The authors have declared that no competing interests exist.
‡ These authors also contributed equally to this work
Among health care personnel working regular hours or rotating shifts can affect parameters of general health and nutrition. We have investigated physical activity, sleep quality, metabolic activity and stress levels in health care workers from both groups.
We prospectively recruited 46 volunteer participants from the workforce of a University Medical Department of which 23 worked in rotating shifts (all nursing) and 21 non-shift regular hours (10 nursing, 13 clerical staff). All were investigated over 7 days by multisensory accelerometer (SenseWear Bodymedia® armband) and kept a detailed food diary. Physical activity and resting energy expenditure (REE) were measured in metabolic equivalents of task (METs). Quality of sleep was assessed as Pittsburgh Sleeping Quality Index and stress load using the Trier Inventory for Chronic Stress questionnaire (TICS).
No significant differences were found for overall physical activity, steps per minute, time of exceeding the 3 METs level or sleep quality. A significant difference for physical activity during working hours was found between shift-workers vs. non-shift-workers (p<0.01) and for shift-working nurses (median = 2.1 METs SE = 0.1) vs. non-shift-working clerical personnel (median = 1.5 METs SE = 0.07, p<0.05). Non-shift-working nurses had a significantly lower REE than the other groups (p<0.05). The proportion of fat in the diet was significantly higher (p<0.05) in the office worker group (median = 42% SE = 1.2) whereas shift-working nurses consumed significantly more carbohydrates (median = 46% SE = 1.4) than clerical staff (median = 41% SE = 1.7). Stress assessment by TICS confirmed a significantly higher level of social overload in the shift working group (p<0.05).
In this prospective cohort study shift-working had no influence on overall physical activity. Lower physical activity during working hours appears to be compensated for during off-hours. Differences in nutritional habits and stress load warrant larger scale trials to determine the effect on implicit health-associated conditions.
The effects on health in individuals being on different working schedules (daytime, night-time or rotating) are a widely discussed topic and cover not only metabolic syndrome and its risk factors like obesity [
In a generic large scaled study Japanese shift-workers were found to have a higher prevalence of gastric ulcera then day time workers (2.38 vs 1.03%) [
Moreover a statistical analysis of a large Scandinavian working cohort comparing day-time workers and shift workers showed a. significantly elevated body-mass-index (BMI) >30 kg∙m-2 for female workers of all age groups and for male workers in the third, fourth and fifth decade of life. Significantly elevated triglyceride levels above 1.7% were observed for female shift workers in their fourth and sixth decade of life [
Employees in the health care industry are frequently facing different working patterns that might influence their health and both physical and psychological wellbeing [
A prospective 4-follow-up study in male and female nurses showed a significantly elevated relative risk of 5.0 (95% confidence-interval 2.1–14.6) for de novo developing a component of metabolic syndrome in night-shift workers compared to only day-shift workers [
High prevalence of poor sleeping quality measured by a questionnaire was found examining resident physicians doing 24 hours of shift work [
Epidemiological studies reported that declining physical activity (PA) or an increasing sedentary activity is associated with higher risks for health such as poor insulin sensitivity [
A lack of moderate PA is considered to be responsible for metabolic disorders such as metabolic syndrome. A decrease of 68%–81% for risks of less likely having abdominal obesity, hypertriglyceridemia, and low HDL cholesterol levels was seen in a study cohort using a SenseWear armband and spending more than 60 minutes per day with MVPA compared to those spending less than 30 minutes per day of MVPA[
Sedentariness as a possible predictor for developing metabolic syndrome was found with a significantly higher hazard ratio of 5.10 (95% confidence interval 2.15 to 12.11; p<0.001) in night shift workers compared to day time workers with a hazard ratio of 2.92 (95% confidence interval 1.64 to 5.18; p = 0.017) [
Other data suggest a relative risk of 1.79 (95% confidence interval 1.06–3.01) for breast cancer in rotating night-shift workers compared with non-shifting workers [
Less bouts of moderate to MVPA were observed in night and evening shift workers compared to day shift workers. A rotating shift schedule was associated with decreased sedentary but more light physical activity in the National Health and Nutrition Examination Survey [
Health care personnel, especially nurses, are a well investigated population for health studies. Since 1976, the “Nurses’ Health Study” examines nurses for health-risks and lifestyle parameters like e.g. smoking and nutrition [
Investigations of individual physical activity are becoming more attractive due to the increasing availability of easy to handle fitness tracking devices. Unfortunately, due to the inconsistent design of studies and heterogeneous results a generalization of observations is not possible. It appears probable that circadian rhythm and sleep is negatively affected by shift work [
In addition to physical activity and sleeping patterns eating habits have been investigated in shift workers and evaluated for their impact for the risk of metabolic syndrome [
The so called healthy worker effect proposes that the apparently better health of workers contrasts always with the general population. Therefore workers need to be compared to appropriate controls [
In health care many employees have to work either in a daytime or shift-working schedule and this group has not been extensively characterized regarding their physical activity, sleeping rhythm and nutritional behaviour. Our aim was to clarify the impact of shift working in these groups.
