Background
The impact of globalization and the increasing demand for 24/7 workers has been a cornerstone issue for epidemiologists, occupational health psychologists, and policy-makers for some time [
1‐
3]. Working non-standard schedules, defined as work outside of the traditional 9 AM to 5 PM, Monday through Friday pattern, impacts work (e.g., job behavior and job attitudes), health (e.g., physical and mental health and health behaviors) as well as quality of life (e.g., work-family conflict, divorce) [
4]. As the workplace becomes increasing complex through developments in organizational design, technological advances, and work arrangements [
1], scholars are paying closer attention to work schedule factors that extend beyond non-traditional work hours, such as mandatory overtime [
5,
6] and the irregularity of shifts [
7], suggesting a greater need to accurately evaluate the nature and structure of schedules. Since the circadian disruption and resulting health consequences of night work are well established [
8], shift irregularity is gaining attention due to its compounding nature. For example, schedule irregularity not only disrupts sleep, but it can have an additional negative effect on recovery and social life, which would not be fully captured by assessing night work alone. While initiatives like the European Working Time provide rights to workers through limits on weekly working hours, provisions for adequate breaks across workdays, and weeks as well as adding extra protections during night work, this is not the case for the United States where the Fair Labor Standards act provides provisions for overtime pay, yet does not limit to the amount of hours an employee can work in a week nor require employers to give breaks to their employees.
Working time can be characterized according to a series of domains that include 1) length; 2) time of day; 3) intensity; as well as social aspects of working hours which include 4) control; 5) predictability; 6) free time and 7) variability of working time [
7]. This characterization is based upon the known biological mechanisms by which working time impacts health and well-being through physiological, behavioral, and psychosocial mechanisms [
8,
9]. Working time impacts include fatigue, and disruption of circadian rhythms, sleep, and social schedules. Working time schedule characteristics can often have numerous health impacts with complex relationships. For example, shift work has been linked to both circadian misalignment with evidence of disturbed sleep impacts both independently as well through the pathway of circadian disruption [
10]. Measurement of working time variables may be performed through quantitative and/or qualitative methods. Administrative databases from human resource applications may provide detailed quantitative data on some aspects of working time such as length, time of day, intensity, free time, and variability, but may not fully capture the social aspects of working time within the domains of control or predictability, such as when a worker is on call or had to come to work unexpectedly [
7,
11,
12]. Surveys allow for subjective assessment of working time [
12], but their use and applicability depend on the quality of their development and length, with shorter measures that prevent survey fatigue more desirable. Overall, there is no gold standard.
Typically, working time scales are unidimensional constructs that assess one aspect of schedules. An advantage of focusing on one schedule feature is that the measure will be short, but as a result, it sacrifices capturing nuances about a worker’s time—which may account for more variability in outcomes. Measures of working time can vary from each other in several ways. They may focus exclusively on the length and frequency of overtime [
13], for example, but do not assess whether that overtime interfered with a person’s ability to have a personal life outside of work or when the overtime occurs [
14]. Or, they may focus on an employee’s satisfaction with their schedule; but in return, they fail to capture whether the satisfaction has to do with a specific time of day [
15]. The unique characteristics of essential service jobs (e.g., health care, corrections, transportation), where in the United States extended and rotating shifts are the norm and the prospect of working mandatory double shifts without advance notice is a foregone conclusion, suggests that a unidimensional measure of working time will consistently fall short of quantifying these workers’ exposures. To date, no comprehensive working time measure exists for workers, necessitating the need for a context-specific scale that evaluates multiple dimensions of work [
5].
Therefore, the primary goal of this study is to identify survey items that fully describe working time characteristics, develop a parsimonious working time assessment scale, and test its reliability and validity for workers that are exposed to a variety of working time exposures with respect to length, time of day, intensity as well as social aspects of working hours (control, predictability, free time and variability). We choose to focus on three populations of workers –transportation workers, correctional officers, and manufacturing workers – due to their exposure to a variety of working time characteristics [
16] as well as to increase the generalizability of our results. Our goal was to create a work time scale: 1) using a psychometrically reputable procedure; 2) that is able to predict quality of life outcomes; and 3) that is appropriate for full-time workers with non-standard schedules.
