# Influence of COVID-19 on Stress at Work During the First Wave of the Pandemic Among Emergency Health Care Workers

Marie Dupuy, Frédéric Dutheil, Al'ai Alvarez, Thomas Godet, Oluwaseun

John Adeyemi, Maëlys Clinchamps, Jeannot Schmidt, Céline Lambert,

Jean-Baptiste Bouillon-Minois

► To cite this version:

Marie Dupuy, Frédéric Dutheil, Al'ai Alvarez, Thomas Godet, Oluwaseun John Adeyemi, et al. Influence of COVID-19 on Stress at Work During the First Wave of the Pandemic Among Emergency Health Care Workers. Disaster Medicine and Public Health Preparedness, 2023, 17, pp.e455. 10.1017/dmp.2023.134. hal-04320777

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Submitted on 22 Jan 2024

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# Influence of COVID-19 on stress at work during the first wave of the pandemic among emergency health care workers

Marie Dupuy<fnref n="1" />*, Frédéric Dutheil<fnref n="2" />, Al'ai Alvarez<fnref n="3" />, Thomas Godet<fnref n="4" />, Oluwaseun John Adeyemi<fnref n="5" />, Maëlys Clinchamps<fnref n="6" />, Jeannot Schmidt<fnref n="7" />, Céline Lambert<fnref n="8" />, Jean-Baptiste Bouillon-Minois<fnref n="7" />

**Competing interests:** The authors have declared that no competing interests exist.

**Word count:** 2683

**Key points:** Does the apparition of a new pandemic induce more stress than daily job among emergency healthcare workers?

**Findings:** Emergency healthcare workers had higher stress levels between the two waves of the pandemic, considered as "control" period. But this period had also a higher level of daily admission.

<footnote n="1">¹ Université Clermont Auvergne, CHU Clermont-Ferrand, Emergency Medicine, F-63000 Clermont-Ferrand, France</footnote>

<footnote n="2">² Université Clermont Auvergne, CNRS, LaPSCo, Physiological and Psychosocial Stress, CHU Clermont-Ferrand, Emergency Medicine, F-63000 Clermont-Ferrand, France</footnote>

<footnote n="3">³ Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA 94304, USA.</footnote>

<footnote n="4">⁴ CHU Clermont-Ferrand, Department of Anesthesiology and Critical Care, Reproduction and Developmental Diseases (R2D2) Unit, EA 7281, F-63003 Clermont-Ferrand, France.</footnote>

<footnote n="5">⁵ Ronald O. Perelman Department of Emergency Medicine, NYU School of Medicine, New York University Langone Health, New York, NY 10012, USA</footnote>

<footnote n="6">⁶ Université Clermont Auvergne, CNRS, LaPSCo, Physiological and Psychosocial Stress, CHU Clermont-Ferrand, Occupational and Environmental Medicine, F-63000 Clermont-Ferrand, France</footnote>

<footnote n="7">⁷ Université Clermont Auvergne, CNRS, LaPSCo, Physiological and Psychosocial Stress, CHU Clermont-Ferrand, Occupational and Environmental Medicine, WittyFit, F-63000 Clermont-Ferrand, France</footnote>

<footnote n="8">⁸ CHU Clermont-Ferrand, DRCI, Biostatistics unit, F-63000 Clermont-Ferrand, France</footnote>

<footnote>*Corresponding author: Dr. Jean-Baptiste Bouillon-Minois, Emergency Medicine, CHU Clermont-Ferrand, Tel: +33 6 74360423, jbbouillon-minois@chu-clermontferrand.fr</footnote>
Meaning: Overcrowding of Emergency Department induce more stress than a new pandemic. Taking care of our healthcare workers should be a priority for all politics.
# Title: Influence of COVID-19 on stress at work during the first wave of the pandemic among emergency health care workers

## Abstract

**Importance:** For more than 2 years, COVID-19 has forced worldwide healthcare systems to adapt their daily practice. These adaptations add to the already stressful demands of providing timely medical care in an overcrowded healthcare system. Specifically, the COVID-19 pandemic added stress to an already overwhelmed emergency and critical care health care workers (HCWs) on the frontlines during the first wave of the pandemic.

**Objective:** This study assessed comparative subjective and objective stress among frontline HCWs using a visual analog scale and biometric data, specifically heart rate variability (HRV).

