Empirical Studies on the Relationship Between Wearable Stress Detection and Workplace Productivity

Kristoko Dwi Hartomo; Muhammad Zaki; Gilang Kartika  Hanum; Nur Silawati; Adele  Valerry

Authors

Kristoko Dwi Hartomo Satya Wacana Christian University https://orcid.org/0000-0003-0237-851X
Muhammad Zaki Universitas Trisakti https://orcid.org/0000-0002-5746-2359
Gilang Kartika Hanum University of Raharja https://orcid.org/0000-0002-5985-7485
Nur Silawati UR Mart https://orcid.org/0009-0001-6595-9365
Adele Valerry Ilearning Incorporation https://orcid.org/0009-0009-1433-1058

Keywords:

Wearable Technology, Stress Detection, Workplace Productivity, Employee Well-Being, Physiological Monitoring

Abstract

Workplace stress has been widely recognized as a critical factor influencing employee health, performance, and organizational outcomes. Recent advancements in wearable technologies provide real-time physiological data that open new opportunities for monitoring and managing stress in professional settings. This study aims to empirically investigate the relationship between wearable-based stress detection and workplace productivity, focusing on how continuous monitoring can enhance well-being and performance. A quantitative approach was employed with 250 participants across three corporate sectors, where wearable devices measured physiological indicators such as heart rate variability and skin conductance, while productivity was assessed through task completion rates and self-reported efficiency. Statistical analyses, including correlation, regression, and moderation analysis, were conducted to examine the strength of associations. Findings reveal a significant negative correlation between elevated stress levels and productivity metrics, while participants using wearable feedback interventions demonstrated improved stress awareness and a 15% increase in task efficiency compared to the control group. In conclusion, wearable stress detection presents a promising tool for enhancing workplace productivity by enabling proactive stress management, highlighting the importance of integrating technology, psychology, and organizational practices to foster healthier and more effective work environments.

References

[1] M. Saxena, N. M. Avanesh, L. Niranjan, and P. Mohanty, “Wearable technologies leading to employee performance and productivity: Exploring the mediating role of mental health and wellbeing,” in Technological Enhancements for Improving Employee Performance, Safety, and Well-Being. IGI Global, 2025, pp. 111–130.

[2] M. N. Ayubi and A. Retnowardhani, “Optimizing learning experiences: A study of student satisfaction with lms in higher education,” Aptisi Transactions on Technopreneurship (ATT), vol. 7, no. 2, pp. 527–541, 2025.

[3] J. E. Naranjo, C. A. Mora, D. F. Bustamante Villag´omez, M. G. Mancheno Falconi, and M. V. Garcia, “Wearable sensors in industrial ergonomics: enhancing safety and productivity in industry 4.0,” Sensors, vol. 25, no. 5, p. 1526, 2025.

[4] M. H. R. Chakim, U. Rahardja, E. D. Astuti, E. Erika, and C. T. Hua, “The social empowerment role of the penta helix entrepreneurship ecosystem in driving the national economy,” ADI Pengabdian Kepada Masyarakat, vol. 6, no. 1, pp. 1–13, 2025.

[5] U. Yadav and S. Soni, “Adoption of wearable technology in the workplace: A study of employee perceptions and behavioral intentions,” in Progressive Computational Intelligence, Information Technology and Networking. CRC Press, 2025, pp. 462–468.

[6] T. S. Goh, D. Jonas, B. Tjahjono, V. Agarwal, and M. Abbas, “Impact of ai on air quality monitoring systems: A structural equation modeling approach using utaut,” Sundara Advanced Research on Artificial Intelligence, vol. 1, no. 1, pp. 9–19, 2025.

[7] M. Awada, B. B. Gerber, G. M. Lucas, and S. C. Roll, “Stress appraisal in the workplace and its associations with productivity and mood: Insights from a multimodal machine learning analysis,” Plos one, vol. 19, no. 1, p. e0296468, 2024.

[8] M. Awada, B. Becerik-Gerber, G. Lucas, and S. C. Roll, “Predicting office workers’ productivity: A machine learning approach integrating physiological, behavioral, and psychological indicators,” Sensors, vol. 23, no. 21, p. 8694, 2023.

[9] B. Kristianto, C. Dewi, H. D. Purnomo, K. D. Hartomo, and S. Z. M. Hashim, “Utilizing the yolov8 model for accurate hand recognition with complex background,” PeerJ Computer Science, vol. 11, p. e3244, 2025.

[10] G. Taskasaplidis, D. A. Fotiadis, and P. D. Bamidis, “Review of stress detection methods using wearable sensors,” IEEe Access, vol. 12, pp. 38 219–38 246, 2024.

