3D Virtual Reality Performance Metrics as a Future Fatigue Biomarker in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. Questionnaires
2.3. VR-OTS—Virtual Reality–Oculomotor Test System
2.4. Statistical Analysis
3. Results
Self-Assessment Correlation
4. Discussion
4.1. RT Slowing in ME/CFS
4.2. Dynamic of RT Improvements Across Rounds
4.3. Age Limitations
4.4. Lack of Correlation with Questionnaires
4.5. Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
| ME/CFS | Myalgic encephalomyelitis/chronic fatigue syndrome |
| PEM | Post-exertional malaise |
| RT | Reaction time |
| EM-means | Estimated marginal means |
| PCS | Post-COVID syndrome |
| 2D | Two-dimensional |
| 3D | Three-dimensional |
| VR-OTS | Virtual reality–oculomotor test system |
| HAQ-DI | Health Assessment Questionnaire Disability Index |
| FACIT | Functional Assessment of Chronic Illness Therapy |
| SE | Standard error |
| CI | Confidence interval |
| PROM | Patient-reported outcome measures |
Appendix A
| Characteristic | ME/CFS N = 29 1 | Control N = 13 1 | p-Value 2 |
|---|---|---|---|
| Age (years) | 43 (39, 46) | 48 (44, 52) | 0.12 |
| Sex | 0.4 | ||
| Male (number) | 14 (48%) | 8 (62%) | |
| Female (number) | 15 (52%) | 5 (38%) |
| ME/CFS | p-Value | Control | p-Value | |
|---|---|---|---|---|
| RT change from round 1 to round 2 | ||||
| Whole cohort 275″ | −26.6% (−16.9% to −37.0%) | <0.0001 | −21.1% (−11.8% to −31.0%) | <0.0001 |
| Pairwise contrast between groups R2: 39.7% (15.8% to 68.5%%) | 0.0005 | |||
| Subset 275″ | −35.3% (−21.1% to −51.2%) | <0.0001 | −15.3% (+2.2% to −36.0%) | 0.105 |
| Pairwise contrast between groups R2: 61.5% (16.2% to 124.6%) | 0.0044 | |||
| RT change from round 2 to round 3 | ||||
| Whole cohort 275″ | −4.6% (+3.4% to −13.2%) | 0.3766 | −5.7% (+2.4% to −14.3%) | 0.2326 |
| Pairwise contrast between groups R3: 41.1% (16.9% to 70.2%) | 0.0003 | |||
| Subset 275″ | −0.9% (+9.7% to −12.7%) | 0.980 | −7.6% (+8.8% to −26.8%) | 0.554 |
| Pairwise contrast between groups R3: 72.1% (23.8% to 139.4%) | 0.0012 | |||
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| Characteristics | ME/CFS N = 60 1 | Control N = 60 1 | p-Value 2 |
|---|---|---|---|
| Age (years) | 47 (38, 58) | 29 (24, 56) | 0.007 |
| Sex | 0.6 | ||
| Male (number) | 27 (45%) | 30 (50%) | |
| Female (number) | 33 (55%) | 30 (50%) | |
| Duration of ME/CFS (days) | 3232 (2051, 6592) | NA | |
| Bell Score | 30 (20, 30) | 100 (100, 100) | <0.001 |
| Chalder Fatigue Scale | 11 (9, 11) | 0 (0, 1) | <0.001 |
| FACIT 3 | 16 (11, 23) | NA | |
| HAQ 4 | 1.00 (0.63, 1.63) | NA | |
| Brain fog (y/n) | 45 (75%) | 0 (0%) | <0.001 |
| Reduced ability to concentrate (y/n) | 46 (77%) | 0 (0%) | <0.001 |
| PEM 5/Fatigue (y/n) | 52 (87%) | 0 (0%) | <0.001 |
| Binocular fusion | 51/60 | 51/60 | |
| ME/CFS | p-Value | Control | p-Value | |
|---|---|---|---|---|
| RT change from round 1 to round 2 | ||||
| Disparity 275″ | −26.6% (−16.9% to −37.0%) | <0.0001 | −21.1% (−11.8% to −31.0%) | <0.0001 |
| Pairwise contrast between groups R2: 39.7% (15.8% to 68.5%) | 0.0005 | |||
| Disparity 550″ | −20.7% (−12.6% to −29.4%) | <0.0001 | −19.6% (−11.6% to −28.2%) | <0.0001 |
| Pairwise contrast between groups R2: 29.1% (8.6% to 53.5%) | 0.0038 | |||
| Disparity 1100″ | −20.0% (−13.0% to −27.3%) | <0.0001 | −17.3% (−10.5% to −24.4%) | <0.0001 |
| Pairwise contrast between groups R2: 23.5% (5.7% to 44.4%) | 0.0081 | |||
| RT change from round 2 to round 3 | ||||
| Disparity 275″ | −4.6% (+3.4% to −13.2%) | 0.3766 | −5.7% (+2.4% to −14.3%) | 0.2326 |
| Pairwise contrast between groups R3: 41.1% (16.9% to 70.2%) | 0.0003 | |||
| Disparity 550″ | −1.5% (+5.3% to −8.8%) | 0.8672 | −6.4% (+0.7% to −14.1%) | 0.0874 |
| Pairwise contrast between groups R3: 35.4% (13.8% to 61.0%) | 0.0006 | |||
| Disparity 1100″ | −2.9% (+3.1% to −9.2%) | 0.5022 | −7.8% (−1.6% to −14.4%) | 0.0085 |
| Pairwise contrast between groups R3: 29.4% (10.7% to 51.3%) | 0.0012 | |||
| Author | Disease | Article/ Review | Key Finding | Type of Finding and Method | Limitation as ME/CFS Tool | Compared to Current Study |
|---|---|---|---|---|---|---|
| Scherbakov et al. [1] | ME/CFS | Article | Peripheral endothelial dysfunction | No direct functional correlation | No functional information | N/A |
| Clarke et al. [2] | ME/CFS | Review | Review of potential blood biomarkers | No direct functional correlation | No functional information | N/A |
| Freitag et al. [3] | ME/CFS | article | Autoantibodies to vasoactive G-PCR correlate with symptom severity | Correlation with fatigue symptoms (PROM) | No functional information | N/A |
