Inequalities in participation and time spent in moderate-to-vigorous physical activity: analysis using the Health Surveys for England 2008, 2012, and 2016

Evidence is unclear on whether inequalities in average levels of moderate-to-vigorous physical activity (MVPA) reflect differences in participation, differences in the amount of time spent active, or both. Using self-reported data from 24 882 adults (Health Survey for England 2008, 2012, 2016), we examined gender-specific inequalities in these separate aspects for total and domain-specific MVPA. Hurdle models can accommodate continuous data with excess zeros and positive skewness. Such models were used to assess differences between income groups in three aspects: (1) the probability of doing any MVPA, (2) the average hours/week spent in MVPA, and (3) the average hours/week spent in MVPA conditional on participation (MVPA-active). Inequalities were summarised on the absolute scale using average marginal effects (AMEs) after confounder adjustment.


Introduction
Being physically active increases cardiometabolic health and reduces risk of cardiovascular-related morbidity and mortality [1]. Inequalities in physical activity (PA) contribute to social gradients in health [2] [3]. However, previous studies on inequalities in adult PA have produced inconsistent findings due, at least in part, to heterogeneity in analysis techniques such as choice of PA indicator and whether assessment was made for total or domain-specific PA [4] [5].
The direction and/or magnitude of inequalities can also vary by whether PA is analysed as a binary, ordinal or continuous variable. Whilst enabling assessment with regard to PA recommendations, categorising a continuous variable such as the hours-per-week spent in moderate-to-vigorous PA (MVPA) into a binary [4][6] or ordinal variable [7][8] loses extensive information in the discretisation and is suboptimal both in terms of power and bias [9]. Yet analysing the continuous variable is also problematic. First, analyses based on the mean can mask inequalities in other parts of the distribution (e.g. at the lower-or upper-tails) [10]. Quantile regression facilitates assessment across continuous distributions; evidence suggests larger inequalities at the upper-tail of the body mass index (BMI) distribution [11] [12]. Secondly, MVPA distributions are not typically normally distributed but are characterised by excess zeros (persons not doing any) and positive skewness (high MVPA for a small number of highly active adults) [13], with each aspect potentially having different determinants [14].
Hurdle models such as those proposed by Cragg [15] can handle continuous MVPA data with excess zeros and positive skewness [13] as they treat participation and the amount of time spent active (conditional on participation) separately. Although used in the economics literature, especially for sports participation [14] [16], no epidemiological studies to date have used hurdle models to quantify inequalities in MVPA, despite the potential advantages for policy-makers and practitioners. Using nationally-representative health survey data, we applied hurdle models to quantify inequalities in total and domainspecific MVPA. We hypothesised that adults in high-income versus low-income households are more likely to participate in MVPA, and that conditional on doing any MVPA, spend more time on average being active.

Study sample
Data came from the Health Survey for England (HSE): this dataset is used to monitor progress on numerous national health objectives, including PA [17] [18]. Details about the HSE sample design and data collection are described elsewhere [19]. Briefly, the HSE annually draws a nationally-representative sample of people living in private households in England using multistage stratified probability sampling with postcode sectors as the primary sampling unit and the Postcode Address File as the household sampling frame. All adults in selected households are eligible for interview. Fieldwork takes place continuously through the year. Trained interviewers measured participants' height and weight and assessed their demographic characteristics, self-reported health, and health behaviours including PA using computer-assisted personal interviewing. We used the most recent surveys (2008,2012,2016) [21]. The PASBAQ has demonstrated moderate-weak convergent validity in comparison with non-synchronous accelerometry [22]. PASBAQ assesses frequency (number of days in the last four weeks) and duration (of an average episode of at least ten minutes) in four leisure-time domains [23]: (i) "light" and "heavy" domestic activity; (ii) "light" and "heavy" manual work (e.g. 'Do-It-Yourself' (DIY)); (iii) walking (with no distinction between walking for leisure or travel); and (iv) sports/exercise (ten specific and six 'other' activities). "Heavy" domestic and manual activities were classed as moderately-intensive. Walking intensity was assessed by a question on usual walking-pace (responses: slow, average, fairly brisk, or fast); moderate-intensity was classed as a fairly brisk or fast pace. Intensity of sports/exercise was determined as indexed in the metabolic equivalent (METs) compendium [24][25] and a follow-up question on whether the activity had made the participant "out-of-breath or sweaty". In addition to leisure-time PA, participants engaged in any paid or unpaid work answer questions on occupational PA. Our analyses classed three activities -walking, climbing stairs or ladders, and lifting, carrying, or moving heavy loads -as moderate-intensity PA for participants working in occupations identified a-priori as moderately-intensive [17].
Time spent in domain-specific MVPA was calculated as the product of frequency and duration, converted from the last four weeks to hours/week. For sports/exercise, time in vigorous-intensity activities was multiplied by two when combined with moderate-intensity activities to calculate 'equivalent' hours/week as specified in MVPA guidelines [26]. Total MVPA was calculated by summing across the five domains, and was truncated at a maximum of 40 hours/week to minimise unrealistic values.

