Cardiorespiratory fitness, occupational aerobic workload and age: workplace measurements among blue-collar workers

Eligible workplaces were workplaces that employed blue-collar workers from the cleaning, manufacturing and transportation sectors and gave consent to allow measurements to take place during working hours. Workers from eligible workplaces were invited to participate in the baseline health check, questionnaire, and subsequent diurnal measurement (Jørgensen et al. 2019). Workers were included if they responded to the baseline questionnaire, participated in the baseline health check, and agreed to 24-h activity measurement over 1–4 consecutive days. Workers were excluded from cardiorespiratory fitness testing if they suffered from hypertension—identified as blood pressure of  ≥ 160 or  ≥ 100 mmHg for systole and diastole respectively at the health check—or self-reported angina pectoris, or declared the use of heart or lung medicine. Moreover, workers with a history of disc herniation, considerable musculoskeletal pain in the lower back area, or fever on the day of testing were excluded from cardiorespiratory fitness testing. No measurements were taken in the case of pregnancy and individuals reporting allergy to bandages or adhesives were excluded from the diurnal heart rate measurements. We only included workers who provided valid data for cardiorespiratory fitness and 24-h heart rate in our analysis.

Baseline measurements included digital questionnaires, anthropometrical measurements, and objective measurements of cardiorespiratory fitness and heart rate. Cardiorespiratory fitness was assessed using a submaximal cycle ergometer test (Åstrand and Ryhming 1954), on an Ergomedic 874 E cycle ergometer (Monark AB, Varberg, Sweden). This test measures the heart rate required to achieve a given power output (work conducted) to provide an estimate of VO_2max that takes into account both age and gender. Initial power output estimation was made using the information on age and estimated fitness. Typical output was estimated at between 60 and 90 W at a cadence of 60 revolutions per minute. Tests lasted a maximum of 10 min and were terminated once a steady-state heart rate was registered—defined as a heart rate change of less than 5 beats/min from 5 to 6 min. If a heart rate of less than 110 beats/min was registered after the first minute, power output was increased to achieve a registered heart rate at or above 60% of the estimated maximum or at least 120 beats/min. Heart rate was measured with a handheld pulse oximeter (Nellcor OxiMax N-65, United States), fixed to the fingertip. The combination of power output and heart rate were then used in the estimation of maximal oxygen uptake (Åstrand and Ryhming 1954).

Following baseline measurements, a single Actiheart device (Actiheart, CamNTech Ltd., Cambridge, England) was used to measure 24-h heart rate, over four consecutive days. Actiheart consists of a single electrocardiography node attached at the apex of the sternum and connected to a secondary node, which is attached over any intercostal space on the left side of the ribcage. Workers were also requested to log work, leisure, and sleep periods, and periods without wearing the monitors in a diary provided after device placement. Furthermore, workers were instructed to remove any monitors causing irritation or discomfort.

For heart rate data to be considered a valid representation of aerobic workload, the Actiheart needed to be worn for at least one valid day. A valid day was defined as  ≥ 4 h of heart-rate data collection during work hours or  ≥ 75% of the individual's average work hours. We also excluded measurements with beat error  > 50%.

The calculation of heart rate reserve (%HRR) was as follows (Karvonen 1957):

where HR_min was the minimum heart rate over an average of ten beats using a moving window during the course of the whole measurement period and HR_max was estimated according to (Tanaka et al. 2001):

We chose to use HR_min because heart rate, like blood pressure, can be much influenced by mental stress or physical activities if measured in a clinical health setting. Since we measured the heart rate continuously during the 24 h of the day, we could therefore subtract the minimum heart rate throughout the 24 h. Thus, to attain the most precise estimation of HRR we used the lowest heart rate period over the day, which we believe provides the most valid estimate of the true sleeping heart rate of an individual.