**Design, setting and Participants:** This is a prospective, observational study using surveys and heart rate monitoring among HCWs who work in three frontline healthcare units (emergency department, mobile intensive care unit, and intensive care unit) in the University Hospital of Clermont-Ferrand, France. Two sessions were performed: one during the first wave of the pandemic (April 10 to May 10, 2020) and one after the first wave of the pandemic (June 10 to July 15, 2020).

**Main outcomes and measures:** The primary outcome is the difference in stress levels between the two time points. Secondary objectives were the impact of overcrowding, sociodemographics, and other variables on stress levels. We also assessed the correlation between subjective and objective stress levels.
**Results:** Among 199 HCWs, 98 participated in biometric monitoring, 84 had biometric and survey data, and 12 with only biometric data. Subjective stress was higher during the second time point compared to the first (4.39±2.11 vs. 3.16±2.34 *p*=0,23). There were higher objective stress levels with a decrease in HRV between the first and the second time points. Furthermore, we found higher patient volumes as a source of stress during the second time point. We did not find any significant correlation between subjective and objective stress levels.

**Conclusion and relevance:** HCWs had higher stress levels between the two waves of the pandemic. Overcrowding in the emergency department is associated with higher stress levels. We did not find any correlation between subjective and objective stress among intensive care and emergency HCWs during the first wave of the pandemic.

**Key points:** COVID-19, Stress, Pandemic, Emergency Department, Heart Rate Variability, Visual Analogue Scale
# INTRODUCTION

For more than 2 years, Coronavirus-19 (COVID-19) pandemic impacted an already overwhelmed healthcare system. The local epidemic quickly became a global pandemic that caused worldwide governments to adapt¹. During the first pandemic wave (from April 10 to May 10, 2020), no vaccine was available, with some areas being more overwhelmed than others²,³. Healthcare workers (HCWs), especially emergency and intensive care HCWs, were on the frontlines managing patients affected by this novel disease⁴. Occupational stress leads to burnout and impacts individual morbidity and mortality⁵. Emergency HCWs are particularly at risk for stress and burnout because of the complex interaction between life-threatening emergencies, emergency department (ED) overcrowding, vicarious trauma⁶⁻⁸, lack of sleep, unhealthy diet⁹, and accumulated fatigue¹⁰,¹¹. Frontline workers were more likely to experience burnout, post-traumatic stress disorders, and poor sleep quality¹²⁻¹⁶.

Furthermore, HCWs, especially those on the frontlines, reported high rates of depression, anxiety, and insomnia compared to the general public¹⁷.

The Karasek Job-demand scale or the Perceived Stress Scale is a validated tool that assesses subjective stress at work¹⁸. However, because of its length and complexity, the time commitment for completion makes this test impractical in the clinical setting. The Visual Analog Scale (VAS) was initially developed to assess pain¹⁹. It has also been used and validated to evaluate stress²⁰. VAS is attractive because it is simple to implement, easy to understand, and time efficient. Currently, VAS is the most common tool used by occupational physicians to assess stress among workers²⁰,²¹. In addition, stress can be objectively evaluated using biomarkers²²,²³(p8),²⁴⁻²⁶ such as heart-rate variability (HRV)²⁷.

Measuring HRV is noninvasive, easy to use, reproducible, and measurable over more extended periods (over 24 hours)²⁸. Linked to the autonomous nervous system, HRV reflects
the cardiovascular response to regulatory impulses affecting heart rhythm <sup>29</sup>. Stress among
HCWs has been widely studied with the overwhelming observation that this pandemic
induced stress, burnout, and post-traumatic stress disorder <sup>30-32</sup>. Given the dearth of
information on stress response during the first wave of the pandemic, we conducted a study
among emergency and intensive care unit HCWs. The main objective was to assess the
impact of COVID-19 on the subjective and objective stress levels at work. Secondary
objectives were to assess the correlation between subjective stress using VAS and objective
stress using HRV among HCWs.

METHODS

Study design

We performed a prospective, observational study between April 10 to July 15, 2020, in three departments of the University Hospital of Clermont-Ferrand, France: ED, mobile intensive care unit, and intensive care unit (ICU). The study was divided into two sessions: the first one during the first wave of the COVID-19 pandemic (April 10 to May 10, 2020) and the second one during the interval between the first wave and the 2nd wave (June 10 to July 15, 2022).

