Exploring Mental Fatigue and Burnout in the Workplace: A Survival Analysis Approach

Authors

DOI:

https://doi.org/10.4108/eetpht.10.5719

Keywords:

Mental fatigue, Kaplan Meier, Nelson-Aalen, fatigue, Cox proportional-hazards, survival curve, burnout

Abstract

INTRODUCTION: Employee burnout is a prevalent concern in contemporary workplaces, adversely impacting both individual well-being and organizational productivity.

OBJECTIVES:  In this paper, we conducted a comprehensive analysis of a dataset focusing on mental fatigue and burnout among employees, employing various survival analysis techniques including Kaplan Meier, Nelson-Aalen, and Cox proportional-hazards models, as well as Frailty Models and Competing Risks Analysis.

METHODS:  This research underscored significant associations between mental fatigue, burnout, and adverse outcomes, emphasizing the critical need for early identification and intervention. Kaplan Meier analysis revealed differences in survival probabilities, while Nelson-Aalen analysis depicted cumulative hazard functions over time. Cox proportional-hazards models identified mental fatigue and burnout as significant predictors of adverse events, supported by Frailty Models accounting for individual-level variability. Additionally, Competing Risks Analysis elucidated the simultaneous occurrence of multiple adverse events among employees experiencing burnout.

RESULTS: This research underscored significant associations between mental fatigue, burnout, and adverse outcomes, emphasizing the critical need for early identification and intervention. Kaplan Meier analysis revealed differences in survival probabilities, while Nelson-Aalen analysis depicted cumulative hazard functions over time. Cox proportional-hazards models identified mental fatigue and burnout as significant predictors of adverse events, supported by Frailty Models accounting for individual-level variability. Additionally, Competing Risks Analysis elucidated the simultaneous occurrence of multiple adverse events among employees experiencing burnout.

CONCLUSION: This study contributes valuable insights into understanding and addressing mental fatigue in the workplace, highlighting the importance of evidence-based interventions to support employee well-being and organizational resilience. The insights gained from this analysis inform evidence-based strategies for mitigating burnout-related risks and promoting a healthier work environment.

Downloads

Download data is not yet available.

References

G. Darnai et al., “The neural correlates of mental fatigue and reward processing: A task-based fMRI study,” Neuroimage, vol. 265, p. 119812, Jan. 2023, doi: 10.1016/j.neuroimage.2022.119812. DOI: https://doi.org/10.1016/j.neuroimage.2022.119812

G. Sauch Valmaña, Q. Miró Catalina, N. Carrasco-Querol, and J. Vidal-Alaball, “Gender, Mental Health and Socioeconomic Differences in Fibromyalgia: A Retrospective Cohort Study Using Real-World Data from Catalonia,” Healthcare, vol. 11, no. 4, p. 530, Feb. 2023, doi: 10.3390/healthcare11040530. DOI: https://doi.org/10.3390/healthcare11040530

Y. Zhang, H. Guo, Y. Zhou, C. Xu, and Y. Liao, “Recognising drivers’ mental fatigue based on EEG multi-dimensional feature selection and fusion,” Biomed. Signal Process. Control, vol. 79, p. 104237, Jan. 2023, doi: 10.1016/j.bspc.2022.104237. DOI: https://doi.org/10.1016/j.bspc.2022.104237

X. Xu, J. Tang, T. Xu, and M. Lin, “Mental Fatigue Degree Recognition Based on Relative Band Power and Fuzzy Entropy of EEG,” Int. J. Environ. Res. Public Health, vol. 20, no. 2, p. 1447, Jan. 2023, doi: 10.3390/ijerph20021447. DOI: https://doi.org/10.3390/ijerph20021447

Sreeshakthy M. and Preethi J., “Classification of emotion from EEG using hybrid radial basis function networks with elitist PSO,” in 2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO), Jan. 2015, pp. 1–4. doi: 10.1109/ISCO.2015.7282340. DOI: https://doi.org/10.1109/ISCO.2015.7282340

M. Sreeshakthy, J. Preethi, and A. Dhilipan, “A Survey On Emotion Classification From Eeg Signal Using Various Techniques and Performance Analysis,” Int. J. Inf. Technol. Comput. Sci., vol. 8, no. 12, pp. 19–26, Dec. 2016, doi: 10.5815/ijitcs.2016.12.03. DOI: https://doi.org/10.5815/ijitcs.2016.12.03

J. Preethi and S. Sowmiya, “Emotion recognition from EEG signal using ISO-FLANN with firefly algorithm,” in 2016 International Conference on Communication and Signal Processing (ICCSP), Apr. 2016, pp. 1932–1936. doi: 10.1109/ICCSP.2016.7754508. DOI: https://doi.org/10.1109/ICCSP.2016.7754508

I. Zorzos, I. Kakkos, S. T. Miloulis, A. Anastasiou, E. M. Ventouras, and G. K. Matsopoulos, “Applying Neural Networks with Time-Frequency Features for the Detection of Mental Fatigue,” Appl. Sci., vol. 13, no. 3, p. 1512, Jan. 2023, doi: 10.3390/app13031512. DOI: https://doi.org/10.3390/app13031512

