Abstract
Introduction
The rapid and extensive expansion of virtual reality (VR) across all medical specialties, including neurorehabilitation, has significantly enhanced and broadened the treatment options for individuals with neurological disorders. The objective of the present study was to carry out an economic analysis of the MYO Armband® device in combination with specifically designed video games for upper limb (UL) rehabilitation in people with multiple sclerosis (MS).
Methods
A randomized controlled trial was conducted. The sample was randomly assigned to two groups: an experimental group (EG) that received UL rehabilitation through serious games developed by the research team and controlled with the MYO Armband® gesture sensor plus conventional therapy, and a control group (CG) that received conventional therapy. Both groups underwent two 60-minute sessions per week for eight weeks. Wrist and forearm range of motion, grip muscle strength and coordination and gross UL dexterity were evaluated pre-treatment, post-treatment and during a follow-up period of 2 weeks without receiving any treatment. Also, satisfaction and compliance (attendance) were recorded in both groups. The healthcare provider’s perspective was used to identify, measure, and assess resource use across multiple scenarios. Differences in health outcomes and direct healthcare costs were compared from baseline to the end of follow-up in both groups. The aim was to estimate the incremental cost-effectiveness ratio (ICER) in order to assess the efficiency of the MYO Armband in improving participants’ health outcomes.
Results
The total costs in the CG amounted to 5,721.1 euros compared to 6,240.4 euros for the EG, with a cost difference between intervention and control of 35 euros per patient. The MYO Armband® intervention showed a favorable cost-effectiveness ratio on the more affected side in the active range of motion, with statistically significant gains costing 7.5 euros extra per point (grade) increment in palmar flexion, 13.5 euros extra in pronation, and 5.4 euros extra in supination. One additional point (grade) gained in dorsiflexion had an incremental cost of 6.2 euros, although this difference was not statistically significant. Furthermore, the incremental costs for gaining one point in treatment adherence (percentage) were 5.9 euros.
Conclusion
The use of MYO Armband® in the therapy of patients with MS offers promising results in maintaining their range of motion of the UL at a relatively modest additional cost. While these preliminary results will need to be corroborated by future works, the intervention appears to be cost-effective.
Trial registration This randomised controlled trial has been registered at ClinicalTrials.gov Identifier: NCT04171908.
Keywords: Multiple sclerosis, MYO armband, Virtual reality, Cost-effective, Video games, Upper limb, Rehabilitation, Economic evaluation
Introduction
The rapid spread of virtual reality (VR) across all medical specialties, including neurorehabilitation, has significantly enhanced and broadened the treatment options for individuals with neurological disorders [1]. These devices, which vary widely in cost, can be utilized to improve sensorimotor function, cognitive abilities, and other therapeutic targets [2–4]. Furthermore, the integration of VR into repetitive, intensive rehabilitation protocols promotes active patient participation and motivation, mitigating the decline in therapeutic adherence often observed due to the chronic nature and/or progression of certain neurological conditions [5]. These factors strongly support the implementation of VR technologies to facilitate experience-dependent neuroplasticity, which serves as a fundamental mechanism for motor learning.
However, certain barriers hinder the adoption of VR in neurorehabilitation, particularly those related to the cost of these devices compared to conventional therapy [6, 7]. Additionally, the continuous rise in healthcare costs associated with the management of chronic conditions has raised concerns about the sustainability of healthcare systems, including rehabilitation treatment for neurological disorders. In these cases, not only is it essential for technologies to be effective, but their economic viability also needs to be ensured [7]. Concurrently, to strengthen the financial sustainability of healthcare systems, public authorities are facing the challenging task of balancing two key objectives. On the one hand, they must facilitate access to health technologies and promote policies that offer potential benefits to citizens. On the other, they must ensure that such access remains compatible with the financial viability of the healthcare systems themselves [8, 9].
In this regard, economic evaluations of healthcare interventions can provide valuable insights [10] helping us to compare and determine the relationship between the additional cost (or potential savings) of a given technology compared to other alternatives, which are usually standard practice, and its potential health benefits. These evaluations complement assessments of efficacy, safety, and quality by incorporating the dimension of efficiency, thereby providing decision-makers with comprehensive evidence. Moreover, these analyses do not preclude the consideration of other critical factors, such as equity, which are essential for the public funding and integration of new health technologies and services [11, 12].
Very few studies have been conducted into analyzing the cost-effectiveness and cost-utility of VR devices in neurorehabilitation. Thomas et al. [13] evaluated the cost-effectiveness of a home-based intervention using the Nintendo Wii® through a video game called Mii-vitaliSe in people with multiple sclerosis (MS). In this study, the cost of a Nintendo Wii® unit was estimated at approximately 300 GBP, while the average cost of implementing Mii-vitaliSe was 684 GBP per person. The authors concluded that this device represented a cost-effective option for managing chronic conditions such as MS. Other studies suggest that VR could lead to cost savings, primarily due to the low cost of certain systems and the reduction of transportation-related expenses when using tele-rehabilitation platforms, compared to conventional in-clinic interventions [14–16]. However, evidence on the costs of VR interventions in neurorehabilitation remains extremely limited, and Thomas et al.’s study is, to our knowledge, the only example of a formal cost analysis with VR focused on upper limb in people with MS. This scarcity highlights the need for further research to provide data on costs which take into account the duration of application, clinical effect units, improvements in quality of life, and the potential impact on additional healthcare and social care resource utilization.
