Heather Poupore-King, Postdoctoral Faculty Sponsor
Exploration of the trait-activation model of pain catastrophizing in Native Americans: results from the Oklahoma Study of Native American pain risk (OK-SNAP).
Scandinavian journal of pain
OBJECTIVES: Native Americans (NAs) have the highest prevalence of chronic pain of any racial/ethnic group. This issue has received little attention from the scientific community. One factor that may contribute to racial pain disparities is pain catastrophizing. Pain catastrophizing is a construct related to negative pain outcomes in persons with/without chronic pain. It has been suggested that the relationship between trait catastrophizing and pain is mediated by situation-specific (state) catastrophizing. The present study has 2 aims: (1) to investigate whether state pain catastrophizing mediates the relationship between trait catastrophizing and experimental pain (e.g., cold, ischemic, heat and electric tolerance), and (2) to investigate whether this relationship is stronger for NAs.METHODS: 145 non-Hispanic Whites (NHWs) and 137 NAs completed the study. Bootstrapped indirect effects were calculated for 4 unmoderated and 8 moderated mediation models (4 models with path a moderated and 4 with path b).RESULTS: Consistent with trait-activation theory, significant indirect effects indicated a tendency for trait catastrophizing to be associated with greater state catastrophizing which in turn is associated with reduced pain tolerance during tonic cold (a*b=-0.158) and ischemia stimuli (a*b=-0.126), but not during phasic electric and heat stimuli. Moderation was only noted for the prediction of cold tolerance (path a). Contrary to expectations, the indirect path was stronger for NHWs (a*b for NHW=-.142).CONCLUSIONS: Together, these findings suggest that state catastrophizing mediates the relationship between trait catastrophizing and some measures of pain tolerance but this indirect effect was non-significant for NAs.
View details for DOI 10.1515/sjpain-2021-0174
View details for PubMedID 35289511
Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach.
2021; 16 (7): e0255277
Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health problem. The emergence and/or worsening of chronic pain are known sequelae of IPV; however, not all those who experience IPV develop chronic pain. To mitigate its development, it is critical to identify the factors that are associated with increased risk of pain after IPV. This proof-of-concept study used machine-learning strategies to predict pain severity and interference in 47 young women, ages 18 to 30, who experienced an incident of IPV (i.e., physical and/or sexual assault) within three months of their baseline assessment. Young women are more likely than men to experience IPV and to subsequently develop posttraumatic stress disorder (PTSD) and chronic pain. Women completed a comprehensive assessment of theory-driven cognitive and neurobiological predictors of pain severity and pain-related interference (e.g., pain, coping, disability, psychiatric diagnosis/symptoms, PTSD/trauma, executive function, neuroendocrine, and physiological stress response). Gradient boosting machine models were used to predict symptoms of pain severity and pain-related interference across time (Baseline, 1-,3-,6- follow-up assessments). Models showed excellent predictive performance for pain severity and adequate predictive performance for pain-related interference. This proof-of-concept study suggests that machine-learning approaches are a useful tool for identifying predictors of pain development in survivors of recent IPV. Baseline measures of pain, family life impairment, neuropsychological function, and trauma history were of greatest importance in predicting pain and pain-related interference across a 6-month follow-up period. Present findings support the use of machine-learning techniques in larger studies of post-IPV pain development and highlight theory-driven predictors that could inform the development of targeted early intervention programs. However, these results should be replicated in a larger dataset with lower levels of missing data.
View details for DOI 10.1371/journal.pone.0255277
View details for PubMedID 34324550