Background: Frailty exists as a complex condition that leads to higher disability rates, insti- tutionalization, and risk of death among older adults. The growing number of older adults requires more than a focus on longer life expectancy. The focus should be shifted towards helping seniors stay healthy while preserving their independence and quality of life. The existing difference between life expectancy and healthy life expectancy requires public health strategies that focus on preventing frailty development. Frailty is not an inevitable consequence of aging, but is shaped by modifiable factors in the physical, psychological, and social domains. Objective: This study aimed to explore frailty transitions among adults aged 80 and older, utilizing a radical constructivist approach within a system-theoretical framework. The research focused on four primary questions: (1) What key factors related to frailty transitions have been identified in the existing literature? (2) What distinct patterns of social participation can be empirically observed in this demographic? (3) How stable are these profiles, and do individuals transition between them? (4) Can frailty transitions be modeled as a stochastic process, and how do social participation profiles and biological reserves interact to shape these dynamics? Methods: A narrative review of the literature was conducted to identify key factors related to frailty. Empirically, a combined data set from the BUTTERFLY study and the Belgian Aging Studies (BAS) was analyzed. Social participation profiles were derived using clustering techniques on BAS variables. The evolution of the frailty state (robust vs. prefrail/frail) was modeled us- ing continuous-time Markov models, incorporating covariates such as age, appendicular lean mass (ALM), fatigue, and social participation groups. Results: Frailty transitions showed significant asymmetry; the prefrail/frail state exhibited high persistence and limited reversibility, which aligns with the concept of attractor states within a constrained system. Older age, reduced ALM, and fatigue were significant predictors of transitions towards frailty. Social participation profiles were found to be relatively stable but dynamic: specific subgroups (e.g., low engagement profiles) exhibited higher risks of adverse frailty transitions. The effects of the interaction between ALM and social participation underscore that no single subsystem dictates frailty trajectories; rather, it is their coupling that shapes individual pathways. Conclusion: Frailty progression is not a linear or inevitable process; rather, it is influenced by dynamic interactions across various domains. The continuous-time Markov model offers a solid framework for measuring these interactions. By defining frailty as an emergent and modifiable char- acteristic of interconnected systems, this study contributes to a more comprehensive understanding of aging. It offers actionable insights for designing more targeted, holistic interventions to support healthy longevity.
Steenhout, I, Buyl, R & Jansen, B 2025, 'Frailty Progression and Social Engagement in Aging Populations: Frailty Progression and Social Engagement in Aging Populations', Paper presented at RSSB Annual Meeting 2025, Brussels, Belgium, 17/11/25 - 18/11/25 pp. 20-21. <https://tverdebo.ulb.ac.be/resources/Programme.pdf>
Steenhout, I., Buyl, R., & Jansen, B. (2025). Frailty Progression and Social Engagement in Aging Populations: Frailty Progression and Social Engagement in Aging Populations. 20-21. Paper presented at RSSB Annual Meeting 2025, Brussels, Belgium. https://tverdebo.ulb.ac.be/resources/Programme.pdf
@conference{a8c2160654e0453fa7526bd0a96e3d8c,
title = "Frailty Progression and Social Engagement in Aging Populations: Frailty Progression and Social Engagement in Aging Populations",
abstract = "Background: Frailty exists as a complex condition that leads to higher disability rates, insti- tutionalization, and risk of death among older adults. The growing number of older adults requires more than a focus on longer life expectancy. The focus should be shifted towards helping seniors stay healthy while preserving their independence and quality of life. The existing difference between life expectancy and healthy life expectancy requires public health strategies that focus on preventing frailty development. Frailty is not an inevitable consequence of aging, but is shaped by modifiable factors in the physical, psychological, and social domains. Objective: This study aimed to explore frailty transitions among adults aged 80 and older, utilizing a radical constructivist approach within a system-theoretical framework. The research focused on four primary questions: (1) What key factors related to frailty transitions have been identified in the existing literature? (2) What distinct patterns of social participation can be empirically observed in this demographic? (3) How stable are these profiles, and do individuals transition between them? (4) Can frailty transitions be modeled as a stochastic process, and how do social participation profiles and biological reserves interact to shape these dynamics? Methods: A narrative review of the literature was conducted to identify key factors related to frailty. Empirically, a combined data set from the BUTTERFLY study and the Belgian Aging Studies (BAS) was analyzed. Social participation profiles were derived using clustering techniques on BAS variables. The evolution of the frailty state (robust vs. prefrail/frail) was modeled us- ing continuous-time Markov models, incorporating covariates such as age, appendicular lean mass (ALM), fatigue, and social participation groups. Results: Frailty transitions showed significant asymmetry; the prefrail/frail state exhibited high persistence and limited reversibility, which aligns with the concept of attractor states within a constrained system. Older age, reduced ALM, and fatigue were significant predictors of transitions towards frailty. Social participation profiles were found to be relatively stable but dynamic: specific subgroups (e.g., low engagement profiles) exhibited higher risks of adverse frailty transitions. The effects of the interaction between ALM and social participation underscore that no single subsystem dictates frailty trajectories; rather, it is their coupling that shapes individual pathways. Conclusion: Frailty progression is not a linear or inevitable process; rather, it is influenced by dynamic interactions across various domains. The continuous-time Markov model offers a solid framework for measuring these interactions. By defining frailty as an emergent and modifiable char- acteristic of interconnected systems, this study contributes to a more comprehensive understanding of aging. It offers actionable insights for designing more targeted, holistic interventions to support healthy longevity.",
author = "Iris Steenhout and Ronald Buyl and Bart Jansen",
year = "2025",
month = nov,
day = "17",
language = "English",
pages = "20--21",
note = "RSSB Annual Meeting 2025 ; Conference date: 17-11-2025 Through 18-11-2025",
url = "https://rssb.be/activities/annual-meeting/program/",
}