Evidence map›Paper›PMID 42291390›Full record

ArticleTurkish journal of physical medicine and rehabilitation2026

The effect of microprocessor-controlled prostheses on walking pattern and energy consumption in above-the-knee amputees.

Sabahat Gaye Baldug, Ümüt Güzelküçük, Emre Adıgüzel, Yasin Demir

Abstract read
In one paragraph

Article in Turkish journal of physical medicine and rehabilitation, 2026. 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

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3 · Its place in the literature

Who cites it

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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

4 authors.

Sabahat Gaye BaldugDepartment of Physical Medicine and Rehabilitation, Isparta City Hospital, Isparta, Türkiye.ORCID 0009-0001-7032-1569
Ümüt GüzelküçükDepartment of Physical Medicine and Rehabilitation, University of Health Sciences, Gülhane Medical School, Gaziler Physical Terapy and Rehabilitation Research and Training Hospital, Ankara, Türkiye.ORCID 0000-0002-9325-3888
Emre AdıgüzelDepartment of Physical Medicine and Rehabilitation, Ankara Bilkent City Hospital, Ankara, Türkiye.ORCID 0000-0002-2447-5065
Yasin DemirDepartment of Physical Medicine and Rehabilitation, University of Health Sciences, Gülhane Medical School, Gaziler Physical Terapy and Rehabilitation Research and Training Hospital, Ankara, Türkiye.ORCID 0000-0002-0978-6826

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: The aim of this study was to investigate microprocessor-controlled (MIC) and non-microprocessor-controlled (non-MIC) prostheses in above-the-knee amputees in terms of energy consumption and gait pattern. Patients and methods: Between June 2018 and February 2019, a total of 34 unilateral transfemoral male amputee patients (mean age: 40.6±13.2 years; range, 19 to 65 years) were included in this cross-sectional study. The patients were divided into two groups as Group 1 (n=17) using an MIC knee unit prosthesis and Group 2 (n=17) using a mechanical (n=5), pneumatic (n=8), or hydraulically (n=4) controlled knee unit prosthesis. Outcome measurements included three-dimensional gait analysis, Locomotor Capabilities Index-5 (LCI-5), exercise tolerance test, Satisfaction with Prosthesis Questionnaire (SAT-PRO), Short Form-36 (SF-36) Health Survey Questionnaire, and 6-min walk test (6MWT). Results: In the MIC prosthesis group, amputated side walking speed (p=0.007), intact side walking speed (p=0.020), amputated side stride length (p=0.008), and amputated side single support phase duration (p=0.049) were significantly higher, while amputated side stride time (p=0.029) was significantly lower compared to the non-MIC prosthesis group. Energy consumption value was significantly lower in the MIC prosthesis group (p=0.005). The MIC prostheses were found to provide a significant improvement in physical health component compared to non-MIC prostheses (p<0.05). Satisfaction with the current prosthesis was significantly higher in the MIC prosthesis group (p=0.002). The gait capacity was found to be significantly higher in the MIC prosthesis group (p<0.001). Conclusion: Our study results suggest that MIC prostheses should be used more widely in the rehabilitation process and daily lives of above-the-knee amputees.

Indexed as

Above-the-knee amputeeenergy consumptiongait analysismicroprocessor-controlled prosthesis.

Identifiers

PMID42291390
PMCPMC13261395

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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.