Studies in psychosomatic medicine are characterized by analyses that typically compare groups. This nomothetic approach leads to conclusions that apply to the average group member but not necessarily to individual patients. Idiographic studies start at the individual patient and are suitable to study associations that differ between time points or between individuals. We illustrate the advantages of the idiographic approach in analyzing ambulatory assessments, taking the association between depression and physical activity after myocardial infarction as an example.
Five middle-aged men who had myocardial infarction with mild to moderate symptoms of depression were included in this study. Four of these participants monitored their physical activity and depressive symptoms during a period of 2 to 3 months using a daily self-registration form. The time series of each individual participant were investigated using vector autoregressive modeling, which enables the analysis of temporal dynamics between physical activity and depression.
We found causal heterogeneity in the association between depression and physical activity. Participants differed in the predominant direction of effect, which was either from physical activity to depression (n = 1, 85 observations, unstandardized effect size = −0.183, p = .03) or from depression to physical activity (n = 2, 65 and 59 observations, unstandardized effect sizes = −0.038 and −0.381, p < .001 and p = .04). Also, the persistency of effects differed among individuals.
Vector autoregressive models are suitable in revealing causal heterogeneity and can be easily used to analyze ambulatory assessments. We suggest that these models might bridge the gap between science and clinical practice by translating epidemiological results to individual patients.
PEP = Psycho-Educational Prevention Module
BDI = Beck Depression Inventory
PCI = percutaneous coronary intervention
CABG = coronary artery bypass graft
LVEF = left ventricular ejection fraction
BMI = body mass index
VAR = vector autoregressive modeling