Social-ecological predictors of physical activity patterns: A longitudinal study of women from socioeconomically disadvantaged areas

Preventive Medicine 132 (2020) 105995

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Social-ecological predictors of physical activity patterns: A longitudinal study of women from socioeconomically disadvantaged areas

T

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Verity Clelanda, , Fiona Cockera, Jana Canaryb, Megan Teychennec, David Crawfordc, Anna Timperioc, Kylie Ballc a

Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia Department of Developmental Studies, University of Alaska Fairbanks, Fairbanks, AK, United States of America c Institute for Physical Activity and Nutrition, Deakin University, Melbourne, Victoria, Australia b

A R T I C LE I N FO

A B S T R A C T

Keywords: Cohort study Exercise Gender Inequalities Prevention

Limited longitudinal evidence of the predictors of physical activity (PA) patterns over time exists, particularly among high-risk groups such as women living in socioeconomically disadvantaged areas. This study aimed to: 1) describe leisure-time PA (LTPA) and transport-related PA (TRPA) patterns over time; and 2) identify individual, social and physical environmental predictors of LTPA and TRPA patterns over five years. Baseline (2007–08) data were collected and analysed (2016–18) from n = 4349 women (18–46 years) from disadvantaged areas of Victoria, Australia. Three- and five-year follow-up data were collected in 2010–11 (n = 1912) and 2012 (n = 1560). LTPA and TRPA were self-reported using the International Physical Activity Questionnaire, and patterns categorised as consistently low, persistently increasing, persistently decreasing, or inconsistent. Compared to a consistently low LTPA pattern, greater family support predicted both persistent decreases (odds ratio [OR] 1.20, 95% CI 1.05–1.36) and persistent increases (OR 1.17, 95% CI 1.04–1.32) in LTPA, while access to childcare predicted inconsistent LTPA patterns (OR 1.66, 95% CI 1.03–2.65). For both LTPA and TRPA, PA enjoyment predicted persistent increases (LTPA: OR 1.05, 95% CI 1.02–1.10; TRPA: OR 1.03, 95% CI 1.00–1.07), persistent decreases (LTPA: OR 1.04, 95% CI 1.00–1.08; TRPA OR 1.04, 95% CI 0.99–1.08), and inconsistent patterns (LTPA: OR 1.04, 95% CI 1.02–1.07; TRPA: OR 1.03, 95% CI 1.01–1.06). Although directionality was inconsistent, and the magnitude of effects were small, PA enjoyment, family social support for PA and access to childcare warrant further investigation and consideration as potentially key factors impacting PA patterns among women living in socioeconomically disadvantaged areas.

1. Introduction Despite physical inactivity being recognised as a risk to public health, current efforts to promote physical activity (PA) are having limited impact, demonstrated by the low proportion of adults around the world meeting PA recommendations (Hallal et al., 2012). Further, there has been little change in the population prevalence of PA participation over time (Chau et al., 2017). It is well-documented that women participate in lower levels of PA than do men (Bauman et al., 2012), and increases in men's participation in PA (around 5%) are more than double that seen for women (< 2%) (Chau et al., 2017). In a 2012 Lancet series, it was argued current strategies to promote PA may be mis-targeted due to an abundance of correlational studies with cross-sectional designs, and a subsequent lack of understanding of the longitudinal determinants of PA (Bauman et al., 2012). Other

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knowledge deficits (Bauman et al., 2012) included: a narrow focus on mostly individual-level correlates that fails to acknowledge broader social and physical environmental factors; a lack of attention to populations at high risk of inactivity (such as those experiencing socioeconomic disadvantage); and limited consideration of non-leisure domains of discretional PA, such as transport. Some longitudinal studies have mapped PA trajectories over time (Barnett et al., 2008; Uijtdewilligen et al., 2015; Parsons et al., 2005; Brown et al., 2009), but only two have extended beyond two time points (Barnett et al., 2008; Uijtdewilligen et al., 2015); these focused on leisure-time PA (LTPA) only. PA typically occurs across four domains: leisure (e.g. sport, exercise), domestic (e.g. cleaning, yard work), occupational (e.g. job activity) and transport (e.g. walking/cycling to get to/from places). While occupational and domestic activities are usually utilitarian (e.g. non-discretionary), transport-related PA (TRPA)

Corresponding author at: Private Bag 23, Hobart, Tasmania 7000, Australia. E-mail address: [email protected] (V. Cleland).

https://doi.org/10.1016/j.ypmed.2020.105995 Received 5 August 2019; Received in revised form 10 December 2019; Accepted 13 January 2020 Available online 15 January 2020 0091-7435/ © 2020 Published by Elsevier Inc.

