Physical Inactivity From Adolescence to Young Adulthood: The Relevance of Various Dimensions of Inequality in a Swedish Longitudinal Sample

Abstract

As physical inactivity may track from adolescence to adulthood, it is important to identify social determinants of physical inactivity in early life. However, most studies have measured socioeconomic position as one dimension. We examine whether multiple dimensions of socioeconomic position, in addition to other dimensions of inequality (i.e., gender, immigrant background), associate with physical inactivity at two time points in youth. Longitudinal data were drawn from the Swedish Level of Living Survey (N = 765) and analysed by gender-stratified logistic regression. Among girls, low parental social class (odds ratio [OR] = 2.63, 95% confidence interval [CI; 1.28, 5.42]) and income (OR = 2.28, 95% [CI 1.12, 4.65]) were associated with physical inactivity, while immigrant background (OR = 2.33, 95% CI [1.03, 5.23]) and a low level of parental education (OR = 3.38, 95% CI [1.15, 9.95]) predicted physical inactivity among women. Among boys, low parental income (OR = 3.27, 95% CI [1.39, 7.69]) was associated with physical inactivity, whereas immigrant background (OR = 2.29, 95% CI [1.04, 5.03]) predicted physical inactivity among men. Our results suggest that physical inactivity is socially patterned, but different dimensions of social stratification should not be considered interchangeable as they may operate independently, through intersection with gender, and at different time points in youth in increasing the risk of physical inactivity.

Keywords

adolescents longitudinal physical activity/exercise social inequalities socioeconomic position young adults

There is substantial evidence that early life engagement in physical activity is associated with physical activity in adulthood (Hirvensalo & Lintunen, 2011; Malina, 2001; Telama, 2009). Although the strength of such associations can depend on the time span under investigation, the measurement of physical activity or inactivity, and adjustment for related factors, it is the conclusion of two recent review articles that physical activity, and even more so physical inactivity, tracks from adolescence to young adulthood and later adulthood (Hirvensalo & Lintunen, 2011; Telama, 2009). This has important implications for public health promotion. As physical inactivity is considered one of the foremost modifiable risk factors for adult noncommunicable diseases (World Health Organization, 2009), it is prudent that researchers investigate the social determinants of early life physical inactivity so as to curb the development of health inequalities from a life course perspective (Graham & Power, 2004).

While a number of studies have addressed whether adolescents’ engagement in physical activity is unequally distributed among socioeconomic groups, there remains a lack of research that accounts for the effects of multiple dimensions of inequality (Hanson & Chen, 2007). The use of one dimension is problematic as it can encourage interpreting different dimensions as interchangeable (Geyer, Hemström, Peter, & Vågerö, 2006). Of course, education, social class, and income are interrelated in that they each reflect social standing in society, and education provides qualifications that can determine occupational class, and in turn income range (Lahelma, Martikainen, Laaksonen, & Aittomäki, 2004). However, correlations between the three dimensions are typically moderate, and different dimensions may have independent effects due to specific mechanisms (Torssander & Erikson, 2010), which are also outcome-specific (Geyer et al., 2006). Indeed different dimensions of socioeconomic position are associated with different perceived barriers to physical activity among adults (Borodulin et al., 2016). Regarding adolescents’ physical inactivity, more education may afford parents greater knowledge of the importance of physical fitness, while different social classes may be associated with class-specific behaviour regarding physical activity, and more disposable income may better enable parents to enrol their children in leisure-time athletic activities. Greater understanding for how dimension-specific social disadvantage translates into early life physical inactivity would allow for better tailoring of physical activity interventions to young people.