In this context, the MYO Armband® has been suggested as a semi-immersive VR tool for upper limb (UL) rehabilitation in people with MS. This device, worn on the forearm, integrates eight surface electromyography sensors and a 9-DOF inertial measurement unit, capturing 3D motion data at 200 Hz (sEMG) and 50 Hz (IMU). It detects gestures like wrist flexion and hand grip, transmitting data via Bluetooth to a USB receiver. The device also includes haptic feedback and a rechargeable battery, offering a novel approach to neurorehabilitation through muscle activity data for assessment and VR therapy with serious games. Studies have demonstrated its effectiveness in improving active range of motion and grip strength, with high patient satisfaction, strong acceptance, and excellent therapeutic adherence [17]. This has had a positive impact on MS rehabilitation, positioning the MYO Armband® as a precise, feasible, and safe treatment tool that enhances the intensity, variability, and number of repetitions during therapy sessions. However, to date, its cost-effectiveness and cost-utility have not been explored. Generating such economic data would be essential for assessing the feasibility of covering its costs in settings such as patient associations or rehabilitation centers, and would enable decision-makers to weigh up not only the clinical benefits for patients but also the efficiency gains derived from its implementation in these contexts.
Therefore, the objective of the present study was to carry out an economic evaluation of the MYO Armband® device in combination with specifically designed games for UL rehabilitation in patients with MS. The present cost analysis was deliberately restricted to in-person, therapist-supervised interventions. While the MYO Armband® could be used in home-based rehabilitation contexts, the current study focused on face-to-face sessions in order to ensure standardized implementation and to provide a clear estimation of costs under supervised clinical conditions.
Methods
Design
A randomized controlled trial (RCT) (NCT04171908) was conducted in accordance with the Consolidated Standards of Reporting Trials (CONSORT), with clinical outcomes published by the research team. In this trial, a sample size of 30 participants (15 per arm) was estimated, assuming a medium effect size in gross manual dexterity and a 10% potential loss to follow-up [17].
The sample was randomized into two study groups: the experimental group (EG) and the control group (CG). The EG received a conventional physical therapy program, combined with a VR protocol and video games specifically designed for the UL in individuals with MS, while the CG received only the conventional physical therapy program. All interventions were conducted at the Leganés Multiple Sclerosis Association (ALEM) in Madrid, Spain.
This protocol was approved by the Ethics and Research Committee of Rey Juan Carlos University (Madrid, Spain), with reference number 2,310,202,119,821. All selected participants provided written consent through the informed consent document.
In conducting the study, we followed the recommendations set out in the economic evaluation guide proposed by the Spanish technology assessment agencies (RedETS) [18]. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) were also used to report the health economic evaluation [19].
Participants
The inclusion criteria for the study were as follows: age between 20 and 65 years; a confirmed diagnosis of MS according to the McDonald criteria [20]; disease duration of more than two years; an Expanded Disability Status Scale (EDSS) score between 3.0 and 7.5 points; stable medical treatment for at least six months prior to the intervention; UL muscle tone not exceeding 2 points on the Modified Ashworth Scale; muscle strength of at least 3 points in the UL according to manual muscle testing; absence of cognitive impairment.
The exclusion criteria were: diagnosis of another neurological disease or musculoskeletal disorder other than MS; diagnosis of cardiovascular, respiratory, metabolic, or other condition that could interfere with the study; exacerbation or hospitalization within three months prior to the assessment protocol or during the therapeutic intervention process; prior treatment with intravenous or oral steroid cycles; presence of uncorrected visual impairments; history of photosensitive epilepsy triggered by video game use.
Intervention
Participants were randomized into two groups, with 15 allocated to the EG and the remaining 15 to the CG. Both groups received two weekly sessions, each lasting 60 min, over a period of eight weeks (a total of 16 sessions per group).
The CG received a specific conventional physical therapy intervention delivered by an expert physiotherapist specialized in the care of people with MS. This intervention was based on conventional physical therapy exercises [21, 22], including joint mobilization of the shoulder, elbow, wrist, and fingers, strengthening of forearm and hand muscles, training of both gross and fine manipulative dexterity, and the practice of functional tasks aimed at replicating the movements included in the games specifically designed for the EG intervention. The CG received treatment for the UL for the more and less affected side, and it was applied in the same way to all participants in this group.
The EG received the same conventional physical therapy treatment (45 min) together with a semi-immersive VR intervention using the MYO Armband® sensor (15 min) and video games specifically designed for this protocol for the UL. The intervention was conducted by two physical therapists experienced in technology-assisted rehabilitation. Participants were seated in front of a table positioned at mid-trunk height, with the elbow flexed at 90° and the forearm in a neutral pronation-supination position. Before each video game session, a gesture calibration was performed to individualize the treatment for each participant and improve the accuracy of the MYO Armband® sensor. This calibration involved training the system’s gesture classifier to optimize performance and reduce errors caused by the manual placement of the armband. To apply the principle of distributed practice and avoid early fatigue, each session focused on alternating UL. These games simulated movements commonly used in conventional physical therapy protocols, such as hand opening and closing, wrist flexion and extension, finger pinch, and forearm pronation and supination. Overall, eight gestures, including a resting arm position, were used to guide the rehabilitation process. Manual assistance was provided by the physical therapist when necessary. This protocol was previously demonstrated to be feasible and safe for patients with MS in a prior study [23].
