How Personality and Affective Responses Are Associated with Skepticism Towards Virtual Reality in Medical Training

Abstract

Virtual reality (VR) offers a safe, immersive environment for medical training, but some users remain skeptical about a broader implementation. Our study aims to explore how personality traits, affective responses, and task-related perceptions correlate with attitudes towards VR-based medical emergency training. Forty-seven medical students participated in a 30-minute VR emergency training. Personality traits were assessed using the short version of the Big Five Inventory beforehand, while affective responses (using the Positive and Negative Affect Schedule, PANAS), stress, and motivation were measured before and after the training. Participants also rated the sessions’ difficulty, cognitive challenge, and technical maturity of the VR program and their acceptance of VR for training and examination purposes. Cluster analysis identified three groups: Cluster 1, characterized by low technical affinity, limited prior VR experience, and high extraversion, demonstrated the greatest increase in negative affective responses and the lowest VR acceptance. In contrast, cluster 3, with high technical affinity and neuroticism, experienced more positive affective responses and increased motivation, expressing high acceptance of VR for training purposes but some reservation regarding its use in examinations. Cluster 2 displayed balanced affective responses and strong support for VR use in both settings. Thematic analysis identified perceived lack of control due to insufficient medical knowledge, technical issues, and simulation sickness as sources of negative affective responses. In conclusion, personality and affective responses may play a significant role in shaping the attitude towards VR training applications. Uncovering emotional barriers to VR adoption among skeptical users and understanding their underlying reasons may inform future strategies for overcoming them. Given the relatively small sample size, results of this preliminary study should be expanded through further examination of diverse populations and a broader range of VR applications.

Introduction

Virtual reality (VR) technology has gained significant momentum in medical education, serving as a versatile tool for teaching both technical and nontechnical skills within a safe, controlled environment.¹ Its ability to provide immersive, hands-on experiences that mimic real-world clinical scenarios supports repetitive practice without risk to patients, fostering competence and confidence.^2,3 Despite its potential, the widespread implementation of VR in medical education faces several barriers, e.g., the current maturity and cost of hardware and software.⁴ Furthermore, user experience and the prevalence of simulation sickness impact the acceptance and integration of VR-based training programs into medical education.¹

While technological advances have improved VR systems, some users continue to express reservations about VR, as observed in recent implementation studies.^5–8 Research suggests the acceptance of VR may be linked to demographic factors such as age or gender^5,9,10 as well as general technology acceptance.^11,12 However, the role of psychological determinants, such as personality traits and emotional states, remains relatively underexplored.¹³ Previous research has linked conscientiousness and agreeableness to performance metrics in a VR assembly training¹⁴ and identified anxiety as a barrier to adoption of VR for learning.¹⁵ Furthermore, a VR-based assessment framework has been developed to measure personality traits.¹⁶ While these studies mainly assessed stable traits using abstract or purpose-built scenarios, evidence on the link between traits, affective responses, and acceptance in complex medical training remains scarce.

To address these gaps, this study investigates person-specific factors influencing acceptance of VR technology in medical education. In our study, all participating students had already taken part in a standardized VR-based medical emergency training to establish a basis for forming a representative judgment. We then examined the relationship between static personality traits (using the Big Five Inventory [BFI]), affective responses during the VR experience (via the Positive and Negative Affect Schedule [PANAS]), stress levels, and motivation, and how these factors relate to the acceptance of VR as a training or examination tool.

Research Questions

Can distinct participant clusters be identified based on person-specific factors, personality traits, affective and cognitive responses, task-related perceptions (stress and motivation), as well as ratings of the VR program’s difficulty and cognitive challenge?

How do usability and acceptance relate to these clusters?

How do affective and cognitive responses evolve among clusters during training?

What underlying causes contribute to VR rejection?

