Employment, work hours and weight gain among middle-aged women

Transcription

1 International Journal of Obesity (2013) 37, & 2013 Macmillan Publishers Limited All rights reserved /13 ORIGINAL ARTICLE among middle-aged women NAu 1, K Hauck 2 and B Hollingsworth 1 OBJECTIVE: To investigate the influence of employment and work hours on weight gain and weight loss among middle-aged women. DESIGN: Quantile regression techniques were used to estimate the influence of employment and hours worked on percentage weight change over 2 years across the entire distribution of weight change in a cohort of middle-aged women. A range of controls was included in the models to isolate the effect of work status. SUBJECTS: A total of 9276 women aged years at baseline who were present in both the 1996 and 1998 surveys of the Australian Longitudinal Study of Women s Health. The women were a representative sample of the Australian population. RESULTS: Being out of the labour force or unemployed was associated with lower weight gain and higher weight loss than being employed. The association was stronger at low to moderate levels of weight gain. Among employed women, working regular (35 40), long (41 48) or very long (49 þ) hours was associated with increasingly higher levels of weight gain compared with working part-time hours. The association was stronger for women with greater weight gain overall. The association between unemployment and weight change became insignificant when health status was controlled for. CONCLUSIONS: Employment was associated with more weight gain and less weight loss. Among the employed, working longer hours was associated with more weight gain, especially at the higher levels of weight gain where the health consequences are more serious. These findings suggest that as women work longer hours they are more likely to make lifestyle choices that are associated with weight gain. International Journal of Obesity (2013) 37, ; doi: /ijo ; published online 19 June 2012 Keywords: weight gain; women; labour force participation; employment; work hours INTRODUCTION In most Organisation for Economic Co-operation and Development (OECD) nations, including Australia, over 60% of adults are now overweight or obese. 1 3 This represents a serious public health concern. Evidence suggests that women are more susceptible to major weight gain than men. 4,5 Middle-age is a stage in life that for many women is characterised by fewer child-raising responsibilities, menopause, a return to employment and executive career peaks. 6 It is also the stage when overweight and obesity prevalence is at its peak. 3 Although weight gain is fundamentally caused by an imbalance between energy intake and expenditure, the underlying causes of the recent marked increase in obesity prevalence are not well understood. It has been suggested though that one potential contributor is the increase in female employment and work hours. 7 Over the past 3 decades, during which obesity rates have soared, female employment in Australia has also increased, from 45% in 1980 to 59% in Of all age-groups, the greatest increase occurred among women of middle-age (45 54 years). 9 In contrast, male employment rates declined slightly over this period, from 78 to 72%. Not only has the employment rate increased, but the proportion of workers working longer hours has risen. Between 1985 and 2005, the proportion of full-time employed women in Australia working 50 or more hours per week increased from 9 to 16%. 9 Similar trends are observed in other OECD countries. 10,11 There are clearly economic benefits and rewards associated with increased employment for women, but studies have shown that compared with non-employed women, employed women spend less time on meal preparation and grocery shopping and are more likely to purchase prepared (energy-dense) foods. 12,13 They also spend fewer hours exercising 14 and sleeping, 15 which are both risk factors for obesity. 15 By reducing time spent on other (healthful) activities, the rise in female employment and work hours may have contributed to population weight gain and rising obesity rates. 7 Although a sizable literature finds that maternal employment is found to affect the body weight of children, 13,16 less attention has been paid to the relationship between employment patterns and the body weight of the women themselves. Findings from existing evidence remains inconclusive. Shields 17 found that men who moved from standard hours to longer hours had much higher odds of experiencing unhealthy weight, but no association was found for women. Au and Hollingsworth 18 found that young women who worked full-time were more likely to experience weight gain than those who worked part-time. Courtemanche 19 established that for both men and women, working longer work hours lead to weight gain. No study has focussed on women of middle-age, despite the increase in employment rates and high risk of obesity among this population group. This study uses two models to examine the direct relationship between (a) employment status and weight gain among 1 Centre for Health Economics, Monash University, Melbourne, Victoria, Australia and 2 Imperial College London, Business School, London, UK. Correspondence: Dr N Au, Centre for Health Economics, Monash Uiversity, Clayton, Melbourne, Victoria 3800, Australia. Nicole.au@monash.edu Received 3 February 2012; revised 18 April 2012; accepted 29 April 2012; published online 19 June 2012

