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Military members and veterans exhibit higher rates of injuries and illnesses such as posttraumatic stress disorder (PTSD) because of their increased exposure to combat and other traumatic scenarios. Novel treatments for PTSD are beginning to emerge and increasingly leverage advances in gaming and other technologies, such as virtual reality. Without assessing the degree of technology acceptance and perception of usability to the end users, including the military members, veterans, and their attending therapists and staff, it is difficult to determine whether a technology-based treatment will be used successfully in wider clinical practice. The Unified Theory of Acceptance and Use of Technology model is commonly used to address the technology acceptance and usability of applications in 5 domains.
Using the Unified Theory of Acceptance and Use of Technology model, the purpose of this study was to determine the technology acceptance and usability of multimodal motion-assisted memory desensitization and reconsolidation (3MDR) on a virtual reality system in the primary user group (military members and veterans with treatment-resistant PTSD, 3MDR therapists, and virtual reality environment operators).
This mixed methods embedded pilot study included military members (n=3) and veterans (n=8) with a diagnosis of combat-related PTSD, as well as their therapists (n=13) and operators (n=5) who completed pre-post questionnaires before and on completion of 6 weekly sessions of 3MDR. A partial least squares structural equation model was used to analyze the questionnaire results. Qualitative data from the interviews were assessed using thematic analysis.
Effort expectancy, which was the most notable predictor of behavioral intention, increased after a course of 3MDR with the virtual reality system, whereas all other constructs demonstrated no significant change. Participants’ expectations of the technology were met, as demonstrated by the nonsignificant differences in the pre-post scores. The key qualitative themes included feasibility and function, technical support, and tailored immersion.
3MDR via a virtual reality environment appears to be a feasible, usable, and accepted technology for delivering 3MDR to military members and veterans who experience PTSD and 3MDR therapists and operators who facilitate their treatment.
Military services commonly involve engagement in high-risk activities, whether during physical training, daily trade-related tasks, overseas deployment or in response to natural disasters. Such activities place military members, individually and collectively, at a heightened risk of physical and psychosocial injury. Canadian Armed Forces (CAF) military members and veterans exhibit higher rates of injuries and illnesses, such as posttraumatic stress disorder (PTSD), major depressive disorder, generalized anxiety disorder, substance abuse, sleep disorders, and mild traumatic brain injury compared to their civilian counterparts [
Multimodal motion-assisted memory desensitization and reconsolidation (3MDR) is an innovative, technology-assisted, exposure-based trauma therapy that holds promise for treating combat-related PTSD (crPTSD). 3MDR is a structured, personalized, exposure-based, virtual reality (VR)–supported intervention developed in the Netherlands and used with military members and veterans with PTSD in the Netherlands, the United Kingdom, the United States, Israel, and Canada [
The 3MDR intervention comprises 10 sessions, including selecting images and music, trauma processing, and reconsolidation, and six 90-minute therapy sessions in the VRE, including a 30-minute debrief [
Initial randomized controlled trials with 3MDR participants have shown a reduction in PTSD symptoms, which was maintained over time [
Technology offers health care professionals a variety of benefits, including improving the efficacy, efficiency, safety, and cost-effectiveness of assessments, interventions, data collection, data analysis, reporting, record keeping, and communication. The acceptance of such technologies by health care professionals is an important topic for health care professionals and researchers [
The use of digital and mobile health innovations is becoming widespread in military and veteran populations [
The purpose of this mixed methods pilot study is to use the Unified Theory of Acceptance and Use of Technology (UTAUT) model to determine the technology acceptance and usability of 3MDR within a VRE in the end user groups of (1) military members and veterans with crPTSD, (2) 3MDR therapists, and (3) VRE operators. On the basis of previous research in this area, it is hypothesized that performance expectancy (PE) and facilitating conditions (FC) will be the most influential variables on behavioral intentions (BI) and use, respectively. In addition, it is hypothesized that social influence (SI) will have the least influence on BI.
