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Does Physically Demanding Work Hinder a Physically Active Lifestyle in Low Socioeconomic Workers? A Compositional Data Analysis Based on Accelerometer Data.

ABSTRACT: Leisure time physical activity (LTPA) is strongly associated with socioeconomic position (SEP). Few studies have investigated if demanding occupational physical activity (OPA) could impede a physically active lifestyle in low SEP groups. The aim of this study was to investigate the association between OPA and LTPA among low SEP men and women. We used cross-sectional data from 895 low SEP workers who wore accelerometers for 1⁻5 consecutive workdays. The associations between the relative importance of activities performed during work and leisure time were assessed using compositional regression models stratified on sex. Compositional isotemporal substitution models were used to assess the implication of increasing occupational walking, standing, or sitting on LTPA. We found dissimilarity in LTPA between the sexes, with men spending more waking leisure time sedentary than women (men ~67%, women ~61%), suggesting women performed more household tasks. In men, the associations between OPA and LTPA were weak. In women, the strongest association was observed between the relative importance of occupational walking and leisure time standing (β = -0.16; p = 0.01), where reallocating 15 min work time to occupational walking showed an expected decrease in leisure time standing of 7 min. If this time was spent on additional sedentary leisure time, it could have adverse health consequences.

SUBMITTER: Rasmussen CL

PROVIDER: S-EPMC6068990 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundWorkers with physically demanding work may be at risk for injury, illness or other adverse health outcomes due to exposure to different occupational hazards, especially at higher age. Sensor technology applications may be useful in the workplace to unobtrusively measure and monitor work exposures and provide workers with real-time feedback or access to data on demand. Many aspects might impede the implementation of sensor technology applications in the workplace, which should be taken into consideration for a successful implementation. Moreover, needs and preferences of workers regarding the use of sensor technology applications during work performance need to be identified. Therefore, the aim of this study was to identify worker needs and preferences regarding the use of sensor technology applications in the workplace.MethodsFour on-site focus group sessions were conducted in four different companies among workers with physically demanding work (n = 30). Semi-structured interview schedules were used to identify which work exposures should be measured, by which kind of sensor technology applications, under which (pre)conditions, how to motivate long-term use of sensor technology applications, and which type of feedback is preferred. For data analysis, a content-analysis with an inductive approach was performed.ResultsParticipants mentioned that they want to use wearable sensor technology applications to measure and monitor physical job demands, occupational heat stress, noise and fatigue. Factors associated with quality, comfort and perceived ease of use were identified as potential barriers for implementation in the workplace. Long-term motivation was attributed to the ability to manage and monitor work exposures, positive feedback and data ownership. Participants indicated a need to both receive real-time feedback and access to data on demand.ConclusionsSensor technology applications may support workers with physically demanding work to measure and monitor their work exposures. Potential barriers for implementation such as privacy aspects and quality, comfort and perceived ease of use of sensor technology applications need to be well considered to ensure successful implementation of sensor technology applications in the workplace.

Project description:ObjectivesPrevious research has shown strong associations between occupational physical activity (OPA) and need for recovery (NFR). However this research has only utilized self-reported measures of OPA which may be biased. Thus, there is a need for investigating if the previously documented association between self-reported OPA and NFR can be found when using technical measures of OPA. There is also the need to investigate whether older workers are particularly susceptible to increased NFR, since age-related declines in physical capacity mean that it is likely these workers will have a higher NFR for a given physical activity. The aim of this study was to investigate the association between technically measured OPA and NFR, and whether this relationship is modified by age.MethodsThis study utilized data from the Danish Physical Activity Cohort with Objective Measurements cohort-comprising Danish workers (n = 840) from the cleaning, manufacturing, and transportation sectors. OPA was measured by accelerometers attached to the thigh and upper back for at least one work day and classified into four physical behaviour categories (sedentary, standing, light, or moderate/vigorous). NFR was measured using a shortened version of the Danish NFR scale. Analysis was conducted using linear regression and isotemporal substitution analyses for compositional data.ResultsThe overall association between OPA and NFR was statistically significant in the unadjusted model (P < 0.001), but not when adjusted for age, sex, occupation, and shift work (P = 0.166). Isotemporal substitution showed small but significant reductions in NFR when increasing sedentary time relative to other behaviours (adjusted: ΔNFR = -0.010 [-0.019; -0.001]). There were no significant interactions between age and OPA (P = 0.409).ConclusionsThis study found significant associations between OPA and NFR, but the effect sizes were small. Reallocating 30 min to sedentary behaviours from other behaviours was associated with a reduced NFR, but the effect size may not be practically relevant. Moreover, no clear modifying effects of age were identified.

