Sociocultural & biobehavioral influences on pain expression and assessment

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This protocol measures pain-related facial responses in a diverse population to measure whether nonverbal responses to pain vary as a function of biological and sociocultural factors. We will then measure whether individuals (both healthy volunteers and medical providers) pay attention to different features of pain or assess pain differently in in-group relative to out-group individuals, and whether we can develop interventions to reduce any biases in attention or pain assessment. This year, we continued data collection for our first sub-study on the protocol, and began to prepare stimuli for sub-studies 2 and 3. The first sub-study includes two visits for most participants. During the first visit, we assess eligibility via eligibility checklist, urine screen, nursing assessment, and a standard clinical interview diagnostic. During the second visit, we use video recording and facial electromyography to measure facial responses to painful stimuli. We administer three types of noxious stimuli: electric shock, thermal pain, and cold water. In FY20 we had 19 new participants complete at least one visit and 16 participants who completed all tasks (3 FY19 subjects completed in FY20). This places us at 87 total participants who have completed at least one visit and 64 participants who completed both visits. Data collection will be complete when we have 100 participants who have completed both visits. We are using specialized software to measure facial responses via video and to avoid implicit biases that could affect results if we used human coders. We will measure whether sex differences are observed in facial responses that are similar to sex differences in pain, as well as whether we see differences in facial responses or sex differences as a function of race, ethnicity, or identity centrality of race or sex. We have begun analyzing facial and autonomic data from the study to establish our data processing pipeline. Following completion, sub-study 1 participants are asked whether they want their images to be included in a database that will be shown to other participants. Images of participants who opt into this database will be used as stimuli for our subsequent sub-studies 2 and 3, which use eye tracking to measure how individuals (both naive participants and medical providers) view and judge pain in others that they perceive to be similar or different from them. Through this, we hope to gain insight on behavioral mediators of health disparities in pain. If we identify targets that contribute to differences in perceived pain, we will use these to develop interventions and see if individuals can improve accuracy in sub-study 3. We have begun making stimulus clips from participants who opted into the database and are in the process of designing sub-studies 2 and 3. Obviously the need to halt healthy volunteer research due to the COVID19 pandemic was a set-back for data collection progress on this protocol. However, we successfully shifted gears to focus on data analysis, as well as other experiments that involve online data collection. Fortunately we have received approval to resume healthy volunteer research. We are in the process of modifying this protocol to allow for screening and data collection through secure remote platforms prior to having participants visit NIH in person, which will reduce the duration of the face to face procedures and provide protection of both participants and laboratory staff. We have modified our experiment room so that participants and experimenters will be in different rooms and interact through a video monitoring and intercom system, thereby allowing us to ensure safety and complete sub-study 1 without obscuring facial responses due to masks. In addition to the data collection and analysis mentioned above, we published one commentary that is relevant to this line of research, in which we argued that studies of facial responses must include a diverse sample, particularly at this time when artificial intelligence is being trained on facial databases (Dildine & Atlas, 2019). We also have been preparing a systematic review on the relationship between pain and discrimination, and conducting several IRB-exempt online studies that measure perception of pain in computer generated facial expressions which are also relevant for this line of work.