Virtual Reality Systems for Upper Limb Motor Function Recovery in Patients With Spinal Cord Injury: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Patients with spinal cord injury (SCI) usually present with different motor impairments, including a deterioration of upper limb motor function (ULMF), that limit their performance of activities of daily living and reduce their quality of life. Virtual reality (VR) is being used in neurological rehabilitation for the assessment and treatment of the physical impairments of this condition. OBJECTIVE: A systematic review and meta-analysis was conducted to evaluate the effectiveness of VR on ULMF in patients with SCI compared with conventional physical therapy. METHODS: The search was performed from October to December 2019 in Embase, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, Medline, Physiotherapy Evidence Database (PEDro), PubMed, and Cochrane Central Register of Controlled Trials. The inclusion criteria of selected studies were as follows: (1) comprised adults with SCI, (2) included an intervention with VR, (3) compared VR intervention with conventional physical therapy, (4) reported outcomes related to ULMF, and (5) was a controlled clinical trial. The Cochrane Collaboration's tool was used to evaluate the risk of bias. The RevMan 5.3 statistical software was used to obtain the meta-analysis according to the standardized mean difference (SMD) and 95% CIs. RESULTS: Six articles were included in this systematic review. Four of them contributed information to the meta-analysis. A total of 105 subjects were analyzed. All of the studies used semi-immersive or nonimmersive VR systems. The statistical analysis showed nonsignificant results for the Nine-Hole Peg Test (SMD -0.93, 95% CI -1.95 to 0.09), muscle balance test (SMD -0.27, 95% CI -0.82 to 0.27), Motricity Index (SMD 0.16, 95% CI -0.37 to 0.68), Jebsen-Taylor Hand Function Test (JTHFT) subtests (writing, SMD -0.10, 95% CI -4.01 to 3.82; simulated page turning, SMD -0.99, 95% CI -2.01 to 0.02; simulated feeding, SMD -0.64, 95% CI -1.61 to 0.32; stacking checkers, SMD 0.99, 95% CI -0.02 to 2.00; picking up large light objects, SMD -0.42, 95% CI -1.37 to 0.54; and picking up large heavy objects, SMD 0.52, 95% CI -0.44 to 1.49), range of motion of shoulder abduction/adduction (SMD -0.23, 95% CI -1.48 to 1.03), shoulder flexion/extension (SMD 0.56, 95% CI -1.24 to 2.36), elbow flexion (SMD -0.36, 95% CI -1.14 to 0.42), elbow extension (SMD -0.21, 95% CI -0.99 to 0.57), wrist extension (SMD 1.44, 95% CI -2.19 to 5.06), and elbow supination (SMD -0.18, 95% CI -1.80 to 1.44). Favorable results were found for the JTHFT subtest picking up small common objects (SMD -1.33, 95% CI -2.42 to -0.24). CONCLUSIONS: The current evidence for VR interventions to improve ULMF in patients with SCI is limited. Future studies employing immersive systems to identify the key aspects that increase the clinical impact of VR interventions are needed, as well as research to prove the benefits of the use of VR in the rehabilitation of patients with SCI in the clinical setting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".