<%BANNER%>

Virtual human technology

Permanent Link: http://ufdc.ufl.edu/UFE0042949/00001

Material Information

Title: Virtual human technology Patient demographics and healthcare training factors in pain observation and treatment recommendations
Physical Description: 1 online resource (38 p.)
Language: english
Creator: WANDNER,LAURA D
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2011

Subjects

Subjects / Keywords: AGE -- BIASES -- PAIN -- RACE -- SEX -- TECHNOLOGY -- VIRTUAL
Clinical and Health Psychology -- Dissertations, Academic -- UF
Genre: Psychology thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Patients? sex, race, and age have been found to affect others? perception of their pain. However, the influence of these characteristics on treatment recommendations from lay persons and healthcare providers is understudied. To address this issue, 75 undergraduates and 107 healthcare-trainees used a web-based delivery system to view video clips of virtual human (VH) patients presenting with different standardized levels of pain. Subjects then rated the VHs? pain intensity and recommended the amount of medical treatment the virtual humans should receive. Results indicated that compared to undergraduates, healthcare-trainees (HTs) perceived African Americans and older adults as having less pain, but were more willing to recommend medical treatment for these patients than were undergraduate participants. HTs and undergraduates rated female, African American, older, and high pain expressing adults as having greater pain intensity than male, Caucasian, younger, and lower pain expressing adults. Moreover, they also recommended that female, older, and high pain expressing adults receive more medical treatment than male, younger and lower pain expressing adults. This study found that both the demographic characteristics of the VH and whether the participants were undergraduates or healthcare trainees influenced the ratings of pain assessment and treatment recommendations. The findings are consistent with the previous VH literature showing that VH characteristics are important cues in the perception and treatment of pain. However, this is the first study to identify differences in pain-related decisions between individuals who are pursuing healthcare careers and those who are not. Finally, not only does this study serve as further evidence for the validity and potential of VH technology, it also confirms prior research which has shown that biases regarding patient sex, race, and age can affect pain assessment and treatment.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by LAURA D WANDNER.
Thesis: Thesis (M.S.)--University of Florida, 2011.
Local: Adviser: Robinson, Michael E.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2011
System ID: UFE0042949:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042949/00001

Material Information

Title: Virtual human technology Patient demographics and healthcare training factors in pain observation and treatment recommendations
Physical Description: 1 online resource (38 p.)
Language: english
Creator: WANDNER,LAURA D
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2011

Subjects

Subjects / Keywords: AGE -- BIASES -- PAIN -- RACE -- SEX -- TECHNOLOGY -- VIRTUAL
Clinical and Health Psychology -- Dissertations, Academic -- UF
Genre: Psychology thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Patients? sex, race, and age have been found to affect others? perception of their pain. However, the influence of these characteristics on treatment recommendations from lay persons and healthcare providers is understudied. To address this issue, 75 undergraduates and 107 healthcare-trainees used a web-based delivery system to view video clips of virtual human (VH) patients presenting with different standardized levels of pain. Subjects then rated the VHs? pain intensity and recommended the amount of medical treatment the virtual humans should receive. Results indicated that compared to undergraduates, healthcare-trainees (HTs) perceived African Americans and older adults as having less pain, but were more willing to recommend medical treatment for these patients than were undergraduate participants. HTs and undergraduates rated female, African American, older, and high pain expressing adults as having greater pain intensity than male, Caucasian, younger, and lower pain expressing adults. Moreover, they also recommended that female, older, and high pain expressing adults receive more medical treatment than male, younger and lower pain expressing adults. This study found that both the demographic characteristics of the VH and whether the participants were undergraduates or healthcare trainees influenced the ratings of pain assessment and treatment recommendations. The findings are consistent with the previous VH literature showing that VH characteristics are important cues in the perception and treatment of pain. However, this is the first study to identify differences in pain-related decisions between individuals who are pursuing healthcare careers and those who are not. Finally, not only does this study serve as further evidence for the validity and potential of VH technology, it also confirms prior research which has shown that biases regarding patient sex, race, and age can affect pain assessment and treatment.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by LAURA D WANDNER.
Thesis: Thesis (M.S.)--University of Florida, 2011.
Local: Adviser: Robinson, Michael E.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2011
System ID: UFE0042949:00001


This item has the following downloads:


Full Text

PAGE 1

1 VIRTUAL HUMAN TECHNOLOGY: PATIENT DEMOGRAPHICS AND HEALTHCARE TRAINING FACTORS IN PAIN OBSERVATION AND TREATMENT RECOMMENDATIONS By LAURA DOVER WANDNER A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORID A IN PARTIAL FULFILLMENT OF THE R EQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2011

PAGE 2

2 2011 Laura Dover Wandner

PAGE 3

3 To my family who has inspired, encouraged, and supported me throughout my life

PAGE 4

4 ACKNOWLEDGMENTS I want to thank my mentor, Dr. Michael Robinson, for his support and guidance both throughout this project and during my time at the University of Florida. In addition I want to thank Dr. Lauren Stutts, Dr. Ashraf Alquadah, Dr. Jason Craggs, Dr. Cindy Scipio, and Dr. Adam Hirsh for their guidance in writing the journal article associated with my Master thesis. Also, I want to recognize the members of my supervisory committee : Dr. Stephen Boggs Dr. Patricia Durning, and Dr. Vonetta Dotson Finally, I want to recognize my family, friends, and colleagues for their support throughout this project.

