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Journal of Occupational & Environmental Medicine:
doi: 10.1097/JOM.0b013e31824e1058
Original Articles

A Cross-Sectional Analysis of the Association Between Night-Only or Rotating Shift Work and Overweight/Obesity Among Female Nurses and Midwives

Zhao, Isabella RN, BN, BN (Hons I); Bogossian, Fiona RN, DipAppSci, BAppSci, MPH, PhD, MACMI; Turner, Catherine RN, BA, Grad Dip, Ed, MN, PhD

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Author Information

From the School of Nursing and Midwifery, The University of Queensland, Brisbane, Queensland, Australia.

Address correspondence to: Isabella Zhao, RN, BN, BN (Hons I), School of Nursing and Midwifery, The University of Queensland, Brisbane, Australia (i.zhao@uq.edu.au).

This project is supported by grants from the Australian Research Council (LP0562102, SR0566924), Australian National Health and Medical Research Council (2005002108), and New Zealand Health Research Council (456163). Industry Partners providing additional funding include Queensland Health, the South Australian Department of Health, Injury Prevention and Control Australia (Pty Ltd), Nursing Council of New Zealand, and the Macquarie Bank Foundation. Corporate sponsors include Virgin Blue, Virgin Atlantic, and MessageNet.

The authors declare no conflict of interest.

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Abstract

Objective: To examine the associations between shift work types and overweight/obesity among female nurses and midwives.

Methods: A cross-sectional study. Measurement included exposure variables: rotating shift work and night-only shift work; outcome variables: overweight and obesity; and potential confounding and associated variables: modifiable lifestyle factors, general health status, menopausal status, and work pattern.

Results: Among the 2086 participants, almost 60% were overweight/obese (31.7% overweight; 27.1% obese). After we adjusted the selected confounders, we found that rotating shift workers were 1.02 times more likely to be overweight/obese than day workers (P = 0.007; 95% confidence interval [95% CI], 1.004 to 1.03; and P = 0.02; 95% CI: 1.004 to 1.04, respectively). Night-only shift work was found to be significantly associated with obesity only (P = 0.031; relative risk, 1.02; 95% CI, 1.002 to 1.04).

Conclusions: Rotating shift work was associated with both overweight and obesity; and night-only shift work was associated with obesity, not overweight.

Overweight and obesity have reached global epidemic proportions. In 2008, more than 500 million people older than 20 years were obese with a body mass index (BMI) ≥ 30.1 Since 1980, obesity rates have increased more than threefold in some areas of North America, the United Kingdom, Eastern Europe, the Middle East, the Pacific Islands, Australasia, and China.2 Over the past decade, there has been a 9% increase in the Australian adult population who are either overweight or obese.3

One quarter of Australian women aged 18 years and more are overweight; and 17% were obese as of 2008.4 The prevalence of being overweight or obese or both has also increased in the United Kingdom and New Zealand.5,6 In the United Kingdom, 25% of women and 24% of men older than 15 years were obese in 2008;7 and similar obesity prevalence (26.5%) was also reported in New Zealand in 2007.8 Overweight and obesity have serious health consequences, and are associated with many chronic conditions, such as Type II diabetes, cardiovascular disease, osteoarthritis, hypertension and stroke, and certain forms of cancer.921 In addition, the increasing prevalence of overweight/obesity may also negatively affect maintaining a healthy workforce22 and pose greater threat to the already strained nursing and midwifery professions.

Limited research has studied the impact of occupational characteristics on BMI among nurses and midwives.23 Shift work is a defining occupational characteristic in the nursing and midwifery professions; and has been identified as one of the risk factors that may impact being overweight/obese. Research findings suggest that shift work may increase the likelihood of being overweight or obese or both by at least 39%.2427 Our previous research found that shift workers are 1.3 times more likely to be overweight/obesity compared with day workers.28

Following our findings that shift work was associated with higher likelihood of being overweight and obese, but not associated with being underweight,28 a further investigation into which particular shift type has the strongest association with overweight/obesity was carried out. The common non–day shift types in nursing and midwifery professions are rotating shift (three shifts rotate) and night-only shift. Therefore, this study aimed to examine (1) the association between rotating shift work and overweight/obesity and (2) the association between night-only shift work and overweight/obesity among nurses and midwives to determine if either shift type carries a greater risk.

To determine the direct association between shift types and the development of overweight/obesity, we identified from the literature a number of modifiable risk factors: diet quality; physical activity; smoking; and alcohol consumption, which we then adjusted as potential confounders. Additional variables, including the participants' perceived general physical and mental health, menopausal status, and work pattern, were also included in the analysis because these variables were identified as being associated with overweight/obesity in the literature.