We defined this second time point as “control,” considering operationally, clinical practice tended to return to pre-COVID practices. The inclusion criteria were all HCWs (physicians, nurses, and nurse's aids) who volunteered to participate in the study. Exclusion criteria included pregnancy, refusal to participate, and being a trainee. At the beginning of each shift, one investigator installed a portable monitor belt that measures heart rate (HR) and HRV. Study volunteers were asked to wear the belt for the duration of the shift.

Furthermore, a survey was created by three investigators (M.C., F.D., and J-B.B-M.) regarding
stress assessment and the necessity to study the impact of COVID-19 on stress among
emergency and ICU HCWs. Completion of the survey took approximately 3 minutes and was
divided into 3 parts: 1) Sociodemographics – age, sex, height, weight, body mass index (BMI), profession, seniority, physical activity, and tobacco, 2) The 13 VAS questions – stress to caring for a COVID-19 patient, stress at work, stress at home, fatigue, sleep quality, anxiety, mood, burnout, job control, job demand, leadership support, support from colleagues, and job satisfaction. This part was answered at the beginning and end of the shift, 3) Patient acuity and demographics – number of patients personally cared for by the study participants, including life-threatening emergencies and the number of procedures deemed high-risk for COVID-19 exposure <sup>33</sup> (endotracheal intubation, thoracostomy, nasogastric aspiration, and nasopharyngeal swabbing of patients under investigation for COVID-19), number of admissions in the ED and number of COVID-19-related admissions by shift.

## Data collection

All data from questionnaires were manually extracted in an Excel® sheet. HRV data were downloaded daily using Bioharness Zephyr® software. Analysis from Zephyr was performed with the Kubios® software. All HR records available were analyzed. We deleted incorrect data due to artifacts using the very-low filter on the Kubios® software<sup>34-37</sup>. When available, the questionnaires were linked to the biometric data monitoring with an anonymized number.

The following biometric parameters were collected: time-domain measurement – RR interval (Interval between every heartbeat), the standard deviation of the regular sinus beats (SDNN), HR mean, root mean square of successive differences between normal (RMSSD), percentage of adjacent NN intervals that differ from each other by more than 50 milliseconds (pNN50), HRV triangular index (HTI), Triangular Interpolation of the NN interval histogram (TINN), the standard deviation of heart rate (STDHR) and frequency-domain measurements to separate HRV into its component VLF (very low frequency), LF (low
frequency), and HF (high frequency) rhythms that operate within different frequency ranges

## Data analysis

Categorical data were expressed as a number of subjects and associated percentages, and continuous data as mean ± standard deviation or median [25th; 75th percentile] according to the statistical distribution. Characteristics of the participants were compared according to the two sessions using the chi-squared test or Fisher's exact test for categorical variables and the Student's t-test or Mann-Whitney test (if the assumptions of the t-test were not met) for quantitative ones. Normality was verified by the Shapiro-Wilk test and/or histogram, and homoscedasticity by the Fisher-Snedecor test. Factors associated with subjective stress were studied using linear mixed models, considering the participant as a random effect, to account for the repeated measurements per participant. Objective stress was compared according to the two sessions using linear mixed models, considering the session as a fixed effect and the participant as a random effect. If necessary, a logarithmic transformation of the dependent variable was made to achieve normality. Correlations between subjective and objective stress at work (VAS and HRV, respectively) were assessed with Spearman's correlation coefficients (noted ρ). These coefficients were interpreted as follows (absolute value): ≥ 0.70 (strong correlation), 0.50-0.69 (moderate correlation), 0.30-0.49 (low correlation), 0.00-0.29 (no or negligible correlation)³⁹. Statistical analysis was performed using the Stata software (version 15; StataCorp, College Station, Texas, USA). All tests were two-sided, with an alpha level set at 0.05. No correction for multiple testing was applied in the analysis of secondary outcomes or subgroup analysis ⁴⁰. Findings from these analyses were interpreted as exploratory.
## Ethics

A French Ethics committee (Comité de Protection des Personnes Sud-Est VI, University hospital of Clermont-Ferrand) approved this study protocol in April 2020 (reference 2020/CE15), and the protocol was registered in ClinicalTrials (NCT04954105).