PAGE 5

5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 8 LIST OF ABBREVIATIONS ................................ ................................ ............................. 9 ABSTRACT ................................ ................................ ................................ ................... 10 1 INTRODUCTION ................................ ................................ ................................ .... 12 Gender ................................ ................................ ................................ .................... 12 Race/Ethnicity ................................ ................................ ................................ ......... 13 Age ................................ ................................ ................................ ......................... 14 Virtual Human Technology ................................ ................................ ...................... 15 2 METHOD ................................ ................................ ................................ ................ 18 Participants ................................ ................................ ................................ ............. 18 Procedure ................................ ................................ ................................ ............... 18 Statistical Analyses ................................ ................................ ................................ 20 3 RESULTS ................................ ................................ ................................ ............... 22 ................................ ............ 22 Pain Intensity Ratings ................................ ................................ ............................. 22 VH Group ................................ ................................ ................................ ......... 22 VH Sex ................................ ................................ ................................ ............. 22 VH Race ................................ ................................ ................................ ........... 22 VH Age ................................ ................................ ................................ ............. 23 VH Pain Expression ................................ ................................ ......................... 23 Age Covariate ................................ ................................ ................................ ... 23 Healthcare Reco mmendations ................................ ................................ ................ 24 Participant Group ................................ ................................ ............................. 24 VH Sex ................................ ................................ ................................ ............. 24 VH Race ................................ ................................ ................................ ........... 24 VH Age ................................ ................................ ................................ ............. 24 VH Pain Expression ................................ ................................ ......................... 25 4 DISCUSSION ................................ ................................ ................................ ......... 29 LIST OF REFERENCES ................................ ................................ ............................... 34

PAGE 6

6 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 38

PAGE 7

7 LIST OF TABLES Table P age 3 1 Means (M) and standard deviations (SD) of pain intensity and recommendation for medical help ................................ ................................ ....... 28

PAGE 8

8 LIST OF FIGURES Figure P age 2 1 Young, Caucasian, female, high pain ................................ ................................ 21 2 2 Old, Black, male, low pain ................................ ................................ .................. 21 3 1 Race by group interaction for pain inte nsity ratings ................................ ............ 26 3 2 Age by group interaction for pain intensity ratings ................................ ............. 26 3 3 Race by group interaction for healthcare recom mendations ............................. 27 3 4 Age by group interaction for healthcare recommendations ............................... 27

PAGE 9

9 LIST OF ABBREVIATION S ANCOVA Analysis of covariance ANOVA Analysis of variance AU Action units FACS Facial action coding system GLM General linear model HTs Health care trainees VAS Virtual analogue scale VH Virtual human

PAGE 10

10 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science V I RTUAL HUMAN TECHNOLOG Y: PATIENT DEMOGRAPHICS AND HEALTHCARE TRAINING FACTORS IN PAIN OBSERVATION AND TREATMENT RECOMMENDATIONS By Laura Dover Wandner May 2011 Chair: Michael Robinso n Major: Psychology pain. However, the influence of these characteristics on treatment recommendations from lay persons and healthcare providers is understudied. To addres s this issue, 75 undergraduates and 107 healthcare trainees used a web based delivery system to view video clips of virtual human (VH) patients presenting and recommend ed the amount of medical treatment the virtual humans should receive. Results indicated that compared to undergraduates, healthcare trainees (HTs) perceived African Americans and older adults as having less pain, but were more willing to recommend medical treatment for these patients than were undergraduate participants. HTs and undergraduates rated female, African American, older, and high pain expressing adults as having greater pain intensity than male, Caucasian, younger, and lower pain expressing adu lts. Moreover, they also recommended that female, older, and high pain expressing adults receive more medical treatment than male, younger and lower pain expressing adults.

PAGE 11

11 This study found that both the demographic characteristics of the VH and whether the participants were undergraduates or healthcare trainees influenced the ratings of pain assessment and treatment recommendations. The findings are consistent with the previous VH literature showing that VH characteristics are important cues in the perce ption and treatment of pain. However, this is the first study to identify differences in pain related decisions between individuals who are pursuing healthcare careers and those who are not. Finally, not only does this study serve as further evidence for the validity and potential of VH technology, it also confirms prior research which has shown that b iases regarding patient sex, race, and age can affect pain assessment and treatment.

