Evaluating the performance of personality-based profiling in predicting physical activity

Abstract

Background: Profiling or clustering individuals based on personality and other characteristics is a common statistical approach used in marketing, medicine, and social sciences. This approach improves data simplicity, supports the implementation of a data-driven decision-making process, and guides intervention strategies, such as personalized care. However, the clustering process involves loss of information owing to the discretization of continuous variables. Although any loss of information may be practically or pragmatically acceptable, the amount of information lost and its influence on predicting external outcomes have not yet been systematically investigated.

Methods: We assessed the accuracy of predicting physical activity using the clustering approach and compared it with the dimensional approach, where variables are used as continuous regressors. This analysis is based on survey data from a sample of 20,573 individuals regarding physical activity and psychological traits, including the Big-Five personality traits.

Results: A four-cluster solution, supported by the standard criterion for determining the number of clusters, achieved no more than 60-70% prediction accuracy of the dimensional approach employing the raw dimensional scale as explanatory variables.

Conclusion: The cluster solution suggested by conventional statistical criteria may not be optimal when clusters are used to predict external outcomes.

Keywords: Clustering; Personality; Physical activity; Prediction; Profiling.

Fig. 1

Evaluation of number of clusters based on a statistical metric. (A) Evaluation of K-means by Gap statistic. A higher value indicates better clustering. Error bars indicate standard errors. (B) Evaluation of number of clusters by BIC. A lower value indicates better clustering

Fig. 2

Cluster centers for the four cluster solutions. (A) Cluster solution with K-means using only Big-Five (Variable Set 1). (B) Cluster solution with K-means with full variable sets (Variable Set 4). Error bars indicate standard error of the mean. (C) Cluster solution with GMM using only Big-Five (Variable Set 1). (D) Cluster solution with GMM with full variable sets (Variable Set 4). The solid lines represent clustering of results using the entire data, while symbols indicate the results of clustering using training data and test data separately

Fig. 3

Distribution of clusters on the principal component scores obtained by PCA for Variable Set 1 (Big-Five only). (A) K-means, (B) GMM, (C) Principal component loadings, and (D) Distribution of PC scores. PC = Principal Component

Fig. 4

Evaluation of predictive accuracy for a different number of clusters. R² for total PA of test data when using Variable Set 1 (A) and Variable Set 4 (B) are plotted. Error bars indicate 95% confidence interval obtained by the bootstrap samples (both test data and training data). The solid horizontal line indicates the R² values of the dimensional approach model presented in Table 1 (dashed lines represent 95% CI). The colored lines represent the R² values of the clustering-approach model (blue for K-means and orange for GMM). Error bars indicate 95% CI