Outcome measures
The following outcome measures were administered to both groups at the beginning of the intervention, at the end of the protocol and during a follow-up 15 days after receiving no treatment: active range of joint motion of the UL assessed using goniometry, handgrip strength evaluated with a Jamar® dynamometer for both sides, and coordination and motor dexterity assessed with the Box and Blocks Test (BBT) for both sides. Also, user satisfaction was assessed with the Client Satisfaction Questionnaire (CSQ-8) and their percentage of attendance at the therapy sessions (adherence) were registered. The description of the outcome measures applied is detailed in the previously published article by Marcos-Antón et al. [17].
Use of resources, costs and economic evaluation
The cost analysis was approached from the perspective of the funder (the National Health System) or the health care provider. This means that only direct healthcare costs were included in the economic evaluation. Thus, we identified, measured, and valued all resources required in both the intervention and control groups, as if a healthcare provider would deliver the intervention and usual care to their insured. We used 2024 as the base year for the estimated costs.
The resources to be valued included a series of elements common to the treatments evaluated and a series of differential elements. Among the former, it was observed that the physical spaces required to carry out the rehabilitation therapies and the time dedicated by the health professionals (physiotherapists) were the same in the intervention and CG. Additional resources required by the EG included the use of a laptop computer, a software license (Matlab) and the MYO Armband® sensor. We defined the economic assessment of these resources as the intervention costs.
The analysis shows all the costs (the intervention and control costs) in order to: (i) facilitate comparison of the study with other studies that wish to replicate it; and (ii) facilitate analyses that plan to scale up the evaluation for a larger population. However, the most relevant costs for economic evaluation are the differential costs. These costs are also known as incremental costs, and show the cost of switching from the usual care to the intervention assessed.
The monetary valuation of the common resources was based on the market price of renting the space needed to carry out the treatments for 8 weeks, taking into account that each patient would require 2 hourly sessions per week of rehabilitation (240 h total for each group). The cost of the physiotherapists’ treatment was obtained from their gross salaries, also considering the number of hours of treatment for each group.
The intervention costs were valued at market prices. In the case of the MYO Armband® device and the computer, a depreciation period of 2 years was also taken into account in the base case. To make a conservative estimate, 1 year of use of the MYO Armband® and 1 year of use of the computer were imputed as the cost of the intervention. The cost of acquiring the annual Matlab® license was also imputed.
The sensitivity analyses used different amortization periods for the MYO Armband® sensor of 1 or 2 years, and of the computer used (2 to 3 years). Also, in another scenario, it was considered that the healthcare facility already had the appropriate software in place and the purchase of a Matlab® licence was not required. Additionally, the most conservative assumptions were discarded, and the actual usage time (8 weeks) was imputed for the assessment of differential resources (SA6). Finally, the change of venue was also taken into account when using the intervention with a higher number of patients (30 and 40 patients per group, instead of 15) (Table 1).
Table 1.
Sensitivity scenarios applied in the economic evaluation
| Scenario | Description |
|---|---|
| SA1 | The amortisation period of MYO applied was 4 years. |
| SA2 | The amortisation period of MYO applied was 1 year. |
| SA3 | The amortisation period of the computer program was 3 years |
| SA4 | The amortisation period of the computer program was 2 years |
| SA5 | No amortisation period (free license) |
| SA6 | Extending intervention duration up to 8 weeks |
| SA7 | Increasing the number of patients (lowering healthcare costs per patient) to up to 30 participants. |
| SA8 | Increasing the number of patients (lowering healthcare costs per patient) to up to 40 participants |
Several cost-effectiveness analyses were performed. All of them had the same numerator in common: the difference between the costs per patient in the EG and the CG, with the most important difference between the analyses found in the denominator: the difference in mean health-related effects of EG vs. CG. Thus, we obtained the Incremental Cost-effectiveness Ratio (ICER), where the cost differences remained the same, but the health-related results (the denominator) were switched from the health-related effects included in the RCT.
Statistical analysis of clinical outcomes
Statistical analysis was performed using the SPSS statistical software system (SPSS Inc., Chicago, IL; v28.0). Descriptive analysis of the qualitative data (age, gender, more affected side, MS type, disease evolution and EDSS score) was performed using means, medians, percentages and ranges. Univariate sensitivity analyses were also performed, in line with the types of sensitivity analyses proposed for cost analysis. In addition, we assessed the differences in health-related effects in the intervention and CG using Student’s t-test, assuming unequal variances, using a p-value < 0.05 as a significant difference.
Results
Sociodemographic data
In the RCT published by the research team [17], 50 patients were assessed for eligibility. 19 of them were excluded from the study for not meeting inclusion criteria and 1 participant was excluded prior to the allocation due to incompatibility with the intervention protocol sessions and schedules. Finally, 30 participants, 14 male and 16 female, completed the study, with ages ranging from 29 to 62 years (mean age 48.27 ± 7.06 years). In 14 participants, the most affected side was the left side, while the right side was the most affected for the remaining 16 participants. The type of MS was relapsing-remitting (RRMS) in 15 participants, secondary progressive (SPMS) in 10 participants and primary progressive (PPMS) in 5 participants. The duration of the disease was 15.23 (± 9.34) years. The median score on the EDSS scale was 6.0 [IQR: 1.6].
