Sitting Behaviors and Mental Health among Workers and Nonworkers: The Role of Weight Status

Objective. To explore the associations between sitting time in various domains and mental health for workers and nonworkers and the role of weight status. Design. Cross-sectional analyses were performed for 1064 respondents (47% men, mean age 59 years) from the Doetinchem Cohort Study 2008-2009. Sedentary behavior was measured by self-reported time spent sitting during transport, leisure time, and at work. Mental health was assessed by the Mental Health Inventory (MHI-5). BMI was calculated based on measured body height and weight. Results. Neither sitting time during transport nor at work was associated with mental health. In the working population, sitting during leisure time, and particularly TV viewing, was associated with poorer mental health. BMI was an effect modifier in this association with significant positive associations for healthy-weight non-workers and obese workers. Conclusion. Both BMI and working status were effect modifiers in the relation between TV viewing and mental health. More longitudinal research is needed to confirm the results and to gain insight into the causality and the underlying mechanisms for the complex relationships among sedentary behaviors, BMI, working status, and mental health

Impact of physical activity on healthcare costs: a systematic review

BACKGROUND: This systematic review aims to describe the relation between physical inactivity and healthcare costs, by taking into account healthcare costs of physical-inactivity-related diseases (common practice), including physical-activity-related injuries (new) and costs in life-years gained due to avoiding diseases (new), whenever available. Moreover, the association between physical inactivity and healthcare costs may both be negatively and positively impacted by increased physical activity. METHODS: A systematic review was conducted, including records reporting on physical (in)activity in relation to healthcare costs for a general population. Studies were required to report sufficient information to calculate the percentage of total healthcare costs potentially attributable to physical inactivity. RESULTS: Of the 264 records identified, 25 were included in this review. Included studies showed substantial variation in the assessment methods of physical activity and in type of costs included. Overall, studies showed that physical inactivity is related to higher healthcare costs. Only one study included costs of healthcare resources used in prolonged life when physical-inactivity-related diseases were averted, showing net higher healthcare costs. No study included healthcare costs for physical-activity-related injuries. CONCLUSIONS: Physical inactivity is associated with higher healthcare costs in the general population in the short-term. However, in the long-term aversion of diseases related with physical inactivity may increase longevity and, as a consequence, healthcare costs in life-years gained. Future studies should use a broad definition of costs, including costs in life-years gained and costs related to physical-activity-related injuries

Adhering to the 2017 Dutch Physical Activity Guidelines: A Trend over Time 2001-2018.

Recently, new physical activity (PA) guidelines were adopted in the Netherlands consisting of two components: (1) addressing duration of moderate and vigorous PA, (2) bone and muscle strengthening activities. The aim of this study is to retrospectively assess the long-term trend in fulfilling the criteria of the new PA guidelines and to gain insight into which activities contribute to changes over time. Data were available for 2001–2018 of a nationally representative sample of approximately 7000 Dutch citizens aged 12 years and over using the Short Questionnaire to Assess Health-enhancing physical activity (SQUASH). Multiple logistic regression analysis was performed by age, sex, and level of education. Overall, a positive trend was found from 39.9% adherence in 2001 to 46.0% in 2018. Adherence levels among adolescents decreased and increased among adults and seniors. Intermediate and higher educated groups showed positive trends over time whereas a stable trend was observed among lower educated. Activities contributing most to changes over time were sports, leisure time walking, and strenuous occupational activities. In the period 2001–2018, though an increasing trend was found, less than half of the population was sufficiently active. Special effort is necessary to reach adolescents, seniors, and lower educated groups in PA promotion programs

A systematic review of financial incentives given in the healthcare setting; do they effectively improve physical activity levels?

BACKGROUND: According to current physical activity guidelines, a substantial percentage of the population in high-income countries is inactive, and inactivity is an important risk factor for chronic conditions and mortality. Financial incentives may encourage people to become more active. The objective of this review was to provide insight in the effectiveness of financial incentives used for promoting physical activity in the healthcare setting. METHODS: A systematic literature search was performed in three databases: Medline, EMBASE and SciSearch. In total, 1395 papers published up until April 2015 were identified. Eleven of them were screened on in- and exclusion criteria based on the full-text publication. RESULTS: Three studies were included in the review. Two studies combined a financial incentive with nutrition classes or motivational interviewing. One of these provided a free membership to a sports facility and the other one provided vouchers for one episode of aerobic activities at a local leisure center or swimming pool. The third study provided a schedule for exercise sessions. None of the studies addressed the preferences of their target population with regard to financial incentives. Despite some short-term effects, neither of the studies showed significant long-term effects of the financial incentive. CONCLUSIONS: Based on the limited number of studies and the diversity in findings, no solid conclusion can be drawn regarding the effectiveness of financial incentives on physical activity in the healthcare setting. Therefore, there is a need for more research on the effectiveness of financial incentives in changing physical activity behavior in this setting. There is possibly something to be gained by studying the preferred type and size of the financial incentive

20-year individual physical activity patterns and related characteristics.