The study population consisted of employees of University Medicine Greifswald, a tertiary care medical centre in Western Pomerania, Germany. The study was approved by the Local Ethics Committee of the University of Greifswald (‘Ethikkommission Universitätsmedizin Greifswald’, Felix-Hausdorff-Str. 3, 17487 Greifswald, Germany) on July 2013. All volunteers signed informed consent. Volunteers were recruited among the nursing and administration staff between 2013 and 2014. All participants were aged above 18 years and had an unremarkable medical history except for e.g. occasional back pain or arterial hypertension. No financial compensation was offered, but all participants were offered to receive their individual results after termination of the study. Participants were processed as outlined in
The shift-working group (SG) only consisted of nursing staff (male and female) from different wards of University Medicine Greifswald. They rotated through day-, evening- and night-shifts. The non-shift-working group (NG) included nurses that worked in outpatient departments, diagnostic units or solely on day-shift on the wards (NN), as well as office administrative staff (NO).
Age, weight, height, waist and hip circumferences, left- or right-handedness, nicotine consumption, blood pressure and heart rate were recorded at the beginning of the study. Besides BMI waist-to-hip-ratio (WHR) was calculated. The recent Expert Consultation of the World Health Organization acknowledges the use of WHR and the waist circumference solely as a parameter for abdominal obesity. Appropriate cut-off points for different ethnicities and schemes for scaling are still controversial. Although World Health Organization’s possible cut-offs of about ≥0.90 cm/cm for men and ≥0.85 cm/cm for women are suggested to indicate a ‘substantially increased risk of metabolic complications’ [
The WHR considers the distribution of body fat and has been suggested to represent a better predictor for the occurrence of cardiovascular diseases and their risk factors than BMI e.g. better indicating dyslipidaemia or hypertension [
Body composition was measured using bioelectrical impedance analysis (BIA, Nutriguard-M and the included NutriPlus® software by Data Input GmbH Pöcking, Germany). This non-invasive method is frequently used in both clinical routine as well as in trials for assessment of dietary and physical conditions. A special focus was laid on the assessment of body fat, body water and body cellular mass (BCM). For better interpersonal comparability the ratio of BCM and extracellular mass (ECM), BCM/ECM index, and the phase angle as a parameter for muscular cell vitality were determined.
The physical activity was measured (actigraphy) by the SensewearPro3 armband (SWA, Bodymedia Inc. Pittsburgh, PA, USA). This device is a multisensory research-grade accelerometer worn at the upper arm (regio brachialis posterior), registering longitudinal and transversal acceleration, number of steps, rate of heat-dissipation, galvanic skin response, body and ambient temperature. Data were recorded constantly every minute. SWA is a valid and reliable tool for total energy expenditure and resting energy expenditure (REE) when compared to the gold standard indirect calorimetry [
Physical activity was quantified as metabolic equivalents of task (MET). The equivalence of 1 MET is defined by the turnover of 3.5 ml oxygen per kg body weight per minute. In other words, it can be defined as the turnover of 1 kcal (= 4.2 kJ) per kg body weight per minute. In normo-metabolic individuals 1 MET represents the activity level of 1 minute of quiet sitting [
The American College of Sports Medicine and the American Heart Association recommends 30 minutes (i.e. 2.1% of an entire day’s time) of MVPA preferably in 10 min bouts, per day for healthy adults [
All subjects were asked to wear the armband continuously for 1 week to cover five working days and an equal distribution of different shift-types in the SG subgroup although this was not always feasible. In addition, all subjects had to record the start and the end of their working hours, sleeping and resting hours, and optionally, their leisure time activities in a log handed to them at the beginning of the study.
