Effect of Transcranial Direct Current Stimulation Augmented with Motor Imagery and Upper-Limb Functional Training for Upper-Limb Stroke Rehabilitation: A Prospective Randomized Controlled Trial

Background: Combining transcranial direct current stimulation (tDCS) with other therapies is reported to produce promising results in patients with stroke. The purpose of the study was to determine the effect of combining tDCS with motor imagery (MI) and upper-limb functional training for upper-limb rehabilitation among patients with chronic stroke. Methods: A single-center, prospective, randomized controlled trial was conducted among 64 patients with chronic stroke. The control group received sham tDCS with MI, while the experimental group received real tDCS with MI. Both groups performed five different upper-limb functional training exercises coupled with tDCS for 30 min, five times per week for two weeks. Fugl-Meyer’s scale (FMA) and the Action Research Arm Test (ARAT) were used to measure the outcome measures at baseline and after the completion of the 10th session. Results: Analysis of covariance showed significant improvements in the post-test mean scores for FMA (F (414.4) = 35.79, p < 0.001; η2 = 0.37) and ARAT (F (440.09) = 37.46, p < 0.001; η2 = 0.38) in the experimental group compared to the control group while controlling for baseline scores. Conclusions: Anodal tDCS stimulation over the affected primary motor cortex coupled with MI and upper-limb functional training reduces impairment and disability of the upper limbs among patients with chronic stroke

The impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia: Simulation approach

Objectives: This paper aims to measure the impact of the implemented nonpharmaceutical interventions (NPIs) in the Kingdom of Saudi Arabia (KSA) during the pandemic using simulation modeling. Methods: To measure the impact of NPI, a hybrid agent-based and system dynamics simulation model was built and validated. Data were collected prospectively on a weekly basis. The core epidemiological model is based on a complex Susceptible-Exposed-Infectious-Recovered and Dead model of epidemic dynamics. Reverse engineering was performed on a weekly basis throughout the study period as a mean for model validation which reported on four outcomes: total cases, active cases, ICU cases, and deaths cases. To measure the impact of each NPI, the observed values of active and total cases were captured and compared to the projected values of active and total cases from the simulation. To measure the impact of each NPI, the study period was divided into rounds of incubation periods (cycles of 14 days each). The behavioral change of the spread of the disease was interpreted as the impact of NPIs that occurred at the beginning of the cycle. The behavioral change was measured by the change in the initial reproduction rate (R0). Results: After 18 weeks of the reverse engineering process, the model achieved a 0.4 % difference in total cases for prediction at the end of the study period. The results estimated that NPIs led to 64 % change in The R0. Our breakdown analysis of the impact of each NPI indicates that banning going to schools had the greatest impact on the infection reproduction rate (24 %). Conclusion: We used hybrid simulation modeling to measure the impact of NPIs taken by the KSA government. The finding further supports the notion that early NPIs adoption can effectively limit the spread of COVID-19. It also supports using simulation for building mathematical modeling for epidemiological scenarios