Virtual Reality and Serious Games for Stress Reduction with

Application in Work Environments

I. Ladakis

, V. Kilintzis

, D. Xanthopoulou

and I. Chouvarda

Lab. of Computing, Medical Informatics and Biomedical Imaging Technologies, Aristotle University of Thessaloniki,

Thessaloniki, Greece

School of Psychology, Aristotle University of Thessaloniki, Thessaloniki, Greece

Keywords: Virtual Reality, VR, Serious Games, Stress, Sensor, Biomedical Signals, Heart Rate, EDA.

Abstract: This paper proposes a VR – based gamified approach aiming to reduce stress levels in work environments.

This idea employs a standalone VR headset for immersion in a peaceful virtual environment, guided deep

breathing exercises, sensing of heart rate and electrodermal activity for the estimation of stress, and feedback

to the VR environment. For the qualitative and quantitative assessment of stress levels two sensors were used,

Scosche Rhythm+ for monitoring heart rate signal (HR) and Moodmetric Ring for monitoring the EDA

(electrodermal activity) signal. As a preliminary work, a series of stress-inducing batteries were created,

including different stressful conditions that may resemble real life conditions (e.g., at work). Experiments

with volunteers were conducted, to investigate the stress response before and during a stressful situation, as

well as after VR-based relaxation. The signal fluctuation over time and the correlation between HR/EDA

signals were explored towards selecting the optimal metric for the representation of the real stress level. The

results of this preliminary study are relevant for the timely estimation of stress levels and the provision of a

simple and useful tool for the immediate decrease of stress in various real-life environments such as work

environments.

1 INTRODUCTION

The rapid evolution of VR technologies has created

great expectations regarding their potential

exploitation in the medical research domain or in the

therapeutic procedure (Pourmand, 2017). At the same

time, the development of serious games emerges as

an alternative solution to classic therapeutic

approaches (Lau, 2017). Serious games promise to

convert parts of therapy and prevention into a

pleasant procedure, enhancing patients’

psychological involvement, without limiting the

quality of the expected therapy results.

The exploitation of VR technology for the

development and implementation of serious games or

gamified approaches aiming at confronting anxiety,

stress or other neuropsychological issues (Valmaggia,

2016) is widely accepted by the scientific community,

since the advantages of this approach seem to have

potential (Ferreira, 2002). Furthermore, the

simultaneous collection of biomedical signals could

indicate certain stressors, opening new approaches in

stress prevention. The characterization of stress level

would be based on the observation of the collected

signals and the environmental conditions that

stimulate stress.

The aim of this paper is to propose a serious game

in VR environment for stress reduction with potential

implication in work environments. Specifically, our

main interest is to contribute in stress reduction in

office environments, where the emergence of stress

could be related with high levels of potentially

stressful working conditions (i.e., job demands) and

lacking resources to deal with these demands

(Karasek, 1979). To do so, we test the effectiveness

of an VR intervention aiming at promoting relaxation

techniques to reduce stress, such as deep-breathing

(McCallie, Blum, & Hood, 2006; Richardson &

Rothstein, 2008).

The above issue has not been widely addressed.

Thoondee and Oikonomou (Thoondee, 2017)

developed a VR application for Oculus Rift that tried

to address work stress by exposing workers to

peaceful environments in order to facilitate their

relaxation during breaktime. A similar approach

(Ahmaniemi, 2017) used Gear VR headset and

Ladakis, I., Kilintzis, V., Xanthopoulou, D. and Chouvarda, I.

Virtual Reality and Serious Games for Stress Reduction with Application in Work Environments.

DOI: 10.5220/0010300905410548

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF, pages 541-548

ISBN: 978-989-758-490-9

541

various sensors for the collection of physiological

signals, such as electrocardiogram (ECG),

photoplethysmogram (PPG), galvanic skin response

(GSR) and blood pressure (BP), to detect and address

work stress. User experience was investigated via

questionnaire. Results suggested that the GSR signal

better captured stress than the other collected signals.

Another approach (Salkevicius, 2019) was mainly

trying to trace stress levels with the collection of

physiological signals during the exposure in VR

environments. The collected physiological signals

were blood volume pressure (BVP), galvanic skin

response (GSR) and skin temperature.

Our idea is to develop an integrated system of

sensors and VR serious gaming in order to

immediately assess and address stress, combining

ideas that have been tested in the existing literature.

