Impact of work schedule characteristics on teacher mental health and burnout symptoms while remote working.

BACKGROUND: During the COVID-19 pandemic, teachers quickly shifted to remote teaching with many teachers experiencing increased work demands with limited resources, affecting both mental health and work. METHODS: Within a cross-sectional study, we evaluated the relationship between one type of work demand, non-standard work schedule characteristics, and depressive and burnout symptoms in kindergarten through 8th grade U.S. teachers working remotely in May 2020. We further assessed the impact of COVID-19 and work resources. Work schedule characteristics were self-assessed across six domains on a 5-point frequency scale from always (1) to never (5). We used multilevel Poisson models to calculate prevalence ratios (PRs) and 95% confidence intervals (CIs). RESULTS: In fully adjusted models, frequently working unexpectedly was associated with a higher prevalence of depressive symptoms (PR = 1.18, 95% CI = 1.07-1.31, p < 0.01), high emotional exhaustion (PR = 1.17, 95% CI = 1.05-1.30, p < 0.01), and high depersonalization (PR = 1.40, 95% CI = 1.02-1.92, p = 0.03). Remote work resources were significantly associated with a lower prevalence of depressive symptoms (PR = 0.88, 95% CI = 0.79-0.98, p = 0.02). There was a linear association between low coworker support and a low sense of personal accomplishment (PR = 0.68, 95% CI = 0.53-0.87, p < 0.01). CONCLUSIONS: Frequently having to work unexpectedly while remote teaching was associated with symptoms of depression and burnout during the COVID-19 pandemic. Workplaces should support predictable working times to lessen the disruption caused by unexpected work to promote worker well-being.

Associations between observed time sitting at work and musculoskeletal symptoms: a repeated-measures study of manufacturing workers.

Time sitting at work is known to affect health overall, but its specific effects on musculoskeletal symptoms are unclear. We evaluated the relationship between observed time sitting at work and self-reported musculoskeletal symptoms among 195 manufacturing workers. Longer time sitting at work was significantly associated with lower prevalence of neck/shoulder (prevalence ratio [PR] = 0.70, 95% confidence interval [CI] [0.68, 0.72]; p < 0.001) and arm/wrist/hand (PR = 0.46, 95% CI [0.31, 0.69]; p < 0.001) musculoskeletal symptoms. Associations remained largely unchanged after adjusting for job type or occupational postures and load. Time sitting at work was associated with musculoskeletal symptoms, and should be taken into consideration as part of interventions to prevent musculoskeletal disorders (MSDs) and promote health of manufacturing workers.

Continuous physiological signal measurement over 24-hour periods to assess the impact of work-related stress and workplace violence.

Work-related stress has long been recognized as an essential factor affecting employees' health and wellbeing. Repeated exposure to acute occupational stressors puts workers at high risk for depression, obesity, hypertension, and early death. Assessment of the effects of acute stress on workers' wellbeing usually relies on subjective self-reports, questionnaires, or measuring biometric and biochemical markers in long-cycle time intervals. This study aimed to develop and validate the use of a multiparameter wearable armband for continuous non-invasive monitoring of physiological states. Two worker populations were monitored 24 h/day: six loggers for one day and six ICU nurses working 12-hr shifts for one week. Stress responses in nurses were highly correlated with changes in heart rate variability (HRV) and pulse transit time (PTT). A rise in the low-to high-frequency (LF/LH) ratio in HRV was also coincident with stress responses. HRV on workdays decreased compared to non-work days, and PTT also exhibited a persistent decrease reflecting increased blood pressure. Compared to loggers, nurses were involved in high-intensity work activities 45% more often but were less active on non-work days. The wearable technology was well accepted by all worker participants and yielded high signal quality, critical factors for long-term non-invasive occupational health monitoring.

The development and initial validation of a new working time scale for full-time workers with non-standard schedules.