Actiheart devices were initialized and downloaded using Actiheart software. All were set to short-term recording mode, allowing inter-beat intervals to be captured for up to 440,000 heartbeats. Once downloaded, the inter-beat intervals recorded on Actiheart were further processed and checked for errors using Acti4 software using methods described in full previously (Kristiansen et al. 2011). In brief, Acti4 software splits 24-h heart rate measurements into periods of work, leisure, and sleep time based on the information contained in participant diaries (Skotte and Kristiansen 2014). Inter-beat intervals were then resampled at a 4 Hz frequency through the implementation of a linear interpolation scheme and the calculations outlined above were completed (Eqs. 1, 2).

Inter-beat intervals corresponding to less than 36 beats/min or greater than 200 beats/min were discarded (Skotte and Kristiansen 2014). Additionally, intervals differing by more than 15% compared to their neighbouring intervals or containing an error rate greater than 50% were discarded (Skotte and Kristiansen 2014).

Age was determined based on the date of birth of the participant and sex was determined using the question “Are you male or female?” self-rated general health was determined by the question “How will you rate your overall health?” rated on a scale of 1 = very good, 2 = good, 3 = fairly good, 4 = poor, 5 = very poor. Participant’s use of medication was determined using the question “Have you in the last three months been taking prescription medication?” with a dichotomous ‘yes/no’ response category. This question was followed by “If yes, what kind of medication?” also using a dichotomous ‘yes/no’ response category. Occupational sector was determined according to the workplace, and occupation itself was determined by the question “What is your present main occupation?” with the response categories: 1 = blue-collar, 2 = white-collar and 3 = manager. For our analyses, these two variables (sector and occupation) were combined into a new occupation variable in the following way: blue-collar workers from each of the three occupational sectors (cleaning, manufacturing, transportation) were classified according to their sector, while white-collar workers and managers from each of the sectors were classified together as ‘administration workers’. Shift workers were identified with the question “At what time of the day do you usually work in your main occupation?” with category responses “fixed day work” and “night/varying/other”. Participants reported their workability on a 0–10 scale in response to “Please rate your present work ability?”.

Three outcomes were used in this study. These were; mean %HRR at work (%HRRmean), maximum %HRR at work (%HRRmax; the maximum %HRR above which the participant has spent at least 1 min), and the proportion of time at work spent at or above 30% of HRR (≥ 30%HRR). 30%HRR was chosen as a cut-off due to the suggestions of previous literature (Wu and Wang 2002; Korshøj et al. 2015). The association between the exposure (cardiorespiratory fitness) and each of these outcomes were investigated independently using linear regression modelling.

We chose to express the time spent above and below 30% of HRR as a proportion of an approximately 8-h workday. Therefore, this data was first transformed into isometric log-ratio coordinates in accordance with the principles of compositional data analyses (CoDA). Simply put, this method allows for the time spent above and below 30% HRR to be expressed relative to each other. As such, instead of investigating only the time spent above 30% HRR, we investigate the time spent above 30% HRR relative to the time spent below 30% HRR. In this manner, we can account for individual differences in the daily composition of HRR (i.e. the proportion a 24-h day with a HRR above or below 30% of an individual’s overall HRR).

The secondary analysis involved stratification by occupational group and age. We chose to stratify by the occupational group due to differences in activity levels between each of these groups. However, this stratification revealed that there was also a wide variation in physical activity levels within occupational groups. Therefore, we decided post-hoc to further stratify according to quartiles of steps-per-hour at work as a proxy for the pace or intensity of work, thus providing a rough approximation of work pace regardless of the occupational group. We also chose to stratify by age (using quartiles) to understand the identified interaction between cardiorespiratory fitness and age. We chose quartiles to maintain a balanced number of participants between groups and because we had no specific reason to choose any other value(s). In all cases, stratification was based upon the adjusted model (i.e. all potential confounders were included in the model except the variable used to stratify). All analyses were conducted in R (R Core Team 2018)/Studio (RStudio Team 2016) using packages: compositions (van den Boogaart et al. 2018), robcompositions (Matthias et al. 2019), car (Fox 2018), lmtest (Hothorn et al. 2018), and the tidyverse suite of packages (Wickham et al. 2019).