This study used a mixed methods, embedded study design with a pre-post quasi-experimental approach. A quantitative approach using partial least squares structural equation modeling (PLS-SEM) and other nonparametric statistics was the primary method of data collection; a qualitative thematic analysis was secondary to this. This study was embedded in a larger study that used a mixed methods staggered entry clinical trial to test the efficacy, effectiveness, and safety of 3MDR [
The UTAUT model was developed based on previous theories and models for the acceptance and adoption of technologies and consumer products that address the perceived technology acceptance of a user group with the goal of predicting use behavior (
The Unified Theory of Acceptance and Use of Technology Model [
The UTAUT model addresses the perceived expectations of technological acceptance of new technology in five constructs: PE, effort expectancy (EE), and SI (direct determinants of BI), as well FC and BI, which have a direct impact on use behavior [
BI is defined as the intention to use technology, and use is defined as the actual use [
The UTAUT has been used in recent years as a model and framework for addressing technology use and acceptance in health care [
The target study sample size was set at a minimum of 40 military members and veterans to account for a 20% dropout rate and allow for power at 32 participants. With 4 latent variables, for 80% significance at a 5% significance level, the sample size required for this study was 24 (
Recruitment of regular and reserve CAF military members and veterans was conducted by word of mouth among potential participants and their mental health providers as convenience and snowball sampling. Service providers supporting CAF military members and veterans, after being informed of the study via word of mouth and institutional email, informed patients who met the study inclusion and exclusion criteria. Potential participants who showed interest in participation were provided with a
Recruitment of the 3MDR therapists and operators was initiated via email circulated by key stakeholders associated with the 3MDR studies at 7 sites within Canada, the Netherlands, the United Kingdom, and the United States. 3MDR therapists and operators interested in participating in the study were instructed to email the research team to indicate consent to be contacted. Participants who met the inclusion criteria were forwarded a web-based consent form via a secure server (REDCap [Research Electronic Data Capture]) or hard copy, and an interview time was scheduled. Potential participants were informed that engagement in the study was voluntary.
This study received approval from University of Alberta Research Ethics Board (Pro00084466) and CAF Surgeon General Research Program (E2019-02-250-003-0003).
The 3MDR study participants included regular and reserved CAF military members and veterans aged 18 to 60 years under the care of a mental health clinician or service provider working at or associated with a Canadian Forces Base, an Operational Stress Injury Clinic, or Veterans Affairs Canada. All participants met the Diagnostic and Statistical Manual–Fifth Edition [
The 3MDR therapists and operators included in this study were English-speaking current or previous 3MDR therapists and operators who were trained by the developer of 3MDR. Participants must have completed a full course of 3MDR delivery with at least one patient (ie, had completed six 3MDR platform sessions using a VRE).
A demographic questionnaire was provided via email to participants through the REDCap server or in hard copy form. Variables collected from patient participants included age, sex, marital status, employment status, military status, enrollment era, rank, element, and years of service. For the 3MDR therapists and operators, the collected variables included the participants’ sex, profession, role in delivering 3MDR, years using 3MDR, location, VRE used, and level of education.
Two UTAUT questionnaires specific to the end users were developed specifically for this study. Version 1 (time point 0 [T0]) included questions in the future tense, whereas version 2 (time point 1 [T1]) included the same questions but was modified to reflect the past tense. The 12 questions’ outcome measures were based on a Likert scale, with a score of 1 to 7 assigned to each question, with 1 being
The UTAUT questionnaires were completed by patients, therapists, and operators before and after 6 sessions of the 3MDR. The questionnaires were administered by a member of the research team before the qualitative semistructured interviews. Version 1 of the UTAUT questionnaire was presented before its first introduction to the CAREN and 3MDR. This version was future tense oriented and intended to measure expectations of the technology. After completing this questionnaire, the participants engaged in 3MDR for 6 sessions over approximately 6 weeks before completing the version 2 UTAUT questionnaire. This version was written in the past tense, intending to measure the actual intention to use technology once the participants had some experience with it.