BACKGROUND: Working time characteristics have been used to link work schedule features to health impairment; however, extant working time exposure assessments are narrow in scope. Prominent working time frameworks suggest that a broad range of schedule features should be assessed to best capture non-standard schedules. The purpose of this study was to develop a multi-dimensional scale that assesses working time exposures and test its reliability and validity for full-time workers with non-standard schedules. METHODS: A cross-sectional study was conducted using full-time, blue-collar worker population samples from three industries - transportation (n = 174), corrections (n = 112), and manufacturing (n = 99). Using a multi-phased approach including the review of scientific literature and input from an advisory panel of experts, the WorkTime Scale (WTS) was created and included multiple domains to characterize working time (length, time of day, intensity, control, predictability, and free time). Self-report surveys were distributed to workers at their workplace during company time. Following a comprehensive scale development procedure (Phase 1), exploratory factor analysis (EFA) (Phase 2) and, confirmatory factor analysis (CFA) (Phase 3; bivariate correlations were used to identify the core components of the WTS and assess the reliability and validity (Phase 4) in three samples. RESULTS: Phase 1 resulted in a preliminary set of 21 items that served as the basis for the quantitative analysis of the WTS. Phase 2 used EFA to yield a 14-item WTS measure with two subscales ("Extended and Irregular Work Days (EIWD)" and "Lack of Control (LOC)"). Phase 3 used CFA to confirm the factor structure of the WTS, and its subscales demonstrated good internal consistency: alpha coefficients were 0.88 for the EIWD factor and 0.76-0.81 for the LOC factor. Phase 4 used bivariate correlations to substantiate convergent, discriminant, and criterion (predictive) validities. CONCLUSIONS: The 14-item WTS with good reliability and validity is an effective tool for assessing working time exposures in a variety of full-time jobs with non-standard schedules.

Precarious Work Schedules and Sleep: A Study of Unionized Full-Time Workers.

Unlike precarious employment which is temporary and insecure, with inadequate pay, benefits, and legal protections, precarious work schedules can affect workers with permanent full-time jobs in sectors where employment has historically been secure, well-compensated, and even unionized. Precarious work schedules - characterized by long shifts, non-daytime hours, intensity and unsocial work hours - are increasingly prevalent. Relations between precarious work schedules and poor health are not well understood, and less is known about how to attenuate this relation. We examined the indirect effects of precarious work schedules on fatigue and depressive symptoms through sleep quantity. Two moderators - schedule flexibility and sleep quality - were examined as buffers of these associations. Workers from the Departments of Correction and Transportation in a northeast state (N = 222) took surveys and reported on demographics, work schedule characteristics, schedule flexibility, sleep quality and quantity, fatigue, and depressive symptoms. Results revealed that precarious work schedules had indirect effects on fatigue and depressive symptoms through sleep quantity. Schedule flexibility moderated the relation between precarious work schedules and sleep quantity, such that workers with greater schedule flexibility had more hours of sleep. Sleep quality moderated the association between sleep quantity and fatigue and depressive symptoms, such that workers reported greater fatigue and depressive symptoms when they had poorer sleep quality. Findings have direct applicability for developing initiatives that enhance Total Worker Health® through individual and organizational changes. Supplementary Information: The online version contains supplementary material available at 10.1007/s41542-022-00114-y.

Investigating the relationship between working time characteristics on musculoskeletal symptoms: a cross sectional study.

How working hours are organized can have a substantial effect on workers' health. Using a questionnaire and cross-sectional study design, we evaluated the relationship between working time characteristics, job demands, and health behaviors on musculoskeletal symptoms among 316 Department of Transportation (N = 174) and Department of Corrections (N = 142) workers. Low schedule control was associated with neck/shoulder musculoskeletal symptoms (prevalence ratio: 1.20, 95% confidence interval: 1.06-1.34, p < 0.01), and working long (>48 hours per week) hours more frequently was associated with leg/foot musculoskeletal symptoms (prevalence ratio: 1.26 95% confidence interval: 1.06-1.50, p = 0.02). Some working time characteristics were associated with musculoskeletal symptoms, and should be taken into consideration as part of interventions to prevent musculoskeletal disorders and promote health of workers.

Comparison of the Wrist-Worn Fitbit Charge 2 and the Waist-Worn Actigraph GTX3 for Measuring Steps Taken in Occupational Settings.

