Evidence map›Paper›PMID 28580370›Full record

ArticleInternational journal of physical medicine & rehabilitation2016

Robotic Assist-As-Needed as an Alternative to Therapist-Assisted Gait Rehabilitation.

Shraddha Srivastava, Pei Chun Kao, Darcy S Reisman, John P Scholz, Sunil K Agrawal, Jill S Higginson

Open access · greenAbstract read
In one paragraph

Article in International journal of physical medicine & rehabilitation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
3.1field-weighted citation impact, top 9% 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

15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 53 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Exploiting telerobotics for sensorimotor rehabilitation: a locomotor embodiment.Journal of neuroengineering and rehabilitation · 2021
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  8. Human-AI Cooperation in Healthcare and Rehabilitation.Delaware journal of public health · 2026
    Article
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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

6 authors at 2 institutions in 1 country.

Shraddha SrivastavaDepartment of Health Sciences and Research, Medical University of South Carolina, Charleston, SC 29425, USA.
Pei Chun KaoDepartment of Physical Therapy, University of Massachusetts Lowell, Lowell, MA 01854, USA.
Darcy S ReismanDepartment of Physical Therapy, University of Delaware, Newark, DE 19713, USA.
John P ScholzDepartment of Physical Therapy, University of Delaware, Newark, DE 19713, USA.
Sunil K AgrawalDepartment of Mechanical Engineering, Columbia University, USA.
Jill S HigginsonDepartment of Mechanical Engineering, University of Delaware, USA.
Medical University of South Carolina · USUniversity of Massachusetts Lowell · US

Funding

Robotic Exoskeletons, FES, and Biomechanics: Treating Movement DisordersR01HD038582 · NICHD · UNIVERSITY OF DELAWARE · PI BINDER-MACLEOD, STUART A. · 2002 to 2012
$6.1M
NICHD NIH HHS R01 HD038582
6 · The paper itself

Abstract

objectiveBody Weight Supported Treadmill Training (BWSTT) with therapists' assistance is often used for gait rehabilitation post-stroke. However, this training method is labor-intensive, requiring at least one or as many as three therapists at once for manual assistance. Previously, we demonstrated that providing movement guidance using a performance-based robot-aided gait training (RAGT) that applies a compliant, assist-as-needed force-field improves gait pattern and functional walking ability in people post-stroke. In the current study, we compared the effects of assist-as-needed RAGT combined with functional electrical stimulation and visual feedback with BWSTT to determine if RAGT could serve as an alternative for locomotor training.

methodsTwelve stroke survivors were randomly assigned to one of the two groups, either receiving BWSTT with manual assistance or RAGT with functional electrical stimulation and visual feedback. All subjects received fifteen 40-minutes training sessions.

resultsClinical measures, kinematic data, and EMG data were collected before and immediately after the training for fifteen sessions. Subjects receiving RAGT demonstrated significant improvements in their self-selected over-ground walking speed, Functional Gait Assessment, Timed Up and Go scores, swing-phase peak knee flexion angle, and muscle coordination pattern. Subjects receiving BWSTT demonstrated significant improvements in the Six-minute walk test. However, there was an overall trend toward improvement in most measures with both interventions, thus there were no significant between-group differences in the improvements following training.

conclusionThe current findings suggest that RAGT worked at least as well as BWSTT and thus may be used as an alternative rehabilitation method to improve gait pattern post-stroke as it requires less physical effort from the therapists compared to BWSTT.

Indexed as

Assist-as-neededBody weight supported treadmill trainingForce-fieldGait rehabilitationLocomotionRobotic exoskeletonStroke

Identifiers

PMID28580370
PMCPMC5450822
OpenAlexW2536750604

What OpenQuestion holds

Textmetadata
LicenceTDM
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.