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Fig. 1. Summary of participants in the READI study at T1, T2 and T3.

general population samples, telling us little about the factors that may be important for supporting PA among those experiencing socioeconomic disadvantage. A lack of longitudinal studies investigating multiple levels of influence on PA behaviour means there are gaps impeding advancements in knowledge, policy and practice (Bauman et al., 2012). To tackle these research gaps and provide crucial information to inform intervention development, this paper draws on data from a prospective cohort study of adult women living in socioeconomically disadvantaged neighborhoods. It aims to: 1) describe LTPA and TRPA patterns over five years; and 2) identify the longitudinal individual, social and environmental determinants of LTPA and TRPA patterns over five years. Understanding why some people manage to increase or maintain their activity levels over time will provide insights into how to best promote and support ongoing PA.

represents an underexplored discretionary PA behaviour with potential for public health gain. Social-ecological models provide a useful framework for understanding behaviours such as PA (Sallis and Owen, 2002; Stokols, 1996). They hypothesise that multiple levels of influence interact to shape behaviour: individual (e.g. self-efficacy, enjoyment), social (e.g. social support from family, friends, colleagues), physical environmental (e.g. neighborhood walkability, aesthetics) and policy (e.g. urban planning) level factors. The existing longitudinal evidence has predominantly focused on demographic predictors of PA. While useful for identifying population groups at risk, these studies shed little light on the complex inter-relationships between modifiable individual (e.g. psychological, cognitive), social (e.g. social support) and physical environmental (e.g. neighborhood safety, aesthetics) factors highlighted by social-ecological models (Sallis and Owen, 2002; Stokols, 1996) as important for PA. No longitudinal studies have examined multiple levels of influence on non-leisure domains of PA. Further, these studies have focused on 2

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2. Methods

value from the T2 value (T2-T1), and the T2 value from the T3 value (T3-T2). These differences were standardised (by subtracting the group mean from each individual value and dividing by the standard deviation: z = X − μ / σ), and classified as increasing, decreasing, consistently low or inconsistent (see Appendix Table 2). Finally, a categorical ‘PA patterns’ outcome variable was generated for LTPA and TRPA, separately (see Appendix Table 3).

2.1. Ethics Three surveys were conducted for the Resilience for Eating and Activity Despite Inequality (READI) study in 2007–2008 (T1), 2010–2011 (T2), and 2012–2013 (T3) (Ball et al., 2013; Cleland et al., 2010a). This study was approved by the Deakin University Human Research Ethics Committee and all participants provided written informed consent. The READI study had a specific focus on adult women living in socioeconomically disadvantaged areas, and of childbearing age (18–45 years). Here, we report secondary analysis of this data, adhering to the STROBE reporting guidelines (von Elm et al., 2014) (see checklist in Appendix Table 1).

2.3.2. Baseline exposure variables Baseline exposure variables were selected to broadly encompass the individual, social and physical-environmental constructs of the socioecological model (Sallis and Owen, 2002; Stokols, 1996). Individual factors from baseline assessments included PA self-efficacy (five items using a five-point Likert scale (Marcus et al., 1992)), PA enjoyment (six items on a seven-point Likert scale (Kendzierski and DeCarlo, 1991)), outcome expectancies (six items on a four-point scale (Lechner et al., 2006)), perceived behavioural control (one item on a seven-point Likert scale (Giles-Corti and Donovan, 2003)), behavioural skills related to goal setting and planning for PA (two items (Giles-Corti and Donovan, 2003) using four- and six-point Likert scales), and priorities relating to family and PA commitments (three items using a six-point Likert scale). Social factors from baseline assessments included access to childcare to enable PA (yes, no, or not applicable/don't have children), dog ownership (yes/no), social support from family to be physically active (three items on a five-point Likert scale), and social support from friends/work colleagues to be physically active (three items on a fivepoint Likert scale) (Sallis et al., 1987). Social norms were assessed by three novel items developed for this questionnaire on a five-point Likert scale: “Lots of women I know walk or cycle”, “Lots of women I know do other forms of exercise or play sport”, and “Lots of women I know don't do much PA.” Perceived physical environmental factors from baseline assessments included perceptions of neighborhood personal safety (three items), neighborhood aesthetics (five items), and the neighborhood ‘PA environment’ (seven items) (Mujahid et al., 2007). Each environmental factor was assessed on a 5-point Likert scale. Internal reliability (Cronbach's alpha) for all of the individual, social and physical environmental measures have been previously reported (Cleland et al., 2010a).