OBJECTIVES: If consumer-based monitors such as Fitbit can measure activity accurately, it could provide opportunities for improved assessment of physical activity in general and at work for research purposes. The accuracy of the Fitbit has hardly been investigated in an occupational setting. METHODS: We compared measurements of steps taken at work, out-of-work, and in total of a wrist-worn Fitbit to a waist-worn Actigraph. Seventeen participants wore the Fitbit and Actigraph for 1 full workday. RESULTS: Compared with the Actigraph, the Fitbit consistently recorded more steps [mean steps at work Fitbit = 7850 (6974), Actigraph = 4396 (1991); out-of-work Fitbit = 6414 (5691), Actigraph = 4116 (3502); total Fitbit = 13 478 (10 666), Actigraph = 8009 (5167)]. CONCLUSIONS: Compared with the Actigraph, the Fitbit consistently recorded more steps at work, out-of-work, and in total. The Fitbit and Actigraph recordings were more consistent for measuring steps taken out-of-work compared with at work. Steps counts recorded by the Fitbit, especially in occupational settings, may be inaccurate.

Worker perspectives on the impact of non-standard workdays on worker and family well-being: A qualitative study.

BACKGROUND: Non-standard work schedules (NSWSs), occurring outside of regular and predictable daytime hours, may negatively affect worker and family health. This qualitative study sought to understand worker perspectives on the health and well-being impacts of NSWSs among full-time, transportation maintainers, correctional, and manufacturing workers. METHODS: Forty-nine workers participated in 8 focus groups. Data were transcribed and analyzed with ATLAS.ti, using the constant comparative method to identify themes and sub-themes. RESULTS: Workers reported that long work hours and irregular and unpredictable schedules posed the biggest obstacles to their well-being. Workers reported that NSWSs were associated with behavior impacts (poor family and social connections, poor eating, poor sleep, lack of exercise recovery), physical health impacts (exhaustion, weight gain) and extended work exposures (increased stress, increased accidents). CONCLUSIONS: This highlights the importance of developing and implementing effective workplace interventions to address these barriers to health and health behaviors.

Evaluation of the HearWell Pilot Program: A Participatory Total Worker Health® Approach to Hearing Conservation.

Our objective was to pilot test HearWell, an intervention created to preserve hearing among highway maintainers, by using a participatory Total Worker Health® (TWH) approach to designing, implementing and evaluating interventions. Regional maintenance garages were randomized to control (n = 6); HearWell (n = 4) or HearWell Design Team (n = 2) arms. Maintainer representatives from the HearWell Design Team garages identified barriers to hearing health and collaborated to design interventions including a safety leadership training for managers, a noise hazard management scheme to identify noise levels and indicate the hearing protection device (HPD) needed, and a comprehensive HearWell training video and protocol. These worker-designed interventions, after manager input, were delivered to the HearWell Design Team and the HearWell garages. Control garages received standard industry hearing conservation training. Periodic surveys of workers in all 12 garages collected information on the frequency of HPD use and a new hearing climate measure to evaluate changes in behaviors and attitudes over the study period and following interventions. An intention-to-treat approach was utilized; differences and trends in group HPD use and hearing climate were analyzed using a mixed-effects model to account for repeated measures from individual participants. The HearWell Design Team maintainers reported the highest frequency of HPD use. Hearing climate improved in each group 6 months following intervention implementation, with the largest increase and highest value for the HearWell Design Team workers. The HearWell pilot intervention showed promising results in improving HPD use through a participatory TWH approach to hearing conservation. Furthermore, results suggest that employee participation in hearing conservation programs may be necessary for maximal effectiveness.

Technology Use and Preferences for Minority Diabetes Self-Management Application.

We describe technology use and preferences of minority patients with diabetes who participated in focus groups in order to help design a mobile/online health application (or, app) to assist with diabetes self-management. Self-management apps should include health-related data and suggestions about food. The authors close by recommending additional considerations for future self-management mobile/online apps.

Working Time Characteristics and Mental Health among Corrections and Transportation Workers.

