Evaluation of the stress reactive countermeasure effectiveness in workplace: a randomized, controlled, cross-over study

doi:10.21203/rs.3.pex-2297/v1

Method Article

Evaluation of the stress reactive countermeasure effectiveness in workplace: a randomized, controlled, cross-over study

https://doi.org/10.21203/rs.3.pex-2297/v1

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This protocol serves to examine the effectiveness of a workplace-based reactive stress countermeasure (e.g., an action or device intended to prevent or mitigate undesirable effects of acute stress). It’s designed specifically for the context where decrease of acute stress reaction is desired while preserving vigilance and avoiding sleepiness. The study adopts a cross-over, controlled design. The order of interventions is randomized and blinded to the data collector and data analyst. This approach ensures increased validity and reliability of research outcomes while minimizing the number of participants required. The protocol employs affordable state-of-the-art subjective indicators measured using questionnaires, as well as objective physiological indicators of arousal (heart rate, respiration rate, electrodermal activity, frontal EEG). Baseline stress levels are measured, followed by induced acute stress using the Maastricht Acute Stress Task, and subsequent sessions of either countermeasure or control. Data analysis includes descriptive, time series, and ANCOVA or general linear models approaches. The protocol is estimated to take approximately 2 to 2.5 hours for completion.

stress

countermeasure

psychology

physiology

workplace

controlled study

crossover study

human factors

In today's work environment, individuals often face high levels of stress due to various factor (e.g., personal issues, responsibilities, heavy workloads, planning, and unforeseen events). Experiencing these stressors can create sense of overload which can have detrimental effects on their well-being, performance but also safety in the workplace [1]. This should be of utmost interest for employers because stress is directly linked to productivity and has a significant impact on economic growth [2]. In France specifically, the cost of work-related stress on society was estimated to €1,167 - €1,975 million, or to 14.4 – 24.2% of the total spending of social security occupational illnesses and work injuries [3].

Existing strategies for stress management have primarily focused on cognitive-behavioural interventions, multimodal programs, education on relaxation techniques, and organization-centred approaches [4, 5]. However, these interventions mainly emphasize preventive measures and skill development, without adequately addressing the need for reactive countermeasures tailored to high-stress work environments. Industries such as aerospace, maritime, logistics, healthcare, first responders, and the military require solutions that effectively mitigate stress while maintaining optimal levels of alertness. The current gap lies in the absence of countermeasures that decrease stress responses without compromising alertness, which is crucial for practical application in demanding job contexts. The recognition of this gap has spurred the development of numerous techniques and solutions by various companies and research institutions.

However, there are several challenges. First challenge lies in the conceptualization and measurement of stress response. The nebulous nature of the stress concept and the potential for comprehension bias make it difficult to define and measure accurately. Furthermore, different fields such as psychology, physiology, human factors, and sociology have divergent definitions of stress that may not align well [6]. Researchers often rely on metrics within their own expertise, resulting in fragmented understanding.

Next, stress response is a complex phenomenon that encompasses multiple layers of physiological, psychological, and behavioural reactions [6]. These responses do not necessarily occur simultaneously, further complicating the measurement process and data interpretation. Moreover, the available markers of stress are typically indirect. While emerging novel real-time objective measurement methods show promise [7], current state-of-the-art approaches rely on interviews, questionnaires, or physiological markers such as hormones, biomarkers, and indicators of sympathetic nervous system activity. Video recordings and interviews provide in-depth understanding of individuals behaviour and opinions, but are quite time consuming. On the other hand, questionnaires allow for rapid data collection and direct comparisons across cases and settings. Next, analysing hormones and biomarkers can be costly and requires access to laboratory facilities, which may not be available to all research facilities or companies. Monitoring physiological signals offers a practical and affordable approach.

Another problem stems from the desired state individuals are expected to achieve. Several stress mitigation strategies are focused solely on relaxation [4]. However, relaxation techniques can inadvertently decrease alertness, increase fatigue, and induce sleepiness, which is undesirable in a workplace setting. Therefore, it is important to incorporate methods for assessing fatigue and sleepiness. Similarly to stress, there is currently no widely accepted objective measure for an individual's fatigue level. Various approaches, such as measuring activity, EEG, eye-gaze, facial feature recognition, and reaction times, have been explored [8]. Other approaches use subjective sleepiness, such as Karolinska Sleepiness Scale (KSS). Addressing sleepiness when examining the effectiveness of stress countermeasures is an innovative and crucial aspect of the study design, providing valuable insights and raising awareness of countermeasure effectiveness.

