Evidence map›Paper›PMID 27065539›Full record

ArticleJournal of physical therapy science2016

Application of RFID technology-upper extremity rehabilitation training.

Chih-Chen Chen, Yu-Luen Chen, Shih-Ching Chen

Open access · diamondAbstract read
In one paragraph

Article in Journal of physical therapy science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.5field-weighted citation impact, top 18% 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

3 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
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

3 authors at 3 institutions in 1 country.

Chih-Chen ChenDepartment of Management Information Systems, Hwa Hsia University of Technology: 111 Gongzhuan Rd., Zhonghe Dist., New Taipei City 235, Taiwan.
Yu-Luen ChenDepartment of Digital Technology Design, National Taipei University of Education, Taiwan.
Shih-Ching ChenDepartment of Physical Medicine and Rehabilitation, Taipei Medical University and Hospital, Taiwan.
Hwa Hsia University of Technology · TWNational Taipei University of Education · TWTaipei Medical University Hospital · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

[Purpose] Upper extremity rehabilitation after an injury is very important. This study proposes radio frequency identification (RFID) technology to improve and enhance the effectiveness of the upper extremity rehabilitation. [Subjects and Methods] People use their upper extremities to conduct daily activities. When recovering from injuries, many patients neglect the importance of rehabilitation, which results in degraded function. This study recorded the training process using the traditional rehabilitation hand gliding cart with a RFID reader, RFID tags in the panel, and a servo host computer. [Results] Clinical evidence, time taken to achieve a full score, counts of missing the specified spots, and Brunnstrom stage of aided recovery, the proximal part of the upper extremity show that the RFID-based upper extremity training significantly and reduce negative impacts of the disability in daily life and activities. [Conclusion] This study combined a hand-gliding cart with an RFID reader, and when patients moved the cart, the movement could be observed via the activated RFID tags. The training data was collected and quantified for a better understanding of the recovery status of the patients. Each of the participating patients made progress as expected.

Indexed as

Hand-gliding cartRehabilitationRFID

Identifiers

PMID27065539
PMCPMC4793003
OpenAlexW2324937071

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.