All processed parameters, including the individual METs (through the temporal dimension), were standardized to the individual’s effective wearing time to ensure general comparability. The value of subjects’ individual METs was calculated by SWA’s attached software, through a special algorithm.
Furthermore, we analysed the percentage of time exceeding the 3 METs level, steps per minute and the percentage of time in all activity categories (see above), which were recorded by the armband. Through the subjects log recording we could extract all these parameters for the entire working period.
Owing the fact that 1 MET represents the activity of quiet sitting in the presence of a normal metabolism; METs in resting subjects can represent the individual REE. REE was calculated on the basis of the MET-level of 1 hour activity during the early sleeping phase that was retrieved from the subjects’ log. The mean REE (calculated in METs) of all measurements was calculated for each subject.
Depending on the individual body composition and diet a REE of <0.9 METs defines hypometabolism, between 0.9 and 1.1 normometabolism and >1.1 hypermetabolism.
The SensewearPro armband provides the opportunity to evaluate the sleeping habits of its wearer. Laying and sleeping times were registered and sleeping hours were identified according to the diary. The mean ratio of all hours of lying and all hours of sleeping represents the individual sleeping efficiency (in percent). Actigraphy is, in general, regarded as valid to assess sleeping quality [
All participants were asked to keep a food diary for 7 days. All non-nutritional beverages (pure water and unsweetened tea) were neglected for the sake of compliance and practicability. The data was analysed using the OptiDiet© software (GOE GmbH Linden, Germany). Intake of macronutrients and micronutrients were quantified using an integrated database of the software. The average percentage intake of fat, carbohydrates, proteins was determined by the software. Intake sugar (sucrose) as a subset of carbohydrate as analysed especially again
The Trier Inventory for Assessment of Chronic Stress (TICS) is a validated 57-item questionnaire [
Statistical analysis and graphical editing were done using SAS 9.3 software (SAS Institute Cary, NC, USA) and Sigma Plot 11.0 (Systat Software Inc. San Jose, CA, USA).
If not otherwise indicated the median and standard error (SE) is presented for descriptive statistics, to ensure robust statistics. Nevertheless for general data the mean-value is also delivered (see General Data).
For categorical variables Fisher’s Exact Test was performed. In case of continuous variables ANOVA was used. ANOVA’s assumptions of normal distribution were tested with the Shapiro-Wilk test and the requirement of homogeneity of variances was tested with Levene’s test and the more robust Brown-Forsythe test.
When ANOVA assumptions were violated, the non-parametric Wilcoxon–Mann–Whitney test was used to compare two samples (group comparison) and Kruskal-Wallis test to compare three samples (subgroup analysis). Since continuity correction for Wilcoxon–Mann–Whitney test can be neglected only for larger sample sizes or absence of ties, a correction of 0.5 was kept.
The post-hoc analysis for the three subgroups could be done for ANOVA agreements performing Tukey’s test, controlling the type I experimentwise error rate. For the non-parametric test and categorical variables the subgroup analysis was done using Bonferroni correction for multiple testing.
Unless otherwise indicated a p-value of <0.05 was considered to be significant. For the subgroup analysis the levels of significance refer to the first step of testing for difference and not to the post-hoc analysis.
The study population categorized in shift working and non-shift working employees and their subgroups are characterized in
(sub)population characteristics | shift-working group |
non-shift-working group (N = 21) | non-shift-working nursing-staff subgroup (N = 10) | non-shift-working office-staff subgroup (N = 11) |
---|---|---|---|---|
Age (yr) | 31±1.9 (27) | 41.62±2.0 (43) |
46.7±1.5 (47) |
37±3.0 (33) |
Height (cm) | 172±2.4 (170) | 168±1.3 (167) | 167.5±1.3 (167.5) | 169±2.1 (167) |
Weight (kg) | 76.35±4.2 (72) | 72±3.3 (72) | 75.7±5.1 (74) | 68.6±4.2 (65) |
BMI in (kg·m-2) | 25.7±1.3 (23.8) | 25.4±1.1 (24.2) | 27.1±2 (26.0) | 23.9±1.07 (23.9) |
Waist-to-hip-ratio | 0.86±0.01(0.86) | 0.84±0.02 (0.85) | 0.84±0.02 (0.86) | 0.84±0.03 (0.85) |
Armband-wearing-rate (%) | 96.5±1.00 (98.4) | 93.0±2.4 (97.6) | 89.6±4.8 (97.5) | 96.1±1.3 (97.8) |
Packyears (in years) | 4.3±1.2 (3) |
1.8±0.8 (0) | 2.5±1.5 (0) | 1.18±0.6 (0) |
Phase angle (°) | 6.08±0.16 (6.00) | 5.93±0.13 (6.00) | 5.92±0.16 (5.70) | 5.95±0.22 (6.00) |
Body cellular mass (%) | 52.1±0.8(52) | 51.5±0.7 (51.7) | 51.5±0.8 (50.5) | 51.48±1.03 (51.8) |
Body fat (%) | 26.0±2.24 (25.2) | 27.09±1.57 (24.6) | 30.5±2.7 (33.5) | 24.0±1.3 (23.6) |
values are means ± SE additionally; median-values are in brackets.
