The study, published in Scientific Reports, drew on five biennial waves of the Korean Longitudinal Study of Aging (KLoSA) between 2014 and 2022, a nationally representative panel survey of adults aged 45 and older. The research team, led by Gyeong-min Lee and Jae-hyun Kim of Dankook University together with colleagues at Gachon University and Seoul National University, set out to answer two questions with direct policy consequences. First, does the multidimensional Active Ageing Index (AAI) follow different trajectories across the retirement transition in metropolitan versus non-metropolitan areas? Second, do the individual characteristics that push retirees into ‘low active ageing’ differ by region?

The Active Ageing Index, built on standards proposed by the United Nations Economic Commission for Europe, is not a simple health score. In this Korean adaptation it combined three domains — health, participation and security — measured through eleven indicators. Health captured self-rated health, psychological distress on the CES-D10 scale, functional limitations in daily and instrumental activities, and chronic disease burden. Participation included labor force involvement, caregiving for family or grandchildren, volunteering and membership in social, religious or civic groups. Security rested on equivalized household income, pension status and homeownership. Each domain was normalized to a scale of zero to one using the United Nations Development Programme formula familiar from the Human Development Index, and the total AAI was their mean.

Because retirees are not a random cross-section of society, the team first had to make the regional comparison fair. From an initial pool of 8,238 respondents, they identified 4,801 people aged 55 or older who experienced retirement during the study period — 58.5 percent of those with complete key variables. Event-aligned analysis required an identifiable retirement wave and at least one AAI observation at the retirement wave and an adjacent time point, leaving 1,307 eligible retirees. Propensity score matching, using 1:1 nearest-neighbor matching with a caliper of 0.25, then balanced metropolitan residents of Seoul, Gyeonggi-do and Incheon against everyone else, producing a matched repeated-measures sample of 872 participants split evenly between the two groups. Metropolitan and non-metropolitan retirees, who differed in age, smoking and physical activity before matching, were statistically indistinguishable afterward.

The statistical machinery then turned to trajectories. Using piecewise generalized estimating equation models with region-by-time interactions, the researchers aligned each person’s data around their own retirement date: one wave before, the retirement wave itself, and up to two waves after. The results showed a clear dip at the transition itself. The pre-retirement time coefficient was negative and highly significant (β = −0.0925, p < 0.001), meaning total AAI fell from the wave before retirement to the retirement wave. After that, the index was stable: the post-retirement slope was essentially flat and not statistically significant (β = 0.0032, p = 0.315). Crucially, neither the main effect of metropolitan residence nor any region-by-time interaction approached significance, indicating that the shape of the trajectory was the same in the capital region and elsewhere.

But here the study delivers its most technically instructive twist. Labor force participation is one of the indicators inside the AAI’s participation domain — which means the index and the event of retirement overlap conceptually. Drop out of work, and your AAI drops partly by definition. In a sensitivity analysis that removed labor force participation from the participation domain, the retirement-wave decline in the revised total AAI shrank to statistical insignificance (β = −0.0076, p = 0.218). The apparent cliff, in other words, was largely a measurement artifact of the index’s construction. Yet something genuinely important survived: the revised participation index showed a significant negative post-retirement slope (β = −0.0196, p = 0.012), suggesting that non-work engagement — caregiving, volunteering, social groups — continues to erode after people leave employment. Retirement may not damage overall active ageing so much as slowly hollow out its participatory core.

The second analysis shifted from trajectories to risk. Among matched retirees whose AAI was at or above 0.5 at the retirement wave, the team used Cox proportional hazards models to examine who crossed below that midpoint threshold first. During follow-up, 170 of 553 participants transitioned to low active ageing while 383 were censored. Kaplan–Meier curves with log-rank tests showed no difference between regions (χ² = 0.443, p = 0.506), and metropolitan residence itself carried no elevated hazard (HR = 1.122, 95% CI 0.827–1.521, p = 0.460). Interactions between region and individual characteristics were likewise non-significant across the board.

Two individual factors did stand out with striking consistency. Retirees aged 75 and older faced a 70 percent higher hazard of transitioning to low active ageing than those aged 55 to 64 (HR = 1.698, 95% CI 1.080–2.669, p = 0.022). Regular physical activity, meanwhile, cut the hazard by roughly 30 percent (HR = 0.695, 95% CI 0.509–0.949, p = 0.022). The proportional hazards assumption held, verified with Schoenfeld residuals. In plain terms: getting older is dangerous for active ageing, and moving your body is protective — regardless of your postal code.

The authors are careful about what the null regional findings do and do not mean. The absence of significant interactions does not prove that place never matters for ageing well; it shows that the crude KLoSA metropolitan/non-metropolitan distinction failed to capture measurable heterogeneity in aligned AAI change. International research, including studies from China showing sharp east-versus-west differences and European sub-national AAI comparisons, suggests that finer-grained contextual features — local services, transport, community resources and social capital — may drive place-based differences that broad geographic labels obscure. The study’s event-aligned design also carries limits: KLoSA’s biennial rhythm means retirement timing is known only to the survey wave, only one pre-retirement observation exists, and the authors explicitly caution that changes around retirement should not be read as causal effects of retirement itself.

For a nation that crossed into super-aged status in 2025, with adults 65 and older now 20.3 percent of the population, the implications are pointed. Korea’s institutional peculiarities — mandatory retirement allowed at 60 while pension eligibility arrives later — create a vulnerable gap in which work exits, income security and daily routines must be renegotiated simultaneously. This study suggests that blanket regional targeting is the wrong lens for that gap. Instead, the risk of sliding into low active ageing concentrates among the oldest retirees and those who do not exercise regularly, and the quiet decay of social participation after work ends may be the signal worth watching. If retirement is a window of opportunity, as earlier reviews have proposed, these results indicate the window is best used not by moving to the city or fleeing it, but by staying active and staying connected.

Subject of Research: Active ageing trajectories across the retirement transition among Korean older adults

Article Title: Active ageing across the retirement transition in Korea: regional trajectories and risk of low active ageing

Article References: Lee, G.-M., Cho, H.-K., Hong, J.-W., Moon, J. Y., & Kim, J.-H. (2026). Active ageing across the retirement transition in Korea: regional trajectories and risk of low active ageing. Scientific Reports, 16(1), Article 32847. https://doi.org/10.1038/s41598-026-61165-7

Image Credits: AI Generated

DOI: 10.1038/s41598-026-61165-7

Keywords: active ageing, retirement, Active Ageing Index, KLoSA, South Korea, regional disparities, older adults, physical activity, longitudinal study, Cox proportional hazards, propensity score matching, social participation