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METHODS

Participants

Participants were derived from the Nurses and Midwives' e-cohort Study (NMeS), a longitudinal population-based study funded by the Australian Research Council and a range of industry partners (www.e-cohort.net). The purpose of NMeS is to examine factors associated with both workforce and health outcomes in a cohort of nurses and midwives in Australia, New Zealand, and the United Kingdom. Details of methodology have been published elsewhere29 and the NMeS was granted ethical clearance by The University of Queensland. The baseline survey, open from April 1, 2006 to March 30, 2008, resulted in 10,120 registrants of whom 7604 were nurses or midwives. Each participant was given a unique ID and no personal information could be identified in the analysis data sets.

To be included in the study sample, a participant needed to have identified as female; working as a nurse or midwife or both; not pregnant at the time of data collection; her weight and height; her modifiable lifestyle factors; and working either shift work (rotating shift work or night shifts only) or day-only shift (including day shifts only without weekends, or day shifts only with weekends).

Although 7604 nurses and midwives participated in the baseline survey, they did not necessarily answer all the questions. There were 1506 participants who did not provide data of their height or weight. and 1985 participants did not provide detailed information about the types of shift work. Another 1687 participants did not provide full information about their modifiable lifestyle factors. Among the remaining participants, only female nurses and midwives were selected because male participants constituted a small proportion (<5%). Furthermore, we sought to counterbalance the existing male dominance in populations drawn for shift work research30 and to remove the effects of gender as a potential confounding variable.31 After calculation and categorization of BMI, underweight participants were excluded from the analysis. This was justified because only 1% of the cohort was underweight, and our previous study did not show any significant association between shift work and being underweight.28 The above-mentioned criteria were met by 2086 female nurses or midwives or both. Among those, 1212 were day shift workers, 759 were continuous shift workers, and 115 were night-only shift workers.

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Variables
Exposure Variable (Shift Work)

Shift work is defined as work performed primarily outside typical daytime hours and includes evening shifts, rotating shifts, irregular shifts, and flexitime.32 In the NMeS, work schedule was measured by asking the participant to identify which of the following categories described his or her current shift pattern: (1) day shifts only without weekends; (2) day shifts only with weekends; (3) continuous shift work (3 shifts rotating); (4) evening shifts only; (5) night shifts only; (6) morning and evening shifts only; and (7) evening and night shifts only. For both purposes of this study, the first two response categories were considered as day-only work, continuous shift work corresponded to the definition of rotating shift work, and those who indicated night shifts only were included in night-only shift work. The other three responses to the question describing participants' shift pattern were excluded because they did not meet either of the aims of our study.

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Outcome Variables (Overweight and Obesity)

According to the World Health Organization's BMI classification standards,33 a healthy, normal BMI ranges from 18.5 to 24.9. Overweight and obesity refer to a BMI of 25.0 to 29.9 and 30.0 or more, respectively.

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Factors Selected for Controlling Confounding Effects
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Modifiable lifestyle factors

Overweight/obesity is caused by higher energy intake than expenditure, a function of diet and physical activity level, and other lifestyle habits.34 The association between smoking and overweight/obesity is complex35 and the impact of alcohol consumption on one's weight change has not yet been established.36 Therefore, in this study, diet quality, physical activity, and smoking and alcohol consumption were selected as modifiable lifestyle factors to better understand the contribution of the interaction of those factors and shift work on overweight/obesity.

Diet quality was measured by using the Australian Recommended Food Score (ARFS).37 The ARFS is a 74-item instrument in which each item has a “yes/no” response format. This instrument has been developed and validated to be a quick and easy way to assess individuals on the quality of diet based on a set of dietary targets.37,38 The maximum ARFS is 74 and scores were categorized into five categories according to different cut-points in which higher scores are indicative of higher diet quality.

Physical activity was assessed by the International Physical Activity Questionnaire (IPAQ). This questionnaire is used internationally to obtain a comparable estimate of physical activity, and to measure the prevalence and impact of the sedentary lifestyle. The validity of IPAQ as an estimate of level of physical activity has been established,3942 and the test–retest reliability has been evaluated in other studies.39,41,4345 The guidelines for data processing and scoring of IPAQ are available online (http://www.ipaq.ki.se/scoring.pdf). For the purposes of this study, IPAQ scores have been treated as a categorical variable (low, moderate, and high).46

Smoking status was assessed by items from Nurses' Health Studies (NHS).47 The NHS was established in 1976, expanded in 1989, and continues to the present. As the largest and longest investigation of factors influencing women's health, the NHS has been highly regarded in epidemiologic research. In this study, the items relating to smoking required the respondents to identify as a “current smoker,” “former smoker,” or “never smoker.”

