Abstract
Diabetes Mellitus is a chronic illness that can be managed in various ways. However, maintaining a healthy lifestyle is a crucial factor that can significantly improve treatment outcomes. Unfortunately, many patients fail to adopt the lifestyle changes recommended by their doctors. Monitoring patients’ routines and identifying areas where they may be going wrong can be challenging. To address this issue, sensor-based systems have been developed. However, patients may be reluctant to share the necessary information. To overcome this hurdle, a taxonomic system based on business process models has been designed. This system uses text-based data from patients, obtained from various social media groups related to Diabetes, where they express their feelings about the condition. Diabetic individuals often share their emotions with others who are facing similar challenges. This textual data is analyzed to understand the lifestyle choices of diabetic patients. The information is then mapped onto process models to identify areas where patients may need additional support. The results of this analysis provide insights into the degree to which diabetic individuals are following a healthy lifestyle.
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References
Association, A.D.: Diagnosis and classification of diabetes mellitus. Diab. Care 33(Suppl 1), S62–S69 (2010)
Ramachandran, A., et al.: The Indian diabetes prevention programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1). Diabetologia 49, 289–297 (2006)
Yoshioka, N.: Psychological reluctance to insulin therapy: as an illness side of diabetes mellitus. Diabetology Int. 9(2), 82-83. Springer (2018)
Mirahmadizadeh, A., Delam, H., Seif, M., Banihashemi, S.A., Tabatabaee, H.: Factors affecting insulin compliance in patients with type 2 diabetes in South Iran, 2017: We are faced with insulin phobia. Iranian J. Med. Sci. 44(3), 204–213 (2019)
Han, Y., Han, M., Lee, S., Jehad Sarkar, A.M., Lee, Y.K.: A framework for supervising lifestyle diseases using long-term activity monitoring. Sensors 12(5), 5363–5379 (2012)
Bilal, H.S.M., Khattak, A.M., Lee, S.: Sedentary behavior-based user life-log monitoring for wellness services. In: Chang, C.K., Chiari, L., Cao, Yu., Jin, H., Mokhtari, M., Aloulou, H. (eds.) ICOST 2016. LNCS, vol. 9677, pp. 283–292. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39601-9_25
Rus, H.M., Cameron, L.D.: Health communication in social media: message features predicting user engagement on diabetes-related facebook pages. Ann. Behav. Med. 50(5), 678–689 (2016)
De la Torre-DĂez, I., DĂaz-Pernas, F.J., AntĂłn-RodrĂguez, M.: A content analysis of chronic diseases social groups on facebook and twitter. Telemed. e-Health 18(6), 404–408 (2012)
Petrovski, G., Zivkovic, M., Stratrova, S.S.: Social media and diabetes: can facebook and skype improve glucose control in patients with type 1 diabetes on pump therapy? one-year experience. Diabetes Care 38(4), e51–e52 (2015)
Shaw, R.J., Johnson, C.M.: Health information seeking and social media use on the Internet among people with diabetes. Online J. Pub. Health Inform. 3(1) (2011)
Fergie, G., Hilton, S., Hunt, K.: Young adults’ experiences of seeking online information about diabetes and mental health in the age of social media. Health Expect. 19(6), 1324–1335 (2016)
Lee, R., Whitley, H.: Use of social media to support patients with diabetes mellitus. Consultant Pharmacist® 29(1), 53–57 (2014)
Chomutare, T., Tatara, N., Årsand, E., Hartvigsen, G.: Designing a diabetes mobile application with social network support. In: HIC, pp. 58–64 (2013)
Eggleston, E.M., Weitzman, E.R.: Innovative uses of electronic health records and social media for public health surveillance. Curr. Diab. Rep. 14, 1–9 (2014)
Santoro, E., Castelnuovo, G., Zoppis, I., Mauri, G., Sicurello, F.: Social media and mobile applications in chronic disease prevention and management. Front. Psychol. 6, 567 (2015)
Sohail, A., Dominic, P.D.: A gap between business process intelligence and redesign process. In: 2012 International Conference on Computer & Information Science (ICCIS), vol. 1, pp. 136–142. IEEE (2012)
Sánchez Ferreres, J.: Comparació de descripcions textuals i formals de models de processos. Universitat Politècnica de Catalunya (2016)
Gallagher, R., Zelestis, E., Hollams, D., Denney-Wilson, E., Kirkness, A.: Impact of the healthy eating and exercise lifestyle programme on depressive symptoms in overweight people with heart disease and diabetes. Eur. J. Prev. Cardiol. 21(9), 1117–1124 (2014)
Sohail, A., Haseeb, A., Rehman, M., Dominic, D.D., Butt, M.A.: An intelligent graph edit distance-based approach for finding business process similarities. Comput. Mater. Continua 69(3), 3603–3618 (2021)
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Sohail, A., Ali, S., Butt, M.A., Rana, M.K.S., Anjum, M.S., Tariq, M.I. (2023). Lifestyle Monitoring of Diabetic Patients Using Business Process on Social Media. In: Balas, V.E., Jain, L.C., Balas, M.M., Baleanu, D. (eds) Soft Computing Applications. SOFA 2020. Advances in Intelligent Systems and Computing, vol 1438. Springer, Cham. https://doi.org/10.1007/978-3-031-23636-5_38
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DOI: https://doi.org/10.1007/978-3-031-23636-5_38
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