This protocol aims to address the necessity of measuring the effectiveness of novel reactive stress countermeasures for workplace using scientifically valid and reliable methods within a replicable research design. Employing a cross-over research design with parallel groups offers several advantages such as reducing inter-individual variability and sample size requirements to achieve statistical power [6]. Participants receive two sequential interventions, separated by a period to minimize any potential carryover effects from one intervention to the next following. The order of interventions is block-randomized with 1:1 allocation. Depending on the study context, the order of intervention may also be blinded to the experimenter and/or data analyst, as we did in our study, further enhancing result validity. It is crucial for the participants to represent the target population to strengthen the validity of the results. The protocol is suitable for use in controlled environments and is thus applicable to research studies conducted in universities as well as research and development projects in private companies.

To evaluate effectiveness of a reactive stress countermeasure, first acute stress response needs to be sufficiently and ethically elevated in similar levels for every participant. The method of stress elevation needs to be repeatable, objective, controllable, non-invasive, and deployable in laboratory settings. Because of inter-individual variability in sensitivity to various types of stressors as well as differences in effect of various stressors on humans [7], an ideal approach is to use multiple types of stressors. For this purpose, the Maastricht Acute Stress Test (MAST) is suitable as it is a standardized protocol combining physiological, cognitive, and socio-evaluative components. It consists of 5 min of preparation phase and 10m of acute stress phase. Because this is a case-control crossover design, participants will be invited for a follow up session in 3 weeks, as this was proven to be a period free of habituation or sensitization to the effect of MAST [21]. This session will differ only in counting part and in the post-MAST intervention.

Our protocol encompasses state-of-the-art methods to measure the stress response and sleepiness employing both physiological and subjective indicators. Regarding the physiological, our study uses heart rate and its variability [9]. The instructions for measurement are based on previously published standards [10-12]. 5 minutes long recordings will be analysed using SDNN as a marker of the parasympathetically mediated respiration sinus arrhythmia and rMSSD as a vagally mediated change, according to the Shaffer and Ginsberg [13]. For more fine-grained analysis of stress reaction, these metrics along with AVNN can be applied on 50s intervals [14]. Next, EEG signal is recorded for analysis of stress or drowsiness indicators. We wanted to address frontal areas which have been mostly associated with stress reaction [15, 16]. Our study uses 8-channel EEG device, therefore we opted to place electrodes in positions in between Fp1/F3, Fp2/F4, F3/P3, F4/P4, PZ/OZ, and in CZ according to International 10-20 EEG system. We also use M1 and M2 as references, and E1 and E2 to control for eye blinking. Lastly, electrodermal activity is measured as a fast-reacting indicator of sympathetic activity. Regarding the questionnaires, the protocol offers simple 10-point scale for momentary stress evaluation, Physiological Arousal Scale [17] for subjective perception of physiological stress indicators, and 10 item Perceived Stress Scale for chronic stress estimation [18]. We also use Positive and Negative Affect Schedule [19] for assessment in overall changes in positive and negative emotions. For sleepiness, Karolinska Sleepiness Scale [20] is used to evaluate momentary sleepiness, Epworth Sleepiness Scale [21] is used to evaluate sleepiness as a trait, and single question on overall subjective sleep quality over the last month from Pittsburgh Sleep Inventory [22].

Anthropometry

Weight scale – our study used Mi Body Composition Scale 2 (Xiaomi Communications Co., Ltd.)

Stadiometer – our study used wall mounted retractable measuring tape (Yuecoom) with precision to 0.1 cm. The definition of measurement followed Anthropometry Procedures Manual by National Health and Nutrition Examination Survey (NHANES), 2007.

Measuring tape – our study used measuring tape included in the Nox A1s Polysomnograph (Nox Medial USA, Alpharetta, United States), with precision to 0.1 cm. Definitions of measurements followed the WHO STEPwise Approach to Surveillance (STEPS) and are findable in Waist Circumference and Waist-Hip Ratio

Report of a WHO Expert Consultation, Geneva, 2008.

Questionnaires

Perceived Stress Scale – 10 (PSS-10) – standardized and validated questionnaire for assessment of perception of unpredictability, uncontrollability, and overloading over the past month. It has been used widely in the field of stress research for chronic stress assessment (Reis et al., 2010).

Perceived stress – participants rate how stressed they feel at this moment using a discrete scale ranging from 1 to 10.

Physiological Arousal (PAQ) – this questionnaire assesses how a person perceives their physiological arousal to be at any given time (Dieleman, 2019).