Alcohol consumption was assessed by asking participants to indicate how often, on average, they drink one glass, bottle, can of beer (heavy), or beer (light), or red or white wine, or spirits (eg, vodka). These items were based on the semiquantitative Food Frequency Questionnaire from the 1984 Survey of the Nurses' Health Study.48 The validity and reliability of the Food Frequency Questionnaire have been reported.4953 In Australia, the National Health and Medical Research Council has developed population guidelines for low-risk drinking.54 The guidelines address both short- and long-term risks in terms of “standard drinks” consumed per week. In this substudy, four categories were proposed for the measurement of alcohol consumption: “abstains from alcohol consumption,” “low-risk drinkers,” “risky drinkers,” and “high-risk drinkers.”

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Other confounding variables selected

General physical and mental health was measured by using the SF-36 Health Survey, which is a generic outcome measure designed to examine a person's perceived health status.55 The SF-36 can be divided into two aggregate summary measures of the physical component (physical component score [PCS]) and the mental component (mental component score [MCS]),56 which reflect one's perceived general physical health and general mental health, respectively. The SF-36 has been widely used in nearly 4000 publications to date.57 The most complete information about the history and development of the SF-36, its psychometric evaluation, studies of reliability and validity, and normative data is available in the first of three SF-36 user's manuals.58 Previous studies showed significant associations between overweight/obesity and poor levels of perceived health status, particularly in terms of physical wellbeing.5961 Therefore, general physical and mental health was adjusted in the analysis of this study.

Menopausal status was obtained by asking the participants whether their menstrual periods have ceased permanently. Menopausal status was selected as one of the confounders because some recent studies have reported that menopause is associated with weight gain.62,63

Participants were asked to choose their current work pattern; categories included:“permanent full-time,” “permanent part-time,” “casual,” “temporary full-time,” and “temporary part-time.” In this study, categories were combined into full-time work, part-time work, and casual work. To our knowledge, there is no study that explores the effects of work pattern on overweight/obesity, so we determined to include work pattern in our study to explore the association.

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STATISTICAL METHOD

All statistical analyses were undertaken using Stata version 9.2.64 A significance level of 0.05 was determined for all statistical tests. Descriptive statistics were carried out to calculate mean and standard deviation for continuous variables. Frequencies were presented for categorical variables. Bivariate analysis included one-way analysis of variance for continuous variables among the three groups (assumptions for using analysis of variance were checked and met); and Pearson chi-square tests were used for the categorical variables. Multivariable modeling was performed using a modified Poisson regression approach.65 In this approach, dichotomous variables (overweight: yes/no; obesity: yes/no) were created. In the multivariable analysis, all of the selected potential confounders were included.

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RESULTS

In our study sample, 2086 female nurses and midwives were aged 21 to 69 (43.4 ± 9.6) years. Among the three exposure groups, rotating shift workers were almost 5 years younger than day-only and night-only shift workers (40.5 ± 10.2 vs 45.2 ± 8.7 and 45.3 ±9.0 years, respectively, P < 0.001). Of the participants, 1212 were day-only workers, 759 were rotating shift workers, and 115 were night-only shift workers. In this sample, almost 60% of the participants were classified as being overweight (31.7%) or obese (27.1%). The highest percentage of overweight was found in rotating shift workers (33.3%), with the highest percentage of obesity found in night-only shift workers (31.3%) (Table 1).

Table 1
Table 1
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Regarding modifiable lifestyle factors, the majority of the participants reported having high quality of diet (61.5%) and high level of physical activity (51.7%). The proportion of participants reporting high diet quality was higher in night-only shift workers (67.8%) than the other two groups. The group with the highest percentage reporting high level of physical activity was rotating shift workers (59.7%), followed by night-only shift workers (53%) (Table 1). The majority of participants (56%) reported never having smoked a cigarette, 11.7% were current smokers, and 32.4% were former smokers. Night-only shift workers had the highest proportion of current smokers (16.5%) (Table 1). Three quarters of the participants identified themselves as low-risk drinkers and approximately 10% of them were risky or high-risk drinkers. Comparing the three groups, we identified day-only workers as having the highest percentage of risky or high-risk drinkers (11.8%) (Table 1).