2 middle-aged women, and (b) work hours and weight gain among employed middle-aged women. Particular contributions of this study are, first, the focus on middle-aged women from a large and representative cohort of middle-aged women from the Australian Longitudinal Study of Women s health (ALSWH), and second the use of quantile regression (QR) 20 methods to analyse the relationship between employment patterns and weight gain across the distribution of weight gain for this important subgroup of the population. Previous studies have not been able to provide a complete picture of the relationship across the entire distribution of weight change. Unlike mean-based regression techniques, such as ordinary least squares (OLS), or analyses with binary indicators of weight gain, QR provides information about the relationship between covariates and weight change at different points along the distribution of weight change (from high levels of weight loss to high levels of weight gain). It does so by allowing for variation in the relationship at different levels of weight change. This is of particular value when the degree of weight change (such as major weight gain) is of interest. In addition, QR estimates are more robust than standard estimations when there are outliers (women with very small or large changes in body weight), or the weight change distribution is not normal. 21 Although previous studies on the relation between employment and weight gain that use OLS answer the question on average, does employment influence changes in weight?, our study can answer: does employment influence weight change differently for women with average weight gain compared to women with severe weight gain (or minor weight gain or even various degrees of weight loss)?. QR can uncover differences in the impact of important causal factors on changes in weight, which would be masked by estimates evaluated at the average; in particular, QR allows for the possibility that the impact of those factors on weight loss and weight gain is different, and possibly even opposing. QR is particularly suited to the analysis of body weight changes because it does not restrict the analysis to (often arbitrarily) nominated cutoff points for clinically relevant amounts of weight, as is demanded by analyses that rely on binary indicators for moderate or major weight change. The results of this study provide important information to policymakers on the relationship between work patterns and body weight changes of middle-aged women. Ultimately, they may assist in the design of targeted public health policies aimed at curbing weight gain and improving the health of women. MATERIALS AND METHODS Data The ALSWH is a population-based survey, which collects detailed information on the health, demographic characteristics and well being of women in three age cohorts (younger, middle-aged and older). Women were randomly selected from the Australian national health insurance database for the baseline survey in The study uses self-complete questionnaires mailed to each participant at 2 3 year intervals. Further details and information on sample design and response rates can be found at 22 This study utilised information from the first two surveys (1996 and 1998) of the middle-aged cohort. The women were born from and were aged years in the first survey. Retention rates for the second survey were 91%. After excluding observations with missing data, the total sample included 9276 women and the employed sample included 6108 women. Empirical approach We employed QR 20 to estimate the impact of labour status on weight change across the entire distribution of weight change, controlling for factors that may confound the relationship between labour status and weight change. Labour status is either represented by employment status (for the total sample) or work hours (for the employed sample). (Note, quantiles are synonymous to percentiles the 0.95 quantile equals the 95th percentile.) For comparison, all models were also estimated using OLS regression, assuming uniform response of weight gain across the distribution. Analyses were conducted using Stata. 23 The dependent variable was a continuous measure of the respondent s percentage change in self-reported body weight from the first (baseline) to the second (follow-up) survey. Weight change was measured at a later point of time than all the explanatory variables (including employment and work hours) to establish temporal order. An advantage of using percentage weight change (instead of a direct measure of weight change) is that it takes into account initial body weight, which can influence the likelihood and size of weight gain, particularly among women. 