[11] H. Hijry, S. M. R. Naqvi, K. Javed, O. H. Albalawi, R. Olawoyin, C. Varnier, and N. Zerhouni, “Real time worker stress prediction in a smart factory assembly line,” IEEE Access, 2024.

[12] J. Kallio, E. Vildjiounaite, J. Tervonen, and M. Bordallo L´opez, “A survey on sensor-based techniques for continuous stress monitoring in knowledge work environments,” ACM Transactions on Computing for Healthcare, vol. 6, no. 3, pp. 1–31, 2025.

13] P. H. P. Tan, M. Tukiran, and D. Wuisan, “Innovation practices and external support for msme performance and survival in indonesia,” International Journal of Cyber and IT Service Management (IJCITSM), vol. 5, no. 2, pp. 120–133, 2025.

[14] E. Lazarou and T. P. Exarchos, “Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices,” AIMS neuroscience, vol. 11, no. 2, p. 76, 2024.

[15] B. M. Booth, H. Vrzakova, S. M. Mattingly, G. J. Martinez, L. Faust, and S. K. D’Mello, “Toward robust stress prediction in the age of wearables: Modeling perceived stress in a longitudinal study with information workers,” IEEE Transactions on Affective Computing, vol. 13, no. 4, pp. 2201–2217, 2022.

[16] A. Kadim, I. Yusnita, A. Sutarman, R. Lesmana, and F. A. Ramahdan, “Assessing the impact of corporate governance and strategic leadership on economic growth and market stability,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 6, no. 2, pp. 177–187, 2025.

[17] T. Pujiati, H. Setiyowati, B. Rawat, N. P. L. Santoso, and M. G. Ilham, “Exploring the role of artificial intelligence in enhancing environmental health: Utaut2 analysis,” Sundara Advanced Research on Artificial Intelligence, vol. 1, no. 1, pp. 37–46, 2025.

[18] K. Srinivasan, F. Currim, C. M. Lindberg, J. Razjouyan, B. Gilligan, H. Lee, K. J. Canada, N. Goebel, M. R. Mehl, M. M. Lunden et al., “Discovery of associative patterns between workplace sound level and physiological wellbeing using wearable devices and empirical bayes modeling,” npj Digital Medicine, vol. 6, no. 1, p. 5, 2023.

[19] C. Belletier, M. Charkhabi, G. Pires de Andrade Silva, K. Ametepe, M. Lutz, and M. Izaute, “Wearable cognitive assistants in a factory setting: a critical review of a promising way of enhancing cognitive performance and well-being,” Cognition, Technology & Work, vol. 23, no. 1, pp. 103–116, 2021.

[20] F. G. Antonaci, E. C. Olivetti, F. Marcolin, I. A. Castiblanco Jimenez, B. Eynard, E. Vezzetti, and S. Moos, “Workplace well-being in industry 5.0: a worker-centered systematic review,” Sensors, vol. 24, no. 17, p. 5473, 2024.

[21] S. Purnama, C. S. Bangun, and E. P. Mahadewi, “Predicting consumer purchase intention in personal shopper services using big data analytics and sem,” International Journal of Cyber and IT Service Management (IJCITSM), vol. 5, no. 1, pp. 105–119, 2025.

[22] S. A. Khowaja, A. G. Prabono, F. Setiawan, B. N. Yahya, and S.-L. Lee, “Toward soft real-time stress detection using wrist-worn devices for human workspaces.” Soft Computing-A Fusion of Foundations, Methodologies & Applications, vol. 25, no. 4, 2021.

[23] P. Traunmuller, A. Jahanjoo, S. Khooyooz, A. Aminifar, and N. TaheriNejad, “Wearable health care devices for monitoring stress and attention level in workplace environments,” arXiv preprint arXiv:2406.05813, 2024.

[24] M. A. Setiawan, H. Hartoyo, K. B. Seminar, B. Sartono, R. Fitriati, and V. Ginting, “Improving e-service quality of indonesian toll road application with entrepreneurship insights,” Aptisi Transactions on Technopreneurship (ATT), vol. 7, no. 2, pp. 503–515, 2025.

[25] F. Sutisna, N. Lutfiani, E. Anderson, D. Danang, and M. O. Syaidina, “E-commerce and digital marketing strategies: Their impact on startupreneur performance using pls-sem,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 6, no. 2, pp. 215–223, 2025.

[26] B. B. Van Acker, P. D. Conradie, P. Vlerick, and J. Saldien, “Employee acceptability of wearable mental workload monitoring: exploring effects of framing the goal and context in corporate communication,” Cognition, Technology & Work, vol. 23, no. 3, pp. 537–552, 2021.

