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This is the first study evaluating the feasibility and usability of a home-based immersive virtual reality (VR) headset-based dexterity training in people with multiple sclerosis.
The immersive VR headset-based dexterity training was specifically developed for this study.
Feasibility, usability, and patient satisfaction was very high qualifying this treatment approach for the use in home-based neurorehabilitation.
The results hint to an efficacies training intervention regarding improvements in manual dexterity.
Impaired manual dexterity is frequent and disabling in patients with multiple sclerosis (MS), affecting activities of daily living and quality of life.
The aim of this study was to evaluate the feasibility, usability and patient engagement/satisfaction of a home-based immersive virtual reality (VR) headset-based dexterity training in person with multiple sclerosis (pwMS). In addition, preliminary efficacy data on the impact of this new training on manual dexterity were collected.
Single arm prospective study. After a waiting period of two weeks, pwMS performed a specifically developed home-based VR headset-based dexterity training using the Oculus quest 2 for two weeks with five training sessions/week, each session for approximately 20 minutes. Primary endpoints were feasibility (measured by the adherence rate), usability (System Usability Scale, SUS) and patient engagement/satisfaction (Custom User Engagement Questionnaire, CUEQ). Secondary exploratory efficacy endpoints, measured before and after the waiting period as well as after the training intervention, were the Nine-hole-Peg-Test (9HPT), Coin rotation task (CRT), Handheld JAMAR dynamometer, Arm Function in Multiple Sclerosis Questionnaire (AMSQ) and the Multiple Sclerosis Impact Scale 29 (MSIS 29).
Eleven pwMS (mean age 49 ± 10.87 SD, mean EDSS 4.28 ± 1.48 SD) participated in the study. Feasibility (adherence rate: 81.8%), usability (median SUS score 94 (IQR = 78-96)) and patient engagement/satisfaction (median 8 on scale of 1-10) of the VR training was very high. In addition, the CRT for the dominant hand improved significantly after training (p = 0.03).
The good results on feasibility, usability, and patient engagement/satisfaction qualify this home-based immersive VR headset-based dexterity training approach for the use in home-based neurorehabilitation in pwMS. Improved fine motor skills for the dominant hand suggest preliminary efficacy, but this needs to be proven in a future randomized-controlled trials.
). Despite increasing therapeutic options to ameliorate the disease course, most patients suffer from persistent neurological deficits over time. Impaired manual dexterity is frequent, affecting approximately three-quarters of patients with MS (pwMS) (
). Impaired manual dexterity occurs early, however is preserved longer in the disease course compared to walking capabilities leading to a different meaningfulness of manual dexterity throughout disease progression (
). In early stages of the disease, arm-hand function is important, for example, for activities such as driving a car or doing creative activities. In later stages with usually lower extremity functions worsening first, upper extremity function is important to maintain daily functions such as dressing and eating, using a wheelchair, performing intermittent catheterization, or using a computer (
Manual dexterity is usually trained with physical- or occupational therapy in a low-frequency outpatient setting using traditional methods such as “hands-on techniques” (Rasová et al., 2022). In a European survey in MS, accessibility to physical therapy was poor with access to outpatient therapy varying from 34% to 41.3% and inpatient therapy varying from 17.4% to 28.5% (
) (Rasová et al., 2022). With recent technological innovations such as the development of applications (apps) for mobile phones and tablets as well as Virtual Reality (VR) Headsets, new treatment options arise that can be used on their own or as a supplement to classical therapies. Compared to traditional methods such as physical- or occupational therapy, or complex and expensive technological approaches requiring inpatient treatments (
), mobile phones, tablets or VR headsets, often already owned by pwMS, can be used as training device independently at home enabling high-frequency home-based training while being efficacious and cost-effective (
). Patients can be treated who otherwise have no access to therapies due to limited mobility, lack of therapy in the region, travel costs, or lack of time which are main reasons not to participate in classical therapies (Rasová et al., 2022).