The main goals are presented in the following points:

1. To develop a calm, peaceful environment where

visual guidance for deep breathing is offered for

stress reduction. The user – system interaction is

reduced to the minimum level for relaxation

purposes.

2. To connect a heart rate sensor with the application

for the acquisition of the signal and to add

visualized feedback depending on the changes in

heart rate values.

3. To simultaneously collect EDA and heart rate

signal in order to assess users’ stress levels and

how these change during the users’ interaction

with the virtual environment.

2 METHODOLOGY

In this section, the chosen software development tools

and sensors is be presented. Then, a brief description

of the steps followed during the project’s

development are mentioned. Lastly, the serious game

and the experiment protocols are presented.

2.1 Sensors

For the purposes of this project the following two

commercial sensors were used.

2.1.1 Scosche Rhythm+

Scosche Rhythm+ is a simple BTLE sensor (Low

Energy Bluetooth Device) that captures heart rate

signals and can be connected to a proper device via

Bluetooth in order to present, analyse and save the

extracted data (https://www.scosche.com/rhythm-

plus-heart-rate-monitor-armband).

This device provides an affordable and easy

solution for monitoring heart rate, with acceptable

accuracy in office situations (not intense exercise) as

previously found (Navalta, 2020), which was suitable

for the purposes of the project. Scosche Rhythm+ was

also the sensor that was opted to be connected to the

VR application for convenience reasons.

It must be noted that the HR signal is made

available every second, which allows the depiction of

HR over time and the calculation of standard

deviation. However, it does not capture other heart

rate variability features (Castaldo, 2019) that might

be more relevant to stress level estimation.

2.1.2 Moodmetric Ring

Moodmetric Ring (https://moodmetric.com/) is a

commercial sensor that uses a patented processing

algorithm to analyse electrodermal activity. This

analysis provides as an output an index number that

indicates the user’s stress levels. The extracted signal

that was used in this project is not the raw signal of

electrodermal activity, but the signal of the above

index number. Despite of the fact that the raw data of

EDA signal could be accessed, we used the

Moodmetric index value signal as its use in relevant

studies has been promising (Pakarinen, 2019). The

stress levels are categorized as showed in figure 1.

Moodmetric Ring is a sensor that assembles

various advantages, such as handiness, easiness to use

and collect objective data of everyday life situations,

comparability between users, robustness to motional

artifacts etc.

Figure 1: Stress levels categorization.

2.2 Development Tools

For the development of the programming part of the

project and the depiction – analysis of the collected

signal, a combination of four software programs were

used.

Unity (https://unity.com/) was used to develop the

main application of the project, the deep breathing

exercise environment, linking it with the VR mask

Oculus Go. Android Studio was used in order to

develop the needed android library for the application

– sensor connection via Bluetooth

(https://developer.android.com/studio). RStudio

(https://rstudio.com/), an integrated development

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environment for R that gives access to free and open-

source libraries for data analysis and visualization,

was used for the depiction of the collected

physiological signals, HR and Moodmetric index

value, during the experimental phase. Lastly,

PsychoPy3 (https://psychopy.org/), an open-source

software written in python that provides an integrated

environment of tools for the development of simple

applications aiming at psychological research, was

used for the development of a Stroop test application

(Scarpina, 2017) needed for the experimental phase

of the project.

2.3 Design Methodology

The steps followed to achieve the goals of the project

are summarized in the following points:

1. Selection and design of the serious game for stress

reduction after reviewing the existing literature

and discussion with special therapists –

psychologists. Deep breathing exercise

visualization in a peaceful virtual environment

was selected.

2. Development of the serious game in Unity game

engine for implementation on android operating

system and, specifically, VR mask Oculus Go.

3. Development of an android library for the

successful connection of the application and the

Scosche Rhythm+ sensor via Bluetooth.

4. Integration of the Bluetooth android library in

Unity as a plug – in to exploit its functionality.

The data collected was displayed in the

application’s environment and saved after each

session with the prospect to introduce

environmental feedback based on the value of

heart rate.

5. Planning of preliminary and final experiment

protocols with the following main stages: initial

relaxation, stress stimulation activities

(Palanisamy, 2011), final relaxation (with and

without the use of deep breathing exercise

application). During the experiments the use of

both sensors was deemed necessary.