The participants were randomized into two groups, with 15 assigned to EG and 15 assigned to the CG. The sociodemographic data of the intervention groups are presented in Table 2. There were no statistically significant differences in terms of age (p = 0.762), disease duration (p = 0.239) and EDSS (p = 0.756) between the GE and the GC.
Table 2.
Participants’ sociodemographic information at baseline by group
| Group (n) | Age (years) Mean (± Standard deviation) |
Gender (male/female) | More affected side (left/right) | MS type | Disease evolution (years) Mean (± Standard deviation) |
EDSS Median [IQR] |
|---|---|---|---|---|---|---|
| Control group (15) | 47.87 (± 6.7) | 5/10 | 5/10 |
8 RRMS 4 SPMS 3 PPMS |
13.20 (± 7.6) | 6.0 [2.5] |
| Experimental group (15) | 48.67 (± 7.63) | 9/6 | 9/6 |
7 RRMS 6 SPMS 2 PPMS |
17.27 (± 10.7) | 6.0 [1] |
EDSS Kurtzke Expanded Disability Status Scale, IQR Interquartile Range, MS Multiple Sclerosis, PPMS Primary-Progressive MS, RRMS Relapsing-Remitting MS, SPMS Secondary-Progressive MS. Data are expressed as mean (± Standard deviation) or median [IQR].
Main clinical findings of the proposed intervention
The clinical effects of the previously published RCT (17) showed significant inter-group differences in active range of motion in forearm supination (p = 0.004) in the group*side*time comparison, and handgrip strength (p = 0.004) in the group*time comparison. Additionally, within-group improvements were statistically significant in active range of motion for palmar flexion, pronation, and forearm supination in the EG (as shown in Table 3). On the other hand, a clinical improvement in handgrip strength was observed in the more affected side in the EG, compared to the CG, whose follow-up evaluation data showed significant worsening when compared to post-treatment measurements. No inter-group differences were recorded for the variables of coordination and motor dexterity (measured with BBT).
Table 3.
Patient-reported outcome pre-intervention and at the end of the follow-up by group
| Outcomes | Group | Pre-intervention Mean (± SD) |
Follow-up Mean (± SD) |
Mean difference between groups (p-value) |
|---|---|---|---|---|
| Range of motion (degrees)* | ||||
|
Dorsiflexion MAS (wrist) |
Experimental | 55.67 (± 10.87) | 58.14 (± 10.67) | |
| Control | 60 (± 15.11) | 58 (± 7.27) | ||
| Mean difference (p-value) | -4.33 (0.45) | 0.14 (0.97) | 5.14 (0.22) | |
|
Dorsiflexion LAS (wrist) |
Experimental | 56.2 (± 12.84) | 57.57 (12.41) | |
| Control | 57.1 (± 12.83) | 58.6 (± 11.18) | ||
| Mean Difference (p-value) | -0.9 (0.87) | -1.03 (0.84) | 0.5 (0.84) | |
| Palmar flexion MAS (wrist) | Experimental | 55.07 (± 9.82) | 57.64 (± 10.59) | |
| Control | 52.4 (± 11.92) | 50.7 (± 10.41) | ||
| Mean Difference (p-value) | 2.67 (0.57) | 6.94 (0.13) | 4.63 (0.04) | |
| Palmar flexion LAS (wrist) | Experimental | 53.73 (± 15.87) | 56.36 (± 14.63) | |
| Control | 53.9 (± 15.69) | 50.5 (± 11.31) | ||
| Mean Difference (p-value) | -0.17 (0.98) | 5.86 (0.3) | 6.47 (0.13) | |
|
Pronation MAS (forearm) |
Experimental | 86.8 (± 2.37) | 90 (0) | |
| Control | 89.5 (± 1.58) | 90 (0) | ||
| Mean Difference (p-value) | -2.7 (0.002) | 2.57 (0.01) | ||
|
Pronation LAS (forearm) |
Experimental | 87.4 (± 2.53) | 90 (0) | |
| Control | 88.2 (± 3.36) | 90 (0) | ||
| Mean Difference (p-value) | -0.8 (0.53) | 0.63 (0.63) | ||
|
Supination MAS (forearm) |
Experimental | 87.07 (± 3.53) | 90.43 (± 0.85) | |
| Control | 88.5 (± 2.42) | 85.7 (± 2.21) | ||
| Mean Difference (p-value) | -1.43 (0.24) | 4.73 (< 0.001) | 6.37 (< 0.001) | |
|
Supination LAS (forearm) |
Experimental | 88.4 (± 1.64) | 89.79 (± 0,7) | |
| Control | 88.2 (± 3.36) | 88.6 (± 3,78) | ||
| Mean Difference (p-value) | 0.2 (0.86) | 1.19 (0.35) | 1.1 (0.5) | |
| Handgrip strength (kilograms) | ||||
| Jamar dynamometry MAS | Experimental | 27.29 (± 11.53) | 29.22 (± 10.98) | |
| Control | 20.04 (± 14.80) | 20.89 (± 13.91) | ||
| Mean Difference (p-value) | 7.24 (0.15) | 8.32 (0.08) | 1.08 (0.48) | |
| Jamar dynamometry LAS | Experimental | 32.12 (± 10.23) | 33.67 (± 10.62) | |
| Control | 22.07 (± 12.79) | 22.25 (± 12.82) | ||
| Mean Difference (p-value) | 9.41 (0.04) | 11.42 (0.01) | 2.01 (0.18) | |