In this regard we already performed home-based research projects with conventional training methods and Tablet App Based Dexterity Trainings in MS (
VR is a computer-generated environment with scenes and objects that appear to be real, making the user feel they are immersed in their surroundings. VR headset devices are one of the technologies with the highest projected potential for growth and likely to become an integral part of our private and professional life (
Advantages of VR are to create realistic risk-free environments that are not realizable and/or financeable in the real world. These VR environments are easily adaptable to the needs of the user, and the possibility of gamification increases patient motivation (Doumas et al., 2022). As further advantage, VR can be performed in the patient's home and monitored at a distance (telerehabilitation). The home-based setting allows frequent training independently of available hospital or community-based rehabilitation programs which makes training interventions available to a larger group of patients (
Regarding dexterity training interventions, hand- and finger tracking technologies additionally enable more effective and precise interaction in a natural fashion and an increased immersion and presence, i.e. the subjective experience of being in a highly-immersive virtual environment (
The aim of this pilot study was therefore to investigate the feasibility, usability and patient engagement/satisfaction of a self-developed home-based immersive dexterity training using a VR Headset (Oculus quest 2) in pwMS. In addition, preliminary efficacy data on the impact of the training program on manual dexterity were collected.
2. Materials and Methods
Participants were recruited consecutively through the corresponding author during regular visits at the MS center of the Luzerner Kantonsspital, Switzerland. Patients with relapsing-remitting MS, secondary-progressive MS, or primary-progressive MS according to the 2017 McDonald's criteria, age between 18-75 years were eligible (
). Main exclusion criteria were relapses and/or steroid treatment within the preceding 60 days, rapidly progressive MS and additional diseases or conditions apart from MS that affect manual dexterity or compromise the adequate performance of the study procedures. Each participant provided written informed consent prior to study entry. The study was carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) and approved by the Ethics Committee Northwest/Central Switzerland EKNZ (BASEC-Nr. 2022-00032).
2.2 Study design
This was a single-center, single-arm observational study. At screening/baseline (Visit 1), demographic data and efficacy measures (= secondary outcome measures) were collected followed by a 2-weeks waiting period. Afterwards efficacy measures were repeated, and pwMS were instructed into the home-based training intervention by the corresponding author (Visit 2). After the 2-weeks training period, efficacy measures were repeated, and feasibility and usability outcome measurements (= primary endpoints) were performed (End of study Visit 3) (Figure 1).
2.3 Study procedures
At screening/baseline, demographic data (age, gender), handedness, disease duration, date of MS diagnosis, MS type, current medication and diseases relevant to the study as well as experience with VR devices (yes/no) were collected. The Expanded Disability Status Scale (EDSS) score was taken from the last routine appointment (
Primary outcome measures. The primary endpoints were feasibility, usability and patient satisfaction. Feasibility was measured by the adherence to the protocol, usability by the System Usability Scale (SUS) and patient satisfaction by a Custom User Engagement Questionnaire (CUEQ).
), feasibility was measured with the adherence rate being the ratio of the number of sessions performed (SP) and the prescribed number of sessions (PS), which is 10, so ((SP/PS) * 100%) = ((SP/10) * 100%). An adherence rate of ≥80% was considered as good (
). SP were extracted from the Oculus quest 2. Each time the training intervention is started is stored on the device inside a SQL lite database (SQLite file). The date and time used are based on the OS of the headset. The data is stored continuously as the application is used. In the first iteration, the data can only be accessed directly on the headset and can be downloaded through adb (developer tool for android).
