Intelligent Mental Workload Mobile Application in Personalized Digital Care Pathway for Lifestyle Chronic Disease
Keikhosrokiani, Pantea; Isomursu, Minna; Korhonen, Olli; Sean, Tan Teik (2024-05-05)
Springer
05.05.2024
Keikhosrokiani, P., Isomursu, M., Korhonen, O., Sean, T.T. (2024). Intelligent Mental Workload Mobile Application in Personalized Digital Care Pathway for Lifestyle Chronic Disease. In: Särestöniemi, M., et al. Digital Health and Wireless Solutions. NCDHWS 2024. Communications in Computer and Information Science, vol 2083. Springer, Cham. https://doi.org/10.1007/978-3-031-59080-1_24
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© 2024 The Author(s). Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
https://creativecommons.org/licenses/by/4.0/
© 2024 The Author(s). Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202405063145
https://urn.fi/URN:NBN:fi:oulu-202405063145
Tiivistelmä
Abstract
In the new healthcare paradigm, personalized digital care pathway enables the provision of tailored information and empowers patients. In healthcare, it is crucial to attend to patients’ physical and emotional requirements. Stress and heavy mental workload can be detrimental to managing chronic lifestyle disorders. However, a reliable, standardized, and widely used paradigm for incorporating mental workload into the digital care pathway for providing long-term personalized care is missing from the current care pathway. Therefore, this study aims to investigate the use of mental workload tools and mobile applications in personalized digital care pathways for managing lifestyle chronic diseases. The study was focused on determining and characterizing the variables that determine mental workload; and then, investigating the ways in which these variables might function as supplementary data sources to enhance the personalization of care pathway. Based on the proposed mental workload tool, data was collected from 304 employees in the manufacturing industry, software development department. An intelligent mobile application was developed to manage and classify mental workload. Ensemble learning algorithms were used for mental workload classification, among which Hard Voting Ensemble Model outperforms the other techniques with 0.97 accuracy. Based on the findings, the most variable factor of mental workload is psychological factors with a median of 3.25, suggesting that individual differences or specific psychological conditions can significantly affect mental workload. Regarding personalization for managing chronic diseases, the mental workload variables may be utilized to individually adjust digital treatments to the specific requirements of every patient in a person-centered care.
In the new healthcare paradigm, personalized digital care pathway enables the provision of tailored information and empowers patients. In healthcare, it is crucial to attend to patients’ physical and emotional requirements. Stress and heavy mental workload can be detrimental to managing chronic lifestyle disorders. However, a reliable, standardized, and widely used paradigm for incorporating mental workload into the digital care pathway for providing long-term personalized care is missing from the current care pathway. Therefore, this study aims to investigate the use of mental workload tools and mobile applications in personalized digital care pathways for managing lifestyle chronic diseases. The study was focused on determining and characterizing the variables that determine mental workload; and then, investigating the ways in which these variables might function as supplementary data sources to enhance the personalization of care pathway. Based on the proposed mental workload tool, data was collected from 304 employees in the manufacturing industry, software development department. An intelligent mobile application was developed to manage and classify mental workload. Ensemble learning algorithms were used for mental workload classification, among which Hard Voting Ensemble Model outperforms the other techniques with 0.97 accuracy. Based on the findings, the most variable factor of mental workload is psychological factors with a median of 3.25, suggesting that individual differences or specific psychological conditions can significantly affect mental workload. Regarding personalization for managing chronic diseases, the mental workload variables may be utilized to individually adjust digital treatments to the specific requirements of every patient in a person-centered care.
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