Using Virtual Human Technology to Provide Immediate Feedback About Participants′ Use of Demographic Cues and Knowledge of Their Cue Use

Published:August 11, 2014DOI:


      • This is the first study to make individuals aware of whether a virtual human's sex, race, or age influences their decision making.
      • We assessed the participants' knowledge of their cue use.
      • Findings suggest that a majority of the individuals who were made aware of their demographic cue use would be willing to participate in an online intervention.


      Demographic characteristics have been found to influence pain management decisions, but limited focus has been placed on participants' reactions to feedback about their use of sex, race, or age to make these decisions. The present study aimed to examine the effects of providing feedback about the use of demographic cues to participants making pain management decisions. Participants (N = 107) viewed 32 virtual human patients with standardized levels of pain and provided ratings for virtual humans' pain intensity and their treatment decisions. Real-time lens model idiographic analyses determined participants' decision policies based on cues used. Participants were subsequently informed about cue use and completed feedback questions. Frequency analyses were conducted on responses to these questions. Between 7.4 and 89.4% of participants indicated awareness of their use of demographic or pain expression cues. Of those individuals, 26.9 to 55.5% believed this awareness would change their future clinical decisions, and 66.6 to 75.9% endorsed that their attitudes affect their imagined clinical practice. Between 66.6 and 79.1% of participants who used cues reported willingness to complete an online tutorial about pain across demographic groups. This study was novel because it provided participants feedback about their cue use. Most participants who used cues indicated willingness to participate in an online intervention, suggesting this technology's utility for modifying biases.


      This is the first study to make individuals aware of whether a virtual human's sex, race, or age influences their decision making. Findings suggest that a majority of the individuals who were made aware of their use of demographic cues would be willing to participate in an online intervention.

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        • Alqudah A.F.
        • Hirsh A.T.
        • Stutts L.A.
        • Scipio C.D.
        • Robinson M.E.
        Sex and race differences in rating others’ pain, pain-related negative mood, pain coping, and recommending medical help.
        J Cyber Ther Rehabil. 2010; 3: 63-70
        • Anderson K.O.
        • Mendoza T.R.
        • Valero V.
        • Richman S.P.
        • Russell C.
        • Hurley J.
        • Gning I.
        • Lynch G.R.
        • Kalish D.
        • Cleeland C.S.
        Minority cancer patients and their providers: Pain management attitudes and practice.
        Cancer. 2000; 88: 1929-1938
        • Aubrun F.
        • Salvi N.
        • Coriat P.
        • Riou B.
        Sex-and age-related differences in morphine requirements for postoperative pain relief.
        Anesthesiology. 2005; 103: 156-160
        • Beal D.
        • Gillis J.S.
        • Stewart T.
        The LENS model: Computational procedures and applications.
        Percept Mot Skills. 1978; 46: 3-28
        • Calderone K.L.
        The influence of gender on the frequency of pain and sedative medication administered to postoperative patients.
        Sex Roles. 1990; 23: 713-725
        • Cooksey R.W.
        Judgment Analysis: Theory, Methods, and Applications.
        Academic Press, San Diego, CA1996
        • Hing E.
        • Cherry D.K.
        • Woodwell D.A.
        National ambulatory medical care survey: 2004 summary.
        Adv Data. 2006; 374: 1-33
        • Hirsh A.T.
        • Alqudah A.F.
        • Stutts L.A.
        • Robinson M.E.
        Virtual human technology: Capturing sex, race, and age influences in individual pain decision policies.
        Pain. 2008; 140: 231-238
        • Hirsh A.T.
        • George S.Z.
        • Robinson M.E.
        Pain assessment and treatment disparities: A virtual human technology investigation.
        Pain. 2009; 143: 106-113
        • Hirsh A.T.
        • Callander S.B.
        • Robinson M.E.
        Patient demographic characteristics and facial expressions influence nurses’ assessment of mood in the context of pain: A virtual human and lens model investigation.
        Int J Nurs Stud. 2011; 48: 1330-1338
        • Horgas A.L.
        • Elliott A.F.
        Pain assessment and management in persons with dementia.
        Nurs Clin North Am. 2004; 39: 593-606
        • Martel M.O.
        • Thibault P.
        • Sullivan M.J.
        Judgments about pain intensity and pain genuineness: The role of pain behavior and judgmental heuristics.
        J Pain. 2011; 12: 468-475
        • Pettigrew T.F.
        • Tropp L.R.
        How does intergroup contact reduce prejudice? Meta-analytic tests of three mediators.
        Eur J Soc Psychol. 2008; 38: 922-934
        • Prkachin K.M.
        The consistency of facial expressions of pain: A comparison across modalities.
        Pain. 1992; 51: 297-306
        • Robinson M.E.
        • Wise E.A.
        Prior pain experience: Influence on the observation of experimental pain in men and women.
        J Pain. 2004; 5: 264-269
        • Stutts L.A.
        • Hirsh A.T.
        • George S.Z.
        • Robinson M.E.
        Investigating patient characteristics on pain assessment using virtual human technology.
        Eur J Pain. 2010; 14: 1040-1045
        • Vallerand A.H.
        • Polomano R.C.
        The relationship of gender to pain.
        Pain Manag Nurs. 2000; 1: 8-15
        • Wandner L.D.
        • Stutts L.A.
        • Alqudah A.F.
        • Craggs J.G.
        • Scipio C.D.
        • Hirsh A.T.
        • Robinson M.E.
        Virtual human technology: Patient demographics and healthcare training factors in pain observation and treatment recommendations.
        J Pain Res. 2010; 3: 241-247
        • Wandner L.D.
        • Hirsh A.T.
        • Torres C.A.
        • Lok B.C.
        • Scipio C.D.
        • Heft M.W.
        • Robinson M.E.
        Using virtual human technology to capture dentists’ decision policies about pain.
        J Dent Res. 2013; 92: 301-305