6. Use of an one-item self-report, questionnaire prior

and after the stress stimulation activities and after

the relaxation exercise to complete the final

experiment protocol and to provide the analysis

with additional information.

7. Implementation of User Experience

Questionnaire (UEQ,

https://www.ueq-online.org/)

8. Depiction of the collected signals and statistical

data extracted from the self-report questionnaires.

2.4 Game Design

The development of the serious game aims to

function both as a guide for deep breathing and as a

peaceful 3D environment that precipitates user’s

relaxation. It consists of one main scene. Before the

construction of the scene, the following free asset

packages were necessary:

 Fantasy Forest Environment asset package for

environmental design. The scene was set in a

forest virtual environment

(https://assetstore.unity.com/packages/3d/environ

ments/fantasy/fantasy-forest-environment-free-

demo-35361) since empirical evidence suggests

that walks and in the nature and relaxation

activities facilitate recovery from job stress (de

Bloom et al., 2017).

 Oculus integration asset package which provided

necessary tools for the final implementation on

Oculus Go.

 Audio files as soundtracks in the application.

The main scene is presented in figure 2. The scene

consists of five objects:

 Terrain: the forest environment of the scene.

 OVRCameraRig: the main camera of the scene, an

extended camera object extracted from Oculus

integration tools.

 Sphere: a centered sphere on main camera that

simulates the rate at which the user is advised to

take breaths. When the sphere’s volume is

augmented, its color turns white and the user is

advised to inhale at the rate of its growth. While

the sphere’s volume is reduced, its color turns red

and the user is advised to exhale at the rate of its

reduction.

 A text component used to display the heart rate

extracted from Scosche Rhythm+.

 An audio file for soundtrack.

Figure 2: Game’s snapshot.

In figure 3 the architecture of the system is

displayed. As the system’s functional diagram

implies, there are three main modules: the virtual

forest environment where all the game objects are

Virtual Reality and Serious Games for Stress Reduction with Application in Work Environments

543

displayed, the sphere that undertakes the role of visual

guidance for deep breathing and background

functionality parts that contributes mainly in Oculus

Go – Scosche Rhythm+ connection and the storage of

the captured HR signal.

Τwo C# code files were developed to determine

the behaviour of the game’s objects.

 Spiral.cs: this file determines the behaviour of the

sphere, thus determines the changes in sphere’s

volume and its colour. Furthermore, this file

ensures that after two and a half minutes the

application will end automatically. This duration

was selected based on Allen et al. (2002) who

found that employees who were trained in two-

minute “mini-relaxations” experienced reductions

in stress equivalent to that of employees who were

taught 20-minute Abbreviated Progressive

Relaxation Training (see also, Pollak Eisen et al.,

2008).

 BLE.cs: this file ensures the application – sensor

connection by activating the Bluetooth service via

the developed android plug – in. Moreover, via

this code the heart rate values are displayed on the

main screen each second and are saved after the

end of the application’s time.

Figure 3: System’s architecture.

2.5 Experiment Protocols

Two different protocols were designed for the

experiments that were conducted, one preliminary

and one final. Volunteers were asked to wear properly

the two sensors throughout the experiments. For the

stress stimulation stage three different activities were

selected because they were found to stimulate stress

levels and to some extend (e.g., calculations)

resemble work tasks that may stimulate stress

reactions. The three activities were: arithmetic

calculations (finding the next element in an

alphanumeric sequence, solving math puzzles, easy

math tasks), a First Person Shooting Game (Xonotic,

https://xonotic.org/) with rapid and intense

environmental changes and the Stroop test.

2.5.1 Preliminary Experiment Protocol

Figure 4: Stages of preliminary experiment protocol.

The preliminary experiments were conducted in order

to modulate a first opinion regarding the two signals

and their accuracy on the indication of stress levels.

As shown in the diagram of figure 4, the preliminary

experiment protocol consisted of the following three

stages:

1. Initial Relaxation stage (5 minutes): the volunteer

was asked to relax for 5 minutes and stop all

his/her activities.

2. Stress stimulation stage (10 – 15 minutes): the

volunteer was asked to carry on with one of the

selected activities.

3. Final Relaxation stage (5 minutes): the volunteer

was asked either to relax for 5 minutes or to wear

Oculus Go and use deep breathing exercise

application for 2,5 minutes and then relax another

2,5 minutes.