Methods

Study design

This study was conducted at a medical school in Germany offering a 6-year curriculum, from March to November 2023. Fifth-year medical students, already familiar with VR-based emergency training, participated during optional supplementary sessions. After informed consent, they completed a 30-minute VR training featuring one of five emergency scenarios: (1) fever and abdominal pain due to sepsis, (2) hematemesis from esophageal variceal bleeding, (3) abdominal pain from biliary pancreatitis, (4) chronic cough caused by pulmonary tuberculosis, and (5) chest pain resulting from acute myocardial infarction. The scenario content was previously described.⁶ Scenarios included tasks such as taking patient history, ordering tests, administering treatments, and arranging interventions. The VR system provided automated feedback, dynamically adjusting vital signs and lab parameters based on an underlying physiological model. At session end, students received a guideline-based evaluation of their performance. During the VR training, tutors were available solely to resolve technical issues, without offering guidance on the medical content.

Measurement instruments

Data collection involved pre- and post-session questionnaires (see Table 1 for instruments).

Table 1.

Assessment Instruments

Variables	Description of the measure	Questionnaire		Source	Scales and response options
Variables	Description of the measure	Pre	Post	Source	Scales and response options
1. Person-specific factors
Demographic information and prior experience	Participants’ information on age, gender, prior experience with digital 3D and VR applications	X		None	4 Items, each with its own response options.
Technical affinity	Self-rated technical affinity	X		None	1 on a 5-point Likert scale ranging from 1 = very low to 5 = very high
2. Personality traits
Personality traits	German short version of the Big Five Inventory (BFI-K), measuring extraversion, agreeableness, conscientiousness, emotional stability and openness.	X		18	21 items on a 5-point Likert scale, participants rated their agreement ranging from 1 = strongly disagree to 5 = strongly agree
3. Affective and cognitive responses
Positive and negative affective responses	German version of the Positive and Negative Affect Schedule (PANAS)	X	X	20	20 items to assess positive (n = 10) and negative (n = 10) affect scores using a 5-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree
Stress	Perception of being stressed during the management of virtual emergencies	X	X	None	Scale ranging from 0–100, with 100 as the maximum
Motivation	Subjective rating of current motivation	X	X	None	1 item on a 5-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree
4. Task-related perceptions
Difficulty and cognitive challenge	Perceived degree of difficulty and cognitive challenge during the scenarios		X	None	2 items on a 9-point Likert scale ranging from 1 = very low to 9 = very high
5. System-related variables
Technical maturity of the VR program	Perceived technical maturity of the VR program		X	None	1 item on a 5-point Likert scale ranging from 1 = very low to 5 = very high
Acceptance	General acceptance of virtual reality for use in (1) training and (2) examinations		X	None	2 items on a 5-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree
Causes of negative affective responses during the VR session	Subjective assessment of causes contributing to negative affective responses during the VR session		X	None	Open-ended question, the responses to which were subjected to thematic analysis.

VR, virtual reality.

Pre-session variables

Participants provided demographic information (age, gender, prior 3D/VR experience) and self-rated technical affinity. Personality traits were assessed using the BFI¹⁷ in its German short form (BFI-K).¹⁸

Pre- and post-session variables

Positive and negative affect were measured before and after the VR scenario using the PANAS¹⁹ in its German version.²⁰ Stress (0–100 scale) and motivation (5-point Likert scale) were also rated. Pre-post differences were calculated, resulting in variables termed as Δ positive affect score, Δ negative affect score, Δ stress, and Δ motivation.

Post-session variables

Participants rated scenario difficulty and cognitive challenge (9-point Likert scale), technical maturity, and VR acceptance for training or exam purposes (5-point Likert scale).

Finally, to explore potential causes for positive and negative affective responses, participants were asked to provide free-text responses explaining changes in affect during the VR session. These responses were then analyzed using thematic analysis.²¹

Ethical considerations

The local institutional review and ethics board judged the project as not representing medical or epidemiological research on human subjects, and thus, a simplified assessment protocol was applied. The project received approval without any reservations under proposal number 20230323–04. Survey data from the questionnaires were collected anonymously via the EvaSys® platform (Lüneburg, Germany). Participation in the study was voluntary, and neither the students’ decision to participate nor the results of their questionnaires had any impact on their academic progress. All data were processed and stored in accordance with the local data protection regulations.

Statistics

Descriptive statistics, including mean (M) and standard deviation (SD), were calculated for all measurement instruments. Group differences were assessed using the Wilcoxon rank-sum test. To detect overarching patterns, changes across all scales (listed in Table 1) were analyzed using cluster analysis. For small sample sizes, multi-dimensional scaling was applied for the k-means algorithm to ensure clear cluster separation.²² Depending on the number of clusters extracted, either a Welch test and/or an Analysis of Variance (ANOVA) was conducted, both of which are robust against violations of normality.^23,24 All calculations and figure generations were performed using GraphPad Prism (version 10.3.1) and R (version 2024.04.2).

