Physical inactivity is currently a global public health concern due to its impact on morbidity and all-cause mortality.1,2 More than 27.5% of the worldwide adult population in 2016 did not reach a sufficient level of physical activity (PA) according to World Health Organization (WHO) guidelines.2 These physical inactivity levels have implied, in turn, a fourth leading cause of death and a huge impact involved, on average, in the 8% of mortality of noncommunicable diseases, such as cardiovascular, respiratory, metabolic, cancer, or mental.1 As a result, physical inactivity was declared a pandemic.1 The revisited version of the WHO PA recommendations,3 launched in 2020, stated that any amount of PA is better than none, and every move counts to encourage society to practice PA but also highlighted the need to limit the time that we should spend sitting or in other types of sedentary behavior (SB, ≤1.5 times the basal metabolic rate), such as reclining or lying postures, television or screen time, and driving automobiles. This latter statement about SB comes from many epidemiological systematic reviews and meta-analyses that have evidenced that sitting time is an independent risk factor for the development of major noncommunicable diseases, even among those that engage in regular PA.4–6 In this sense, the detrimental effects of SB are not far behind those of physical inactivity. High levels of SB may worsen the detriment effects of physical inactivity.7–10 Nonetheless, what constitutes a high level of SB remains uncertain. Some studies have pointed out that 6, 7, or 8 hours per day of sitting time increased the risk of all-cause mortality from self-reported measures (eg, questionnaires),7–10 while objective measures (eg, accelerometery) have established the risk at 9.5 daily hours of SB.11
Both PA and SB depend on the conditions in which people live and work, influencing their health-related lifestyle.12,13 The availability and accessibility to resources in our physical and social environments modulate our healthy opportunities and choices for PA and SB.14 Furthermore, different PA intensities (ie, vigorous, moderate, walking) and types of SB may also be unequally distributed and affected by these socioeconomic living conditions.15,16 There is in fact a social gradient in lifestyle behaviors that is closely related to different individual characteristics, such as occupational status, educational level, and household income,12,14,17 that compound socioeconomic status (SES), among others. People with a disadvantaged SES are more likely to be physically inactive and to experience the deleterious health effects of a sedentary lifestyle.14,18 A previous survey study in the European population reported that low and middle social class were less likely to meet WHO PA recommendations and more likely to present barriers compared with high social class17 in accordance with other health-related lifestyle determinants (eg, tobacco use, alcohol use, or low diet quality), which contributes to social inequalities of ill health.12 However, paradoxically, other studies have shown that sitting time might inversely describe this socioeconomic gradient, that is, the higher the SES, the higher the sedentary time.19,20 Higher levels of overall daily SB have been associated with higher educational level.20 This association could be due to a higher sitting time at work in upper occupational levels (white collar), in addition to leisure-time PA that is greater in high SES, which reflects the fact that PA and SB could differ in their determinants. Nonetheless, these findings are only focused on the German population with limited evidence across the European population. This study, in a broader sample, could yield relevant and robust findings to deeply address these discrepancies to establish and implement public health policies for promoting an active lifestyle, increasing PA, and decreasing SB. In this regard, PA and SB in high-income countries and regions, such as the European Union, have worsened since the 21st century.2 The physical inactivity prevalence in these developed countries was twice that of low-income countries, whereas the highest sitting time was observed in Europe.2,21,22 Studying the differences among socioeconomic groups in both PA and SB may yield crucial insights to establish more specific risk groups in which to implement measures to increase PA and/or decrease SB. Additionally, epidemiological evidence demonstrates that SB can exacerbate the detrimental effects of physical inactivity and even have negative health effects regardless of the amount of PA7–10; therefore, both PA and SB should be conjointly considered as part of an active lifestyle.
Thus, using a large and representative cross-sectional survey,23 we aimed to assess the following in the European adult population (18–64 y old): (1) the general differences and socioeconomic inequalities of the distribution of moderate, vigorous, and walking PA and sitting time; (2) the association of the main, best-known SES indicators with total PA and SB separately; and (3) the association of SES with total PA and SB conjointly. We hypothesized that there are wider population differences in PA than SB and that high socioeconomic groups paradoxically report higher PA and SB levels.
Methods
Data
We employed data from the Eurobarometer 88.4 cross-sectional survey collected in December of 2017.24 This survey comprises an initial total sample of 28,031 interviewed aged 15 and over from the 28 European Union Country Members, with approximately 1000 respondents per country. The sampling method was performed by multistage random sampling, in which potential sampling points were systematically determined in each country according to population size and density by individual unit and type of area stratification. In addition, the gender, age, region, and size of the locality were included in the iteration procedure. Finally, trained interviewers conducted face-to-face interviews in the appropriate national language, randomly selecting one person from each household. More information is provided elsewhere.24 Ethical approval and informed consent from participants were not needed due to the anonymized secondary data employed in the present study.