OBJECTIVES: Mental health disorders are a leading cause of work disability and while the psychosocial workplace environment plays a critical role, working time characteristics are also implicated. We sought to examine the association between working time characteristics and mental health in a cohort of two unionized, full-time worker populations, correctional supervisors, and transportation maintainers. METHODS: Using a cross-sectional study design, we surveyed workers on working time characteristics across seven domains including length of the shift, the intensity or proximity of sequential shifts, the time of day, and social aspects of work hours including predictability, variability, control, and free time. Burnout symptoms (Oldenburg Burnout Inventory) and depressive symptoms (eight-item Centers for Epidemiologic Studies Depression Scale) were assessed along with the psychosocial work environment and health behaviors. We used log-binomial regression models to estimate prevalence ratios and 95% confidence intervals after adjusting for age, gender, and company. RESULTS: A total of 318 workers were surveyed. The majority (72%) of workers reported a fixed shift. The prevalence of burnout symptoms was higher among workers reporting more frequent shift intensity (working 6 or more days in a row) (PR: 1.15, 95% CI: 1.01, 1.31; P = 0.04). Low psychological demands and high worker social support were associated with fewer burnout symptoms, independent of working time characteristics. The prevalence of depressive symptoms was higher in workers reporting more frequent unpredictable work (PR: 1.17, 95% CI: 1.01, 1.35; P = 0.04) and was lower among workers reporting increased schedule control (PR: 0.86, 95% CI: 0.77, 0.96; P = 0.01). Higher worker social support as well as sufficient sleep and adequate exercise were associated with fewer depressive symptoms, independent of working time characteristics. CONCLUSIONS: Different working time characteristics were associated with work-related mental health (i.e. burnout) as well as general mental health (i.e. depressive symptoms). Work intensity was associated with the prevalence of burnout symptoms and the results suggest that the mechanism may be through the need for recovery from the psychosocial exposures of work. Both schedule control and predictability were associated with depressive symptoms and the results suggest that work-life imbalance and lack of recovery may be implicated. Comprehensive prevention policies that consider work organization along with the psychosocial work environment and work-life balance may help to improve workers' mental health.

Development and application of a noise-hazard scheme for road maintainers.

BACKGROUND: Transportation road maintenance and repair workers, or "maintainers," are exposed to hazardous and variable noise levels and often rely on hearing protection devices (HPD) to reduce noise-exposure levels. We aimed to improve upon HPD use as part of the HearWell program that used a Total Worker Health, participatory approach to hearing conservation. METHODS: Full-shift, personal noise sampling was performed during the routine task of brush cutting. Work activities and equipment were recorded and combined with 1-min noise measures to summarize personal noise-exposure levels by equipment. Using noise-monitoring results, HPD noise reduction ratings, and input from worker-based design teams, a noise-hazard scheme was developed and applied to the task and equipment used during brush cutting. RESULTS: Average (standard deviation) and maximum Leq 1-minute, personal noise-exposure levels recorded during brush cutting included chainsaws at 92.1 (7.6) and max of 111 dBA, leaf blowers at 91.2 (7.5) and max 107 dBA, and wood chipper at 90.3 (7.3) and max of 104 dBA. The worker-designed noise-hazard scheme breaks down noise exposures into one of three color bands and exposure ranges: red (over 105 dBA), orange (90-105 dBA), or yellow (85-90 dBA). The scheme simplifies the identification of noise levels, assessment of noise-hazard, and choice of appropriate hearing protection for workers. CONCLUSION: Combining noise-exposure assessment with intervention development using participatory methods, we characterized noise exposure and developed an intervention to educate and assist in protecting workers as they perform noisy tasks.

Safety climate, hearing climate and hearing protection device use among transportation road maintainers.

BACKGROUND: It is important to understand workplace factors including safety climate that influence hearing protection device (HPD) use. We sought to investigate the association between HPD use, safety climate, and hearing climate, a new measure specific to hearing. METHODS: A survey was developed and distributed among transportation "maintainers" who perform road maintenance and repair. A new hearing climate measure was designed by adapting a safety climate measure. HPD use was assessed by asking workers how often they wear HPD while in noise. The differences in safety climate and hearing climate were compared by the frequency of HPD use using analysis of variance. RESULTS: Among 166 maintainers, 54% reported always or almost always wearing HPD while noise exposed. High-frequency HPD users reported a statistically significant higher safety climate (P = 0.004) and hearing climate (P = 0.003). CONCLUSIONS: Hearing climate predicts the frequency of HPD use and may be a useful measure when assessing and improving hearing conservation programs.

