Evidence map›Paper›PMID 33062041›Full record

SynthesisComputational and mathematical methods in medicine2020

Intelligent Rehabilitation Assistance Tools for Distal Radius Fracture: A Systematic Review Based on Literatures and Mobile Application Stores.

Yalan Chen, Yijun Yu, Xin Lin, Zhenwei Han, Zhe Feng, Xinyi Hua, Dongliang Chen, Xiaotao Xu, Yuanpeng Zhang, Guheng Wang

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Computational and mathematical methods in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Technologies in Home-Based Digital Rehabilitation: Scoping Review.JMIR rehabilitation and assistive technologies · 2023
    Article
  4. 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

10 authors.

Yalan ChenDepartment of Medical Informatics, School of Medicine, Nantong University, Nantong 226001, China.
Yijun YuNantong University Xinglin College, Nantong University, Nantong 226236, China.
Xin LinBachelor of Nursing, University of Technology Sydney, Sydney, NSW 2007, Australia.
Zhenwei HanNantong University Xinglin College, Nantong University, Nantong 226236, China.
Zhe FengDepartment of Medical Informatics, School of Medicine, Nantong University, Nantong 226001, China.
Xinyi HuaDepartment of Medical Informatics, School of Medicine, Nantong University, Nantong 226001, China.
Dongliang ChenNantong University Xinglin College, Nantong University, Nantong 226236, China.
Xiaotao XuDepartment of Medical Informatics, School of Medicine, Nantong University, Nantong 226001, China.
Yuanpeng ZhangDepartment of Medical Informatics, School of Medicine, Nantong University, Nantong 226001, China.
Guheng WangDepartment of Hand Surgery, Affiliated Hospital of Nantong University, Nantong 226001, China.ORCID https://orcid.org/0000-0003-3084-4303

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo systematically analyze the existing intelligent rehabilitation mobile applications (APPs) related to distal radius fracture (DRF) and evaluate their features and characteristics, so as to help doctors and patients to make evidence-based choice for appropriate intelligent-assisted rehabilitation.

methodsLiteratures which in regard to the intelligent rehabilitation tools of DRF were systematic retrieved from the PubMed, the Cochrane library, Wan Fang, and VIP Data. The effective APPs were systematically screened out through the APP markets of iOS and Android mobile platform, and the functional characteristics of different APPs were evaluated and analyzed.

resultsA total of 8 literatures and 31 APPs were included, which were divided into four categories: intelligent intervention, angle measurement, intelligent monitoring, and auxiliary rehabilitation games. These APPs provide support for the patients' home rehabilitation guidance and training and make up for the high cost and space limitations of traditional rehabilitation methods. The intelligent intervention category has the largest download ratio in the APP market. Angle measurement tools help DRF patients to measure the joint angle autonomously to judge the degree of rehabilitation, which is the most concentrated type of literature research. Some of the APPs and tools have obtained good clinical verification. However, due to the restrictions of cost, geographic authority, and applicable population, a large number of APPs still lack effective evidence to support popularization.

conclusionPatients with DRF could draw support from different kinds of APPs in order to fulfill personal need and promote self-management. Intelligent rehabilitation APPs play a positive role in the rehabilitation of patients, but the acceptance of the utilization for intelligent rehabilitation APPs is relatively low, which might need follow-up research to address the conundrum.

Indexed as

Mobile ApplicationsArtificial IntelligenceComputational BiologyEvidence-Based MedicineHumansMathematical ConceptsRadius FracturesSelf CareTelemedicineTelerehabilitation

Identifiers

PMID33062041
PMCPMC7542482

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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.