Evidence map›Paper›PMID 36318505›Full record

ArticleGames for health journal2023

Usage of RePlay as a Take-Home System to Support High-Repetition Motor Rehabilitation After Neurological Injury.

David T Pruitt, Y-Nhy Duong-Nguyen, Eric C Meyers, Joseph D Epperson, Joel M Wright, Rachael A Hudson, Jane G Wigginton, Robert L Rennaker Ii, Seth A Hays, Michael P Kilgard

Open access · greenAbstract read
In one paragraph

Article in Games for health journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.6field-weighted citation impact, top 31% of its field
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

7 citing papers in PubMed, 7 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 1 institution in 1 country.

David T PruittTexas Biomedical Device Center, The University of Texas at Dallas, Richardson, Texas, USA.ORCID 0000-0001-8356-0887
Y-Nhy Duong-NguyenTexas Biomedical Device Center, The University of Texas at Dallas, Richardson, Texas, USA.
Eric C MeyersTexas Biomedical Device Center, The University of Texas at Dallas, Richardson, Texas, USA.
Joseph D EppersonTexas Biomedical Device Center, The University of Texas at Dallas, Richardson, Texas, USA.
Joel M WrightTexas Biomedical Device Center, The University of Texas at Dallas, Richardson, Texas, USA.
Rachael A HudsonTexas Biomedical Device Center, The University of Texas at Dallas, Richardson, Texas, USA.
Jane G WiggintonTexas Biomedical Device Center, The University of Texas at Dallas, Richardson, Texas, USA.
Robert L Rennaker IiTexas Biomedical Device Center, The University of Texas at Dallas, Richardson, Texas, USA.
Seth A HaysTexas Biomedical Device Center, The University of Texas at Dallas, Richardson, Texas, USA.
Michael P KilgardTexas Biomedical Device Center, The University of Texas at Dallas, Richardson, Texas, USA.
The University of Texas at Dallas · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Stroke is a leading cause of chronic motor disability. While physical rehabilitation can promote functional recovery, several barriers prevent patients from receiving optimal rehabilitative care. Easy access to at-home rehabilitative tools could increase patients' ability to participate in rehabilitative exercises, which may lead to improved outcomes. Toward achieving this goal, we developed RePlay: a novel system that facilitates unsupervised rehabilitative exercises at home. RePlay leverages available consumer technology to provide a simple tool that allows users to perform common rehabilitative exercises in a gameplay environment. RePlay collects quantitative time series force and movement data from handheld devices, which provide therapists the ability to quantify gains and individualize rehabilitative regimens. RePlay was developed in C# using Visual Studio. In this feasibility study, we assessed whether participants with neurological injury are capable of using the RePlay system in both a supervised in-office setting and an unsupervised at-home setting, and we assessed their adherence to the unsupervised at-home rehabilitation assignment. All participants were assigned a set of 18 games and exercises to play each day. Participants produced on average 698 ± 36 discrete movements during the initial 1 hour in-office visit. A subset of participants who used the system at home produced 1593 ± 197 discrete movements per day. Participants demonstrated a high degree of engagement while using the system at home, typically completing nearly double the number of assigned exercises per day. These findings indicate that the open-source RePlay system may be a feasible tool to facilitate access to rehabilitative exercises and potentially improve overall patient outcomes.

Indexed as

Motor DisordersPersons with DisabilitiesStrokeStroke RehabilitationExercise TherapyHumansGamesNeurological disordersRecoveryRehabilitationTelehealth systems

Identifiers

PMID36318505
PMCPMC9894604
OpenAlexW4307838097

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.