A Personalised Approach to Identifying Important Determinants of Well-being

To develop effective and personalized interventions, it isessential to identify the most critical processes or psychological drivers thatimpact an individual’s well-being. Some processes may be universallybeneficial to well-being across many contexts and people, while others may onlybe beneficial to certain individuals in specific contexts.

We conducted three intensive daily diary studies, each withmore than 50 within-person measurement occasions, across three data sets(n1 = 44; n2 = 37; n3 = 141). We aimedto investigate individual differences in the strength of within-personassociations between three distinct process measures and a variety of outcomes.We utilized a unique idiographic algorithm, known as i-ARIMAX (AutoregressiveIntegrated Moving Average), to determine the strength of the relationship (Beta)between each process and outcome within individuals (“i”). All ofthe computed betas were then subjected to meta-analyses, with individualstreated as the “study”.

The results revealed that the process-outcome links variedsignificantly between individuals, surpassing the homogeneity typically seen inmeta-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 notdemonstrate any group-level links to loneliness but still had significantindividual-level effects that varied from positive to negative.

Using i-ARIMAX might help reduce the number of candidatevariables for complex within-person analyses. Additionally, the size and patternof i-ARIMAX betas could prove useful in guiding personalizedinterventions.