Physical Activity and Sedentary Behavior Assessment
The total amount of both PA and SB was assessed by self-reporting using the short version of the International PA Questionnaire (IPAQ).25 Vigorous, moderate, and walking activity were measured in a typical week, reporting the average time spent per day at each intensity and the corresponding number of days. However, the Eurobarometer evaluates time spent per day at 6 intervals: 0, 30 minutes or less, 31 to 60 minutes, 61 to 90 minutes, 91 to 120 minutes, and more than 120 minutes. We therefore used median values to estimate the average time spent per day, employing a value of 135 minutes for the last interval. Thereby, we calculated weekly vigorous (VPA), moderate (MPA), and walking PA, as well as weekly total PA, as the sum of vigorous, moderate, and walking time, denoted here as health-enhancing PA (HEPA). Overall, SB was also measured in intervals of daily sitting time using the following question: How much time do you spend sitting on a usual day? This may include time spent at a desk, visiting friends, studying, or watching television. The intervals ranged from less than 1 hour, followed by 1 to 1.5 hours¸ with subsequent increments of 1 hour until the last interval, more than 8.5 hours. Here, we also computed median values in minutes using 540 minutes (9 h) for the last interval. Lastly, we established quartiles for both HEPA and sitting time. For weekly HEPA, quartiles were determined according to HEPA’s sample values as ≤105 minutes (Q4); >105 minutes to ≤315 minutes (Q3); >315 minutes to ≤675 minutes (Q2); >675 minutes (Q1); whereas for daily SB, groups were set at ≤4.5 hours (Q4); >4.5 hours to ≤6.5 hours (Q3); >6. 5 hours to ≤8.5 hours (Q2); >8.5 hours (Q1) as in previous studies.6, 9
Socioeconomic Status Indicators
Three different socioeconomic measures were used: educational attainment, occupational social class, and economic issues. Educational attainment was evaluated by the following question: How old were you when you stopped full-time education? (up to 15 y; 16–19 y; 20 y and older; still studying; or no full-time education). No full-time education was considered a missing value (n = 198), whereas those who were studying were reclassified into the 3 remaining levels according to their current age. Then, as in previous studies,26 up to 15 years, 16–19 years, and 20 years and older, when the participants stopped their full-time education, were established as primary, secondary, and university educational attainment, respectively.
Occupational social class was based on the current occupation with the following question: What is your current occupation? The population was then reclassified into 7 groups27 from I (Professionals and managers with higher grades) to III (Routine nonmanual workers) until category VII (nonskilled manual workers). We then reduced occupational social classes into 3 groups according to previous studies28: I–II (high), III–IV (middle), and V–VII (low). Those who were unemployed were asked for their last occupation, considering those who never did any paid work and were students, unable to work, or responsible for ordinary shopping (n = 715) as the V to VII (low) social class.28
Economic issues were measured by the self-reported economic difficulties of paying household bills during the last year with the following question: During the last 12 months, how often have you had difficulties in paying your bills at the end of the month? Three groups were then established (most of the time, from time to time, and almost never or never).
Although educational attainment, occupation, and economic issues represent SES, each of which may have specific associations with the socioeconomic gradient and measure phenomena involving different hypothetical causal mechanisms.29 For example, economic issues denote material deprivation and inaccessibility to resources. Occupation is representative not only of income (better occupations usually imply better salaries) but also of social hierarchy, recognition, and prestige. Lastly, educational level, which is a stable indicator of adulthood, is linked to better critical thinking.
Data Analysis
Descriptive statistics as medians and 10th and 90th percentiles of weekly PA (HEPA, vigorous, moderate, and walking PA) and daily SB were computed by gender; age group (18–24, 25–34, 35–44, 45–54, and 55–64 y); SES; resident place (rural, small urban, and large urban); and marital status (single without children, single with children, multiple without children, and multiple with children). Multiple marital status refers to people who live with a partner. The proportions of weekly HEPA and daily SB quartiles in each SES were also described.
To determine inequality in the population distribution of weekly PA and daily SB, we used the Gini coefficient, which ranges from 0 (maximum equality) to 1 (maximum inequality). The Gini coefficient is calculated from the Lorenz curve using the following formula: Gini = A/(A + B), where A is the area between the Lorenz curve and the perfect line of equality. The letter B corresponds to the area under the Lorenz curve (Supplementary Figure S1 and Supplementary Material S1 [available online]). The Gini coefficient allows for comparisons of inequality distributions among variables with different ranges and proportions. Gini coefficients were reported across age, SES, and place of residence by gender, as well as EU-28 countries.
To assess the associations between SES (independent variables) and weekly HEPA and daily SB (dependent variables), we performed multinomial logistic regressions adjusted by age, gender, marital status, resident place, and either weekly HEPA or daily SB. References were ≤ 105 minutes (Q4) in weekly HEPA, ≤4.5 hours in daily SB (Q4), primary education attainment, low social class (V–VII), and economic issues most of the time. We also tested the joint associations of weekly HEPA and daily SB (dependent variable, 16 groups from the combination of quartiles of weekly HEPA and daily SB) with SES (independent variables) by multinomial logistic regressions using the group with the lowest weekly HEPA and daily SB as the reference and adjusting for the covariates previously described. We performed these joint analyses to complement separate multinomial logistic regressions for weekly PA and daily SB and their association with SES, examining the possible cumulative effects of weekly PA and daily SB. Educational attainment, occupational social class, and economic issues were entered simultaneously in logistic regressions.
For all analyses, we selected the age range of 18–64 years old, as it represents the working-age active population (n = 19,645, 70.1%). Moreover, we excluded respondents with missing data (n = 1230, 4.4%) and incongruent values for PA measurement items in IPAQ (n = 4707, 16.8%); that is, participants who reported, for instance, an average time spent of 31 to 60 minutes and 0 days at the same intensity. The final sample size comprised 13,708 people (48.9%). Additionally, we performed sensitivity analyses in which those who were still studying constituted another educational attainment group. Statistically significant differences were set at P < .05. Rstudio (version 3.6.1, Rstudio, PBC) was employed for all statistical analyses by applying population–survey sampling weights.
