Integrating Salutogenesis into Predictive Analytics: Modelling SOC As a Key Indicator of Resilience and Well–being

Background: Salutogenesis emphasizes factors that promote health and well-being, focusing on strengths rather than diseases. Central to this framework is Sense of Coherence (SOC), which reflects an individual’s capacity to perceive life as comprehensible, manageable, and meaningful. A higher SOC is associated with better stress management, resilience, and overall well-being. Advances in predictive analytics, including statistical modelling and machine learning, offer opportunities to integrate the SOC into models that forecast health outcomes, identify at-risk individuals, and guide interventions. Despite the growing interest, research on SOC in predictive models is fragmented and varies in methods, highlighting the need for a comprehensive review. Objectives: To review the literature on the use of SOC in predictive analyses and evaluate its effectiveness as an indicator of resilience and well-being. Discussion: Evidence shows that SOC is a reliable predictor of psychological resilience and well-being. Predictive models incorporating SOC, ranging from regression to machine learning, often outperform models that rely solely on traditional risk factors. The limitations of this study include variability in SOC measurement, limited longitudinal validation, and heterogeneity in the modelling approaches. Nevertheless, SOC provides important insights into resilience outcomes at the individual and population levels. Conclusion: Integrating SOC into predictive analyses is a promising strategy for assessing resilience and well-being in older adults. Standardizing SOC measurements and combining them with multidimensional data can enhance predictive accuracy and inform personalized health interventions.