Evidence map›Paper›PMID 36188175›Full record

ArticleFrontiers in human neuroscience2022

Diagnostic accuracy of multi-component spatial-temporal gait parameters in older adults with amnestic mild cognitive impairment.

Shuyun Huang, Xiaobing Hou, Yajing Liu, Pan Shang, Jiali Luo, Zeping Lv, Weiping Zhang, Biqing Lin, Qiulan Huang, Shuai Tao and 5 more

Abstract read
In one paragraph

Article in Frontiers in human neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

15 authors.

Shuyun HuangDepartment of Neurology, First People's Hospital of Foshan, Foshan, Guangdong, China.
Xiaobing HouDepartment of Neurology, First People's Hospital of Foshan, Foshan, Guangdong, China.
Yajing LiuDepartment of Neurology, First People's Hospital of Foshan, Foshan, Guangdong, China.
Pan ShangDepartment of Neurology, First People's Hospital of Foshan, Foshan, Guangdong, China.
Jiali LuoDepartment of Neurology, First People's Hospital of Foshan, Foshan, Guangdong, China.
Zeping LvNational Research Center for Rehabilitation Technical Aids, Rehabilitation Hospital, Beijing, China.
Weiping ZhangDepartment of Neurology, First People's Hospital of Foshan, Foshan, Guangdong, China.
Biqing LinDepartment of Neurology, First People's Hospital of Foshan, Foshan, Guangdong, China.
Qiulan HuangDepartment of Neurology, First People's Hospital of Foshan, Foshan, Guangdong, China.
Shuai TaoDalian Key Laboratory of Smart Medical and Health, Dalian University, Dalian, China.
Yukai WangDepartment of Neurology, First People's Hospital of Foshan, Foshan, Guangdong, China.
Chengguo ZhangDepartment of Neurology, First People's Hospital of Foshan, Foshan, Guangdong, China.
Lushi ChenDepartment of Neurology, First People's Hospital of Foshan, Foshan, Guangdong, China.
Suyue PanDepartment of Neurology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
Haiqun XieDepartment of Neurology, First People's Hospital of Foshan, Foshan, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to develop a diagnostic model of multi-kinematic parameters for patients with amnestic mild cognitive impairment (aMCI). Method: In this cross-sectional study, 94 older adults were included (33 cognitively normal, CN; and 61 aMCI). We conducted neuropsychological battery tests, such as global cognition and cognitive domains, and collected gait parameters by an inertial-sensor gait analysis system. Multivariable regression models were used to identify the potential diagnostic variables for aMCI. Receiver operating characteristic (ROC) curves were applied to assess the diagnostic accuracy of kinematic parameters in discriminating aMCI from healthy subjects. Results: Multivariable regression showed that multi-kinematic parameters were the potential diagnostic variables for aMCI. The multi-kinematic parameter model, developed using Timed Up and Go (TUG) time, stride length, toe-off/heel stride angles, one-leg standing (OLS) time, and braking force, showed areas under ROC (AUC), 0.96 [95% confidence interval (CI), 0.905-0.857]; sensitivity, 0.90; and specificity, 0.91. In contrast, a single kinematic parameter's sensitivity was 0.26-0.95 and specificity was 0.21-0.90. Notably, the separating capacity of multi-kinematic parameters was highly similar to Montreal Cognitive Assessment (MoCA; AUC: 0.96 vs. 0.95). Compared to cognitive domain tests, the separating ability was comparable to Auditory Verbal Learning Test (AVLT) and Boston Naming Test (BNT; AUC: 0.96 vs. 0.97; AUC: 0.96 vs. 0.94). Conclusion: We developed one diagnostic model of multi-kinematic parameters for patients with aMCI in Foshan.

Indexed as

amnestic mild cognitive impairmentdiagnostic accuracyinertial-sensor gait analysis systemmulti-kinematic parametersreceiver operating characteristic

Identifiers

PMID36188175
PMCPMC9519852

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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.