This paper examined longitudinal relationships between neighbourhood disadvantage, individual-level SEP, and WfT and how these associations changed over time as people aged. An important reference-point for this research was studies that had examined the association between neighbourhood disadvantage, SEP, and LTPA. A large body of work extending back numerous decades consistently shows a positive association between socioeconomic circumstances and LTPA irrespective of how these concepts are measured ,. By contrast, the relationship between neighbourhood disadvantage, individual-level SEP, and WfT has been investigated in a limited number of studies, all of which are cross-sectional, with mixed and inconsistent findings .
Ageing and walking for transport
At baseline (2007) respondents were aged 40–65 years, and younger persons walked more minutes on average per week than their older counterparts. Between 2007 and 2011 average minutes of WfT remained fairly stable for younger respondents; however, for older persons, average minutes of walking declined markedly over the subsequent five years. These declines in WfT are consistent with findings from the PLACE study in Adelaide, Australia  and parallel those observed in longitudinal studies of LTPA ,. Possibly, and especially for the older respondents in the HABITAT sample, age-related declines in WfT may be partly associated with concomitant declines in health and functional status .
Remaining physically active as we age is beneficial for aerobic capacity, strength, endurance, flexibility, range of motion, and balance control . A reduced capacity or loss of these functions is often associated with reduced autonomy and independence, reduced community and family participation, and a lower quality of life; and for society and the economy, these flow on to increased health care costs and greater use of health- and aged-care systems, reduced rates of workforce participation and earlier retirement, and lower levels of civic engagement such as volunteering . The challenge therefore is to find ways to delay the onset of the diseases and disabilities of ageing, thus enabling people to live longer in good health until death at an advanced old age. Meeting this policy challenge will require national, state, and local governments to configure (design and/or retrofit) neighbourhood environments to facilitate active transport and LTPA for an ageing population, and to develop sustainable individual-level interventions that can promote and support all domains of PA as we age.
Neighbourhood disadvantage and walking for transport
Residents of disadvantaged neighbourhoods were significantly less likely to be classified as ‘never-walkers’: and among those who reportedly walked, residents of disadvantaged neighbourhoods spent significantly more time each week walking for transport-related purposes. Given that active transport is health protective independent of LTPA -, greater minutes of walking among residents of disadvantaged areas may potentially minimise neighbourhood-level socioeconomic inequalities in total PA, and by extension, health inequalities for activity-related chronic disease such as CVD and diabetes.
These findings from Brisbane (Australia) are consistent with patterns of WfT reported by Giles-Corti and Donovan in Perth (Australia)  and Van Dyck et al. in Ghent (Belgium); however, they contrast with those reported by Cerin et al. in Adelaide (Australia) who found no association between neighbourhood SES and weekly minutes of WfT, and a significant positive association between neighbourhood SES and weekly frequency of walking for transport. Methodological differences notwithstanding, these inconsistencies might point to variation between cities in terms of how urban infrastructure is spatially distributed along socioeconomic lines, thus differentially shaping and circumscribing socioeconomic patterns in WfT: national and international comparative research is needed to examine these issues.
The longitudinal evidence showed that minutes of WfT declined over time in all neighbourhoods, irrespective of the neighbourhood’s level of socioeconomic disadvantage. The declines appeared to be steeper for residents of the most disadvantaged neighbourhoods (figure not shown), although the interaction between neighbourhood disadvantage and time was not statistically significant. In Brisbane at least, disadvantaged neighbourhoods have physical environments that are more conducive to WfT : they are typically more residentially dense, have a more interconnected street network, and a more diverse mix of land uses (hence a greater number of destinations within walking distance). In infrastructural terms therefore, disadvantaged neighbourhoods in Brisbane provide the ‘best’ contexts to facilitate and promote WfT: moreover, these environments may be important in offsetting the negative health consequences that often result from exposure to socioeconomic inequality at the individual-level. Even in these highly walkable neighbourhoods however, levels of walking declined over time as people aged. The reasons for these declines are as yet unknown; however, they represent a significant challenge to public health advocates in their attempts to keep people active and healthy in their later years of life. Further, given that WfT often takes place within the neighbourhood environment, declines in walking as we age are likely to translate to a lower level of direct and visible engagement with the local community, with potential negative flow-on effects for neighbourliness and social capital .