Prior studies have also been limited in their treatment and discussion of other dimensions of inequality additional to socioeconomic position. Health inequalities may, for example, develop from structural disadvantages imparted on women and ethnic minority or immigrant groups (Graham, 2009). Female gender is associated with adolescent physical inactivity (for a review, see Sallis, Prochaska, & Taylor, 2000) and socioeconomic advantage may positively affect girls’ participation in physical activity more so than boys’, though the latter is not a consistent finding (Hanson & Chen, 2007). Few studies have investigated whether immigrant background may be associated with young people’s physical inactivity. Yet there is indication that immigrant background is associated with adolescent physical inactivity in the United States (Singh, Stella, Siahpush, & Kogan, 2008), Canada (Kukaswadia, Pickett, & Janssen, 2014), and Sweden, though only for girls (Gillander Gådin & Hammarström, 2002; Kahlin, Werner, Romild, & Alricsson, 2009). It is unknown whether this association may persist in young adulthood or whether it could be explained by socioeconomic position.

In summary, despite the public health relevance of investigating social determinants of early life physical inactivity, most existing studies have narrowly focused on one dimension of socioeconomic position, and very few have included multiple dimensions of socioeconomic position in addition to other dimensions of inequality, like gender and immigrant background. Most prior research has also been cross-sectional (Hanson & Chen, 2007). Considering these limitations we developed three research questions to be examined in a longitudinal sample of Swedish young people:

Research Question 1: What dimensions of inequality are associated with adolescents’ physical inactivity?

Research Question 2: Are the same dimensions of inequality associated with participants’ physical inactivity as young adults?

Research Question 3: To what extent are inequalities in young adults’ physical inactivity explained by prior adolescent physical inactivity?

Method

Study Population

This study uses data from the Swedish Level of Living Survey (LNU) and the supplemental Child-LNU. The LNU is a nationally representative study of 1/1,000 of the Swedish adult population aged 18 to 74 years (Fritzell & Lundberg, 2000; Statistics Sweden, 2003). Information is collected on participants’ living conditions through in-person interviews. In 2000, the Child-LNU included 1,290 adolescents aged 10 to 18 years, recruited through a parent’s participation in the LNU 2000 (corresponding to 86% of all children). Adolescents completed a survey through an audio questionnaire (for more information, see Jonsson & Östberg, 2010). Roughly two thirds (63%) of the sample participated in follow-up in-person interviews for the LNU 2010 when they were young adults aged 20 to 28 years. After accounting for missing data, the final sample comprises 59% (N = 765) of those who originally participated in the Child-LNU. This study was approved by the Regional Ethics Committee of Stockholm (EPN).

Variables

Adolescents’ socioeconomic position is based on parental information reported in the LNU 2000. For adolescents living in two-parent (original parents or stepparent) households (90% of sample), we use data self-reported by each parent to calculate the dominant household educational attainment and social class. Information from one parent was used when data were missing on a second parent (applicable for 10% of participants living in two-parent households). Parental educational attainment reflects the highest level of completed education in the household based on three ordered categories: (1) tertiary degree, (2) upper secondary degree, (3) compulsory degree or less. Parental social class is coded according to the EGP (Erikson–Goldthorpe–Portocarero) schema set forth by Erikson and Goldthorpe (1992) and divided into four unranked groups: (1) higher nonmanual workers (Service I); (2) intermediate (Service II) and lower nonmanual workers (Service IIIa); (3) skilled manual workers (Service VI), unskilled manual workers (Service VII), and routine nonmanual workers (Service IIIb); (4) self-employed workers with fewer than 20 employees (Service IV). Parental income reflects annual household disposable income adjusted by household size and is shown in quartiles. Adolescents are coded as having a Swedish (nonimmigrant) background if at least one parent was born in Sweden.

Physical inactivity was self-reported in adolescence and young adulthood. As Swedish adolescents participate in mandatory physical activity in school, adolescents who did not engage in physical activity during their leisure-time on a weekly basis “in a club or association, for example, football, riding, swimming,” were coded as being inactive. Young adults who did not “pursue any sports, outdoor or exercise activities, for example, long walks” at least multiple times a week were coded as being inactive. Hence, the latter is a more general measure that reflects nonparticipation in organized and unorganized physical activity. Our different dichotomizations of physical inactivity in adolescence and young adulthood reflect this difference.