Results
Higher Socioeconomic Differences and Inequalities by Physical Activity Than Sedentary Behavior
Overall, weekly HEPA corresponded to a median value of 315 minutes (10th–90th: 30–1185 min), while weekly VPA, MPA, and walking were 45 (10th–90th: 0–405 min), 45 (10th–90th: 0–525 min), and 105 minutes (10th–90th: 15–525 min), respectively, with a daily sitting time of 5 hours (10th–90th: 2–9 h; Table 1). By gender, men practiced more weekly HEPA (+45 min), VPA (+75 min), and MPA (+45 min) than women, while walking activity (105 min) was equal; however, women described less daily sitting time (−60 min). Across age groups weekly HEPA, VPA, and MPA decreased while walking remained constant. However, those aged 25–44 were less sedentary (240 min, 10th–90th: 75–480 min). Those with university education attainment, high (I–II) occupational social class, and almost never or never economic issues reported higher levels of weekly HEPA, VPA, and MPA, with no differences in walking activity. On daily sitting time, people with a university education sit more (+60 min) than secondary and primary educational attainment, and in contrast, low (V–VII) social class and economic issues from time to time presented lower daily sitting levels (−60 min). Regarding resident place, all environments showed similar activity levels in all measures, excluding a higher weekly HEPA in small urban (+15 min), lower weekly VPA in large urban areas (−15 min), and a higher daily sitting time in large urban places (+60 min). According to marital status, single parents with children presented a higher weekly HEPA (360 min, 10th–90th: 45–1340.8 min) and lower weekly VPA (0 min, 10th–90th: 0–525 min). In addition, the population with children described lower daily sitting times (−60 min).
Median and 10th and 90th Percentiles of Weekly Physical Activity Components and Daily Sitting Time Across Sociodemographic Variables in the European Union-28, 2017
| n (%) | HEPA/wk | VPA/wk | MPA/wk | Walking/wk | Sitting/d | |
|---|---|---|---|---|---|---|
| Overall | 13,708 (100) | 315 (30–1185) | 45 (0–405) | 45 (0–525) | 105 (15–525) | 300 (120–540) |
| Gender | ||||||
| Men | 6281 (45.8) | 360 (45–1350) | 75 (0–525) | 75 (0–525) | 105 (0–525) | 300 (120–540) |
| Women | 7427 (54.2) | 315 (30–1035) | 0 (0–300) | 30 (0–375) | 105 (15–525) | 240 (75–480) |
| Age, y | ||||||
| 18–24 | 1306 (9.5) | 405 (90–1140) | 75 (0–420) | 90 (0–375) | 105 (30–525) | 300 (120–540) |
| 25–34 | 2541 (18.5) | 375 (75–1185) | 75 (0–450) | 90 (0–525) | 105 (15–525) | 240 (75–480) |
| 35–44 | 3021 (22.1) | 315 (30–1350) | 45 (0–525) | 45 (0–525) | 105 (15–525) | 240 (75–480) |
| 45–54 | 3272 (23.9) | 315 (30–1185) | 15 (0–405) | 45 (0–420) | 105 (0–525) | 300 (120–540) |
| 55–64 | 3568 (26.0) | 315 (0–1140) | 0 (0–375) | 15 (0–450) | 105 (0–525) | 300 (120–540) |
| Education | ||||||
| Primary | 1295 (9.6) | 270 (0–1155) | 0 (0–525) | 0 (0–450) | 105 (0–525) | 240 (75–480) |
| Secondary | 6757 (50.1) | 315 (30–1275) | 15 (0–525) | 45 (0–525) | 105 (9.3–525) | 240 (120–480) |
| University | 5444 (40.3) | 360 (75–1095) | 75 (0–375) | 90 (0–420) | 105 (15–525) | 300 (120–540) |
| Occupational SC | ||||||
| I–II (high) | 2362 (17.2) | 375 (75–1065) | 75 (0–315) | 90 (0–525) | 105 (20.1–525) | 300 (120–540) |
| III–IV (middle) | 4447 (32.5) | 315 (30–1050) | 45 (0–315) | 45 (0–405) | 105 (0–525) | 300 (120–540) |
| V–VII (low) | 6899 (50.3) | 315 (30–1290) | 15 (0–525) | 30 (0–525) | 105 (0–525) | 240 (75–480) |
| Economic issues | ||||||
| Most of the time | 1359 (10.0) | 255 (0–1155) | 0 (0–525) | 0 (0–417.5) | 105 (0–525) | 300 (75–540) |
| From time to time | 3998 (29.6) | 300 (0–1095) | 0 (0–375) | 15 (0–375) | 105 (0–525) | 240 (75–480) |
| Almost never/never | 8172 (60.4) | 360 (60–1230) | 45 (0–420) | 90 (0–525) | 105 (15–525) | 300 (120–540) |
| Resident place | ||||||
| Rural | 3916 (28.6) | 315 (15–1260) | 45 (0–420) | 45 (0–525) | 105 (0–525) | 240 (75–480) |
| Small urban | 4515 (32.9) | 330 (30–1230) | 45 (0–405) | 45 (0–525) | 105 (0–525) | 240 (120–480) |
| Large urban | 5277 (38.5) | 315 (60–1125) | 30 (0–405) | 45 (0–375) | 105 (30–525) | 300 (120–540) |
| Marital status | ||||||
| Single without children | 3405 (25.0) | 330 (45–1155) | 45 (0–420) | 60 (0–420) | 105 (15–525) | 300 (120–540) |
| Single with children | 866 (6.4) | 360 (45–1340.8) | 0 (0–525) | 45 (0–525) | 105 (15–675) | 240 (75–480) |
| Multiple without children | 3823 (28.1) | 315 (30–1095) | 15 (0–375) | 45 (0–420) | 105 (0–525) | 300 (120–540) |
| Multiple with children | 5512 (40.5) | 315 (30–1275) | 45 (0–408.1) | 45 (0–525) | 105 (15–525) | 240 (75–480) |
Abbreviations: HEPA, health-enhancing physical activity; MPA, moderate physical activity; SC, social class; VPA, vigorous physical activity. Note: Multiple marital status refers to people who live with a partner in household.
The proportions of the different weekly HEPA and daily SB quartiles across socioeconomic groups (Figure 1) showed that high SESs presented more weekly HEPA than daily SB. The 2 most active and sedentary quartiles gradually increased from primary education to university education. Regarding occupation, the proportion of weekly HEPA quartiles did not present marked differences between social classes, although the proportion of the less active quartiles was lower in the high (I–II) social class. In daily SB, the least sedentary quartile was also lower in the low (V–VII) social class. Lastly, economic issues also displayed a gradient in weekly HEPA but not for daily SB. Sensitivity analyses showed similar results in which educational attainment continued to present a gradient with higher weekly PA and daily SB at higher educational levels (Supplementary Figure S2 [available online]). In addition, those who were still studying reported even greater daily SB than those with university studies, with slight differences in weekly PA.
