Well-being trajectories in breast cancer and their predictors: A machine-learning approach.

OBJECTIVE: This study aimed to describe distinct trajectories of anxiety/depression symptoms and overall health status/quality of life over a period of 18 months following a breast cancer diagnosis, and identify the medical, socio-demographic, lifestyle, and psychological factors that predict these trajectories. METHODS: 474 females (mean age = 55.79 years) were enrolled in the first weeks after surgery or biopsy. Data from seven assessment points over 18 months, at 3-month intervals, were used. The two outcomes were assessed at all points. Potential predictors were assessed at baseline and the first follow-up. Machine-Learning techniques were used to detect latent patterns of change and identify the most important predictors. RESULTS: Five trajectories were identified for each outcome: stably high, high with fluctuations, recovery, deteriorating/delayed response, and stably poor well-being (chronic distress). Psychological factors (i.e., negative affect, coping, sense of control, social support), age, and a few medical variables (e.g., symptoms, immune-related inflammation) predicted patients' participation in the delayed response and the chronic distress trajectories versus all other trajectories. CONCLUSIONS: There is a strong possibility that resilience does not always reflect a stable response pattern, as there might be some interim fluctuations. The use of machine-learning techniques provides a unique opportunity for the identification of illness trajectories and a shortlist of major bio/behavioral predictors. This will facilitate the development of early interventions to prevent a significant deterioration in patient well-being.

Creating a Culture of Health in Planning and Implementing Innovative Strategies Addressing Non-communicable Chronic Diseases.

Ongoing demographic changes are challenging health systems worldwide especially in relation to increasing longevity and the resultant rise of non-communicable diseases (NCDs). To meet these challenges, a paradigm shift to a more proactive approach to health promotion, and maintenance is needed. This new paradigm focuses on creating and implementing an ecological model of Culture of Health. The conceptualization of the Culture of Health is defined as one where good health and well-being flourish across geographic, demographic, and social sectors; fostering healthy equitable communities where citizens have the opportunity to make choices and be co-producers of healthy lifestyles. Based on Antonovsky's Salutogenesis model which asserts that the experience of health moves along a continuum across the lifespan, we will identify the key drivers for achieving a Culture of Health. These include mindset/expectations, sense of community, and civic engagement. The present article discusses these drivers and identifies areas where policy and research actions are needed to advance positive change on population health and well-being. We highlight empirical evidence of drivers within the EU guided by the activities within the thematic Action Groups of the European Innovation Partnership on Active and Healthy Aging (EIP on AHA), focusing on Lifespan Health Promotion and Prevention of Age-Related Frailty and Disease (A3 Action Group). We will specifically focus on the effect of Culture on Health, highlighting cross-cutting drivers across domains such as innovations at the individual and community level, and in synergies with business, policy, and research entities. We will present examples of drivers for creating a Culture of Health, the barriers, the remaining gaps, and areas of future research to achieve an inclusive and sustainable asset-based community.