36.7 25.8 37.5 257 37.0 63.0 257 81.7 18.3 257 49.3 13.6 37.1
 III manual 425 21.6 31.5 46.8 429 33.2 66.8 429 79.1 20.9 429 52.0 17.2 30.8
 IV and V 262 12.2 28.6 59.2 265 25.3 74.7 265 83.6 16.4 265 54.5 17.9 27.6
 N 1556 1574 1574 1574
 LRT p value <.001 <.001 .35 <.001
 LRT p value (trend) .005 <.001 .58 <.001
Father’s occupational class in 1950
 I and II 341 19.6 36.7 43.7 373 37.2 62.8 387 77.7 22.3 389 33.8 18.3 47.9
 III nonmanual 287 25.1 33.1 41.8 308 40.0 60.0 326 79.7 20.3 311 36.7 19.6 43.7
 III manual 467 20.3 29.8 49.9 500 33.2 66.8 524 83.7 16.3 508 50.7 16.3 33.0
 IV and V 415 17.6 33.7 48.7 451 30.6 69.4 469 80.6 19.4 459 55.8 14.2 30.0
 N 1510 1632 1706 1657
 LRT p .08 .004 .05 <.001
 LRT p (trend) .14 .002 .09 <.001
Intergenerational occupational social mobility
 Manual/manual 469 17.7 30.7 51.6 474 29.0 71.0 474 81.9 18.1 474 57.5 16.0 26.5
 Manual/nonmanual (upward) 364 22.3 30.5 47.3 368 34.8 65.2 368 82.5 17.5 368 48.3 13.8 37.9
 Nonmanual/manual (downward) 172 18.6 31.4 50.0 174 33.8 66.2 174 77.6 22.4 174 40.0 20.7 39.3
 Nonmanual/nonmanual 430 24.9 35.6 39.5 436 40.8 59.2 436 78.8 21.2 436 32.9 18.7 48.4
 N 1435 1452 1452 1452
 LRT p .01 <.001 .19 <.001
Educational qualifications achieved by age 26 years
 No qualifications 600 14.7 31.5 53.8 655 28.4 71.6 683 81.8 18.2 665 57.1 15.9 27.0
 Lower secondary 543 28.0 27.1 44.9 578 36.0 64.0 611 81.4 18.6 586 45.2 17.5 37.3
 Advanced secondary 349 14.9 44.4 40.7 347 40.3 59.7 391 79.9 20.1 379 30.7 17.2 52.1
 Degree level 77 26.0 42.9 31.2 81 50.3 49.7 86 72.7 27.3 84 28.6 13.5 57.9
 N 1569 1688 1771 1714
 LRT p <.001 <.001 .11 <.001
 LRT p (trend) <.001 <.001 .05 <.001
Father’s educational level
 Primary only 855 20.1 30.8 49.1 919 32.0 68.0 963 82.3 17.7 935 52.9 15.7 31.4
 Primary and further education (no qualifications attained) 183 23.5 32.8 43.7 195 35.8 64.2 211 78.9 21.1 199 43.5 18.4 38.1
 Secondary only or primary and further education or higher 201 19.9 34.3 45.8 217 37.6 62.4 225 79.3 20.7 217 36.8 19.4 43.8
 Secondary and higher 226 20.4 38.9 40.7 251 40.8 59.2 262 77.8 22.2 255 31.1 16.6 52.3
 N 1465 1582 1661 1606
 LRT p .25 .006 .14 <.001
 LRT p (trend) .04 <.001 .03 <.001
Intergenerational educational social mobility
 Lower/lower 854 20.4 28.6 51.1 917 30.6 69.4 965 82.0 18.0 933 53.4 16.2 30.3
 Lower/advanced (upward) 152 18.4 48.0 33.6 158 42.7 57.3 167 79.5 20.5 162 37.9 15.9 46.2
 Advanced/lower (downward) 181 27.1 30.4 42.5 200 36.3 63.7 208 81.0 19.0 201 44.0 18.9 37.2
 Advanced/advanced 231 15.2 42.0 42.9 249 42.0 58.0 259 75.8 24.2 252 24.5 17.2 58.3
 N 1418 1524 1599 1548
 LRT p <.001 <.001 .07 <.001

LRT = likelihood ratio test; LTPA = leisure-time physical activity.

All % are row percentages.