Results

Background information of participants

A total of 47 medical students participated in the study. Table 2 depicts the details of participants. The sample included 74% females, with an average of age 25.4 ± 2.6 years, representative of advanced medical students. Overall, participants reported limited prior experience with VR and 3D applications.

Table 2.

Participants Demographics and Prior Experience with 3D and VR Applications

Parameter	N	%
Gender
Female	35	74
Male	12	26
Diverse	0	0
Age in years [M ± SD]	25.4 ± 2.6
Frequency of digital 3D application usage
Never	29	62
x month	10	21
1–3x/month	5	11
1–6x/week	3	6
Daily	0	0
Total duration of prior VR application usage
None	19	40
Less than 1 hours	15	32
Between 1 and 5 hours	11	23
Between 5 and 10 hours	2	4
More than 10 hours	0	0

VR, virtual reality.

Cluster analysis

Figure 1 depicts a graphical representation of the clusters, displaying each participant as an individual data point. Most students were part of cluster 2 (green, n = 18), followed by cluster 3 (blue, n = 17), with the fewest in cluster 1 (red, n = 12). Cluster 3 differed the most from cluster 1, as reflected by their separation. Table 3 presents numerical values of the variables and statistical significance for differences across clusters.

FIG. 1.

Clusters 1 (red), 2 (green), and 3 (blue) with each point representing a participant. Axes represent linear combinations of scaled variables.

Table 3.

Numerical Values (M ± SD; Gender as Absolute Counts) of All Participants (Total) and for Clusters 1, 2, and 3

	Total	Cluster			p value
	(n = 47)	1 (n = 12)	2 (n = 18)	3 (n = 17)	p value
Pre-session variables
Age	25.36 ± 2.64	25.58 ± 2.54	26.28 ± 3.36	24.24 ± 1.15	0.12
Gender (female/male)	35/12	9/3	15/3	11/6	0.45
Technical affinity	3.00 ± 1.04	2.25 ± 0.97	3.11 ± 0.58	3.41 ± 1.23	0.003
Prior use of VR applications	1.91 ± 0.90	1.42 ± 0.67	1.50 ± 0.62	2.71 ± 0.77	<0.001
Extraversion	3.37 ± 0.72	3.67 ± 0.56	3.46 ± 0.69	3.07 ± 0.77	0.02
Agreeableness	3.38 ± 0.74	3.21 ± 0.78	3.31 ± 0.73	3.57 ± 0.72	0.18
Conscientiousness	3.85 ± 0.63	4.08 ± 0.59	3.76 ± 0.68	3.76 ± 0.60	0.21
Neuroticism	3.14 ± 0.72	2.60 ± 0.69	3.08 ± 0.58	3.57 ± 0.64	<0.001
Openness	3.79 ± 0.59	3.68 ± 0.46	3.71 ± 0.73	3.95 ± 0.50	0.20
Pre-/post-session variables
Δ Positive affect score	0.09 ± 0.66	−0.12 ± 0.65	0.25 ± 0.64	0.49 ± 0.34	0.20
Δ Negative affect score	0.15 ± 0.83	0.38 ± 0.55	0.28 ± 0.60	−0.32 ± 0.63	0.003
Δ Stress	10.00 ± 23.27	20.58 ± 27.35	0.17 ± 18.53	12.94 ± 21.8	0.54
Δ Motivation	0.15 ± 0.83	−0.33 ± 0.78	0.28 ± 0.60	0.35 ± 0.93	0.003
Post-session variables
Cognitive challenge	6.66 ± 1.46	7.08 ± 1.38	6.5 ± 1.38	6.53 ± 1.62	0.36
Perceived difficulty	6.04 ± 1.44	6.42 ± 1.38	5.78 ± 1.17	6.06 ± 1.75	0.59
Technical maturity of VR program	3.04 ± 0.88	3.17 ± 0.83	3.61 ± 0.70	2.35 ± 0.61	0.004
Acceptance of VR for training	4.32 ± 1.04	3.33 ± 1.37	4.89 ± 0.32	4.41 ± 0.80	0.01
Acceptance of VR for examination	2.51 ± 1.44	1.17 ± 0.39	3.89 ± 1.02	2.00 ± 1.00	0.34

VR, virtual reality.