A semistructured interview guide was developed to collect qualitative data. The research team conducted individual 40- to 60-minute semistructured interviews either in person or via telephone or a secure Zoom videoconferencing platform with the 3MDR patients, therapists, and operators. All interviews were recorded and subsequently transcribed by the research team.
The research team conducted both quantitative and qualitative analyses. Quantitative analysis was based on the UTAUT, which uses a reflexive path model and PLS-SEM. The expectations from T0 and actual experience from T1 were statistically analyzed using PLS-SEM with a within-sample path model. Structural equation modeling (SEM) is considered a second-generation technique of multivariate analysis that allows researchers to incorporate unobservable variables measured indirectly by indicator variables [
The path model must be analyzed through measurement and structural model assessments [
As PLS-SEM does not assume that data are normally distributed—it relies on a nonparametric bootstrap procedure to test the significance of the estimated path coefficients in PLS-SEM. With bootstrapping, subsamples are created with randomly drawn observations from the original set of data (with replacement) and then used to estimate the PLS path model [
SmartPLS [
Qualitative interview data were subjected to thematic analysis (inductive and deductive) to identify, analyze, and report patterns (themes) in rich detail and allow the researcher to interpret various aspects of the topic [
A concurrent parallel approach following a data transformation model was used in the data analysis process to convert data to compare quantitative statistical results with qualitative findings [
A total of 29 end users of 3MDR participated in this study. The demographic information of the military (3/29, 10%) and veteran (8/29, 28%) sample is displayed in
Sample demographic information of the military and veteran sample (N=11).
Characteristics | Participants, n (%) | ||
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Female | 1 (9) | |
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Male | 10 (91) | |
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30-39 | 2 (18) | |
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40-49 | 6 (55) | |
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50-59 | 3 (27) | |
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Common law | 2 (18) | |
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Divorced | 1 (9) | |
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Married | 5 (45) | |
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Separated | 1 (9) | |
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Single | 2 (18) | |
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Employed | 6 (55) | |
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Unemployed | 5 (45) | |
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Active military member | 3 (27) | |
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Veteran | 8 (73) | |
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1976-1990 | 2 (18) | |
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1991-2000 | 8 (73) | |
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2001-2015 | 1 (9) | |
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Junior NCMa | 6 (55) | |
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Senior NCM | 4 (36) | |
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Unknown | 1 (9) | |
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Air | 2 (18) | |
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Land | 9 (82) | |
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Sea | 0 (0) | |
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5-10 | 2 (18) | |
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11-15 | 1 (9) | |
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≥20 | 8 (73) |
aNCM: noncommissioned member.
Sample demographics of 3MDRa therapists and operators (N=18).
Characteristics | Participants, n (%) | |
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Man | 9 (50) |
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Woman | 9 (50) |
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Canada | 7 (41) |
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The Netherlands | 6 (35) |
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The United Kingdom | 3 (17) |
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United States | 2 (11) |
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Occupational therapist | 1 (6) |
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Clinical psychologist | 6 (33) |
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Nursing | 1 (6) |
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Mental health therapist | 1 (6) |
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Mental health chaplain | 2 (11) |
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Researcher | 8 (44) |
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Technician | 5 (28) |
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No | 16 (89) |
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Yes | 2 (11) |
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Therapist | 13 (72) |
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Operator | 5 (28) |
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<1 | 5 (28) |
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1-3 | 9 (50) |
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3-5 | 3 (17) |
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CARENb | 12 (67) |
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GRAILc | 3 (17) |
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CAREN Light | 2 (11) |
a3MDR: multimodal motion-assisted memory desensitization and reconsolidation.
bCAREN: Computer Assisted Rehabilitation Environment.
cGRAIL: Gait Realtime Analysis Interactive Lab.
