Evaluating the effectiveness and implementation of digital health interventions for patients with spinal cord injury: A systematic review and meta-analysis.
Aug 2026· Complementary Therapies in Medicine· pp.
103424
· 0 citations
Medicine
TL;DR
Current evidence suggests that digital health technologies, as a supplement to conventional rehabilitation, may help improve certain functional outcomes, psychological health, and quality of life in patients with spinal cord injury, while also alleviating pain to some extent.
Abstract
Objective
To evaluate the effectiveness and implementation of digital health interventions (DHIs) for patients with spinal cord injury (SCI) compared with control groups.
Methods
We conducted a systematic review of relevant randomized controlled trials (RCTs). Five electronic databases were searched from inception to January 1, 2026. The analysis examined the effects of DHIs on patients' clinical symptoms, quality of life, psychological status, and other outcomes. Quantitative analyses were performed using Review Manager software (version 5.4), and the results were presented using forest plots. The Cochrane Risk of Bias (RoB 1.0) tool was used to assess risk of bias, and the GRADE approach was applied to evaluate the overall certainty of evidence.
Results
A total of 26 RCTs were included. The meta-analysis showed that DHIs significantly reduced pain in patients with spinal cord injury (SMD = -0.19, 95% CI -0.38 to -0.00, P = 0.05) and improved independent living ability as measured by the Functional Independence Measure (FIM) (MD = 10.69, 95% CI 9.33 to 12.04, P < 0.00001). DHIs also significantly improved depressive symptoms (SMD = -0.25, 95% CI -0.51 to -0.00, P = 0.05) and anxiety symptoms (MD = -1.11, 95% CI -1.99 to -0.23, P = 0.01). In addition, DHIs significantly improved lower limb function, as reflected by improvements in the Timed Up and Go test (TUG) (MD = -0.92, 95% CI -1.60 to -0.23, P = 0.009) and the 10-Meter Walk Test (10MWT) (MD = -1.61, 95% CI -2.93 to -0.28, P = 0.02), and also enhanced balance ability (MD = 30.61, 95% CI 6.97 to 54.26, P = 0.010). However, no significant improvement was observed in upper limb function. DHIs also improved quality of life in the psychological domain (MD = 2.66, 95% CI -0.03 to 5.36, P = 0.05), social domain (MD = 1.52, 95% CI 0.75 to 2.30, P = 0.0001), and environmental domain (MD = 1.10, 95% CI 0.19 to 2.01, P = 0.02). Among the included studies, the reach of digital health technologies ranged from 14.05% to 100%, feasibility ranged from 58.33% to 100%, and adherence ranged from 51.05% to 100%. Only two studies reported mild adverse events. In this review, seven studies were assessed as having a low risk of bias, while 19 studies were rated overall as having a high risk of bias. The certainty of evidence in this review ranged from very low to moderate.
Conclusion
Current evidence suggests that digital health technologies, as a supplement to conventional rehabilitation, may help improve certain functional outcomes, psychological health, and quality of life in patients with spinal cord injury, while also alleviating pain to some extent. However, the certainty of these findings is limited by multiple factors. Future high-quality studies are needed to further strengthen the evidence in these areas.
LLMs are increasingly used for code generation, yet they frequently hallucinate non-existent software packages, creating exploitable entry points into the software supply chain. We make four contributions to this problem. First, we show that prior evaluation methodologies systematically inflate hallucination rates by misclassifying standard-library modules as hallucinations in some languages. For Python, the overestimation reaches 9.4 percentage points. Second, we evaluate seven inference-time defenses for mitigating package hallucinations, including five guided decoding strategies (Greedy, Contrastive, DoLa, Nudging, and Active Layer-Contrastive Decoding), an iterative self-refinement approach (Self-Refine), and a Retrieval-Augmented Generation (RAG)-based defense.. Across eight models spanning five families and four programming languages (Python, JavaScript, Ruby, Rust), RAG reduces the package hallucination rate (PHR) in 18 of 32 model--language configurations. Third, we introduce Package Utility (PU) to assess whether defenses preserve valid and task-relevant recommendations. Among strategies evaluated, Greedy decoding provides the strongest average mitigation--utility trade-off. Fourth, we stress-test all strategies under adversarial prompts seeded with fabricated package names and find that PHR surges by up to 45 percentage points relative to standard prompts, with Ruby consistently the most vulnerable language (80.9--95.2\%). Under adversarial conditions, RAG and Self-Refine outperform all decoding-only strategies, indicating that robust defense requires either external grounding or iterative self-verification when prompts are actively hostile. Our results recast package hallucination as both a measurement problem and a decoding-time control problem, and they demonstrate that the choice of defense must be matched to the threat model and recommendation utility.
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