Virtual Human Technology Research Articles

The influence of patient race, sex, pain-related body postures, and anxiety status on pain management: a virtual human technology investigation.

PurposeThe purpose of this study was to examine mechanisms underlying disparities in pain management among patients with psychological comorbidities. Studies have consistently shown that health care providers, health care trainees, and laypeople are susceptible to biased assessment and treatment decisions for patients presenting with pain. Further, psychological factors may influence the use of demographic and behavioral cues in pain assessment and treatment decisions. The present study employed innovative virtual human technology to capture decision-making approaches at both the group- and individual-level to better elucidate the influence of psychological factors, demographic cues, and pain-related body postures on pain assessment and treatment decisions.Patients and methodsOne hundred and thirty-two providers and trainees in the areas of nursing, physical therapy, and medicine viewed separate, empirically validated virtual human profiles that systematically varied across pain behaviors, anxiety status, race, and sex. Participants provided pain assessment and treatment ratings using a visual analog scale for each virtual human profile.ResultsIdiographic analyses revealed that participants used patient pain-related body postures most consistently and reliably across ratings. Nomothetic analyses showed anxious virtual humans were identified as having more anxiety and more likely to be recommended anti-anxiety medications, especially by female participants.ConclusionThis innovative study successfully explored the influence of patient pain-related body postures, anxiety status, and demographic characteristics on pain management decisions with virtual human technology and a Lens model design. Results of this study can be used to better inform clinical practice, research, and education regarding the influence of patient variables on pain assessment and treatment decisions.

Creating an mHealth App for Colorectal Cancer Screening: User-Centered Design Approach.

BackgroundPatients are increasingly using mobile health (mHealth) apps to monitor their health and educate themselves about medical issues. Despite the increasing popularity of such apps, poor design and usability often lead to suboptimal continued use of these apps and subsequently to poor adherence to the behavior changes at which they are aimed. One solution to these design problems is for app developers to use user-centered design (UCD) principles to consider the context and needs of users during the development process.ObjectiveThis study aimed to present a case study on the design and development process for an mHealth app that uses virtual human technology (VHT) to encourage colorectal cancer (CRC) screening among patients aged 50 years and above.MethodsWe have first provided an overview of the project and discussed its utilization of VHT. We have then reviewed UCD principles and how they can be incorporated into the development of health apps. We have described how we used UCD processes during the app’s development. We have then discussed the unique roles played by communication researchers, computer scientists, clinicians, and community participants in creating an mHealth app that is credible, usable, effective, and accessible to its target audience.ResultsThe principles of UCD were woven throughout the project development, with researchers collecting feedback from patients and providers at all stages and using that feedback to improve the credibility, usability, effectiveness, and accessibility of the mHealth app. The app was designed in an iterative process, which encouraged feedback and improvement of the app and allowed teams from different fields to revisit topics and troubleshoot problems.ConclusionsImplementing a UCD process contributed to the development of an app, which not only reflected cross-disciplinary expertise but also the needs, wants, and concerns of patients.

Impact of Child and Maternal Weight on Healthcare Trainee Clinical Assessment Decision Making: A Virtual Human Study.

Adult literature documents that healthcare providers rely on patient characteristics, such as age, race, and weight, when making clinical decisions. However, little research has examined these biases among pediatric populations. This study aimed to examine the impact of child and maternal weight and race on clinical decision-making of healthcare trainees in the context of a pediatric pain assessment using standardized virtual pediatric patients and mothers. Ninety-two healthcare trainees read a standardized clinical vignette describing a child with chronic pain, which was accompanied by eight virtual human (VH) scenes-each with a child and mother. Scenes varied by the dyad's race, child's weight status, and mother's weight status. For each scene, participants were asked to make six healthcare assessment ratings. Participants rated children (M = 42.44 vs. 48.69; p < 0.001) and mothers (M = 51.06 vs. 65.31; p < 0.001) with obesity as being less likely to adhere to physician recommendations compared with healthy weight children and mothers. Child patients with obesity (M = 38.88 vs. 30.08; p < 0.001) and mothers with obesity (M = 49.71 vs. 43.71; p < 0.001) were also rated as bearing more responsibility for the child's health status compared with healthy weight peers. This study provides evidence that child and mother weight can impact clinical decision-making, as well as for the utility of VH technology in studying decision-making among healthcare trainees and providers.

