Shift work and the risk of cardiovascular disease. A systematic review and meta-analysis including dose–response relationship

This is the final version. Available from Nordic Association of Occupational Safety and Health (NOROSH) via the DOI in this record.Objectives The aim of this review was to assess the risk of cardiovascular disease (CVD) events associated with shift work and determine if there is a dose–response relationship in this association. Method Electronic databases (PubMed, Scopus, and Web of Science) were searched for cohort or case–control control study designs in any population, reporting exposure to shift work as the main contributing factor to estimate CVD risk. For each study, adjusted relative risk (RR) ratios and 95% confidence intervals (CI) were extracted, and used to calculate the pooled RR using random-effect models. Meta-regression analysis was conducted to explore potential heterogeneity sources. Potential non-linear dose–response relationships were examined using fractional polynomial models. Results We included 21 studies with a total of 173 010 unique participants. The majority of the studies were ranked low-to-moderate risk of bias. The risk of any CVD event was 17% higher among shift workers than day workers. The risk of coronary heart disease (CHD) morbidity was 26% higher (1.26, 95% CI 1.10–1.43, I2= 48.0%). Sub-group analysis showed an almost 20% higher risk of CVD and CHD mortality among shift workers than those who did not work shifts (1.22, 95% CI 1.09–1.37, I2= 0% and 1.18, 95% CI 1.06–1.32 I2=0%; respectively). After the first five years of shift work, there was a 7.1% increase in risk of CVD events for every additional five years of exposure (95% CI 1.05–1.10). Heterogeneity of the pooled effect size (ES) estimates was high (I2=67%), and meta-regression analysis showed that sample size explained 7.7% of this. Conclusions The association between shift work and CVD risk is non-linear and seems to appear only after the first five years of exposure. As shift work remains crucial for meeting production and service demands across many industries, policies and initiatives are needed to reduce shift workers’ CVD risk

Associations between indicators of energy intake and expenditure with excess weight and obesity among women in sedentary and less-sedentary jobs

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordData statement: The data used in this study are from the Australian Longitudinal Study on Women’s Health. Restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available free of charge from the Australian Longitudinal Study on Women’s Health, following submissions of an expression of interest form, and approval by the Data Access Committee. Further information can be found at https://www.alswh.org.au/how-to-access-the-data/alswh-data or requested at [email protected] - Data Access and Liaison OfficeThe aim of this study was to compare the associations between indicators of energy intake and expenditure with excess weight and obesity in women who work full-time in sedentary and less sedentary jobs. Data were from 3,444 participants the Australian Longitudinal Study on Women’s Health, who reported their weight, dietary intake, physical activity and occupation in 2009 (baseline), and weight in 2012 (follow-up). Participants were categorised as being in a ‘less sedentary’ or ‘sedentary’ job, based on occupational activity patterns. Odds of excess weight (BMI≥25) at baseline and of being obese (BMI≥30) at follow-up, by indicators of energy intake and expenditure, were compared in the two occupational groups. In multivariate analyses, high non-work sitting time and saturated fat intake were associated with increased odds of obesity at 3-year follow-up in both occupational groups. In the sedentary job group, high physical activity (in leisure and transport) was associated with a 51% reduction in odds of obesity (OR 0.49, 95%CI 0.25-0.97). In the less-sedentary job group, energy intake and high soft drink consumption were associated with markedly increased odds of obesity (OR 1.67 95%CI 1.07-2.61; OR 2.08 95%CI1.42-3.05, respectively). In this cohort of young Australian women, sedentariness at work did not markedly affect the prevalence of excess weight or obesity. Indicators of high energy intake and low energy expenditure were associated with increased odds of both excess weight and obesity, regardless of sedentariness of occupational group

Physical activity and menopausal symptoms in women who have received menopause-inducing cancer treatments: results from the Women's Wellness After Cancer Program.