Predicting Forearm Physical Exposures During Computer Work Using Self-Reports, Software-Recorded Computer Usage Patterns, and Anthropometric and Workstation Measurements.

Objectives: Alternative techniques to assess physical exposures, such as prediction models, could facilitate more efficient epidemiological assessments in future large cohort studies examining physical exposures in relation to work-related musculoskeletal symptoms. The aim of this study was to evaluate two types of models that predict arm-wrist-hand physical exposures (i.e. muscle activity, wrist postures and kinematics, and keyboard and mouse forces) during computer use, which only differed with respect to the candidate predicting variables; (i) a full set of predicting variables, including self-reported factors, software-recorded computer usage patterns, and worksite measurements of anthropometrics and workstation set-up (full models); and (ii) a practical set of predicting variables, only including the self-reported factors and software-recorded computer usage patterns, that are relatively easy to assess (practical models). Methods: Prediction models were build using data from a field study among 117 office workers who were symptom-free at the time of measurement. Arm-wrist-hand physical exposures were measured for approximately two hours while workers performed their own computer work. Each worker's anthropometry and workstation set-up were measured by an experimenter, computer usage patterns were recorded using software and self-reported factors (including individual factors, job characteristics, computer work behaviours, psychosocial factors, workstation set-up characteristics, and leisure-time activities) were collected by an online questionnaire. We determined the predictive quality of the models in terms of R2 and root mean squared (RMS) values and exposure classification agreement to low-, medium-, and high-exposure categories (in the practical model only). Results: The full models had R2 values that ranged from 0.16 to 0.80, whereas for the practical models values ranged from 0.05 to 0.43. Interquartile ranges were not that different for the two models, indicating that only for some physical exposures the full models performed better. Relative RMS errors ranged between 5% and 19% for the full models, and between 10% and 19% for the practical model. When the predicted physical exposures were classified into low, medium, and high, classification agreement ranged from 26% to 71%. Conclusion: The full prediction models, based on self-reported factors, software-recorded computer usage patterns, and additional measurements of anthropometrics and workstation set-up, show a better predictive quality as compared to the practical models based on self-reported factors and recorded computer usage patterns only. However, predictive quality varied largely across different arm-wrist-hand exposure parameters. Future exploration of the relation between predicted physical exposure and symptoms is therefore only recommended for physical exposures that can be reasonably well predicted.

Predicting Directly Measured Trunk and Upper Arm Postures in Paper Mill Work From Administrative Data, Workers' Ratings and Posture Observations.

Objectives: A cost-efficient approach for assessing working postures could be to build statistical models for predicting results of direct measurements from cheaper data, and apply these models to samples in which only the latter data are available. The present study aimed to build and assess the performance of statistical models predicting inclinometer-assessed trunk and arm posture among paper mill workers. Separate models were built using administrative data, workers' ratings of their exposure, and observations of the work from video recordings as predictors. Methods: Trunk and upper arm postures were measured using inclinometry on 28 paper mill workers during three work shifts each. Simultaneously, the workers were video filmed, and their postures were assessed by observation of the videos afterwards. Workers' ratings of exposure, and administrative data on staff and production during the shifts were also collected. Linear mixed models were fitted for predicting inclinometer-assessed exposure variables (median trunk and upper arm angle, proportion of time with neutral trunk and upper arm posture, and frequency of periods in neutral trunk and upper arm inclination) from administrative data, workers' ratings, and observations, respectively. Performance was evaluated in terms of Akaike information criterion, proportion of variance explained (R2), and standard error (SE) of the model estimate. For models performing well, validity was assessed by bootstrap resampling. Results: Models based on administrative data performed poorly (R2 ≤ 15%) and would not be useful for assessing posture in this population. Models using workers' ratings of exposure performed slightly better (8% ≤ R2 ≤ 27% for trunk posture; 14% ≤ R2 ≤ 36% for arm posture). The best model was obtained when using observational data for predicting frequency of periods with neutral arm inclination. It explained 56% of the variance in the postural exposure, and its SE was 5.6. Bootstrap validation of this model showed similar expected performance in other samples (5th-95th percentile: R2 = 45-63%; SE = 5.1-6.2). Conclusions: Observational data had a better ability to predict inclinometer-assessed upper arm exposures than workers' ratings or administrative data. However, observational measurements are typically more expensive to obtain. The results encourage analyses of the cost-efficiency of modeling based on administrative data, workers' ratings, and observation, compared to the performance and cost of measuring exposure directly.