Using Virtual Human Technology to Examine Weight Bias and the Role of Patient Weight on Student Assessment of Pediatric Pain.

The purpose of the study was to investigate the influence of weight bias and demographic characteristics on the assessment of pediatric chronic pain. Weight status, race, and sex were manipulated in a series of virtual human (VH) digital images of children. Using a web-based platform, 96 undergraduate students with health care-related majors (e.g., Health Science, Nursing, Biology, and Pre-Medicine) read a clinical vignette and provided five ratings targeting the assessment of each VH child's pain. Students also answered a weight bias questionnaire. Group-based analyses were conducted to determine the influence of the VH child's weight and demographic cues, as well as greater weight bias on assessment ratings. Male and VH children with obesity were rated as more likely to avoid non-preferred activities due to pain compared to female and healthy weight children, respectively (both p < .001). The pain of VH children with obesity was rated as more likely to be influenced by psychological/behavioral issues compared to the pain of healthy weight VH children (p = .022). African American VH children were rated as experiencing significantly greater pain than Caucasian VH children (p = .037). As child weight increased, low weight bias participants felt more sympathy, while high weight bias participants felt less sympathy (p = .002). Also, low weight bias participants showed increased motivation to help, while high weight bias participants showed less motivation to help, as VH patient weight increased (p = .008). Child weight and evaluator weight bias may be influential in the assessment of pediatric pain. If supported by future research, results highlight the importance of training in evidence-based practice and education on weight bias for students majoring in health-care fields.

Simulation of body extension motion based on dynamics virtualization

With the further development of virtual human simulation technology and motion modeling, it is an important task to construct and simplify the virtual human geometry model. Aiming at the issues including large computational overhead, unnatural model generation gesture, change of kinematic chain structure and high complexity, this paper takes the body extension motion as the research object, uses the forward dynamics calculation method, conducts analysis of dynamic control structure of arm movement, and uses inverse kinematics algorithm to control leg movement. According to the forward dynamics algorithm to study the collision contact model of the foot and the ground, the virtual human could act more naturally and consistently in extension motion and the computational complexity could be effectively reduced.

3D virtual human rapid modeling method based on top-down modeling mechanism

Aiming to satisfy the vast custom-made character demand of 3D virtual human and the rapid modeling in the field of 3D virtual reality, a new virtual human top-down rapid modeling method is put for-ward in this paper based on the systematic analysis of the current situation and shortage of the virtual hu-man modeling technology. After the top-level realization of virtual human hierarchical structure frame de-sign, modular expression of the virtual human and parameter design for each module is achieved gradu-al-level downwards. While the relationship of connectors and mapping restraints among different modules is established, the definition of the size and texture parameter is also completed. Standardized process is meanwhile produced to support and adapt the virtual human top-down rapid modeling practice operation. Finally, the modeling application, which takes a Chinese captain character as an example, is carried out to validate the virtual human rapid modeling method based on top-down modeling mechanism. The result demonstrates high modelling efficiency and provides one new concept for 3D virtual human geometric mod-eling and texture modeling.

The Development of a Technology-Based Hierarchy to Assess Chronic Low Back Pain and Pain-Related Anxiety From a Fear-Avoidance Model.

Previous studies have not examined the assessment of chronic low back pain (CLBP) and pain-related anxiety from a fear avoidance model through the use of motion-capture software and virtual human technologies. The aim of this study was to develop and assess the psychometric propertiesof an interactive, technologically based hierarchy that can be used to assess patients with pain and pain-related anxiety. We enrolled 30 licensed physical therapists and 30 participants with CLBP. Participants rated 21 video clips of a 3-D animated character (avatar) engaging in activities that are typically feared by patients with CLBP. The results of the study indicate that physical therapists found the virtual hierarchy clips acceptable and depicted realistic patient experiences. Most participants with CLBP reported at least 1 video clip as being sufficiently anxiety-provoking for use clinically. Therefore, this study suggests a hierarchy of fears can be created out of 21 virtual patient video clips paving the way for future clinical use in patients with CLBP. This report describes the development of a computer-based virtual patient system for the assessment of back pain-related fear and anxiety. Results show that people with back pain as well as physical therapists found the avatar to be realistic, and the depictions of behavior anxiety- and fear-provoking.