—Proportions of socioeconomic groups in physical activity and sedentary behavior quartiles by educational attainment, occupational social class, and economic issues in the last year. Q4, total physical activity, ≤105 minutes per week; sedentary behavior, ≤4.5 hours per day. Q3, total physical activity, >105 to ≤315 minutes per week; sedentary behavior, >4.5 to ≤6.5 hours per day. Q2, total physical activity, >315 to ≤675 minutes per week; sedentary behavior, >6.5 to ≤8.5 hours per day. Q1, total physical activity, >675 minutes per week; sedentary behavior, >8.5 hours per day. European Union-28, 2017.
Citation: Journal of Physical Activity and Health 20, 3; 10.1123/jpah.2022-0036
—Proportions of socioeconomic groups in physical activity and sedentary behavior quartiles by educational attainment, occupational social class, and economic issues in the last year. Q4, total physical activity, ≤105 minutes per week; sedentary behavior, ≤4.5 hours per day. Q3, total physical activity, >105 to ≤315 minutes per week; sedentary behavior, >4.5 to ≤6.5 hours per day. Q2, total physical activity, >315 to ≤675 minutes per week; sedentary behavior, >6.5 to ≤8.5 hours per day. Q1, total physical activity, >675 minutes per week; sedentary behavior, >8.5 hours per day. European Union-28, 2017.
Citation: Journal of Physical Activity and Health 20, 3; 10.1123/jpah.2022-0036
—Proportions of socioeconomic groups in physical activity and sedentary behavior quartiles by educational attainment, occupational social class, and economic issues in the last year. Q4, total physical activity, ≤105 minutes per week; sedentary behavior, ≤4.5 hours per day. Q3, total physical activity, >105 to ≤315 minutes per week; sedentary behavior, >4.5 to ≤6.5 hours per day. Q2, total physical activity, >315 to ≤675 minutes per week; sedentary behavior, >6.5 to ≤8.5 hours per day. Q1, total physical activity, >675 minutes per week; sedentary behavior, >8.5 hours per day. European Union-28, 2017.
Citation: Journal of Physical Activity and Health 20, 3; 10.1123/jpah.2022-0036
In addition, Gini coefficients showed that the highest inequalities were found in weekly VPA and MPA, especially in women (women: 0.77 and 0.73; men: 0.68 and 0.67, respectively), followed by walking and HEPA (Supplementary Figure S3 [available online]). The lowest inequality was in daily sitting time for both genders (women: 0.30; men: 0.28). Across age, inequality increased in weekly VPA and MPA, while walking and HEPA also increased but only slightly (Supplementary Figure S4 [available online]). Moreover, in men, large urban areas had a higher inequality in weekly VPA (0.69) and MPA (0.68), while in women, rural areas presented the highest inequality in VPA (0.78) (Supplementary Figure S5 [available online]). In contrast, the lowest inequality in all environments was in daily sitting time for both genders.
Focusing on SES (Figure 2), a gradient was observed in weekly VPA, MPA, and HEPA, from higher inequality in lower statuses to lower inequality in higher statuses. In contrast, this gradient was more uniform and flattened in daily SB across socioeconomic groups. Sensitivity analyses also showed this tendency among the groups with primary, secondary, and university education attainment, whereas those who were still studying reported the most equal distributions in weekly PA and daily SB (Supplementary Figure S6 [available online]). At the country level (Supplementary Figure S7 [available online]), the same tendency was projected. Daily sitting inequality varies from 0.19 in the Netherlands to 0.37 in Romania, weekly VPA inequality from 0.53 in Finland to 0.87 in Malta, and weekly MPA inequality from 0.56 in the Netherlands to 0.85 in Malta.
—Gini coefficient inequality across socioeconomic status by education attainment, occupational social class and income’s issues on different physical activity components and sedentary behavior in women and men. A higher Gini coefficient indicates a higher inequality. European Union-28, 2017. HEPA, health-enhancing physical activity; MPA, moderate physical activity; VPA, vigorous physical activity.
Citation: Journal of Physical Activity and Health 20, 3; 10.1123/jpah.2022-0036
—Gini coefficient inequality across socioeconomic status by education attainment, occupational social class and income’s issues on different physical activity components and sedentary behavior in women and men. A higher Gini coefficient indicates a higher inequality. European Union-28, 2017. HEPA, health-enhancing physical activity; MPA, moderate physical activity; VPA, vigorous physical activity.
Citation: Journal of Physical Activity and Health 20, 3; 10.1123/jpah.2022-0036
—Gini coefficient inequality across socioeconomic status by education attainment, occupational social class and income’s issues on different physical activity components and sedentary behavior in women and men. A higher Gini coefficient indicates a higher inequality. European Union-28, 2017. HEPA, health-enhancing physical activity; MPA, moderate physical activity; VPA, vigorous physical activity.