2.2. Sample/participants The 2001 Socio-Economic Index for Areas (SEIFA) was used to classify all postcodes (‘neighborhoods’) in the Australian state of Victoria (Australian Bureau of Statistics, 2001). This area-level indicator of socioeconomic disadvantage is based on the population census that considers factors such as employment, education and income (McLennan, 1998). Neighborhoods in the bottom SEIFA third were considered ‘disadvantaged’, and from these, a randomly selected sample of 40 urban and 40 rural neighborhoods were identified. The Australian electoral roll (compulsory registration at age 18 years) was used to randomly identify 150 women aged 18–45 years from each of the 80 neighborhoods. In three neighborhoods with < 150 eligible women, all women were selected, resulting in a total sampling pool of 11,940 women. Invitations were sent via mail using Dillman protocols (Dillman, 1978, 2000) with reminders sent to non-responders at 7, 17 and 29 across two waves in August–September 2008 and January–February 2009. A total of 4938 completed surveys were returned, 45% of those delivered (excluding from the denominator 861 marked ‘return to sender’ and 13 which were the incorrect gender or deceased (Ball et al., 2013; Cleland et al., 2010a)). Those who moved from the sampled suburb prior to completing the survey (n = 571), who completed the survey but were not the intended participant (n = 3), withdrew their data after completing the survey (n = 2), or were < 17or > 46-years old (n = 13), were deemed ineligible (n = 589) and were excluded, leaving 4349 eligible participants at T1 (Fig. 1). The same contact procedure was administered at T2 and T3. Of these, 1913 completed a second survey (T2: 2010–11) and 1560 completed the third survey (T3: 2012–13). Of the 1560 women who completed all three surveys, LTPA or TRPA data was missing in one or more surveys for 80 participants, leaving 1480 women for final analyses.

2.3.3. Covariates Self-reported demographic characteristics included age, country of birth (Australia/outside Australia), and whether English was spoken at home (yes/no). The following characteristics were measured at baseline only (T1): highest level of education of self and of partner (low = year 12 or less; mid = certificate/trade/diploma; high = tertiary; no partner), weekly gross income of self and of household (< $500, $500–999, ≥$1000, refused to answer), number of people dependent on household income (1–4, ≥5), current regular smoking (yes no), general health (poor/fair/good, very good, excellent), whether past week PA was typical (less than usual, usual, more than usual), body mass index (BMI - from self-reported height and weight (kg/m2)), pregnancy (yes, no/don't know), presence of a serious illness, long-term injury or disability that prevented PA (yes, no), and urban/rural status. Two other covariates recorded whether participants had reported going through menopause (yes or no/don't know) and whether there were children under 5 years in the home (yes or no) at any timepoint.

2.3. Measures The social-ecological model (Stokols, 1996; Sallis et al., 1987) served as the overarching framework guiding the development of the study questionnaire. Specific variables and pathways between the broad model domains were informed by a conceptual model developed by Kamphuis et al. (2006). 2.3.1. Outcomes The long self-administered version of the International Physical Activity Questionnaire (IPAQ-L) was used to estimate past week minutes of PA for leisure and transport purposes (Craig et al., 2003). The IPAQ-L is a reliable and valid instrument which collects information on duration, frequency and intensity of PA. This analysis focused on LTPA and TRPA because these behaviours, unlike occupational and domestic PA, are discretionary. Separate LTPA and TRPA pattern variables were created using PA data from all three time-points. First, the difference in minutes of PA per week between time points was calculated by subtracting the T1

2.4. Statistical analysis Stata version 14.1 (Statacorp, College Station Texas, USA) was used for all analyses (conducted 2016–18). 2.4.1. Descriptive analyses A series of summary statistics (mean and standard deviation [SD], median and inter-quartile range) were calculated for both LTPA and 3

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TRPA by PA pattern at each time point.

Table 1 Characteristics of the sample at each time point.

2.4.2. Univariable analyses Step 1 involved fitting univariable models of all baseline exposure variables (i.e. individual, social, environmental factors) individually with the outcomes (i.e. LTPA patterns and TRPA patterns). Step 2 was as per Step 1, but with the inclusion of a binary baseline PA variable for each respective PA domain (specified as ≤150 min/week or > 150 min/week of any intensity) variable to account for each participant's baseline PA (partially adjusted model). Step 3 involved establishing confounders for the multivariable models using a two-step process. First, we checked for associations between each potential confounder and the outcome variables (LTPA/ TRPA patterns) and the exposure variables (individual, social, environmental factors). If the Wald p-value in at least one category of the exposure or outcome variable was > 10%, it was considered further as a potential confounder. Second, univariable models for each potential confounder variable were fitted and the percentage change in the coefficient calculated following the individual addition of each potential confounder. Confounders that were associated with at least one exposure as well as the outcome, and produced a > 20% change in the exposure's coefficient in at least one category when added, were kept in the model (Hosmer and Lemeshow, 2000).

