Evidence map›Paper›PMID 34828636›Full record

ArticleHealthcare (Basel, Switzerland)2021

Agreement between 3D Motion Analysis and Tele-Assessment Using a Video Conferencing Application for Telerehabilitation.

Kyeongjin Lee

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

1 author.

Kyeongjin LeeDepartment of Physical Therapy, College of Health Science, Kyungdong University, Wonju 24764, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global pandemic of the coronavirus disease 2019 (COVID-19) has highlighted the need for remote healthcare services. This study aimed to evaluate the concurrent validity and reliability of tele-assessment using 3D motion analysis and video conferencing applications. The subjects of this study were 14 Pilates instructors and 14 healthy adults, who repeated five exercises of "side spine stretch", "bridge", "toe taps", "quadruped leg raise", and "cat and cow" five times each. We performed 3D kinematic analysis with 16 infrared cameras while the subject performed each exercise, and the image captured by one webcam was transmitted to the evaluators through a video conferencing application, and eight raters evaluated the mobility, stability, and symmetry of the movement. The result was then compared with the gold standard 3D motion analysis to evaluate the teleassessment system. The concurrent validity of the data obtained using both methods was analyzed. In addition, the inter-rater reliability of the data from the eight raters was evaluated. As a result, mobility showed excellent (ICC > 0.75, ICCs: intraclass correlation coefficients) or good agreement (ICC = 0.6-0.74) with 3D motion analysis and tele-assessment in all motions. The analysis of stability showed high agreement in general, but it was not significant in "cat and cow." Symmetry showed moderate agreement only in "bridge" and "toe taps", showing low agreement compared to other components. In addition, the inter-rater reliability of the tele-assessment showed good agreement (ICC = 0.744). Although there were few components with weaker agreements, the results of this study confirmed that it is a valid and reliable method of tele-assessment using video conferencing applications and showed feasibility as an alternative to the existing face-to-face examination.

Indexed as

kinematicsmobile applicationreliabilitytelerehabilitationvalidity

Identifiers

PMID34828636
PMCPMC8622086

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Registered trials

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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.