2.5.2 Final Experiment Protocol

Figure 5: Stages of final experiment protocol.

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The final experiment protocol was designed based on

the results of the preliminary experiments. This

protocol is an extended version of the previous one

and included self-assessments of stress. Another

difference between the two protocols is the extended

stress stimulation stage, where a combination of the

mentioned activities was implemented to better

approach stressful conditions. Thus, the 6 stages of

the final experiment protocol were (figure 5):

1. Initial Relaxation stage (5 minutes): volunteers

was asked to relax for 5 minutes, thus, stop all

their activities.

2. Questionnaire: volunteers were asked to complete

a one-item self-report scale assessing stress after

the initial relaxation stage.

3. Stress stimulation stage (17 minutes): volunteers

were asked to carry on with three selected

activities. Firstly, they had to perform arithmetic

calculations (5 minutes), then to play a First

Person Shooting Game (Xonotic – 10 minutes)

and finally to take the Stroop test (2 minutes).

4. Questionnaire: volunteers were asked to complete

the one-item self-report scale assessing stress after

stage 3.

5. Final Relaxation stage (5 minutes): volunteers

were asked either to relax for 5 minutes or to wear

Oculus Go and use deep breathing exercise

application for 2,5 minutes and then relax for

another 2,5 minutes.

6. Questionnaire: volunteers were asked to complete

the one-item self-report scale assessing stress after

stage 5.

3 RESULTS

In this section we are going to present indicative

results of preliminary and final experiments. After the

presentation of the results, some of the main

conclusions are going to be summarized.

3.1 Preliminary Experiment Results

The results of the preliminary experiments will be

discussed in this subsection. Two students

volunteered in this part of the experiments, both

female and not quite familiar with VR technologies.

The experiments were regarded as an easy, pleasant

procedure by the volunteers. They were asked to wear

both sensors during the experiments. The diagrams

extracted as results are depicting heart rate signal,

Moodmetric index value signal and the correlation

between the two signals. The shading logic in

Moodmetric index value is following the

categorization presented in figure 1, with 5 zones,

from green corresponding to ‘calm’ (index value 0-

20), to red corresponding to ‘running high’ (index

value 80-100).

The volunteers were asked to follow the protocol,

although there was an extension in the recording time

to get more information about the correlation of the

two signals and their function as stress indicators. The

results that are presented in figure 6 are extracted

from the experiment that used the Stroop test as stress

stimulation exercise. The test began at 17:55 and

finishes 5 minutes later. These results refer to

volunteer 1.

Figure 6: Preliminary experiment – Heart rate –

Moodmetric index value diagrams – Stroop test.

The results do not lead to clear findings. The two

signals seem to correlate with some delay, although

this varied per volunteer. Even though the

Moodmetric index value signal seems to more

accurately depict the changes in stress levels, it is

interesting that whether HR or EDA show more clear

stress-related fluctuations depends also on the

volunteer. The main problem that was addressed

during the preliminary experiments was that none of

the stress stimulation activities seemed to activate

volunteers. To address this problem, this stage of the

experiment was extended in the final protocol as

described above.

Virtual Reality and Serious Games for Stress Reduction with Application in Work Environments

545

3.2 Final Experiment Results

In this subsection, the final experiment results are

discussed. Four volunteers participated in this

experiment, two female (the same as in the

preliminary experiment) and two male students that

were familiar with VR technologies and gaming

environments. As above, the results that will be

presented refer to volunteer 1. In this experiment, the

impact of Oculus Go application was investigated.

Thus, the results will be the depiction of the two

signals without/with the use of Oculus Go mask.

The results of the first volunteer present positive

reaction with the use of Oculus Go application

regarding its impact on stress levels (figures 7, 8).

There seems to be a slight tendency of reduction

during the use of the VR mask. This conclusion is

based on the observation of Moodmetric index value

signal, as heart rate signal on both occasions presents

insignificant changes. The questionnaire results are

displayed in table 1. These results support the

hypothesis of the positive Oculus Go application

impact on stress levels from the point of view of the

volunteer.

Figure 7: Final experiment – Heart rate – Moodmetric index

value diagrams – without Oculus Go intervention. The two

vertical lines delimit the start and the end of the stress

stimulation stage respectively.