The psychometric properties of the raw data of the survey items used to measure the latent variables are shown in
The results of the measurement model evaluation, including the factor analysis, internal consistency (Cronbach α), convergent validity (AVE), and composite reliability, are shown in
To evaluate discriminant validity, cross-loading, Fornell-Larcker Criterion, and heterotrait-monotrait ratio (
The measure of lateral collinearity of the structural model demonstrated inner variance inflation factor values <5 for 10 (66.67%) latent variables, with the exception of indicator variables number 1 (5.649), 2 (9.215), and 3 (5.410) for PE, 11 (5.584) for FC, and 14 (7.392) for BI. The coefficient of determination (
A multigroup analysis with the PLS path model attempted to compare pre-post scores; however, this was not possible because of sample size restrictions. Instead, a Wilcoxon signed-ranks test was used to determine if there were any statistically significant changes in scores from before the technology was used (before to T0) to after the occurrence of the 3MDR course (after to T1). This showed a significant pre-post increase in the EE score only (
Psychometric properties of indicators used to measure latent variables.
Exogenous latent variables (indicators) | Values, meana (SDb) | Values, medianc | |
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Using the CARENd system improved my medical condition (patient) Using the CAREN improved the medical condition of my patient (therapist and operator) |
5.714 (1.082) | 6 |
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Using the CAREN system had a positive effect on my medical condition (patient) Using the CAREN system had a positive effect on the medical condition of my patient (therapist and operator) |
5.643 (0.961) | 6 |
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The CAREN system improved my quality of life (patient) The CAREN system had improved the quality of life of my patient (therapist and operator) |
5.357 (1.060) | 6 |
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Interacting with the CAREN system was easy for me (patient, therapist, and operator) |
6.429 (0.632) | 6 |
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I believe my interaction with the system was clear and understandable (patient, therapist, and operator) |
6.500 (0.516) | 7 |
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I found the system easy to use (patient, therapist, and operator) |
6.429 (0.516) | 6 |
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People who are important to me think that I should be involved in using the CAREN system (patient, therapist, and operator) |
5.214 (1.496) | 6 |
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I would use the CAREN system because my colleagues will use it too to improve their medical condition (patient) I used the CAREN system because my colleagues used it too to improve the medical condition of my patient (therapist and operator) |
3.714 (1.944) | 6 |
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In general, my organization has supported my involvement in this initiative (patient, therapist, and operator) |
6.286 (1.290) | 6 |
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I believe guidance was available to me during my interaction with the CAREN system (patient, therapist, and operator) |
6.571 (0.507) | 6 |
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I believe specialized instruction concerning the interaction with the CAREN system was available to me (patient, therapist, and operator) |
6.500 (0.640) | 6 |
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A specific person (or group) was available for assistance with CAREN system difficulties (patient, therapist, and operator) |
6.500 (0.834) | 6 |
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I am willing to use the CAREN system in the next weeks (patient, therapist, and operator) |
6.571 (0.632) | 6 |
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I plan I would use the CAREN system if I am willing to do so (patient, therapist, and operator) |
6.071 (1.246) | 6 |
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I predict I will use the CAREN system in the future (patient, therapist, and operator) |
5.857 (1.438) | 6 |
aRaw mean scores of items within the scale, where each item is measured on a 7-point Likert scale; 1=strongly disagree, and 7=strongly agree. The higher the indicator score, the more agreement with the statement.
bSD of raw scores.
cMedian scores of each question.
dCAREN: Computer Assisted Rehabilitation Environment.
Results of the validity and reliability evaluation of the measurement model.
Latent variables, indicator variables, and outer loadingsa | Cronbach αb | AVEc,d | CRe,f | ||
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0.951 | .957 | 0.918 | 0.971 |
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0.962 | .957 | 0.918 | 0.971 |
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0.961 | .957 | 0.918 | 0.971 |
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0.913 | .797 | 0.698 | 0.872 |
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0.662 | .797 | 0.698 | 0.872 |
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0.906 | .797 | 0.698 | 0.872 |
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0.953 | .921 | 0.853 | 0.946 |
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0.950 | .921 | 0.853 | 0.946 |
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0.866 | .921 | 0.853 | 0.946 |
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0.261 | .460 | 0.455 | 0.978 |
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0.983 | .460 | 0.455 | 0.978 |
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0.912 | .460 | 0.455 | 0.978 |
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0.915 | .918 | 0.860 | 0.948 |
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0.972 | .918 | 0.860 | 0.948 |
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0.893 | .918 | 0.860 | 0.948 |
aOuter loadings ≥0.5 indicate indicator reliability. With a reflective model, internal consistency is measured by Cronbach α.
bCronbach α ≥.7 indicates good indicator reliability.
cAVE: average variance extracted.
dAVE ≥0.5 indicates convergent validity.
eCR: composite reliability.
fCR ≥0.5 indicates good internal consistency.
gPE: performance expectancy.
hEE: effort expectancy.
iFC: facilitating conditions.
jSI: social influence.
kBI: behavioral intentions.
