Background Stroke remains one of the major chronic illnesses worldwide that health care organizations will need to address for the next several decades. Individuals poststroke are subject to levels of cognitive impairment and mental health problems. Virtual reality (VR)-based therapies are new technologies used for cognitive rehabilitation and the management of psychological outcomes. Objective This study performed a meta-analysis to evaluate the effects of VR-based therapies on cognitive function and mental health in patients with stroke. Methods A comprehensive database search was performed using PubMed, MEDLINE (Ovid), Embase, Cochrane Library, and APA PsycINFO databases for randomized controlled trials (RCTs) that studied the effects of VR on patients with stroke. We included trials published up to April 15, 2021, that fulfilled our inclusion and exclusion criteria. The literature was screened, data were extracted, and the methodological quality of the included trials was assessed. Meta-analysis was performed using RevMan 5.3 software. Results A total of 894 patients from 23 RCTs were included in our meta-analysis. Compared to traditional rehabilitation therapies, the executive function (standard mean difference [SMD]=0.88, 95% confidence interval [CI]=0.06-1.70, P=.03), memory (SMD=1.44, 95% CI=0.21-2.68, P=.02), and visuospatial function (SMD=0.78, 95% CI=0.23-1.33, P=.006) significantly improved among patients after VR intervention. However, there were no significant differences observed in global cognitive function, attention, verbal fluency, depression, and the quality of life (QoL). Conclusions The findings of our meta-analysis showed that VR-based therapies are efficacious in improving executive function, memory, and visuospatial function in patients with stroke. For global cognitive function, attention, verbal fluency, depression, and the QoL, further research is required. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42021252788; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=252788

No abstract

No abstract

The spatiotemporal fusion technique has the advantages of generating time-series images with high-spatial and high-temporal resolution from coarse-resolution to fine-resolution images. A hybrid fusion method that integrates image blending (i.e., spatial and temporal adaptive reflectance fusion model, STARFM) and super-resolution (i.e., very deep super resolution, VDSR) techniques for the spatiotemporal fusion of 8 m Formosat-2 and 30 m Landsat-8 satellite images is proposed. Two different fusion approaches, namely Blend-then-Super-Resolution and Super-Resolution (SR)-then-Blend, were developed to improve the results of spatiotemporal fusion. The SR-then-Blend approach performs SR before image blending. The SR refines the image resampling stage on generating the same pixel-size of coarse- and fine-resolution images. The Blend-then-SR approach is aimed at refining the spatial details after image blending. Several quality indices were used to analyze the quality of the different fusion approaches. Experimental results showed that the performance of the hybrid method is slightly better than the traditional approach. Images obtained using SR-then-Blend are more similar to the real observed images compared with images acquired using Blend-then-SR. The overall mean bias of SR-then-Blend was 4% lower than Blend-then-SR, and nearly 3% improvement for overall standard deviation in SR-B. The VDSR technique reduces the systematic deviation in spectral band between Formosat-2 and Landsat-8 satellite images. The integration of STARFM and the VDSR model is useful for improving the quality of spatiotemporal fusion.