Evidence map›Paper›PMID 39686162›Full record

ArticleSensors (Basel, Switzerland)2024

Validity and Reliability of Gait Speed and Knee Flexion Estimated by a Novel Vision-Based Smartphone Application.

Kam Lun Leung, Zongpan Li, Chen Huang, Xiuping Huang, Siu Ngor Fu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
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.

Kam Lun LeungDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China.
Zongpan LiDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China.
Chen HuangDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China.
Xiuping HuangDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China.
Siu Ngor FuDepartment of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China.

Funding

The Hong Kong Jockey Charities Trust S/N Ref 2022/0024
6 · The paper itself

Abstract

Patients with knee osteoarthritis walk with reduced speed and knee flexion excursion in the early stance phase. A slow walking speed is also associated with falls in older adults. A novel vision-based smartphone application could potentially facilitate the early detection of knee osteoarthritis and fall prevention. This study aimed to test the validity and reliability of the app-captured gait speed and peak knee flexion during the initial stance phase of gait. Twenty adults (aged 23-68 years) walked at self-selected comfortable walking speeds while the gait speed and knee flexion were simultaneously measured using retroreflective sensors and Xsens motion trackers and the app in two separate sessions for validity and reliability tests. Pearson's

Indexed as

Mobile ApplicationsSmartphoneWalking SpeedAdultAgedFemaleGaitHumansKnee JointMaleMiddle AgedOsteoarthritis, KneeRange of Motion, ArticularReproducibility of ResultsWalkingYoung Adultfallknee flexionknee osteoarthritissmartphone applicationwalking speed

Identifiers

PMID39686162
PMCPMC11644766

What OpenQuestion holds

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LicenceCC BY
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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.