5 (Note, all descriptive statistics and regression analyses were also estimated using weight change as the dependent variable (with initial weight as a covariate) and the conclusions were qualitatively the same as those presented here using % weight change). For the employment status models, key explanatory variables were binary indicators for whether or not the respondent was employed, unemployed (not working, but looking for work) or not in the labour force (LF) (not working and not looking for work). For the work hours models, key explanatory variables were binary indicators for working part-time (1 34 h per week), regular full-time (35 40 h), long hours (41 48 h per week) or very long hours (X49 h per week). Additional covariates for both models were age in years, ethnic background, marital status and region of residence. Socioeconomic standing has been shown to have an important role in determining weight gain and healthy lifestyles. 24,25 As such, indicators were included for highest education level attained, and ability to manage on household income. Indicators of the women s stage in menopause were included because the loss of ovarian function can increase body weight. 26 Smoking status was included as smoking and smoking cessation are associated with body weight. 27,28 Additional covariates for the work hours models were occupation groups (manager; professional; tradesperson; service; and manual labour) because type of occupation may influence weight gain through job-related exercise 29 or sitting time. 30 All explanatory variables were measured at baseline. See Supplementary Table S1 (Supplementary Information section) for descriptions of all covariate categories. Following previous studies, we did not include health status measures in the models. 31 Health status may be endogenous in models of body weight, either because unobserved factors impact on both health status and body weight, or because reverse causation results in body weight impacting on health status (that is, poor health, for example, type 2 diabetes or cardiovascular disease, may be a consequence of chronic weight gain). For comparison, we estimated additional models that included indicators of self-reported health status (poor, fair, good, very good or excellent) measured at baseline. Also in accordance with the literature, 18,19 we do not include lifestyle choices among the controls. Lifestyle factors that are influenced by employment characteristics (for example, diet and physical activity) are part of the causal pathway between employment and body weight. Including them as additional regressors constitutes overadjustment, and may lead to the erroneous inference that employment has no effect on body weight. However, to gain some information on the role of lifestyle factors as a potential mechanism through which employment patterns may influence weight gain, we present descriptive statistics on the association between employment status and two lifestyle factors: physical activity and alcohol risk levels. These lifestyle factors were selected because there is evidence that they impact on weight gain 32,33 and may also be associated with employment status. 14,34 Although there are many more potential mechanisms such as calorie intake or sleep duration, accurate information on such factors is not available in the survey. RESULTS Descriptive statistics For the total sample, the mean weight change between the first and second surveys was 1.5% of initial weight (or 0.95 kg), with 55% of the sample gaining weight between surveys and only 31% of the sample losing weight. Most women (76%) were employed, or not in the LF (21%) with only 1% unemployed. Of the employed sample, working part-time was most common (50%) whereas working very long hours was least common (7%). Table 1 shows the mean percentage weight change by employment status and work hours, and the mean percentage weight gain among women who gained or lost weight. On average, 719 & 2013 Macmillan Publishers Limited International Journal of Obesity (2013)

3 720 Table 1. Mean percentage weight change from survey 1 2 by employment status and work hours % Weight change % Weight change among gainers a % Weight change among losers a N Mean SD N Mean SD N Mean SD Total Sample (5.99) (4.04) (3.90) Employed * (5.89) (3.98) * (3.82) Not in the LF * (6.27) (4.22) * (4.09) Unemployed * (6.25) (4.21) * (4.22) Employed Sample (5.87) (3.97) (3.80) Part-time hours (5.72) * (3.83) (3.77) Regular hours (5.93) * (4.08) (3.75) Long hours (6.07) * (3.88) (4.16) Very long hours (6.25) * (4.49) (3.56) Abbreviation: LF, labour force. Standard errors in parentheses. a Gainers are those who gained weight, losers are those who lost weight. *Indicates statistically significant differences at the 5% level between subgroups using a one-way analysis of variance (ANOVA) test. Note: Employed sample used for work hour subgroups is less than the employed subgroup from total sample due