Similar patterns were observed for father’s occupational class in 1950, although differences between manual and nonmanual occupational classes were generally reduced. Compared with participants who remained in the manual occupational class, those from a similar background but who were upwardly mobile by age 36 reported more sedentary behavior during the working day and less walking in men only, and increased LTPA in both men and women. Compared with men who remained in the nonmanual occupational class, men who were downwardly mobile reported less sedentary behavior, more walking, and less LTPA. In women whose fathers were nonmanual occupational class there were similar patterns, although the magnitudes of the differences were reduced.

In women who were nonmanual occupational class at age 36, there were residual differences in LTPA between those with manual and nonmanual occupational class fathers. This effect also was observed in those who were manual occupational class at age 36.

Although the effects of occupational class on physical activity were most often seen as a manual/nonmanual split, the effects of educational class were more linear. In both men and women, having more advanced educational qualifications was associated with increased sedentary behavior during the working day and decreased walking, but also with increased LTPA. Similar patterns were observed for study members' father's educational level, although the magnitudes of the associations were generally reduced.

Those with upward intergenerational mobility into the advanced educational class reported more sedentary behavior during the day and less walking but more LTPA. Similarly, study participants demonstrating downward educational mobility reported less sedentary behavior during the working day and more walking (men only) but less LTPA (women only).

There was some evidence of a residual effect of father's educational class. Among study members of advanced educational class, having a father of advanced rather than lower educational class led to increased sedentary behavior during the working day (men only), reduced walking (men only), and increased LTPA (women only). A similar residual effect was seen for sedentary behavior in male study members of lower educational class.

Repeating the analysis using most likely latent classes in unweighted logistic regressions made little difference to the percentage of study participants corresponding to each level of SEP and did not affect the conclusions drawn (results not shown).

In models with adjustment for the effects of seasonal variation of physical activity the estimated associations changed very little (results not shown).

Repeating the analyses using women's own occupational class at age 36 led to an amplification of the effects of occupational class and intergenerational occupational mobility on sedentary behavior and, to a lesser extent, walking in women, although the LTPA results were essentially unchanged (results not shown). When mother's rather than father's educational level was used (and intergenerational educational mobility defined on this basis), the direction and overall strength of associations were generally very similar (results not shown).

Discussion

In a large, population-based, prospective study we found SEP and intergenerational social mobility to be associated with previously identified latent class variables for different types of physical activity and an additional observed variable for sedentary behavior. Manual occupational classes and lower educational classes, both for the study member and their father, were associated with lower levels of sedentary behavior during the working day and greater levels of walking activity, most likely through the subject having a type of job that requires more walking. Greater levels of LTPA (particularly sport and leisure activity) were found to be more common in those of nonmanual occupational class and those with more advanced educational qualifications, most likely as a conscious compensation for the detrimental effect on their health of having a more sedentary occupation.

The large differences in physical activity generally found between study members whose SEP (occupational or educational class) changed from their father's and study members whose SEP remained the same as their father's suggests that it was largely their own SEP that determined their pattern of physical activity rather than their parents’, illustrating the positive potential of social mobility. However, the residual effect of father's SEP in those with the same SEP in adulthood suggests that when SEP changes between generations, it may take further generations before the full implications are felt.

Our findings suggest that it is important to consider several types of activity rather than extrapolating from only one in studies of physical activity. We cannot be certain whether doing more LTPA (generally those of greater SEP) amounts to more total physical activity than being less sedentary and walking more (lower SEP). People who are particularly active during their working day may well be too tired to engage in greater levels of activity in their leisure time.

The observed associations were often less clear in female respondents. Although this may be attributable to less distinct separation of the latent classes (21), it may also indicate that using the occupational class of the head of household (usually a male) at age 36 years is a relatively poorer measure of SEP in women, leading to attenuation. Using women's own occupational class at age 36 led to stronger associations with sedentary behavior and, to a lesser extent, walking. Although women's own occupational class may naturally be more strongly associated with occupation-based physical activity—sedentary behavior was based on time sitting down during the day—head of household's occupational class is likely to provide a more reliable general measure of SEP at age 36 years because many women in this cohort were at home looking after children.

The data used in the present analysis were collected from 1977 to 1999 and secular trends in physical activity and women’s employment may mean that the relationships observed in this cohort have changed in later cohorts. In recent years, decreases in occupational physical activity coupled with an upward trend in sports participation have been noted in the UK (24). In addition, the increase in the female labor market (25) is likely to have led to more similar patterns of occupational activity across the sexes.