| Bizjak et al. [4] | ME/CFS and PCS | Article | Mitochondrial differences | Functional with exercise testing | Requires exercise testing, which may trigger PEM | Study focusses on mitochondrial function and compares with exercise testing. The focus is on physical performance, rather than neurological. |
| Shan et al. [6] | ME/CFS | Review | Review of neuroimaging characteristics including fMRI | Functional with fMRI | Requires fMRI access | Shows slower signal responses and altered brain area recruitment in fMRI studies, supporting the findings of the current study. |
| Lange et al. [29] | ME/CFS | Article | Slower processing speed across timepoints, independent of exercise test on same visit | Traditional tests and CogState clinical functional assessment tests (computerized cognitive assessment) with tasks reflecting psychomotor speed, attention, learning memory, and learning efficiency | Multiple separate tests administered once at beginning and end of a clinic visit and at home after clinic visits on conventional devices. Although tasks are said to reflect multiple facets of cognition, response time is the only outcome reported as significant. | The findings support the current study; rather than three successive administrations in the current study, multiple tasks were administered over a larger period of time. Additionally, three-dimensional tasks are reported to impose greater cognitive loads [21]. |
| Scribano et al. [17] | Alzheimer’s disease | Review | Review of VR and AI methods to detect preclinical Alzheimer’s disease | Some functional markers using VR and AI algorithms | N/A | Reviews functional testing using VR in the detection of preclinical Alzheimer’s disease. |
| Culicetto et al. [18] | Parkinson’s disease | Review | Review of VR/eye tracking as diagnostic and disease markers | Functional with VR glasses and other eye trackers | N/A | Reviews functional testing using VR as diagnostic marker of Parkinson’s disease. |
| Faúndez et al. [19] | Persistent postural-perceptual dizziness | Article | Eye tracking and VR spatial navigation test shows deficits in affected patients | Functional with VR glasses and normal screens (compared) | N/A | Tests spatial cognition in persistent postural-perceptual dizziness and vestibular disorders. |
| Kara et al. [22] | Mild traumatic brain injury | Article | Fusion capacity is reduced in mild traumatic brain injury | Functional with VR-OTS | N/A | Same test, in contrast to mild traumatic brain injury, where binocular fusion capacity was reduced. ME/CFS patients had the same rate of fusion as controls. |
| Güttes et al. [23] | PCS | Article | Reaction time deficits | Functional with VR-OTS | N/A | Same test-similar to the current study PCS patients had slower RT compared to controls. |
| Kelly et al. [30] | PCS | Article | VR tests showed neural deficits | Functional with 14 tests on a VR eye-tracking device targeting oculomotor, vestibular, reaction time and cognitive tests | N/A | This study supports the findings of slower RT also reported in our test; although their discussion draws links to ME/CFS, this is not supported by data. |
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Share and Cite
Ladek, A.-M.; Priebe, L.; Harrer, T.; Harrer, E.; Michelson, G.; Knauer, T.S.; Dias-Nunes, D.X.; Mardin, C.Y.; Bergua, A.; Hohberger, B. 3D Virtual Reality Performance Metrics as a Future Fatigue Biomarker in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). Biomedicines 2026, 14, 855. https://doi.org/10.3390/biomedicines14040855
Ladek A-M, Priebe L, Harrer T, Harrer E, Michelson G, Knauer TS, Dias-Nunes DX, Mardin CY, Bergua A, Hohberger B. 3D Virtual Reality Performance Metrics as a Future Fatigue Biomarker in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). Biomedicines. 2026; 14(4):855. https://doi.org/10.3390/biomedicines14040855
Chicago/Turabian StyleLadek, Anja-Maria, Leonie Priebe, Thomas Harrer, Ellen Harrer, Georg Michelson, Thomas S. Knauer, Diogo X. Dias-Nunes, Christian Y. Mardin, Antonio Bergua, and Bettina Hohberger. 2026. "3D Virtual Reality Performance Metrics as a Future Fatigue Biomarker in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)" Biomedicines 14, no. 4: 855. https://doi.org/10.3390/biomedicines14040855
APA StyleLadek, A.-M., Priebe, L., Harrer, T., Harrer, E., Michelson, G., Knauer, T. S., Dias-Nunes, D. X., Mardin, C. Y., Bergua, A., & Hohberger, B. (2026). 3D Virtual Reality Performance Metrics as a Future Fatigue Biomarker in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). Biomedicines, 14(4), 855. https://doi.org/10.3390/biomedicines14040855