The findings of our study support that cardiorespiratory fitness is of importance for the aerobic workload. Although we showed that this finding is consistent across all levels of work intensity (using the steps per hour as a proxy) (Table 5), we argue that this finding is particularly important for blue-collar workers. This is because blue-collar workers are at increased risk for health impairments and early drop-out from the working market compared with higher educated occupational groups with less physical demanding work (Sewdas et al. 2019). In other words, blue-collar workers in manual jobs (e.g. construction, manufacturing, service and eldercare) have a greater need for initiatives to reduce the aerobic workload—such as offering tailored exercise at the workplace for sustaining and improving cardiorespiratory fitness (Korshøj et al. 2016; Hallman et al. 2017; Lidegaard et al. 2018; Lund Rasmussen et al. 2018). Apart from increasing fitness, tailoring of manual work tasks to the cardiorespiratory fitness of workers may also be a solution for reducing the relative aerobic workload of individual workers—promoting balance between the capacity of workers and their tasks (which, currently, does not seem to be occurring Merkus et al. 2019; Oakman et al. 2019). Such tailoring would likely focus on equitable distribution in how work tasks are allocated (i.e., relative to capacity), but could also include other work modifications such as increased opportunity for rest breaks, shorter time at work, fewer days at work and help from fitter colleagues.

Our findings also suggest that age has an influence on the relationship between cardiorespiratory fitness and aerobic workload at work, with the strongest effect in the age span between 46 and 51 years of age. As we see no physiological explanation why the effect of cardiorespiratory fitness on HRR would decrease above this age, it is reasonable that this drop may be either due a tailoring towards less physically demanding manual work to the older workers and/or a ‘healthy worker effect’ (Mohren et al. 2010; Gommans et al. 2015; Zacher 2015). If that is the case, our findings suggest that cardiorespiratory fitness becomes more important for maintaining a healthy workload as age increases. Because cardiorespiratory fitness generally decreases with age (Hodgson and Buskirk 1977; Klabunde 2011), and because statutory retirement age in most countries is increasing (OECD 2017), the promotion of cardiorespiratory fitness as the workforce is aging is vital for achieving longer healthy working lives—particularly among blue-collar workers who generally have poor cardiorespiratory fitness (Kenny et al. 2016).

A major strength of the current study is the device-worn measurement of heart rate, measured over several consecutive days in unconstrained workplace settings. A potential limitation of our study is that the participants included in our study may not be representative of the general population. However, the demographics of our study are very similar to the overall DPHACTO cohort profile (Jørgensen et al. 2019). The primary difference between our study and the overall DPHACTO cohort was that participants in this study had higher self-rated health than those in the overall cohort (respectively, 71 vs. 61% of participants self-rated their general health as good or very good). This seems likely that, due to health issues, not all participants could perform the cardiorespiratory fitness test. This would have removed those workers with the worst health and may partly explain the potential healthy worker effect discussed above.

Another potential limitation is the use of a submaximal test for cardiorespiratory fitness. We chose to use a submaximal test instead of a maximal test for several reasons. First, among workers (particularly blue collar) not used to exercising, maximal heart rate and maximal oxygen consumption cannot be achieved in a single exercise test as it requires several sessions over a period to feel safe and to learn to reach peak aerobic intensity. Second, because a high proportion of the workers are not in very good health, we would have had a large number of participants ‘drop out’ from the test. These two reasons would have increased the likelihood that the maximal test would introduce bias in cardiorespiratory fitness. Furthermore, the test needed to be performed at the workplaces, which do not permit maximal testing for safety reasons. As such, we used a well-acknowledged and validated sub-maximal cycling test (Åstrand and Ryhming 1954).

Finally, as the majority of participants came from the manufacturing sector, this does somewhat limit our conclusions regarding other sectors. However, the fact that stratified analyses were performed and (while acknowledging the small sample sizes) there were no differences identified, mean that this issue is unlikely.