Regarding the usability of the system, the SUS was performed after completing the intervention (Visit 3). The SUS is a well-validated questionnaire, consisting of a 10-item Likert scale. Each item was scored from 0 (“strongly disagree”) to 4 (“strongly agree”) which takes three usability criteria into account: effectiveness, efficiency, and satisfaction. The total score is obtained by multiplying the mean sum value by 2.5. The SUS score has a range of 0% to 100% with higher values indicating better usability. A score of 70% to 100% represents acceptable to excellent usability (
A Custom User Engagement Questionnaire (CUEQ), which was previously developed by us and applied in prior feasibility trials, was used to evaluate the training intervention regarding user engagement and satisfaction of the training intervention more specifically (
). The CUEQ contains two parts. Firstly, seven specific questions related to the content of the VR-based training intervention are scored on a scale ranging from 1 (“bad”) to 5 (“excellent”) (ie, Q1 = Were the exercises fun to play?; Q2 = Have you improved your fine motor skills?; Q3 = Was it easy to integrate the exercises into daily life?; Q4 = Were the explanations for the execution of each exercise sufficient?; Q5 = Would you recommend this application?; Q6 = Can you take the VR headset easily on the go?; and Q7 = Do you notice improvements in everyday life regarding fine motor skills?). Secondly, participants gave an overall rating of satisfaction with the training intervention ranging from 1 (“very poor quality”) to 10 (“excellent quality”).
In addition, participants could give further written feedback regarding the training intervention including engagement and satisfaction and were asked to suggest improvements.
Secondary outcome measures. Secondary endpoints were performed every visit to get preliminary and exploratory data on the efficacy of the training intervention on manual dexterity (Figure 1).
The Nine Hole Peg Test (9HPT) is reliable (ICC values 0.80–0.99), valid and sensitive in detecting impaired dexterity in pwMS (
). The time to complete the task was recorded twice on both hands and mean values were taken for each hand. If patients could not perform the 9HPT, an arbitrarily chosen value of 300 seconds was taken.
The Coin Rotation Task (CRT) is reliable and valid in assessing manual dexterity in pwMS (
). The time to perform 20 half turns was measured twice on both hands and mean values were taken for each hand. If patients could not perform the CRT, an arbitrarily chosen value of 300 seconds was taken.
The handheld JAMAR dynamometer is a reliable (ICC values 0.85 – 0.98) and valid test to measure isometric grip strength of the hands in healthy subjects and in pwMS (
). It is performed in an upright seating position with 90° flexion of the elbow next to the body. The highest value (kilograms force) of two maximum voluntary grip strength movements was taken for each hand.
The “Arm Function in Multiple Sclerosis Questionnaire” (AMSQ) is a patient recorded outcome measure (PROM) evaluating the impact of manual dexterity on ADL in pwMS. The AMSQ was validated in several languages showing good validity, test-retest reliability and inter-observer reliability (
The Multiple Sclerosis Impact Scale (MSIS-29) is valid and reliable in measuring the overall impact of MS on ADL. It contains 29 items comprising to a physical (MSIS-29 physical) and psychological impact scale (MSIS-29 psychological). All items are scored from ‘not at all’ to ‘extremely’ on a five-point Likert scale (
As safety endpoints, adverse events and dropouts were collected at visits 2 and 3.
Training intervention: The VR headset-based training intervention was specifically developed for this project by the corresponding author in collaboration with the Start-ups “12 Parsec” (Oberfeld 3, 6037 Root, Switzerland), “Holonautic AG” (Felmis-Allee 11, 6048 Horw, Switzerland) and “Westhive” (Hardturmstrasse 161, 8005 Zürich, Switzerland). The commercially available Oculus quest 2 (Meta) was used as VR-Device (Figure 2). It is a standalone “all in one” immersive VR headset delivered with two hand-held controllers and an embedded hand- and finger tracking technology (https://www.meta.com). The detailed description of the training intervention and its development regarding medical and technical aspects will be publish separately (Kamm et al., in preparation). The training intervention derived from previous successful home-based training interventions with conventional training methods and Tablet App Based Dexterity Trainings in MS in which trainings were performed five days/week for four weeks for evaluating efficacy (
). Being primarily a feasibility study, we however shortened the training period to two weeks, however with high intensity.
Six different training exercises were developed with the goal to address different key functions of the hand/arm from finger to full arm movements. In this regard, the training exercises especially covered “pinch grip“, “bending/stretching/circling fingers”, “wrist rotation”, “target-orientated finger” and “target-orientated full arm movements” as summarized as follows and illustrated in Figure 2.