T1 (N = 4349)

T2 (N = 1913)

T3 (N = 1560)

n

n

n

%

Age < 25 years 741 17.0 25–35 years 1299 29.9 35–45 years 1894 43.6 46 years 358 8.2 Mean, S.d. 34.4 8.1 Country of birth Australia 3851 88.5 Other 480 11.0 English spoken at home Yes 4114 94.6 No 216 5.0 Education level (self) Low 965 22.2 Medium 2211 50.8 High 1113 25.6 Education level (partner) Low 668 16.1 Medium 1763 42.4 High 576 13.8 No partner 1153 27.7 Avg. gross personal income < AUD $120/ 591 13.6 week AUD $120–AUD 1316 30.3 $499/week AUD $500–AUD 1282 29.5 $999/week ≥AUD $1000/ 430 9.9 week Don't know 134 3.1 Refuse answer 354 8.1 Avg. gross household income < AUD $120/ 55 1.3 week AUD $120–AUD 357 8.2 $499/week AUD $500–AUD 1120 25.8 $999/week ≥$1000/week 1501 34.5 Don't know 414 9.5 Refuse answer 362 8.3 Number of people dependant on income One–four people 3557 81.8 Five or more 746 17.2 Children at home < 5 years of age Yes 1233 28.4 No 3116 71.6 General health Poor 79 1.8 Fair 554 12.7 Good 1799 41.4 Very good 1508 34.7 Excellent 392 9.0 Body mass index 2 < 18.5 kg/m 145 3.3 18.5– < 25 kg/m2 2008 46.2 2 25– < 30 kg/m 1037 23.8 ≥30 kg/m2 1159 26.6 Smoking status Never smoked 2184 50.2 Use to smoke 1066 24.5 Smoke 411 9.5 occasionally Smoke regularly 685 15.8 Pregnancy status Pregnant 230 5.3 Not pregnant 4079 93.8 Menopause status Reached 130 3.0 menopause

2.4.3. Multivariable analyses Step 4 involved fitting multinomial logistic regression models to both the LTPA and TRPA patterns variables to estimate the odds of persistent increases, persistent decreases or inconsistent PA patterns compared to a consistently low PA pattern (selected a priori as the reference group). Models included all variables that had a Likelihood Ratio Test p-value < 30% in Step 2, and only variables with a Wald pvalue of ≤0.15 in at least one outcome category were kept (fully adjusted model) (Hosmer and Lemeshow, 2000). The Hosmer-Lemeshow goodness-of-fit test for multinomial logistic regression (Fagerland, 2012) was applied to each of the final models to assess their fit to the data. Final confounders in the multivariable model for LTPA patterns were age, partner education level, general health, gross weekly income (self), and number of people dependent on this income. Final confounders in the multivariable model for TRPA patterns were age, gross weekly income (self), and BMI. 3. Results 3.1. Characteristics of the sample Mean (SD) time between T1 and T2 survey completions was 3.0 (0.1) years, and between T2 and T3 was 2.0 (0.1) years. Loss to followup analyses revealed significant differences between those included and excluded from the final analysis sample on most baseline confounder variables, and continuous and categorical predictors of outcome (Appendix Table 4). However, the magnitude of the differences between the two groups on all variables was modest, with the difference less than five percentage points for most categorical variables. Table 1 presents sociodemographic, health and PA characteristics of the sample at each time point. 3.2. Leisure-time and transport-related PA Median minutes/week of LTPA and TRPA at each time point for the PA patterns variables indicated distinct patterns of change (Fig. 2). The ‘consistently low’ group had notably lower LTPA and TRPA values at each time point. The ‘persistently increasing’ groups typically started with a lower mean PA value than the inconsistent group but had the highest PA by T3. The ‘persistently decreasing’ groups had the highest mean PA at T1 but decreased to a PA level similar to the ‘consistently low’ group by T3. There was low concordance between LTPA and TRPA

%

206 356 787 540 39.0

10.8 18.6 41.1 28.2 7.7

%

161 198 592 606 41.2

10.3 12.7 37.9 38.8 7.5

1767 146

92.4 7.6

1436 124

92.1 7.9

1862 50

97.3 2.6

1524 35

97.7 2.2

375 932 599

19.6 48.7 31.3

271 738 542

17.4 47.3 34.7

285 847 308 399

15.5 46.1 16.7 21.7

224 696 282 323

14.4 44.6 18.1 20.7

233

12.2

163

10.4

485

25.4

365

23.4

595

31.1

533

34.2

333

17.4

324

20.8

31 158

1.6 8.3

19 119

1.2 7.6

16

0.8

16

1.0

109

5.7

100

6.4

460

24.0

339

21.7

884 102 202

46.2 5.3 10.6

834 60 150

53.5 3.8 9.6

1536 363

80.3 19.0

1248 302

80.0 19.4

536 1376

28.0 72.0

369 1191

23.7 76.3

36 182 752 729 209

1.9 9.5 39.3 38.1 10.9

21 135 615 600 180

1.3 8.7 39.4 38.5 11.5

39 783 493 422

2.0 40.9 25.8 22.1

32 663 407 368

2.1 42.5 26.1 23.6

930 612 131

48.6 32.0 6.8

764 530 101

49.0 34.0 6.5

235

12.3

159

10.2

82 1823

4.3 95.3

61 1496

3.9 95.9

122

6.4

209

13.4

(continued on next page)