PAGE 12

12 CHAPTER 1 INTRODUCTION Pain is the number one reason why individual s seek medical attention, and it is the number one cause of disability. 1 However, healthcare profess ionals find pain particularly challenging to assess and treat because pain is subjective. 2, 3 Because pharmacotherapy with analgesic medications is the cornerstone of pain management, overly conservative approaches to such medications may deny adequate pain relief to large numbers of patients. Also, s ince pain is subjective, healthcare professionals have related variables and the p report of pain; however, the assessment is ultimately inferential. 4 The subjective nature of pain lends itself to biases that can affect pain assessment, especially for healthcare professionals who have had less training about pain management 5 Although there are a number of potential influences, the following three areas of patient demographics are e xplored to examine their effects on pain assessment: sex, race and age Gender There has been an increased interest in sex and gender differences in pain. Women and men have been found to experience pain differently in both clinical and experimental pain assessment settings 6, 7 Clinically, women are more likely to experience a number of health related problems and rates of disability and have higher rates of healthcare utilization than men 7, 8 Experimental differences indicate that women have significantly lower thermal pain thresholds and tolerance than men 8 In addition to these clinical and experimental differences, research suggests that social learning is a contributor to how women and men perceive pain. Research has fou nd

PAGE 13

13 that there tends to be greater differences in pain ratings between men and women in experimental pain studies than clinical pain studies. 7, 9 One study found that manipulating expectations alters differences between women and men exp eriencing experimental pain. 10 Similarly, the study found that when men and women were given gender specific pai n tolerance expectations, they did not differ in their tolerance, threshold, or pain rating. Research has also shown that people report significant differences in their pain expectations for others. 11 Researchers suggest that gender stereoty pes influence both 12 Specifically, healthcare providers and healthcare trainees report differences in pain perception between men and women 13, 14 Research also indicates that there are physicians under medicated female patients with cancer pain relative to male pat ients. 15 Moreover, nurses have been shown to administer less analgesic medications to women than to men, and it also has been found that phys icians prescribe less pain medications to women than to men following abdominal surgery. 16, 17 Race/Ethnicity It is also important to examin e the racial/ethnic differences in the pain experience, especially as the demographics of the United States change. Ethnic differences in pain perception have been reported by a number of investigators for experimental and clinical pain. 18 21 Experimentally, African Americans and Hispanics have been shown to demonstrate lower pain tolerance, lower cold pressor pain tolerance, and to experience higher pain than Caucasians. 18, 19 Clinically, studies have found that African Americans report experiencing higher levels of pain with medical

PAGE 14

14 conditions such as migr aine headaches and post operative pain. 20, 21 A frican Americans also reported higher levels of pain and disability than Caucasians in a multidisciplinary pain center. 18 It is also important to note that there are more robust differences in pain responses among individuals of different races in experimental studies than clinical studies, which suggests that social learning biases may be contribut ing to the problem. 19 Resea rch has also indicated that disparities in pain management among racial and ethnic minorities have been reported for a variety of pain conditions and treatment settings. 22 This suggests that African American and Hispanic patients are more likely to h ave their pain undertreated compared to Caucasian patients. 22 For example, in one study, minority cancer patients were found to be more likely to have the severity of their pain underestimated by their physicians than Caucasian patients. 23 Another study found that doctors underestimated the pain severity of about 7 0 % of African American patients and about 6 0 % of Hispanic patients. 24 I naccurate pain assessment and treatment for ethnic minorities can lead to delayed healing and unnecessary suffering. 25 Age A ge is a nother important demographic facto r to examine because pain is a common and significant problem for many older adults. Studies report that about 45% to 85% of older individuals indicate that they experience at least one current pain condition. 26 28 Although pain is common in older adults, it is often under recognized compared to pain in younger adults. 29, 30 A contributor to this problem is that healthcare settings often do not regularly screen older ad ults for pain conditions One study found that about 40% of elderly patients were ever screened for a pain condition. 31 Another

PAGE 15

15 difficulty is that older adults have been found to under report their pain experience relative to younger adults. 32 Diagnosis of pain conditions is especially difficult in older adults because of the high rates of chronic and acute hea lth conditions, especially musculoskeletal conditions such as arthritis, knee pain, back pain, cancer, and surgical procedures. 33 30 However, relatively li about pain management. Preliminary data su ggest that at least some health care professionals and healthcare trainees use age as a significant cue in determining the level of pain me dication needed. 13, 34 Biases concerning the amount of pain experienced by older adults have important implications for the amount and quality of tre atment older adults receive. 35 Virtual Human Technology Virtual human (VH) technology is a novel way of investigating differences in pain assessment. Three previous studies have used this technology to examine whether participants assess and would treat the pain of VHs wh o differ by sex, race, age, and pain expression differently. 14, 36 Th e advantage of using VH technology is that the facial features and pain expressions can be standardized without the biases of interest being present in the construction of the stimuli. VH technology may also increase the likelihood that a healthcare profe ssional will report his/her perceptions and treatment opinions with less social desirability bias since the patient is not present. Hirsh et al. (2009a) found that sex, race, age, and the expression of pain were prominent predictors of pain intensity and the recommendation for medical treatment in a sample of 75 undergraduates. When the VHs were either women or high pain