Discussion
We developed a 14-item WorkTime scale (WTS) that characterized working time characteristics based on an established framework [
7]. Within three distinct populations of full-time work forces, exploratory factor analysis identified two subscales including one reflective of extended and irregular work days (EIWD) and another reflective of lack of schedule control (LOC). The WTS demonstrated good convergent validity, showing significant correlations with both schedule-related measures as well as psychosocial and sleep outcomes.
Based on the Härmä et al. framework [
7], we anticipated that the scale would load upon six dimensions, corresponding to schedule length, time of day, intensity, control, predictability, and free time. Yet, upon evaluation for the three populations within the study, the inherent inter-relatedness of these schedule factors revealed patterns that could be grouped as either EIWD or LOC. For example, if a worker exhibits a pattern of having extended or irregular work days (EIWD)– as evidenced by working more than 12 hours a day (Q1), occasionally working early morning hours (Q4), evening hours (Q6) and overnight hours (Q7) – they will experience worsened work and life outcomes. Moreover, if this same worker were frequently on call (Q11), had to work unexpectedly on their days off (Q14), and unexpectedly worked longer hours than scheduled (Q15), they would exhibit an overlapping yet differential set of adverse outcomes when compared to EIWD. The results of our initial validation of the WTS aligns with research demonstrating the interrelatedness of working time features in outcomes important for longevity at work [
35]. We extend the literature on working time exposures by going beyond single-item assessments and evaluating patterns of exposures, which has its benefits. Specifically, due to its multi-item nature, the WTS captures more of the working time domains than other scales, which has the distinct advantage of yielding a scale with greater content validity and reliability than single-item working time exposure measures [
36]. In practice, this means that the WTS will enable investigators to make stronger inferences about the adverse impact of working time characteristics, particularly when comparing different worker populations.
With the two factor composite measure, we were allowed to link the working time exposures with health and well-being outcomes among the three populations. In fact, schedule control has been shown to be an important predictor of poor self-related health [
37], high schedule control or worker flexibility, is associated with better health and well-being [
38]. Yet, low job control has been showing increasing trends in the US workforce from 2002 to 2014 [
39]. The components of EIWD, which are long work hours and irregular work schedules, have also been independently linked to poor health outcomes. Long work hours have been linked to poor cardiovascular health, mental health, health behaviors, and sleep as well as increased fatigue and workplace accidents [
40]. Irregular work hours have been linked to increased work-life conflict as well as poor cardiovascular and mental health [
38].
The study is strengthened by the use of an established framework [
7] to characterize working time across numerous dimensions. Furthermore, the study comprehensively captured working time exposures across all jobs, rather than just one job held by each individual, which in the current populations applied to 29% of survey respondents. In addition, three distinct populations were used to develop and validate the survey measure to expand the generalizability of the results.
Yet, the study is not without weakness. Several items were dropped in this study. For example, “I had two or more days off in a row (Q9)” was one item dropped. One reason for this may be that measuring the number of free days may not be the same as capturing the extent of EIWD. For example, a person can have a demanding and irregular schedule for the days they are actually at work, while still having free days off within a week. Moreover, another item dropped pertained to “I had control over my work schedule (Q13)”. Perhaps, the wording of this item was too vague to differentiate among people, and it is better to use more specific items that capture control of schedule features. This may be one explanation for why the LOC factor ended up with 4 items; they were all detailed (e.g. I unexpectedly had to work more than an hour later than I was scheduled to work) and could better differentiate among people’s lack of control at work.