Citation: Journal of Physical Activity and Health 20, 3; 10.1123/jpah.2022-0036
Associations of Socioeconomic Status With Physical Activity and Sedentary Behavior
Multinomial logistic regressions (Figure 3) showed that higher SES increased the probability of practicing more weekly HEPA; however, this association was attenuated or even eliminated in the most active quartile (Q1). The population with secondary or university education was more likely to correspond to Q3, Q2, and Q1 of the weekly HEPA compared with primary educational attainment. By occupation, high (I–II) and middle (III–IV) social classes were also associated with higher weekly HEPA; however, the association was not statistically significant for the middle (III–IV) social class at the most active quartile. In addition, those who reported economic issues never or almost never were likely to perform more weekly HEPA, whereas having economic issues from time to time was also associated with more weekly HEPA, except in the most active quartile. In attending daily SB, socioeconomic indicators showed a dose–response association of SES in educational attainment and occupational social class with daily sitting time. Compared with the less sedentary quartile (Q4), those with a secondary or university education were gradually associated with more daily sitting time across quartiles. High (I–II) and middle (III–IV) occupational social classes also increase the risk of spending more daily sitting time across quartiles. Nonetheless, those with economic issues from time to time and never or almost never were less likely to belong to the second and most sedentary quartiles. When a sensitivity analysis was performed, the multinomial logistic regression models yielded the same results, and even those who were still studying were more likely to report more weekly HEPA and daily SB than those with university education attainment (Supplementary Figure S8 [available online]). Joint associations of the weekly HEPA and daily SB quartiles with SES also presented this gradient (Figure 4). Higher educational attainment and occupational social class were associated with more weekly HEPA, increasing this association even more with a higher daily sitting time. However, those who reported economic issues never or almost never were more likely to perform more weekly PA in the 2 lowest sitting-time quartiles. Lastly, sensitivity analysis with joint associations confirmed these results (Supplementary Figure 9 [available online]).
—Forest plot of odds ratio and 95% CIs for the association between socioeconomic factors and belonging to the different quartiles of total physical activity and sedentary behavior. Q1 represents the highest time spent of these behaviors. References were Q4 (total physical activity, ≤105 min/wk; sedentary behavior, ≤4.5 h/d), Primary education level, V to VII low social class and economic issues most of the time. Estimates were adjusted by age, gender, marital status, resident place, and either log10 weekly HEPA time or daily sitting time. CI indicates confidence interval. HEPA, health-enhancing physical activity. Q3, total physical activity, >105 to ≤315 minutes per week; sedentary behavior, >4.5 to ≤6.5 hours per day. Q2, total physical activity, >315 to ≤675 minutes per week; sedentary behavior, >6.5 to ≤8.5 hours per day. Q1, total physical activity, >675 minutes per week; sedentary behavior, >8.5 hours per day. SC, social class. European Union–28, 2017.
Citation: Journal of Physical Activity and Health 20, 3; 10.1123/jpah.2022-0036
—Forest plot of odds ratio and 95% CIs for the association between socioeconomic factors and belonging to the different quartiles of total physical activity and sedentary behavior. Q1 represents the highest time spent of these behaviors. References were Q4 (total physical activity, ≤105 min/wk; sedentary behavior, ≤4.5 h/d), Primary education level, V to VII low social class and economic issues most of the time. Estimates were adjusted by age, gender, marital status, resident place, and either log10 weekly HEPA time or daily sitting time. CI indicates confidence interval. HEPA, health-enhancing physical activity. Q3, total physical activity, >105 to ≤315 minutes per week; sedentary behavior, >4.5 to ≤6.5 hours per day. Q2, total physical activity, >315 to ≤675 minutes per week; sedentary behavior, >6.5 to ≤8.5 hours per day. Q1, total physical activity, >675 minutes per week; sedentary behavior, >8.5 hours per day. SC, social class. European Union–28, 2017.
Citation: Journal of Physical Activity and Health 20, 3; 10.1123/jpah.2022-0036
—Forest plot of odds ratio and 95% CIs for the association between socioeconomic factors and belonging to the different quartiles of total physical activity and sedentary behavior. Q1 represents the highest time spent of these behaviors. References were Q4 (total physical activity, ≤105 min/wk; sedentary behavior, ≤4.5 h/d), Primary education level, V to VII low social class and economic issues most of the time. Estimates were adjusted by age, gender, marital status, resident place, and either log10 weekly HEPA time or daily sitting time. CI indicates confidence interval. HEPA, health-enhancing physical activity. Q3, total physical activity, >105 to ≤315 minutes per week; sedentary behavior, >4.5 to ≤6.5 hours per day. Q2, total physical activity, >315 to ≤675 minutes per week; sedentary behavior, >6.5 to ≤8.5 hours per day. Q1, total physical activity, >675 minutes per week; sedentary behavior, >8.5 hours per day. SC, social class. European Union–28, 2017.
Citation: Journal of Physical Activity and Health 20, 3; 10.1123/jpah.2022-0036
—Forest plot of odds ratio and 95% confidence intervals (CIs) of socioeconomic factors with joint associations of total physical activity and sedentary behavior quartiles. References were lowest physical activity and sedentary behavior; primary education level, V to VII low social class and economic issues most of the time. Estimates were adjusted by age, gender, marital status, and resident place. European Union-28, 2017.
Citation: Journal of Physical Activity and Health 20, 3; 10.1123/jpah.2022-0036
—Forest plot of odds ratio and 95% confidence intervals (CIs) of socioeconomic factors with joint associations of total physical activity and sedentary behavior quartiles. References were lowest physical activity and sedentary behavior; primary education level, V to VII low social class and economic issues most of the time. Estimates were adjusted by age, gender, marital status, and resident place. European Union-28, 2017.
Citation: Journal of Physical Activity and Health 20, 3; 10.1123/jpah.2022-0036
—Forest plot of odds ratio and 95% confidence intervals (CIs) of socioeconomic factors with joint associations of total physical activity and sedentary behavior quartiles. References were lowest physical activity and sedentary behavior; primary education level, V to VII low social class and economic issues most of the time. Estimates were adjusted by age, gender, marital status, and resident place. European Union-28, 2017.