Assessment of the Influence of Demographic and Professional Characteristics on Health Care Providers’ Pain Management Decisions Using Virtual Humans

Disparities in health care associated with patients' gender, race, and age are well documented. Previous studies using virtual human (VH) technology have demonstrated that provider characteristics may play an important role in pain management decisions. However, these studies have largely emphasized group differences. The aims of this study were to examine dentists' and physicians' use of VH characteristics when making clinical judgments (i.e., cue use) and to identify provider characteristics associated with the magnitude of the impact of these cues (β-weights). Providers (N=152; 76 physicians, 76 dentists) viewed video vignettes of VH patients varying in gender (male/female), race (white/black), and age (younger/older). Participants rated VH patients' pain intensity and unpleasantness and then rated their own likelihood of administering non-opioid and opioid analgesics. Compared to physicians, dentists had significantly lower β-weights associated with VH age cues for all ratings (p<0.001; d>0.69). These effects varied by provider race and gender. For pain intensity, professional differences were present only among non-white providers. White providers had greater β-weights than non-white providers for pain unpleasantness but only among men. Provider differences regarding the use of VH age cues in non-opioid analgesic administration were present among all providers except non-white males. These findings highlight the interaction of patient and provider factors in driving clinical decision making. Although profession was related to use of VH age cues in pain-related clinical judgments, this relationship was modified by providers' personal characteristics. Additional research is needed to understand what aspects of professional training or practice may account for differences between physicians and dentists and what forms of continuing education may help to mitigate the disparities.

Framework and Assessment of Conversational Virtual Humans as Role-players in Simulated Social Encounters with People

As technology advances, people increasingly converse with computer conversational agents serving in a personal assistant role such as Siri or Cortona. Beyond the personal assistant role, a virtual humans may substitute for a person in another role during a social encounter in a simulated environment. From an experiential or pedantic training perspective, the resulting simulated social encounter may provide a forum by which real people may gain social skills. This article reviews advances in technology and science of virtual humans in faceto-face social encounters with people. An assessment framework is proposed based on a modification of the Schroeder, Adesope, &amp; Gilbert framework [1]. Conversational attributes include verbal content and expression and nonverbal behavior such as head position or inclination, micro and macro facial or body emotional expression, eye gaze and direction, and other facial and other bodily gestures. Using that framework, ten instantiations of virtual humans are analyzed with respect to simulating a person in a bi-directional conversation. Findings and discussion address the question: can a virtual human truly be a social partner with a person for the purpose of social skills training? Within a social skills learning setting, analysis raises concern about the impact of textual data concurrently displayed with verbal message content and expression, and nonverbal behavior. Suggested future advanced technology research includes virtual human eye tracking technology in order to a better understand the cognitive science and behavioral responses associated with periods of engagement and boredom during interactions with a person.

An Approach for Integrated Analysis of Human Factors in Remote Handling Maintenance

Considering dangerous environmental conditions, maintenance of radioactive equipment can be performed by remote handling maintenance (RHM) system. The RHM system is a sophisticated man-machine system. Therefore, human factors analysis is an inevitable aspect considered in guaranteeing successful and safe task performance. This study proposes an approach for integrated analysis of human factors in RHM so as to make the evaluating process more practical. In the approach, indicators of accessibility, health safety, and fatigue are analyzed using virtual human simulation technologies. The human error factors in the maintenance process are analyzed using the human error probability (HEP) based on the success likelihood index method- (SLIM-) analytic hierarchy process (AHP). The psychological factors level of maintenance personnel is determined with an expert scoring. The human factors for the entire RHM system are then evaluated using the interval method. An application example is present, and the application results show that the approach can support the evaluation of the human factors in RHM.

Effect of a perspective-taking intervention on the consideration of pain assessment and treatment decisions.

ObjectivesPain is often poorly managed, highlighting the need to better understand and treat patients’ pain. Research suggests that pain is assessed and treated differently depending on patient sex, race, and/or age. Perspective-taking, whereby one envisions the perspective of another, has been found to reduce racial disparities in pain management. This study used virtual human (VH) technology to examine whether a perspective-taking intervention impacts pain management decisions.MethodsNinety-six participants were randomized to an online treatment or control group and viewed 16 video clips of VHs with standardized levels of pain. Participants provided ratings on the VHs’ pain intensity and their willingness to administer opioids to them. The intervention group received a brief perspective-taking intervention that consisted of having participants imagine how the patient’s suffering could affect his/her life, whereas the control group was asked to wait for the next VH videos to load. A LENS model analysis was used to investigate both group level (nomothetic) and individual level (idiographic) decision policies. A LENS model of analysis is typically used as an analog method for capturing how groups of people and individuals use information in their environment to form judgments.ResultsNomothetic results found that participants rated pain higher and were more likely to prescribe opioids to VHs postintervention, irrespective of group. Idiographic results, however, found that the use of cues to make pain management decisions was mitigated by the perspective-taking group. The participants in the perspective-taking group were more likely to think about pain and the patients’ perspective during the intervention, while control participants were more likely to reflect on the VHs’ sex, race, or age.ConclusionA brief intervention may alter participants’ pain management decisions. These results indicate that a brief intervention might be an initial step toward aligning observers’ pain management ratings with those of the patient. Future research is needed to replicate findings in a health care population.