ObjectiveThis randomized controlled trial tested a digitally-delivered whole-of-lifestyle program for women previously treated for cancer. We investigated (1) associations between self-reported physical activity (PA) and menopausal symptoms and (2) if the intervention was associated with beneficial changes in PA and menopausal symptoms.MethodsWomen were randomized to intervention (n = 142) or control (n = 138). The intervention targeted lifestyle behavior including PA. Self-reported PA (International Physical Activity Questionnaire - Short Form) and menopausal symptom (Green Climacteric Scale, GCS) data were collected at baseline, with measures repeated at 12 weeks (end of intervention) and 24 weeks (to assess sustainability). Generalized estimating equation models explored associations between PA and GCS scores. Mixed-effects generalized equation models analyzed changes within and between groups in PA and GCS scores.ResultsTotal GCS scores were 1.83 (95% CI: 0.11-3.55) and 2.72 (95% CI: 1.12-4.33) points lower in women with medium and high levels of PA, respectively, than in women with low levels of PA. Total average GCS scores were 1.02 (0.21-2.26) and 1.61 (0.34-2.87) points lower in those undertaking moderate or vigorous intensity PA, respectively. Time spent walking, and performing moderate and vigorous PA were not different between intervention and control. The average GCS decrease of 0.66 points (95% CI: 0.03-1.29; p time = 0.03) over 24 weeks was not different between groups.ConclusionThis exploratory study established a stepwise association between moderate and vigorous PA and a lower total menopausal symptom score. The intervention did not appear to increase self-reported PA in women treated for early stage breast, reproductive, and blood cancers

Effects of the Active Choices Program on Self-Managed Physical Activity and Social Connectedness in Australian Defence Force Veterans: Protocol for a Cluster-Randomized Trial.

BACKGROUND: A stepped-down program is one in which clients transition from the care of a health professional to self-managed care. Very little is known about the effectiveness of stepped-down physical activity (PA) programs for military service veterans. OBJECTIVE: This study will test Active Choices, a stepped-down behavioral support program designed to help Australian Defence Force veterans and their dependents who are clients of the Department of Veterans' Affairs, transition from treatment by an exercise physiologist or physiotherapist to self-managed PA. METHODS: The study is a parallel-group, randomized trial, with city-based exercise physiology or physiotherapy practices that recruit eligible Department of Veterans' Affairs clients assigned to Active Choices or a comparison program. The study aims to recruit 52 participants (26 in each group). The Active Choices program will consist of 2 face-to-face (Weeks 1, 12) and 2 telephone (Weeks 4 and 8) consultations. During these sessions, the participant and Active Choices consultant will utilize an evidence-based resource booklet to review the key benefits of an active lifestyle, build an action plan for PA preferences, set and review goals, self-monitor progress relative to set goals, and discuss strategies to overcome PA barriers. Linking participants to local PA communities to overcome social isolation will be a program priority. The comparison program will consist of 2 consultations (Weeks 1 and 12) and use fewer behavioral support strategies (education, self-monitoring, and action planning only) than Active Choices. Outcome measures will be administered at baseline, end-intervention (12 weeks), and follow-up (24 weeks) to assess changes in moderate intensity self-managed PA, psychological well-being, and social connectedness. We will also measure health service utilization and costs as well as PA choices across the intervention period. End-intervention interviews will capture participant experiences. RESULTS: Due to the impacts of the COVID-19 pandemic on human research activities in Australia, participant recruitment will commence when it is safe and feasible to do so. CONCLUSIONS: Findings will provide valuable pilot data to support up-scaling of the program and larger effectiveness trials with regional and rural as well as city-based Australian Defence Force veterans and their dependents. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry (ANZCTR): ACTRN12620000559910; https://www.anzctr.org.au/ACTRN12620000559910.aspx. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/21911

The active living gender's gap challenge: 2013-2017 Eurobarometers physical inactivity data show constant higher prevalence in women with no progress towards global reduction goals

BACKGROUND: The World Health Organization (WHO) considers physical inactivity (PIA) as a critical noncommunicable factor for disease and mortality, affecting more women than men. In 2013, the WHO set a 10% reduction of the PIA prevalence, with the goal to be reached by 2025. Changes in the 2013-2017 period of physical inactivity prevalence in the 28 European Union (EU) countries were evaluated to track the progress in achieving WHO 2025 target. METHODS: In 2013 and 2017 EU Special Eurobarometers, the physical activity levels reported by the International Physical Activity Questionnaire of 53,607 adults were analyzed. Data were considered as a whole sample and country-by-country. A χ2 test was used to analyze the physical inactivity prevalence (%) between countries, analyzing women and men together and separately. Additionally, PIA prevalence was analyzed between years (2013-2017) for the overall EU sample and within-country using a Z-Score for two population proportions. RESULTS: The PIA prevalence increased between 2013 and 2017 for the overall EU sample (p <  0.001), and for women (p = 0.04) and men (p < 0.001) separately. Data showed a higher PIA prevalence in women versus men during both years (p <  0.001). When separately considering changes in PIA by gender, only Belgium's women and Luxembourg's men showed a reduction in PIA prevalence. Increases in PIA prevalence over time were observed in women from Austria, Croatia, Germany, Lithuania, Malta, Portugal, Romania, and Slovakia and in men from Bulgaria, Croatia, Czechia, Germany, Italy, Lithuania, Portugal, Romania, Slovakia, and Spain. CONCLUSIONS: PIA prevalence showed an overall increase across the EU and for both women and men between 2013 and 2017, with higher rates of PIA reported for women versus men during both years. PIA prevalence was reduced in only Belgium's women and Luxembourg's men. Our data indicate a limited gender-sensible approach while tacking PIA prevalence with no progress reaching global voluntary reductions of PIA for 2025

