Applying P-Technique Factor Analysis to Explore Person-Specific Models of Readiness-to-Exercise

Abstract

Recent research in exercise prescription and periodization has emphasized the importance of subjective experience, both in medium- and long-term monitoring, but also in the acute experience. Emerging evidence also highlights an important role of subjective readiness (pre-exercise mental and physical states) in determining how exercise is experienced, and in acutely modifying the prescribed exercise intensity. The concept of “readiness-to-exercise” shows promise in enabling and informing this acute decision-making to optimize the experiences and outcomes of exercise. While subjective experiences can be effectively assessed using psychometric scales and instruments, these are often developed and deployed using cross-sectional samples, with resulting structures that reflect a normative pattern (nomothetic). These patterns may fail to reflect individual differences in sensitivity, experience and saliency (idiographic). We conducted this research with the primary aim of comparing the nomothetical and idiographic approaches to modeling the relatively novel concept of readiness-to-exercise. Study 1 (nomothetic) therefore analyzed data collected from 572 participants who completed a one-time survey using R-technique factor analysis. Results indicated a four-factor structure that explained 60% of the variance: “health and fitness;” “fatigue;” “vitality” and “physical discomfort.” Study 2 (idiographic) included a sample of 29 participants who completed the scale multiple times, between 42 and 56 times: permitting intra-individual analysis using separate P-technique factor analyses. Our analyses suggested that many individuals displayed personal signature, or “profiles” of readiness-to-exercise that differed in structure from the nomothetic form: only two participants' personal signatures contained four structures as modeled in Study 1, whereas the majority demonstrated either two or three factors. These findings raise important questions about how experiential data should be collected and modeled, for use in research (conceptual development and measurement) and applied practice (prescribing, monitoring)—as well as in more applied research (implementation, effectiveness).