Over 60 - (r)evolution of technology use among Bulgarian participants in project SAAM

  • Nadejda Nikolaeva Miteva BILSP
  • Zlatka Gospodinova Balkan Institute for Labour and Social Policy
  • Vera Veleva Balkan Institute for Labour and Social Policy
  • Yordan Dimitrov Balkan Institute for Labour and Social Policy
  • Kaloyan Haralampiev Sofia University St. Kliment Ohridski
Keywords: active ageing, seniors, coaching, smart technologies, SAAM


Project SAAM (Supporting Active Ageing through Multimodal coaching) aimed at providing new methods supporting Europe’s ageing population to remain active and live independently at home for as long as possible. This article presents the case study of designing the SAAM system for and and testing it together with seniors, sharing the experience from Bulgaria. The article particularly focuses on project results in the area of technology uptake among seniors. SAAM proved to be a system with significant potential to meet seniors’ active ageing needs, because of its high level of personalisation. On the other hand, most Bulgarian seniors participating in the project managed to adapt to having the SAAM system present in their lives regardless of their previous experience with technologies or lack thereof. Thus, SAAM contributed to answering some of the open questions in the field of active ageing. Still, observing the interplay between the complex and multi-domain system’s functioning and seniors’ experience with it inevitably raised new questions to be answered in the future;


1. Andreoni, G. et al. 2021. Other Advanced Research Initiatives in Elderly Care and Fragility Prevention, in Digital Health Technology for Better Aging: A multidisciplinary approach. Cham: Springer International Publishing, pp. 327–359. [online] Available at: https://doi.org/10.1007/978-3-030-72663-8_20 [Accessed: 2021].
2. Blomkvist, S., 2006. The User as a Personality: A Reflection on the Theoretical and Practical Use of Personas in HCI Design. Uppsala: Uppsala University. [online] Available at: https://www.it.uu.se/research/publications/reports/2006-049/2006-049-nc.pdf [Accessed: 2021].
3. Demsar, J., Curk T., Erjavec, A., Gorup, C., Hocevar, T., Milutinovic, M., Mozina, M., et al. 2013. Orange: Data Mining Toolbox in Python. Journal of Machine Learning Research, 14, pp. 2349−2353.
4. Dodge, R. et al. 2012. ‘The challenge of defining wellbeing’, International Journal of Wellbeing, 2. DOI: 10.5502/ijw.v2i3.4.
5. Eibe, F., Hall, M. and Witten, I. 2016. The WEKA Workbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann: Fourth Edition.
6. Genealogies of Knowledge. 2018. Software. [online] Available at: http://genealogiesofknowledge.net/software/#modnlp [Accessed: 2021].
7. Haider, F. et al. 2018. SAAMEAT: Active Feature Transformation and Selection Methods for the Recognition of User Eating Conditions, in Proceedings of the 20th ACM International Conference on Multimodal Interaction. New York, NY, USA: Association for Computing Machinery, pp. 564–568. DOI: 10.1145/3242969.3243685.
8. Haider, F., Fuente, S. de la and Luz, S. 2020. An Assessment of Paralinguistic Acoustic Features for Detection of Alzheimer’s Dementia in Spontaneous Speech, IEEE Journal of Selected Topics in Signal Processing, 14(2), pp. 272–281. DOI: 10.1109/JSTSP.2019.2955022.
9. Haider, F. and Luz, S. 2019. Attitude Recognition Using Multi-resolution Cochleagram Features, in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3737–3741. DOI: 10.1109/ICASSP.2019.8682974
10. IBM Corporation. 2016. TwoStep Cluster Analysis. [online] Available at: https://www.ibm.com/docs/en/spss-statistics/24.0.0?topic=base-twostep-cluster-analysis [Accessed: 2021].
11. International Longevity Centre UK. 2019. Older People’s Quality of Life Questionnaire (OPQOL-35). [online] https://ilcuk.org.uk/wp-content/uploads/2019/03/OPQOL-full-questionnaire.pdf [Accessed: 2021].
12. Kumar, K. M., and Ramma Mohan Reddy, A. 2017. An efficient k-means clustering filtering algorithm using density based initial cluster centres. Information Sciences 418-419, pp. 286-301. [online] DOI: https://doi.org/10.1016/j.ins.2017.07.036 [Accessed: 2021].
13. Luz, S., and Sheehan, S. 2020. Methods and visualization tools for the analysis of medical, political and scientific concepts in genealogies of knowledge. Palgrave Commun 6(49), pp. 1-20.
14. R Core Team. 2021. R: A language and environment for statistical computing. R Foundation for Statistical Computing: Vienna. [online] https://www.R-project.org/ [Accessed: 2021].
15. Science Direct. 2021. Multidimensional Scaling. [online] https://www.sciencedirect.com/topics/computer-science/multidimensional-scaling [Accessed: 2021]
16. Steinke, F., Bading, N., Fritsch, T., and Simonsen, S. 2014. Factors influencing trust in Ambient Assisted Living Technology: A scenario-based analysis. Gerontechnology 81-100. DOI: https://doi.org/10.4017/gt.2013.
17. Tan, P.-N., Steinbach, M., Karpatne, A., and Kumar, V. 2019. Cluster Analysis: Basic Concepts and Algorithms. In Introduction to Data Mining (Second Edition), by Pang-Ning Tan, Michael Steinbach, Anuj Karpatne and Vipin Kumar, 525-612. Pierson. [online] https://www-users.cse.umn.edu/~kumar001/dmbook/index.php [Accessed: 2021].
How to Cite
Miteva, N., Gospodinova, Z., Veleva, V., Dimitrov, Y., & Haralampiev, K. (2022). Over 60 - (r)evolution of technology use among Bulgarian participants in project SAAM. Vanguard Scientific Instruments in Management, 17. Retrieved from https://vsim-journal.info/index.php?journal=vsim&page=article&op=view&path[]=273