Finger circling (Index finger)
Overall, ten training session were planned within two weeks, with the first of these sessions being supervised at baseline in the hospital within the instruction. The following nine training sessions at home were unsupervised. It was recommended to perform all six training exercises each training session in random order. Overall, a training session lasted approximately 20 minutes. All training exercises were performed in a seated position to avoid falls and injury, and the participants’ hands were used to conduct all training programs through hand/finger tracking technology and both hands were trained in each exercise in alternating fashion (one or the other hand).
2.4 Statistical Analyses
All statistical analyses were performed using Statistical Package for Social Science (SPSS) for Windows, version 27.0; SPSS; Inc. Chicago, IL, USA. The level of significance was set at p < 0.05. Descriptive statistics were used to present baseline and clinical characteristics. Non-parametric Friedman's ANOVA were done to compare observations (T0 = Visit 1: Screening/Baseline; T1 = Visit 2: Start Training; T2 = Visit 3: End Training/Study) for all outcome measures (presented as median score and Interquartile ranges, IQR) on the same subjects. With regard to efficacy of the training intervention, secondary outcome parameters before and after the training intervention (T1/T2) were compared to changes of secondary outcome parameter before and after the waiting period without training (T0/T1). Post-hoc Dunn-Bonferroni-Tests were done to compare single observations (T0 vs T1, T0 vs T2, T1 vs T2). The effect sizes of the individual comparisons were calculated. In this regard, the effect size of the Dunn-Bonferroni-test was calculated, which corresponds to the effect size of a rank sum test, r. The classification by Cohen (1992) was used to assess the magnitude of the effect size (r = .10 corresponds to a weak effect; r = .30 corresponds to a medium effect; r = .50 corresponds to a strong effect). Furthermore, we conducted Spearman correlational analyses between usability/adherence scores and demographic and clinical baseline characteristics to analyze a possible impact of these characteristics on usability/adherence.
From April to July 2022, fifteen pwMS were asked to participate and eleven pwMS started and completed the study. Four pwMS declined because they did not want to participate in a study. No adverse events or dropouts were reported. Detailed clinical and demographic characteristics of the study cohort are presented in Table 1. There were no additional medication or diseases relevant to the study.
Table 1Demographic and clinical characteristics (n=11)
49.20 ± 10.87
Gender, n (%)
Handedness, n (%)
Disease duration (y)
15.38 ± 9.95
4.28 ± 1.48
44.64 ± 14.49
MS type, n (%)
Immunomodulatory therapy, n (%)
VR-Experience, n (%)
Values are mean ± SD or as otherwise indicated; SD, standard deviation; y, years; n, number of patients; EDSS, Expanded Disability Status Scale; RRMS, relapsing remitting multiple sclerosis; SPMS, secondary progressive multiple sclerosis; PPMS, primary progressive multiple sclerosis; VR-Experience, Experience with Virtual Reality headset before study participation.
The adherence to the training protocol was good. Participants completed on average 8.18 sessions ± 1.88 (Standard Deviation (SD)) of the planned 10 sessions, being 81.8%.
Regarding the influence of baseline and disease characteristics on feasibility, a higher score in the CRT, in terms of more dexterous difficulties, significantly correlated with a higher adherence/feasibility (Spearman's rho -.781, p<0.01) whereas the other parameters did not (Table 2).
Table 2Influence of Baseline characteristics on Feasibility and Usability
Values are presented as Spearman's rho, *p<0.05; **p<0.01; EDSS, Expanded Disability Status Scale; SDMT, Single Digit Modality Test; 9HPT, Nine-Hole Peg Test; CRT, Coin Rotation Task; AMSQ, Arm Function in Multiple Sclerosis Questionnaire; JAMAR, handheld JAMAR dynamometer, D, dominant hand; ND, non-dominant hand
The median SUS score was very high being 94 (IQR = 78-96). Regarding the influence of baseline and disease characteristics on usability, the EDSS showed a significant correlation with higher EDSS scores corresponding to a reduced usability (Spearman's rho -.715, p=0.013). All other parameters including gender showed no significant correlations (Table 2). In addition, usability and feasibility did not influence each other.