| Manual dexterity and coordination (number of cubes) | ||||
| BBT MAS | Experimental | 45.80 (± 10.78) | 50.33 (± 11.19) | |
| Control | 35.87 (± 13.62) | 41.07 (± 15.51) | ||
| Mean Difference (p-value) | 9.93 (0.04) | 9.27 (0.07) | -0.67 (0.74) | |
| BBT LAS | Experimental | 50.00 (± 10.62) | 55.13 (± 11.53) | |
| Control | 39.47 (± 13.72) | 43.46 (± 12.43) | ||
| Mean Difference (p-value) | 10.53 (0.03) | 11.67 (0.01) | 1.13 (0.49) | |
Bold text denotes statistical significance at 95% ; BBT: Box and Blocks Test; LAS: less affected side; MAS: more affected side. *Normal range of motion: dorsiflexion: 60 degrees; palmar flexion: 60 degrees; pronation: 80 degrees; supination: 80 degrees (taken from: Soucie JM, Wang C, Forsyth A, Funk S, Denny M, Roach KE, Boone D; Hemophilia Treatment Center Network. Range of motion measurements: reference values and a database for comparison studies. Haemophilia. 2011 May;17(3):500-7)
The EG achieved a treatment attendance rate of 97.08% (± 5.21), compared to 91.25% (± 7.34) in the CG. The difference was statistically significant (F = 5.43, p = 0.029). Finally, the EG obtained 90 (± 11.9) points out of 100 in the CSQ-8.
Cost analysis
The cost difference between the intervention and the usual care was 37,6 euros per patient, while sensitivity analyses modifying the amortization periods SA1-SA4 give varying results between 35 and 52 additional euros per patient. In the case where no software license is required (SA5), the additional cost per patient dropped to 20 euros. SA6 is the most favorable result, in which the more conservative assumptions in the base case were discarded and only the depreciation of the MYO Armband®, computer and license during the 8 weeks of the study was imputed. In this case, the additional cost per patient fell by an additional 6 euros. Finally, SA7 and SA8 show the advantages, in the form of economies of scale, of centralizing the care of a larger number of patients in a single center (Table 4).
Table 4.
Direct healthcare costs (EUR 2024) per patient by group
| Intervention group | Control group | Difference* | |
|---|---|---|---|
| Base Case | 419.0 | 381.4 | 37.6 |
|
SA1 The amortisation period of MYO applied was 4 years |
416.0 | 381.4 | 34.6 |
|
SA2 The amortisation period of MYO applied was 1 year |
425.0 | 381.4 | 43.6 |
|
SA3 Amortisation period of the computer: 3 years |
423.7 | 381.4 | 42.3 |
|
SA4 Amortisation period of the computer: 2 years |
433.2 | 381.4 | 51.8 |
|
SA5 Free licence software |
401.5 | 381.4 | 20.1 |
|
SA6 Considering amortisation of real duration of intervention (8 weeks) |
387.2 | 381.4 | 5.8 |
|
SA7 30 participant per group |
400.2 | 381.4 | 18.8 |
|
SA8 40 participant per group |
395.5 | 381.4 | 14.1 |
Units: EUR
Base case: The amortisation period of MYO applied was 2 years; Amortization period of the computer: 4 years; software annual license imputed; 15 patients per group.
SA scenarios. Only the change from the base scenario is indicated; all other assumptions are held constant.
Economic evaluation
Table 5 shows the ICER results of the base case and the different sensitivity analyses carried out. The results of the economic evaluations carried out, taking into account the joint range of motion, show more favorable results on the most affected sides. If we focus on the results where the differences in clinical variables were statistically significant, the choice of the MYO Armband® intervention vs. usual care shows that gaining one additional point in palmar flexion on the more affected side, one additional point in pronation on the more affected side and one additional point in supination on the more affected side would require an investment of 8.1, 14.6 and 5.9 euros (in the base case), respectively. In exchange for gaining one additional point in dorsiflexion on the more affected side, an investment of 6.2 euros would be required, although the clinical difference between the two groups is not statistically significant. In the case of the ICER results for the range of motion measures on the least affected side, the differences between the clinical measures were not statistically significant either.
Table 5.