*p<0.05
***p<0.001
BMI and WHR did not show significant differences within the groups and between subgroups. The median BMI and the median WHR in all subpopulation was within the range of healthy individuals. For BMI a tendency towards overweight was found for all subgroups, whereas the NN subgroup has the highest median BMI of all subgroups.
Smoking habits estimated by the number of pack years indicated that individuals on a shift schedule smoked significantly more than employees working during daytime only (4.3±1.2 vs. 1.8±0.8 pack years).
A potential bias for the percental SWA wearing rate in the main groups could be assumed, apartly beholding a very conservative view at the two tailed p-value (p = 0.0926) and the one-tailed p-value (p = 0.0463).
Overall average activity in METs was not significantly different between the shift- and non-shift working group and ranged at around 1.6 METs (
However, when analysing the median physical activity during business hours shift-working individuals were significantly more active than non-shift workers (2.1 vs. 1.7 METs, p<0.01). In addition, there was a significant difference between shift-working nurses and (non-shift working) office workers (2.095 vs. 1.52 METs, p<0,05) (
There was also a significant difference in the percentage of time exceeding more than 3.0 METs per minute of wearing during working phases for shift-workers and non-shift workers.
We tend to define the parameter “percentage of time exceeding more than 3.0 METs per minute of wearing” for overall time of as analogous to the common MVPA, in which 30 minutes of MVPA (i.e. 2.1% of an entire day’s time) [
In working phases we also found significant differences between shift- and non-shift-workers regarding the percentage of time reaching levels of low (<3.0 METs) and medium activity (3.0–6.0 METs).
We then focussed on the number of steps (per minute of wearing) as a health-related activity parameter (
The boxes cover the first quartile on the bottom and the third quartile on the top. Whiskers reach from the minimum to the maximum value excluding outliers (illustrated by dots). Shift-working group and shift-working nursing-staff subgroup cover identical cohorts. Steps per minute were calculated based on the ratio of overall steps and overall armband wearing time in minutes. Dotted line represents the 10000 steps per day boundary (i.e. 6.94 steps·min-1). NG, non-shift-working group; SG, shift-working group; SN, shift-working nursing-staff subgroup; NO, non-shift-working office-staff subgroup; NN, non-shift-working nursing-staff subgroup *p<0.05.
When comparing individual METs (
During the working phases the difference in number of steps per minute was greatest between the NG and SG cohort (p<0.0001, data not shown) and there were significant pairwise differences between all subgroups.
Correlation of METs at work and steps per minute while working was significantly different (p<0.05) and showed strong correlation (Spearman‘s rho = 0.763) (Figs
No significant differences in REE were found in the SG and NG groups (
The boxes cover the first quartile on the bottom and the third quartile on the top. Whiskers reach from the minimum to the maximum value excluding outliers (illustrated by dots). Shift-working group and shift-working nursing-staff subgroup cover identical cohorts. The dotted lines bounds the limits for hypermetabolic state (REE>1.1 METs) and hypometabolic state (REE<0.9 METs). NG, non-shift-working group; SG, shift-working group; SN, shift-working nursing-staff subgroup; NO, non-shift-working office-staff subgroup; NN, non-shift-working nursing-staff subgroup *p<0.05
Owing to the fact that the REE for woman and man is different, the difference stays significant when only female study subjects are compared. The drawn barriers for different types of metabolism also indicated that non-shift workers, especially NN, are in the majority hypometabolic.