3.3 Patient engagement and satisfaction
Questions 1 to 5 of the CUEQ were rated high with median scores of 3 to 5, corresponding to “good”, “very good” or “excellent”. Questions 6 and 7 were rated lower, with a median score of 2 corresponding to “poor” (Figure 3).
The overall rating of the training intervention was very high with a median VAS score of 8 (IQR = 7-9) on a scale from 1-10.
The individual written free-text feedback was overall very positive. Regarding improvements of the VR training, some pwMS suggested individual adjustments of the level of difficulty, incentives such as “reaching a new level” or “collecting points” for motivation, and more different trainings. In addition, participants gave practical suggestions for further development and raised technical issues.
There were no significant changes for all outcomes during the waiting period between Visit 1 (T0, Baseline) and Visit 2 (T1, Start of Training) (Table 3).
Table 3Endpoints at baseline, start and end of training.
Friedman's test, p-value
Values are presented as median with the 25th and 75th percentiles (IQR), Abbreviations: AMSQ = Arm Function in Multiple Sclerosis Questionnaire; 9HPT, Nine-Hole Peg Test; CRT, Coin Rotation Test; MSIS-29, Multiple Sclerosis Impact Scale.
*Dunn Bonferroni Post Hoc test difference between Start and End of Training, z = 0.86, p = 0.04 (Effektstärke r = 0.3), between Baseline und End of Training, z = 0.91, p = 0.03 (Effektstärke r = 0.3). No significant difference between Baseline and Start of training, p = 0.92.
After the training intervention (Visit 3), the AMSQ scores did not significantly change, χ2(2) = 2.64, p = 0.27. Similarly, the 9HPT, JAMAR, CRT (non-dominant hand), and MSIS scores did not significantly change (9HPTdominant hand χ2(2) = 3.80, p = 0.15, 9HPTnon-dominant hand χ2(2) = 0.16, p = 0.92, JAMARdominant hand χ2(2) = 4.90, p = 0.09, JAMARnon-dominant hand χ2(2) = 0.05, p = 0.97, CRTnon-dominant hand χ2(2) = 1.00, p = 0.61, MSIStotal χ2(2) = 0.86, p = 0.71, MSISphysical χ2(2) = 0.55, p = 0.76, MSISpsychological χ2(2) = 1.77, p = 0.41).
There was a signifcant improvement for the CRT of the dominant hand, χ2(2) = 6.87, p = 0.03. This signifcant improvement was related to change from Start to End of Training, Z = 0.91, p = 0.03, with a medium effect size (r = 0.3) (Table 3).
This study aimed to evaluate the feasibility, usability and patient engagement/satisfaction of a new immersive home-based VR headset-based dexterity training in pwMS. To do so, a heterogenic cohort of pwMS was recruited with an age range of 23-65 years and an EDSS range from 0-6 (Table 1). None of the participants had experience with VR devices prior to study entry.
The feasibility of the training intervention was good with a high adherence to the training protocol (81.8%), which is further supported by the absence of adverse effects or dropouts.
Baseline and disease characteristics such as age, disability or cognition did not negatively affect feasibility which is important because manual dexterity is more pronounced in more disabled patients (
). Interestingly, pronounced impaired manual dexterity measured by the CRT was correlated with a higher adherence. This could be due to a higher motivation of patient with dexterous difficulties, which would be positive regarding this training approach.
The usability of the training intervention was very high with a SUS score of 94 (IQR = 78-96). This result is reflected by the high overall rating of the training intervention with a median of 8 on the VAS.