G. D’Arrigo, D. Leonardis, S. Abd ElHafeez, M. Fusaro, G. Tripepi, and S. Roumeliotis, “Methods to Analyse Time-to-Event Data: The Kaplan-Meier Survival Curve,” Oxid. Med. Cell. Longev., vol. 2021, pp. 1–7, Sep. 2021, doi: 10.1155/2021/2290120. DOI: https://doi.org/10.1155/2021/2290120

L. A. Vale-Silva and K. Rohr, “Long-term cancer survival prediction using multimodal deep learning,” Sci. Rep., vol. 11, no. 1, p. 13505, Jun. 2021, doi: 10.1038/s41598-021-92799-4. DOI: https://doi.org/10.1038/s41598-021-92799-4

“Are Your Employees Burning Out?”, [Online]. Available: https://www.kaggle.com/datasets/blurredmachine/are-your-employees-burning-out

Pratik Shukl, “A Complete Guide To Survival Analysis In Python,” Aspiring Mach. Learn. Eng., [Online]. Available: https://www.kdnuggets.com/2020/07/complete-guide-survival-analysis-python-part1.html

J. G. Burneo, V. Villanueva, R. C. Knowlton, R. E. Faught, and R. I. Kuzniecky, “Kaplan–Meier analysis on seizure outcome after epilepsy surgery: Do gender and race influence it?,” Seizure, vol. 17, no. 4, pp. 314–319, Jun. 2008, doi: 10.1016/j.seizure.2007.10.002. DOI: https://doi.org/10.1016/j.seizure.2007.10.002

I. Etikan, “The Kaplan Meier Estimate in Survival Analysis,” Biometrics Biostat. Int. J., vol. 5, no. 2, Feb. 2017, doi: 10.15406/bbij.2017.05.00128.

J. Kishore, M. Goel, and P. Khanna, “Understanding survival analysis: Kaplan-Meier estimate,” Int. J. Ayurveda Res., vol. 1, no. 4, p. 274, 2010, doi: 10.4103/0974-7788.76794.

J. Kiessling, A. Brunnberg, G. Holte, N. Eldrup, and K. Sörelius, “Artificial Intelligence Outperforms Kaplan–Meier Analyses Estimating Survival after Elective Treatment of Abdominal Aortic Aneurysms,” Eur. J. Vasc. Endovasc. Surg., Jan. 2023, doi: 10.1016/j.ejvs.2023.01.028. DOI: https://doi.org/10.1016/j.ejvs.2023.01.028

Ayat Mubarak Karamalla Elamin and Altaiyb Omer Ahmed Mohmmed, “The Cox regression and Kaplan-Meier for time-to-event of survival data patients with renal failure,” World J. Adv. Eng. Technol. Sci., vol. 8, no. 1, pp. 097–109, Jan. 2023, doi: 10.30574/wjaets.2023.8.1.0183. DOI: https://doi.org/10.30574/wjaets.2023.8.1.0183

C. Li, Y. Gao, C. Lu, and M. Guo, “Identification of potential biomarkers for colorectal cancer by clinical database analysis and Kaplan–Meier curves analysis,” Medicine (Baltimore)., vol. 102, no. 6, p. e32877, Feb. 2023, doi: 10.1097/MD.0000000000032877. DOI: https://doi.org/10.1097/MD.0000000000032877

C. C. A. Reddy, Chandan K., Healthcare Data Analytics. CRC Press is an imprint of Taylor & Francis Group, an Informa business, 2015.

scikit-, “Introduction to Survival Analysis with scikit-survival,” scikit-survival, [Online]. Available: https://scikit-survival.readthedocs.io/en/stable/user_guide/00-introduction.html

J. Kishore, M. Goel, and P. Khanna, “Understanding survival analysis: Kaplan-Meier estimate,” Int. J. Ayurveda Res., vol. 1, no. 4, p. 274, 2010, doi: 10.4103/0974-7788.76794. DOI: https://doi.org/10.4103/0974-7788.76794

V. S. Stel, F. W. Dekker, G. Tripepi, C. Zoccali, and K. J. Jager, “Survival Analysis I: The Kaplan-Meier Method,” Nephron Clin. Pract., vol. 119, no. 1, pp. c83–c88, Jun. 2011, doi: 10.1159/000324758. DOI: https://doi.org/10.1159/000324758

S. Lacny et al., “Kaplan-Meier Survival Analysis Overestimates the Risk of Revision Arthroplasty: A Meta-analysis,” Clin. Orthop. Relat. Res., vol. 473, no. 11, pp. 3431–3442, Nov. 2015, doi: 10.1007/s11999-015-4235-8. DOI: https://doi.org/10.1007/s11999-015-4235-8