Cost-effectiveness analysis of joint range of motion, grip strength and coordination. MYO Armband® intervention vs. usual care
| Range of motion | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| ICER | Base Case | SA1 | SA2 | SA3 | SA4 | SA5 | SA6 | SA7 | SA8 |
| Dorsiflexion MAS wrist | 6.8 | 6.2 | 7.8 | 7.6 | 9.3 | 3.6 | 1.0 | 3.4 | 2.5 |
| Dorsiflexion LAS wrist | 75.2 | 69.2 | 87.2 | 84.7 | 103.5 | 40.3 | 11.5 | 37.6 | 28.2 |
| Palmar flexion MAS wrist | 8.1 | 7.5 | 9.4 | 9.1 | 11.2 | 4.3 | 1.2 | 4.1 | 3.0 |
| Palmar flexion LAS wrist | -5.8 | -5.3 | -6.7 | -6.5 | -7.9 | -3.1 | -0.9 | -2.9 | -2.2 |
| Pronation MAS | 14.6 | 13.5 | 17.0 | 16.5 | 20.1 | 7.8 | 2.2 | 7.3 | 5.5 |
| Pronation LAS | 59.7 | 55.0 | 69.2 | 67.2 | 82.2 | 32.0 | 9.2 | 29.8 | 22.4 |
| Supination MAS | 5.9 | 5.4 | 6.8 | 6.6 | 8.1 | 3.2 | 0.9 | 3.0 | 2.2 |
| Supination LAS | 34.2 | 31.5 | 39.6 | 38.5 | 47.1 | 18.3 | 5.2 | 17.1 | 12.8 |
| Grip strength | |||||||||
| ICER | Base Case | SA1 | SA2 | SA3 | SA4 | SA5 | SA6 | SA7 | SA8 |
| Jamar MAS Evolution | 32.06 | 34.82 | 40.35 | 36.43 | 45.17 | 15.88 | 5.34 | 16.03 | 12.02 |
| Jamar LAS Evolution | 25.27 | 27.45 | 31.81 | 28.72 | 35.61 | 12.52 | 4.21 | 12.64 | 9.48 |
| Coordination | |||||||||
| ICER | Base Case | SA1 | SA2 | SA3 | SA4 | SA5 | SA6 | SA7 | SA8 |
| BBT MAS Evolution | -51.67 | -56.13 | -65.04 | -58.72 | -72.82 | -25.60 | -8.61 | -25.84 | -19.38 |
| BBT LAS Evolution | 30.37 | 32.99 | 38.23 | 34.51 | 42.80 | 15.05 | 5.06 | 15.18 | 11.39 |
Statistically significant clinical results indicated in bold type.
Likewise, the results for the grip strength and coordination variables are shown for information purposes, although in this case the null hypothesis of zero difference between the results of the evolution of the variables in both groups cannot be rejected either.
Finally, the ICER was also analyzed using the treatment follow-up rate as the outcome measure. In this case, evaluated on average, gaining 1% point of adherence would require an additional 5.94 euros. The sensitivity analyses performed provide a range of variation for the ICER from an additional 0.99 euros per percentage point of extra adherence (SA6) to an additional 8.37 euros per percentage point of extra adherence (SA4).
Discussion
The objective of this paper was to analyze the cost-effectiveness of the MYO Armband® device in combination with specifically designed games for UL rehabilitation in patients with MS. Our findings highlight that adding a semi-immersive technological device and a set of video games designed ad doc for the treatment of motor control disorders in the UL in subjects with MS was a cost-effective strategy in terms of range of motion. The total cost of the conventional intervention was 5,721.1 euros compared to 6,240.4 euros as the total cost of the experimental treatment, for a sample of 15 patients by type of intervention.
To our knowledge, this is the first study that has analyzed the economic feasibility and health care cost of applying a VR system focused on the UL with an outpatient sample of MS patients. As our research group has previously already published [7], to date, very few studies have addressed this issue in the field of neurorehabilitation. However, the scientific literature seems to clearly indicate, for certain neurological disorders, that VR is effective for functional recovery (including improvement in gait and UL function) for patients who have experienced stroke, and patients with traumatic brain injury, spinal cord injuries, cerebral palsy, Parkinson´s disease and MS [7]. Nonetheless, the widespread use of this technology in the neurorehabilitation field is limited by several issues, including economic barriers to the adoption of these devices linked to the lack of cost-effectiveness studies.
Previously, Winser et al. [24, 25] identified that physiotherapy involving static stretching, aerobic exercise, strengthening exercise, and balance training was cost-effective for MS patients. As the authors showed, very few studies have evaluated the cost-effectiveness of physiotherapy treatments in neurological disorders at a general level and, particularly, in the field of MS. By the other hand, an Italian hospital compared costs between a mixed rehabilitation model—combining conventional therapy and robotic/technology-based treatments supervised by a single physiotherapist —and purely traditional one-on-one therapy for the upper limb in people with neurological disorders (among them, multiple patients). The mixed approach saved €49.60 per patient cycle, with a >98% probability of being less expensive, especially when increasing the number of robotic treatments per cycle. The study also highlighted that the model is economically sustainable only if one therapist can supervise about four patients at the same time (a 1:4 ratio) [26]. Most of the existing literature about cost-effectiveness in neurological rehabilitation seems to be restricted to North America, Europe (mostly in UK), Australia and New Zealand and it mostly focuses on stroke, Parkinson´s disease and cognitive impairment [27].
