The article titled “Lifestyle and Psychosocial Determinants of Quality of Life in Turkish Community-Dwelling Older Adults: A Multivariate and Hierarchical Regression Approach” offers a multidimensional examination of the factors shaping quality of life in later life within the Turkish sociocultural context. Positioned at the intersection of gerontology, public health, and behavioral science, the study moves beyond single-factor explanations and instead adopts a holistic analytical lens integrating lifestyle behaviors, psychosocial dynamics, and sociodemographic structures. In doing so, it contributes to the growing international literature that frames ageing not merely as a biomedical trajectory but as a biopsychosocial process shaped by structural inequalities, behavioral practices, and mental health conditions.
Grounded conceptually in the World Health Organization’s Healthy Ageing Framework and the biopsychosocial model, the study conceptualizes quality of life as a composite outcome emerging from the interaction between intrinsic capacity and environmental context. Within this framework, diet quality and physical activity represent modifiable behavioral components, depression reflects a proximal psychosocial determinant, and socioeconomic indicators function as structural conditions influencing exposure to risks and access to supportive resources. This theoretically informed positioning enables the authors to test not only direct associations but also the relative explanatory power of different determinant clusters.
Methodologically, the research is based on a cross-sectional dataset collected from 966 older adults aged 65 and above residing in the central district of Edirne, Türkiye. Participants were recruited through 15 Family Health Centers using stratified sampling to ensure representativeness. Data collection combined validated psychometric scales, nutritional assessments, anthropometric measurements, and structured questionnaires. Key instruments included the WHOQOL-OLD-BREF for quality of life, the Geriatric Depression Scale for depressive symptoms, the International Physical Activity Questionnaire for activity levels, and the Healthy Eating Index-2015 for dietary quality. This multimodal measurement strategy strengthened construct validity by capturing biological, behavioral, and psychosocial dimensions simultaneously.
The analytical design employed multiple linear regression, hierarchical regression, and logistic regression models. This layered modeling approach enabled the authors to examine incremental variance contributions and to test whether psychosocial factors exert stronger effects on quality of life than lifestyle or demographic indicators. Hierarchical modeling was particularly critical, as it operationalized the theoretical structure by entering variables in blocks: sociodemographic factors first, lifestyle behaviors second, and psychosocial variables last.
Findings consistently identified depression as the strongest predictor of quality of life across all statistical models. Higher depressive symptomatology was associated with markedly lower quality-of-life scores, underscoring the centrality of psychological resilience and emotional well-being in later life. Physical activity emerged as the second most influential determinant, positively associated with functional capacity, social engagement, and perceived well-being. These results collectively reinforce the argument that functional participation and mental health outweigh purely biomedical indicators in explaining life satisfaction among older adults.
Diet quality, while statistically associated in preliminary models, displayed a weaker and model-sensitive relationship. Its significance diminished after adjusting for psychosocial variables, suggesting indirect or mediated pathways rather than an independent effect. This attenuation highlights the possibility that nutritional behaviors influence quality of life through psychological well-being, functional status, or socioeconomic conditions rather than through direct experiential pathways.
Sociodemographic disparities further shaped vulnerability profiles. Lower income, limited education, and physical inactivity increased the likelihood of falling into the lowest quality-of-life quartile. These structural inequalities interacted with psychosocial burdens, reinforcing cumulative disadvantage in ageing trajectories. Gendered patterns were also observed descriptively, with women exhibiting higher depression risk and obesity prevalence, while men reported higher physical activity. However, gender did not remain an independent predictor in multivariable models.
An important contextual contribution of the study lies in its cultural localization. The authors argue that Türkiye’s familial caregiving structures, nutrition traditions, and socioeconomic inequalities create a distinctive ageing environment. Consequently, findings derived from Western populations may not fully translate to middle-income settings characterized by different dietary practices, mental health access patterns, and social support systems.
Despite its robust sampling and analytical rigor, the study acknowledges temporal limitations. The dataset was collected in 2016, preceding major socioeconomic shifts and the COVID-19 pandemic. Therefore, findings should be interpreted as historically situated rather than directly generalizable to current policy environments.
In conclusion, the article advances a person-centered understanding of healthy ageing by demonstrating that psychosocial resilience and functional engagement constitute the core determinants of quality of life. Nutritional and biomedical indicators remain relevant but operate largely through indirect pathways. For policymakers and practitioners, the evidence suggests that interventions prioritizing mental health support, physical activity promotion, and social participation may yield the greatest gains in later-life well-being.
Reference: Cemali, Ö., & Dağdeviren, H. N. (2026). Lifestyle and psychosocial determinants of quality of life in Turkish community-dwelling older adults: A multivariate and hierarchical regression approach. BMC Geriatrics. Advance online publication. https://doi.org/10.1186/s12877-026-07128-z
Mini Dictionary (Key Concepts)
Quality of Life (QoL): A multidimensional construct encompassing physical health, psychological state, social relationships, functional autonomy, and environmental satisfaction in an individual’s lived experience.
Healthy Ageing Framework: A WHO-developed model defining healthy ageing as the process of maintaining functional ability through the interaction of intrinsic capacity and environmental supports.
Depression (Geriatric Depression): A psychosocial condition characterized by persistent sadness, withdrawal, and loss of motivation, significantly impairing functional ability and perceived well-being in older adults.
Physical Activity (IPAQ-based): Any bodily movement requiring energy expenditure, operationalized through frequency, duration, and intensity metrics to assess functional engagement.
Diet Quality (HEI-2015): An index measuring adherence to nutritional guidelines based on adequacy and moderation components such as fruit, vegetable, sodium, and saturated fat intake.
Hierarchical Regression. A stepwise statistical modeling technique that enters variables in theoretically informed blocks to assess incremental explanatory power.Biopsychosocial Model: An integrative framework positing that health outcomes emerge from the interaction of biological conditions, psychological states, and social environments