Age and family composition (dual- or single-parent household) in adolescence were considered potential confounders and adjusted for in all analyses.

Data Analysis

To determine how different dimensions of social inequality relate to adolescent and young adult physical inactivity, we employed multivariate logistic regression analysis using robust standard errors (using Stata 13.1). Regression estimates are presented as odds ratios (ORs), though, of note, our results did not differ substantially if we obtained average marginal effects, considered more reliable than odds ratios when comparing logistic regression models as they do not reflect the models’ unobserved heterogeneity (Mood, 2010).

To explore whether dimensions of inequality may intersect to impact physical inactivity, we stratified results by gender to compare social determinants for young women (Table 2) and men (Table 3), and calculated additive scale interactions (discussed in text when significant), a method recommended for assessing excess risk due to simultaneously occupying intersecting positions of disadvantage (Bauer, 2014). Interactions were calculated using the Synergy Index (S) and the attributable proportion due to interaction (AP); the former has been shown to be most robust to the inclusion of covariates (Bauer, 2014), while the latter may be more robust when using odds ratios to calculate additive interactions (Kalilani & Atashili, 2006). A S > 1 indicates a synergistic interaction, which is significant if the 95% confidence interval (CI) does not overlap with 1, and an AP > 0 indicates a synergistic interaction, significant if the CI does not include 0 (Kalilani & Atashili, 2006).

Results

Descriptive statistics are presented in Table 1. Our sample was evenly split by gender (51% female) with a mean adolescent age of 13. Physical inactivity was more prevalent among adolescent girls than boys (35% vs. 27%), while young adult physical inactivity was more common among men than women (43% vs. 34%).

Table 1.

Descriptive Statistics.

	Young men (N = 373)		Young women (N = 392)		Gender difference significance test
Study variables	n	%	n	%	Gender difference significance test
Adolescent age, M ± SD, years	13.5 ± 2.5		13.4 ± 2.5		ns
Family composition
Two-parent household	333	89.3	357	91.1
Single-parent household	40	10.7	35	8.9	ns
Immigrant background
No	337	90.4	357	91.1
Yes	36	9.7	35	8.9	ns
Parental educational attainment
Tertiary degree	93	24.9	103	26.3
Upper secondary degree	250	67.0	265	67.6
≤Compulsory degree	30	8.0	24	6.1	ns
Parental social class
Higher nonmanual	107	28.7	104	26.5
Intermediate/lower nonmanual	115	30.8	130	33.2
Routine nonmanual/manual	92	24.7	109	27.8
Self-employed	59	15.8	49	12.5	ns
Parental income
1st (highest) quartile	101	27.1	91	23.2
2nd quartile	92	24.7	102	26.0
3rd quartile	90	24.1	100	25.5
4th (lowest) quartile	90	24.1	99	25.3	ns
Adolescent physical inactivity
≥Weekly leisure-time organized activity	272	72.9	255	65.1
<Weekly leisure-time organized activity	101	27.1	137	35.0	p < .05
Young adult physical inactivity
>Weekly physical activity	213	57.1	259	66.1
≤Weekly physical activity	160	42.9	133	33.9	p < .05

Note. Significant differences by gender determined through chi-square test or t test when appropriate. Nonsignificant (p > .05) differences denoted with ns.

Table 2 shows associations between dimensions of inequality, adolescent physical inactivity, and young adult physical inactivity for women. In bivariate models, adolescent physical inactivity was associated with immigrant background (OR = 3.71, p < .01), lowest parental educational attainment (OR = 3.24, p < .05) compared with attainment of a tertiary degree, and was respectively patterned with parental social class and income. In the full model (Model 1), the association with social class and income remained statistically significant. In young adulthood, all examined dimensions of social inequality were significantly associated with physical inactivity in bivariate models (with the exception of parental income, where lowest income reached significance at the 10% level). However, immigrant background and parental educational attainment remained most strongly associated with young women’s physical inactivity when all dimensions were included simultaneously (Model 1). Adolescent physical inactivity was also associated with physical inactivity in young adulthood (OR = 1.95, p < .01), and its inclusion in the full model (Model 2) attenuated the effect of immigrant background, leaving parental educational attainment and adolescent physical inactivity as the strongest predictors of young women’s physical inactivity.