Positive and Negative Affect Schedule (PANAS) -– well-validated scale assessing a person’s affect. It consists of 20 items that must be ranked on a 1-5 Likert scale to provide an overall scoring for Positive and Negative Affect (Watson, Clark, Tellegen, 1988).

Pittsburgh Sleep Questionnaire (PSQI) – a standardized inventory used for assessment of sleep quality in clinical populations, also validated for non-clinical population (Grander et al., 2016). The study recommends its use as a tool for measurement of sleep satisfaction. Our study used only one question from this inventory, evaluating overall sleep quality (4 item scale from Very good to Very bad).

Karolinska Sleepiness Scale (KSS) – this scale measures how sleepy an individual feels at that time. It is a single question 9-point scale ranging from extremely alert to extremely sleepy -– fighting sleep (Shahid et al., 2012).

Epworth Sleepiness Scale (ESS) – a short scale intended to measure daytime sleepiness. This can be helpful in diagnosing sleep disorders. Individuals rate their probability to fall asleep during eight different situations on a scale from 0 to 3. This questionnaire was previously validated and proved reliable on a French sample (Kaminska et al., 2010).

Physical properties of the laboratory

Room thermometer – aërQ Temperature & Humidity Sensor was used, with <0.4°C temperature and < 3 % humidity accuracy.

Biosignal measurement

Respiration rate (RR) – our study used respiratory induced plethysmography thorax channel from Nox A1s Polysomnograph (Nox Medial USA, Alpharetta, United States), 200 Hz Sampling, 1x Flow/Pressure Channel -5cmH20 to +50cmH2O input pressure range, DC-80 Hz, <1 mmH2O noise.

Electrocardiography (ECG) – our study used bipolar ECG electrodes from Nox A1s Polysomnograph (Nox Medial USA, Alpharetta, United States) with ±1024mV input range, < 3 µVrms noise, 512 kHz sampling rate, 24-bit AD

Electroencephalography (EEG) – our study used 8 unipolar EEG electrodes from Nox A1s Polysomnograph (Nox Medial USA, Alpharetta, United States) with ±3.2mV input range < 1 µVrms noise, 512 kHz sampling rate.

Electrodermal activity (EDA) –our study used sensor from biosignalsplux EDA sensor (PLUX – Wireless Biosignals (Bitalino), Lisabon, Portugal) with input range, 0-25μS (with VCC = 3.3V), 0-2.8Hz sampling rate. Sensor is connected to BITalino Core BT from same company. We set the frame rate of PPG at 100Hz.

Photoplethysmography (PPG) – our study used reflective sensor (PLUX – Wireless Biosignals (Bitalino), Lisabon, Portugal) of wavelength ~520nm (green), with input voltage 3.0 -5.5V. Sensor is connected to BITalino Core BT from same company. We set the frame rate of PPG at 100Hz.

Blood pressure (BP) – our study used GE Tonoport VI (PAR Medizintechnik GmbH & Co. KG)

Electrodes – for ECG sensors we used Ambu WhiteSensor WS (Ambu Ltd, Cambs, United Kingdom), for EDA sensors we used Ambu BlueSensor N (Ambu Ltd, Cambs, United Kingdom).

Skin preparation gel – our study used Nuprep skin preparation gel (Weaver and Company, Aurora, USA), the mild abrasive gel to clean and reduce the impedance of the skin before applying the electrodes.

Conductive paste - our study used Ten20 Conductive Paste (Weaver and Company, Aurora, USA) for EEG electrodes attachment.

Electrode cream – our study used EC2 Electrode Cream (Natus Medical Incorporated, Middleton, WI, USA).

Maastricht Acute Stress Task (MAST)

MAST instructions – for instructions please reach to the authors of the MAST (Prof. dr. Tom Smeets, Department of Medical and Clinical Psychology, Tilburg University, [email protected]).

Chair and table – participants in our study sit on an adjustable bar stool in front of a table with computer so they can comfortably put their feet in and out of the cold water while also seeing the instructions on the external monitor.

Computer and monitor – our study used laptop connected to an external monitor to present questionnaires and instructions for MAST.

Web camera videorecording – our study used Bandicam (Bandicam Company, Irvine, CA, USA) screen recording software for recording of participants using camera integrated in the laptop.

MS Office – our study used tools such as PowerPoint and Microsoft Forms to deliver questionnaires and instructions for MAST.

