Traducción al portugués brasileño, adaptación transcultural, fiabilidad y validación de la Functional Ambulation Classification para la categorización de deambulación postictus en un entorno clínico

Repetitive peripheral sensory stimulation for motor recovery after stroke: a scoping review

BACKGROUND AND PURPOSE

Enhancing afferent information from the paretic limb can improve post-stroke motor recovery. However, uncertainties exist regarding varied sensory peripheral neuromodulation protocols and their specific impacts. This study outlines the use of repetitive peripheral sensory stimulation (RPSS) and repetitive magnetic stimulation (rPMS) in individuals with stroke.

METHODS

This scoping review was conducted according to the JBI Evidence Synthesis guidelines. We searched studies published until June 2023 on several databases using a three-step analysis and categorization of the studies: pre-analysis, exploration of the material, and data processing.

RESULTS

We identified 916 studies, 52 of which were included (N = 1,125 participants). Approximately 53.84% of the participants were in the chronic phase, displaying moderate-to-severe functional impairment. Thirty-two studies used RPSS often combining it with task-oriented training, while 20 used rPMS as a standalone intervention. The RPSS primarily targeted the median and ulnar nerves, stimulating for an average of 92.78 min at an intensity that induced paresthesia. RPMS targeted the upper and lower limb paretic muscles, employing a 20 Hz frequency in most studies. The mean stimulation time was 12.74 min, with an intensity of 70% of the maximal stimulator output. Among the 114 variables analyzed in the 52 studies, 88 (77.20%) were in the "s,b" domain, with 26 (22.8%) falling under the "d" domain of the ICF.

DISCUSSION AND CONCLUSION

Sensory peripheral neuromodulation protocols hold the potential for enhancing post-stroke motor recovery, yet optimal outcomes were obtained when integrated with intensive or task-oriented motor training.

Cross-cultural adaptation of the Functional Ambulation Categories (FAC) in Danish patients with acquired brain injury

PURPOSE

The Functional Ambulation Categories (FAC) is a measurement tool used to classify walking ability according to the amount of physical support required. In this study, the FAC is translated into Danish and the reliability and validity of the FAC are established in a population of patients with acquired brain injury (ABI).

METHODS

The study took place in a neuro-rehabilitation setting. The translation process followed an accepted forward-backward translation method in six steps. For the assessment of clinometric properties a cross-sectional study design was applied. Interrater reliability was assessed with weighted Cohen's kappa. Construct validity of the FAC was assessed with Spearman's rho by correlating the FAC to walking velocity assessed with the 10-meter walk test and to the number of daily steps assessed with accelerometers.

RESULTS

The FAC was successfully translated into Danish. For the assessment of clinometric properties, 53 patients were included. Almost perfect interrater reliability was found (κ = 0.92, p < 0.0001). Construct validity was high between FAC and walking velocity (r2 = 0.87, p < 0.0001) and moderate between FAC and the number of daily steps (r2 = 0.62, p < 0.0001).

CONCLUSION

The Danish version of the FAC was found reliable and valid for use in-patients with ABI.

Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept

Introduction: To understand the current practices in stroke evaluation, the main clinical decision support system and artificial intelligence (AI) technologies need to be understood to assist the therapist in obtaining better insights about impairments and level of activity and participation in persons with stroke during rehabilitation. Methods: This scoping review maps the use of AI for the functional evaluation of persons with stroke; the context involves any setting of rehabilitation. Data were extracted from CENTRAL, MEDLINE, EMBASE, LILACS, CINAHL, PEDRO Web of Science, IEEE Xplore, AAAI Publications, ACM Digital Library, MathSciNet, and arXiv up to January 2021. The data obtained from the literature review were summarized in a single dataset in which each reference paper was considered as an instance, and the study characteristics were considered as attributes. The attributes used for the multiple correspondence analysis were publication year, study type, sample size, age, stroke phase, stroke type, functional status, AI type, and AI function. Results: Forty-four studies were included. The analysis showed that spasticity analysis based on ML techniques was used for the cases of stroke with moderate functional status. The techniques of deep learning and pressure sensors were used for gait analysis. Machine learning techniques and algorithms were used for upper limb and reaching analyses. The inertial measurement unit technique was applied in studies where the functional status was between mild and severe. The fuzzy logic technique was used for activity classifiers. Conclusion: The prevailing research themes demonstrated the growing utility of AI algorithms for stroke evaluation.