Predicting real-world emotion and health from spontaneously assessed linguistic distancing using novel scalable technology.

Abstract

Individuals who are better at regulating their emotions have been shown to have better physical and mental health outcomes. One promising emotion regulation strategy is psychological distancing, which involves appraising a stimulus with objectivity or spatial/temporal distance. Language-based psychological distancing (linguistic distancing [LD]) refers to the degree to which one implements psychological distancing naturally via language. A crucial, underexamined mechanism that may account for real-world emotion and health self-reports is spontaneous (i.e., implicit) LD. Using HealthSense, a novel, scalable, mobile health assessment application, we collected lexical transcriptions for personally specific negative and positive events as well as emotion and health-relevant data over 14 days (data collected in 2021) and examined how implicit LD during negative and positive events relates to well-being over time. Primary analyses revealed that higher LD during negative events was associated with lower levels of stress as well as greater emotional and physical well-being within persons. LD during positive events on 1 day predicted greater reports of happiness 2 days later within persons. LD during positive events was associated with fewer symptoms of depression and LD during negative events was associated with greater physical well-being among persons. Exploratory analyses revealed that average depression, rumination, and perceived stress across the 2 weeks were significantly negatively associated with LD during negative events between persons. The present results expand understanding of the relationship between LD and mental and physical health risks and motivate future research on low-burden, scalable interventions involving LD.