Intercorrelations between study variables measured by the FLCa and HTMTb.c.
Measures and latent variables | BId | EEe | FCf | PEg | SIh | ||||||
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BI | 0.927 | —i | — | — | — | |||||
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EE | 0.467 | 0.835 | — | — | — | |||||
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FC | 0.378 | 0.529 | 0.924 | — | — | |||||
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PE | 0.220 | −0.262 | 0.062 | 0.958 | — | |||||
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SI | 0.469 | 0.305 | 0.403 | 0.166 | 0.675 | |||||
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BI | — | — | — | — | — | |||||
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EE | 0.486 | — | — | — | — | |||||
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FC | 0.371 | 0.695 | — | — | — | |||||
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PE | 0.224 | 0.316 | 0.122 | — | — | |||||
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SI | 0.574 | 0.595 | 0.468 | 0.391 | — |
aFLC: Fornell-Larcker Criterion.
bHTMT: heterotrait-monotrait ratio.
cDiagonals are the square root of the average variance extracted of the latent variables and indicate the highest in any column or row.
dBI: behavioral intentions.
eEE: effort expectancy.
fFC: facilitating conditions.
gPE: performance expectancy.
hSI: social influence.
iNot applicable.
Structural model evaluation and hypothesis testing (prediction of BIa).
Relationship | Standard β (SE) | T value | Effect size ( |
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PEc>BI | .293 (0.544) | 0.869 | .39 | 0.112 (−0.808 to 1.249) |
EEd>BI | .455 (0.444) | 0.812 | .40 | 0.215 (−0.819 to 0.747) |
SIe>BI | .278 (0.337) | 0.734 | .47 | 0.104 (−0.446 to 0.799) |
FCf>BI | .007 (0.364) | 0.014 | .99 | 0.004 (−0.569 to 0.887) |
aBI: behavioral intentions.
bEffect size (
cPE: performance expectancy.
dEE: effort expectancy.
eSI: social influence.
fFC: facilitating conditions.
Partial least square path model; path analysis model of Unified Theory of Acceptance and Use of Technology predicting BI.
Results of the Wilcoxon signed-ranks test for pre-post changes in latent variable ranksa.
Latent variables | Significance ( |
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BIb | 33 (9.715) | .57 |
EEc | 65 (11.214) | .004d |
FCe | 32 (15.843) | .20 |
PEf | 39.5 (11.147) | .56 |
SIg | 13.5 (8.178) | .27 |
aTotal:
bBI: behavioral intentions.
cEE: effort expectancy.
dStatistical significance at
eFC: facilitating conditions.
fPE: performance expectancy.
gSI: social influence.
Thematic analysis was conducted by analyzing the responses to the open-ended questions from the UTAUT questionnaires and interviews after 3MDR for the participants, therapists, and operators. Three themes emerged: (1) feasibility and function, (2) technical support, and (3) tailored immersion (
Thematic analysis results of open-ended questions from the Unified Theory of Acceptance and Use of Technology questionnaire and qualitative interviews.