Differences in the prevalence of musculoskeletal symptoms among female and male custodians.

BACKGROUND: The prevalence of musculoskeletal symptoms among custodians is high. We sought to compare musculoskeletal symptoms between female and male custodians and to explore how task might affect this relationship. METHODS: A cross-sectional study was performed among 712 custodians who completed a survey assessing upper extremity, back, and lower extremity musculoskeletal symptoms and exposure to cleaning tasks. Chi-square tests and logistic regression analyses were used to test for associations between gender, cleaning tasks, and musculoskeletal symptoms. RESULTS: Gender was significantly (P < 0.05) associated with musculoskeletal symptoms in χ(2) tests and multivariate analyses. The prevalence ratio of symptoms among women was roughly 50% higher than men, regardless of the tasks that workers performed. CONCLUSIONS: The prevalence of musculoskeletal symptoms differed for female and male custodians and appeared to be consistent across a range of job tasks. Am. J. Ind. Med. 59:841-852, 2016. © 2016 Wiley Periodicals, Inc.

Time Course of Heart Rate Variability Response to PM2.5 Exposure from Secondhand Smoke.

PURPOSE: Exposure to secondhand smoke (SHS) has been associated with decreased heart rate variability (HRV). However, the time course of this association is unclear. Therefore, the objective of this study was to investigate the association between 15-240 minute SHS-related fine particulate matter (PM2.5) moving averages and indices of HRV. METHODS: With a panel study design, we used personal monitors to continuously measure PM2.5 and HRV of 35 participants who were exposed to SHS for approximately 6 hours. RESULTS: We observed negative, significant associations between 5-minute HRV indices and 15 minute PM2.5 moving averages and 240 minute PM2.5 moving averages: there was a significant (p<0.01) 7.5% decrease in the 5-minute square root of the mean squared differences of successive normal heart beats associated with (RMSSD), and a significant (p<0.01) 14.7% decrease in the 5-minute high frequency (HF) power associated with the 15 minute PM2.5 moving averages; there was also a significant (p<0.01) 46.9% decrease in the 5-minute RMSSD, and a significant (p<0.01) 77.7% decrease in the 5-minute high frequency (HF) power associated with the 240 minute PM2.5 moving averages. CONCLUSIONS: Our findings that exposure to SHS related PM2.5 was associated with HRV support the hypothesis that SHS can affect the cardiovascular system. The negative associations reported between short and longer term PM2.5 and HRV indicate adverse effects of SHS on the cardiovascular system.

A Comparison of Two Strategies for Building an Exposure Prediction Model.

Cost-efficient assessments of job exposures in large populations may be obtained from models in which 'true' exposures assessed by expensive measurement methods are estimated from easily accessible and cheap predictors. Typically, the models are built on the basis of a validation study comprising 'true' exposure data as well as an extensive collection of candidate predictors from questionnaires or company data, which cannot all be included in the models due to restrictions in the degrees of freedom available for modeling. In these situations, predictors need to be selected using procedures that can identify the best possible subset of predictors among the candidates. The present study compares two strategies for selecting a set of predictor variables. One strategy relies on stepwise hypothesis testing of associations between predictors and exposure, while the other uses cluster analysis to reduce the number of predictors without relying on empirical information about the measured exposure. Both strategies were applied to the same dataset on biomechanical exposure and candidate predictors among computer users, and they were compared in terms of identified predictors of exposure as well as the resulting model fit using bootstrapped resamples of the original data. The identified predictors were, to a large part, different between the two strategies, and the initial model fit was better for the stepwise testing strategy than for the clustering approach. Internal validation of the models using bootstrap resampling with fixed predictors revealed an equally reduced model fit in resampled datasets for both strategies. However, when predictor selection was incorporated in the validation procedure for the stepwise testing strategy, the model fit was reduced to the extent that both strategies showed similar model fit. Thus, the two strategies would both be expected to perform poorly with respect to predicting biomechanical exposure in other samples of computer users.