I. Etikan, “The Kaplan Meier Estimate in Survival Analysis,” Biometrics Biostat. Int. J., vol. 5, no. 2, Feb. 2017, doi: 10.15406/bbij.2017.05.00128. DOI: https://doi.org/10.15406/bbij.2017.05.00128

E. Carnero Contentti et al., “Neuromyelitis optica spectrum disorders with and without associated autoimmune diseases,” Neurol. Sci., Jan. 2023, doi: 10.1007/s10072-023-06611-4. DOI: https://doi.org/10.1007/s10072-023-06611-4

C. Elhardt, R. Schweikert, R. Kamnig, E. Vounotrypidis, A. Wolf, and C. M. Wertheimer, “Recurrence of perforation and overall patient survival after penetrating keratoplasty versus amniotic membrane transplantation in corneal perforation,” Graefe’s Arch. Clin. Exp. Ophthalmol., Jan. 2023, doi: 10.1007/s00417-022-05914-0. DOI: https://doi.org/10.1007/s00417-022-05914-0

S. Karim et al., “Gene expression study of breast cancer using Welch Satterthwaite t-test, Kaplan-Meier estimator plot and Huber loss robust regression model,” J. King Saud Univ. - Sci., vol. 35, no. 1, p. 102447, Jan. 2023, doi: 10.1016/j.jksus.2022.102447. DOI: https://doi.org/10.1016/j.jksus.2022.102447

I. Ismiguzel, “Hands-on Survival Analysis with Python”, [Online]. Available: https://towardsdatascience.com/hands-on-survival-analysis-with-python-270fa1e6fb41

J. Xidian Uni , “Literature review on MHD Peristaltic Transport of non-Newtonian fluids through channels/Tubes,” v., vol. 14, no. 5, May 2020, doi: 10.37896/jxu14.5/263. DOI: https://doi.org/10.37896/jxu14.5/263

J. T. Rich, J. G. Neely, R. C. Paniello, C. C. J. Voelker, B. Nussenbaum, and E. W. Wang, “A practical guide to understanding Kaplan‐Meier curves,” Otolaryngol. Neck Surg., vol. 1 43, no. 3, pp. 331–336, Sep. 2010, doi: 10.1016/j.otohns.2010.05.007. DOI: https://doi.org/10.1016/j.otohns.2010.05.007

Mohammadbagher Gorji, "The Effect of Job Burnout Dimension on Employees Performance", International Journal of Social Science and Humanity, Vol. 1, No. 4, November 2011.

Chellatamilan, N. S. Kumar, B. Valarmathi, Effective deployment of multicloud customizable chatbot application for covid-19 datasets, in: Operationalizing Multi-Cloud Environments, Springer International Publishing, 2021, pp. 361–379. doi:10.1007/978-3-030-74402-120. DOI: https://doi.org/10.1007/978-3-030-74402-1_20

T. Chellatamilan, B. Valarmathi, K. Santhi, and, Research trends on deep transformation neural models for text analysis in NLP applications, International Journal of Recent Technology and Engineering IJRTE 9 (2) (2020) 750–758. doi:10.35940/ijrte.b3838.079220. DOI: https://doi.org/10.35940/ijrte.B3838.079220

K. Santhi, B. Valarmathi, T. Chellatamilan, Depth impurity pruned strategies for extracting high utility itemsets, International Journal of Engineering Technology 7 (3.4) (2018) 52. doi:10.14419/ijet.v7i3.4.16747. DOI: https://doi.org/10.14419/ijet.v7i3.4.16747

K. Santhi, T.Chellatamilan, B. Valarmathi, Pfbtree for big data memory management system, Indian Journal of Public Health Research Development 9 (6)(2018) 531. doi:10.5958/0976-5506.2018.00666.6. DOI: https://doi.org/10.5958/0976-5506.2018.00666.6

S. Nivetha, B. Valarmathi, K. Santhi, T. Chellatamilan, Detection of type 2 diabetesusing clustering methods balanced and imbalanced pima indian extendeddataset, in: Proceeding of the International Conference on Computer Networks,Big Data and IoT (ICCBI - 2019), Springer International Publishing, 2020, pp.610–619. doi:10.1007/978-3-030-43192-169. DOI: https://doi.org/10.1007/978-3-030-43192-1_69

He Sun1, Kim G. Soh2, Samsilah Roslan3, Mohd Rozilee Wazir Norjali Wazir2, Alireza Mohammadi4, Cong Ding2, Zijian Zhao1*,Nature exposure might be the intervention to improve the self-regulation and skilled performance in mentally fatigue athletes: A narrative review and conceptual framework, Front. Psychol., 02 August 2022, Sec. Performance Science,Volume 13 – 2022, https://doi.org/10.3389/fpsyg.2022.941299 DOI: https://doi.org/10.3389/fpsyg.2022.941299

Downloads

Published

10-04-2024

How to Cite

1.
Eswar Reddy R, K S. Exploring Mental Fatigue and Burnout in the Workplace: A Survival Analysis Approach. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Apr. 10 [cited 2024 May 3];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5719