Thermometer – our study used Digital LCD Fish Aquarium Marine Vivarium Thermometer with measuring range -50°C +70°C (Zacro)

Ice trays and freezer – to ensure that the water is in desired range 4°C to 2°C, we are using ice from ice trays and a fridge freezer. For the volume of the box (9L) and temperature of tap water 18°C and room temperature of 22°C, we need about 7 cups of ice to get water to the desired level.

Box – participants put their feet in a box with volume of (9L).

Reactive stress countermeasure

This section is dependent on the researchers utilising this protocol. To ensure intellectual property protection, a proprietary countermeasure employed in our study cannot be disclosed within this protocol. Feel free to reach Human design Group for more information.

Invitation of the participant

1. Send email with an invitation to experiments, including information to withdraw alcohol and vigorous physical activity 24 prior to test, avoid eating, coffee or smoking 2h before test, to bring lightweight clothing for somatometry and their own towel and soap.

Preparation before participant arrives

2. Prepare all materials.

a) Electronic devices such as participants computer, polysomnograph, BITalino Core, blood pressure cuff – check the battery status of all.

b) Sensors, tools, electrodes and paste, box with cold water and ice.

c) Open and prepare all necessary programs and software.

d) Printed informed consent and supporting documents for researcher.

Preparation when participant arrives

3. Explaining experiment, signing informed consent.

4. Anthropometry of participant (height, weight, waist and hip circumference).

5. Mark physical properties of the room (temperature, humidity).

6. Mount all biosignal monitoring devices on the participant, according to the manual provided by the devices manufacturers.

a) For EEG, we are placing the electrodes approximately in Fp1/F3, Fp2/F4, F3/C3, F4/C4, CZ, PZ/OZ, E1, E2 positions according to the 10-20 system.

b) For ECG, we are placing one electrode on RA (2cm below the clavicle of the right arm in fossa infraclavicularis, medial to the m. deltoideus) and other LL (Lower left abdominal quadrant, in the anterior axillary line betaeen the last rib and the iliac crest) position according to the Mason-Likar’s lead placement.

c) For EDA, participants' skin is prepared. If it requires cleaning, it is gently washed only with lukewarm water. If it is too oily, conductive paste is gently applied. For optimal signal and elimination of the electrolyte interaction with sweat gland ducts, investigators have to wait 5-10 minutes before baseline data collection.

7. Start recording the signal and check its quality. Do necessary corrections or adjustments if needed.

Baseline measurement

8. Ask the participant to start filling the questionnaires (demographics and cofactors, PSS-10, PSQI item, VAS stress, PAQ, KSS, ESS, and PANAS)

9. Boil a kettle and pour hot water to a cup for baseline measurement of EDA.

10. After 5-10 minutes of recording, baseline measurement is started.

11. Ask the participant to sit with knees at 90° angle, both feet flat on the floor, hands on thighs and eyes closed for 5 minutes.

12. Take the blood pressure baseline measurement.

13. To address changes quality of the EDA signal, preliminary measurements are taken. The participant is asked to perform an apnoea phase in which the participant holds their breath for about 20 seconds. This follows 30 seconds of rest. Then ask the participant to hold on to a cup of hot water for a few seconds. This follows 30s of rest.

14. Each of these phases must be time stamped for later analysis.

Maastricht Acute Stress Task

15. Put the box with cold water under the participant's bar stool.

16. Ask the participant to read the instructions for MAST.

a. Timestamp beginning and end of instructions reading.

17. Proceed with MAST

a. For detailed instructions on MAST please write to prof. dr. Tom Smeets, Department of Medical and Clinical Psychology, Tilburg University, [email protected].

18. Timestamp beginning and end of MAST execution.

19. Measure post-MAST blood pressure.

20. Deliver post-MAST questionnaires (VAS stress, PAQ, KSS, PANAS).

Reactive stress countermeasure

21. Guide participant to the execution of reactive countermeasure

a. Timestamp beginning and end of countermeasure execution.

22. Measure post-countermeasure blood pressure.

23. Deliver post-countermeasure questionnaires (VAS stress, PAQ, KSS, PANAS).

End of experiment

24. Dismount all devices from participant.

25. Mark physical properties of the room (temperature, humidity).

26. Walk out the participant.

27. Clear and clean the box with cold water.

28. Clear all sensors, tools and put devices on charger.

29. Back-up all files in appropriate data storage place.

Participant selection

Unsuitable population

Participants shall not attend the research if they have cardiovascular anomaly detectable on the ECG. Other exclusion criteria were adopted from authors of MAST. These include cardiovascular diseases, severe physical illnesses (e.g., fibromyalgia), hypertension, endocrine disorders, current, or lifetime psychopathology, substance abuse, heavy smoking (>10 cigarettes/day) or being on any kind of medication known to affect the HPA axis.