In this cohort, the average PCS and the average MCS were 51.5 and 46.7, respectively. Both the physical and mental components of SF-36 that reflect the perceived physical and mental health were inside the expected range (20 to 58 for PCS and 17 to 62 for MCS),61 and were fairly similar across the three groups. The average age of the participants was approximately 43 (±9.6) years, and the majority (72.8%) reported to have ceased menstrual periods permanently. Although the proportions of menopausal women were fairly similar between day-only and night-only shift workers (68.8% vs 66.1%) (Table 1), a much higher percentage of menopausal women was found in rotating shift workers. Approximately half of the participants worked full-time, with the highest percentage of full-time workers being day-only workers (59%) and the lowest being night-only shift workers (26.1%). More than half of the night-only shift workers worked part-time and almost 20% of them worked on a casual basis (Table 1).

Rotating shift workers were found to have a higher percentage of being overweight than day-only workers and were approximately 5 years younger than day-only workers (P < 0.0001). After adjusting all the selected potential confounding variables in the multivariable analysis, we found that rotating shift workers were 1.02 times more likely to be overweight/obese than day-only workers (P = 0.001; 95% confidence interval [95% CI], 1.004to1.03; and P = 0.02; 95% CI, 1.004to1.04, respectively) (Tables 2 and 3). Night-only shift workers were found to have a higher percentage of being obese than day-only workers. After adjusting all the selected potential confounding variables in the multivariable analysis, we found that night-only shift workers were 1.02 times more likely to be obese thanday-only workers (P = 0.031; 95% CI, 1.002 to1.04) (Table 5). The association between night-only shift and overweight was no longer statistically significant in the final multivariable analysis (Table 4).

Table 2
Table 2
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Table 3
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Table 4
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Table 5
Table 5
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DISCUSSION

Our study found that 58.8% of the total participants were either overweight (31.7%) or obese (27.1%), which suggests that the nursing and midwifery population face the problem of being overweight and obese. The results demonstrate that the nursing and midwifery population had a higher prevalence of overweight and obesity than the general population across the three countries. This finding is supported by another prevalence study of obesity using NMeS baseline data.66 Overweight and obesity in groups of health professionals is surprising, given that they may be considered more informed of the consequences of, and the strategies to prevent or overcome, overweight and obesity than the general population. Moreover, the percentages of being overweight and obese in both rotating and night-only shift workers were higher than those of day workers. The higher percentages of overweight and obesity in this study may be caused by sampling bias with more overweight/obese nurses and midwives completing the sedentary electronic survey; however, there should be no reason to expect that sampling bias would differ between shift workers and day workers.

The findings from our study suggest that rotating shift work was associated with both overweight and obesity and night-only shift work was associated with obesity, not overweight. The results could be explained by the fact that new graduate nurses usually have to work on the rotating shift for the first few years of their nursing career to get oriented to the different tasks required for each shift. The effects of shift work on BMI would exhibit slowly and gradually. More senior nurses could opt for night-only shifts after being exposed to all kinds of shift work, particularly rotating shift. They might already have a much higher baseline BMI (obesity) when they started on night-only shifts. This speculation should be tested in future longitudinal studies to establish the temporal relationship. In this study, the measures of effect were not strong; it should be noted that they did not contain the null value (1.000), suggesting the increased risk of overweight and obesity through exposure to these shift work types. Therefore, the impact of shift work on BMI ought not to be overlooked, especially because the study has considered modifiable lifestyle factors and other significant factors in the analysis. Stakeholders and policy makers should give consideration in the future to the health and well-being of rotating shift workers when planning workforce retention strategies. Rotating shift work carried a greater risk than other types of shift work (such as night-only shifts). In a study of 587 nurses and midwives, a much higher percentage of the participants chose to work on night shifts only, as opposed to rotating shifts.67 This decision could be influenced by the ability to share childcare responsibilities and domestic commitments to allow them time at home with their young children and enjoy the added financial incentive.67

Rotating shift work was found to be more of a concern with overweight/obesity than night-only shift in this study, a finding supported by other research studies. A study in Italy comparing day workers (0700 to 1600 hours) to shift workers (three regular rotating shifts) found a statistically significant relationship between the rotating shift and higher BMI, after taking into account fasting insulin levels and other selected variables.68 Parkes26 reported that increase in BMI was more marked in the day–night rotating shift work over successive years of exposure (r = 0.19; P = 0.0025).26 Another cross-sectional study of 1612 female factory workers found that the rotating shift workers including nights (2 or 3 shifts including night) faced significantly higher risks of being overweight after adjusting for age and other variables (adjusted odds ratio, 1.6; 95% CI, 1.28 to2.06).27 On the contrary, evidence relating to night-only shift and overweight/obesity is mixed. A prospective cohort study conducted among female nurses concerning the impact of shift work on weight gain showed that more female nurses on night work exhibited excessive weight gain than nurses on day work (>7kg; odds ratio, 2.9; 95% CI, 1.2–6.9).24 Another longitudinal study that followed up on a group of nurses for 4 years concluded that the risk of developing metabolic syndrome (moderate degrees of visceral obesity, dyslipidaemia, abnormal blood pressure, and serum glucose levels in the same individual) is strongly associated with night-shift work in nurses.69 However, another longitudinal study reported no significant differences in the weights and BMIs of night and day-only shift workers.70 Poor diet and inadequate exercise were regarded as fundamental causes of overweight/obesity; however, none of these studies adjusted both factors in their analysis. In addition, some studies also recruited only female participants;24,27 however, menopausal status was not considered in the analysis.