The acknowledged association between greater SEP and greater levels of leisure-time or moderate-vigorous activity (11) was clearly replicated in our study. Cleland et al. (13) found that persistently high SEP and upward social mobility from childhood to adulthood were associated with increases in physical activity. Although our study did not allow us to examine changes in physical activity, we found that high SEP in childhood or adult life, or upward intergenerational social mobility were associated with greater levels of LTPA. However, we also found these groups to correspond to lower levels of walking and greater sedentary behavior.

In addition, Cleland et al. found that childhood SEP had no lasting impact on physical activity levels once adult SEP was taken into account. Similar findings have been reported in other studies (26, 27). In our analysis, however, we found a residual effect of father’s SEP for some types of physical activity. In a Dutch prospective cohort of 25- to 74–year-old subjects, van de Mheen et al. (28) similarly found childhood SEP to be associated with frequent physical activity after adjustment for current SEP, although only in female subjects.

There is much strength to this analysis. Several different physical activities were examined and the concordance of our conclusions using two different subdomains of SEP suggests our findings are robust. The LCA approach identified clearly separated latent classes which provided a good fit to the data, although for walking in females the separation was less clear.

All study participants with at least one nonmissing variable within a given type of physical activity were included in that LCA under the assumption of missing at random. The missing at random assumption is difficult to assess but seems reasonable given the strong correlations observed between most variables within the same type of physical activity (20).

A second missing data assumption is that study participants excluded from the final logistic regression models can be considered missing completely at random. As with any long-running cohort study, some attrition as the result of deaths and emigration is unavoidable, and avoidable loss as the result of refusal and failure to trace is relatively low in this study (29). The effective sample sizes ranged between 2821 and 3564, or between 72.8% and 92.0% of those alive and still living in the UK at the start of follow-up for this study. Exclusion was found to be associated with educational level for several of the physical activity-SEP combinations, with lower educational level resulting in increased exclusion, reflecting the greater attrition at lower educational levels previously reported in this cohort (29). However, no associations were found between exclusion and other SEP variables. The effective sample sizes compare favorably with the number of study participants successfully contacted at each data collection (16). Because the 3035 study participants successfully contacted at age 53 have been found to be broadly representative of native-born adults living in England, Scotland, and Wales at the time of data collection (29), we are confident that our samples were similarly broadly representative.

However, there are also limitations. Data availability determined at what ages and to what extent we could examine different types of physical activity, with only a single measure of sedentary behavior being available. Measures of physical activity obtained from questionnaires may be prone to nondifferential measurement error (30). The retrospectively self-reported measures may have led to recall bias, potentially differentially through social desirability and approval influencing the responses (31). Although the physical activity data were almost always collected between spring and autumn, misclassification caused by seasonal variability of activity behaviors (32) may have been present. However, adjustment for the season of data collection made very little difference to the estimated associations.

In addition, some of the physical activity items may be differentially relevant to people in different socioeconomic groups. For example, those of lower SEP may be less likely to have homes with gardens, so would by necessity do less gardening. This may partially confound apparent social differences in physical activity (33).

This descriptive analysis has made no attempt to disentangle the complex relationships between socioeconomic position, physical activity, and the many potential confounding or mediating variables between the two, such as health status, mobility limitation, and obesity. Each of these could be considered as either a cause or an effect of low levels of physical activity, and a rigorous investigation of these issues is beyond the scope of the present analysis. As such, we cannot rule out the possibility that the observed associations may be at least partly the result of unmeasured confounders.

An alternative approach to that used in the present analysis would have been to include all the physical activity response variables in a single LCA to derive overarching physical activity latent classes. We decided against this approach because we wanted to capture specific types of physical activity that would also be applicable to different settings and to maintain comparability with other cohorts, as most studies concentrate on a single type of physical activity.

In conclusion, this study found clear evidence of social differences in different types of physical activity. Persistently high SEP and upward social mobility were associated with greater levels of LTPA but also with greater levels of sedentary behavior during the working day and less walking. In addition, the lack of strong correlation between most of the types of physical activity suggests that studies examining relationships between physical activity and health should consider many types of activity rather than extrapolating from only one.

Acknowledgments

This work was supported by Kidney Research UK (grant number RP34/2009) to R.J.S. and D.N.; the UK Medical Research Council to M.P., D.K., and G.D.M.; and the Australian National Health and Medical Research Council to G.D.M. Data collection was funded by the UK Medical Research Council. None of the funders had any role in the analysis or interpretation of the data, the writing of the report, or the decision to submit the paper for publication.