4

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and persistent decreases (17%) in LTPA. Having access to childcare was associated with increased odds of inconsistent LTPA (66%). The final multinomial logistic regression model for TRPA patterns is presented in Table 2. Hosmer-Lemeshow tests indicated that the final model fit the data well (0.96). Higher enjoyment was associated with increased odds of inconsistent (3%) and persistent increases (3%) in TRPA over five years. No other associations were detected.

Table 1 (continued) T1 (N = 4349)

T2 (N = 1913)

T3 (N = 1560)

n

n

n

%

1346

86.3

197 1355

12.6 86.9

1098 345 110

70.4 22.1 7.1

623 937

39.9 60.1

%

%

Not reached 4172 95.9 1782 93.2 menopause Illness, injury, or disability preventing physical activity Yes 489 11.2 213 11.1 No 3815 87.7 1692 88.4 Typical past week PA Yes 2836 65.2 1294 67.6 No, less than usual 1117 25.7 484 25.3 No, more than 339 7.8 126 6.6 usual Urban/rural status Urban 2016 46.4 765 40.0 Rural 2333 53.6 1148 60.0

4. Discussion This study aimed to determine individual, social and physical environmental predictors of PA patterns over five years among women living in socioeconomically disadvantaged areas. This is the first study internationally to examine these relationships longitudinally in this atrisk population group. Stable patterns of PA were relatively uncommon, with persistent increases, persistent decreases and inconsistent patterns over time prevalent for both LTPA and TRPA. While few associations were evident in multivariable models, the direction of associations were not always in the expected direction and the magnitude of some were small, PA enjoyment, family support and access to childcare appear to be related to more favourable LTPA patterns. Enjoyment of PA was further associated with more favourable TRPA patterns. Results revealed no association between physical environmental factors and LTPA or TRPA patterns. Changing patterns of both LTPA and TRPA were common, with < 20% of the sample demonstrating consistently low PA. This may be due to the PA pattern classification method, but more likely is related to the many significant transitions that occur at this life-stage (Allender et al., 2008). Participants were 18–46 years at baseline, and while not examined in this study, were likely to experience any number of situational changes related to employment, further education, residential status, partnering and parenting. For those who were older, menopause and health-related concerns become more prevalent. While beyond the scope of this analysis, these transitions may explain the common fluctuating (inconsistent, increasing, decreasing) patterns of PA observed. For example, that access to childcare predicted inconsistent patterns of PA may simply reflect the impact of motherhood on PA. It is also possible that physical and mental health problems, which are more common in socioeconomically disadvantaged populations (Australian Institute of Health and Welfare, 2016), impacted on participants' ability to be active, although this too was not examined in this study. Enjoyment, social support from family and access to childcare appear to be potentially important, as they demonstrated associations with more favourable PA patterns (i.e. better than remaining consistently low). Our earlier work (e.g. Cleland et al., 2010a,b) as well as other studies of adults (Trost et al., 2002) have cross-sectionally identified these and numerous other factors as correlates of PA. Few

classifications (Spearman's rho = 0.07; Kappa coefficient = 55.7%), as shown in Appendix Table 5. Descriptive statistics for the PA variables at each time point by PA patterns are provided in Appendix Table 6. 3.3. Univariable analyses Of the individual factors, higher self-efficacy, enjoyment, outcome expectancies, behavioural control and behavioural skills increased odds of inconsistent LTPA (compared to consistently low LTPA), with enjoyment and outcome expectancies also associated with higher odds of persistently increasing LTPA (Appendix Table 7). Of the social factors, access to childcare was associated with increased odds of inconsistent LTPA, higher levels of family support for PA were associated with higher odds of increasing, decreasing and inconsistent LTPA, and higher PA norms were associated with increased odds of persistently increasing LTPA. No associations were evident between any environmental factors and LTPA patterns. Higher baseline PA enjoyment was associated with increased odds of inconsistent TRPA over five years (Appendix Table 8). No other individual, social or environmental factors were associated with TRPA patterns. 3.4. Multivariable analyses The final multinomial logistic regression model for LTPA patterns over five years is presented in Table 2. Hosmer-Lemeshow tests indicated that the final model fit the data well (0.87). Greater PA enjoyment was associated with persistent increases (4%), inconsistent (4%), and persistent decreases (5%) in LTPA. Greater family support for PA was associated with increased odds of persistent increases (20%)