PAGE 16

16 expressing adults, they were rated by participants as having higher pain intensity. The study also found that VHs who were female, older adults, and expressing higher pain were more likely to be recommended for medical treatment. In Hirsh et al. (2009b), nurses who assessed the VH profiles indicated that they perceived female, African American, older, and higher pain expressing adults to be experiencing greater pain. Moreover, the nurses were more likely to suggest opioid treatment if the VH was female, African American, older, and expressing more pain. Stutts, Hirsh, George, and Robinson (2010) assessed healthcare trainees (HTs) pe older and high pain expressing VHs to have higher pain intensity ratings, higher pain unpleasantness, higher negative moods, worse coping, and as being more in need of medic al help. Although the previous work indicated that both laypersons and healthcare trainees (HTs) are influenced by patient demographic cues (i.e, age, race, sex, pain expression) when making pain related decisions, it is not clear whether the influences o f these cues are the same or different in these two groups. This question is best addressed by comparing the decision policies of laypersons and HTs in the same statistical analysis. If there is a difference between the decisions of laypersons and HTs, th at difference might suggest that healthcare training or self selection as a healthcare professional is related to different rates of cue use or potential bias in pain observation. If patient characteristics sex, race, age, or pain expression influence healthcare professionals in assessing or treating a patient, it could adversely affect the 14, 16, 23, 31, 37 Healthcare professionals not only treat patients,

PAGE 17

17 but they also serve as model s and educators for future generations of healthcare professionals. Thus, the current study examines whether healthcare trainees physical therapy, nursing, medical, and dental students use patient race, sex and age cues differently than do undergradua te students when making judgments about pain. The results of such analyses may ultimately lead to education efforts aimed at reducing biases among providers and the general public and improved patient care.

PAGE 18

18 CHAPTER 2 METHOD Participants Participants included 75 undergraduate students and 107 healthcare trainees (HTs) from the University of Florida. The undergraduate population consisted of 53 women and 22 men and included 62 Caucasians and 13 African Americans. The average age of the undergraduate s tudents was 21.01 years, with an age range of 18 28 years. The HT group consisted of 34 physical therapy students, 30 nursing students, 25 medical students, and 18 dental students. The HT population was made up of 83 women and 24 men and included 74 Cauca sians, 10 Hispanics, 13 Asians, 3 African age of the sample was 24.62 years, with an age range of 19 48 years. All participants were compensated $15 for their particip ation. Procedure This study was approved by the University of Florida Institutional Review Board. The study used a web based delivery model. After giving consent electronically, participants completed a demographic questionnaire and then viewed the VH vid eos and read the VH vital signs and clinical vignette The participants viewed each video for 20 seconds. For each VH video participants read a clinical vignette either about a VH with chronic lower back pain (HTs) or a VH with abdominal pain (undergrad uate students) and viewed the vital signs of each VH video. The VHs were created with the People Putty software program that has been used in previous studies. 14, 36 The VHs had four personal characteristics that were systematically manipulated: sex (male, female), race (Caucasian, African American),

PAGE 19

19 age (young adult, older a dult), and pain expression (low, high). The VH expressed pain through facial expressions that were coded based on the Facial Action Coding System (FACS). 38 The FACS is based on facial muscle movements and distinguishes 44 different action units (AUs). However, an abbreviated version of FACS was used in this study. The study focused on 4 AUs that represent the core features of pain expr ession (brow lowering, tightening of the orbital muscles surrounding the eye, nose wrinkling/upper lip raising, and eye closure). 14, 36, 39, 40 Figure s 2 1 and 2 2 are examples of two of the VH faces that was used in this study. To control for order effects, VH videos were presented randomly. The participants were required to complete one VH video before viewing the next one. Also, they were not permitted to revisit a completed VH video. The participants read a s et of instructions that provided information on how to answer the pain assessment and treatment ratings using the Visual Analogue Scales (VASs). The participants rated each VH on two VASs on a 1 sensat treatment A total of 16 unique scenarios were created to represent all possible cue combinations. The undergraduate students viewed all 16 VH videos The HTs obser ved 32 VH videos where they observed each cue combination twice. In order for the data to be comparable, only the first VH video of the pair was used; thus, only 16

PAGE 20

20 VH videos were included in the study. The undergraduate students and the HTs viewed the s ame 16 VH videos and clinical vignettes The study took approximately 1 1 hours for the participants to complete. The participants were then debriefed regarding the concept of the study. Statistical Analyses All data analyses were performed using SPSS f or Windows (Version 17). Descriptive statistics were conducted to summarize the demographic and background characteristics of the sample. Because the two groups differed significantly by age (3.6 years), r = .459, p =.000, correlations between age and the key dependent variables were conducted. These correlations were used to determine if assumptions of covariance analyses were met to include age as a covariate. Analysis of Variance (ANOVA), under the General Linear Model (GLM) was performed to examine th e group differences (undergraduate students vs. HTs) in the rating of two dimensions of pain personal characteristics (sex, race, age, and pain expression). Where appropriat e, age was used as a covariate to control for group differences in age. This study consists of a secondary analysis of the data from two previous dissertations conducted at the Center for Pain Research and Behavioral Health at the University of Florida ; ho wever, the aims of the current study are distinct and the questions addressed were not previously investigated in the prior work.