Figure 8: Final experiment – Heart rate – Moodmetric index

value diagrams – with Oculus Go intervention. The three

vertical lines delimit the start, the end of the stress

stimulation stage and the end of Oculus Go intervention

respectively.

The rest of the results are less clear. The two first

volunteers (female) were not that familiar with First

Person Shooting Games and this fact seemed to affect

positively the capacity of the protocol’s combination

of activities to stimulate stress. On the other hand, the

last two volunteers (male) were more familiar

reacting with this virtual environment and the stress

stimulation was not that successful. Furthermore, it

was again observed that whether HR or EDA show

more clear stress-related fluctuations depends on the

volunteer. The HR standard deviation signal

presented better correlation with Moodmetric index

value signal, suggesting that it could work as a better

stress indicator than HR. The Moodmetric index

value signal fluctuations continued to present a

clearer picture of stress levels, while the changes of

HR over time were insignificant in most occasions.

Finally, all volunteers stated in the questionnaire that

the use of the mask and deep breathing application

actually relieved them of stress, despite the fact that

this was not depicted on the collected signals on all

occasions. The use of Oculus Go application was

translated into slightly faster tendencies to recover

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from the previously increased stress levels. Thus,

there was indeed a better match of the Oculus Go with

the self-reports.

Having regarded as a more accurate stress indicator

the Moodmetric index value signal, most of the results

showed that the use of mask relieves slightly from

stress. Nevertheless, the stress stimulation stage of the

protocol has to be reconsidered in order to come to

more concrete conclusions.

Table 1: Questionnaire results with and without Oculus Go

intervention. The letter V stands for volunteer.

Questionnaire scale: 1 – 5 (No stress – Extreme stress).

Volunteers Question 1 Question 2 Question 3

V1 without 2 3 3

V1 with 3 3 2

V2 without 2 2 2

V2 with 2 3 2

V3 without 3 2 3

V3 with 2 2 1

V4 without 2 3 2

V4 with 3 3 2

3.3 User Experience Questionnaire

(UEQ)

The User Experience Questionnaire was

implemented to collect opinions for the developed

Oculus Go application. Ten volunteers accepted to

test the application. The age of participants ranges

from 23 to 59. Most of the answers were encouraging.

The technology of Oculus Go mask certainly played

decisive role in this direction, as most of the

volunteers were not familiar with VR mask

technology.

Figure 9: User experience questionnaire – boxplot of

answers per question.

The results of the UEQ are presented in figure 9.

Most of the lower and higher mean scores interrelate

with positive comments about the system pointing out

mainly its attractiveness, with one exception that

refers to the system’s predictability. The mean score

does not contain necessarily enough information

. The

recitation of the UEQ is necessary. These results

suggest that the application is intuitive, user –

friendly, easy to learn and to use and quite clear

regarding its purposes. The VR environment is

regarded as satisfactory, pleasant or even attractive.

4 DISCUSSION

The purpose of this paper was to present the

development of a VR application for the reduction of

stress levels. To this end, two experiment protocols

were designed and conducted, and indicative results

were presented. Although the number of volunteers

that participated in the experiments does not allow

safe conclusions, the results indicate the potential of

the project, but also the need for new experiment

protocols, more elaborate signal processing and

enhancements on VR environment.

Limitations of this attempt are the small number of

volunteers who participated in the experiments and the

need to enrich and standardize the stress stimulation

stage in experiment protocols to better simulate

working environment conditions and responses by

different stress-related categories of people.

Future work has to invest in the direction of the VR

system enhancement and its harmonic cooperation

with commercial sensors. Access to instantaneous

heart rate and HRV features would better enable the

quantification of stress levels. Overall, it is necessary

to detect the right stress indicators in order to design a

more robust system with the capacity to accurately

address the problem of work stress. One of the possible

future expansion of this work could be the design of a

suite of VR serious games for the treatment of chronic

stress or anxiety disorders.

ACKNOWLEDGEMENTS

We have to thank all four volunteers who accepted to

help us during the phase of experiments and also

those who accepted to test the application and gave us

useful feedback via UEQ.

REFERENCES

Pourmand, A., Davis, S., Lee, D., Barber, S., & Sikka, N.

(2017). Emerging utility of virtual reality as a

Virtual Reality and Serious Games for Stress Reduction with Application in Work Environments

547

multidisciplinary tool in clinical medicine. Games for

Health Journal, 6(5), 263-270.