Fig. 2. Median minutes of (a) leisure time and (b) transport-related physical activity per week by physical activity change category over time. 5

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Table 2 Multinomial logistic regression models for associations with LTPA and TRPA patterns over time. Reference: consistent

Persistently decreasing

Inconsistent

Persistently increasing

OR

95% CI

OR

95% CI

OR

95% CI

1.04

1.00–1.09

1.04

1.02–1.07

1.05

1.02–1.10

1.20

1.05–1.36

1.03

0.96–1.12

1.17

1.04–1.32

0.70 1.03

0.29–1.70 0.39–2.75

1.66 1.72

1.03–2.65 1.00–2.95

1.13 1.59

0.51–2.49 0.65–3.90

1.04

0.99–1.08

1.03

1.01–1.06

1.03

1.00–1.07

1.10

0.97–1.24

1.03

0.96–1.11

1.01

0.90–1.13

1.03

0.75–1.43

0.87

0.73–1.03

0.95

0.71–1.28

a

LTPA Individual factors PA enjoyment Social factors Family support Child care (ref: No) Yes N/A TRPAb Individual factors PA enjoyment Social factors Family support Environmental factors Safety

LTPA: leisure-time physical activity; TRPA: transport-related physical activity; OR: Odds Ratios. Reference group is consistently low. a Adjusted for baseline weekly LTPA, age, number dependent on income, general health, partner education, personal income. b Adjusted for baseline weekly TRPA, age, body mass index, personal income.

longitudinal studies have examined ‘social support’ as a predictor of PA (Shaw et al., 2010; Middelweerd et al., 2017), with only one identifying an association although using a global, rather than behaviour-specific, measure of social support (Shaw et al., 2010). In the current study, changes in other variables (as opposed to baseline indicators only) may better predict PA patterns. These factors may have greater time-specificity, meaning that their influence is short-term (and hence only observed cross-sectionally), while the factors we identified as important may have longer-lasting impacts (and hence were observed longitudinally). It is also possible that among women living in socioeconomically disadvantaged areas, the effect of these factors on PA may differ (i.e. be strengthened or weakened) due to other competing challenges (e.g. financial) specific to this population group. Where associations were observed, these factors did remain relatively stable with moderate to high correlations over time (Spearman's Rho 0.58–0.65 for enjoyment, 0.54–0.73 for childcare, 0.41–0.54 for family support). Both enjoyment of PA and family social support predicted PA patterns that differed from consistently low (persistently increasing, persistently decreasing, and for enjoyment, inconsistent). Given those classified with consistently low PA had lower median LTPA values at each time point than women demonstrating the three other patterns, this finding may reflect an association with higher levels of PA, as observed in the cross-sectional literature. These factors require further in-depth investigation to disentangle mechanisms and better understand temporality and may also require particular attention in strategies to promote PA in this population group. Finding no physical environmental predictors were associated with PA patterns is in contrast to expectations based on a social-ecological model (Sallis and Owen, 2002; Stokols, 1996). However, the present findings are consistent with other studies examining the relative importance of different sources of influence (Cleland et al., 2010a; GilesCorti and Donovan, 2003; Pan et al., 2009), suggesting that proximal factors (i.e. individual, social) may be more influential than more distal environmental factors. It is also plausible that no association was seen with environmental-level variables because of the LTPA and TRPA measures. These included all leisure and transport-related activities, and therefore may not be conceptually aligned with what the environmental factors examined in this study might be expected to be impact. For example, given that survey questions related to the environment specifically examined local neighborhoods, LTPA undertaken during a work break or walking to public transport are unlikely to relate to neighborhood safety, aesthetics or walking environment.

Further studies should ensure context-specific and conceptually aligned behavioural measures are employed (Giles-Corti et al., 2005; Ball et al., 2006).

4.1. Limitations Potential limitations of this study include response bias, although a reasonable response rate was apparent, and heterogeneity was observed in the baseline sample. Attrition bias is a potential limitation, although the baseline characteristics of those that did and did not participate at all three timepoints did not markedly differ. Further, the self-report measures of both exposures and outcomes may result in under- or overestimates of behaviour due to recall or social desirability biases. Despite social-ecological models highlighting policy-level factors as important for PA, these were not considered. Further, the social-ecological model does not specify the temporal or directional nature of relationships between variables and behaviour, and hence it is possible that the application of such a model in longitudinal research may be limited. Future work that includes policy-level influences on PA and explores the temporal and directional nature of factors specified in the socialecological model is warranted. A key strength of this work is that it is the first prospective study to specifically focus on a disadvantaged population group. It is also one of very few to include rural as well as urban-dwelling participants, and seemingly the first to do so longitudinally. Further, while examining all elements of the social-ecological framework is critical to understanding behaviour, few studies assess these in the same model, and none longitudinally.