## RESULTS

### Characteristics of the population

April 10 to July 15, 2020, we included 199/283 volunteer participants for 192 records, 182 (94.8%) of whom responded to the survey questions regarding stress at work (subjective stress) and 96 (50.0%) participants provided HR data entries (objective stress). The flowchart is shown in Figure 1. We had a total of 98 (51.0%) subjective stress responders: 94 (95.9%) during the first session and 4 (4.1%) during the second session. We also had 12 (6.3%) responders provide objective biometric data entry without their subjective stress data: 11 (91.7%) during the first session and 1 (8.3%) during the second session. We had 84 (43.8%) responders provide both subjective and objective stress data: 53 (63.1%) during the first session and 31 (36.9%) during the second time session.

The participants' demographics were mostly female (61.5%), with a mean age of 33.7 ± 7.9 years old and a mean BMI of 23.0 ± 3.3 kg/m². 49 (25.5%) were smokers, and 95/190 (50.0%) had a physical activity superior to the World Health Organization recommendations (≥ 2.5 hours per week). Of the study respondents, 93 (48.4%) were physicians, 76 (39.6%) were nurses, 13 (6.8%) were ambulance drivers, and 10 (5.2%) were nurse's aids. Of the patient demographics/acuity survey respondents, 52/141 (36.9%) faced life-threatening emergencies, and 28/141 (19.9%) high-risk COVID-19 transmission procedures. The characteristics of participants were similar between the two sessions (Table 1). However, there was a significant difference in the median number of patient volumes per day, the
median number of COVID-19 patients, the work location, and the number of patients per
worker (Table 1).

## Main objective: impact of COVID-19 on stress

### Subjective stress

The mean stress level at work was 3.40 ± 2.34, without any difference between the two
session (3.16 ± 2.34 versus 4.39 ± 2.11, p=0.23). No difference was found according to sex,
job, and physical activity. However, a higher stress level was found among smokers (p=0.03)
and participants who had a life-threatening emergency during their shift (p=0.006) (Table 2).
Furthermore, at the beginning of the shift, a moderate correlation (0.52) was observed
between the stress of taking care of a COVID-19 patient and anxiety, and a low correlation
between stress at home (0.35), tiredness (0.37), burnout (0.40), and job demand (0.38)
(Table 3).

### Objective stress

We found no significant difference between the two sessions in all frequency-domain
measurements (Figure 2). However, during the second session, time-domain measurements,
TINN and PNN50, were significantly lower compared to the first session signaling higher
stress levels, respectively 760 ms vs 630 ms for TINN and 31.5% vs 20.6% for PNN50. In
addition, SDNN and HR were lower during the second session but not statistically significant
(122.9 ms vs 99.5 ms p=0.06 for SDNN and 98.0/min vs 93.3/min p=0.08 for HR, respectively)
(Figure 2).
## Secondary objective: correlation between objective and subjective stress

We did not find any correlation between objective and subjective stress except for times-domain measurements – RR (-0.23, p=0.04) and HTI (-0.28, p=0.01), considering as low Spearman's correlation coefficients. However, no other domain was significant - -0.14 for SDNN, 0.17 for HR, -0.15 for STDHR, -0.12 for RMSSD, -0.06 for PNN50 and -0.06 for TINN. Regarding frequency domain measurements, no correlation was significant - -0.17 for VLF, -0.10 for LF and HT and 0.13 for LF/HF. No linear measurement was significant (-0.13 for SD1 and -0.16 for SD2). (Supplementary Table)

## DISCUSSION

We found moderate stress levels among emergency HCWs during the first wave and the control period of the novel pandemic in a low-incidence place. Interestingly, our data show that the novel pandemic did not increase the stress levels of emergency HCWs beyond the expected daily stress. Furthermore, we did not find a strong relationship between subjective (VAS) and objective (HRV) data. Prior literature supports this observation ⁴¹.

