A dynamical approach is used to avoid isolating systems and instead view systems as interacting together. The current study applied a dynamical approach to heart rate variability and personality. There were two main research questions that this study sought to answer with a dynamical analysis of heart rate variability and personality: “Can we listen to a heartbeat and draw connections to behavior and personality?” and “Is dynamical analysis more effective than traditional analysis at finding correlations between heart rate variability and personality?” To answer these questions a dynamical analysis of heart rate variability was conducted (detrended fluctuation analysis; DFA) along with traditional analysis (standard deviations of NN intervals, SDNN, and root mean squared of successive deviations, RMSSD) and then correlations between heart rate variability measures and personality traits from the Big Five Inventory, Positive and Negative Affect schedule, and State-Trait Anxiety Inventory were examined. Data for this study came from the Rapid Automatic & Adaptive Model for Performance Prediction (RAAMP2) Dataset that was part of The Multimodal Objective Sensing to Assess Individuals with Context (MOSAIC) project. There were no statistically significant correlations between heart rate variability and personality. However, there were notable correlations between extraversion and SDNN and RMSSD and between positive affect and SDNN and RMSSD. We found that SDNN and RMSSD were more closely correlated to each other compared to DFA to either measure. This suggests that DFA can provide information that SDNN and RMSSD do not. Future research can explore dynamic analysis of heart rate variability and other nested systems.