Socioeconomic position ascertainment and confounders
Household income was our chosen marker of socioeconomic position (SEP). The household reference person reports annual gross household income via a showcard (31 bands ranging from 'less than £520' to '£150 000+'). Household income is equivalised

Descriptive estimates
Data was pooled over the three surveys to increase precision (prior analyses revealed no change in associations over time). Differences in age, self-rated health, current smoking, and BMI were estimated by income, using Rao-Scott tests for independence [28]. For total and domain-specific MVPA, we computed descriptive estimates for four outcomes: (i) % doing any; (ii) % 'sufficiently' active (i.e. at least 2.5 hours/week MVPA [26]); (iii) average hours/week MVPA (range: 0 to 40 hours/week); and (iv) average hours/week MVPA among those doing any (range: 0.042 to 40 hours/week; hereafter referred to as MVPA-active).
Outcomes (iii) and (iv) represent unconditional and conditional (on participation) means, respectively. We decided, a-priori, to conduct gender-stratified analyses due to expected differences in inequalities as reported in the literature [7][8] [29]. Income-specific estimates were directly age-standardised within gender using the pooled data as standard. Pairwise differences between income groups (low-income households as reference) were evaluated on the absolute scale using a linear combination of the coefficients [30].

Hurdle models
To handle continuous MVPA data with excess zeros and positive skewness, we used the hurdle model proposed by Cragg, which comprises two parts: a selection/participation model and a latent model [15]. statistical code is available from the corresponding author.

Characteristics by income
Information on confounders by income is presented in additional file 1. Poorer self-rated health and higher smoking levels were evident among adults in low-income households (both P<0.001). BMI status also varied by income (P<0.001 for both genders), with higher obesity levels especially among women in low-income households. Tables 1 and 2 show the descriptive estimates for total and domain-specific MVPA for all adults and by income for men and women, respectively. Overall, 85% of men (n=9254) and 81% of women (n=10 947) did any MVPA; 66% of men (n=7120) and 56% of women (n=7537) were 'sufficiently' active. Men and women spent on average 9.7 and 6.8 hours/week respectively in total MVPA; however, these distributions showed excessive zeros and positive skewness (Figure 1). Among those doing any MVPA, men and women spent on average 11.5 and 8.4 hours/week respectively in total MVPA. The largest difference between MVPA and MVPA-active means was for occupational PA; among men, these were 2.5 and 15.2 hours/week respectively.

Descriptive estimates
Inequalities were evident in descriptive analyses in each aspect for total MVPA and for sports/exercise. Differences between high-income versus low-income households in total MVPA were 2.2 hours/week among men (95% CI: 1.7, 2.8; P<0.001) and 1.8 hours/week among women (95% CI: 1.3, 2.2; P<0.001); the same pattern, but with narrower effect sizes, was found for total MVPA-active (men: 0.9 hours/week, 95% CI: 0.4, 1.5; P=0.002; women: 1.0 hours/week, 95% CI: 0.7, 1.4; P<0.001). Likewise, differences in sports/exercise for men and women in high-income versus low-income households were 1.9 hours/week (95% CI: 1.6, 2.2; P<0.001) and 1.5 hours/week (95% CI: 1. Multivariable hurdle models Table 3 shows the AMEs from estimated hurdle models corresponding to the absolute difference in the marginal means for the binary outome of participation, and the continuous outcomes of MVPA and MVPA-active (AMEs are graphically shown in Figure 2).
Higher MVPA in high-income versus low-income households was robust to confounder adjustment for total MVPA and for sports/exercise (P<0.001 for all outcomes and both genders). For example, at fixed values of the confounding variables, differences between high-income versus low-income households in sports/exercise MVPA were 2.2 hours/week among men (95% CI: 1.7, 2.6) and 1.7 hours/week among women (95% CI: 1.4, 2.1); differences in sports/exercise MVPA-active were 1.3 hours/week (95% CI: 0.7, 1.9) and 1.0 hours/week (95% CI: 0.5, 1.6) for men and women, respectively.