Citation: Journal of Physical Activity and Health 20, 3; 10.1123/jpah.2022-0036
Discussion
There were greater inequalities in PA, especially in VPA and MPA, than in SB, which was more equally distributed, with smaller differences in the adult population (18–64 y) of the European Union. These inequalities in the distribution of PA were smaller in high SES, where higher levels of HEPA were also observed. Likewise, higher SES, mainly by educational attainment and occupation, was associated with higher levels of HEPA but also paradoxically with higher SB, analyzing both PA and SB separately and jointly. However, this gradient regarding inequality of distribution was only observed in moderate and vigorous activity but not for sitting time. This pattern was also reflected by the lower inequality in HEPA in the highest socioeconomic group, while sitting time was distributed more uniformly across the European population, with slight differences ranging from 4 to 5 hours per day, despite its clear association with SES.
Although many reports support this socioeconomic gradient in PA,14,17 the evidence is scarce regarding SB, even more so in comparisons and inequalities between both behaviors. A cross-sectional study in German adults showed that education attainment but not income was associated with higher self-reported sitting time, while other environmental physical aspects were not correlated.20 Another study in Australian adults showed that the higher SES (measured by education and incomes), the higher sitting time spent on computers and the lower television and driving time.30 Thus, it seems that not all socioeconomic associations are equal across different sedentary activities, and the highest contribution to the total is by occupational SB according to these studies. Work domain is also a major contributor of PA,31 but this contribution could differ by SES since a high social class population reports more total PA by a higher contribution of leisure time PA, as some systematic reviews have suggested.32–34 In contrast, a longitudinal study with 9 years of follow-up in Australian women observed that nonuniversity education and more working hours were associated with higher sitting time, while those with higher occupation social classes presented less sitting time.35 Overall, a pooled cross-sectional study in England reported that high social class groups were more likely to practice PA and do moderate to vigorous PA with no differences in average walking activity.32 Such a paradoxical association between higher SES, PA, and SB may have hypothetical explanations, where general levels of both behaviors may hide subtle differences in the domains of PA and SB. Higher SES, in addition to higher educational attainment and incomes, are often characterized by sedentary occupations with predominantly intellectual skills.34,36 Moreover, these upper socioeconomic positions tend to have greater access to sports facilities and practice more leisure time PA, which could explain this paradox.14 Another hypothetical explanation of a higher SES mainly associated with educational attainment could result from a higher self-perception and individual awareness to prioritize and develop a healthy lifestyle. Conversely, lower socioeconomic positions usually have repetitive manual jobs, which are associated with standing working positions, and less PA and more sitting time in their leisure time, as well as reside in deprived environments.13,14,37
Lastly, according to our results, education level and occupation may increase both total PA and sitting time, while having no economic issues seems to be associated with higher weekly HEPA and lower daily sitting time. These findings of the economic issues on PA and SB contrast with the educational attainment and occupational social class, opening the need to carry out much more specific research on individual and household economic aspects in their relationship with weekly PA and daily SB. Furthermore, we have provided updated PA comparisons on the inequality distribution across socioeconomic groups using additional inequality measures, such as Gini coefficients, instead of solely absolute or relative PA differences between groups. However, the association of SES with PA and SB that occur in adulthood might differ in older adults, where there is a decrease in PA upon retirement and widespread SB.38
Implications and Future Research
Taken together, our findings suggest that low inequality and socioeconomic differences in sitting time may indicate that SB is spread throughout the population, with some factors that may worsen it. This highlights the huge negative impact of SB on health, especially among those who do not meet WHO PA recommendations but also on physically active people. However, the implementation of action plans to reduce SB in the entire population must be specific. In the work domain in those more sedentary occupations usually represented by white-collar positions (high SES), working activities should be allowed to be performed with the option of alternating between standing and sitting positions, as well as breaking prolonged sitting periods. However, low-socioeconomic (manual) occupations involve long periods of standing positions or strenuous PA, which can discourage leisure-time PA.33,39 This phenomenon is of great importance because excessive occupational PA is linked to poorer health and premature all-cause mortality and, in contrast, leisure time PA is related to beneficial effects.40 Furthermore, SES cannot suppose a limitation on the availability and accessibility to PA facilities and services and, ultimately, HEPA practice. In addition, the socioeconomic gradient of the HEPA could be mainly determined by the inequality distribution of moderate and vigorous PA with low levels and high inequalities in low-socioeconomic groups. However, socioeconomic inequalities are greater in PA than in SB. As such, the contribution to social inequalities in health in terms of an active lifestyle (ie, PA and SB) would be greater by socioeconomic differences in PA than SB. In brief, public health policies should reduce socioeconomic inequalities to promote PA in low-socioeconomic populations to narrow social inequities on health but also counter SB with special attention to those with high educational attainment and high (white collar) occupational social class. The domains may focus on work and active mobility (public transport, cycling paths, trails, and so on) and better intraenvironmental connectivity, also ensuring and encouraging access for all SES that may also increase total PA by enhancing light and walking PA.
Nonetheless, there are still many gaps regarding SB that should be addressed in future research. “Motives” that enhance such behavior are unknown, as are the domains in which higher levels are produced (household, leisure, or work). More research is needed on the social, environmental, and global determinants of sitting time across the life course to implement efficient and effective strategies because these factors may differ from those of PA. In addition to the study of the potential causes that promote SB and establish high-sedentary population groups, more evidence is still needed regarding the detrimental impact of sitting time on individual health, either epidemiological or experimental, to develop better SB guidelines about what constitutes prolonged sitting time, excessive SB levels, and harmful cut-off points.
Strengths and Limitations
Our study has some limitations that should be considered in the extrapolation of the results. The cross-sectional design does not allow causal relationships between SES and PA or sitting time to be established. The use of self-reported data may overestimate PA and underestimate sitting time, in addition to the use of intervals that yield less accurate data.25 Furthermore, light PA was only represented by walking, which in fact is classified as moderate activity and measured by IPAQ in bouts of 10 minutes as the minimum. Additionally, the short version of IPAQ is not capable of distinguishing between the different domains of practice of PA (leisure, work, transport, and household) and types of SB (eg, work, television time, driving), not allowing a deeper analysis. Furthermore, the use of economic issues might imply some self-reported biases; however, this indicator could be even more useful in measuring the relative purchasing power of each country. Lastly, educational attainment was based on the age at which people ceased their studies, which could also contain biases in high educational attainment due to discrepancies between countries in their educational systems. Despite these limitations, our study comprised a large and representative sample size of the European population in which the associations and inequalities of 3 major SES indicators (education, occupation, and incomes) were addressed not only in PA but also in SB.