Again, most baseline and disease characteristics did not influence usability which is important regarding realizations of such training interventions in patients with impaired manual dexterity. However, general disability measured with the EDSS impaired usability which must be considered in the further development of VR headset-based training interventions, for example with a more profound instruction of the training intervention in more disabled patients (Table 2). Usability, examined by the SUS, covered the use of the Oculus quest 2 and the training intervention, however with a clear focus on the training intervention. Handling of the Oculus quest 2 did not present a relevant problem in our cohort as most of the feedback was related to the training intervention and should become a lesser problem in the future if VR devices are increasingly used in daily life. Importantly, usability did not influence adherence rate in our study which means that despite subjective problems in using the device, the training intervention could be performed properly.
User engagement and satisfaction with the training intervention was good. The training intervention could be implemented well into everyday life, was fun, and participants would highly recommend it to other pwMS (Figure 3).
Participants enjoyed the training intervention and found the approach interesting and innovative which are positive signs for the therapeutic use of VR headset devices in general and especially at home. Regarding the feedback, the training intervention could be improved by adjusting the level of difficulty to patient's performance, incentives such as “reaching a new level” or “collecting points” for motivation, and more different trainings. This valuable feedback will be implemented in upcoming training interventions.
Research on feasibility and usability of immersive VR headset-based dexterity training in pwMS is sparse and most published studies had a non-immersive hospital-based approach often using commercial games (
). Regarding home-based unsupervised settings, we however did not find corresponding literature to compare our result with.
One very recently published study examining a 4-weeks immersive upper limb VR training in pwMS, used the Oculus Rift in a hospital-based setting with a supervising physiotherapist. Usability, also rated with the SUS, was only moderate (45.9 ± 11.1), with lower scores assigned to items representing user-friendliness. The authors discuss that this might be critical when using VR-based trainings unsupervised like in home-based settings and that an adequate initial training of the system, which was not administered in their study since the training was fully supervised, could improve usability (
). In our study, participants were instructed in detail regarding the use of the VR device due to the home-based nature of the training and written instructions were handed out as well. In addition, usability was one key focus in the creation of the specifically developed training intervention. These aspects probably contributed to the excellent usability even in a population with no prior experience with VR devices.
Our study was not powered or designed to examine the effectiveness of this new VR dexterity training. However, after the training intervention, there was a significant improvement of the CRT of the dominant hand and positive trends of the JAMAR and 9HPT of the dominant hand in a population not considerably affected by impaired manual dexterity. This may hint to an efficacies training intervention, especially if performed more than 2 weeks. In a previous study we could for example show, that a 4-weeks conventional home-based dexterity training program significantly improved manual dexterity and dexterity-related ADL in pwMS (
). Similarly, participants in our study noticed an improvement in manual dexterity after completing the training intervention with a median value of 3 in CUEQ question 2 (4 participants <3, 6 participants ≥ 3), however without a relevant positive impact on daily life (median value of 2 in CUEQ question 7) as well (Figure 3).
The main limitation is the small sample of participants. However, for piloting a new training, this is an adequate sample, as we previously showed (van Beek et al., 2019). A generalization regarding the diversity of all pwMS is however not possible. To evaluate feasibility, we recorded at which dates participants performed the training intervention. As it was a home-based study, participants were not supervised and therefore, the exact performance of the training intervention regarding which exercises were performed for how long, which hands were used, how many repetitions were made, is unknown. Measuring adherence and the performance of the training intervention more detailed (performed exercises, duration of exercising, hands used etc.) using the Oculus quest 2 is in principle possible and another strength of VR devices which will be addressed in future studies. In addition, VR devices could be used to evaluate efficacy outcome parameter as well and initial research in this regard was recently published (
This is the first study evaluating the feasibility and usability of a home-based immersive VR headset-based dexterity training in pwMS. Feasibility, usability, and patient engagement/satisfaction of the training intervention was very high qualifying this treatment approach for the use in home-based neurorehabilitation. In addition, the preliminary efficacy results hint to an efficacies training intervention regarding improvements in manual dexterity and further randomized-controlled trials in this regard are warranted.