Socio-demographic, behavioural and cognitive correlates of work-related sitting time in German men and women

Background: Sitting time is ubiquitous for most adults in developed countries and is most prevalent in three domains: in the workplace, during transport and during leisure time. The correlates of prolonged sitting time in workplace settings are not well understood. Therefore, the aim of this study was to examine the gender-specific associations between the socio-demographic, behavioural and cognitive correlates of work-related sitting time. Methods: A cross-sectional sample of working German adults (n = 1515; 747 men; 43.5 ± 11.0 years) completed questionnaires regarding domain-specific sitting times and physical activity (PA) and answered statements concerning beliefs about sitting. To identify gender-specific correlates of work-related sitting time, we used a series of linear regressions. Results The overall median was 2 hours of work-related sitting time/day. Regression analyses showed for men (β = -.43) and for women (β = -.32) that work-related PA was negatively associated with work-related sitting time, but leisure-related PA was not a significant correlate. For women only, transport-related PA (β = -.07) was a negative correlate of work-related sitting time, suggesting increased sitting times during work with decreased PA in transport. Education and income levels were positively associated, and in women only, age (β = -.14) had a negative correlation with work-related sitting time. For both genders, TV-related sitting time was negatively associated with work-related sitting time. The only association with cognitive correlates was found in men for the belief ‘Sitting for long periods does not matter to me’ (β = .10) expressing a more positive attitude towards sitting with increasing sitting durations. Conclusions: The present findings show that in particular, higher educated men and women as well as young women are high-risk groups to target for reducing prolonged work-related sitting time. In addition, our findings propose considering increasing transport-related PA, especially in women, as well as promoting recreation-related PA in conjunction with efforts to reduce long work-related sitting times

Relationship of device measured physical activity type and posture with cardiometabolic health markers: pooled dose–response associations from the Prospective Physical Activity, Sitting and Sleep Consortium

Aims/hypothesis: The aim of this study was to examine the dose–response associations of device-measured physical activity types and postures (sitting and standing time) with cardiometabolic health. Methods: We conducted an individual participant harmonised meta-analysis of 12,095 adults (mean ± SD age 54.5±9.6 years; female participants 54.8%) from six cohorts with thigh-worn accelerometry data from the Prospective Physical Activity, Sitting and Sleep (ProPASS) Consortium. Associations of daily walking, stair climbing, running, standing and sitting time with a composite cardiometabolic health score (based on standardised z scores) and individual cardiometabolic markers (BMI, waist circumference, triglycerides, HDL-cholesterol, HbA1c and total cholesterol) were examined cross-sectionally using generalised linear modelling and cubic splines. Results: We observed more favourable composite cardiometabolic health (i.e. z score <0) with approximately 64 min/day walking (z score [95% CI] −0.14 [−0.25, −0.02]) and 5 min/day stair climbing (−0.14 [−0.24, −0.03]). We observed an equivalent magnitude of association at 2.6 h/day standing. Any amount of running was associated with better composite cardiometabolic health. We did not observe an upper limit to the magnitude of the dose–response associations for any activity type or standing. There was an inverse dose–response association between sitting time and composite cardiometabolic health that became markedly less favourable when daily durations exceeded 12.1 h/day. Associations for sitting time were no longer significant after excluding participants with prevalent CVD or medication use. The dose–response pattern was generally consistent between activity and posture types and individual cardiometabolic health markers. Conclusions/interpretation: In this first activity type-specific analysis of device-based physical activity, ~64 min/day of walking and ~5.0 min/day of stair climbing were associated with a favourable cardiometabolic risk profile. The deleterious associations of sitting time were fully attenuated after exclusion of participants with prevalent CVD and medication use. Our findings on cardiometabolic health and durations of different activities of daily living and posture may guide future interventions involving lifestyle modification