Conclusions
Both total PA and SB followed the same socioeconomic gradient; that is, the high SES groups showed paradoxically more HEPA but also sitting time. Nonetheless, SB presented smaller differences and lower inequalities among socioeconomic groups, in contrast to vigorous and moderate activity, which presented an unequal distribution that increased from high to low SES. Public health policies should address socioeconomic inequalities to promote PA, especially in low SES population, but should also consider SB.
Acknowledgments
The authors thank the GESIS Leibniz Institute for the Social Sciences for the availability of the data employed in this study. The analyses and content of this work are the sole responsibility of the authors.
References
- 1.↑
Kohl HW, Craig CL, Lambert EV, et al. The pandemic of physical inactivity: global action for public health. Lancet. 2012;380(9838):294–305. doi:10.1016/S0140-6736(12)60898-8
- 2.↑
Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Glob Health. 2018;6(10):e1077–e1086. doi:10.1016/S2214-109X(18)30357-7
- 3.↑
Ding D, Mutrie N, Bauman A, Pratt M, Hallal PRC, Powell KE. Physical activity guidelines 2020: comprehensive and inclusive recommendations to activate populations. Lancet. 2020;396(10265):1780–1782. doi:10.1016/S0140-6736(20)32229-7
- 4.↑
Thorp AA, Owen N, Neuhaus M, Dunstan DW. Sedentary behaviors and subsequent health outcomes in adults: a systematic review of longitudinal studies, 1996–2011. Am J Prev Med. 2011;41(2):207–215. doi:10.1016/j.amepre.2011.05.004
- 5.
Wilmot EG, Edwardson CL, Achana FA, et al. Sedentary time in adults and the association with diabetes, cardiovascular disease and death: systematic review and meta-analysis. Diabetologia. 2012;55(11):2895–2905. doi:10.1007/s00125-012-2677-z
- 6.↑
Ekelund U, Steene-Johannessen J, Brown WJ, et al. Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with mortality? A harmonised meta-analysis of data from more than 1 million men and women. Lancet. 2016;388(10051):1302–1310. doi:10.1016/S0140-6736(16)30370-1
- 7.↑
Chau JY, Grunseit AC, Chey T, et al. Daily sitting time and all-cause mortality: a meta-analysis. PLoS One. 2013;8(11):e80000. doi:10.1371/journal.pone.0080000
- 8.
Patel AV, Bernstein L, Deka A, et al. Leisure time spent sitting in relation to total mortality in a prospective cohort of US adults. Am J Epidemiol. 2010;172(4):419–429. doi:10.1093/aje/kwq155
- 9.↑
Patterson R, McNamara E, Tainio M, et al. Sedentary behavior and risk of all-cause, cardiovascular and cancer mortality, and incident type 2 diabetes: a systematic review and dose response meta-analysis. Eur J Epidemiol. 2018;33(9):811–829. doi:10.1007/s10654-018-0380-1
- 10.↑
Stamatakis E, Gale J, Bauman A, Ekelund U, Hamer M, Ding D. Sitting time, physical activity, and risk of mortality in adults. J Am Coll Cardiol. 2019;73(16):2062–2072. doi:10.1016/j.jacc.2019.02.031
- 11.↑
Ekelund U, Tarp J, Steene-Johannessen J, et al. Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: systematic review and harmonised meta-analysis. BMJ. 2019;366:l4570. doi:10.1136/bmj.l4570
- 12.↑
García-Mayor J, Moreno-Llamas A, De la Cruz-Sánchez E. High educational attainment redresses the effect of occupational social class on health-related lifestyle: findings from four Spanish national health surveys. Ann Epidemiol. 2021;58:29–37. doi:10.1016/j.annepidem.2021.02.010
- 13.↑
Owen N, Sugiyama T, Eakin EE, Gardiner PA, Tremblay MS, Sallis JF. Adults’ sedentary behavior: determinants and interventions. Am J Prev Med. 2011;41(2):189–196. doi:10.1016/j.amepre.2011.05.013
- 14.↑
O’Donoghue G, Kennedy A, Puggina A, et al. Socio-economic determinants of physical activity across the life course: a “DEterminants of DIet and Physical ACtivity” (DEDIPAC) umbrella literature review. PLoS One. 2018;13(1):e0190737. doi:10.1371/journal.pone.0190737
- 15.↑
Varo JJ, Martínez-González MA, De Irala-Estévez J, Kearney J, Gibney M, Martínez JA. Distribution and determinants of sedentary lifestyles in the European Union. Int J Epidemiol. 2003;32(1):138–146. doi:10.1093/ije/dyg018
- 16.↑
Oftedal S, Vandelanotte C, Duncan MJ. Patterns of diet, physical activity, sitting and sleep are associated with socio-demographic, behavioral, and health-risk indicators in adults. Int J Environ Res Public Health. 2019;16(13):2375. doi:10.3390/ijerph16132375
- 17.↑
Moreno-Llamas A, García-Mayor J, De la Cruz-Sánchez E. Physical activity barriers according to social stratification in Europe. Int J Public Health. 2020;65:1477–1484. doi:10.1007/s00038-020-01488-y
- 18.↑
Staiano AE, Harrington DM, Barreira TV, Katzmarzyk PT. Sitting time and cardiometabolic risk in US adults: associations by sex, race, socioeconomic status and activity level. Br J Sports Med. 2014;48(3):213–219. doi:10.1136/bjsports-2012-091896
- 19.↑
Wallmann-Sperlich B, Bucksch J, Schneider S, Froboese I. Socio-demographic, behavioral and cognitive correlates of work-related sitting time in German men and women. BMC Public Health. 2014;14(1):1259. doi:10.1186/1471-2458-14-1259