Demographic, health-related, and work-related factors associated with body mass index and body fat percentage among workers at six Connecticut manufacturing companies across different age groups: a cohort study.

BACKGROUND: Effective workplace interventions that consider the multifactorial nature of obesity are needed to reduce and prevent obesity among adults. Furthermore, the factors associated with obesity may differ for workers across age groups. Therefore, the objective of this study was to identify demographic, health-related, and work-related factors associated with baseline and changes in body mass index (BMI) and body fat percentage (BFP) and among Connecticut manufacturing workers acrossage groups. METHODS: BMI and BFPof 758 workers from six Connecticut manufacturing companies were objectively measuredat two time points approximately 36 months apart. Demographic, health-related, and work-related factors wereassessed via questionnaire. All variables were included in linear regression models to identify factors associated with baseline and changes in BMI and BFP for workers in 3 age groups: <45 years (35 %), 45-55 years (37 %), >55 years (28 %). RESULTS: There were differences in baseline and changes in BMI and BFP among manufacturing workers across age groups. Being interested in changing weight was significantly (p < 0.01) associated with higher baseline BMI and BFP across all age categories. Other factors associated with higher baseline BMI and BFP differed by age group and included: male gender (BMI p = 0.04), female gender (BFP p < 0.01), not having a college education (BMI p = 0.01, BFP p = 0.04), having childcare responsibilities (BMI p = 0.04), and working less overtime (p = 0.02) among workers in the <45 year age category, male gender (BMI p = 0.02), female gender (BFP p < 0.01) and reporting higher stress in general (BMI p = 0.04) among workers in the 45-55 year age category, and female gender (BFP p < 0.01) and job tenure (BFP p = 0.03) among workers in the >55 year age category. Few factors were associated with change in BMI or BFP across any age category. CONCLUSIONS: Among manufacturing workers, we identified associations between individual, health-related, and work-related factors and baseline BMIand BFP that differed by age. Such results support the use of strategies tailored to the challenges faced by workers in specific age groups rather than adopting a one size fits all approach. Effective interventions should consider a full range of individual, health-related, and work-related factors. More work must be done to identify factors or strategies associated with changes in obesity over time.

Characterization of Urinary Phthalate Metabolites Among Custodians.

Phthalates, a ubiquitous class of chemicals found in consumer, personal care, and cleaning products, have been linked to adverse health effects. Our goal was to characterize urinary phthalate metabolite concentrations and to identify work and nonwork sources among custodians using traditional cleaning chemicals and 'green' or environmentally preferable products (EPP). Sixty-eight custodians provided four urine samples on a workday (first void, before shift, end of shift, and before bedtime) and trained observers recorded cleaning tasks and types of products used (traditional, EPP, or disinfectant) hourly over the work shifts. Questionnaires were used to assess personal care product use. Four different phthalate metabolites [monoethyl phthalate (MEP), monomethyl phthalate (MMP), mono (2-ethylhexyl) phthalate (MEHP), and monobenzyl phthalate (MBzP)] were quantified using liquid chromatography mass spectrometry. Geometric means (GM) and 95% confidence intervals (95% CI) were calculated for creatinine-adjusted urinary phthalate concentrations. Mixed effects univariate and multivariate modeling, using a random intercept for each individual, was performed to identify predictors of phthalate metabolites including demographics, workplace factors, and personal care product use. Creatinine-adjusted urinary concentrations [GM (95% CI)] of MEP, MMP, MEHP, and MBzP were 107 (91.0-126), 2.69 (2.18-3.30), 6.93 (6.00-7.99), 8.79 (7.84-9.86) µg g(-1), respectively. An increasing trend in phthalate concentrations from before to after shift was not observed. Creatinine-adjusted urinary MEP was significantly associated with frequency of traditional cleaning chemical intensity in the multivariate model after adjusting for potential confounding by demographics, workplace factors, and personal care product use. While numerous demographics, workplace factors, and personal care products were statistically significant univariate predictors of MMP, MEHP, and MBzP, few associations persisted in multivariate models. In summary, among this population of custodians, we identified both occupational and nonoccupational predictors of phthalate exposures. Identification of phthalates as ingredients in cleaning chemicals and consumer products would allow workers and consumers to avoid phthalate exposure.