Evidence map›Paper›PMID 34713150›Full record

ArticleFrontiers in digital health2021

Design and Evaluation of an Online Squat Fitness System: Lessons Learned During the Early COVID-19 Pandemic in Japan.

Tianyi Wang, Masamitsu Kamon, Shima Okada, Shuji Sawada, Rui Ogawa, Naruhiro Shiozawa, Shuichi Machida

Abstract read
In one paragraph

Article in Frontiers in digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Tianyi WangDepartment of Robotics, Faculty of Science and Engineering, Ritsumeikan University, Kusatsu, Japan.
Masamitsu KamonDepartment of Robotics, Faculty of Science and Engineering, Ritsumeikan University, Kusatsu, Japan.
Shima OkadaDepartment of Robotics, Faculty of Science and Engineering, Ritsumeikan University, Kusatsu, Japan.
Shuji SawadaCenter of Innovation (COI) Project Center, Juntendo University, Tokyo, Japan.
Rui OgawaCenter of Innovation (COI) Site, Tokyo University of the Arts, Tokyo, Japan.
Naruhiro ShiozawaFaculty of Sport and Health Science, Ritsumeikan University, Kusatsu, Japan.
Shuichi MachidaCenter of Innovation (COI) Project Center, Juntendo University, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 has changed our lives and limited our ability to have adequate physical activity (PA). It is necessary to replace outdoor PA with home-based fitness. However, people lack access, skills, and even motivation for home-based fitness. To address these issues, we designed a free access self-monitoring and coaching and music-based interactive online squat fitness system. Body weight squat was utilized for fitness exercise and evaluated based on three indices: knee width, hip depth, and rhythm. An online survey on changes in exercise due to the COVID-19 pandemic and exercise habits was conducted to investigate the effect of the COVID-19 pandemic on PA. We collected data from 557 respondents 5 months after the system first released and analyzed 200 visitors' performance on squat exercise and the other relevant parameters. Visitors were divided into three groups according to their age: younger, middle, and older groups. Results showed that the younger group had better squat performance than the middle and older groups in terms of hip depth and rhythm. We highlighted the lessons learned about the system design, fitness performance evaluation, and social aspects, for future study of the design and development of similar home-based fitness systems. We provided first-hand results on the relation between the COVID-19 pandemic and physical exercise among different age groups in Japan, which was valuable for policy making in the post-COVID-19 era.

Indexed as

COVID-19 pandemiclessons learnedonline fitnessphysical activitysquat

Identifiers

PMID34713150
PMCPMC8521995

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LicenceCC BY
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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.