### Period of measurement

We performed the first session during the first wave of the pandemic in France from April 10, to May 7, 2020. At that time, scientists did not know much about the virus, its contagiousness, and its transmissibility ⁴². Since diagnosis using polymerase chain reaction was not readily available compared to today, we used chest tomography as a surrogate for diagnosing COVID-19 ⁴³.
Furthermore, no vaccine was available during the time of the study. Emergency and ICU HCWs treated and cared for possible or known COVID-19 patients with only PPE, adding to moral distress. The French Auvergne region was relatively protected from high volumes of COVID-19 admissions during the first pandemic wave following the French government declaring lockdown on March 17, 2020. For example, the average daily entries at the Clermont-Ferrand ED is 160 patients per day. On March 18, 2020, the patient volume was only 60 patients<sup>2,3</sup>. Additionally, all non-urgent surgery and medical hospitalization were canceled. These new constraints decreased overcrowding during the first lockdown<sup>2,3</sup>, and the French Auvergne region took care of patients transferred from other locations<sup>39</sup>. This situation was not anticipated at the time we began the study. We anticipated the ED to be overwhelmed, similar to other EDs<sup>44</sup>, and expected a higher stress level in the first session than in the second session. However, our study showed that the control period was more stressful than the pandemic's first wave based on objective stress data using HRV. The main explanation we found is overcrowding, with the number of patients in the ED being significantly higher in the second period, aggravated by the lack of beds available to admit patients. Similar to pre-pandemic observations, overcrowding significantly impacts increasing stress among nurses and physicians<sup>45</sup>.

## Subjective stress

During more than 24 months, COVID-19 and its impact on emergency HCWs have been studied. We hypothesize that the pandemic increased the stress levels of HCWs. The best performing concept to assess stress is the job-demand-control model created and validated by Karasek in 1981<sup>18</sup>. The Job Content Questionnaire is derived from Karasek's model and has been developed and validated in several languages<sup>18</sup>. Studies have highlighted its
psychometrics properties, especially in a French population of 24,486 workers⁴⁶. However, this tool is difficult to use daily in the population of HCWs because of its length (18 items) and complexity. Similarly, studies show that the participation rate decreases with the length of the questionnaires and decreases in attention and concentration⁴⁷. The VAS was developed to assess occupational stress²⁰,²¹. It is a suitable tool for clinical activity and has good psychometric characteristics⁴⁸,⁴⁹.

## Heart rate variability,

However, when people are less able to cope with stressors or if the duration of stress is too long or often repeated, the autonomic nervous system activity is reduced or unbalanced, inducing anxiety and depression⁵⁰. Baseline HR and HRV are measures of stress highlighting, a sympathetic shift in sympathovagal balance, and reduced complexity of the cardiac signal⁵¹. Although we did not find any statistically significant baseline HRV between the two sessions, we were able to find a tendency. Second session HRV seems to be lower than firsts.
We hypothesize that our sample size is not strong enough. However, we will not be able to perform such study before decades. Indeed, the first wave of the COVID-19 pandemic was the first worldwide pandemic since the Spanish flu in early 20th century. No HCWs have already live this type of pandemic. To perform the exact same study with a stronger sample, we need a long term wash out period to study new HCWs free of pandemic as worker. We can only hypothesize on our results – and so on tendencies. One of the main differences between the two periods was the number of daily consults, which is known to be stressfull⁵².
However, we had a lot of cofounding factors that could decrease its impact.

SDNN explores short-term stress, i.e. less than two hours, while PNN50 and TINN explore both acute and long-term stress. SDNN and stress index positively correlate when the stress level is correctly adapted. SDNN increases when stress increases. Our data showed that
SDNN levels were lower in the second period, assuming lower stress levels; in the context of long-term stress, it is important to remember that SDNN is the best marker for long-term stress. On the contrary, PNN50 is low during stress exposure, explores short-time variability, and is influenced by both acute and long-term stress<sup>53,54</sup>. We found that PNN50 was lower during the second period highlighting higher stress levels. Lastly, TINN, a well-known HRV parameter to be higher when the stress is lower, is also higher during the first period, signaling lower stress levels during the first period<sup>51</sup>.

## Limitations

Our study has some limitations. Our study period covered the first wave of the pandemic, which had comparably less impact in the French Auvergne region, mainly due to early lockdown and public adherence. It may be interesting to perform new measurements during different periods of the COVID-19 pandemic (e.g., first wave, second wave, when the vaccine became available, after the third wave), and also during "non-COVID-19" and during pandemic recovery periods.

Additionally, we used Bioharness Zephyr®, and participants were asked to place the belt themselves after a demonstration, which introduced a lot of artifacts and uninterpretable data. To counter this issue, we used the very-low artifact correction option of Kubios® software. It may be relevant to train participants to use the belt before the beginning of the measurement. Data collection was only obtained during regular business hours and missed those working overnights. Nocturnist schedules may pose unique stressors.