Theme | Illustrative quote |
Feasibility and function |
“The look of the program, it just looks a bit outdated and its small, can be more attractive to make it more user-friendly...but it does work, then with the new session [we] have a.PDF file with all the pictures and associations and units of distress, walking speed average...[with] this new system [documentation] looks way better—not just a sheet with all the information...being able to download the data in a clean way.” [T13] “Improve resolution of photos.” [T7] |
Technical support |
“I suppose that there is a lot of moving pieces, so it is technology dependant, so if something goes down and there is a glitch it throws a monkey wrench in it. You need ‘techy’ people.” [T1] “I would just give the pictures to the operator, everything worked fine. I feel, like I said, I feel comfortable being in that, or working with that technology.” [T5] |
Tailored immersion |
“I want to make it more personalized, now the virtual reality has been chosen by a developer who thinks this is the correct virtual environment, but I think this is the wrong way around. I think we should let our patients decide which virtual environment they want to walk in.” [T20] “When people walk fast they are also walking fast to the picture. I would like to have an option to increase the length of the tunnel, so people can walk fast, but so the photo doesn’t come up as fast.” [T21] |
Overall, the CAF military members, veterans, therapists, and operators found that 3MDR was feasible and functional within their given environments for the purpose of the research study. That said, the end users, particularly therapists and operators, noted a number of items that they felt could be improved to enhance the overall functioning and patient experience in hopes that it would lead to better outcomes. Improvements to the technology that would assist with the delivery and functionality of 3MDR for therapists and operators included more streamlined documentation, ease of downloading of data, and overall intuitiveness of the software. This theme fits within the construct of EE, as many of the suggested modifications and improvements targeted to improve the ease of use of the overall 3MDR system elements [
Similarly, CAF military members, veterans, therapists, and operators identified that they felt satisfied with the level of technical support they received. The participants felt that the team was knowledgeable and able to operate the hardware and software with a high level of competence. When there were glitches, they could be troubleshooted and resolved within a reasonable amount of time. Therapists generally felt that the 3MDR operator was their main source of technological support and that the operators were proficient in providing this. The operators generally felt that they were able to receive support from other VRE operators globally, who had also used 3MDR. The experience of the operators with software and hardware support from vendors was variable, with some reporting that the vendor’s expertise with technology geared toward physical health interventions rather than mental health was a barrier. The theme of technical support falls under the construct of FC as it regards that organizational and technical infrastructure exists to support the use of the system [
The desire to customize the experience of 3MDR for the patient through technology was the strongest theme among the CAF military members, veterans, therapists, and operators. The vast majority of feedback regarding the technological aspects of 3MDR and the associated VRE provided recommendations on how population-specific stakeholders should be used to adapt the hardware and software for future tailoring of the 3MDR intervention. 3MDR therapists desired to have the ability to tailor aspects of the software, such as the length of the tunnel, length of the image exposure, default VRE, and the number of images, in real time based on their clinical observations and needs. Therapists and participants also identified the need to make 3MDR software and hardware accessible to those who may have reduced mobility and who may use a wheelchair. Tailored immersion is correlated with the construct of PE. The desired customization of software and hardware stems from the belief that the system will help the patient or participant attain gains in performance or improved outcomes regarding their PTSD symptoms.
In this preliminary study, the UTAUT model was used as the theoretical foundation for understanding the behavioral intention of CAF military members and veterans with crPTSD, as well as their therapists and operators, in using 3MDR. On the basis of the study results, 3MDR delivered within a VRE appears to be promising as a feasible, usable, and accepted technological intervention for end users, with EE being the most notable predictor of BI and deemed to be the most important to end users. Although the qualitative data support this, it is worth noting that none of the latent variables yielded statistical significance with PLS-SEM. There was also no significant difference detected between the patient end user (CAF military members and veterans) and the health care end user (therapists and operators) scores for PE, EE, SI, FC, or BI. The analysis of the open-ended questions and qualitative interviews revealed several subthemes that can be attributed to the latent variables, including EE, PE, and FC of the UTAUT, as well as BI as a construct. To triangulate the quantitative and qualitative data, possible explanations for the results were formulated [
Overall, end users rated all the latent variables (PE, EE, FC, and SI) and BI favorably for the technological aspect of 3MDR. The data demonstrated that participants generally agreed or strongly agreed with the statements made in the UTAUT questionnaires, especially for the variables of PE, EE, and FC. The results of PLS-SEM analysis demonstrated good internal consistency, convergent validity, composite reliability, and discriminant validity of the indicators, with a moderate predictive accuracy of the model.
