An Investigation of Patients' and Doctors' Autonomic Nervous System Responses Throughout News-Focused Medical Consultations.

Although it is clear that people experience physiological arousal in anticipation of news-focused medical consultations, our knowledge of people's experiences during and throughout these consultations is scarce. We examine interbeat interval responses (IBI) of patients and doctors during real-life medical consultations to understand how the experiences of both parties change throughout these encounters and whether they differ from each other. We also examine how the type of news delivered affects responses. We measured the IBI responses of patients and their oncologists throughout 102 consultations in which providers delivered news (classified as good, bad, or status quo) to patients about a recent computerized tomography scan. We observed two distinct phases of consultations: an initial "news" delivery phase and a subsequent "information" phase. During the news phase, on average, patients' IBI responses rapidly increased-indicating less autonomic arousal over time - whereas doctors' responses did not change over time. In contrast, throughout the information phase, on average, both patients' and doctors' responses remained steady. During the information phase, responses differed based on news type: on average, status quo consultations involved an increase in autonomic arousal, whereas good and bad news consultations involved no changes. Lastly, we observed significant variability in patients' responses during both phases. In sum, on average, patients (but not doctors) experience decreases in autonomic arousal while news is being delivered, suggesting that anticipatory distress regarding these consultations wanes quickly. However, our results also indicate that patients' experiences vary from one another, and future research should focus on factors explaining this variability.

Cancer classification using machine learning and HRV analysis: preliminary evidence from a pilot study.

Most cancer patients exhibit autonomic dysfunction with attenuated heart rate variability (HRV) levels compared to healthy controls. This research aimed to create and evaluate a machine learning (ML) model enabling discrimination between cancer patients and healthy controls based on 5-min-ECG recordings. We selected 12 HRV features based on previous research and compared the results between cancer patients and healthy individuals using Wilcoxon sum-rank test. Recursive Feature Elimination (RFE) identified the top five features, averaged over 5 min and employed them as input to three different ML. Next, we created an ensemble model based on a stacking method that aggregated the predictions from all three base classifiers. All HRV features were significantly different between the two groups. SDNN, RMSSD, pNN50%, HRV triangular index, and SD1 were selected by RFE and used as an input to three different ML. All three base-classifiers performed above chance level, RF being the most efficient with a testing accuracy of 83%. The ensemble model showed a classification accuracy of 86% and an AUC of 0.95. The results obtained by ML algorithms suggest HRV parameters could be a reliable input for differentiating between cancer patients and healthy controls. Results should be interpreted in light of some limitations that call for replication studies with larger sample sizes.

Physiological linkage during interactions between doctors and cancer patients.

INTRODUCTION: Doctors and patients influence each other when interacting and, as a result, can become similar to each other in affect and behavior. In the current work, we examine whether they also become similar to each other on a moment-to-moment basis in their physiological responses. Specifically, we examine physiological linkage-how much a doctor's (or patient's) physiological response predicts a patient's (or doctor's) response at a subsequent time interval-and whether this changes over the course of doctor-patient relationships (measured as the number of consultations held for each unique doctor-patient dyad). METHODS: We collected interbeat interval responses (IBI) continuously during consultations between oncologists and patients undergoing cancer treatment (N = 102 unique doctor-patient interactions) at a hospital in Austria. RESULTS: Physiological linkage varied by an interaction between role (doctor vs. patient) and relationship length (in a non-linear, quadratic pattern). Patients showed significant positive linkage to their doctors (i.e., doctors' physiological responses positively, significantly predicted patients' responses) in relationships that spanned three to eight consultations together. Patients were not linked to their doctors in shorter or longer relationships. Doctors were never significantly linked to their patients, meaning that patients' physiological responses never predicted doctors' responses. CONCLUSION: These results reveal that, by influencing patients' physiological responses on a moment-to-moment basis, doctors may have even more influence over patients' physiology than previously known.