Evidence map›Paper›PMID 33019759›Full record

ArticleInternational journal of environmental research and public health2020

Design and Evaluation of an Augmented Reality-Based Exergame System to Reduce Fall Risk in the Elderly.

Meiling Chen, Qingfeng Tang, Shoujiang Xu, Pengfei Leng, Zhigeng Pan

Open access · goldAbstract read
In one paragraph

Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.9field-weighted citation impact, top 8% 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

16 citing papers in PubMed, 46 citations in OpenAlex.

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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 2 institutions in 1 country.

Meiling ChenSchool of Health Management, Hangzhou Normal University, Hangzhou 310036, China.
Qingfeng TangSchool of Health Management, Hangzhou Normal University, Hangzhou 310036, China.
Shoujiang XuSchool of Health Management, Hangzhou Normal University, Hangzhou 310036, China.ORCID 0000-0002-9756-6435
Pengfei LengSchool of Health Management, Hangzhou Normal University, Hangzhou 310036, China.
Zhigeng PanInstitute of VR and Intelligent System, Hangzhou Normal University, Hangzhou 310036, China.
Hangzhou Normal University · CNZhejiang Chinese Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Falls are a major public health concern in today's aging society. Virtual reality (VR) technology is a promising method for reducing fall risk. However, the absence of representations of the user's body in a VR environment lessens the spatial sense of presence. In terms of user experience, augmented reality (AR) can provide a higher degree of presence and embodiment than VR. We developed an AR-based exergame system that is specifically designed for the elderly to reduce fall risk. Kinect2.0 was used to capture and generate 3D models of the elderly and immerse them in an interactive virtual environment. The software included three functional modules: fall risk assessment, cognitive-motor intervention (CMI) training, and training feedback. The User Experience Questionnaire (UEQ-S) was used to evaluate user experience. Twenty-five elders were enrolled in the study. It was shown that the average scores for each aspect were: pragmatic quality score (1.652 ± 0.868); hedonic quality score (1.880 ± 0.962); and overall score was 1.776 ± 0.819. The overall score was higher than 0.8, which means that the system exhibited a positive user experience. After comparing the average score in a dataset product of UEQ-S Data Analysis Tool, it was found that the pragmatic quality aspect was categorized as good, while the hedonic quality aspect was categorized as excellent. It revealed a positive evaluation from users.

Indexed as

Accidental FallsAugmented RealityVirtual RealityAgedExerciseFemaleHumansMaleaugmented realitycognitive–motor interventionexergamefall riskUEQ-Suser experience

Identifiers

PMID33019759
PMCPMC7579303
OpenAlexW3089958661

What OpenQuestion holds

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LicenceCC BY
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.