EE is the degree of ease associated with the use of a system [
This is contrary to the hypothesis that FC is the strongest predictor based on previous literature regarding patients and health care professionals in a North American context [
As previously mentioned, PE refers to the degree to which an individual believes that using the system will help the person attain gains in performance [
SI is the degree to which an individual perceives that it is important that others believe that they should use the new system [
FC is the degree to which an individual believes that organizational and technical infrastructure exists to support the use of the system [
Although PLS-SEM is ideal for exploratory research and flexible with its nonparametric lack of assumptions regarding data distribution, several limitations need to be considered. First, measurement errors always exist to some degree and are challenging to quantify accurately. PLS-SEM bias refers to the tendency of the path model relationships to be frequently underestimated, whereas the parameters of the measurement model, such as the outer loadings, are overestimated when compared with covariance-based SEM. Measurement errors can also be introduced by variables such as the participants’ understanding of the questionnaire items. In addition, the administrative burden of the study, when combined with other outcome measures attributed to the greater clinical trial with which this study was affiliated, may have caused some participants to rush through final questionnaires or experience fatigue and a reduced level of engagement. Second, the lack of global goodness-of-fit measures is an unavoidable drawback of PLS-SEM. Finally, the small sample size because of COVID-19 related shutdowns made it impossible to incorporate the moderator variables of age and gender, as was originally planned in the research model, and the desired sample power was not met (
The technology acceptance and usability of 3MDR within a VRE, as well as other interventions using technology, warrant evaluation within military and civilian health care contexts and at multiple user levels, including the patient, health care professional, and organization. This also extends to the use of web-based health care technologies where the patient is in a separate location from the health care professionals—a practice that is becoming increasingly widespread, especially in the wake of a global pandemic [
Numerous military personnel and veterans from around the globe who have returned from deployment continue to struggle with the symptoms of PTSD. Despite the plethora of research, publications, and attention that PTSD has received in recent years, many questions remain regarding the complexities of treating the psychological symptoms attributed to this diagnosis. 3MDR challenges traditional conventions and configurations. It is important to incorporate the study of technology acceptance and usability into the implementation of novel VR-supported health care processes to ensure that technological advances aimed at assisting patients will be embraced by the primary intended users. This is important at the micro, meso, and macro levels, especially within unique organizational contexts such as military and health care systems. 3MDR appears to be a promising intervention for crPTSD, with good acceptability by end users, including CAF military members and veterans, as well as 3MDR therapists and operators. The future for the usability of 3MDR is promising, and new and exciting intervention avenues for crPTSD will emerge because of continued research. As civilian and military health care systems increasingly integrate technological innovations to improve the services and care provided to their patients, research must continue to address questions of technological acceptance of the intervention before its wide-scale adoption.
multimodal motion-assisted memory desensitization and reconsolidation
average variance extracted
behavioral intentions
Canadian Armed Forces
Computer Assisted Rehabilitation Environment
combat-related posttraumatic stress disorder
effort expectancy
facilitating conditions
performance expectancy
partial least square
partial least squares structural equation modeling
posttraumatic stress disorder
Research Electronic Data Capture
structural equation modeling
social influence
time point 0
time point 1
Unified Theory of Acceptance and Use of Technology
virtual reality
virtual reality environment
The research team would like to acknowledge that this research was supported by the Royal Canadian Legion Alberta/Northwest Territories Command, Government of Alberta Grant, Glenrose Rehabilitation Hospital Foundation, and Government of Canada Innovation for Defense Excellence and Security Grant. The research team would also like to thank the study participants for sharing their time and experiences; the funders for enabling the study; the Glenrose Rehabilitation Hospital, Canadian Armed Forces, Alberta Health Services, the Royal Canadian Legion Alberta/Northwest Territories, and Veterans Affairs Canada for their continued support; and Ms Melissa Voth for her help in formatting the paper for publication.
EV created the 3MDR but would not stand to benefit financially were it to be adopted into routine clinical practice.