Table 2.

Social Differences in Physical Inactivity in Adolescence and Young Adulthood for Women, Logistic Regression Analysis (OR).

	Adolescent physical inactivity				Young adult physical inactivity
	Bivariate		Model 1		Bivariate		Model 1		Model 2
Dimensions of inequality	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI
Immigrant background
No	Reference		Reference		Reference		Reference		Reference
Yes	3.71	[1.77, 7.80]	2.21	[0.95, 5.17]	2.34	[1.15, 4.76]	2.33	[1.03, 5.23]	2.14	[0.94, 4.85]
Parental educational attainment
Tertiary degree	Reference		Reference		Reference		Reference		Reference
Upper secondary degree	1.14	[0.70, 1.86]	0.88	[0.50, 1.54]	2.14	[1.26, 3.63]	2.08	[1.15, 3.77]	2.14	[1.17, 3.92]
≤Compulsory degree	3.24	[1.27, 8.29]	1.38	[0.48, 3.92]	4.78	[1.83, 12.5]	3.44	[1.22, 9.69]	3.38	[1.15, 9.95]
Parental social class
Higher nonmanual	Reference		Reference		Reference		Reference		Reference
Intermediate/lower nonmanual	2.13	[1.17, 3.89]	2.01	[1.02, 3.94]	2.07	[1.14, 3.75]	1.77	[0.93, 3.37]	1.66	[0.86, 3.18]
Routine nonmanual/manual	3.64	[1.97, 6.72]	2.63	[1.28, 5.42]	2.50	[1.35, 4.62]	1.61	[0.80, 3.25]	1.46	[0.71, 2.99]
Self-employed	1.59	[0.71, 3.54]	1.32	[0.58, 3.00]	2.19	[1.04, 4.61]	1.78	[0.81, 3.93]	1.75	[0.77, 3.94]
Parental income
1st (highest) quartile	Reference		Reference		Reference		Reference		Reference
2nd quartile	2.10	[1.09, 4.04]	1.65	[0.84, 3.25]	1.26	[0.68, 2.31]	1.00	[0.52, 1.93]	0.95	[0.49, 1.84]
3rd quartile	2.01	[1.03, 3.89]	1.35	[0.65, 2.80]	1.17	[0.63, 2.18]	0.75	[0.38, 1.48]	0.72	[0.36, 1.42]
4th (lowest) quartile	3.85	[1.98, 7.49]	2.28	[1.12, 4.65]	1.85	[0.99, 3.46]	1.02	[0.50, 2.07]	0.93	[0.45, 1.92]
Adolescent physical inactivity
≥Weekly leisure-time organized activity					Reference				Reference
<Weekly leisure-time organized activity					1.95	[1.26, 3.02]			1.67	[1.05, 2.66]

Note. N = 392. OR = odds ratio; CI = confidence interval. Bivariate models adjusted for age and family composition. Model 1 adjusted for same variables as bivariate models and immigrant background, parental educational attainment, parental social class, and parental income. Model 2 adjusted for same variables as Model 1 and adolescent physical inactivity. Significant (p < .05) associations are in boldface.

Table 3 shows equivalent results for men. Among men, social determinants were not as consistently associated with physical inactivity, though low parental income was associated with physical inactivity (OR = 3.27, p < .01; Model 1). A diminished risk could be attributed to upper secondary parental education versus tertiary education (OR = 0.48, p < .05). In young adulthood, low income was still a risk factor for physical inactivity (OR = 1.81, p < .05); increased risk could also be seen for young adults with self-employed parents (OR = 1.94, p < .05) and those with an immigrant background (OR = 2.21, p < .05). However, these differences became insignificant in the full model. Physical inactivity in adolescence was also associated with physical inactivity in young adulthood (OR = 2.37, p < .001) but did not fully explain the effect of immigrant background (Model 2).

