Evidence map›Paper›PMID 36876176›Full record

ArticleJournal of sports science & medicine2023

Inertial Sensor-Based Assessment of Static Balance in Athletes with Chronic Ankle Instability.

Yuta Koshino, Moeko Akimoto, Daiki Kawahara, Kentaro Watanabe, Tomoya Ishida, Mina Samukawa, Satoshi Kasahara, Harukazu Tohyama

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of sports science & medicine, 2023. 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
1.7field-weighted citation impact, top 19% 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, 7 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

8 authors at 2 institutions in 1 country.

Yuta KoshinoFaculty of Health Sciences, Hokkaido University, Sapporo, Japan.
Moeko AkimotoFaculty of Health Sciences, Hokkaido University, Sapporo, Japan.
Daiki KawaharaFaculty of Health Sciences, Hokkaido University, Sapporo, Japan.
Kentaro WatanabeFaculty of Health Sciences, Hokkaido University, Sapporo, Japan.
Tomoya IshidaFaculty of Health Sciences, Hokkaido University, Sapporo, Japan.
Mina SamukawaFaculty of Health Sciences, Hokkaido University, Sapporo, Japan.
Satoshi KasaharaFaculty of Health Sciences, Hokkaido University, Sapporo, Japan.
Harukazu TohyamaFaculty of Health Sciences, Hokkaido University, Sapporo, Japan.
Hokkaido University · JPSapporo University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Balance Error Scoring System (BESS), a subjective examiner-based assessment, is often employed to assess postural balance in individuals with chronic ankle instability (CAI); however, inertial sensors may enhance the detection of balance deficits. This study aimed to compare the BESS results between the CAI and healthy groups using conventional BESS scores and inertial sensor data. The BESS test (six conditions: double-leg, single-leg, and tandem stances on firm and foam surfaces, respectively) was performed for the CAI (n = 16) and healthy control (n = 16) groups with inertial sensors mounted on the sacrum and anterior shank. The BESS score was calculated visually by the examiner by counting postural sway as an error based on the recorded video. The root mean square for resultant acceleration (RMS

Indexed as

Ankle JointJoint InstabilityPostural BalanceAccelerationAthletesChronic DiseaseHumansSpineWearable Electronic Devicesankle sprainBalance Error Scoring Systempostural controlpostural stabilityWearable sensor

Identifiers

PMID36876176
PMCPMC9982532
OpenAlexW4313648577

What OpenQuestion holds

Textmetadata
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.