- 20.↑
Wallmann-Sperlich B, Bucksch J, Hansen S, Schantz P, Froboese I. Sitting time in Germany: an analysis of socio-demographic and environmental correlates. BMC Public Health. 2013;13(1):196. doi:10.1186/1471-2458-13-196
- 21.↑
Milton K, Gale J, Stamatakis E, Bauman A. Trends in prolonged sitting time among European adults: 27 country analysis. Prev Med. 2015;77:11–16. doi:10.1016/j.ypmed.2015.04.016
- 22.↑
Hallal PC, Andersen LB, Bull FC, et al. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012;380(9838):247–257. doi:10.1016/S0140-6736(12)60646-1
- 23.↑
STROBE Statement. STROBE Statement – checklist of items that should be included in reports of observational studies (© STROBE Initiative). Int J Public Health. 2008;53(1):3–4. doi:10.1007/s00038-007-0239-9
- 24.↑
European Commission. Eurobarometer 88.4. TNS Opinion, Brussels. GESIS Data Archive, Cologne. GESIS; 2017. doi:10.4232/1.13065
- 25.↑
Craig CL, Marshall AL, Sjöström M, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–1395. doi:10.1249/01.MSS.0000078924.61453.FB
- 26.↑
Kino S, Bernabé E, Sabbah W. Socioeconomic inequality in clusters of health-related behaviors in Europe: latent class analysis of a cross-sectional European survey. BMC Public Health. 2017;17:497. doi:10.1186/s12889-017-4440-3
- 27.↑
Chan TW, Goldthorpe JH. Class and status: the conceptual distinction and its empirical relevance. Am Sociol Rev. 2007;72(4):512–532. doi:10.1177/000312240707200402
- 28.↑
Domingo-Salvany A, Bacigalupe A, Carrasco JM, Espelt A, Ferrando J, Borrell C. Propuestas de clase social neoweberiana y neomarxista a partir de la Clasificación Nacional de Ocupaciones 2011. Gac Sanit. 2013;27(3):263–272. doi:10.1016/j.gaceta.2012.12.009
- 29.↑
Geyer S, Hemström Ö, Peter R, Vågerö D. Education, income, and occupational class cannot be used interchangeably in social epidemiology. Empirical evidence against a common practice. J Epidemiol Community Health. 2006;60(9):804–810. doi:10.1136/jech.2005.041319
- 30.↑
Stamatakis E, Grunseit AC, Coombs N, et al. Associations between socio-economic position and sedentary behavior in a large population sample of Australian middle and older-aged adults: The Social, Economic, and Environmental Factor (SEEF) study. Prev Med. 2014;63:72–80. doi:10.1016/j.ypmed.2014.03.009
- 31.↑
Strain T, Wijndaele K, Garcia L, et al. Levels of domain-specific physical activity at work, in the household, for travel and for leisure among 327 789 adults from 104 countries. Br J Sports Med. 2020;54(24):1488–1497. doi:10.1136/bjsports-2020-102601
- 32.↑
Scholes S, Mindell JS. Inequalities in participation and time spent in moderate-to-vigorous physical activity: a pooled analysis of the cross-sectional health surveys for England 2008, 2012, and 2016. BMC Public Health. 2020;20(1):361. doi:10.1186/s12889-020-08479-x
- 33.↑
Straker L, Holtermann A, Lee I-M, van der Beek AJ, Stamatakis E. Privileging the privileged: the public health focus on leisure time physical activity has contributed to widening socioeconomic inequalities in health. Br J Sports Med. 2021;55(10):525–526. doi:10.1136/bjsports-2020-103356
- 34.↑
Beenackers MA, Kamphuis CB, Giskes K, et al. Socioeconomic inequalities in occupational, leisure-time, and transport related physical activity among European adults: a systematic review. Int J Behav Nutr Phys Act. 2012;9:116. doi:10.1186/1479-5868-9-116
- 35.↑
Van Uffelen JGZ, Heesch KC, Brown W. Correlates of sitting time in working age Australian women: who should be targeted with interventions to decrease sitting time? J Phys Act Health. 2012;9(2):270–287. doi:10.1123/jpah.9.2.270
- 36.↑
Chau JY, van der Ploeg HP, Merom D, Chey T, Bauman AE. Cross-sectional associations between occupational and leisure-time sitting, physical activity and obesity in working adults. Prev Med. 2012;54(3–4):195–200. doi:10.1016/j.ypmed.2011.12.020
- 37.↑
McNeill LH, Kreuter MW, Subramanian SV. Social environment and physical activity: a review of concepts and evidence. Soc Sci Med. 2006;63(4):1011–1022. doi:10.1016/j.socscimed.2006.03.012
- 38.↑
Moreno-Llamas A, García-Mayor J, De la Cruz-Sánchez E. How Europeans move: a moderate-to-vigorous physical activity and sitting time paradox in the European Union. Public Health. 2022;203:1–8. doi:10.1016/j.puhe.2021.11.016
- 39.↑
Bláfoss R, Micheletti JK, Sundstrup E, Jakobsen MD, Bay H, Andersen LL. Is fatigue after work a barrier for leisure-time physical activity? Cross-sectional study among 10,000 adults from the general working population. Scand J Public Health. 2019;47(3):383–391. doi:10.1177/1403494818765894
- 40.↑
Holtermann A, Krause N, Van Der Beek AJ, Straker L. The physical activity paradox: six reasons why occupational physical activity (OPA) does not confer the cardiovascular health benefits that leisure time physical activity does. Br J Sports Med. 2018;52(3):149–150. doi:10.1136/bjsports-2017-097965
