ArticlePloS one2016
Democratizing Neurorehabilitation: How Accessible are Low-Cost Mobile-Gaming Technologies for Self-Rehabilitation of Arm Disability in Stroke?
Article in PloS one, 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.
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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 36 citations in OpenAlex.
- Systematic review of novel technology-based interventions for ischemic stroke.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2021Pooled it
- The Role of Engagement in Teleneurorehabilitation: A Systematic Review.Frontiers in neurology · 2020Pooled it
- Guided Self-rehabilitation Contract vs conventional therapy in chronic stroke-induced hemiparesis: NEURORESTORE, a multicenter randomized controlled trial.BMC neurology · 2019Trial
- Stroke Survivors' Interaction With Hand Rehabilitation Devices: Observational Study.JMIR biomedical engineering · 2024Article
- Modernising grip dynamometry: Inter-instrument reliability between GripAble and Jamar.BMC musculoskeletal disorders · 2022Article
- Self-Directed Exergaming for Stroke Upper Limb Impairment Increases Exercise Dose Compared to Standard Care.Neurorehabilitation and neural repair · 2021Article
- Self-Rehabilitation Guided by a Mobile Application After Anterior Cruciate Ligament Reconstruction Leads to Improved Early Motion and Less Pain.Arthroscopy, sports medicine, and rehabilitation · 2021Article
- Review: How Can Intelligent Robots and Smart Mechatronic Modules Facilitate Remote Assessment, Assistance, and Rehabilitation for Isolated Adults With Neuro-Musculoskeletal Conditions?Frontiers in robotics and AI · 2021Review
- Accuracy and Acceptability of Wearable Motion Tracking for Inpatient Monitoring Using Smartwatches.Sensors (Basel, Switzerland) · 2020Article
- Feasibility of Wearable Sensing for In-Home Finger Rehabilitation Early After Stroke.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2020Article
- Motor dexterity and strength depend upon integrity of the attention-control system.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Balancing the playing field: collaborative gaming for physical training.Journal of neuroengineering and rehabilitation · 2017Article
- Elasticity improves handgrip performance and user experience during visuomotor control.Royal Society open science · 2017Article
- SITAR: a system for independent task-oriented assessment and rehabilitation.Journal of rehabilitation and assistive technologies engineeringArticle
- GripAble: An accurate, sensitive and robust digital device for measuring grip strength.Journal of rehabilitation and assistive technologies engineeringArticle
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
Abstract
Motor-training software on tablets or smartphones (Apps) offer a low-cost, widely-available solution to supplement arm physiotherapy after stroke. We assessed the proportions of hemiplegic stroke patients who, with their plegic hand, could meaningfully engage with mobile-gaming devices using a range of standard control-methods, as well as by using a novel wireless grip-controller, adapted for neurodisability. We screened all newly-diagnosed hemiplegic stroke patients presenting to a stroke centre over 6 months. Subjects were compared on their ability to control a tablet or smartphone cursor using: finger-swipe, tap, joystick, screen-tilt, and an adapted handgrip. Cursor control was graded as: no movement (0); less than full-range movement (1); full-range movement (2); directed movement (3). In total, we screened 345 patients, of which 87 satisfied recruitment criteria and completed testing. The commonest reason for exclusion was cognitive impairment. Using conventional controls, the proportion of patients able to direct cursor movement was 38-48%; and to move it full-range was 55-67% (controller comparison: p>0.1). By comparison, handgrip enabled directed control in 75%, and full-range movement in 93% (controller comparison: p<0.001). This difference between controllers was most apparent amongst severely-disabled subjects, with 0% achieving directed or full-range control with conventional controls, compared to 58% and 83% achieving these two levels of movement, respectively, with handgrip. In conclusion, hand, or arm, training Apps played on conventional mobile devices are likely to be accessible only to mildly-disabled stroke patients. Technological adaptations such as grip-control can enable more severely affected subjects to engage with self-training software.
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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.