Abstract Background Traditional variable-centred approaches often analyse physical behaviours (sedentary behaviour SB, light physical activity LPA, and moderate-to-vigorous physical activity MVPA) in isolation, potentially masking their combined effects on outcomes. This study applied latent profile analysis, a person-centred approach, to identify naturally occurring physical behaviour profiles in older adults and examined their associations with physical fitness and physical function. Methods This cross-sectional study included 1,095 older Portuguese adults (≥ 65 years; 765 females). SB, LPA, and MVPA were assessed using accelerometry (Actigraph; Pensacola, Florida) on the right hip and expressed as percentages of waking time. Latent profile analysis was used to identify distinct profiles based on these percentages. Physical fitness was evaluated by Senior Fitness Test battery and handgrip strength. Physical function was assessed using the 12-item Composite Physical Function questionnaire. Generalised linear models, adjusted for age, were used to examine associations between profiles and outcomes. Results Three distinct profiles emerged for both sexes: “balanced movers” (~ 50% SB, ~ 46% LPA, ~ 4% MVPA), “intermediate movers” (~ 66% SB, ~ 32% LPA, ~ 2% MVPA), and “highly sedentary” (~ 80% SB, ~ 20% LPA, < 1% MVPA). Compared to the “highly sedentary” groups, both “balanced movers” and “intermediate movers” demonstrated better performance on most physical fitness tests and reported higher physical function. Notably, “intermediate movers”, performed similarly to “balanced movers” in most measures. Conclusions Distinct physical behaviour profiles exist among older Portuguese adults. Profiles characterised by lower SB and higher LPA, even when not fully meeting MVPA recommendations (“intermediate movers”), were associated with better physical fitness and physical function compared to the “highly sedentary” profile. This underscores the importance of reducing SB and promoting LPA along with MVPA. By uncovering these behavioural profiles among older adults, latent profile analysis provides valuable insights to guide the development of more personalized interventions for healthy ageing.
Zymbal et al. (2026) studied this question.