Education level and walking for transport
Lower educated groups were significantly more likely to be classified as “never-walkers”; however, there were no education differences in minutes walked among those who reported walking. Moreover, there were no significant interactions between education, time, and WfT, thus levels of walking declined at a similar rate between 2007 and 2011 for all respondents irrespective of their level of education (figure not shown). At this stage we can only speculate about why low educated groups were less likely to walk for transport as very little research has investigated this issue. Higher educated groups tend to have a greater level of awareness of the links between motorised travel and environmental problems (e.g. pollution, greenhouse gas emissions) which could promote increased levels of WfT among this group . Cerin et al.  also suggests that lower educated groups may be less positively pre-disposed to transport walking as they are less likely to perceive the health benefits of an active lifestyle, including participating in LTPA, which possibly reflects a lower responsiveness to health promotion messages. The findings of this present paper, and the interpretive evidence reported by Cerin et al. , need to be viewed circumspectly against a backdrop of a small number of studies that have examined the relationship between education and WfT, and which are highly variable in terms of how walking has been measured. Walking has been operationalised using indicators that capture any WfT (yes/no), walking to work ,, walking to public transit , minutes WfT in general ,,,, walking for transport for ≥30 minutes per day ,, frequency of WfT , and WfT at a moderate or brisk pace . This diverse body of work has produced mixed and sometimes inconsistent findings and generated a complex picture of the relationship between education and WfT that is not easily summarised: thus no clear trends or patterns can be reliably discerned. By extension, any efforts to better understand educational differences in WfT are arguably premature as the field is still someway from reaching a consensus about the form and direction of the relationship: replication studies are needed to provide a deeper evidence-base to advance knowledge and to more robustly inform policy and promotion efforts to increase WfT among all education groups.
Occupation and walking for transport
The odds of never walking were significantly higher for white- and blue-collar workers and those engaged in home duties relative to managers and professionals. There were no differences between managers and professionals and retirees in the odds of never walking. Each of these relationships remained largely unchanged after adjustment for neighbourhood disadvantage; however, they changed markedly with further adjustment for education and household income. Specifically, the odds relative to managers and professionals became significantly lower for white collar workers and retirees; they remained significantly higher for blue collar workers, although substantially reduced; and they were attenuated to non-significance for home duties. A key message from these pre- and post-adjusted findings is the need to specify models and test relationships between socioeconomic variables in a theoretically informed way (e.g. via the use of DAGs) to avoid inaccurate population inferences and erroneous conclusions and policy recommendations.
Among those who walked for transport, differences in minutes walking in the previous week were observed between the employed and non-employed, but no differences were found between the occupation groups. Over the period 2007 to 2011, those classified as home duties and the retired, walked for transport approximately 11 minutes and 15 minutes less each week respectively than managers and professionals. Presumably, more minutes of walking among managers and professionals (and also among white- and blue-collar workers) reflected their travel to work which often involves the use of public transport. No known studies have examined the association between occupation and WfT, and the few that have investigated walking differences by employment status have produced mixed results. In contrast to this present study, Van Dyck et al’s study of Belgian adults aged 18–65 years  found that minutes of WfT were significantly higher among the non-employed. Similarly, Cole et al’s study of Australian adults aged 18 years and over  found no bivariate association between being in paid work (yes/no) and WfT for males, and a significant association for females, with employed females being more likely to report that they walked at a moderate or brisk pace. This same study however found no association between paid work and WfT for at least 150 minutes per week.
Our longitudinal results indicated that minutes spent WfT in the previous week remained relatively stable over the five-year study period for the occupation groups and those classified as home duties. Retired respondents by contrast exhibited a marked decline in average minutes of WfT between 2007 and 2011. The steeply downward trend for retirees possibly reflects a number of interacting factors including: a transition out of paid employment and hence less use of public transport and work-related walking ; older age; poorer health and functioning associated with ageing; increased fear and concerns about safety and crime in the neighbourhood  and therefore fewer walking trips to local destinations (e.g. shops, health care services); and a greater reliance on a motor vehicle.