to missing information on work hours for some respondents. Table 2. Percentage distribution of lifestyle factors by employment status (total sample) and work hours (employed sample) at Survey 1 Lifestyle factor Total sample Employed sample N Employed Not in LF Unemployed P-value a N Part-time Regular hours Long hours Very long hours P-value a (N ¼ 7023) (N ¼ 2120) (N ¼ 133) (N ¼ 3029) (N ¼ 1822) (N ¼ 806) (N ¼ 451) Physical activity None o0.01 Low Moderate High Alcohol risk No risk o o0.01 Low risk o o0.01 High risk Smoking status Never-smoker o Ex-smoker Smoker o a P-value for difference between employment or work hour subgroups using a one-way analysis of variance (ANOVA) test. Alcohol risk was derived using data on frequency and quantity of alcohol consumption reported. Categories were: No risk (never or rarely drinks alcohol); Low risk (up to two drinks per day); and High risk (three or more drinks per day). Notes: Physical activity levels were derived using a composite score based on frequency of participation in vigorous (e.g. jogging, aerobics) and moderate (walking, gardening) exercise lasting for 20 min or more in a normal week. Categories were: None (no activity); Low (vigorous activity 1 2 times or moderate activity 2 4 times or equivalent combination); Moderate (vigorous activity 3 5 times or moderate activity 5 8 times or equivalent combination); and High (vigorous activity 45 times or moderate activity 48 times or equivalent combination). women in all employment categories gained weight. For the total sample, employed women had the greatest mean weight change (1.6%), and the smallest mean weight loss among losers ( 4.8%). Among the employed sample, mean changes in weight increased with work hours (from 1.5% for part-time hours to 1.9% for very long hours), and the mean gain in weight among gainers also increased with work hours (from 5.3% for part-time hours to 5.8% for very long hours). These descriptive statistics are suggestive of a positive relationship between work hours and weight gain. See Supplementary Table S1 (Supplementary Information) for mean percentage weight change for all other covariates in the model. Table 2 shows the percentage distribution of lifestyle factors by employment status and work hours. For the total sample, physical activity levels did not significantly differ by employment status. However, for the employed sample, women who worked very long hours had a higher proportion engaged in no physical activity (35.9%) than women who worked fewer hours (Po0.01). This is suggestive of a positive association between work hours and physical inactivity among the employed. Alcohol risk levels differed by both employment status and work hours. Employed women were least likely to have no alcohol risk, that is, they were more likely to drink alcohol (Po0.01) and among the employed, women working very long or long hours were more likely to drink at high-risk levels (three or more drinks per day) (P ¼ 0.01). These statistics are suggestive of a positive association between alcohol intake and hours spent in employment, and provide some clues as to how employment patterns may affect lifestyle choices, and subsequently, body weight. QR: employment status and weight change Table 3 shows the employment status coefficient estimates at selected quantiles for the total sample. The estimates represent percentage weight changes at each quantile of the weight change distribution. Because the relationship between employment patterns and weight gain is of primary interest, the table predominantly displays quantiles at and above the 0.50 quantile (median) as these represent positive changes in weight. The corresponding International Journal of Obesity (2013) & 2013 Macmillan Publishers Limited

4 Table 3. Quantile regression estimates relationship between employment status and percentage weight change 721 Variable Q 0.10 Q 0.20 Q 0.50 Q 0.60 Q 0.70 Q 0.80 Q 0.90 Q 0.95 OLS Base: N ¼ 9276 a Not in the LF 0.725* 0.451* 0.355* 0.420* ** (0.305) (0.229) (0.171) (0.195) (0.193) (0.201) (0.291) (0.400) (0.152) Unemployed w w w (1.069) (0.800) (0.597) (0.677) (0.673) (0.700) (1.017) (1.269) (0.531) Weight change b Health: N ¼ 9184 c Not in the LF w w 0.432* * 0.410** (0.320) (0.223) (0.178) (0.184) (0.185) (0.208) (0.292) (0.401) (0.155) Unemployed w (1.140) (0.773) (0.614) (0.632) (0.644) (0.720) (0.990) (1.385) (0.537) Weight change b Abbreviations: LF, labour force; OLS, ordinary least squares. a Base model includes all covariates except health status variables. b Percentage change in weight at each quantile of the distribution of weight change. c Health model includes health status variables in addition to all other covariates. *Significant at 5% level, **1% level, w 10% level. Standard errors are in parentheses. Note: Reference category is Employed. percentage weight change at each presented quantile is also shown. For those who gained weight, weight gain ranges from 1.4% at the median to 11.7% at the 0.95 quantile. For an average middle-aged woman weighing 69 