Trial reportJournal of neuroengineering and rehabilitation2018
Trainer in a pocket - proof-of-concept of mobile, real-time, foot kinematics feedback for gait pattern normalization in individuals after stroke, incomplete spinal cord injury and elderly patients.
Trial report in Journal of neuroengineering and rehabilitation, 2018. 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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 3 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Sensors are Capable to Help in the Measurement of the Results of the Timed-Up and Go Test? A Systematic Review.Journal of medical systems · 2020Pooled it
- Enabling Older Adults' Health Self-Management through Self-Report and Visualization-A Systematic Literature Review.Sensors (Basel, Switzerland) · 2020Pooled it
- Task-Dependent Performance of Wearable Multimodal Biofeedback in Physical Rehabilitation: A Longitudinal Post-Stroke Case Study.Healthcare (Basel, Switzerland) · 2026Article
- Evaluating the Usability of Inertial Measurement Units for Measuring and Monitoring Activity Post-Stroke: A Scoping Review.Sensors (Basel, Switzerland) · 2025Article
- Assessment of Foot Strike Angle and Forward Propulsion with Wearable Sensors in People with Stroke.Sensors (Basel, Switzerland) · 2024Article
- Data-driven characterization of walking after a spinal cord injury using inertial sensors.Journal of neuroengineering and rehabilitation · 2023Article
- Robotic Biofeedback for Post-Stroke Gait Rehabilitation: A Scoping Review.Sensors (Basel, Switzerland) · 2022Article
- Reducing Slip Risk: A Feasibility Study of Gait Training with Semi-Real-Time Feedback of Foot-Floor Contact Angle.Sensors (Basel, Switzerland) · 2022Article
- Review of Real-Time Biomechanical Feedback Systems in Sport and Rehabilitation.Sensors (Basel, Switzerland) · 2022Review
- Biofeedback for Post-stroke Gait Retraining: A Review of Current Evidence and Future Research Directions in the Context of Emerging Technologies.Frontiers in neurology · 2021Review
- Feasibility of a real-time pattern-based kinematic feedback system for gait retraining in pediatric cerebral palsy.Journal of rehabilitation and assistive technologies engineeringArticle
- The most used questionnaires for evaluating the usability of robots and smart wearables: A scoping review.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWalking disabilities negatively affect inclusion in society and quality of life and increase the risk for secondary complications. It has been shown that external feedback applied by therapists and/or robotic training devices enables individuals with gait abnormalities to consciously normalize their gait pattern. However, little is known about the effects of a technically-assisted over ground feedback therapy. The aim of this study was to assess whether automatic real-time feedback provided by a shoe-mounted inertial-sensor-based gait therapy system is feasible in individuals with gait impairments after incomplete spinal cord injury (iSCI), stroke and in the elderly.
methodsIn a non-controlled proof-of-concept study, feedback by tablet computer-generated verbalized instructions was given to individuals with iSCI, stroke and old age for normalization of an individually selected gait parameter (stride length, stance or swing duration, or foot-to-ground angle). The training phase consisted of 3 consecutive visits. Four weeks post training a follow-up visit was performed. Visits started with an initial gait analysis (iGA) without feedback, followed by 5 feedback training sessions of 2-3 min and a gait analysis at the end. A universal evaluation and FB scheme based on equidistant levels of deviations from the mean normal value (1 level = 1 standard deviation (SD) of the physiological reference for the feedback parameter) was used for assessment of gait quality as well as for automated adaptation of training difficulty. Overall changes in level over iGAs were detected using a Friedman's Test. Post-hoc testing was achieved with paired Wilcoxon Tests. The users' satisfaction was assessed by a customized questionnaire.
resultsFifteen individuals with iSCI, 11 after stroke and 15 elderly completed the training. The average level at iGA significantly decreased over the visits in all groups (Friedman's test, p < 0.0001), with the biggest decrease between the first and second training visit (4.78 ± 2.84 to 3.02 ± 2.43, p < 0.0001, paired Wilcoxon test). Overall, users rated the system's usability and its therapeutic effect as positive.
conclusionsMobile, real-time, verbalized feedback is feasible and results in a normalization of the feedback gait parameter. The results form a first basis for using real-time feedback in task-specific motor rehabilitation programs.
trial registrationDRKS00011853 , retrospectively registered on 2017/03/23.
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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.