Furthermore, there were some missing data for each variable (not all participants provided both HRV data and the VAS survey). The VAS survey is based on the Karasek model for measuring stress, although it has not been validated. Biometric data obtained were limited
to HRV and did not include respiration rates, depth, or amplitude of respiration. Although less studied, evidence for HRV biomarkers is mostly focused on acute stress. Also, we did not study the impact of COVID-19 on neuroendocrine indexes such as plasmatic cortisol or catecholamine levels. Finally, we recruited a sample of young, healthy participants, which impairs the generalization of our results, although it represents the population of our departments.

## CONCLUSION

COVID-19 alone did not increased stress levels during the first wave of the pandemic. This is likely because the French Auvergne region faced fewer COVID-19 patients during the first wave. We found that a higher volume of patients that daily consult in the ED, active smoking, and exposure to life-threatening emergencies were associated with higher stress levels at work during the COVID-19 pandemic. We also found no significant correlation between subjective and objective stress at work.
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Figures legend

Figure 1: Flowchart of the study.

Figure 2: Comparison of heart rate variability according to the two sessions.

Table 1: Characteristics of the population.

Table 2: Factors associated with subjective stress at work measured by a visual analog scale.

Table 3: Correlation between work and personal environment (at the beginning and end of the shift) and subjective stress at work (measured by a visual analog scale).

<figure><img src="image_2.png" /></figure>
Effect of the pandemic on stress
measured using heart rate variability

Frequency domain

<figure><img src="image_3.png" /></figure>

Time domain

<figure><img src="image_4.png" /></figure>
<table><caption>Table 1: Characteristics of the population.</caption><thead><tr><th></th><th>Total<br>(n=192)</th><th>Session 1<br>(n=156)</th><th>Session 2<br>(n=36)</th><th>p-value</th></tr></thead><tbody><tr><td>Age (years)</td><td>33.7 ± 7.9</td><td>34.0 ± 8.2</td><td>32.4 ± 6.9</td><td>0.25</td></tr><tr><td>Female sex</td><td>118 (61.5)</td><td>98 (62.8)</td><td>20 (55.6)</td><td>0.42</td></tr><tr><td>Body mass index (kg/m²)</td><td>23.0 ± 3.3</td><td>23.1 ± 3.4</td><td>22.8 ± 2.9</td><td>0.64</td></tr><tr><td>Tobacco</td><td>49 (25.5)</td><td>37 (23.7)</td><td>12 (33.3)</td><td>0.23</td></tr><tr><td>Physical activity ≥ 2.5 hours per week</td><td>95/190 (50.0)</td><td>74/154 (48.1)</td><td>21/36 (58.3)</td><td>0.27</td></tr><tr><td>VAS of stress at work (/10)</td><td>3.40 ± 2.34</td><td>3.16 ± 2.34</td><td>4.39 ± 2.11</td><td>0.23</td></tr><tr><td>Job</td><td></td><td></td><td></td><td>0.08</td></tr><tr><td>Ambulance driver</td><td>13 (6.8)</td><td>13 (8.3)</td><td>0 (0.0)</td><td></td></tr><tr><td>Nurse's aid</td><td>10 (5.2)</td><td>10 (6.4)</td><td>0 (0.0)</td><td></td></tr><tr><td>Nurse</td><td>76 (39.6)</td><td>62 (39.7)</td><td>14 (38.9)</td><td></td></tr><tr><td>Physician</td><td>93 (48.4)</td><td>71 (45.5)</td><td>22 (61.1)</td><td></td></tr><tr><td>Workplace</td><td></td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>Urgent care</td><td>6 (3.1)</td><td>2 (1.3)</td><td>4 (11.1)</td><td></td></tr><tr><td>Triage</td><td>6 (3.1)</td><td>4 (2.6)</td><td>2 (5.6)</td><td></td></tr><tr><td>Trauma room</td><td>21 (10.9)</td><td>15 (9.6)</td><td>6 (16.7)</td><td></td></tr><tr><td>Emergency department</td><td>48 (25.0)</td><td>28 (17.9)</td><td>20 (55.6)</td><td></td></tr><tr><td>COVID-19 unit</td><td>66 (34.4)</td><td>62 (39.7)</td><td>4 (11.1)</td><td></td></tr><tr><td>Mobile intensive care unit</td><td>45 (23.4)</td><td>45 (28.8)</td><td>0 (0.0)</td><td></td></tr><tr><td>Number of patients per day</td><td>97 [91; 106]</td><td>96 [91; 104]</td><td>138 [102; 152]</td><td>&lt;0.001</td></tr><tr><td>Number of COVID patients per day</td><td>19 [3; 23]</td><td>22 [19; 23]</td><td>0 [0; 3]</td><td>&lt;0.001</td></tr><tr><td>Number of patients per worker per<br>day</td><td>7 [5; 15]</td><td>6 [4; 14]</td><td>15 [7; 20]</td><td>&lt;0.001</td></tr><tr><td>Life-threatening emergencies</td><td>52/141 (36.9)</td><td>42/106 (39.6)</td><td>10/35 (28.6)</td><td>0.24</td></tr><tr><td>High-risk actions</td><td>28/141 (19.9)</td><td>22/106 (20.8)</td><td>6/35 (17.1)</td><td>0.64</td></tr></tbody></table>

