Evidence map›Paper›PMID 37272500›Full record

ArticleNeurorehabilitation and neural repair2023

Autonomous Control of Music to Retrain Walking After Stroke.

Ashley N Collimore, Anna V Roto Cataldo, Ashlyn J Aiello, Regina Sloutsky, Karen J Hutchinson, Brian Harris, Terry Ellis, Louis N Awad

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In one paragraph

Article in Neurorehabilitation and neural repair, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 3 pooled it
–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

13 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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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

8 authors.

Ashley N CollimoreDepartment of Physical Therapy, College of Health and Rehabilitation Sciences, Sargent College, Boston University, Boston, MA, USA.
Anna V Roto CataldoDepartment of Physical Therapy, College of Health and Rehabilitation Sciences, Sargent College, Boston University, Boston, MA, USA.
Ashlyn J AielloDepartment of Physical Therapy, College of Health and Rehabilitation Sciences, Sargent College, Boston University, Boston, MA, USA.ORCID 0000-0002-9472-7336
Regina SloutskyDepartment of Physical Therapy, College of Health and Rehabilitation Sciences, Sargent College, Boston University, Boston, MA, USA.
Karen J HutchinsonDepartment of Physical Therapy, College of Health and Rehabilitation Sciences, Sargent College, Boston University, Boston, MA, USA.
Brian HarrisDepartment of Physical Therapy, College of Health and Rehabilitation Sciences, Sargent College, Boston University, Boston, MA, USA.
Terry EllisDepartment of Physical Therapy, College of Health and Rehabilitation Sciences, Sargent College, Boston University, Boston, MA, USA.
Louis N AwadDepartment of Physical Therapy, College of Health and Rehabilitation Sciences, Sargent College, Boston University, Boston, MA, USA.ORCID 0000-0002-0159-8011

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPost-stroke care guidelines highlight continued rehabilitation as essential; however, many stroke survivors cannot participate in outpatient rehabilitation. Technological advances in wearable sensing, treatment algorithms, and care delivery interfaces have created new opportunities for high-efficacy rehabilitation interventions to be delivered autonomously in any setting (ie, clinic, community, or home).

methodsWe developed an autonomous rehabilitation system that combines the closed-loop control of music with real-time gait analysis to fully automate patient-tailored walking rehabilitation. Specifically, the mechanism-of-action of auditory-motor entrainment is applied to induce targeted changes in the post-stroke gait pattern by way of targeted changes in music. Using speed-controlled biomechanical and physiological assessments, we evaluate in 10 individuals with chronic post-stroke hemiparesis the effects of a fully-automated gait training session on gait asymmetry and the energetic cost of walking.

resultsPost-treatment reductions in step time (Δ: -12 ± 26%,

conclusionsThe closed-loop control of music can fully automate walking rehabilitation that markedly improves walking after stroke. Autonomous rehabilitation delivery systems that can safely provide high-efficacy rehabilitation in any setting have the potential to alleviate access-related care gaps and improve long-term outcomes after stroke.

Indexed as

Gait Disorders, NeurologicMusicStrokeStroke RehabilitationAgedGaitHumansWalkingdigital therapeuticeconomymusicrhythmsymmetrywalking

Identifiers

PMID37272500
PMCPMC10272623

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Registered trials

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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.