Differences between clusters were analyzed using a two-way ANOVA, with p-Values below the 0.05 significance level highlighted in bold.

The clusters differed significantly on eight variables. Cluster 1 had the lowest technical affinity, minimal previous VR experience, highest extraversion, and lowest neuroticism. It reported the greatest increase in negative affect scores, decreased motivation, and the lowest acceptance of VR despite rating its technical maturity as adequate. Cluster 3, with high technical affinity and VR experience, low extraversion, and high neuroticism, experienced a reduction in negative affect scores and increased motivation. While rating the VR program’s maturity lowest, this cluster strongly accepted VR for training but was cautious about its use for exams. Although not statistically significant, it had the highest proportion of male participants. Cluster 2 was intermediate in personality traits and reported increases in both positive and negative affect, alongside higher motivation. It rated the VR program as highly mature and supported its use for training and exams.

Analysis of PANAS scores highlighted a potential psychological basis of VR skepticism in cluster 1 (Figure 2). While clusters 2 and 3 saw increases in all positive affect items, cluster 1 experienced declines, especially in feeling “strong” (−0.42), “active” (−0.33), and “interested” (−0.33). Cluster 1 also reported higher negative affect scores, notably “upset” (0.58), “jittery” (0.58), and “distressed” (0.42). Clusters 2 and 3 also showed slight increases in negative items like “ashamed” (0.32), “scared” (0.26), and “guilty” (0.24). All numerical values are listed in Supplement Table 1.

FIG. 2.

Changes in individual positive (top) and negative (bottom) affect scores during the VR training session, measured by the PANAS, comparing cluster 1 (red) to clusters 2 and 3 (gray). Values (M ± SD) are presented as post-pre differences of scores.

Thematic analysis

Thematic analysis of free-text responses (n = 15) from cluster 1 participants revealed four key themes likely contributing to the observed decrease in positive and increase in negative affect scores (Table 4): The most frequently reported theme was insufficient medical knowledge to manage the cases, particularly with deteriorating patients. This was followed by technical problems, difficulties with system handling, and symptoms of simulation sickness.

Table 4.

Themes Associated with Negative Affective Responses in Cluster 1. Multiple Responses Were Allowed, Percentages Refer to the Number of Given Responses

Theme	Responses(n)	Percentage(%)	Example of quote
Insufficient medical knowledge	6	40	“I felt helpless due to the worsening vital signs of the virtual patient.”
Technical problems	4	27	“I got stuck in a wall after teleporting, so I couldn’t focus on the case.”
Operating the VR system	3	20	“I felt overwhelmed by the complicated handling of the VR controller buttons.”
Symptoms of simulation sickness	2	13	“I got nausea from wearing the VR headset.”
Total	15	100

VR, virtual reality.

Discussion

Through an analysis of personality traits, affective and cognitive responses, as well as, task-related perceptions during VR-based emergency training, this study sheds light on factors that may contribute to skepticism toward the implementation of VR technology into medical education.

Firstly, students in cluster 1 showed significantly lower acceptance of VR, associated with low technical affinity and limited prior VR experience—factors linked to VR acceptance^9,12 and performance¹⁰ in previous studies. Participants grouped in cluster 1 also displayed higher extraversion and lower neuroticism. With this personality profile, they may have been less engaged due to the solitary nature of the VR training, which contrasts with their preference for social applications.²⁵ Vice versa, individuals with higher neuroticism levels (cluster 3) are more likely to accept self-centered digital applications such as the training program used in our study.²⁶ Further studies may explore how personality traits influence acceptance of social or multi-user immersive VR training applications. Importantly, gender distribution in our sample was skewed, with males unevenly distributed across clusters. While prior research suggests that males may show higher initial acceptance of VR-based training,^9,11 the small sample size prevented a systematic analysis of gender effects in this study.