Project description:BackgroundPoor psychosocial work conditions are known to foster negative health consequences. While the existing literature on this topic focus mainly on white-collar workers, the influence of different aspects of the psychosocial work environment in physically demanding jobs remain understudied. Likewise, senior workers represent a population of the workforce at increased risk of adverse health outcomes and premature exit from the labour market. This study investigates the association between psychosocial work factors and perceived stress among the senior work force.MethodsUtilizing cross-sectional findings, this study reports associations between psychosocial factors (organizational justice, cooperation and collegial support, decision latitude, clarity of tasks, and quality of leadership) and the outcome of perceived stress quantified by Cohen's Perceived Stress Scale (CPSS). Currently employed senior workers with physically demanding jobs were included in the analyses (n = 3386). Associations were modeled using general linear models with weights to make the estimates representative.ResultsFor all individually adjusted psychosocial variables, the category of "good" was consistently associated with lower stress scores compared to the categories of both "moderate" and "poor" (all p < 0.0001). Likewise, in the mutually adjusted analysis, the category of "good" was statistically different from "poor" for all included variables, while the category of "moderate" remained different from "poor" for "clarity of tasks", "cooperation and collegial support", and "decision latitude".ConclusionsAmong senior workers with physically demanding jobs, poor ratings of organizational factors related to the psychosocial work environment are consistently associated with high stress scores. Blue-collar occupations focusing primarily on physical risk factors are recommended to increase awareness on psychosocial aspects that may be relevant to the local work environment.

Project description:BackgroundPhysical activity has beneficial effects on the health of cancer survivors. We aimed to investigate accelerometer-assessed physical activity and sedentary time in cancer survivors, and describe activity profiles. Additionally, we identify demographic and clinical correlates of physical activity, sedentary time and activity profiles.MethodsAccelerometer, questionnaire and clinical data from eight studies conducted in four countries (n = 1447) were pooled. We calculated sedentary time and time spent in physical activity at various intensities using Freedson cut-points. We used latent profile analysis to identify activity profiles, and multilevel linear regression analyses to identify demographic and clinical variables associated with accelerometer-assessed moderate to vigorous physical activity (MVPA), sedentary time, the highly active and highly sedentary profile, adjusting for confounders identified using a directed acyclic graph.ResultsParticipants spent on average 26 min (3%) in MVPA and 568 min (66%) sedentary per day. We identified six activity profiles. Older participants, smokers and participants with obesity had significantly lower MVPA and higher sedentary time. Furthermore, men had significantly higher MVPA and sedentary time than women and participants who reported less fatigue had higher MVPA time. The highly active profile included survivors with high education level and normal body mass index. Haematological cancer survivors were less likely to have a highly active profile compared to breast cancer survivors. The highly sedentary profile included older participants, males, participants who were not married, obese, smokers, and those < 12 months after diagnosis.ConclusionsCancer survivors engage in few minutes of MVPA and spend a large proportion of their day sedentary. Correlates of MVPA, sedentary time and activity profiles can be used to identify cancer survivors at risk for a sedentary and inactive lifestyle.

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Does Physically Demanding Work Hinder a Physically Active Lifestyle in Low Socioeconomic Workers? A Compositional Data Analysis Based on Accelerometer Data.

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