kg, these imply a 2-year gain of about 1 kg and 8 kgs, respectively. Weight loss at the 0.10 quantile was 5.6% or 3.8 kg for a woman of average weight. For comparison, the coefficient estimates from the OLS regression are presented in the last column of Table 3. In the base model (1), the not in the LF estimates are negative and significant for women whose percentage change in weight is at the 0.50 or 0.60 quantiles. This indicates that women who were not in the LF experienced lower gains in body weight at the 0.50 and 0.60 quantiles of the weight change distribution than the reference category of employed women. The coefficients can be interpreted as unit changes in the percentage gain in weight. For example, at the 0.60 quantile (2.7% weight gain), women who were not in the LF had a percentage gain in weight that was 0.42% points lower than employed women. Women who were unemployed also experienced significantly lower gains in weight (by 1.14% points) compared with employed women at the 0.70 quantile. Overall, the results suggest that being employed is associated with higher gains in weight at low to moderate levels (around 1.4 to 4.2%) of the weight gain distribution. At the 0.10 and 0.20 quantiles, which represent a weight loss of 5.6 and 2.8%, respectively, being not in the LF was associated with significantly lower levels of weight change (higher levels of weight loss) than being employed. Being unemployed was also associated with more weight loss at the 0.20 quantile. These estimates suggest that among all women who lost weight over the 2 years, employed women lost less weight than women who were either unemployed or not in the LF. The inclusion of health status variables (model 2) made little difference to the relationship between being not in the LF and weight change at the 0.20, 0.50 and 0.60 quantiles, but increased the negative association at the 0.95 quantile. At this extreme end of the weight change distribution (weight gain of 11.7%), being not in the LF was associated with a 0.88% point lower gain in weight than being employed. This implies that the base model estimates of the association between being not in the LF and weight gain may be conservative at the upper tail of the weight change distribution. The association between unemployment and weight change was insignificant at all presented quantiles when health status variables were included. This suggests part of the negative association in the base model (1) at quantiles 0.20 and 0.70 was related to health status. Figure 1. Employment status estimates from QR and OLS regression across the distribution of percentage weight change. Estimates are from the base case model for the total sample. There are no estimates for quantiles because weight did not change at these quantiles. The graph shows the size and direction of the association between being not in the LF (compared to being employed) and % weight change at different quantiles of weight change after controlling for all covariates in the model. The OLS coefficient is the same at all quantiles, but the QR coefficients show that the association between being not in the LF and weight change differs depending on the quantile of weight change that the woman experiences. The relationship between employment status and weight change for the base case model is illustrated in Figure 1 for being not in the LF. The QR estimates (and 95% CI) along the distribution of weight change are shown together with the OLS estimate. The graph shows the association between being not in the LF and weight change (compared with being employed) at different points along the weight change distribution, after controlling for all covariates in the model. It highlights that the QR estimates are significantly below zero for lower and moderate quantiles (indicating greater weight loss and less weight gain than employed women). The OLS coefficient, shown for comparison, is constant across quantiles because it is estimated at mean weight change. The QR estimates clearly deviate from the OLS estimates at upper and lower quantiles, indicating that the OLS estimates & 2013 Macmillan Publishers Limited International Journal of Obesity (2013)

5 722 Table 4. Quantile regression estimates relationship between work hours and percentage weight change for the employed sample Q 0.10 Q 0.20 Q 0.50 Q 0.60 Q 0.70 Q 0.80 Q 0.90 Q 0.95 OLS Base: N ¼ 6108 a Regular hours * 0.534* * (0.375) (0.271) (0.224) (0.243) (0.209) (0.227) (0.353) (0.455) (0.177) Long hours * 0.921** w 0.853** * 0.488* (0.523) (0.377) (0.309) (0.335) (0.287) (0.311) (0.488) (0.617) (0.244) Very long hours * ** w (0.651) (0.474) (0.392) (0.427) (0.367) (0.398) (0.620) (0.783) (0.310) Weight change b Health: N ¼ 6049 c Regular hours w 0.595* w (0.385) (0.253) (0.214) (0.217) (0.240) (0.254) (0.377) (0.489) (0.178) Long hours ** 0.880** ** w 1.322* 0.514* (0.532) (0.352) (0.295) (0.300) (0.331) (0.349) (0.525) (0.670) (0.245) Very long hours w * w (0.664) (0.441) (0.374) (0.381) (0.421) (0.444) (0.663) (0.840) (0.311) Weight change