Laboratory set-up

High workload of data collectors

At least two data collectors should be present during the study. One data collector is assigned for somatometry and is responsible for positioning the participant, taking measurements, saying the measurement to the recorder. Measuring instrument needs to be set on the participant until the other researchers repeat the measured value and confirm it. This person is also responsible to guide participants through the MAST task.

Other data collector(s) are assisting the examiner and help obtaining correct measurements. They may help with positioning of participants, checking if the participants posture for measurements is correct and if the measurement instruments are used correctly (e.g., the tape is horizontal and tense enough). Next, they are responsible for correct data recording and entering them to the system. They do this by repeating the numbers after the examiner and marking it. The recorder must have the same training as the examiner to be able to recognize errors in measurement or in reading the different instruments. If an error occurs, then the recorder should inform the examiner and the measurement needs to be repeated. Lastly, recorder guides through the process of the study by following the protocol and saying the name of the next measurement or the next step listed. One also have to watch all signals to unsure that there is not displacement of electrodes which would impair signal.

Measurement

Participant is not calm (rushed, is thirsty, needs to use the toilet, etc.)

Investigator should ensure before measurement that participants are comfortable, have the appropriate clothing and a towel, and don’t need to use the toilet.

Reliability of somatometry measurement

All somatometry measurements are taken from the right side of the participant’s body. The examinator is taking the measurements, the recorder assists, control the quality of measurement, and records the values.

Reliability of questionnaires assessment

The data collectors are responsible to eliminate external factors that could possibly influence the results of experiment. Useful information on this topic provides Brenninkmeijer et al. (2019).

Reliability of physiological measurements

Data collectors should maintain standardized conditions for measurement to decrease effect of possible confounding variables. This includes minimization of external influences such us presence of other people, sounds, adhering to the sequence of measurement.

Participant sneezed/coughed/sighed/talked during the baseline

All above mentioned behaviours should be avoided especially during baseline recording because these can bias the signal. If they appear, the participant is asked to refrain from them. Baseline requires 5 minutes of data without other stimuli. Should the participant veer from the true baseline conditions, it should be noted in an observations log.

Signal from electrodermal activity is bad

EDA signal is sensitive to much extraneous data which needs to be minimized. First, sensors should be properly attached to the participant’s body. There is a portion of the population who show no change when using EDA sensors. The signal also requires a bit of time before showing natural fluctuations. If there is no change even after waiting a few minutes, this participant may belong to the population who does not show much EDA change. Proceed as normal – during data analysis, it will be evident if this is the case.

EEG data

A detailed troubleshooting manual is provided by Farrens et al. (2021).

Data analysis

Inter-researcher variability

Signal processing should be performed by the same trained researcher, possibly with the aid of algorithms for an automatic ECG selection (Plaza-Florido, 2020). Selection of signal segments suitable for analysis is to some degree subjective which could lead to increase of iner- and intra-researchers of HRV parameters derived from short-term recordings.

Electrodermal activity confounders

EDA signal is sensitive to movement of the body, talking, sneezing, coughing or deep breathing and sighs. Investigators might consider measuring these variables synchronously (video camera with sound, actigraphy) to address their possible confounding effect. Spontaneous arm movements might require looking at the spectral band of the EDA signal, as brisk movements typically result in the representation of a typical 0.37 Hz component in the EDA signal.

Whole procedure

Approx. 2.5 hours.

Sections

Preparation before participant arrives: 20-30 minutes.

Preparation after participant arrives: 45 minutes.

Baseline measurement: 30 minutes.

Maastricht Acute Stress Task: 15 minutes.

Reactive stress countermeasure: 15 minutes.

End of experiment: 15 minutes.

The hypothesis is that the effect of the applied reactive stress countermeasure is superior to the control condition of resting with no external stimulus. The effect should mitigate acute stress reaction.

To determine superiority of the stress countermeasure over control condition, we evaluate the magnitude of decrease in blood pressure (systolic and diastolic), perceived stress, physiological arousal, negative emotions, and increase in positive affect. Next, we evaluate the magnitude and speed of change in heart rate variability, skin conductance level and skin conductance response. Also, relative gamma and alpha are assessed as a proxz of relaxation and beta as a marker of focus. We also examine differences in subjective and physiological markers of sleepiness such as Theta-Alpha Power Ratio.

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