A systematic review of the associations between various kinds of shift work and people's modifiable lifestyle factors and BMI reported that shift workers were more likely to exercise less when compared with day workers.71 However, our study found a higher level of physical activity in both groups of shift workers compared to day workers, in spite of the evidence in this study that shift workers had higher risks of being overweight or obese or both. It should be noted that IPAQ assesses all kinds of physical activity, including job-related physical activity; transportation physical activity; housework, house maintenance, and caring for family; and recreation, sport and leisure-time physical activity.46 Nurses and midwives on shift work may on the whole undertake more physical tasks at work (8 hours or longer per shift) than those working day shift, because a proportion of day-only workers would include those in more sedentary roles, such as nurse managers and teaching academics. This could result in a comparatively higher estimation of overall physical activity among shift workers. Appropriateness of using IPAQ to measure physical activity in the nursing population is arguable and questioned; and studies are needed to address this question. In addition, work patterns among nurses and midwives were also found to be very different from the general population, with 53.5% of nurses and midwives working full-time in our study compared with approximately 70% in the general population.72 The percentage of full-time nurses or midwives or both was lower among shift workers (48.5% in rotating shift workers and 26.1% in night-only shift workers), indicating that shift workers were more likely to choose to work part-time or on a casual basis. Furthermore, the rotating shift workers had a higher percentage of menopausal women than day- or night-only shift workers. This finding is quite surprising given that rotating shift workers were approximately 5 years younger (average 40.5 years). It could be speculated that rotating shift work might affect women's hormones, resulting in early menopause. However, we did not find any study reporting relationships between shift work and early menopause. This assumption should be tested in future studies.

Our study drew upon a large representative sample of female nurses and midwives across Australia, New Zealand, and the United Kingdom (n = 2086)73 and considered the role of modifiable lifestyle factors (diet quality, physical activity, smoking, and alcohol consumption) and other important variables (menopause, perceived health status, and employment type) in determining the association between the different kinds of shift work and overweight/obesity. Our study investigated the particular type of shift work and its association with overweight/obesity and is the first of its kind to include the relevant modifiable lifestyle factors and other important variables in the analysis of the associations. There were also some potential limitations to consider when interpreting the results. First, the study was cross-sectional in design, which precluded the possibility to draw definite conclusions regarding causality and temporal relationships between the exposure and outcome variables. A longitudinal study is underway to produce higher levels of evidence about the complex associations between exposure to different kinds of shift work and overweight or obesity or both. In addition, the large number of participants excluded from the study sample due to incomplete information about the key variables could result in the weak associations found between the two types of shift work and overweight/obesity. Completeness of data collection from electronic survey is harder to achieve and is regarded as one of the issues to be addressed in conducting surveys electronically. Furthermore, potential “healthy worker effect” bias due to selection out of the study cohort must be considered when interpreting the results. In our study, it may be that work scheduling may have been impacted by individual health status; for example, some shift workers may have given up working shifts due to health reasons, which may have resulted in underestimation of the reported associations. Moreover, study results were based on self-reported data that may be affected by social desirability (eg, responses to the lifestyle questions) and the ability to report information accurately (eg, height and weight data); however, there should be no reason to expect that response bias would differ among the three different shifts. Also, other factors that may have an impact on body weight were not included in the analysis (such as thyroid disease or substitute treatment), because the original survey did not collect data for these variables. Interpretation of the study results should be cautioned regarding this aspect. Finally, the study sample included only female nurses and midwives. If the effects of gender can be controlled, then the associations between the two kinds of shift work and overweight or obesity or both can be determined more conclusively.