Table 3.

Social Differences in Physical Inactivity in Adolescence and Young Adulthood for Men, Logistic Regression Analysis (OR).

	Adolescent physical inactivity				Young adult physical inactivity
	Bivariate		Model 1		Bivariate		Model 1		Model 2
Dimensions of inequality	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI
Immigrant background
No	Reference		Reference		Reference		Reference		Reference
Yes	1.21	[0.55, 2.65]	0.64	[0.27, 1.54]	2.21	[1.08, 4.55]	2.06	[0.94, 4.48]	2.29	[1.04, 5.03]
Parental educational attainment
Tertiary degree	Reference		Reference		Reference		Reference		Reference
Upper secondary degree	0.58	[0.34, 0.99]	0.48	[0.25, 0.92]	1.18	[0.72, 1.93]	0.92	[0.51, 1.63]	1.04	[0.57, 1.88]
≤Compulsory degree	1.85	[0.77, 4.44]	1.27	[0.43, 3.78]	1.39	[0.61, 3.22]	0.99	[0.37, 2.68]	0.93	[0.34, 2.57]
Parental social class
Higher nonmanual	Reference		Reference		Reference		Reference		Reference
Intermediate/lower nonmanual	0.51	[0.26, 0.99]	0.50	[0.24, 1.04]	1.32	[0.76, 2.27]	1.15	[0.62, 2.16]	1.28	[0.67, 2.42]
Routine nonmanual/manual	1.10	[0.59, 2.06]	0.97	[0.43, 2.16]	1.45	[0.81, 2.60]	1.05	[0.50, 2.20]	1.05	[0.49, 2.25]
Self-employed	1.24	[0.62, 2.48]	1.17	[0.53, 2.58]	1.94	[1.01, 3.74]	1.67	[0.81, 3.46]	1.64	[0.78, 3.48]
Parental income
1st (highest) quartile	Reference		Reference		Reference		Reference		Reference
2nd quartile	1.11	[0.55, 2.23]	1.48	[0.71, 3.10]	1.19	[0.66, 2.16]	1.19	[0.63, 2.25]	1.12	[0.59, 2.15]
3rd quartile	1.38	[0.70, 2.74]	2.18	[1.02, 4.66]	1.81	[1.01, 3.26]	1.75	[0.90, 3.41]	1.57	[0.79, 3.11]
4th (lowest) quartile	2.65	[1.31, 5.36]	3.27	[1.39, 7.69]	1.71	[0.92, 3.20]	1.26	[0.61, 2.61]	1.04	[0.49, 2.23]
Adolescent physical inactivity
≤Weekly leisure-time organized activity					Reference				Reference
<Weekly leisure-time organized activity					2.37	[1.47, 3.82]			2.44	[1.47, 4.04]

Note. N = 373. OR = odds ratio; CI = confidence interval. Bivariate models adjusted for age and family composition. Model 1 adjusted for same variables as bivariate models and immigrant background, parental educational attainment, parental social class, and parental income. Model 2 adjusted for same variables as Model 1 and adolescent physical inactivity. Significant (p < .05) associations are in boldface.

Due to aforementioned differential associations between immigrant background and adolescent physical inactivity for girls versus boys, we also tested for and found a significant synergistic interaction using the attributable proportion of interaction (AP = 0.687, 95% CI [0.392, 0.982]) between immigrant background and female gender on adolescent physical inactivity, indicating that girls with an immigrant background have an excess risk of physical inactivity. A high but nonsignificant estimate was also found using the Synergy Index (S = 7.241, 95% CI [0.892, 58.78]); the wide confidence interval may reflect the small number of cases in our sample.