References

  • 1.Warburton D.E., Nicol C.W., Bredin S.S. Health benefits of physical activity: the evidence. CMAJ. 2006;174:801–809. doi: 10.1503/cmaj.051351. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Haskell W.L., Lee I.M., Pate R.R., Powell K.E., Blair S.N., Franklin B.A. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation. 2007;116:1081–1093. doi: 10.1161/CIRCULATIONAHA.107.185649. [DOI] [PubMed] [Google Scholar]
  • 3.Craig C.L., Russell S.J., Cameron C., Bauman A. Twenty-year trends in physical activity among Canadian adults. Can J Public Health. 2004;95:59–63. doi: 10.1007/BF03403636. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Jakes R.W., Day N.E., Khaw K.T., Luben R., Oakes S., Welch A. Television viewing and low participation in vigorous recreation are independently associated with obesity and markers of cardiovascular disease risk: EPIC-Norfolk population-based study. Eur J Clin Nutr. 2003;57:1089–1096. doi: 10.1038/sj.ejcn.1601648. [DOI] [PubMed] [Google Scholar]
  • 5.Hu F.B., Li T.Y., Colditz G.A., Willett W.C., Manson J.E. Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women. JAMA. 2003;289:1785–1791. doi: 10.1001/jama.289.14.1785. [DOI] [PubMed] [Google Scholar]
  • 6.Stamatakis E., Hirani V., Rennie K. Moderate-to-vigorous physical activity and sedentary behaviours in relation to body mass index-defined and waist circumference-defined obesity. Br J Nutr. 2009;101:765–773. doi: 10.1017/S0007114508035939. [DOI] [PubMed] [Google Scholar]
  • 7.Dunstan D.W., Salmon J., Owen N., Armstrong T., Zimmet P.Z., Welborn T.A. Associations of TV viewing and physical activity with the metabolic syndrome in Australian adults. Diabetologia. 2005;48:2254–2261. doi: 10.1007/s00125-005-1963-4. [DOI] [PubMed] [Google Scholar]
  • 8.Ford E.S., Kohl H.W., 3rd, Mokdad A.H., Ajani U.A. Sedentary behavior, physical activity, and the metabolic syndrome among U.S. adults. Obes Res. 2005;13:608–614. doi: 10.1038/oby.2005.65. [DOI] [PubMed] [Google Scholar]
  • 9.Hu F.B., Leitzmann M.F., Stampfer M.J., Colditz G.A., Willett W.C., Rimm E.B. Physical activity and television watching in relation to risk for type 2 diabetes mellitus in men. Arch Intern Med. 2001;161:1542–1548. doi: 10.1001/archinte.161.12.1542. [DOI] [PubMed] [Google Scholar]
  • 10.Katzmarzyk P.T., Church T.S., Craig C.L., Bouchard C. Sitting time and mortality from all causes, cardiovascular disease, and cancer. Med Sci Sports Exerc. 2009;41:998–1005. doi: 10.1249/MSS.0b013e3181930355. [DOI] [PubMed] [Google Scholar]
  • 11.Gidlow C., Johnston L.H., Crone D., Ellis N., James D. A systematic review of the relationship between socio-economic position and physical activity. Health Education J. 2006;65:338–367. [Google Scholar]
  • 12.Corder K., Ogilvie D., van Sluijs E.M. Invited commentary: physical activity over the life course—whose behavior changes, when, and why? Am J Epidemiol. 2009;170:1078–1081. doi: 10.1093/aje/kwp273. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Cleland V.J., Ball K., Magnussen C., Dwyer T., Venn A. Socioeconomic position and the tracking of physical activity and cardiorespiratory fitness from childhood to adulthood. Am J Epidemiol. 2009;170:1069–1077. doi: 10.1093/aje/kwp271. [DOI] [PubMed] [Google Scholar]
  • 14.Karvonen S., Rimpelä A.H., Rimpelä M.K. Social mobility and health related behaviours in young people. J Epidemiol Community Health. 1999;53:211–217. doi: 10.1136/jech.53.4.211. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Watt H.C., Carson C., Lawlor D.A., Patel R., Ebrahim S. Influence of life course socioeconomic position on older women’s health behaviors: findings From the British Women’s Heart and Health Study. Am J Public Health. 2009;99:320–327. doi: 10.2105/AJPH.2007.129288. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Wadsworth M., Kuh D., Richards M., Hardy R. Cohort Profile: the 1946 National Birth Cohort (MRC National Survey of Health and Development) Int J Epidemiol. 2006;35:49–54. doi: 10.1093/ije/dyi201. [DOI] [PubMed] [Google Scholar]