Household income and walking for transport
Household income did not strongly differentiate whether or not respondents WfT. Members of households earning between $26,000 and $51,999 had a significantly raised odds (23%) of never walking compared with members of the highest income households: no other income differences were found. This association remained unchanged after adjustment for neighbourhood disadvantage, but was attenuated to non-significance after further adjustment for education and occupation: this suggests that part of the association between income and not walking is due to the unmeasured influence of a respondent’s educational attainment and their occupational status.
Members of the lowest income households who were classified as walkers walked significantly more minutes per week than members of the highest income households. Similar findings have been consistently observed in other studies ,,,-. Limited access to a motor vehicle is posited as the main reason why members of low income households walk more for transport ,, although greater time constraints in high income households due to longer working hours  and a preference for PA during leisure time might also account for some of these differences in WfT .
In this present study, the relationship between household income and minutes of WfT was attenuated to non-significance after adjustment for neighbourhood disadvantage, thus the disproportionate concentration of low income households in disadvantaged neighbourhoods, and the more walkable environments of these neighbourhoods in Brisbane , partly accounted for the higher levels of WfT observed among members of low income households. Previous studies investigating the relationship between household income and WfT which didn’t adjust for neighbourhood disadvantage may therefore have over-estimated the extent to which income per se directly influenced one’s propensity to walk for transport-related purposes.
Between 2007 and 2011, minutes of WfT declined for all income groups; however, the declines were steeper for respondents from the lowest income households. During the later years of adulthood, members of poorer households typically experience higher rates of chronic degenerative disease and a greater loss of physical function than their more affluent counterparts ,, hence steeper declines in walking in low income households may reflect the greater burden of disease borne by this group. Poorer health and function in adulthood is more often experienced by those from disadvantaged backgrounds in childhood, and those who were exposed to more episodes of accumulated socioeconomic disadvantage over the life course . Hence, the greatest gains in keeping older people physically active and healthy, and reducing health inequalities, are likely to result from policy investments that improve social, economic and environmental conditions in both early and later life.
The random effects: their interpretation and implications
Mixed-effects linear regression models were used to examine associations between ageing and average minutes WfT, and change in WfT by neighbourhood disadvantage and individual-SEP. The random coefficients produced by these models offer useful insights into how average time spent WfT varies between neighbourhoods, between individuals, and within individuals, and what factors might contribute to this variation. The between-neighbourhood variation captured the extent to which average minutes of WfT in the 200 neighbourhoods varied around the overall average for Brisbane city. Before and after adjustment for age, sex, and each of the individual-level socioeconomic factors, the between-neighbourhood variation accounted for the smallest proportion of the total variance in WfT (~1% in each model), although all of the variance terms were significantly different from zero. Subsequent adjustment for neighbourhood disadvantage reduced the between-neighbourhood variance in average minutes of WfT by between 39% and 57%, reflecting the fact that advantaged and disadvantaged neighbourhoods in Brisbane differ in the extent to which their built environments are conducive to walking for transport-related purposes .
The between-individual variation summarises the extent to which average minutes of WfT for each of the sampled respondents varied around the overall average for the population of mid-aged adults living in Brisbane between 2007 and 2011. The between-individual variation in WfT accounted for approximately 20% of the total variation in WfT. When the null model (Model 1, Table 3) and all other models were compared, the magnitude of the between-individual variance was reduced by a maximum of 3.8%, thus age and sex and each of the socioeconomic factors accounted for only a small amount of the variation in WfT among Brisbane residents.
The within-individual variance captured the extent to which each individual varied over time in their reporting of WfT. Relative to the other two sources of variation, the within-individual variance was disproportionately large, accounting for approximately 79% of the total variance in WfT. The large within-individual variance suggested that there was very little temporal stability in peoples’ reporting of WfT: this was confirmed on examination of a sample of individual growth-plots which showed substantial between-wave heterogeneity in reported minutes of WfT. Asking respondents every two years to recall minutes of walking in the previous week appears problematic: a seven-day reference period is narrow and may not capture ‘usual’ activity making it difficult to detect systematic change. This has important implications for the conceptualisation and measurement of PA questions in longitudinal research, as questions that accurately elicit information about minutes of activity in cross-sectional studies won’t necessarily perform well in longitudinal studies . It is therefore recommended that researchers direct attention to developing time-sensitive questions that are more ideally suited to reliably capturing change in PA, also being mindful of the time-lapse between data collection waves. In longitudinal studies it may be preferable to capture ‘usual’ behaviour rather than ‘last 7 days’, despite the tendency for the former behaviour to be over-reported.