b Abbreviation: OLS, ordinary least squares. Note: reference category is part-time hours. a Base model includes all covariates except health status variables. b Percentage change in weight at each quantile of the distribution of weight change. c Health model includes health status variables in addition to all other covariates. *Significant at 5% level, **1% level, w 10% level. Standard errors are in parentheses. Figure 2. Work hour estimates from QR and OLS regression across the distribution of percentage weight change. Estimates are from the base case model for the employed sample. There are no estimates for quantiles because weight did not change at these quantiles. Graph a shows the size and direction of the association between working long hours (compared with working part time) and % weight change at different quantiles of weight change after controlling for all covariates in the model. Graph b shows the same but for very long work hours. The OLS coefficient is the same at all quantiles, but the QR coefficients show that the association between work hours and weight change differs depending on the quantile of weight change that the woman experiences. are a poor predictor of the association between employment status and weight change among those with extreme changes in weight. QR: work hours and weight change Work hour group coefficient estimates at selected quantiles for the employed sample are shown in Table 4. Again, mostly estimates for quantiles at and above the median are displayed as these represent weight gain. The median corresponds to a weight gain of about 1.5%, whereas the 0.95 quantile of the weight change distribution corresponds to a weight gain of 11.8%. The coefficient estimates for working regular (35 40) hours were positive and significant at the upper quantiles. At the 0.70 and 0.80 quantiles, working regular hours was associated with approximately % points higher weight gain than parttime work hours, whereas at the extreme 0.95 quantile, it was associated with approximately 0.94% points higher weight gain. The coefficients for working long (41 48) hours were positive and significant across most of the presented quantiles along the distribution of weight gain, and for working very long (49 þ) hours, the coefficients were also positive and significant at the 0.70 and 0.95 quantile. Two key findings can be drawn. First, the coefficients for working long hours were larger at each quantile than the coefficients for regular hours, and where significant, the coefficients for working very long hours were even larger still than those for working long hours. This implies that women who spent more hours at work experienced more weight gain relative to women who worked part-time. Second, coefficients tend to increase with higher quantiles. For example, working long hours at the median was associated with a percentage weight gain that was higher by 0.79% points, whereas at the 0.95 quantile, it was associated with a percentage weight gain that was 1.41% points higher. This demonstrates the impact of working longer hours on weight gain was considerably greater at higher levels of weight gain a finding that could have serious International Journal of Obesity (2013) & 2013 Macmillan Publishers Limited

6 health implications, given that at the 0.95 quantile of the weight change distribution, women gained close to 12% of their body weight in only 2 years. Such variation across the distributions of weight change would not be detected in estimates using meanbased regression analyses. The inclusion of health status variables (model 2) made little difference to the results. The 0.10 and 0.20 quantile estimates show the relationship between work hours and weight loss. Under both the base case estimates, and estimates including health status, the coefficients at these quantiles were not statistically significant, suggesting that there was no relationship between work hours and weight loss. This implies that although women who worked longer hours had problems with heavier weight gain among those who gained weight, work hours made little difference to weight loss achieved among those who lost weight. The relationship between work hours and % weight change after controlling for all covariates in the base case model is illustrated in Figure 2 for (a) long hours and (b) very long hours (part-time hours is the comparator). The graphs show that unlike the OLS coefficient, the QR coefficients are able to demonstrate the variation in size and direction of the association between work hours and weight change at different points along the distribution of weight change. The variation is particularly pronounced at the extreme ends of the weight change distribution, where the health impacts are likely to be more serious. DISCUSSION The results demonstrated that compared with employed women, women who were not in the LF or unemployed either gained less weight or lost more weight over the 2-year period. These findings suggest that not working may have some protective effect against