Evidence map›Paper›PMID 40692806›Full record

ReviewWearable technologies2025

Clinical and instrument-based assessment of balance, gait, and motor functions in pediatric cerebral palsy: A systematic review.

Dilnoza Karibzhanova, Prashant K Jamwal, Amna Riaz Khawaja, Zaidagul Kystaubayeva, Akim Kapsalyamov, Meiram Shakenov, Sunil Agrawal

Abstract readReview
In one paragraph

Review in Wearable technologies, 2025. 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
–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

3 citing papers in PubMed.

  1. Article
  2. Review
  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

7 authors.

Dilnoza KaribzhanovaSchool of Medicine, Nazarbayev University, Astana, Kazakhstan.ORCID https://orcid.org/0000-0003-4285-8585
Prashant K JamwalSchool of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan.
Amna Riaz KhawajaSchool of Medicine, Nazarbayev University, Astana, Kazakhstan.ORCID https://orcid.org/0009-0000-0923-6341
Zaidagul KystaubayevaDepartment of Family Medicine, Astana Medical University, Astana, Kazakhstan.
Akim KapsalyamovFaculty of Engineering and Mathematics, Hochschule Bielefeld University of Applied Sciences, Bielefeld, Germany.
Meiram ShakenovDepartment of Rehabilitation, National Center for Children's Rehabilitation, Astana, Kazakhstan.
Sunil AgrawalDepartment of Mechanical Engineering and the Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Specialists globally employ various clinical scales and instruments to assess balance, gait, and motor functions in children with cerebral palsy (CP). Selecting appropriate assessment tools is essential for planning studies, developing effective treatment strategies, and tracking clinical outcomes. Given the diversity in assessment needs - whether evaluating dynamic, functional, or static balance - there is a need to identify the most suitable tools for each aspect. Therefore, the primary objective of this review is to critically analyze current clinical and instrument-based assessment methods in the literature to determine the most effective approaches for pediatric CP. This systematic review retrieved 1,812 papers, of which only 23 met the inclusion criteria and presented assessment methods for evaluating balance and motor functions in pediatric CP. These methods were further organized into clinical and instrument-based assessment groups. Among clinical examinations, the Pediatric Balance Scale and Gross Motor Function Measures were considered gold standards and featured in eight studies. In contrast, postural sway measured with the Biodex Balance System, Gait Stability Indices from the GAITRite system, and EMG sensing were the predominant instrument-based observations. Despite this variety, a consensus on the best assessment methods remains lacking. This review highlights the potential of integrating AI-driven metrics that combine clinical and instrument-based data to enhance precision and individualized care. Future research should focus on creating integrated, individualized profiles to better capture the unique capabilities of children with CP, enabling more personalized and effective intervention strategies.

Indexed as

biomechanicsembedded electronicsfeedback devicesperformance characterisation

Identifiers

PMID40692806
PMCPMC12277211

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.