Furthermore, survey measures of any exposure are prone to exposure misclassification, and more objective measures of working time, for example from payroll data are preferred. Often times, payroll data has shift start and stop times to calculate pay and overtime for the company and allow for the calculation of numerous aspects of working time including length, time of day, intensity, free time, and variability, and sometimes control, as is the case for hospital based systems that track desired and received shifts [
7]. Yet, payroll data often lacks information on the social aspects of working time, especially predictability and control, and only accounts for one job, not all which can be better captured via a questionnaire. Likewise, payroll data is often difficult to obtain as companies may lack the ability to de-identify data and are reluctant to share what may be sensitive information with researchers. A thorough capture of working time exposures would include both payroll data along with survey items to fill in working time exposure such as predictability or control.
Prior studies examining the validity of self-reported survey items on work schedule characteristics show heterogeneity in exposure misclassification based on work schedule type, yet support a bias towards the null [
12]. We asked workers to assess the frequency of working time exposures over the past year, as part of an epidemiological study. It is our intent to correlate these working time exposures with health outcomes where the mechanism of action is on the time scale of years rather than a month. However, given that we are asking people to assess exposures over a long period of time, the exposures are prone to recall bias and the actual report of exposure may be a reflection of more recent exposure rather than trends over the past year. This could either under- or over-estimate exposure depending on the variability of the working time exposures over the course of a year.
Future use of the WTS should include more varied worker populations with new survey items to assess additional facets of nonstandard work arrangements and work organizational factors in general. For this validation study we chose three different study populations to increase the generalizability of our results. While all were full-time employees with benefits, the populations differed with respect to job function as well as the distribution of age, gender, and family income. The healthy worker survivor bias may have played a role where workers more suited to the challenges of poor work time exposures more highly represented in the study populations [
41]. Furthermore, the study populations were predominately male, white and married. The WTS factors and composite measure may look different among workforces that have higher percentages of females, non-white workers, single workers, as well as within industries with lower wages and fewer/no benefits.
Future scales should consider the incorporation of survey items to assess work schedule variability. Identifying whether workers work the same days each week, start and end at the same time each workday, or work the same number of hours every week would provide additional information on work schedule variability. An expanded scale along with the 14 items already identified could be informative for assessing working time exposures across a broader range of full-time workers.
The WTS can be used in combination with other work organizational factors to assess and ultimately improve the well-being of workers. In addition to working time characteristics, other salient working arrangement characteristics can impact the health and well-being of workers. All of the worker populations studied were full-time and permanent employees with access to health benefits. This is in contrast to contingent work which is characterized by less secure work arrangements with changes in employment based on employer demand [
42] or precarious employment which is characterized by some degree of insecurity; temporariness; inadequate pay; vulnerability to unfair treatment; lack of ability to negotiate benefits, pay, work schedule, and leave; as well as the lack of a social safety net [
43]. There is often overlap between workers characterized as contingent or precarious, with both groups experiencing non-standard work arrangements and possibly non-standard work schedules. Precarious employment has been linked to poor mental health [
44‐
47], general health [
46] and mortality [
44]. There may be benefit in exploring the interaction between contingent and precarious work arrangements and working time factors as a driver of these adverse outcomes. The large majority (99.4%) of workers in this population reported a family income of over $25,000 which, depending on family size, correlates with living above the poverty guidelines. Evidence suggests that workers in lower socioeconomic positions are exposed to greater worker organizational hazards including job insecurity which plays a role in perpetuating occupational health disparities [
48]. Future research should continue to examine the interaction between low socioeconomic position and worktime pay and working time factors. Overall, the WTS can be used alongside work characterizations including benefit status as well as contingent or precarious employment to further identify hazardous work schedule characteristics.
Furthermore, it should be noted that working time characteristics are highly contextual to the laws, practices and norms within industries, states, and countries. For example, overall Europeans work less hours per week and weeks per year than US Americans, namely 14% fewer hours were documented within the period of 1983–2015 [
49]. With respect to the WTS, the survey items are broad enough to capture working time characteristics across a wide range of industries, states and countries. However, the average values of the survey and the frequency of poor working time conditions would likely vary.
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