Back

Neurotechnology-based intensive upper-extremity supplementary training for inpatients with sub-acute stroke: A feasibility study

Binyamin Netser, R.; Handelzalts, S.; Goldhamer, N.; Avni, I.; Yeshurun Tayer, A.; Koren, Y.; Bibas Levy, O.; Kramer, S.; Bar Haim, S.; Smuelof, L.

2023-11-18 rehabilitation medicine and physical therapy
10.1101/2023.11.18.23298626 medRxiv
Show abstract

BackgroundIntensive and high-dose upper extremity training, concentrating on movement quality in the early phase after a stroke, can enhance motor recovery compared to standard care. Unfortunately, such programs do not exist due to limited resources, patient compliance, and administrative challenges. ObjectiveTo examine the feasibility and potential efficacy, and to evaluate the resources of an intensive technology-based upper extremity training emphasizing movement quality during inpatient stroke rehabilitation. MethodsTwelve subjects with hemiparesis underwent 40 60-minute sessions over a 4-week period, in addition to standard care. The training included two game-based virtual reality platforms to practice proximal (tech 1) and distal (tech 2) movements with daily assessments. ResultsEight subjects completed the entire protocol, three subjects completed 34-38 sessions, and one subject completed only 27 sessions. The mean time on each task was 35{+/-}4 (tech 1) and 37{+/-}2 (tech 2) minutes per hour. The intervention was perceived as motivating and enjoyable [Intrinsic Motivation Inventory (IMI) enjoyment and interest= 6.49{+/-}0.66 out of 7] and was not associated with pain [Visual Analogue Scale (VAS) mean= 2.00{+/-}2.32]. Subjects showed large improvements in all impairment measurements (mean FMA delta= 16.5 points and ARAT delta= 22.9 points). ConclusionsThe results support the feasibility of a high-dose high-intensity supplementary training protocol during sub-acute hospitalization and provide suggestive evidence of its efficacy.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.