A Personalised Approach to Identifying Important Determinants of Well-being.

Purpose: To develop effective and personalized interventions, it is essential to identify the most critical processes or psychological drivers that impact an individual's well-being. Some processes may be universally beneficial to well-being across many contexts and people, while others may only be beneficial to certain individuals in specific contexts. Method: We conducted three intensive daily diary studies, each with more than 50 within-person measurement occasions, across three data sets (n1 = 44; n2 = 37; n3 = 141). We aimed to investigate individual differences in the strength of within-person associations between three distinct process measures and a variety of outcomes. We utilized a unique idiographic algorithm, known as i-ARIMAX (Autoregressive Integrated Moving Average), to determine the strength of the relationship (Beta) between each process and outcome within individuals ("i"). All of the computed betas were then subjected to meta-analyses, with individuals treated as the "study". Results: The results revealed that the process-outcome links varied significantly between individuals, surpassing the homogeneity typically seen in meta-analyses of studies. Although several processes showed group-level effects, no process was found to be universally beneficial when considered individually. For instance, processes involving social behavior, like being assertive, did not demonstrate any group-level links to loneliness but still had significant individual-level effects that varied from positive to negative. Discussion: Using i-ARIMAX might help reduce the number of candidate variables for complex within-person analyses. Additionally, the size and pattern of i-ARIMAX betas could prove useful in guiding personalized interventions. [ABSTRACT FROM AUTHOR]