ArticlePloS one2022
Foundational ingredients of robotic gait training for people with incomplete spinal cord injury during inpatient rehabilitation (FIRST): A randomized controlled trial protocol.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04781621 (Foundational Ingredients of Robotic Gait Training for People With Spinal Cord Injury During Inpatient Therapy), which is not on this map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Foundational Ingredients of Robotic Gait Training for People With Spinal Cord Injury During Inpatient Therapy
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Effects of an exoskeleton robot on motor function in patients with spinal cord injuries: a systematic review and meta-analysis.Systematic reviews · 2025Pooled it
- Impact of Robotic-Assisted Gait Therapy on Depression and Anxiety Symptoms in Patients with Subacute Spinal Cord Injuries (SCIs)-A Prospective Clinical Study.Journal of clinical medicine · 2023Article
- Biopsychosocial effects and experience of use of robotic and virtual reality devices in neuromotor rehabilitation: A study protocol.PloS one · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAs technological advances allow the use of robotic exoskeleton devices with gait training, there is a critical need to establish a robotic gait training (RGT) program to meet the needs of people with spinal cord injury (SCI) during inpatient rehabilitation. The purposes of this study are to prospectively examine the efficacy of a stakeholder informed RGT program compared to usual care gait training (UC) during inpatient rehabilitation in people with incomplete SCI and compare the intensity of RGT and UC gait training during inpatient rehabilitation. STUDY
design128 patients with incomplete SCI admitted to our inpatient rehabilitation facility will be screened for eligibility and randomized to either the RGT or UC group. RGT sessions will use the Ekso robotic exoskeleton [class II medical device (United States FDA)]. UC sessions will use traditional gait training approaches such as manually assisted overground gait training with walkers and orthotics and body weight-supported treadmill training (BWSTT). Our primary outcome is gait function as characterized by the Walking Index for Spinal Cord Injury-II (WISCI-II). Secondary outcomes are gait speed, Spinal Cord Independence Measure (SCIM), Numeric Pain Rating Scale (NPRS), Fatigue Severity Scale (FSS), Penn Spasm Frequency Scale (PSFS), Patient Health Questionnaire-9 (PHQ-9), General Anxiety Disorder- 7 (GAD-7), International Spinal Cord Injury Quality of Life Basic Data Set, and a Qualitative Questionnaire. Assessments of primary and secondary outcomes will occur at admission and discharge from inpatient rehabilitation. General or generalized linear models will be used to analyze differences between groups for all measures. CLINICAL IMPACT: Successful completion of this study will provide a usable, replicable, stakeholder informed RGT intervention for use with individuals with incomplete SCI during inpatient rehabilitation.
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Registered trials
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.