Wellness and active ageing among older adults (65+): case in Albania
DOI:
https://doi.org/10.59476/mtt2026.v2i22.787Keywords:
active ageing, wellness, older adults, demographic ageing, wellbeing, AlbaniaAbstract
Albania’s demographic and social landscape is increasingly shaped by the growing proportion of individuals aged 65 years and over. As life expectancy rises and demographic ageing becomes more pronounced, older adults represent an increasingly important population group that requires enhanced social, health and community-based support. Nevertheless, many older people in Albania continue to face challenges related to mental well-being, social isolation, loneliness, limited access to organized activities, and a lack of sustainable wellness initiatives tailored to their needs.
This study examines demographic ageing trends and selected indicators of wellness and active ageing among older adults in Albania. The analysis is based on Population and Housing Census data from 2001, 2011 and 2023, complemented by Labour Force Survey data and social inclusion indicators. A descriptive demographic trend analysis and linear trend regression models are used to assess changes in population structure and labour market participation among individuals aged 65 years and over.
The findings reveal a substantial increase in the older population, accompanied by important challenges related to health conditions, disability, poverty risk, and social exclusion. At the same time, improvements in educational attainment and increased labor force participation among some groups of older adults indicate opportunities to promote active ageing. The results highlight the multidimensional nature of wellness in later life, emphasizing that active ageing extends beyond healthcare to include social participation, economic security, community engagement, dignity and independence.
The study underscores the importance of developing accessible, community-based initiatives that support healthy, active ageing. The findings provide evidence that may inform the design of policies to improve the well-being and quality of life of older adults in Albania.
References
1. Berkman, L. F., Glass, T., Brissette, I., & Seeman, T. E. (2000). From social integration to health: Durkheim in the new millennium. Social Science & Medicine, 51(6), 843–857. https://doi.org/10.1016/s0277-9536(00)00065-4
2. Bowling, A., & Dieppe, P. (2005). What is successful ageing and who should define it? BMJ, 331(7531), 1548–1551. https://doi.org/10.1136/bmj.331.7531.1548
3. Fisher, J. W. (2011). The four domains model: Connecting spirituality, health and well-being. Religions, 2(1), 17–28. https://doi.org/10.3390/rel2010017
4. Hettler, B. (1980). Wellness promotion on a university campus. Family & Community Health, 3(1), 77–95. https://doi.org/10.1097/00003727-198005000-00008
5. Keyes, C. L. M. (2002). The mental health continuum: From languishing to flourishing in life. Journal of Health and Social Behavior, 43(2), 207–222. https://doi.org/10.2307/3090197
6. Rowe, J. W., & Kahn, R. L. (1997). Successful aging. The Gerontologist, 37(4), 433–440. https://doi.org/10.1093/geront/37.4.433
7. Travis, J. W., & Ryan, R. S. (2004). The wellness workbook (3rd ed.). Celestial Arts.
8. Walker, A. (2002). A strategy for active ageing. International Social Security Review, 55(1), 121–139. https://doi.org/10.1111/1468-246X.00118
9. WHO. (2002). Active ageing: A policy framework. World Health Organization. https://iris.who.int/server/api/core/bitstreams/0418705f-1c82-4fe2-82d6-d4faee89dfa0/content
10. WHO. (2020). Healthy ageing and functional ability. World Health Organization. https://www.who.int/news-room/questions-and-answers/item/healthy-ageing-and-functional-ability
11. WHO. (2023). WHO`s work on the UN Decade of Healthy Ageing (2021–2030). World Health Organization. https://www.who.int/initiatives/decade-of-healthy-ageing
12. WHO. (2025). Ageing and health. World Health Organization.
https://www.who.int/news-room/fact-sheets/detail/ageing-and-health
13. U.S. Census Bureau. (2014). Subnational Projections Toolkit User’s Guide, Version 2.0. https://www2.census.gov/software/sptoolkit/documentation/sptoolkitusersguide.pdf
14. Ismail, M. H., Yusof, M. M., & Yusof, N. (2026). Forecasting Labour Force Trends Among Older Persons in Malaysia Using Time Series Analysis. Journal of Computational Innovation and Analytics, 5(1), 72–83. https://doi.org/10.32890/jcia2026.5.1.5
15. Papke, L. E., & Wooldridge, J. M. (1996). Econometric methods for fractional response variables with an application to 401(k) plan participation rates. Journal of Applied Econometrics, 11(6), 619–632. https://www.jstor.org/stable/2285155
16. Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts. https://otexts.com/fpp3/
17. National Institute of Standards and Technology. (2012). Linear least squares regression. In NIST/SEMATECH e-Handbook of Statistical Methods. https://www.itl.nist.gov/div898/handbook/pmd/section1/pmd141.htm
18. Mamingi, N. (2014). Forecasting unemployment in Barbados. Central Bank of Barbados.
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