Specifically, regarding the studies focused on VR, one paper [14] previously compared in-clinic rehabilitation with VR and an at-home intervention using VR in patients who had suffered a stroke. Islam et al. [15] and Adie et al. [28] compared an intervention using VR and a conventional rehabilitation in people with stroke. Thomas et al. [13] compared a Nintendo Wii plus usual care intervention with usual care for balance, gait, mobility and hand dexterity in MS sufferers. Finally, Farr et al. [16] compared supervised and unsupervised VR groups to evaluate home-based VR therapy in children with cerebral palsy. These studies were conducted in Spain, Denmark, Norway and Belgium (as a multicentric international study) and the UK. The results related to VR interventions showed that semi-immersive VR devices could involve savings (mainly derived from the low prices of the systems analyzed and transportation services if they are applied through telerehabilitation programs) compared to in-clinic interventions in people who experienced a stroke [14]. However, Islam et al. [15] showed equal improvements for conventional approaches in people with stroke for UL rehabilitation, while Adie et al. [28] did not find such improvements, as the VR intervention was more expensive than the conventional UL rehabilitation in people who experienced a stroke. Thomas et al. [13] and Farr et al. [16] showed the advantages derived from using semi-immersive virtual device systems in subjects with MS and cerebral palsy.
In our cost analysis of the intervention proposed in the previously conducted RCT [17], the MYO Armband® intervention showed more favorable cost-effectiveness on the more affected side in the active range of motion, with statistically significant gains costing 7.5 euros per point (grade) in palmar flexion, 13.5 euros in pronation, and 5.4 euros in supination. An additional point (grade) in dorsiflexion cost 6.2 euros, although this difference was not statistically significant. It should be noted that no minimal clinically important differences (MCID) have yet been established for elbow and wrist range of motion in MS. While a standard error of measurement of about 5° has been reported, this does not define clinical meaningfulness, highlighting the need for future clinical work to establish such thresholds.
However, in contrast, from a cost analysis perspective, no significant differences were identified in handgrip strength measures (assessed using the Jamar® dynamometer) or in manual dexterity and coordination (measured with the BBT) between the intervention based on VR and conventional physiotherapy in the pre-treatment to follow-up comparison, considering that the first one was more expensive.
These findings are in line with those reported in the study by Adie et al. [28], where, as previously mentioned, the use of the Nintendo Wii™ to improve UL function in patients with stroke was not found to be superior to conventional physiotherapy and was, in fact, more expensive. However, the authors suggest that these results might have been influenced by a ceiling effect in the outcome measures employed, or by the fact that the video games used in the treatment protocol were not specifically designed for stroke rehabilitation, among other possible factors. In Thomas et al. [13] study, the mean cost of delivering Mii-vitaliSe was £684 per person with MS. In our study, the mean cost of delivering MYO Armband® therapy plus conventional treatment was 35 euros per person. In fact, this cost is significantly lower if the number of patients treated increases due to the presence of economies of scale in the distribution of fixed costs.
From our perspective, the absence of differences in grip strength and manual dexterity between the pre-intervention and follow-up assessments might be attributed to different hypotheses: (1) the relatively short intervention period of only eight weeks. Future studies should consider implementing protocols with longer durations and greater intensity of therapy to achieve more substantial outcomes. However, the risk of fatigue could limit the application of more intensive intervention strategies with these patients. Nevertheless, in a neurodegenerative, progressive, and chronic condition such as MS, the absence of changes in these variables, that is, the non-progression of the disease, might also reflect an interesting aspect worth noting. (2) the MYO Armband®, which relies on sEMG signals and serious games, is primarily designed to modulate neuromuscular control rather than directly enhance muscle force production. Thus, the improvements observed in range of motion may reflect better neuromuscular synchronization, and motor control, rather than true gains in contractile strength. In line with this, sEMG-based training is more likely to optimize motor unit recruitment efficiency than to induce hypertrophy or strength increases. (3) Given the impaired nerve conduction and axonal integrity in MS, such an approach may be insufficient to elicit measurable improvements in muscle strength, while still supporting functional gains in motor performance. Therefore, our findings suggest that improvements tend to occur in the specific functions trained within the virtual environment. Since muscle strength was not directly targeted, no meaningful gains in this domain were observed. Nevertheless, it is worth highlighting that, despite the lack of significant differences in these variables between the experimental and control groups, and the increased cost of therapy in the EG due to the need for specific equipment and devices, the adherence to the treatment was significantly higher in the EG, with a very high satisfaction rate. This suggests that patients were more engaged and motivated by the experimental therapy with VR.
In our study, we did not identify any savings, but this is a logical result considering the time horizon used. On the other hand, it is noteworthy that the intervention required a limited number of resources and that the results obtained were promising. In particular, the additional resources invested in exchange for an improvement in the range of motion of the most affected side is low, both for the dimensions of palmar flexion, pronation and supination. Given that other studies have identified that the socetal costs associated with increased disability generated by the disease are very high [29–33], delaying disease progression through interventions such as the one evaluated may be not only efficient (good value for money) but even cost-saving, especially if we consider spillover effects on couples and families [34–36]. In any case, future research that incorporates a societal perspective is required to explore this issue further, as no previous economic evaluations of the MYO Armband® have taken these costs into account.
The sensitivity analyses show a wide variability in the range of results obtained. In this regard, it is worth noting that the base case was established from a conservative perspective. Effective implementation of the MYO Armband® intervention in routine practice on an ongoing basis in a clinical service means that scenarios considered in sensitivity analyses 5, 6 and 7 would be more realistic. Therefore, although the results vary depending on the assumptions made for the allocation of costs per patient, the conclusions obtained from the analysis can be considered robust.