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CONCLUSIONS

To the best of our knowledge, this is the first study conducted among nurses and midwives across Australia, New Zealand, and the United Kingdom investigating the association between shift work type and overweight/obesity with the consideration of the modifiable lifestyle factors and other significant variables as confounders. Our findings suggest that continuous shift work is associated with higher risk of being overweight and obese, and night-only shift is associated with being obese in female nurses and midwives. Studies that may yield higher levels of evidence are being undertaken by the authors to better understand the causal relationships on these associations. If similar results are found, it could serve as a theoretical basis for policy makers regarding interventions to minimize the social disruption for all shift workers and to maintain a healthy nursing and midwifery workforce.

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ACKNOWLEDGMENTS

The authors thank the following industry partners providing in-kind support for the project: Queensland Nursing Council, Nurses and Midwives Board of New South Wales, Nurses Board of Tasmania, Nurses Board of Western Australia, Nurses Board of the Australian Capital Territory, and the Nursing Council of New Zealand.

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REFERENCES

1. World Health Organization. Obesity and Overweight: Fact Sheet No. 311. Updated February 2011. http://www.who.int/mediacentre/factsheets/fs311/en/.

2. World Health Organization. Global strategy on diet physical activity and health. Available at: http://www.who.int/mediacentre/factsheets/fs311/en/index.html. Published 2006. Accessed May 12, 2010.

3. National Health and Medical Research Council. Overweight and obesity. Available at: http://www.nhmrc.gov.au/your_health/facts/obesity.htm. Published 2009. Accessed May 10, 2010.

4. Australian Institute of Health and Welfare. Australia's Health 2006. Canberra, Australia: AIHW; 2006.

5. Ministry of Health. The Health of New Zealand: Total Population. Wellington, New Zealand: Ministry of Health; 2004.

6. NHS Information Centre for Health and Social Care. Health-related behaviour. United Kingdom Health Statistics 2010. Edinburgh: Scottish Government; 2010.

7. The NHS Information Centre Lifestyles Statistics. Statistics on Obesity, Physical Activity and Diet: England, 2010. The Health and Social Care Information Centre; 2010.

8. New Zealand ministry of Health. A Portrait of Health. Key results of the 2006/07 New Zealand Health Survey. Wellington: Ministry of Health; 2008.

9. Montani JP, Antic V, Yang Z, Dulloo A. Pathways from obesity to hypertension: from the perspective of a vicious triangle. Int J Obes. 2002;26:S28–S38.

10. Wolk R, Shamsuzzaman ASM, Somers VK. Obesity, sleep apnea, and hypertension. Hypertension. 2003;42:1067–1074.

11. Helmrich SP, Ragland DR, Leung RW, Paffenbarger RS. Physical activity and reduced occurrence of non-insulin-dependent diabetes mellitus. N Engl J Med. 1991;325:147–152.

12. Antikainen R, Jousilahti P, Tuomilehto J. Systolic blood pressure, isolated systolic hypertension and risk of coronary heart disease, strokes, cardiovascular disease and all-cause mortality in the middle-aged population. J Hypertens. 1998;16:577–583.

13. IARC Working Group. IARC Working Group on the evaluation of cancer-preventive strategies. In: Vanio H, Bianchini F, eds. IARC Handbooks of Cancer Prevention, Vol. 6. Weight Control and Physical Activity. Lyon, France: IARC Press; 2002.
14. Carroll KK. Obesity as a risk factor for certain types of cancer. Lipids. 1998;33:1055–1059.

15. Wolk A, Lindblad P, Adami HO. Nutrition and renal cell cancer. Cancer Causes Control. 1996;7:5–18.

16. Colditz GA, Willett WC, Stampfer MJ, et al. Weight as a risk factor for clinical diabetes in women. Am J Epidemiol. 1990;132:501–513.

17. Willett WC, Manson JE, Stampfer MJ, et al. Weight, weight change, and coronary heart disease in women: risk within the ‘normal’ weight range. JAMA. 1995;273:461–465.
18. Manson JE, Colditz GA, Stampfer MJ, et al. A prospective study of obesity and risk of coronary heart disease in women. N Engl J Med. 1990;322:882–889.

19. Kurth T, Gaziano JM, Rexrode KM, et al. Prospective study of body mass index and risk of stroke in apparently healthy women. Circulation. 2005;111:1992–1998.

20. Syed IY, Davis BL. Obesity and osteoarthritis of the knew: hypotheses concerning the relationship between ground reaction forces and quadriceps fatigue in long-duration walking. Med Hypotheses. 2000;54:182–185.