weight gain and may help promote weight loss. This may be related to those women having more time to spend on maintaining a healthy body weight. The association between unemployment and weight change was no longer significant after controlling for health status, which suggests that despite having more leisure time than employed persons, those who were unemployed did not have a greater ability to prevent weight gain (or lose weight) than their employed counterparts after health status was accounted for. This result relates to previous findings of a positive association between unemployment and weight gain, 35 obesity 36,37 and poor health more broadly, 38,39 which may be explained by the socioeconomic disadvantage, lower income and lower social support associated with being unemployed, 36,39 for which we can only partially control for in our models. Among employed women, those who worked longer (X35) hours tended to experience more weight gain. The strength of the association between longer work hours and weight gain was higher at the upper quantiles of the weight gain distribution, and the percentage weight gain increased as work hours increased. Health implications are serious at the upper end of the weight gain distribution, where the relationship was found to be strongest. Previous research on employment status and health has largely focussed on the positive association between employment and health. This study highlights that when it comes to weight-related health, employed people are not a homogenous group. Simply classifying a person as employed can hide disparities in the degree of weight gain related to working fewer or more hours per week. The results of this study support previous findings of a higher likelihood of weight gain among full-time employed young adult women compared with those who were only part-time employed. 18 They are also in line with previous work that has found obesity and a higher body mass index to be consequences of working longer hours. 19 It is possible that the positive association between work hours and weight gain may be mediated through more time spent in employment, which may reduce the time spent preparing home-cooked meals, 12,13 exercising 14 and sleeping. 40 The descriptive statistics presented in Table 2 support the hypothesis that a lack of physical activity may be a potential mechanism, at least among employed women. Results from a previous study suggest that this may be particularly relevant for women in full-time sedentary occupations. 41 Other evidence suggests that sitting time or higher levels of work-related stress may be potential mechanisms. 29,42 The statistics shown in Table 2 also suggest that drinking alcohol at risky levels is positively associated with working longer hours among the employed sample. This supports studies that have shown that increased drinking is associated with workrelated stress 43 and employment in occupations where drinking is considered the norm. 33 Although alcohol intake has been found to be positively associated with weight gain, 32,44 there is also conflicting evidence that alcohol intake is inversely related to weight gain. 45 Therefore, the role of alcohol intake as a potential mechanism between work hours and weight gain is unclear. The existing literature and the results in this study highlight that further research into the potential mechanisms between work hours and weight gain among middle-aged women is needed. Use of longitudinal data allows for a unique investigation into the population of women who are at most risk of obesity. 3 A limitation of this study is that self-reported data were used, and it is recognised that women tend to underestimate their weight and overestimate their height. 46 However, the bias in the ALSWH has been found to be small, 47 and measures of weight change may be accurate if the underestimation of weight is consistent within individuals over time. Our study suggests that as women work longer hours they are more likely to make lifestyle choices that are associated with weight gain. As such, policies that inform the population of this health concern and that assist women who work long hours to reduce the time costs of sustaining a healthy diet and physical activity routine may have positive benefits. CONFLICT OF INTEREST The authors declare no conflict of interest. ACKNOWLEDGEMENTS The Australian Longitudinal Study on Women s Health was conceived and developed by researchers at the Universities of Newcastle and Queensland, and is funded by the Australian Government Department of Health and Ageing. REFERENCES 1 Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, JAMA 2010; 303: The NHS Information Centre. Statistics on Obesity, Physical Activity and Diet: England, The Health and Social Care Information Centre: London, Australian Bureau of Statistics. National Health Survey. Cat. No Table 17. Measured body mass index and ABS: Canberra 2009; Report no.: Cat No Ball K, Crawford D, Ireland P, Hodge A. 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