Affective associations towards running: fuzzy patterns of implicit-explicit interaction in young female runners and non-runners

Abstract

Empirical evidence demonstrates that high concordance and low discrepancy of implicit and explicit affective processes facilitate consistent exercise behavior. Novice runners often have difficulties implementing their running behavior on a regular basis resulting in irregular running behavior. To investigate the potential value of affective associations 89 young female runners (regular and irregular) and non-runners were recruited. Affective associations towards running were measured through a Single-Target Implicit Association Test on the implicit level and by self-report on the explicit level. Implicit-explicit interaction (IEI) scores (i.e., implicit-explicit concordance and discrepancy) were derived from principal component analysis. Fuzzy k-means cluster analysis was used to identify patterns of interacting implicit-explicit affective associations. The resulting clusters were assessed for differences in previous running experience, current running behavior, motivational and intentional aspects. Four meaningful overlapping clusters were found and labeled according to their prevalent IEI patterns (i.e., "positive non-discrepant", "positive discrepant", "negative discrepant", "negative non-discrepant"). Significant differences between clusters were found for past running experience, current running behavior, motivational and intentional aspects. The results indicate that running behavior varies between and within patterns of affective associations. In line with previous findings, positive non-discrepant implicit and explicit affective associations are linked to more consistent running behavior, while negative non-discrepant affect is associated with non-runners. However, the occurrence of discrepant implicit-explicit affective associations in young women differing in running behavior, motivation, and intention broadens the view of the complex relationship between affective processes and exercise behavior. In conclusion, individualized interventions that take into account the implicit-explicit interaction of affective associations besides well-known cognitive self-regulatory resources may prove more effective for individuals who struggle to run regularly.

Keywords: affective associations; fuzzy cluster analysis; implicit-explicit interaction; running behavior; young women.

Figure 1

Illustration of trial structure (A) and task procedure (B) of the ST-IAT.

Figure 2

Illustration of distributional information of implicit and explicit affective association variables as violin plots (mean values are displayed as dashed horizontal lines).

Figure 3

Illustration of implicit-explicit interaction sorted by IEC scores associations in descending order and the corresponding z-standardized implicit and explicit (associations n = 89). PCA, principal component analysis.

Figure 4

Illustration of implicit-explicit interaction sorted by IED scores in descending order and the corresponding z-standardized implicit and explicit associations (n = 89). PCA, Principal Component Analysis.

Figure 5

Illustration of k-means (A) and fuzzy k-means (m = 2.0) (B) cluster solutions (k = 4) on their principal components indicating participant IDs, cluster centers and observations with unclear cluster membership (max. m < 0.5). IEC, implicit-explicit concordance; IED, implicit-explicit discrepancy. Cluster centers are depicted as rectangles.

Figure 6

Illustration of implicit-explicit interaction and current running behavior grouped by fuzzy k-means cluster solution (k = 4, m = 2.0).

Figure 7

Bar charts displaying frequency distributions of current running behavior and past regular running experience.

Figure 8

Boxplots of motivational and intentional variables grouped by fuzzy clusters.