Evidence map›Paper›PMID 28284228›Full record

SynthesisJournal of neuroengineering and rehabilitation2017

Interactive wearable systems for upper body rehabilitation: a systematic review.

Qi Wang, Panos Markopoulos, Bin Yu, Wei Chen, Annick Timmermans

Registry-linked trialOpen access · goldAbstract readSystematic Review
In one paragraph

Synthesis in Journal of neuroengineering and rehabilitation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05877183 ('Smart Reminder'), which is not on this map. Cited by 137 papers, 13 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
137citing papers in PubMed, 13 pooled it
29.5field-weighted citation impact, top 1% 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.

NCT05877183 naunknown statusnot on this mapstarted 2023, after this paper: background citation

'Smart Reminder': a Randomized Controlled Trial on the Effect of a Wearable Device to Provide Telerehabilitation for Persons With Stroke

TypeinterventionalSponsorThe Hong Kong Polytechnic UniversityRan2023 to 2024Enrolled40ConditionsStroke, Wearable DeviceArmsWearable device -'Smart Reminder', Sham device
3 · Its place in the literature

Who cites it

137 citing papers in PubMed, 13 syntheses or guidelines pooled it, 348 citations in OpenAlex.

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  4. Quality of mobility measures among individuals with acquired brain injury: an umbrella review.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2022
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  18. Measuring upper limb function in children with hemiparesis with 3D inertial sensors.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2017
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77 more citing papers are in PubMed but not listed here.

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

5 authors at 3 institutions in 3 countries.

Qi WangDepartment of Industrial Design, Eindhoven Technology University, Eindhoven, The Netherlands.
Panos MarkopoulosDepartment of Industrial Design, Eindhoven Technology University, Eindhoven, The Netherlands.
Bin YuDepartment of Industrial Design, Eindhoven Technology University, Eindhoven, The Netherlands.
Wei ChenCenter for Intelligent Medical Electronics, Department of Electronic Engineering, Fudan University, Shanghai, China. w_chen@fudan.edu.cn.ORCID 0000-0003-3720-718X
Annick TimmermansBIOMED REVAL Rehabilitatio Research Institute, Faculty of Medicine and Life Sciences, Hasselt University, Diepenbeek, Belgium.
Eindhoven University of Technology · NLHasselt University · BEShanghai Medical College of Fudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe development of interactive rehabilitation technologies which rely on wearable-sensing for upper body rehabilitation is attracting increasing research interest. This paper reviews related research with the aim: 1) To inventory and classify interactive wearable systems for movement and posture monitoring during upper body rehabilitation, regarding the sensing technology, system measurements and feedback conditions; 2) To gauge the wearability of the wearable systems; 3) To inventory the availability of clinical evidence supporting the effectiveness of related technologies.

methodA systematic literature search was conducted in the following search engines: PubMed, ACM, Scopus and IEEE (January 2010-April 2016).

resultsForty-five papers were included and discussed in a new cuboid taxonomy which consists of 3 dimensions: sensing technology, feedback modalities and system measurements. Wearable sensor systems were developed for persons in: 1) Neuro-rehabilitation: stroke (n = 21), spinal cord injury (n = 1), cerebral palsy (n = 2), Alzheimer (n = 1); 2) Musculoskeletal impairment: ligament rehabilitation (n = 1), arthritis (n = 1), frozen shoulder (n = 1), bones trauma (n = 1); 3) Others: chronic pulmonary obstructive disease (n = 1), chronic pain rehabilitation (n = 1) and other general rehabilitation (n = 14). Accelerometers and inertial measurement units (IMU) are the most frequently used technologies (84% of the papers). They are mostly used in multiple sensor configurations to measure upper limb kinematics and/or trunk posture. Sensors are placed mostly on the trunk, upper arm, the forearm, the wrist, and the finger. Typically sensors are attachable rather than embedded in wearable devices and garments; although studies that embed and integrate sensors are increasing in the last 4 years. 16 studies applied knowledge of result (KR) feedback, 14 studies applied knowledge of performance (KP) feedback and 15 studies applied both in various modalities. 16 studies have conducted their evaluation with patients and reported usability tests, while only three of them conducted clinical trials including one randomized clinical trial.

conclusionsThis review has shown that wearable systems are used mostly for the monitoring and provision of feedback on posture and upper extremity movements in stroke rehabilitation. The results indicated that accelerometers and IMUs are the most frequently used sensors, in most cases attached to the body through ad hoc contraptions for the purpose of improving range of motion and movement performance during upper body rehabilitation. Systems featuring sensors embedded in wearable appliances or garments are only beginning to emerge. Similarly, clinical evaluations are scarce and are further needed to provide evidence on effectiveness and pave the path towards implementation in clinical settings.

Indexed as

ClothingAccelerometryBiomechanical PhenomenaHumansMovementPostureRange of Motion, ArticularRehabilitationInteractive feedbackMotion sensingPosture monitoringRehabilitationUpper bodyWearable technology

Identifiers

PMID28284228
PMCPMC5346195
OpenAlexW2591746674

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.