Evidence map›Paper›PMID 40338407›Full record

ArticleAnnals of biomedical engineering2025

Gait Characteristics Associated with Walking Speed in Postoperative Patients with Adult Spinal Deformity Extracted by Machine Learning.

Naoto Mano, Kimihiko Mori, Tomohiro Ushikubo, Takayuki Konishi, Shun Ito, Jin Kuramoto, Masayuki Ishihara, Kimitaka Hase

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Article in Annals of biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing 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

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Naoto ManoDepartment of Physical Medicine and Rehabilitation, Graduate School of Medicine, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka, 573-1010, Japan. naotoman830@icloud.com.ORCID http://orcid.org/0000-0002-1770-4229
Kimihiko MoriDepartment of Physical Therapy, Faculty of Rehabilitation, Kansai Medical University, 18-89, Uyamahigashimachi, Hirakata, Osaka, 573-1136, Japan.
Tomohiro UshikuboDepartment of AI Research and Development, Anima Corporation, 3-65-1, Shimoishihara, Chofu, Tokyo, 182-0034, Japan.
Takayuki KonishiDepartment of Physical Medicine and Rehabilitation, Kansai Medical University Hospital, 2-3-1 Shinmachi, Hirakata, Osaka, 573-1191, Japan.
Shun ItoDepartment of Physical Medicine and Rehabilitation, Kansai Medical University Hospital, 2-3-1 Shinmachi, Hirakata, Osaka, 573-1191, Japan.
Jin KuramotoDepartment of Physical Medicine and Rehabilitation, Kansai Medical University Hospital, 2-3-1 Shinmachi, Hirakata, Osaka, 573-1191, Japan.
Masayuki IshiharaDepartment of Orthopaedic Surgery, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka, 573-1010, Japan.
Kimitaka HaseDepartment of Physical Medicine and Rehabilitation, Kansai Medical University, 2-5-1 Shinmachi, Hirakata, Osaka, 573-1010, Japan.

Funding

Kansai Medical University Grant D2(#2020)
6 · The paper itself

Abstract

purposePatients with adult spinal deformity (ASD) are unable to walk faster even after spinal fixation. Gait rehabilitation that focuses on movements associated with reduced speed may help improve gait function. This study aimed to identify the gait characteristics associated with walking velocity in postoperative patients with ASD.

methodsThree-dimensional gait analysis was performed in 62 postoperative patients with ASD and 105 healthy volunteers. A dimensionality reduction method, based on data from the healthy cohort, was applied to 194 gait characteristics of the patients. Then, the Markov Chain Monte Carlo method sampled the gait characteristics related to speed, and k-means clustering categorized the gait patterns. The relationships between the extracted gait characteristics and velocity were assessed using multiple regression analysis within each cluster.

resultsHip sagittal angle and moment (β = - 0.47 to 0.42) were significantly associated with postoperative velocity in all the patients (R

conclusionLarger hip joint movement may play a crucial role in enhancing gait velocity because the spinal fixation limits spino-pelvic movement. To improve gait speed, postoperative older adults with ASD who walk slowly may require new rehabilitation strategies that exert more ankle plantarflexion moment and develop an adequate stance phase to facilitate hip extension.

Indexed as

GaitMachine LearningWalkingWalking SpeedAdultAgedFemaleHumansMaleMiddle AgedPostoperative PeriodAdult spinal deformityClusteringGait analysisMachine learningRehabilitationWalking speed

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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.