Data are presented as a number of subjects (percentages), mean ± standard deviation, or median [25th; 75th percentile]. VAS: visual analog scale. Session 1: during the first wave of the pandemic (April 10 to May 10, 2020); session 2: the time interval after the first wave of the pandemic (June 10 to July 15, 2020).
<table><caption>Table 2: Factors associated with subjective stress at work measured by a visual analog scale.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Total</th><th colspan="3">Session 1</th><th colspan="3">Session 2</th></tr><tr><th>n</th><th>Mean ± SD<br>or p</th><th>p-value</th><th>n</th><th>Mean ± SD<br>or p</th><th>p-value</th><th>n</th><th>Mean ± SD<br>or p</th><th>p-value</th></tr></thead><tbody><tr><td>Age (years)</td><td>182</td><td>-0.01</td><td>0.85</td><td>147</td><td>-0.01</td><td>0.92</td><td>35</td><td>0.03</td><td></td></tr><tr><td>Sex</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Female</td><td>112</td><td>3.67 ± 2.37</td><td>0.38</td><td>92</td><td>3.42 ± 2.28</td><td>0.69</td><td>20</td><td>4.80 ± 2.47</td><td></td></tr><tr><td>Male</td><td>70</td><td>2.97 ± 2.25</td><td></td><td>55</td><td>2.74 ± 2.39</td><td></td><td>15</td><td>3.83 ± 1.40</td><td></td></tr><tr><td>Body mass index (kg/m²)</td><td>182</td><td>0.00</td><td>0.99</td><td>147</td><td>0.00</td><td>1.00</td><td>35</td><td>-0.02</td><td></td></tr><tr><td>Tobacco</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>136</td><td>3.13 ± 2.29</td><td>0.03</td><td>113</td><td>2.97 ± 2.30</td><td>0.27</td><td>23</td><td>3.87 ± 2.11</td><td></td></tr><tr><td>Yes</td><td>46</td><td>4.21 ± 2.34</td><td></td><td>34</td><td>3.79 ± 2.38</td><td></td><td>12</td><td>5.38 ± 1.81</td><td></td></tr><tr><td>Physical activity</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>&lt; 2.5 hours per week</td><td>92</td><td>3.15 ± 2.21</td><td>0.70</td><td>78</td><td>2.96 ± 2.21</td><td>0.54</td><td>14</td><td>4.21 ± 1.93</td><td></td></tr><tr><td>≥ 2.5 hours per week</td><td>88</td><td>3.69 ± 2.46</td><td></td><td>67</td><td>3.43 ± 2.48</td><td></td><td>21</td><td>4.50 ± 2.26</td><td></td></tr><tr><td>Job</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Paramedic</td><td>96</td><td>3.59 ± 2.67</td><td>0.19</td><td>82</td><td>3.26 ± 2.61</td><td>0.69</td><td>14</td><td>5.57 ± 2.15</td><td></td></tr><tr><td>Physician</td><td>86</td><td>3.18 ± 1.90</td><td></td><td>65</td><td>3.05 ± 1.95</td><td></td><td>21</td><td>3.60 ± 1.71</td><td></td></tr><tr><td>Number of patients per day</td><td>124</td><td>0.04</td><td>0.64</td><td>89</td><td>0.01</td><td>0.93</td><td>35</td><td>0.025</td><td></td></tr><tr><td>Number of possible<br>COVID-19 patients</td><td>124</td><td>-0.01</td><td>0.27</td><td>89</td><td>-0.10</td><td>0.34</td><td>35</td><td>0.183</td><td></td></tr><tr><td>Number of patients per<br>worker per day</td><td>142</td><td>0.12</td><td>0.15</td><td>107</td><td>0.22</td><td>0.02</td><td>35</td><td>-0.24</td><td></td></tr><tr><td>Life-threatening<br>emergencies</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>89</td><td>3.54 ± 2.30</td><td>0.006</td><td>64</td><td>3.37 ± 2.44</td><td>0.07</td><td>25</td><td>4.00 ± 1.89</td><td></td></tr><tr><td>Yes</td><td>52</td><td>4.32 ± 2.26</td><td></td><td>42</td><td>4.07 ± 2.18</td><td></td><td>10</td><td>5.35 ± 2.43</td><td></td></tr><tr><td>High-risk aerosolizing<br>procedures</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>113</td><td>3.58 ± 2.26</td><td>0.07</td><td>84</td><td>3.34 ± 2.26</td><td>0.03</td><td>29</td><td>2.28 ± 2.14</td><td></td></tr><tr><td>Yes</td><td>28</td><td>4.84 ± 2.29</td><td></td><td>22</td><td>4.82 ± 2.39</td><td></td><td>6</td><td>4.92 ± 2.06</td><td></td></tr></tbody></table>

