Evidence map›Paper›PMID 40046843›Full record

ArticleDigital health

Development of an interactive biosensing application for assessing finger dexterity.

Michal Greenberg Abrahami, Yehuda Warszawer, Alon Kalron, Emanuel Shirbint, Maria Didikin, Anat Achiron

Abstract read
In one paragraph

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

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

6 authors.

Michal Greenberg AbrahamiMultiple Sclerosis Center, Sheba Medical Center, Tel Hashomer, Israel.
Yehuda WarszawerMultiple Sclerosis Center, Sheba Medical Center, Tel Hashomer, Israel.ORCID https://orcid.org/0000-0001-7919-807X
Alon KalronMultiple Sclerosis Center, Sheba Medical Center, Tel Hashomer, Israel.
Emanuel ShirbintMultiple Sclerosis Center, Sheba Medical Center, Tel Hashomer, Israel.
Maria DidikinMultiple Sclerosis Center, Sheba Medical Center, Tel Hashomer, Israel.
Anat AchironMultiple Sclerosis Center, Sheba Medical Center, Tel Hashomer, Israel.ORCID https://orcid.org/0000-0002-2020-3126

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Accurate finger function assessment is crucial for monitoring the performance of daily hand activities. However, specialized digital applications are lacking for evaluating various finger tasks. This study aims to develop a custom digital biosensing application to assess finger dexterity. Methods: We developed a digital biosensing application compatible with smartphones and tablets that enables 3-min testing of finger dexterity, measuring velocity and accuracy for each finger and each movement orientation. Data were collected for the dominant hand from a large cohort of healthy volunteers to establish population norms values. Results: The construction of the application involved a comprehensive, multi-stage process designed to ensure functionality, user-friendliness, and cross-platform compatibility using the Flutter framework by Google with specific adaptations for Android and iOS. To evaluate the application and construct population norms, 318 healthy subjects, 197 females and 121 males, mean ± age 37.7 ± 13.5 years, were tested. Velocity was faster for the vertical and horizontal tests than all other tests and fastest for finger 2, while the pinch test was the slowest for all fingers. Deviation from any required test orientation was more evident for the circle test and mainly for finger 5, while the vertical and horizontal orientations were the most unerring. Analysis of finger dexterity by age disclosed better performance in the younger age group (<35 years); no effect of gender for both velocity and deviation was observed. Conclusions: The developed digital application allows immediate evaluation of finger dexterity. The established population norms can provide a comparative standard for assessing patients with disorders like multiple sclerosis, sensory neuropathy, or stroke.

Indexed as

accuracy.dexteritydigital applicationFingerstouch-screenvelocity

Identifiers

PMID40046843
PMCPMC11881130

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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.