Quality of life in older adults is influenced by multiple interconnected lifestyle factors. The study highlights the importance of adopting a holistic perspective by examining diet quality, physical activity, and depression together among community-dwelling older adults. Considering these components collectively offers more comprehensive insights that can support the development of community-based approaches. Integrating such multidimensional data with findings from large-scale studies would provide valuable guidance for designing more targeted and effective interventions to improve the quality of life in older individuals. This study aims to identify key predictors—especially diet quality, physical activity, and depression—among older adults in Edirne’s city center. A cross-sectional study was conducted between January and May 2016 in 15 Family Health Centers (FHCs) in Edirne, involving 966 older adults. The study was conducted using data collected in 2016. Data collection included questionnaires on mini nutritional assessment (MNA), international physical activity questionnaire short form (IPAQ-SF), quality of life questionnaires (WHOQOL-OLD-BREF), and geriatric depression scale (GDS), plus anthropometric measurements. Food intake was analyzed with nutritional information system (BEBIS) and healthy eating index- 2015 (HEI-2015). Statistical analyses using SPSS 22.0 included descriptive statistics, multiple linear, hierarchical, and logistic regressions, with significance set at p < 0.05. Results from multiple linear regression showed that depression risk was the strongest predictor of quality of life (β = 0.35, p < 0.001), followed by physical activity (β = 0.11, p < 0.001) and diet quality (β = 0.10, p < 0.001). Hierarchical regression confirmed depression as the most significant predictor (β = 0.33, p < 0.001), with physical activity (β = 0.09, p = 0.002) and diet quality (β = −0.09, p = 0.003) also contributing; the model explained 23.6% of variance (R² = 0.236). Logistic regression revealed that lower education (OR = 0.49), lower income (OR = 0.52), low physical activity (OR = 0.66), and depression (OR = 0.20) significantly decreased the odds of good quality of life ( p < 0.05). Age, gender, and diet quality were not statistically significant predictors. Older adults’ quality of life appears to be more strongly shaped by psychosocial—particularly depressive symptoms—and lifestyle factors, such as physical activity, than by biomedical indicators. The inverse association observed between diet quality and quality of life should therefore be interpreted with caution. Although a negative association between diet quality and quality of life was observed in initial models, this relationship was not sustained in hierarchical analyses, suggesting that diet quality may influence quality of life indirectly rather than acting as an independent determinant. The use of data collected in 2016 and the modest explanatory power of the models should be considered when interpreting these findings and their generalizability, underscoring the importance of multidimensional approaches to support healthy ageing.
Cemali et al. (Mon,) studied this question.