BACKGROUND: This study aims to describe individual leisure-time physical activity patterns among Dutch adults over a 20-year period, and to compare baseline characteristics of participants with different patterns. METHODS: The study population consisted of 2,518 adults (53% women) aged 26–65 years at baseline, measured every 5 years over a 20-year period. Self-reported physical activity measurements (from 1994 to 2017) were used to compose five (predefined) patterns: stable active, becoming active, becoming inactive, stable inactive, and varying physical activity. Multivariate logistic regression analyses were used to compare baseline socio-demographic, lifestyle, and health-related characteristics of these patterns. RESULTS: The total population shows a stable percentage being active in each round (between 55 and 58%). However over a period of 20 years, 32.6% of the participants were stable active, 19.9% were stable inactive, 15.2% became active, 11.6% became inactive, and 20.8% had varying physical activity behaviour. Compared to participants who were stable active, becoming active was associated with being 46–55 years old, having an intermediate level of education, and smoking, at baseline. Participants who became inactive were less likely to be 46–55 years old and more likely to be obese. Stable inactivity was associated with an intermediate level of education, low adherence to dietary guidelines, smoking, low levels of alcohol use and a moderate/poor perceived health. Participants with a varying physical activity level were more likely to have low adherence to dietary guidelines and to smoke. CONCLUSIONS: Almost half of the participants changed their physical activity behaviour over 20 years. Baseline age, level of education, smoking, alcohol consumption, adherence to dietary guidelines, weight status and perceived health were associated with different physical activity patterns

Patient-Reported Mobility, Physical Activity, and Bicycle Use after Vulvar Carcinoma Surgery

Patients treated for vulvar carcinoma may experience losses in mobility and physical activity. In this study, we assess the prevalence and severity of mobility problems using patient-reported outcomes of three questionnaires: EQ-5D-5L to estimate QoL and perceived health; SQUASH to estimate habitual physical activity; and a problem-specific questionnaire on bicycling. Patients treated for vulvar carcinoma between 2018 and 2021 were recruited, and 84 (62.7%) responded. The mean age was 68 ± 12 years (mean ± standard deviation). Self-reported QoL and perceived health were 0.832 ± 0.224 and 75.6 ± 20.0, respectively. Dutch physical activity guidelines were met by 34.2% of participants. Compared to baseline values, the times spent walking, bicycling, and participating in sports were all reduced. During bicycling, patients experienced moderate or severe pain in the skin of the vulva (24.5%), pain in the sit bones (23.2%), chafing (25.5%), or itching (8.9%). Overall, 40.3% experienced moderate or severe bicycling problems or could not bicycle, 34.9% felt that their vulva impeded bicycling, and 57.1% wished to make more or longer bicycling journeys. To conclude, vulvar carcinoma and its treatment reduce self-reported health, mobility, and physical activity. This motivates us to investigate ways to reduce discomfort during physical activities, and help women regain their mobility and self-reliance.<br/

Non-occupational physical activity levels of shift workers compared with non-shift workers

Objectives Lack of physical activity (PA) has been hypothesised as an underlying mechanism in the adverse health effects of shift work. Therefore, our aim was to compare non-occupational PA levels between shift workers and non-shift workers. Furthermore, exposure- response relationships for frequency of night shifts and years of shift work regarding non-occupational PA levels were studied. Methods Data of 5980 non-shift workers and 532 shift workers from the European Prospective Investigation into Cancer and Nutrition-Netherlands (EPIC-NL) were used in these cross-sectional analyses. Time spent (hours/week) in different PA types (walking/cycling/exercise/chores) and intensities (moderate/vigorous) were calculated based on self-reported PA. Furthermore, sports were operationalised as: playing sports (no/yes), individual versus non-individual sports, and nonvigorous- intensity versus vigorous-intensity sports. PA levels were compared between shift workers and nonshift workers using Generalized Estimating Equations and logistic regression. Results Shift workers reported spending more time walking than non-shift workers (B=2.3 (95% CI 1.2 to 3.4)), but shift work was not associated with other PA types and any of the sports activities. Shift workers who worked 1-4 night shifts/month (B=2.4 (95% CI 0.6 to 4.3)) and ≥5 night shifts/month (B=3.7 (95% CI 1.8 to 5.6)) spent more time walking than non-shift workers. No exposure-response relationships were found between years of shift work and PA levels. Conclusions Shift workers spent more time walking than non-shift workers, but we observed no differences in other non-occupational PA levels. To better understand if and how PA plays a role in the negative health consequences of shift work, our findings need to be confirmed in future studies