5. Conclusions This longitudinal study among women from socioeconomically disadvantaged areas identified enjoyment, family social support for PA and access to childcare as potentially important factors influencing PA patterns over time. The limitations notwithstanding, these findings provide direction for future work to better understand determinants of PA behaviour. The findings may also help to inform the development of programs and policies to increase PA participation among women experiencing socioeconomic disadvantage, which should consider these factors in their planning and implementation. 6

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Author contribution statement

social and environmental correlates of physical activity among women living in socioeconomically disadvantaged neighbourhoods. Soc. Sci. Med. 70 (12), 2011–2018 2010. Cleland, V.J., Ball, K., Salmon, J., Timperio, A.F., Crawford, D.A., 2010b. Personal, social and environmental correlates of resilience to physical inactivity among women from socio-economically disadvantaged backgrounds. Health Educ. Res. 25 (2), 268–281. Craig, C.L., Marshall, A.L., Sjostrom, M., et al., Aug 2003. International physical activity questionnaire: 12-country reliability and validity. Med. Sci. Sports Exerc. 35 (8), 1381–1395. Dillman, D.A., 1978. Mail and Telephone Surveys: The Total Design Method. WileyInterscience, New York. Dillman, D.A., 2000. Mail and Internet Surveys: The Total Design Method. Wiley, New York. von Elm, E., Altman, D.G., Egger, M., et al., 2014. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: guidelines for reporting observational studies. Int. J. Surg. 12 (12), 1495–1499 Dec. Fagerland, M.W., 2012. A generalized Hosmer-Lemeshow goodness-of-fit test for multinomial logistic regression models. Stata J. 12 (3), 447. Giles-Corti, B., Donovan, R.J., 2003. Relative influences of individual, social environmental, and physical environmental correlates of walking. Am. J. Public Health 93 (9), 1583–1589 Sep. Giles-Corti, B., Timperio, A., Bull, F., Pikora, T., 2005. Understanding physical activity environmental correlates: increased specificity for ecological models. Exerc. Sport Sci. Rev. 33 (4), 175–181 Oct. Hallal, P.C., Andersen, L.B., Bull, F.C., Guthold, R., Haskell, W., Ekelund, U., 2012. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet 380 (9838), 247–257 Jul 21. Hosmer, D.W., Lemeshow, S., 2000. Applied Logistic Regression. Wiley, New York. Kamphuis, C.B., Giskes, K., de Bruijn, G.J., Wendel-Vos, W., Brug, J., van Lenthe, F.J., Oct 2006. Environmental determinants of fruit and vegetable consumption among adults: a systematic review. Br. J. Nutr. 96 (4), 620–635. Kendzierski, D., DeCarlo, K.J., 1991. Physical activity enjoyment scale: two validation studies. J. Sport Exerc. Psychol. 13, 50–64. Lechner, L., Bolman, C., Van Dijke, M., Jun 2006. Factors related to misperception of physical activity in The Netherlands and implications for health promotion programmes. Health Promot. Int. 21 (2), 104–112. Marcus, B.H., Selby, V.C., Niaura, R.S., Rossi, J.S., 1992. Self-efficacy and the stages of exercise behavior change. Res. Q. Exerc. Sport 63 (1), 60–66 Mar. McLennan, W., October 1998. 1996 Census of Population and Housing: Socioeconomic Index for Areas. Australian Bureau of Statistics, Canberra. Middelweerd, A., Te Velde, S.J., Abbott, G., Timperio, A., Brug, J., Ball, K., 2017. Do intrapersonal factors mediate the association of social support with physical activity in young women living in socioeconomically disadvantaged neighbourhoods? A longitudinal mediation analysis. PLoS One 12 (3), e0173231. Mujahid, M.S., Diez Roux, A.V., Morenoff, J.D., Raghunathan, T., 2007. Assessing the measurement properties of neighborhood scales: from psychometrics to ecometrics. Am. J. Epidemiol. 165 (8), 858–867 Apr 15. Pan, S.Y., Cameron, C., Desmeules, M., Morrison, H., Craig, C.L., Jiang, X., 2009. Individual, social, environmental, and physical environmental correlates with physical activity among Canadians: a cross-sectional study. BMC Public Health 9, 21. Parsons, T.J., Manor, O., Power, C., 2005. Changes in diet and physical activity in the 1990s in a large British sample (1958 birth cohort). Eur. J. Clin. Nutr. 59 (1), 49–56 Jan. Sallis, J., Owen, N., 2002. Ecological models of health behavior. In: Glanz, K., Rimer, B.K., Lewis, F.M. (Eds.), Health Behavior and Health Education: Theory, Research & Practice, Vol 3rd edition. Jossey-Bass, San Francisco, USA, pp. 462–484. Sallis, J.F., Grossman, R.M., Pinski, R.B., Patterson, T.L., Nader, P.R., 1987. The development of scales to measure social support for diet and exercise behaviors. Prev. Med. 16 (6), 825–836 Nov. Shaw, B.A., Liang, J., Krause, N., Gallant, M., McGeever, K., Nov 2010. Age differences and social stratification in the long-term trajectories of leisure-time physical activity. J. Gerontol. B Psychol. Sci. Soc. Sci. 65 (6), 756–766. Stokols, D., 1996. Translating social ecological theory into guidelines for community health promotion. Am. J. Health Promot. 10 (4), 282–298 Mar–Apr. Trost, S.G., Owen, N., Bauman, A.E., Sallis, J.F., Brown, W., 2002. Correlates of adults’ participation in physical activity: review and update. Med. Sci. Sports Exerc. 34 (12), 1996–2001 Dec. Uijtdewilligen, L., Peeters, G.M., van Uffelen, J.G., Twisk, J.W., Singh, A.S., Brown, W.J., 2015. Determinants of physical activity in a cohort of young adult women. Who is at risk of inactive behaviour? J. Sci. Med. Sport 18 (1), 49–55 Jan.

