Your age may not tell the whole story about how healthy you are

By Tarun Sai LomteReviewed by Susha Cheriyedath, M.Sc.Oct 19 2026

Can five simple lifestyle measures tell us how well we're aging? Swiss researchers investigated what everyday habits and personal well-being might reveal about functional age.

Study: Lifestyle factors associated with biofunctional age in a cross-sectional study of adults aged 35 and older. Image Credit: Prathankarnpap / Shutterstock

In a recent study published in the journal Scientific Reports, researchers examined lifestyle factors associated with bio-functional age (BFA) and developed a preliminary index to assess functional health.

Population growth and longer life expectancy reflect improvements in living conditions and medical care, accompanied by a mounting burden of disability, chronic diseases, and functional decline in later life. The global proportion of older people (≥ 60 years) is projected to reach 22% by 2050. The Decade of Healthy Aging 2021–30, an international initiative, aims to improve older people's lives and promote healthy aging.

The initiative recognizes the significance of social inclusion, mental and physical health, economic well-being, and self-determination throughout life. The concept of BFA, or bio-psychosocial age, has been introduced to evaluate aging beyond chronological terms, as people differ in their cognitive, physical, social, and emotional functioning. BFA is a multidimensional measure of functional capacity that integrates information from physical, emotional–social, and cognitive–mental domains. Unlike molecular measures of biological aging, it reflects functional health rather than cellular aging or life expectancy.

Lifestyle factors are important determinants of healthy aging. Smoking, body mass index (BMI), dietary habits, physical activity, and alcohol intake have been identified as contributors to aging outcomes. Regular physical activity, abstinence from smoking, and a healthy BMI are associated with healthy aging. While evidence suggests lifestyle factors interact, and even cluster, how combined lifestyle patterns relate to bio-functional aging remains poorly understood.

About the study

In the present study, the researchers investigated lifestyle factors associated with BFA and developed a preliminary lifestyle index (LI) for assessing bio-functional status (BFS). German-speaking adults aged 18–65 years were enrolled as part of another study at a university hospital in Switzerland between 2012 and 2014. Pregnancy, acute illness, and illiteracy were exclusion criteria for the original cohort; participants younger than 35 years were excluded from the present analysis because the BFA model requires a minimum chronological age (CA) of 35 years.

Data on participants' CA, lifestyle behaviors, social status, occupational status, and personal and family medical history were collected using a structured assessment. Lifestyle behaviors included alcohol intake, sleep quality, smoking, and physical activity. Social status variables were parental status, partnership status, and satisfaction with sexual life and relationship. Subjects also completed questionnaires for anxiety, depression, stress, and quality of life.

The researchers evaluated BFS using a comprehensive test battery, with parameters split across four domains: physical performance and nutrition, age-dependent sensory functions, mental and psychomotor performance, and social and emotional areas. BFA was derived from factor analysis and sex-specific regression of functional age. The BFA-Index was calculated as the difference between BFA and CA.

The team developed and estimated the composite LI on a 10-point scale based on five parameters: BMI, physical activity, smoking, sleep quality, and satisfaction with sexual life, with each contributing up to two points. The highest BMI score was assigned to values below 18.5 kg/m². Next, they examined the relationships between individual lifestyle factors, the LI, and BFA using a correlation analysis. Additional statistical tests assessed group differences and associations, including chi-square, Kruskal-Wallis, and Mann-Whitney U tests and ordinal logistic regression.

Findings

The original cohort comprised 630 participants, 74% of whom were female, with a mean age of 39.5 years. After excluding those under 35 years, the analysis included 312 participants (mean age approximately 53 years). Most participants were female (79%), physically active (70%), and non-smokers (59%). On average, the BFA was 44.1 year-equivalents (ye), and the BFA-Index was −8.8 ye, indicating that participants' functional age was lower than their CA.

The average LI was 5.7 points. A higher LI was modestly but significantly associated with lower BFA, even after adjusting for chronological age. Regarding individual lifestyle factors, higher scores for BMI categories, physical activity, and satisfaction with sexual life were significantly negatively correlated with BFA. Physical activity showed the strongest association with lower BFA among the five individual lifestyle factors. Meanwhile, the smoking-related score showed a positive correlation with BFA, likely reflecting an age effect, as the few current smokers in the sample were younger than former or never smokers, on average. Sleep quality showed no statistically significant association with BFA.

Alcohol consumption was examined separately but excluded from the final index. An initial association between moderate drinking and lower BFA weakened when the scoring was revised to give abstinence the highest score, consistent with public health guidance. The authors suggested that socioeconomic differences may explain the initial finding and cautioned against interpreting it as evidence that alcohol improves healthy aging.

Conclusions

Taken together, the LI, based on a small, easily assessed set of behaviors, showed a significant negative correlation with BFA within this sample, suggesting that the combined lifestyle factors may relate to multi-dimensional aspects of functional aging. The findings support viewing active aging through functional capacity and well-being, accounting for individual circumstances rather than relying on a fixed biological-age target.

The study's limitations include its cross-sectional design (which precludes causal inference), recruitment bias (as participants were primarily recruited from a medical setting), the cohort's relative homogeneity, self-reporting bias, and potential overlap between BFA domains and LI variables. The index was developed and assessed in the same participants, leaving its predictive performance and clinical usefulness uncertain. Further studies are needed to validate the LI in independent, more diverse, and larger cohorts for clinical application.