Lau, H. M., Smit, J. H., Fleming, T. M., & Riper, H. (2017).

Serious games for mental health: are they accessible,

feasible, and effective? A systematic review and meta-

analysis. Frontiers in psychiatry, 7, 209.

Valmaggia, Lucia & Latif, Leila & Kempton, Matthew &

Rus-Calafell, Mar. (2016). Virtual reality in the

psychological treatment for mental health problems: An

systematic review of recent evidence. Psychiatry

Research. 236. 10.1016/j.psychres.2016.01.015.

Ferreira, Nuno. “Serious Games.” Distributed Computer

Graphics. Universidade do Minho. Portugal (2002).

Karasek, R.A. (1979). Job demands, decision latitudes, and

mental strain: Implications for job redesign.

Administrative Science Quarterly, 24 (2), 285-308.

McCallie, M. S., Blum, C. M., & Hood, C. J. (2006).

Progressive muscle relaxation. Journal of human

behavior in the social environment, 13(3), 51-66.

Richardson, K. M., & Rothstein, H. R. (2008). Effects of

occupational stress management intervention

programs: a meta-analysis. Journal of occupational

health psychology, 13(1), 69.

Thoondee, Krsna & Oikonomou, Andreas. (2017). Using

virtual reality to reduce stress at work. 492-499.

10.1109/SAI.2017.8252142.

Ahmaniemi, Teemu & Lindholm, Harri & Müller, Kiti &

Taipalus, Tapio. (2017). Virtual Reality Experience as

a Stress Recovery Solution in Workplace.

10.1109/LSC.2017.8268179.

Salkevicius, Justas & Damasevicius, Robertas &

Maskeliunas, Rytis & Laukiene, Ilona. (2019). Anxiety

Level Recognition for Virtual Reality Therapy System

Using Physiological Signals. Electronics. 8.

10.3390/electronics8091039.

Navalta JW, Montes J, Bodell NG, Salatto RW, Manning

JW, DeBeliso M (2020) Concurrent heart rate validity

of wearable technology devices during trail running.

PLoS ONE 15(8): e0238569.

https://doi.org/10.1371/journal.pone.0238569

Castaldo R, Montesinos L, Melillo P, James C, Pecchia L.

Ultra-short term HRV features as surrogates of short

term HRV: a case study on mental stress detection in

real life. BMC Med Inform Decis Mak. 2019 Jan

17;19(1):12. doi: 10.1186/s12911-019-0742-y. PMID:

30654799; PMCID: PMC6335694.

Pakarinen, T., Pietilä, J., & Nieminen, H. (2019, July).

Prediction of Self-Perceived Stress and Arousal Based

on Electrodermal Activity. In 2019 41st Annual

International Conference of the IEEE Engineering in

Medicine and Biology Society (EMBC) (pp. 2191-

2195). IEEE.

Scarpina, F., & Tagini, S. (2017). The stroop color and

word test. Frontiers in psychology, 8, 557.

Palanisamy, Karthikeyan & M, Murugappan & Yaacob,

Sazali. (2011). A review on stress inducement stimuli

for assessing human stress using physiological signals.

Proceedings - 2011 IEEE 7th International Colloquium

on Signal Processing and Its Applications, CSPA 2011.

10.1109/CSPA.2011.5759914.

de Bloom, J., Sianoja, M., Korpela, K., Tuomisto, M., Lilja,

A., Geurts, S., &Kinnunen, U. (2017). Effects of park

walks and relaxation exercises during lunch breaks on

recovery from job stress: Two randomized controlled

trials. Journal of Environmental Psychology, 51, 14-30.

https://doi.org/10.1016/j.jenvp.2017.03.006

Allen, G.J., Pescatello, L., Coughlan, L., Stephan, C.,

Barzvi, A., & Kwassman, J., et al., A multi-disciplinary,

employee-centered health promotion program:

Research, service, and evaluation. Presented at the

Connecticut Psychological Association, Westbrook,

CT, November, 2002.

Pollak Eisen, K., Allen, G.J., Bollash, M, & Pescatello, L.S.

(2008). Stress management in the workplace: A

comparison of a computer-based and an in-person

stress-management intervention. Computers in Human

Behavior, 24 (2), 486-496.

https://doi.org/10.1016/j.chb.2007.02.003

HEALTHINF 2021 - 14th International Conference on Health Informatics

548