Verity Cleland: Conceptualisation, Methodology, Project administration, Supervision, Roles/Writing – original draft, Writing – review and editing. Fiona Cocker: Formal analysis, Roles/Writing – original draft, Writing – review and editing. Jana Canary: Formal analysis, Roles/Writing – original draft, Writing – review and editing. Megan Teychenne: Methodology, Project administration, Writing – review and editing. David Crawford: Funding acquisition, Methodology, Project administration, Writing – review and editing. Anna Timperio: Funding acquisition, Methodology, Project administration, Writing – review and editing. Kylie Ball: Conceptualisation, Funding acquisition, Methodology, Project administration, Supervision, Writing – review and editing. Acknowledgments The authors gratefully acknowledge the contributions of the Project Manager Dr. Michelle Jackson, and the study participants. VC (ID 100444) and AT (ID 100046) are supported by National Heart Foundation of Australia Future Leader Fellowships; KB was supported by a National Health and Medical Research Council Principal Research Fellowship (ID 1042442). This project was supported by a National Health and Medical Research Council Strategic Award (ID 374241). These funding bodies had no role in the study design; collection, analysis or interpretation of data, writing of the report; or the decision to submit this report for publication. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ypmed.2020.105995. References Allender, S., Hutchinson, L., Foster, C., 2008. Life-change events and participation in physical activity: a systematic review. Health Promot. Int. 23 (2), 160–172 Jun. Australian Bureau of Statistics, 2001. Census of Population and Housing. Socio-Economic Indexes for Areas, Australia, 2001. Australian Bureau of Statistics, Canberra. Australian Institute of Health & Welfare, 2016. Australia’s Health 2016. AIHW, Canberra. Ball, K., Timperio, A.F., Crawford, D.A., 2006. Understanding environmental influences on nutrition and physical activity behaviors: where should we look and what should we count? Int. J. Behav. Nutr. Phys. Act. 3, 33. Ball, K., Cleland, V., Salmon, J., et al., 2013. Cohort profile: the resilience for eating and activity despite inequality (READI) study. Int. J. Epidemiol. 42 (6), 1629–1639 Dec. Barnett, T.A., Gauvin, L., Craig, C.L., Katzmarzyk, P.T., 2008. Distinct trajectories of leisure time physical activity and predictors of trajectory class membership: a 22 year cohort study. Int. J. Behav. Nutr. Phys. Act. 5, 57. Bauman, A.E., Reis, R.S., Sallis, J.F., Wells, J.C., Loos, R.J., Martin, B.W., 2012. Correlates of physical activity: why are some people physically active and others not? Lancet 380 (9838), 258–271 Jul 21. Brown, W.J., Heesch, K.C., Miller, Y.D., 2009. Life events and changing physical activity patterns in women at different life stages. Ann. Behav. Med. 37, 294–305 Jun 9. Chau, J., Chey, T., Burks-Young, S., Engelen, L., Bauman, A., 2017. Trends in prevalence of leisure time physical activity and inactivity: results from Australian National Health Surveys 1989 to 2011. Aust. N. Z. J. Public Health 41 (6), 617–624 Dec. Cleland, V., Ball, K., Hume, C., Timperio, A., King, A., Crawford, D., 2010a. Individual,

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