PAGE 21

21 Figure 2 1. Young, Caucasian, female, high pain Figure 2 2 Old, Black, male, low pain

PAGE 22

22 CHAPTER 3 RESULTS Correlation of A correlation was conducted to examine the association between pain intensity rating and participant age. The correlation was modest but statistically significant, r = .156, p <.05. Therefore, participant age was us ed as a covariate in the ANOVA model for pain intensity rating. A correlation was also conducted to examine the association between medical recommendation ratings and participant age. Since the correlation was not significant (r = .005, p < .95), age was not used as a covariate in the ANOVA model for recommending medical help. Pain Intensity Ratings VH Group The results of the ANOVA on pain intensity ratings indicated a main effect for participant group. Collapsed across the 4 VH cues (sex, race, age, and pain expression), undergraduate student participants gave significantly higher pain intensity ratings to VH patients than did HT participants [ F (1,180) = 4.81, p 2 =.03]. VH Sex A main effect for VH sex also emerged, with female VHs perceived as experiencing more pain than male VHs [ F (1,180)= 22.35, p 2 =.11]. The interaction of patient sex and participant group was not significant for ratings of pain 2 =.000). VH Race A significant main effect for patient race was also found, with African American VHs rated as experiencing more pain intensity than the Caucasian VHs [ F (1,180) =

PAGE 23

23 9.36, p 2 =.05]. The results also identified a significant race by group interaction [ F (1,180) = 13.11, p < 2 =.07]. Specifically, compared to HTs, undergraduate participants gave higher pain intensity ratings to African American VHs than to Caucasian VHs. Figure 3 1 displays the significant race by group interaction for pain intensity ratings. VH Age Similarly, a main effect for patient age also emerged. The pain intensity of older VHs was rated significantly higher than that of younger VHs [ F (1,180) = 36.53, p <.001, 2 =.17]. A significant age by group interaction was also found; compared to HTs, the undergraduate participants rated the pain of older VHs as significantly more intense than that of younger VHs [ F (1,180) = 21.60, p 2 = .11]. Figure 3 2 displays the significant age by group interaction for pain intensity ratings VH Pain Expression A main effect for the VH pain expression was also found As expected, VHs with a high pain expression were rated as having higher pain intensity than those with a low pain expression [ F (1,180) = 519.95, p 2 =.74]. The expression by group interaction was not significant (F=.05, p 2 = .00). Age Covariate As noted above, participant age was significantly, albeit modestly, correlated with pain intensity ratings. The results of the Analysis of Covariance ( ANCO VA ) were essentially the same as those above and indicated that age was not a significant factor in the model. For this reason, the ANCOVA results were not included thesis and are only consider ed in the discussion.

PAGE 24

24 Healthcare Recommendat ions Participant Group The results of the ANOVA on recommendations for medical help ratings indicated no main effects for participant group. Collapsed across the 4 VH cues (sex, race, age, and pain expression) there were no group differences [ F (1,180) = 1.26. p 2 =.01]. VH Sex There was a main effect of VH sex in the recommendation for medical help. Female VHs received significantly higher recommendation ratings than male VHs [ F (1,180) = 2.32. p < 2 =.01]. The interaction of pat ient sex and participant group was not significant for ratings of recommending medical help (F=2.01, p>.05, 2 =.01). VH Race There was no main effect of VH race in the recommendation for medical help, [ F (1,180) = 6.277, p 2 =.03]. There was a significant interaction between VH race and participant group. A larger race effect was seen for HTs than for undergraduates. [ F (1,180) = 6.277, p 2 =.03], such that HTs more frequently recommended the African American VHs for more medic al help than the Caucasian VHs Figure 3 3 displays the race by group interaction for healthcare recommendations. VH Age for medical help, [ F (1,180) = 38.92, p 2 =.18]; older VHs received significantly higher recommendation ratings than did younger VHs. A significant

PAGE 25

25 interaction between age and group was also indicated, [ F (1,180) = 9.03, p <.01, partial 2 =.05]. HTs more often recommended medical treatment for th e older VHs than did undergraduate participants Figure 3 4 displays the age by group interaction for healthcare recommendations. VH Pain Expression Finally, there was a significant main effect for the VH pain expression, such that VHs expressing a high l evel of pain were more often recommended for medical treatment than VHs with a low pain expression [ F (1,180) = 357.43, p <.001, par tial 2 =.67]. The expression by group interaction was not significant (F=9.03, p >.05, partial 2 = .05). Detailed results of the analyses discussed above are presented in Table 1.