[27] R. Aprianto, R. Haris, A. Williams, H. Agustian, and N. Aptwell, “Social influence on ai-driven air quality monitoring adoption: Smartpls analysis,” Sundara Advanced Research on Artificial Intelligence, vol. 1, no. 1, pp. 28–36, 2025.

[28] M. De Choudhury, “Toward improved workplace measurement with passive sensing technologies,” Tech- nology and Measurement around the Globe, p. 46, 2023.

[29] I. Okpala, C. Nnaji, I. Awolusi, and A. Akanmu, “Developing a success model for assessing the impact of wearable sensing devices in the construction industry,” Journal of Construction Engineering and Management, vol. 147, no. 7, p. 04021060, 2021.

[30] D. Gathmyr, U. Suhud, H. Herlitah, H. Hamidah, R. T. H. Safariningsih, and J. Wilson, “Technological advancements in perceived organizational support enhancing healthcare systems towards sustainable development goals,” Aptisi Transactions on Technopreneurship (ATT), vol. 7, no. 2, pp. 516–526, 2025.

[31] M. Herold, S. Simbula, and M. Gallucci, “Can smartphone applications and wearable technologies improve workplace well-being and help manage stress? a systematic review,” Current Psychology, vol. 43, no. 36, pp. 28 650–28 673, 2024.

[32] C. Dewi, D. Manongga, Hendry, E. Mailoa, and K. D. Hartomo, “Deep learning and yolov8 utilized in an accurate face mask detection system,” Big Data and Cognitive Computing, vol. 8, no. 1, p. 9, 2024.

[33] W. Szewczyk, I. Mongelli, and J.-C. Ciscar, “Heat stress, labour productivity and adaptation in europe—a regional and occupational analysis,” Environmental Research Letters, vol. 16, no. 10, p. 105002, 2021.

[34] J. Oh, G. Y. Cho, and H. Kim, “Performance analysis of wearable robotic exoskeleton in construction tasks: Productivity and motion stability assessment,” Applied Sciences, vol. 15, no. 7, p. 3808, 2025.

[35] C.-M. Rosca and A. Stancu, “Fusing machine learning and ai to create a framework for employee well- being in the era of industry 5.0.” Applied Sciences (2076-3417), vol. 14, no. 23, 2024.

[36] S. Canali, B. De Marchi, and A. Aliverti, “Wearable technologies and stress: toward an ethically grounded approach,” International journal of environmental research and public health, vol. 20, no. 18, p. 6737, 2023.

[37] N. Lutfiani, U. Rahardja, S. Wijono, K. D. Hartomo, and H. Purnomo, “Unlocking the potential of ai- enabled startup through digital talent in higher education,” in 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT). IEEE, 2024, pp. 1–6.

[38] M. Bolpagni, S. Pardini, M. Dianti, and S. Gabrielli, “Personalized stress detection using biosignals from wearables: A scoping review,” Sensors, vol. 24, no. 10, p. 3221, 2024.

[39] S. Purnama, B. L. Pradana, G. Khanna, S. Suhandi, A. Rizky, I. N. Hikam, and M. F. Kamil, “The impact of war on the cryptocurrency economy from a management perspective,” International Journal of Cyber and IT Service Management, vol. 4, no. 2, pp. 143–154, 2024.

[40] S. N. Kodithuwakku Arachchige, R. F. Burch V, H. Chander, A. J. Turner, and A. C. Knight, “The use of wearable devices in cognitive fatigue: current trends and future intentions,” Theoretical Issues in Ergonomics Science, vol. 23, no. 3, pp. 374–386, 2022.

[41] L. Kask, N. Bloom, and R. Porta, “Health informatics: Utilization of information technology in health care and patient management,” International Journal of Cyber and IT Service Management, vol. 4, no. 1, pp. 53–58, 2024.

[42] S. Nepal, G. J. Martinez, S. Mirjafari, S. Mattingly, V. D. Swain, A. Striegel, P. G. Audia, and A. T. Campbell, “Assessing the impact of commuting on workplace performance using mobile sensing,” IEEE Pervasive Computing, vol. 20, no. 4, pp. 52–60, 2021.

[43] D. Wuisan, J. W. Manurung, C. Wantah, and M. E. Yuliana, “Entrepreneurial self-employment and work engagement in msmes through autonomy and rewards,” Aptisi Transactions on Technopreneurship (ATT), vol. 7, no. 1, pp. 264–281, 2025.

[44] E. Svertoka, S. Saafi, A. Rusu-Casandra, R. Burget, I. Marghescu, J. Hosek, and A. Ometov, “Wearables for industrial work safety: A survey,” Sensors, vol. 21, no. 11, p. 3844, 2021.