Non-occupational physical activity levels of shift workers compared with non-shift workers

OBJECTIVES: Lack of physical activity (PA) has been hypothesised as an underlying mechanism in the adverse health effects of shift work. Therefore, our aim was to compare non-occupational PA levels between shift workers and non-shift workers. Furthermore, exposure-response relationships for frequency of night shifts and years of shift work regarding non-occupational PA levels were studied. METHODS: Data of 5980 non-shift workers and 532 shift workers from the European Prospective Investigation into Cancer and Nutrition-Netherlands (EPIC-NL) were used in these cross-sectional analyses. Time spent (hours/week) in different PA types (walking/cycling/exercise/chores) and intensities (moderate/vigorous) were calculated based on self-reported PA. Furthermore, sports were operationalised as: playing sports (no/yes), individual versus non-individual sports, and non-vigorous-intensity versus vigorous-intensity sports. PA levels were compared between shift workers and non-shift workers using Generalized Estimating Equations and logistic regression. RESULTS: Shift workers reported spending more time walking than non-shift workers (B=2.3 (95% CI 1.2 to 3.4)), but shift work was not associated with other PA types and any of the sports activities. Shift workers who worked 1-4 night shifts/month (B=2.4 (95% CI 0.6 to 4.3)) and ≥5 night shifts/month (B=3.7 (95% CI 1.8 to 5.6)) spent more time walking than non-shift workers. No exposure-response relationships were found between years of shift work and PA levels. CONCLUSIONS: Shift workers spent more time walking than non-shift workers, but we observed no differences in other non-occupational PA levels. To better understand if and how PA plays a role in the negative health consequences of shift work, our findings need to be confirmed in future studies

Non-occupational physical activity levels of shift workers compared with non-shift workers

OBJECTIVES: Lack of physical activity (PA) has been hypothesised as an underlying mechanism in the adverse health effects of shift work. Therefore, our aim was to compare non-occupational PA levels between shift workers and non-shift workers. Furthermore, exposure-response relationships for frequency of night shifts and years of shift work regarding non-occupational PA levels were studied. METHODS: Data of 5980 non-shift workers and 532 shift workers from the European Prospective Investigation into Cancer and Nutrition-Netherlands (EPIC-NL) were used in these cross-sectional analyses. Time spent (hours/week) in different PA types (walking/cycling/exercise/chores) and intensities (moderate/vigorous) were calculated based on self-reported PA. Furthermore, sports were operationalised as: playing sports (no/yes), individual versus non-individual sports, and non-vigorous-intensity versus vigorous-intensity sports. PA levels were compared between shift workers and non-shift workers using Generalized Estimating Equations and logistic regression. RESULTS: Shift workers reported spending more time walking than non-shift workers (B=2.3 (95% CI 1.2 to 3.4)), but shift work was not associated with other PA types and any of the sports activities. Shift workers who worked 1-4 night shifts/month (B=2.4 (95% CI 0.6 to 4.3)) and ≥5 night shifts/month (B=3.7 (95% CI 1.8 to 5.6)) spent more time walking than non-shift workers. No exposure-response relationships were found between years of shift work and PA levels. CONCLUSIONS: Shift workers spent more time walking than non-shift workers, but we observed no differences in other non-occupational PA levels. To better understand if and how PA plays a role in the negative health consequences of shift work, our findings need to be confirmed in future studies

Comparing national device-based physical activity surveillance systems: a systematic review

Abstract Background Physical activity surveillance systems are important for public health monitoring but rely mostly on self-report measurement of physical activity. Integration of device-based measurements in such systems can improve population estimates, however this is still relatively uncommon in existing surveillance systems. This systematic review aims to create an overview of the methodology used in existing device-based national PA surveillance systems. Methods Four literature databases (PubMed, Embase.com, SPORTDiscus and Web of Science) were searched, supplemented with backward tracking. Articles were included if they reported on population-based (inter)national surveillance systems measuring PA, sedentary time and/or adherence to PA guidelines. When available and in English, the methodological reports of the identified surveillance studies were also included for data extraction. Results This systematic literature search followed the PRISMA guidelines and yielded 34 articles and an additional 18 methodological reports, reporting on 28 studies, which in turn reported on one or multiple waves of 15 different national and 1 international surveillance system. The included studies showed substantial variation between (waves of) systems in number of participants, response rates, population representativeness and recruitment. In contrast, the methods were similar on data reduction definitions (e.g. minimal number of valid days, non-wear time and necessary wear time for a valid day). Conclusions The results of this review indicate that few countries use device-based PA measurement in their surveillance system. The employed methodology is diverse, which hampers comparability between countries and calls for more standardized methods as well as standardized reporting on these methods. The results from this review can help inform the integration of device-based PA measurement in (inter)national surveillance systems