Effects of MOTOmed movement therapy on the mobility and activities of daily living of stroke patients with hemiplegia: a systematic review and meta-analysis

Following stroke, the brain undergoes various stages of recovery where the central nervous system can reorganize neural circuitry (neuroplasticity) both spontaneously and with the aid of behavioral rehabilitation and non-invasive brain stimulation. Multiple neuroimaging techniques can characterize common structural and functional stroke-related deficits, and importantly, help predict recovery of function. Diffusion tensor imaging (DTI) typically reveals increased overall diffusivity throughout the brain following stroke, and is capable of indexing the extent of white matter damage. Magnetic resonance spectroscopy (MRS) provides an index of metabolic changes in surviving neural tissue after stroke, serving as a marker of brain function. The neural correlates of altered brain activity after stroke have been demonstrated by abnormal activation of sensorimotor cortices during task performance, and at rest, using functional magnetic resonance imaging (fMRI). Electroencephalography (EEG) has been used to characterize motor dysfunction in terms of increased cortical amplitude in the sensorimotor regions when performing upper limb movement, indicating abnormally increased cognitive effort and planning in individuals with stroke. Transcranial magnetic stimulation (TMS) work reveals changes in ipsilesional and contralesional cortical excitability in the sensorimotor cortices. The severity of motor deficits indexed using TMS has been linked to the magnitude of activity imbalance between the sensorimotor cortices. In this paper, we will provide a narrative review of data from studies utilizing DTI, MRS, fMRI, EEG, and brain stimulation techniques focusing on TMS and its combination with uni- and multimodal neuroimaging methods to assess recovery after stroke. Approaches that delineate the best measures with which to predict or positively alter outcomes will be highlighted.

The aging population with chronic and age-related diseases has become a global issue and exerted heavy burdens on the healthcare system and society. Neurological diseases are the leading chronic diseases in the geriatric population, and stroke is the leading cause of death in China. However, the uneven distribution of caregivers and critical healthcare workforce shortages are major obstacles to improving disease outcome. With the advancement of wearable health devices, cloud computing, mobile technologies and Internet of Things, mobile health (mHealth) is rapidly developing and shows a promising future in the management of chronic diseases. Its advantages include its ability to improve the quality of care, reduce the costs of care, and improve treatment outcomes by transferring in-hospital treatment to patient-centered medical treatment at home. mHealth could also enhance the international cooperation of medical providers in different time zones and the sharing of high-quality medical service resources between developed and developing countries. In this review, we focus on trends in mHealth and its clinical applications for the prevention and treatment of diseases, especially aging-related neurological diseases, and on the opportunities and challenges of mHealth in China. Operating models of mHealth in disease management are proposed; these models may benefit those who work within the mHealth system in developing countries and developed countries.

This study aimed to investigate the effectiveness of biofeedback cycling training on lower limb functional recovery, walking endurance, and walking speed for patients with chronic stroke. Thirty-one patients with stroke (stroke onset >3 months) were randomly assigned into two groups using a crossover design. One group (N = 16; mean: 53.6 ± 10.3 years) underwent conventional rehabilitation and cycling training (30 minutes/time, 5 times per week for 4 weeks), followed by only conventional rehabilitation for another 4 weeks. The other group (N = 15; mean: 54.5 ± 8.0 years) underwent the same training in reverse order. The bike used in this biofeedback cycling training was the MOTOmed viva2 Movement Trainer. Outcome measures included the lower extremity subscale of Fugl-Meyer assessment (LE-FMA), the 6-minute walk test (6MWT), the 10-meter walk test (10MWT), and the modified Ashworth scale (MAS). All participants were assessed at the beginning of the study, at the end of the 4(th) week, and at the end of the 8(th) week. Thirty participants completed the study, including the cycling training interventions and all assessments. The results showed that improvements in the period with cycling training were significantly better than the noncycling period in the LE-FMA (p < 0.05), 6MWT (p < 0.001), 10MWT (p < 0.001), and MAS (p < 0.001) scores. No significant carryover effects were observed. The improvements on outcome measures were significantly different between the cycling period and the noncycling period after adjusting for potential confounding factors in the multivariate analysis of variance (p < 0.001). The study result indicates that the additional 4-week biofeedback cycling training could lead to improved LE functional recovery, walking endurance, and speed for patients with chronic stroke.