Discussion

Our aim was to explore how different dimensions of inequality associate with physical inactivity in adolescence and young adulthood in a longitudinal sample of Swedish young people. Regarding our first two research questions, we found three notable results: (1) female gender is associated with adolescent physical inactivity but not inactivity in young adulthood, (2) girls’ physical inactivity in both adolescence and young adulthood is more systematically patterned with dimensions of socioeconomic position than boys’ physical inactivity at both time points, (3) a significant interaction was found between female gender and immigrant background on adolescent physical inactivity. Regarding our third research question, we found that (4) physical inactivity in adolescence predicts physical inactivity in young adulthood, but accounting for this association does not eliminate socioeconomic inequalities in young adulthood. These results are discussed in this order in greater depth below.

Numerous studies have reported that adolescent girls are at greater risk for physical inactivity than boys (Sallis et al., 2000), and our results corroborate this finding. Adolescent girls were significantly more likely to be inactive in leisure-time organized physical activity, which Vilhjalmsson and Kristjansdottir (2003) have argued is an important dimension of physical inactivity with regard to gender, as the often-cited gender difference in adolescent physical activity may be attributable to a gender divide in sports club enrolment. That girls are less likely to join sports clubs may be due to greater socialization of boys into organized sports and differential encouragement given to boys over girls to value competition and athleticism (Vilhjalmsson & Kristjansdottir, 2003). However, we also found that being male translated into a greater risk of physical inactivity in young adulthood. Gender differences in physical inactivity in this age-group are largely unexplored in the literature; however, our results are in line with a longitudinal Finnish study that found that boys’ physical activity levels decline more quickly than girls from early adolescence to young adulthood (Telama & Yang, 2000). It is also plausible that our more global measure of physical inactivity in young adulthood better captures the physical activities in which women engage (i.e., more independent and less competitive activities like walking; Vilhjalmsson & Kristjansdottir, 2003). Of note, gender differences in physical inactivity at both time points in this study were not dependent on how we dichotomized the physical inactivity variables.

Further addressing our first two research questions, we found that physical inactivity was more systematically patterned by socioeconomic position, that is, in expected directions, for girls than boys. We found girls to be at greater risk for physical inactivity if their parents held low educational attainment, were not higher nonmanual workers, and had low income. Among these, social class and income were the most important dimensions in adolescence and education was the most important in young adulthood. The patterning among boys did not correspond with socioeconomic disadvantage as systematically. However, boys in families with lower income were more likely to be physically inactive in adolescence and a similar tendency persisted in young adulthood, though it was not statistically significant in the full model. Identifying why specific dimensions of socioeconomic position may impact girls versus boys differently, and during different developmental periods in youth, is a complex endeavour. For instance, it is unclear why only young men with self-employed parents would be at a greater risk of physical inactivity (finding not significant in the full model). It is also difficult to disentangle why adolescent boys with upper secondary educated or intermediate/lower nonmanual parents would be less likely to be inactive compared with boys whose parents have tertiary education or are higher nonmanual workers, respectively (of note, results were similar when checked in the larger sample of adolescents who participated in the Child-LNU). Interpreting results for girls was more straightforward. That parental social class and income largely explained the association between parental education and adolescent girls’ physical inactivity can be partially understood in terms of how different dimensions of socioeconomic position are acquired in the parental generation. As education provides qualifications for more desirable occupations with high salaries, social class and income can be regarded as mediators (or are more important per se) in the association between education and physical inactivity. More specifically, not belonging to the higher nonmanual social class may translate into fewer opportunities for adolescents to participate in organized activities (perhaps due to differential class norms or neighbourhood disadvantage), and it is not unreasonable to expect that if opportunities are few that this would affect girls’ participation more so than boys’, as boys are socialized to participate in sports to a greater degree (Vilhjalmsson & Kristjansdottir, 2003). Additionally, the impact of disposable income on adolescent physical inactivity (found for girls and boys) may be understood in terms of the financial investment often required for enrolment in leisure-time athletic activities. It is less clear why we find a strong independent effect of parental education on young women’s physical inactivity in young adulthood. However, there is evidence that education, when compared with other dimensions of socioeconomic position, is more strongly connected to adult women’s health (Torssander & Erikson, 2010) and health behaviours (Dorner, Strogenner, Hoffmann, Stein, & Niederkrotenthaler, 2013). It is possible that higher education equips parents with a greater awareness of the importance of physical activity and gender equality, which could translate into a greater likelihood for their daughters to participate in physical activities and go on to achieve a high education themselves (which would then in turn also be associated with physical activity). However, more research is needed testing such mechanisms.

