Evidence map›Paper›PMID 36760102›Full record

ArticleInquiry : a journal of medical care organization, provision and financing

The Value of Handgrip Strength and Self-Rated Squat Ability in Predicting Mild Cognitive Impairment: Development and Validation of a Prediction Model.

Han Xiao, Hou Fangfang, Wang Qiong, Zhou Shuai, Zhang Jingya, Lou Xu, Shen Guodong, Zhang Yan

Abstract read
In one paragraph

Article in Inquiry : a journal of medical care organization, provision and financing. 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

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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

3 citing papers in PubMed.

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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

8 authors.

Han XiaoAnhui Medical University, Hefei, P.R. China.
Hou FangfangAnhui Medical University, Hefei, P.R. China.
Wang QiongAnhui Medical University, Hefei, P.R. China.
Zhou ShuaiAnhui Medical University, Hefei, P.R. China.
Zhang JingyaAnhui Medical University, Hefei, P.R. China.
Lou XuAnhui Professional & Technical Institute of Athletics, Hefei, P.R. China.
Shen GuodongUniversity of Science and Technology of China, Hefei, P.R. China.
Zhang YanAnhui Medical University, Hefei, P.R. China.ORCID 0000-0003-3367-5832

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early identification of individuals with mild cognitive impairment (MCI) is essential to combat worldwide dementia threats. Physical function indicators might be low-cost early markers for cognitive decline. To establish an early identification tool for MCI by combining physical function indicators (upper and lower limb function) via a clinical prediction modeling strategy. A total of 5393 participants aged 60 or older were included in the model. The variables selected for the model included sociodemographic characteristics, behavioral factors, mental status and chronic conditions, upper limb function (handgrip strength), and lower limb function (self-rated squat ability). Two models were developed to test the predictive value of handgrip strength (Model 1) or self-rated squat ability (Model 2) separately, and Model 3 was developed by combining handgrip strength and self-rated squat ability. The 3 models all yielded good discrimination performance (area under the curve values ranged from 0.719 to 0.732). The estimated net reclassification improvement values were 0.3279 and 0.1862 in Model 3 when comparing Model 3 to Model 1 and Model 2, respectively. The integrated discrimination improvement values were estimated as 0.0139 and 0.0128 when comparing Model 3 with Model 1 and Model 2, respectively. The model that contains both upper and lower limb function has better performance in predicting MCI. The final prediction model is expected to assist health workers in early identification of MCI, thus supporting early interventions to reduce future risk of AD, particularly in socioeconomically deprived communities.

Indexed as

Alzheimer DiseaseCognitive DysfunctionHand StrengthHumansRisk Assessmentalgorithmclinical prediction modelcognitive functionmachine learningolder adults

Identifiers

PMID36760102
PMCPMC9926366

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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.