PAGE 26

26 Figure 3 1. Race by group interaction for pain intensity ratings Figure 3 2. Age by group interaction for pain intensity ratings

PAGE 27

27 Figure 3 3. Race by group interaction for healthcare recommendations Figure 3 4. Age by group interaction for healthcare recommendations

PAGE 28

28 Table 3 1. Means (M) and standard deviations (S D) of pain i ntensity and recommendation for medical h elp Pain Intensity Recommend Medical Help Undergraduate Students Healthcare Trainees Undergraduate Students Healthcare Trainees N=75 N=107 N=75 N=107 M SD M SD M SD M SD Sex Men 38. 65 14.53 33.74 15.78 44.06 21.16 48.79 25.00 Women 41.14 13.87 36.16 16.46 47.15 20.95 50.13 25.18 Race Caucasian 40.06 13.97 32.97 15.51 45.92 21.18 48.15 25.27 African American 39.73 14.46 36.93 17.07 45.29 20.72 50.76 24.15 Age Young 39.42 13.66 31.32 15.00 44.35 21.18 45.89 25.03 Old 40.37 14.91 38.59 17.89 46.85 20.94 53.03 24.74 Pain Intensity Cue Low Pain Intensity 25.94 14.8 21.26 14.65 31.94 23.99 37.91 26.65 High Pain Intensity 53.86 16.99 48.64 20.31 59.26 20.91 61.00 24.9

PAGE 29

29 CHAPTER 4 DISCUSSION Using VH technology, this study examined the question of whether undergraduate students and healthcare trainees (HTs ) assess pain and suggest treatment differ ently. The overall results of this study demonstrate the ability of web based VH scenarios to elicit and objectively measure sex, race, age, and pain expression influences on decisions about pain assessment and recommendations for treatment. In addition, the approach was sensitive to group differences between un dergraduate students and health care trainees. Notably this study also suggests the hypothesis that healthcare training, or self selection as a healthcare professional, is related to different rate s of cue use, and potential bias in pain observation. and education status (undergraduate vs. HT) influenced the ratings of pain intensity and recommendations for medical help. C ompared to HTs, undergraduates rated both African American and older VHs as having higher pain, however, their recommendations f or treatment ratings were lower for these same VHs. Also of interest is that undergraduates consistently he HTs. However, the HTs consistently recommended more treatment for the VHs. This suggests that even though HTs might perceive their patients as having less pain, they nevertheless are more likely to recommend medical treatment for them. This could refl ect a selection bias in terms of who pursues healthcare as a profession. U ndergraduates might also be particularly sensitive to the pain of others because they are less often exposed to it and thus err on the side of caution. However, HTs might be more i nured to the pain of others because they frequently encounter patients in pain during their professional training 41 Some

PAGE 30

30 research has shown that medical students do not accurately perceive what patients believe about their own health, includi ng about their pain 42 Even though undergraduat es might be particularly responsive to pain in others, they might not feel it is their place or that they have the expertise to make recommendations about treatment. HTs, on the other hand, might feel more comfo rtable with giving such recommendations. Our results could also reflect HTs having more education than undergraduates on the best practices for pain management. T he validity of computer generated pain expression was supported in this study The par ticipants were able to distinguish the level of pain expressed by the VH (low or two levels of pain expression would have yielded non significant results. However, participants in the study consistently identified the VH videos intended to express high pain as having higher pain intensity and the VH videos intended to express low pain as having lower pain intensity. The HTs in the study also consistently reported that the vignettes and the VHs reflected accurate perceptions of what they see while they are working with patients. This study has interesting implications for public healt h. Although the effect sizes for group differences were modest, the use of age, sex and race cues could still have a large impact on healthcare. Healthcare professionals typically see thousands of patients during the course of thei r careers. If the use of these cues reflects a bias toward one demographic group or another, patient outcomes could be adversely affected. Also, healthcare professionals frequently serve as mentors to HTs and may

PAGE 31

31 may be particularly influential given that HTs receive limited pain treatment education 41, 42 Indeed, previous research has found that healthcare providers prescribe less pain medication to women, African Americans, and older patients. Since healthcare professi onals see so many patients and share their information with mentees, potential biases in prescribing medication can affect a large number of patients 15, 23, 24, 31 The results of this study suggest that health edu cat awareness of the differences in pain reporting and perception that can affect the Several study limitations should be considered. The participants were not asked an ope n ended question as to what treatment they would suggest for the VHs, nor were they given the option of gathering additional information before recommending treatment. Differences could have emerged in the type of treatment recommended to VHs of different demographic characteristics. Also, the undergraduates and the HTs pain ratings. Further, the participants who took part in the study were a relatively homogeneous population young and educated. In addition, it is possible that participants were able to determine the intent of the study, and thus adjusted their responses in a socially desirable manner. Finally, the representativeness of the VH videos and the scena rios presented has t o be considered. However, in the pilot work, over 70% of participants indicated that the VH facial expressions were realistic, and 90% indicated that the clinical scenarios were reflective of real post operative scenarios 14

