A Music-Based Digital Therapeutic: Proof-of-Concept Automation of a Progressive and Individualized Rhythm-Based Walking Training Program After Stroke.

Background: The rhythm of music can entrain neurons in motor cortex by way of direct connections between auditory and motor brain regions. Objective: We sought to automate an individualized and progressive music-based, walking rehabilitation program using real-time sensor data in combination with de...

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Publicado en:Neurorehabilitation & Neural Repair Vol. 34; no. 11; pp. 986 - 997
Autores principales: Hutchinson, Karen, Sloutsky, Regina, Collimore, Ashley, Adams, Benjamin, Harris, Brian, Ellis, Terry D., Awad, Louis N.
Formato: pictorial research tables/charts Journal Article
Publicado: Sage Publications Inc. Nov2020
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Nov2020
      vid: 34
      iid: 11
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      pub: Sage Publications Inc.
      place: Thousand Oaks, California
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        atl: A Music-Based Digital Therapeutic: Proof-of-Concept Automation of a Progressive and Individualized Rhythm-Based Walking Training Program After Stroke.
      aug:
        au:
          Hutchinson, Karen
          Sloutsky, Regina
          Collimore, Ashley
          Adams, Benjamin
          Harris, Brian
          Ellis, Terry D.
          Awad, Louis N.
        affil: Sargent College, Boston University, Boston, MA, USA
      sug:
        subj:
          Stroke Rehabilitation
          Automation
          Walking
          Music Therapy
          Human
          Pilot Studies
          Algorithms
          Walking Speed
          Program Development
          Patient Safety
      ab: Background: The rhythm of music can entrain neurons in motor cortex by way of direct connections between auditory and motor brain regions. Objective: We sought to automate an individualized and progressive music-based, walking rehabilitation program using real-time sensor data in combination with decision algorithms. Methods: A music-based digital therapeutic was developed to maintain high sound quality while modulating, in real-time, the tempo (ie, beats per minute, or bpm) of music based on a user's ability to entrain to the tempo and progress to faster walking cadences in-sync with the progression of the tempo. Eleven individuals with chronic hemiparesis completed one automated 30-minute training visit. Seven returned for 2 additional visits. Safety, feasibility, and rehabilitative potential (ie, changes in walking speed relative to clinically meaningful change scores) were evaluated. Results: A single, fully automated training visit resulted in increased usual (∆ 0.085 ± 0.027 m/s, P =.011) and fast (∆ 0.093 ± 0.032 m/s, P =.016) walking speeds. The 7 participants who completed additional training visits increased their usual walking speed by 0.12 ± 0.03 m/s after only 3 days of training. Changes in walking speed were highly related to changes in walking cadence (R 2 > 0.70). No trips or falls were noted during training, all users reported that the device helped them walk faster, and 70% indicated that they would use it most or all of the time at home. Conclusions: In this proof-of-concept study, we show that a sensor-automated, progressive, and individualized rhythmic locomotor training program can be implemented safely and effectively to train walking speed after stroke. Music-based digital therapeutics have the potential to facilitate salient, community-based rehabilitation.
      pubtype: Academic Journal
      doctype:
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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