Subjective examination of disturbed sleeping quality was assessed by PSQI and showed no significant differences in the group- and subgroup-analyses (
All test subjects with PSQI-score>5 in groups and subgroups were labelled as having a disturbed sleeping quality. NG, non-shift-working group; SG, shift-working group; SN, shift-working nursing-staff subgroup; NO, non-shift-working office-staff subgroup; NN, non-shift-working nursing-staff subgroup.
TICS was used to make a comparison between stress affection in different groups.
(WOL) work overload, (SOL) social overload, (PTP) pressure to perform, (WOD) work discontent, (EDW) excessive demands from work, (LSR) lack of social recognition, (SOT) social tensions, (SOI) social isolation, (CWO) chronic worrying, (SSC) stress screening scale. NG, non-shift-working group; SG, shift-working group; SN, shift-working nursing-staff subgroup; NO, non-shift-working office-staff subgroup; NN, non-shift-working nursing-staff subgroup *p<0.05.
TICS defines social overload by a high degree of personal interaction and extended responsibility for other people.
Shift-working employees more frequently complained of work overload). Their stress screening scale, an indicator for global stress load, tended to be higher but not significantly so.
Among non-shift workers a significantly higher lack of social recognition was reported from non-shift-working nurses than from office staff. Comparison of shift-working nurses and clerical staff failed to be significant after Bonferroni correction. According to TICS, lack of social recognition is defined as a mismatch between individual commitment and the expected social gratification.
It should be noted that anybody of the office employees reported on social overload, lack of social recognition or social isolation. Differences were only significant for the first two items before adapting the Bonferroni correction.
Total energy intake (in kcal) was similar between the shift and non-shift working group as well as in subgroup analysis as illustrated in
Analysis of fat consumption revealed a higher percental intake among the office staff (NO) and this difference was significantly different from both the shift working and non-shift working nurses (p<0.01) (
In a microepidemiological approach this study aimed to identify differences of lifestyle habits in individuals employed in health care in a German tertiary medical centre and working on either a shift or a non-shift schedule. We included parameters for physical and metabolic activity, stress perception, sleeping quality and nutritional habits. Groups differed in the way, that shift workers were younger and more often smokers than non-shift workers. These differences are closely in line with the data from the Nurses’ Health Study (NHS) II, where higher smoking behaviour rates were found in earlier and midlife among rotating shift workers [
So the discrepancy in age might be also explained by the fact that older nursing staff changes from shift to a regular work schedule. Notably, women were proportionally overrepresented in the non-shift-working group in our data.
We were able to show the variation of physical activity in nurses on a shift rotation compared to daytime only workers, which appeared significant during working hours. Moreover, they were partly more prone to mental stress, and significant differences were seen in the composition of meals.
The very limited sample of similarly designed studies compared mainly working schedule-associated activity parameters: A Turkish study with day and night-shift working nurses using the SWA found average MET-values of a mean of 1.99 (SD = 0.35) for day shifting and a mean of 1.83 (SD = 0.28) for ordinary service nurses during work [
However, our study’s findings vary from another study proposing less sedentary behaviour and more light activity for rotating shift workers [
We were surprised by the discrepancy between step-rate and overall physical activity (in METs). Step-rate was quantified using the accelerometer method and is frequently used for assessment of physical activity in clinical studies. Conceivably, pedometry based on the accelerometer probably is not as accurate as that of classical pedometers. Yet, there are studies that demonstrated similarities between these two methods in young healthy [
Only a minority of all subjects achieved the recommended number of steps (
The fact that nurses on a shift schedule usually have to walk more during their working hours, for control rounds or food and drug dispensation explains this difference. Furthermore, their activities such as washing and patient mobilisation are physically demanding and, at night, done in smaller teams.
REE was similar in shift or non-shift working individuals. Despite shift-work’s suggested influence on metabolism through circadian desynchronization [
A reduced metabolism (lower REE) was observed in the non-shift nursing subgroup that appeared to contradict our quite homogeneous study group. Considering that higher age reduces metabolism and REE, the higher age of non-shift working nurses can explain this difference (
Additionally, obese individuals tend to deviate from REE-estimating equations [
Stress perception showed variations in our study and we noticed a higher social overload (e.g. “I must frequently care for the well-being of others.”) [
Interestingly, the lack of social recognition (e.g. “Although I do my best, my work is not appreciated.”) [
Conflicting data exist on nutritional habits of (shift)-working employees. Increased energy intake and insufficient intake of vitamins and dietary fibres were previously reported for shift workers [
In addition, the shortage of guaranteed breaks increases the need for a quickly available energy source.