21. Sowers M. Epidemiology of risk factors for osteoarthritis: systemic factors. Curr Opin Rheumatol. 2001;13:447–451.

22. Klarenbach S, Padwal R. Population-based analysis of obesity and workforce participation. Obesity. 2006;14:920–927.

23. Sobal J. Social and cultural influences on obesity. In: Bjorntorp P, ed. The international Textbook of Obesity. Weiley: Chichester; 2001:305–322.

24. Niedhammer I, Lert F, Marne MJ. Prevalence of overweight and weight gain in relation to night work in a nurses' cohort. Int J Obes Relat Metab Disord. 1996;20:625–633.

25. Karlsson B, Knutsson A, Lindahl B. Is there an association between shift work and having a metabolic syndrome? results from a population based study of 27,485 people. Occup Environ Med. 2001;58:747–752.

26. Parkes KR. Shift work and age as interactive predictors of body mass index among offshore workers. Scand J Work Environ Health. 2002;28:64–71.

27. Chee H, Mirnalin K, Maimunah K, et al. Body mass index and factors related to overweight among women workers in electronic factories in Peninsular Malaysia. Asia Pac J Clin Nutr. 2004;13:248–254.

28. Zhao I, Bogossian F, Song S, et al. The association between shift work and unhealthy weight: A cross-sectional analysis from the Nurses and Midwives' e-cohort Study. JOEM. 2011;53:153–158.

29. Turner C, Cohort Profile: The Nurses and Midwives' e-cohort study – a novel electronic longitudinal study. Int J Epidemiol. 2009;38:53–60.

30. Steeland K. Shift work, long hours, and CVD: a review. In: Schnall PL, Belkie K, Landsbergis P, Baker D, eds. The Workplace and Cardiovascular Disease. Philadelphia, PA: Hanley & Belfus; 2000.

31. Bjorntorp P, ed. The International Textbook of Obesity. Weiley: Chichester; 2001.

32. Green-McKenzie J, Shofer FS. Duration of time on shift before accidental blood or body fluid exposure for housestaff, nurses and technicians. Infect Control Hosp Epidemiol. 2006;28:5–9.

33. WHO. World Health Report 2004. Available at: http://www.who.int/hrh/whr04_consultation/en/index.html. Accessed May 12, 2010.

34. Department of Health and Ageing. About overweight and obesity. Available at http://www.health.gov.au/internet/main/Publishing.nsf/Content/. Published 2009. Accessed May 13, 2010.

35. Chiolero A, Faeh D, Paccaud F, Cornuz J. Consequences of smoking for body weight, body fat distribution, and insulin resistance. Am J Clin Nutr. 2008;87:801–809.

36. Koh-Banergee P, Wang Y, Hu F, et al. Changes in body weight and body fat distribution as risk factors for clinical diabetes in US men. Am J Epidemiol. 2004;159:1150–1159.

37. Collins CE, Young AF, Hodge A. Diet quality is associated with higher nutrient intake and self- rated health in mid-aged women. J Am Coll Nutr. 2008;27:146–157.

38. Zars CM, Tangney CC, Keim KS, Domas AJ, Sher T. The development of a diet quality score. J Am Diet Assoc. 2006;106:A21.

39. Halla PC, Victora CG. Reliability and validity of the International Physical Activity Questionnaire (IPAQ). Med Sci Sports Exerc. 2004;36:556.

40. Rutten A, Abu-Omar K. Prevalence of physical activity in the European Union. Soz Praventiv Med. 2004;49:281–289.
41. Hagstromer M, Oja P, Sjostrom M. The international physical activity questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr. 2006;9:755–762.

42. Sjostrom M, Oja P, Hagstromer M, Smith B, Bauman A. Health-enhancing physical activity across European Union countries: the Eurobarometer study. J Public Health. 2006;14:291–300.

43. Craig CL, Marshall AL, Sjöström M, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;38:1381–1395.

44. Hallal PC, Victora CG, Wells JCK, Lima RC, Valle NJ. Comparison of short and full- length International Physical Activity Questionnaires. J Phys Act Health. 2004;1:227–234.
45. Fogelholm MJ, Malmberg J, Suni J, et al. International physical activity questionnaire: validity against fitness. Med Sci Sports Exerc. 2006;28:753–760.

46. International Physical Activity Questionnaire (IPAQ). Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ): short and long forms. Available at: http://www.ipaq.ki.se/scoring.pdf. Published 2005. Accessed March 12, 2010.

47. The Nurses' Health Study. Available at: http://www.channing.harvard.edu/nhs/. Accessed March 10, 2010.

48. Willet WC, Sampson L, Stampfer MJ, et al. Reproducibility and validity of a semiquantitative food frequency questionnaire. Am J Epidemiol. 1985;122:51–65.