Towards virtual ergonomics: aviation and aerospace

Purpose – The purpose of this paper is to highlight state-of-the-art digital human modeling applications in aviation and aerospace industry, generate research interest and promote application of digital human modeling technology among audience of diverse background including researchers, students, trainees, etc. in academia and industry; designers; engineers; and ergonomists associated with aviation and aerospace sectors. Design/methodology/approach – Comprehensive literature search was performed and, subsequently, all publications identified were studied thoroughly at least by abstracts. Available information has been segregated under different headings and depicted systematically for easy understanding by readers. Findings – Virtual human modeling technology has been used in assessing reach and accessibility in aircraft cockpits, creating accurate posture libraries, performing vision analysis for pilots, determining design modifications to accommodate female users, predicting probable pilot behavior in ...

Biomechanics-based in silico medicine: The manifesto of a new science

In this perspective article we discuss the role of contemporary biomechanics in the light of recent applications such as the development of the so-called Virtual Physiological Human technologies for physiology-based in silico medicine. In order to build Virtual Physiological Human (VPH) models, computer models that capture and integrate the complex systemic dynamics of living organisms across radically different space–time scales, we need to re-formulate a vast body of existing biology and physiology knowledge so that it is formulated as a quantitative hypothesis, which can be expressed in mathematical terms. Once the predictive accuracy of these models is confirmed against controlled experiments and against clinical observations, we will have VPH model that can reliably predict certain quantitative changes in health status of a given patient, but also, more important, we will have a theory, in the true meaning this word has in the scientific method. In this scenario, biomechanics plays a very important role, biomechanics is one of the few areas of life sciences where we attempt to build full mechanistic explanations based on quantitative observations, in other words, we investigate living organisms like physical systems. This is in our opinion a Copernican revolution, around which the scope of biomechanics should be re-defined. Thus, we propose a new definition for our research domain “Biomechanics is the study of living organisms as mechanistic systems”.

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

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. PerspectiveThis 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.

(524) The effect of a virtual human perspective-taking intervention on pain assessment and treatment decisions

Pain is often underestimated and poorly managed; therefore, the identification of methods to improve understanding of patients’ pain experiences and providers’ pain management strategies is crucial. Perspective-taking (imagining how the other’s suffering could affect his/her life) serves as a potential method by which to improve pain-related clinical decision making. The aim of the current study was to use virtual human (VH) technology to examine whether a brief, perspective-taking intervention impacts pain management decisions to begin to address the lack of evidence-based interventions.

(516) Using virtual human technology to provide immediate feedback about participants’ use of demographic cues and their knowledge of their cue use

Although pain is among the most common reasons for seeking medical attention, pain assessment and treatment are difficult. Demographic characteristics have been found to influence pain management decisions. Limited research experimentally examines the use of demographic characteristics in making pain management decisions, and even fewer make healthcare providers aware of their use of those characteristics. One hundred and seven individuals viewed 32 virtual human (VH) patients with standardized levels of pain.

The impact of patients’ gender, race, and age on health care professionals’ pain management decisions: An online survey using virtual human technology