Macronutrient analysis of our whole study group showed an increased intake of fat (>30%) and sugar (>10%) but reduced amounts of carbohydrates (<50%) in their diet and is in line with the so called western pattern diet, which is suspected to predispose to obesity and cardiovascular disorders [
The large variation in energy intake seen in the shift working group may indicate disturbed eating habits (e.g. overeating or erratic fluctuations) as a result of shift work. Moreover, data of self-reported food diary should be interpreted with caution as underreporting of absolute food intake or inaccuracies of estimated portion sizes can occur [
There are limitations of our study. We didn’t allow for blinding and stratification so an observer effect cannot be ruled out. Our participants might have practiced a healthier life style during the observation time than usually being aware that their physical activity is recorded.
For the same reason the self-reported food diary can be manipulated by participants resulting in inaccurate information on nutritional habits. Although, physical activity varied widely, particularly among the SN subgroup.
Moreover part time workers were included as well. Nevertheless PA-parameters used in the study were balanced for time of wearing and independent from time-dependent influences.
Another limitation is the small simple size. This fact can weaken the power of the study’s claims. Possible confounding cannot be ruled out. Appropriate statistical tests were used to correct for this limitation. The small sample size might derive from limited voluntary participation and lacking rewards. Another obstacle for participation is due to the practibility of the armband itself (e.g. armband’s weight, interference in daily routine) and time-consuming recording nutrition in a food diary. However, the majority of available studies covering this topic have comparable sample sizes.
Some parameters like BMI and WHR are not easily comparable between different ages and gender. Nevertheless, we found a representative study group covering a wide range of participants.
The equalisation of moderate to vigorous physical activity (MVPA) with percental time of overall METs of ≥ 3.0 also contains certain limitations but has been widely used [
General statistic considerations as well as the study design do not admit the interpretation that the study’s findings in the different examined categories deliver causality. This study has a comparative approach and was not intended to measure health outcomes. Considering that shift work is associated with metabolic health risks and a lack of moderate PA with metabolic diseases, we did not find evidence that shift work increases PA in a manner overcome the increased health risk. Other factors may affect the health of shift workers and shift work appears to be an independent risk factor by itself.
Health behaviour assessed by physical activity showed comparable results for shift- and non-shift (daytime) workers that partly contradict previous reports. Differences were found when volunteer’s people were at work. Most probably office workers compensate for the lack of physical activity during business hours in their free time.
To our knowledge this is the first comparative approach to shift and non-shift workers employed in the health care industry with regard to their physical activity, nutrition, metabolism, chronic stress and sleep. These findings and range of parameters have not been described in a central European cohort yet.
Shift-working had no overall effect on physical activity but was associated with different eating habits and greater stress levels. Office workers appear to compensate for less physical activity during work in their off hours.
Further studies should enrol a larger sample size and longer periods of monitoring, e.g. four weeks. Advanced developments like smaller and lighter fitness-tracking armbands could be used as actigraphy in future studies to encourage study participation. The relation between shift work and impairment should always be noted by comparing shift-working and non-shift-working study groups of similar working environment (e.g. health-care workers).
The amount of MVPA and overall activity measured by METs should be used in future studies due to their clinical relevance and reproducibility.
Considering the theoretical foundation of this study—the biopsychosocial model- further studies should also acquire data about e.g. individual education, social status, income etc. By obtaining a larger and more representative sample size, evaluation of shift-work’s retrospective impact on social parameters such as family structure, career could be realized.
The minimal data set contains all the relevant underlying raw data processed in this manuscript.
(XLSX)
This study was approved by the local ethics committee and supported by unrestricted grants for the Domagk scholarship of J.K, L.J.V. and S.G. provided through donations from Baxter and Nutricia.
body cell mass
bioelectrical impedance analysis
body mass index
extracellular mass
metabolic equivalent of task
moderate to vigorous physical activity
non-shift-working
non-shift-working nursing subgroup
non-shift-working office worker subgroup
physical activity
Pittsburgh-Sleeping-Quality-Index
resting energy expenditure
standard error
shift-working group
shift-working nursing subgroup
SensewearPro3 armband
waist-to-hip-ratio