PAGE 32

32 Future research is warranted to examine the causal relationships between group membership (HTs vs. undergraduates) and cue ANCOVA use. First, there is an age difference between the HTs and the undergraduate participants. Preliminary evidence after examining the results of the 2 X 2 Mixed for the pain ratin g pain intensity suggests more in depth research should be conducted to determine how, and to what extent, age ing hypotheses for the group differences include self selection into a helping profession or the direc t effects of training in health care. It is also possible that the demographic characteristics of the assessor interact with those of the patient to influ ence ratings Second, future studies could be designed that investigate the role of these and other potential factors that might account for the observed differences in pain assessment and treatment ratings. Such studies might include longitudinal design s following first year students to practice or studies might include age matched controls and healthcare providers. Third, t he study results reflect that participants used age and race as cues. I t is important to understand why th os e cues are used. Thus, additional research should be conducted to determine how and why race and age are used in the perception of Currently there are no questionnaires that can be used to probe how and why people use age and race cues in the perception of pain. However, the Center of Pain Research and Behavioral Health is in the process of creating and validating such questionnaires. Finally it is important to find a way to alter the biased cue use. One way to do this would be to use the VHs to create a traini ng module for healthcare trainees that would help them explore their own biases in evaluating the pain of others.

PAGE 33

33 Sensitizing healthcare trainees to the potential for bias in their assessments coul d help them minimize the effect s of demographic cues in t heir pain evaluations which could help improve patient care and outcomes. In summary, this study found that both the characteristics of the VH and the type of participants influenced ratings of pain assessment and treatment recommendations. The findings are consistent with the previous VH literature showing that age, race, sex, and pain intensity characteristics are important cues. However, this is the first study to identify differences in pain related decisions between individuals wh o are pursuing heal thcare careers and those who are not. Finally, not only does this study serve as further evidence for the validity and potential of VH technology, it also confirms prior research which has shown that b iases regarding patient sex, race, and age can affect pain assessment and treatment.

PAGE 34

34 LIST OF REFERENCES 1. Reeve s JL. An interview with James N Campbell, MD, American Pain Society, American Pain Society President, 1994 1995 Am Pain Soc Bull. 1994;17(1). 2. Bendelow D. Pain perceptions, emotions, and gender. Sociol Health Illn. 1993;25:273 294. 3. Coghill RC, McHaffie JG, Yen Y. Neural correlates of interindividual differences in the subjective experience of pain. Proc Natl Acad Sci US A 2003; 100 (14):8538 8542 4. Dworkin SF, Sherma n JJ, ed itor s. Relying on objective and subjective measures of chronic pain: Guidelines for use and interpretation In: Turk DC, Melzack R, editors. Handbook of pain a ssessment New York: Guilford Press; 2001. 5. Paulson M, Dekker AH. Healthcare dispariti es in pain management. J Am Osteopath Assoc 2005;105(6 Suppl 3):S14 17. 6. Robinson ME, Wise EA, Gagnon C, Fillingim RB, Price DD. Influences of gender role and anxiety on sex differences in temporal summation of pain. J Pain. 2004;5(2):77 82. 7. Fillin gim RB, King CD, Ribeiro Dasilva MC, Rahim Williams B, Riley JL. Sex, gender, and pain: A review of recent clinical and experimental findings. J Pain. 2009;10(5):447 485. 8. Fillingim RB, Edwards RR, Powell T. The relationship of sex and clinical pain to experimental pain responses. Pain. 1999;83(3):419 425. 9. Myers CD, Riley JL, Robinson ME. Psychosocial contributions to sex correlated differences in pain. Clin J Pain. 2003;19:225 232. 10. Robinson ME, Gagnon CM, Riley JL, 3rd, Price DD. Altering gender role expectations: Effects on pain tolerance, pain threshold, and pain ratings. J Pain. 2003;4(5):284 288. 11. Robinson ME, Riley JL, Myers CD, et al. Gender role expectation of pain: Relationship to sex differences in pain. J Pain 2001;5:251 257. 12. Robinson ME, Wise EA. Gender bias in the observation of experimental pain. Pain. 2003;104:259 264. 13. Stutts LA, Hirsh AT, George SZ, Robinson ME. Investigating patient characteristics on pain assessment using virtual human technology. Euro J Pain 2010; 14(10):1040 1045