BackgroundPrevious literature indicates that biases exist in pain ratings. Healthcare professionals have been found to use patient demographic cues such as sex, race, and age when making decisions about pain treatment. However, there has been little research comparing healthcare professionals’ (i.e., physicians and nurses) pain decision policies based on patient demographic cues. MethodsThe current study used virtual human technology to examine the impact of patients’ sex, race, and age on healthcare professionals’ pain ratings. One hundred and ninety-three healthcare professionals (nurses and physicians) participated in this online study. ResultsHealthcare professionals assessed virtual human patients who were male and African American to be experiencing greater pain intensity and were more willing to administer opioid analgesics to them than to their demographic counterparts. Similarly, nurses were more willing to administer opioids make treatment decisions than physicians. There was also a significant virtual human-sex by healthcare professional interaction for pain assessment and treatment decisions. The sex difference (male>female) was greater for nurses than physicians. ConclusionsResults replicated findings of previous studies using virtual human patients to assess the effect of sex, race, and age in pain decision-making. In addition, healthcare professionals’ pain ratings differed depending on healthcare profession. Nurses were more likely to rate pain higher and be more willing to administer opioid analgesics than were physicians. Healthcare professionals rated male and African American virtual human patients as having higher pain in most pain assessment and treatment domains compared to their demographic counterparts. Similarly the virtual human-sex difference ratings were more pronounced for nurses than physicians. Given the large number of patients seen throughout the healthcare professionals’ careers, these pain practice biases have important public health implications. This study suggests attention to the influence of patient demographic cues in pain management education is needed.

Pain Assessment and Treatment Decisions for Virtual Human Patients

Laypeople and healthcare professionals use demographic cues when making pain management decisions. These decisions can negatively affect patient outcomes. This study examined whether laypeople base their pain management decisions in part on pain-related postures and demographic cues. Virtual human (VH) technology was used to research whether sex and race, as well as body posture, influenced pain management decisions. Ninety-seven laypersons examined VH patients exhibiting low back pain related body postures whose demographic cues varied by VH sex and VH race. T tests validated that participants were able to distinguish between high pain related body postures and low pain related body postures. The participants assessed male VH patients to be experiencing more pain than female VH patients. This study suggests that participants use sex as a cue when assessing pain. Participants may perceive VH male patients as experiencing high pain intensity if the participants are willing to counter male stereotypes and acknowledge that the male VH patients display pain behaviors.

The influence of sex, race, and age on pain assessment and treatment decisions using virtual human technology: a cross-national comparison.

PurposeStudies in the United States have found that patients’ sex, race, and age influence the pain assessment and treatment decisions of laypeople and medical professionals. However, there is limited research as to whether people of other nationalities make pain management decisions differently based on demographic characteristics. Therefore, the purpose of the following study was to compare pain assessment and treatment decisions of undergraduate students in Jordan and the United States as a preliminary examination of nationality as a potential proxy for cultural differences in pain decisions.MethodsVirtual human (VH) technology was used to examine the influences of patients’ sex (male or female), race (light-skinned or dark-skinned), and age (younger or older) on students’ pain management decisions. Seventy-five American and 104 Jordanian undergraduate students participated in this web-based study.ResultsAmerican and Jordanian students rated pain intensity higher in females and older adults and were more likely to recommend medical help to these groups, relative to males and younger adults. Furthermore, Jordanian participants rated pain intensity higher and were more likely to recommend medical help for all patient demographic groups (ie, sex, race, age) than American participants.ConclusionThis is the first cross-national study that compares pain decisions between undergraduate students. The results suggest that sex, race, and age cues are used in pain assessment and treatment by both Americans and Jordanians, with Jordanians more likely to rate pain higher and recommend medical help to patients. Additional research is needed to determine the cultural determinants of these differences.

The influence of patient's sex, race and depression on clinician pain treatment decisions

Pain treatments often vary across patients' demographic and mental health characteristics. Most research on this topic has been observational, has focused on opioid therapy exclusively and has not examined individual differences in clinician decision making. The current study examined the influence of patient's sex, race and depression on clinicians' chronic pain treatment decisions. We used virtual human technology and lens model methodology to enhance study realism and facilitate a richer understanding of treatment decisions. Clinicians and trainees (n = 100) made treatment decisions (opioid, antidepressant, pain specialty referral, mental health referral) for 16 computer-simulated patients with chronic low back pain. Patients' sex, race and depression status were manipulated across vignettes (image and text). Individual- and group-level analyses indicated that patient's depression status had the strongest and most consistent influence on treatment decisions. Although less influential overall, patient's sex and race were significantly influential for a subset of participants. Furthermore, the results indicated that participants who were influenced by patient's race had less experience in treating chronic pain than those who were not influenced by patient's race [t(11.59) = 4.75; p = 0.001; d = 1.20]. The results of this study indicated considerable variability in participants' chronic pain treatment decisions. These data suggest that interventions to reduce variability in treatment decision making and improve pain care should be individually tailored according to clinicians' decision profiles.