Furthermore, the investment for gaining one point in treatment adherence (attendance percentage) is approximately 6 euros, which also represents a low investment in this variable. This result, coupled with the high customer satisfaction score (obtained through the CSQ-8), provides promising data for the rehabilitation of MS patients using semi-immersive VR technology, meaning that a low investment from institutions with limited economic resources could enhance treatment adherence, which has sometimes been negatively affected by conventional treatments, which are perceived as systematic and monotonous by patients and, as a result, linked to a lack of efficacy due to the lack of motivation and abandonment [5].
As clinical implications and future lines of research, we believe that this study provides a good starting point for institutions that choose to invest in interventions based on the use of VR, specifically with the MYO Armband® sensor and video games designed for the treatment of UL function in patients with MS. It demonstrates that a low economic investment (between 5 and 13.5 euros per degree of improvement) can lead to improvements in forearm and wrist range of motion, particularly on the most affected side in these patients. Nevertheless, to date, it has not been demonstrated that this investment results in superior outcomes in handgrip strength or manual dexterity compared to conventional treatment. However, it has been shown that this investment, in addition to promoting improvements in active range of motion, leads to increased patient satisfaction with the therapy and higher treatment adherence. Future research should focus on patient populations with forearm and wrist range of motion below functional thresholds, as this could allow for a more accurate assessment of intervention-related improvements and help clarify hypothetical ceiling effects and their implications for the economic interpretation of such findings. Therefore, institutions should carefully consider both their own priorities and those of their patients when planning the implementation of such interventions. Adherence to therapy is crucial for optimizing outcomes in neurorehabilitation. Both groups in this study received structured scheduling, motivational support, and engaging exercises, contributing to high attendance, with the EG showing significantly higher adherence. Future work should explore additional strategies to enhance adherence to conventional therapy to maximize functional gains. Future studies should corroborate our findings with longer treatment times and longer follow-up periods and incorporating in the economic evaluation not only the direct implementation costs, but also those associated with training clinical staff to use such technologies, in order to provide a more comprehensive view of their cost-effectiveness. Future studies should establish MCID for elbow and wrist range of motion in MS, building upon the reported 5° standard error of measurement, to enable more clinically meaningful interpretation of intervention effects. Establishing these thresholds would also enhance the assessment of cost-effectiveness, allowing future economic evaluations to determine whether the additional resources invested translate into clinically meaningful benefits for patients.
It is important to highlight certain limitations of this study. First, the sample size was limited, and the results cannot be generalized to the entire population with MS or other neurological conditions, as this research was conducted solely with patients with an EDSS score between 3.0 and 7.5 and a specific disease duration. The size of the study groups also limited the methods applied. For example, univariate sensitivity analyses were performed, but probabilistic analyses were discarded precisely because of the sample size. Second, it would be interesting to explore in future studies the cost-effectiveness of this protocol in patients with MS with different levels of disability, disease duration, greater functional limitation of forearm and wrist range of motion. Also, future studies should incorporate in the economic evaluation not only the direct implementation costs, but also those associated with training clinical staff to use such technologies. Additionally, the sampling method may have resulted in selection bias, as the patients were recruited from a single MS Association in a specific location; despite this, it is certain that our findings could shed light on the implementation of this kind of service in local patient associations with similar material conditions and limited economic resources. Another limitation was that the cost analysis of the VR therapy did not account for setup and cleanup time, including device disinfection. However, these procedures were minimal, as the sessions were conducted by a trained professional and the sensor was small and required only minimal time for disinfection, suggesting only a marginal impact on overall implementation costs. Future research should examine the economic viability and cost-effectiveness of home-based applications of the MYO Armband®. This approach could reduce certain costs (e.g., patient transportation, direct therapist contact hours), but it would also raise new considerations regarding patient compliance, remote monitoring, and safety, which warrant systematic evaluation for adequate clinical effectiveness. Finally, the time horizon of our follow-up valuations was short, so longer follow-up evaluations would be valuable in future studies. In addition, future economic studies should incorporate further healthcare resources (such as outpatient visits, diagnostics test) and the societal costs perspective (including labor productivity losses, direct non health costs), incorporating spillover effects such as the impact on health-related quality of life and wellbeing of patients and caregivers. Likewise, to make the findings comparable, general outcome measures for quality of life should be incorporated, for example, using the Eq. 5D or the SF-6D.
Conclusions
Despite the above limitations, the use of MYO Armband® in the therapy of patients with MS offers promising results in maintaining their range of motion of the UL, at a relatively modest additional cost. While these preliminary results will need to be corroborated by future studies, the intervention appears to be cost-effective.
Author contributions
SMA, RCC, IA, JO wrote the main manuscript text. AJ and EO designed the video games for the intervention project. IA and JO conducted all economic analysis. All authors reviewed the manuscript.
Funding
This work has been funded by CIBER de Fragilidad y Envejecimiento Saludable, Instituto de Salud Carlos III.
Data availability
The data are available from the corresponding authors upon reasonable request.
Declarations
Ethical approval and consent to participate
The study was approved by the Human Ethics Committee of the Rey Juan Carlos University.
Consent for publication
Consent to publish was obtained from all the participants.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Change history
2/12/2026
The original online version of this article was revised: funding details has revised and updated.
Contributor Information
Roberto Cano-de-la-Cuerda, Email: roberto.cano@urjc.es.
Isaac Aranda-Reneo, Email: isaac.aranda@uclm.es.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data are available from the corresponding authors upon reasonable request.