n: number of observations; rho (ρ): Spearman's correlation coefficient; SD: standard deviation. Spearman's correlation coefficients were interpreted as follows (absolute value): ≥ 0.70 (strong correlation), 0.50-0.69 (moderate correlation), 0.30-0.49 (low correlation), 0.00-0.29 (no or negligible correlation). Session 1: during the first wave of the pandemic (April 10 to May 10, 2020); session 2: after the first wave of the pandemic (June 10 to July 15, 2020).
<table><caption>Table 3: Correlation between work and personal environment (at the beginning and end of the shift) and subjective stress at work (measured by a visual analog scale).</caption><thead><tr><th rowspan="2"></th><th colspan="3">Beginning of the shift</th><th colspan="3">End of the shift</th></tr><tr><th>n</th><th>p</th><th>p-value</th><th>n</th><th>p</th><th>p-value</th></tr></thead><tbody><tr><td>Care of possible COVID-19 patient</td><td>182</td><td>0.52</td><td>&lt;0.001</td><td>181</td><td>0.51</td><td>&lt;0.001</td></tr><tr><td>Stress at home</td><td>182</td><td>0.35</td><td>&lt;0.001</td><td>181</td><td>0.38</td><td>&lt;0.001</td></tr><tr><td>Tiredness</td><td>182</td><td>0.37</td><td>&lt;0.001</td><td>181</td><td>0.51</td><td>&lt;0.001</td></tr><tr><td>Sleep quality</td><td>182</td><td>-0.07</td><td>0.36</td><td>181</td><td>-0.07</td><td>0.38</td></tr><tr><td>Anxiety</td><td>182</td><td>0.52</td><td>&lt;0.001</td><td>182</td><td>0.59</td><td>&lt;0.001</td></tr><tr><td>Mood</td><td>182</td><td>0.04</td><td>0.63</td><td>180</td><td>-0.04</td><td>0.56</td></tr><tr><td>Burn out</td><td>182</td><td>0.40</td><td>&lt;0.001</td><td>181</td><td>0.38</td><td>&lt;0.001</td></tr><tr><td>Job control</td><td>180</td><td>0.07</td><td>0.36</td><td>180</td><td>0.11</td><td>0.16</td></tr><tr><td>Job demand</td><td>181</td><td>0.38</td><td>&lt;0.001</td><td>181</td><td>0.40</td><td>&lt;0.001</td></tr><tr><td>Leadership support</td><td>182</td><td>-0.03</td><td>0.73</td><td>182</td><td>0.01</td><td>0.85</td></tr><tr><td>Co-workers' support</td><td>182</td><td>0.04</td><td>0.59</td><td>182</td><td>0.01</td><td>0.87</td></tr><tr><td>Personal job satisfaction</td><td>182</td><td>0.01</td><td>0.94</td><td>182</td><td>-0.10</td><td>0.17</td></tr></tbody></table>

n: number of observations; rho (ρ): Spearman's correlation coefficient. Spearman's correlation coefficients were interpreted as follows (absolute value): ≥ 0.70 (strong correlation), 0.50-0.69 (moderate correlation), 0.30-0.49 (low correlation), 0.00-0.29 (no or negligible correlation). All data were assessed using a visual analog scale.