Discussion
Applying hurdle models to investigate inequalities in total and domain-specific MVPA, we hypothesised that adults in high-income households were more likely both to participate in MVPA than adults in low-income households and, conditional on doing any, to spend more time on average being active. These hypotheses were confirmed in fully-adjusted analyses for total MVPA and for sports/exercise. Heterogeneity in associations was observed for the other activity domains. For example, adults in high-income versus low-income households were more likely to do any walking, yet showed no difference in hours/week walking, whilst among those doing any walking, women in high-income households spent less time walking.
Comparisons with previous studies are difficult due to differences in study characteristics and US [4][6] studies. Our results showing that inequalities differ by domain corroborate both systematic reviews [5] and previous empirical studies [4], reflecting differences across SEP in how MVPA is accrued. In agreement with other reports [8], we found that inequalities in total MVPA were driven in the main by sports/exercise, which contributes a larger proportion of total MVPA for adults, especially men, in high-income households. This result also reflects inequalities in vigorous-intensity sports/exercise (data not shown), which is given twice the weight of moderate-intensity activities in our analyses in accordance with guidelines [26]. Inequalities in total and sports/exercise MVPA were partially offset by the reverse pattern for occupational PA, consistent with previous studies [4], reflecting the higher involvement of lower SEP groups in physically demanding work. Whilst occupational PA is taken into account in monitoring adherence to MVPA guidelines using HSE data [17] Implications for policy Differences in financial resources (especially for sports/exercise) [5]  [44], are key determinants of inequalities in physical activity. Reducing the inequalities presented here for sports/exercise will require interventions to move adults in low-income households from inactivity to activity, and to enable those already active to do more.

Strengths and limitations
Our analyses used novel modelling methods to assess inequalities in MVPA. Although it is well-known that MVPA distributions typically contain excess zeros and positive skewness, no epidemiological studies to date have applied hurdle models to assess inequalities. Such models avoid the loss of information and power that occurs when practitioners typically categorise a continuous variable into a binary or ordinal variable [9]. Precision of our estimates was increased by pooling standardised PA data across survey years. Caution is required, however, when interpreting our findings. First, self-reported PA data has wellknown limitations such as recall and reporting (social desirability) bias [48] [49]. Secondly, the dataset contained a sizeable amount of missing data for income and BMI (~20%); among HSE participants, the probability of having missing income data varies systematically across groups [50], which we minimised to some extent through applying non-response weights. The software routine for estimating hurdle models does not currently permit multiply imputed data, and so our findings may be statistically underpowered to some extent. Thirdly, the choice of potential confounders was limited to some extent by data availability; furthermore, we were unable to account for ethnic differences due to small numbers. As in all studies, our findings could have been influenced by unmeasured confounders. Fourthly, our findings are contingent upon HSE data collection, including the minimum duration of ten minutes (in accord with the contemporaneous UK guidance but differing from recent UK [51] and US [52] guidelines, which acknowledge that PA of any duration enhances health), a specific subset of occupational PA for a selected group of occupations, and the inability to distinguish between walking for leisure and active travel. We acknowledge that different definitions may have led to different conclusions. Finally, we cannot draw causal inferences, as this was a descriptive study based on cross-sectional data.

Conclusion
Monitoring inequalities in MVPA requires assessing different aspects of the distribution within each domain. In the present study, income-based inequalities were evident in the propensity to do any sports/exercise and walking, and for the amount of time spent doing sports/exercise. These findings may assist policy-makers to identify and commission tailored interventions best suited to tackling inequalities, and our methods could be used by practitioners to evaluate their impact.

Availability of data and materials
The HSE datasets generated and/or analysed during the current study are available in via the UK Data Service (UKDS: http://www.uk-dataservice.ac.uk). Syntax to enable replication of our results (using the datasets deposited at the UKDS) is available on request from the corresponding author.

Competing interests
The authors declare that they have no competing interests.

Funding
The Health Survey for England (HSE) was funded by NHS Digital. NHS Digital is the trading name of the Health and Social Care Information Centre. The authors are funded to conduct the annual HSE but this specific study was not funded. NHS Digital had no role in the analysis, interpretation of data, decision to publish or preparation of the manuscript for this specific study.  were in their job (responses: very; fairly; not very; not at all). Estimates are unweighted.

Authors contributions
MVPA: moderate-to-vigorous physical activity; SE: standard error.    Figure 1 Men and women spent on average 9.7 and 6.8 hours/week respectively in total MVPA; however, these distributions showed excessive zeros and positive skewness.

Figure 2
AMEs represent difference between adults in high-income versus low-income households in: (i) any participation (%); (ii) MVPA hours/week (average amongst all adults, including those who did none); and (iii) MVPA-active hours/week (average among those who did any). AMEs evaluated at fixed values of the confounders: for persons aged 35-44 years with very good/good health, never being a regular smoker, and having a normal-weight (BMI 18.5-24.9kg/m2).

Supplementary Files
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