Study strengths and limitations
The study was based on a sample of Brisbane residents who lived at the same address between May 2007 and May 2011: delimiting the study to non-movers dampened the potential negative impact of neighbourhood self-selection, although this may have occurred prior to the baseline data being collected.
Our finding of an association between neighbourhood disadvantage and WfT might be confounded by individual-level socioeconomic factors not included in the models. However, we used the three most commonly employed individual-level indicators of SEP in health research (i.e. education, occupation, and income), and given the correlation among these measures  it is likely they captured the unmeasured influence of other socioeconomic factors not included in the models.
An attrition analysis (not presented here) showed that the probability of loss to follow-up was significantly higher among younger respondents, the least educated, blue collar workers, members of low income households, and residents of disadvantaged neighbourhoods; however, the likelihood of drop-out was significantly lower among the retired, and those who reported walking for transport. Higher rates of loss to follow-up among the socioeconomically disadvantaged may bias the findings towards or away from the null depending on how the losses are associated with walking. Higher rates of attrition among the low SES that are non-differential with respect to WfT will typically only bias towards the null.
Walking for transport was measured by self-report using a question that asked respondents to estimate the total time they spent walking in the last week. Retrospective accounts of time-based activities are prone to substantial recall error ,. Moreover, the extent and direction of recall error often varies by the respondents’ sociodemographic characteristics (e.g. young versus old, high versus low SES). Given this, the error inherent in our measure of weekly walking likely biased associations between neighbourhood disadvantage, individual-level SEP and WfT, although it is not known if the bias resulted in an under- or over-estimation of the effect-sizes relative to their ‘true’ magnitude in the wider population.
The measure of walking was non-specific in its focus hence peoples’ reporting probably captured a diverse range of travel-related activities such as use of public transport for employment, taking children to school, and accessing businesses and services in the local neighbourhood. Associations between neighbourhood disadvantage, individual-level SEP and WfT appear to vary depending on the purpose of the walking and the destination (as noted above). The findings of this study therefore may have shown a different patterning and magnitude with a closer conceptual alignment between the socioeconomic predictor and the walking activity (e.g. full-time home duties and walking children to school, occupation and employment status and walking to public transport, retired and use of health care services). Future research should investigate this issue further as it is consistent with recent calls for greater specificity in our conceptualisation, measurement, and modelling of the determinants of PA, including walking for transport . Further, the measure of walking provided no indication of the exertion-level or intensity of the activity. Walking at a moderate pace is deemed necessary to produce health benefits  and as this study didn’t capture this we were unable to determine if respondents were meeting PA recommendations by walking for transport. This said however, WfT for any intensity or duration is likely to accrue some health benefits relative to no activity .
Finally, for a range of reasons, comparing the findings of this present study with earlier research was difficult. Existing studies have measured WfT in a variety of ways (e.g. categorical or continuous, frequency, minutes, or intensity) using different scenarios (e.g. walking to places in general or to specific destinations such as public transport). Studies used different reference periods when asking about WfT (e.g. daily, weekly, or fortnightly), the analytic models were specified using different types and numbers of socioeconomic indicators and covariates (e.g. self-rated health, body mass index), and the residential contexts in which the studies have been conducted possibly differed on neighbourhood-level factors likely to influence WfT such as residential density, land use mix, street connectivity, and closeness to walkable destinations (e.g. shops, employment, transport nodes). This present study investigated WfT using a sample aged 40–65 in 2007 (baseline), whereas others used either an all-ages sample ,, a sample that included respondents who were 18 years or older ,,, or a sample where respondents were aged between 18 and 65 years ,,,,,. Clearly, the lack of consistency across studies in their designs, samples, methods, measures, and reference periods makes it difficult to reliably compare findings; it also arguably thwarts efforts to further advance our understanding of how and why neighbourhood disadvantage and individual-level SEP are related to WfT.