PAGE 35

35 14. Hirsh AT, George SZ, Robinson ME. Pain assessment and treatment disparities: A virtual human technology investigation. Pain. 2009;143:106 113. 15. Cleeland CS, Gonin R, Hatfield AK, et al. Pain and its treatment in outpatients with metastatic cancer. N ew Eng J Med. 1994;330:592 596. 16. McDonald DD. Gender and ethnic stereotyping and narcotic analgesic administration. Res Nurs Health. 1994;17(1):45 49. 17. Faherty BS, Grier MR. Analgesic medication for elderly people post surgery. Nurs Res. 1984;33(6):369 372. 18. Edwards CL, Fillingim RB, Keefe F. Race, ethnicity and pain. Pain. 2001;94:133 137. 19. Campbell CM, Edwards RR, Fillingim RB. Ethnic differences in responses to multiple experimental pain stimuli. Pain. 2005;113:20 26. 20. White SF, Asher MA, Lai SF, Burton DC. Patients' perception of overall function, pain, and appearnace after primary posterior instrumentation and fusion for idiopathic scoliosis. Spine. 1999;24:1693 1700. 21. Stewart WF, Lipton RB, Liberman J. Variat ion in migraine prevalance by race. Neurology. 1996;47(1):52 59. 22. Green CR, Anderson KO, Baker TA, et al. The unequal burden of pain: Confronting racial and ethnic disparities in pain. Pain Med. 2003;4(3):277 294. 23. Cleeland CS, Gonin R, Baez L, Loe hrer P, Pandya KJ. Pain and treatment of pain in minority patients with cancer. Ann Intern Med. 1997;127(9):813 816. 24. Anderson KO, Mendoza TR, Valero V, et al. Minority cancer patients and their providers: Pain management attitudes and practice. Cancer 2000(88):1929 1938. 25. McNeill JA, Sherwood GD, Starck PL. The hidden error of mismanaged pain: A systems approach. J Pain Symptom Manage. 2004;28(1):47 58. 26. Royal College of Physicians, British Geriatrics Society, British Pain Society. The assessm ent of pain in older people: National guidelines. Concise g uidance to good practice series, Vol 8. London: R CP ; 2007. 27. Helme R, Gibson S. The epidemiology of pain in elderly people. Clin Geriatr Med. 2001;17:417 431.

PAGE 36

36 28. Ferrell B, Ferrell B, Osterweil D. Pain in the nursing home. J Am Geriatr Soc. 1990;38:409 414. 29. Horgas AL, Elliott AF. Pain assessment and management in persons with dementia. Nurs Clin North Am. 2004;39(3):593 606. 30. Herman AD, Johnson TM, Rit chie CS, Parmelee PA. Pain management interventions in the nursing home: A structured review of the literature. J Am Geriatr Soc. 2009;57:1258 1267. 31. Chodosh J, Solomon DH, Roth CP, et al. The quality of medical care provided to vulnerable older patien ts with chronic pain. J Am Geriatr Soc. 2004;52:756 761. 32. Oberle K, Wry J, Paul P, Grace M. Environment, anxiety, and postoperative pain. West J Nurs Res. 1990;12(6):745 753 33. Bookwala J, Harralson TL, Parmelee PA. Effects of pain on functtioning a nd well bein in older adults with osteoarthritis of the knee Psychology and Aging. 2003;18(4):844 850. 34. Roth MT, Moore CG, Ivey JL, Esserman DA, Campbell WH, Weinberger M. The quality of medication use in older adults: Methods of a longitudinal study. Am J Geriatr Pharmacother 2008;6(4):220 233. 35. American Geriatric Society. The management of persistent pain in older persons: AGS panel on persistent pain in older persons. J Am Geriatr Soc. 2002;50:205 224. 36. Hirsh AT, Alquadah AF, Stutts LA, Robi nson ME. Virtual human technology: Capturing sex, race, and age influences in individual pain decision policies. Pain. 2009;140:231 238. 37. Marquie L, Raufaste E, Lauque D, Marine C, Ecoiffier M, Sorum P. Pain rating by patients and physicians: Evidence of systematic pain miscalibration. Pain. 2003;102:289 296. 38. Ekman P, Frieson W. Facial Actional Coding System: A technique for the measurement of facial movement Palo Alto: Consulting Psychologists Press; 1978. 39. Craig KD, Prkachin KM, Grunau R, ed itor s. The facial expression of pain In: Turk D, Melzack R, editors. Handbook of pain assessment, 2 nd ed. New York: Guilford Press; 2001. 40. Prkachin KM. The consistency of facial expression of pain: A comparison across modalities Pain. 1992;51:297 30 6.

PAGE 37

37 41. Simpson K, Kautzman L, Dodd S. The effects of pain management education program on the knowledge level and attitudes of clinical staff. Pain Manag Nurs 2002;3:87 93. 42. Turner GH, Weiner DK. Essential components of a medical student curriculum o n chronic pain management in older adults: Results of modified delphi process. Pain Med 2002;3 :240 252

PAGE 38

38 BIOGRAPHICAL SKETCH Laura Wandner was born in Washington D.C. Laura graduated summa cum laude from Connecticut College in May 2007 with a Bachelor of Arts in psychology and government She is currently residing in Gainesville, Florida and is pursuing a doctorate in clinical and health psychology at the University of Florida. Laura exam and the pain of others, as well as chronic illnesses. Laura is currently furthering her clinical training experience in various settings. Her clinical experience includes conducting psychological assessments of children, structured comprehensive assessments specific to populations of individuals with anxiety disorders, and a range of medical psychology assessments. Current volunteer experiences include providing free brief therapy at a local community center fo r residents of the community in need of psychological services. Laura include a career in a medical setting that involves both clinical and research opportunities.