Virtual human technology: Capturing sex, race, and age influences in individual pain decision policies

Abstract 

Pain assessment is subject to bias due to characteristics of the individual in pain and of the observing person. Few research studies have examined pain assessment biases in an experimental setting. This study employs innovative virtual human technology to achieve greater experimental control. A lens model design was used to capture decision-making policies at the idiographic and nomothetic level. Seventy-five undergraduates viewed virtual humans (VH) that varied in sex, race, age, and pain expression. Participants provided computerized ratings with Visual Analogue Scales on the VH’s pain intensity, pain unpleasantness, negative mood, coping, and need for medical treatment. Idiographic analyses revealed that individuals used pain expression most frequently as a significant cue. Nomothetic analyses showed that higher pain expression VH and female VH were viewed as having higher pain intensity, higher pain unpleasantness, greater negative mood, worse coping, and a greater need to seek medical treatment than lower pain expression VH and male VH, respectively. Older VH were viewed as having worse coping and a greater need to seek medical treatment than younger VH. This innovative paradigm involving VH technology and a lens model design was shown to be highly effective and could serve as a model for future studies investigating pain-related decision making in healthcare providers.

Keywords: Pain assessment, Sex differences, Race differences, Age differences, Virtual technology, Decision policies