We also found that immigrant background was associated with physical inactivity in adolescence (for girls only) and in young adulthood (both genders). A synergistic interaction between immigrant background and female gender in adolescence lent further support to an intersectional interpretation, where being female and having an immigrant background translate into a higher proportion of physically inactive adolescents that is greater than summing the independent effects of being female and having an immigrant background (Bauer, 2014; Kalilani & Atashili, 2006). This finding also supports previous research conducted in Sweden that found an association between immigrant background and physical inactivity for adolescent girls only (Gillander Gådin & Hammarström, 2002; Kahlin et al., 2009). Possible explanations for this association include differences in socioeconomic position (of which we find some support) and other mechanisms outside the scope of this study, including differential norms held by parents regarding girls’ participation in physical activity and discrimination or exclusion of girls with an immigrant background from participating in leisure-time organized athletic activities. However, as we also find that immigrant background is associated with physical inactivity for both men and women in young adulthood, further research should consider how such mechanisms may be specific to time period in youth or type of physical inactivity.

In this study, nonparticipation in leisure-time organized physical activity in adolescence also predicted physical inactivity in young adulthood. Although our measures of physical inactivity differed at the two time points, this was nonetheless an expected finding as previous research has indicated that adolescent sports participation is associated with later life physical inactivity (Hirvensalo & Lintunen, 2011). However, adolescent physical inactivity did not explain socioeconomic inequalities in young adult physical inactivity. For young women, accounting for prior inactivity did attenuate the effect of immigrant background on young adult physical inactivity, though a strong effect of educational attainment remained. For young men, the effect of immigrant background also remained associated with physical inactivity after accounting for prior physical inactivity. This suggests that while some inequalities in physical inactivity (i.e., immigrant background for women) may become embodied already in adolescence, the effects of other dimensions appear or reappear in young adulthood.

This study was limited by its relatively small sample size, which made it difficult to assess differences in physical inactivity by more refined age-groups, shown in other studies to be inversely related to physical activity (Sallis et al., 2000). Our measures of physical inactivity were also constructed differently at each time point, which limited our ability to assess changes in physical inactivity over time; they were also self-reported, which has been associated with underreporting of physical inactivity as compared with more objective measurement (Epstein, Paluch, Coleman, Vito, & Anderson, 1996). However, this study benefitted from the inclusion of multiple dimensions of socioeconomic position, self-reported by parents when their children were adolescents. Together with measures of physical inactivity from both adolescence and young adulthood, this allowed us to examine inequalities in physical inactivity at two time points in youth.

In conclusion we find that physical inactivity is associated with multiple dimensions of inequality in adolescence and young adulthood, and that these dimensions may operate independently and through intersection to affect physical inactivity. In contrast to prior literature, which has often focused on parental education or occupation without accounting for other dimensions of inequality (Hanson & Chen, 2007), we find that less studied dimensions of inequality like parental income and immigrant background may be important to consider when targeting interventions to young people. As physical inactivity in youth is associated with less favourable cardiovascular disease risk factor profiles in adulthood (Twisk, Kemper, & Van Mechelen, 2002), investments in improving disadvantaged young people’s participation in physical activity may represent both an efficient and cost-effective method to reduce later life health inequalities.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was financially supported by the Swedish Research Council for Health, Working Life and Welfare (Grant No. 2013-0159; 2015-00399).

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