Evidence map›Paper›PMID 40682190›Full record

Trial reportJournal of neuroengineering and rehabilitation2025

Variants of active assist robotic therapy: feasibility of virtual assistance and proprioceptive training as gauged by their effects on success and motivation during finger movement training after stroke.

Andria J Farrens, Dylan Reinsdorf, Luis Garcia-Fernandez, Raymond Diaz Rojas, Vicky Chan, Joel Perry, Eric T Wolbrecht, David J Reinkensmeyer

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of neuroengineering and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Andria J FarrensUniversity of California, Irvine, United States. farrensa@uci.edu.
Dylan ReinsdorfUniversity of California, Irvine, United States.
Luis Garcia-FernandezUniversity of California, Irvine, United States.
Raymond Diaz RojasUniversity of California, Irvine, United States.
Vicky ChanUniversity of California, Irvine, United States.
Joel PerryUniversity of Idaho, Moscow, United States.
Eric T WolbrechtUniversity of Idaho, Moscow, United States.
David J ReinkensmeyerUniversity of California, Irvine, United States. dreinken@uci.edu.

Funding

Institute for Clinical and Translational ScienceUM1TR004927 · NCATS · UNIVERSITY OF CALIFORNIA-IRVINE · PI DAN M COOPER, Eric J. Vilain · 2024 to 2026
$12.2M
NCATS NIH HHS UM1 TR004927NIH HHS NCT04818073
6 · The paper itself

Abstract

backgroundStandard robotic therapy for upper extremity stroke rehabilitation provides physical assistance during video game-based training. While effective, it requires complex equipment, focuses on visuo-motor control, and yields variable results—raising the question: can more pragmatic or targeted movement training strategies be developed? To explore this, we pilot tested two novel robotic therapy modes. In the Virtual Assistance mode, game parameters are modulated to promote task completion without physical assistance – potentially useful when only sensors are available, although its acceptability for people with more severe motor impairment is unclear. In the Proprioceptive Training mode, participants play video games through a combination of visual and haptic cues. This approach aims to retrain proprioception but may also be too challenging for individuals with proprioceptive impairment to find it motivating. This study tested the feasibility of both variants across a range of motor and proprioceptive impairments, comparing them to training with Standard robotic therapy.

methodsChronic stroke participants (N = 46) were randomized to receive Standard, Virtual, or Proprioceptive Training. Participants used the FINGER robot to train in three sessions across one week, during which an adaptive algorithm titrated success to ~ 80%. Baseline proprioceptive and motor function were assessed prior to training, and motivation for training was assessed using the Intrinsic Motivation Inventory. Feasibility was evaluated by levels of gameplay success and motivation for training.

resultsParticipants of widely varying motor and proprioceptive ability achieved ~ 80% success for all three modes. However, Virtual Assistance resulted in significantly diminished motivation, due to lower perceived competence when participants were not provided with physical assistance. Participants with impaired proprioception rated Proprioceptive Training engaging, although it was more challenging for them and they required an increased level of assistance.

conclusionBoth training paradigms were feasible for use with chronic stroke survivors and were able to achieve high gameplay success and motivation for training. However, physical assistance appeared to have an advantage over Virtual Assistance in raising training motivation. Proprioceptive Training required high levels of assistance, but was motivating even for people with poor proprioception.

Indexed as

Exercise TherapyFingersMotivationProprioceptionRoboticsStroke RehabilitationAdultAgedFeasibility StudiesFemaleHumansMaleMiddle AgedMovementStrokeVideo Games

Identifiers

PMID40682190
PMCPMC12273468

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.