49. Baghurst KI, Record SJ. Intake and sources in selected Australian subpopulations, of dietary constituents implicated in the aetiology of chronic disease. J Food Nutr. 1983;40:1–15.

50. Rohan TE, Record SJ, Cook MG. Repeatability of estimates of nutrient and energy intake: the quantitative food frequency approach. Nutr Res. 1987;7:125–137.

51. Baghurst KI, Baghurst PA. The measurement of usual dietary intake in individuals and groups. Trans Menzies Found. 1981;3:139–160.
52. Tassie K, Baghurst K, Record S, Nordin B. Usual dietary intake of middle-aged women using the food frequency method. In: Washlqvist M, Truswell A, eds. Recent Advances in Clinical Nutrition. London: John Libbbey; 1985.
53. Gelissen IC, Roberts DCK. Comparison of estimated nutrient intake by two methods: validation of a food frequency questionnaire. J Hum Nutr Diet. 1992;5:215–223.

54. National Health and Medical Research Council. Australian alcohol guidelines: Health risks and benefits. Available at: http://www.nhmrc.gov.au/publications/synopses/ds9syn.htm. Accessed May 12, 2010.

55. Centre for Health Service Development. Instrument Review: SF-36 Health Survey. Sydney: The University of Wollongong, 2006.

56. Ware JE, Kosinski M, Bayliss MS, et al. Comparison of methods for the scoring and statistical analysis of SF 36 health profile and summary measures: summary of results from the medical outcomes study. Med Care. 1995;33:AS264–AS279.

57. Turner-Bowker DM, Bartley PJ, Ware JE Jr. SF-36® Health Survey & “SF” Bibliography: Third Edition (1988–2000). Lincoln, RI: QualityMetric Incorporated, 2002.

58. Ware JE, Snow KK, Kosinski M, Gandek B. SF-36® Health Survey Manual and Interpretation Guide. Boston, MA: New England Medical Center, The Health Institute, 1993.

59. Doll JA, Petersen SEK, Steward-Brown S. Obesity and physical and emotional well-being: associations between body mass index, chronic illness, and the physical and mental components of the SF-36 questionnaire. Obes Res. 2000;8:160–170.

60. Larsson U, Karlsson J, Sullivan M. Impact of overweight and obesity on health-related quality of life – a Swedish population study. Int J Obesity. 2002;26:417–424.
61. Johansen KL, Kutner NG, Young B, et al. Association of body size with health status in patients beginning dialysis. Am J Clin Nutr. 2006;83:543–549.

62. Wing R, Matthews K, Kuller L, et al. Weight gain at the time of menopause. Arch Intern Med. 1990;151:97–103.

63. Kuskowaska-Wolk A, Rossner S. Prevalence of obesity in Sweden: cross-sectional study of a representative adult population. J Int Med. 1990;227:241–246.

64. StataCorp. Statistical Software: Release 9.2. College Station, TX: Stata Corporation; 2001.

65. Zou G. A modified poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159:702–706.

66. Bogossian FE, Long MH, Benefer C, et al. A workforce profile comparison of practising and non-practising midwives in Australia: Baseline data from the Midwives and Nurses e-cohort Study. Midwifery. 2011; 27:342–349.

67. Barton J. Choosing to work at night: a moderating influence on individual tolerance to shift work. J Appl Psychol. 1994;79:449–454.

68. Di Lorenzo L, De Pergola G, Zocchetti C, et al. Effect of shift work on body mass index: results of a study performed in 319 glucose-tolerant men working in a Southern Italian industry. Int J Obes. 2003;27(11):1353–1358.

69. Pietroiusti A, Neri A, Somma G, et al. Incidence of metabolic syndrome among night-shift heathcare workers. Occup Environ Med. 2010;67:514–557.

70. Reeves SL, Newling-Ward E, Gissane C. The effect of shift-work on food intake and eating habits. Nutr Food Sci. 2004;34(5):216–221.

71. Zhao I, Turner C. The impact of shift work on people's daily health habits and adverse health outcomes. Aust J Adv Nurs. 2008;25:8–22.

72. Australian Bureau of Statistics (ABS). Working arrangements, Australia, November 2009. Available at: http://www.abs.gov.au/ausstats/abs@.nsf/Latestproducts/6342.0Main%20Features2November%202009?opendocument&tabname=Summary&prodno=6342.0&issue=November%202009&num=&view= . Accessed March 5, 2011.

73. Schluter PJ, Turner C, Bain C, Huntington A, et al. Work/life balance and health: the Nurses and Midwives e-cohort study. Int Nurs Rev. 2010;58:28–36.

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