  • 17.Taylor H.L., Jacobs D.R., Jr., Schucker B., Knudsen J., Leon A.S., Debacker G. A questionnaire for the assessment of leisure time physical activities. J Chronic Dis. 1978;31:741–755. doi: 10.1016/0021-9681(78)90058-9. [DOI] [PubMed] [Google Scholar]
  • 18.Office of Population Censuses and Surveys . HMSO; London: 1970. Classification of Occupations. [Google Scholar]
  • 19.Nylund K.L., Asparouhov T., Muthen B.O. Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Structural Equation Modeling. 2007;14:535–569. [Google Scholar]
  • 20.Silverwood R.J., Nitsch D., Pierce M., Kuh D., Mishra G.D. Characterising longitudinal patterns of physical activity in mid-adulthood using latent class analysis: results from a prospective cohort study. Am J Epidemiol. 2011;174:1406–1415. doi: 10.1093/aje/kwr266. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Muthén L.K., Muthén B.O. Muthén & Muthén; Los Angeles, CA: 2010. Mplus statistical software, release 6. [Google Scholar]
  • 22.StataCorp . StataCorp; College Station, TX: 2010. Stata statistical software, release 11. [Google Scholar]
  • 23.Muthén L.K., Muthén B.O. 6th ed. Muthén & Muthén; Los Angeles, CA: 1998. 2007. Mplus User’s Guide. [Google Scholar]
  • 24.Stamatakis E., Ekelund U., Wareham N.J. Temporal trends in physical activity in England: the Health Survey for England 1991 to 2004. Prev Med. 2007;45:416–423. doi: 10.1016/j.ypmed.2006.12.014. [DOI] [PubMed] [Google Scholar]
  • 25.Duffield M. Trends in female employment 2002. Labour Market Trends. 2002;110:605–616. [Google Scholar]
  • 26.Brunner E., Shipley M.J., Blane D., Smith G.D., Marmot M.G. When does cardiovascular risk start? Past and present socioeconomic circumstances and risk factors in adulthood. J Epidemiol Community Health. 1999;53:757–764. doi: 10.1136/jech.53.12.757. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Wannamethee S.G., Whincup P.H., Shaper G., Walker M. Influence of fathers’ social class on cardiovascular disease in middle-aged men. Lancet. 1996;348:1259–2363. doi: 10.1016/S0140-6736(96)02465-8. [DOI] [PubMed] [Google Scholar]
  • 28.van de Mheen H., Stronks K., Looman C.W., Mackenbach J.P. Does childhood socioeconomic status influence adult health through behavioural factors? Int J Epidemiol. 1998;27:431–437. doi: 10.1093/ije/27.3.431. [DOI] [PubMed] [Google Scholar]
  • 29.Wadsworth M.E., Butterworth S.L., Hardy R.J., Kuh D.J., Richards M., Langenberg C. The life course prospective design: an example of benefits and problems associated with study longevity. Soc Sci Med. 2003;57:2193–2205. doi: 10.1016/s0277-9536(03)00083-2. [DOI] [PubMed] [Google Scholar]
  • 30.Ferrari P., Friedenreich C., Matthews C.E. The role of measurement error in estimating levels of physical activity. Am J Epidemiol. 2007;166:832–840. doi: 10.1093/aje/kwm148. [DOI] [PubMed] [Google Scholar]
  • 31.Adams S.A., Matthews C.E., Ebbeling C.B., Moore C.G., Cunningham J.E., Fulton J. The effect of social desirability and social approval on self-reports of physical activity. Am J Epidemiol. 2005;161:389–398. doi: 10.1093/aje/kwi054. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Tucker P., Gilliland J. The effect of season and weather on physical activity: a systematic review. Public Health. 2007;121:909–922. doi: 10.1016/j.puhe.2007.04.009. [DOI] [PubMed] [Google Scholar]
  • 33.Shaw B.A., Liang J., Krause N., Gallant M., McGeever K. Age differences and social stratification in the long-term trajectories of leisure-time physical activity. J Gerontol B Psychol Sci Soc Sci. 2010;65:756–766. doi: 10.1093/geronb/gbq073. [DOI] [PMC free article] [PubMed] [Google Scholar]

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