Secondly, the pre-post comparison of affective scores revealed distinct response patterns across the three clusters. Previous research has highlighted VR’s impact on affective responses in diverse contexts—sensory analysis of taste,²⁷ recorded 360° videos of life-events,²⁸ and fear-inducing games.²⁹ In this study, participants of cluster 2 and 3 reported an increase in positive affect scores, notably “enthusiastic,” “inspired,” and “excited,” underscoring VR’s potential to evoke enthusiasm and positive engagement.^28,30 In contrast, cluster 1 showed a decline in most positive affect scores, particularly in feelings of being “strong,” “active,” and “interested.” Similar decreases in “interest” and “enjoyment” were reported in a desktop PC-based simulation with a deteriorating patient.³¹ Vice versa, cluster 1 also reported significant increases in negative affect scores, such as “upset,” “jittery,” and “scared.” This finding is also reflected by the above-mentioned study, which noted negative affective responses such as fear, distress, and anger in challenging VR scenarios.³¹ Physical simulations of disaster scenarios have likewise evoked increases in negative emotions (although the respective affective dimensions were not further specified).³² Overall, the range and specificity of these affective responses highlight VR’s ability to elicit a complex pattern of emotional engagement.

Thirdly, a thematic analysis identified participants’ inability to impact the virtual patient’s outcome, attributed to insufficient medical knowledge, as the primary source of negative affective responses. This aligns with the results of individual PANAS items, where participants felt less “strong” and more “upset” and “scared” post-session, likely indicating a perceived lack of control. In line with these findings, literature identifies a lack of control as a significant stressor in VR-based training,⁶ and a key factor influencing the emotional impact of critical incidents on health care professionals.^33,34 Although this study involved only virtual patients in a safe setting, it underscores the importance of providing reassurance and support post-training, as was done here by a trained tutor. Other themes include technical issues and usability challenges, common barriers to broader VR adoption³⁵ and should be reported to developers, who can incorporate general design recommendations to improve immersive simulations.³⁶

Strengths and limitations

A key strength of this study is its focus on the psychological factors influencing VR adoption, an area often overlooked in favor of technical usability studies. By employing validated instruments like the PANAS and the BFI-K, the study provided robust data on how these variables contribute to VR acceptance and how they change in response to VR training. Thematic analysis further identified specific causes that can elicit negative affective responses and, in turn, negatively influence acceptance.

The study also has limitations. The sample size was relatively small, and participants were drawn from a single institution, which limits the generalizability of the findings. Analyses concerning cluster 1, with 12 participants, remain exploratory in nature. The low proportion of male participants within the clusters limited the analysis of gender-specific differences. Moreover, the VR scenarios focused solely on emergency medical situations, which may not fully represent the range of applications in medical education. Lastly, qualitative data collection was restricted to responses to a specific question, omitting deeper aspects (e.g., attitudes or prior negative experiences) that may influence VR acceptance.

Conclusion

This study examines the interplay between personality, emotional responses, and the acceptance of VR training environments, identifying potential causes of negative emotional user experiences. By uncovering emotional barriers to VR adoption among the most skeptical users and their underlying reasons, strategies for overcoming them can be developed. However, given the relatively small sample size, the study should be considered preliminary. Expanding the research to include larger, more diverse populations and contexts is essential to assess the relevance of the results for other professional fields.

Footnotes

Authors’ Contributions

T.M. was responsible for the conceptualization of the study, supervising the execution, analyzing the survey data, and writing the original draft. J.B. contributed to the conceptualization and performed cluster analysis. L.D. conducted the data acquisition and curation. S.K. was involved in the conceptualization of the study and participated in the writing, review, and editing of the article.

Author Disclosures Statement

T.M. was involved in the software development process of STEP-VR.

Funding Information

No funding was received for this study.

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How Personality and Affective Responses Are Associated with Skepticism Towards Virtual Reality in Medical Training—A Pre-Post Intervention Study

Abstract

Introduction

Research Questions

Methods

Study design

Measurement instruments

Pre-session variables

Pre- and post-session variables

Post-session variables

Ethical considerations

Statistics

Results

Background information of participants

Cluster analysis

Thematic analysis

Discussion

Strengths and limitations

Conclusion

Footnotes

Authors’ Contributions

Author Disclosures Statement

Funding Information

References