Evidence map›Paper›PMID 36585625›Full record

ArticleBMC geriatrics2022

Association of motor index scores with fall incidence among community-dwelling older people.

Xiao Liu, Ayiguli Abudukeremu, Yuan Jiang, Zhengyu Cao, Maoxiong Wu, Kai Zheng, Jianyong Ma, Runlu Sun, Zhiteng Chen, Yangxin Chen and 2 more

Open access · goldAbstract read
In one paragraph

Article in BMC geriatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
2.6field-weighted citation impact, top 9% of its field
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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
  4. 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

12 authors at 4 institutions in 2 countries.

Xiao Liu *Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China.
Ayiguli Abudukeremu *Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China.
Yuan JiangDepartment of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China.
Zhengyu CaoDepartment of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China.
Maoxiong WuDepartment of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China.
Kai ZhengMedical Care Strategic Customer Department, China Merchants Bank Shenzhen Branch, Shenzhen, China.
Jianyong MaDepartment of Pharmacology and Systems Physiology University of Cincinnati College of Medicine, Cincinnati, USA.
Runlu SunDepartment of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China.
Zhiteng ChenDepartment of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China.
Yangxin ChenDepartment of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China.
Yuling ZhangDepartment of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China. zhyul@mail.sysu.edu.cn.
Jingfeng WangDepartment of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, China. wjingf@mail.sysu.edu.cn.
Sun Yat-sen Memorial Hospital · CNKey Laboratory of Guangdong Province · CNSun Yat-sen University · CNUniversity of Cincinnati Medical Center · US

Funding

China Postdoctoral Science Foundation 2020M683123China Postdoctoral Science Foundation 2021M703724Guangdong Basic and Applied Basic Research Foundation 2019A1515110129Guangdong Medical Science and Technology Research Foundation A2021006National High Technology Research and Development Program of Guangzhou 201704020044National High Technology Research and Development Program of Guangzhou 20180304001National Natural Science Foundation of China 21866019National Natural Science Foundation of China 81870170National Natural Science Foundation of China 81970200National Natural Science Foundation of China 82070237National Natural Science Foundation of China 82100347Natural Science Foundation of Guangdong Province 2019A1515011682
6 · The paper itself

Abstract

backgroundSeveral kinds of motor dysfunction have been studied for predicting future fall risk in community-dwelling older individuals. However, no study has tested the ability of the fine motor index (FINEA) and gross motor index (GROSSA) to predict the risk of falling, as well as the specific fall type.

objectiveWe investigated the associations of FINEA/GROSSA scores with fall risk, explained falls, and unexplained falls.

methodsA total of 6267 community-dwelling adults aged ≥ 50 years from the Irish Longitudinal Study on Aging (TILDA) cohort were included. First, the associations of FINEA and GROSSA scores with the history of total falls, explained falls and unexplained falls were assessed in a cross-sectional study and further verified in a prospective cohort after 2 years of follow-up by Poisson regression analysis.

resultsWe found that high FINEA and GROSSA scores were positively associated with almost all fall histories (FINEA scores: total falls: adjusted prevalence ratio [aPR] = 1.28, P = 0.009; explained falls: aPR = 1.15, P = 0.231; unexplained falls: aPR = 1.88, P < 0.001; GROSSA scores: total falls: aPR = 1.39, P < 0.001; explained falls: aPR = 1.28, P = 0.012; unexplained falls: aPR = 2.18, P < 0.001) in a cross-sectional study. After 2 years of follow-up, high FINEA scores were associated with an increased incidence of total falls (adjusted rate ratio [aRR] = 1.42, P = 0.016) and explained falls (aRR = 1.51, P = 0.020) but not with unexplained falls (aRR = 1.41, P = 0.209). High GROSSA scores were associated with an increased incidence of unexplained falls (aRR = 1.57, P = 0.041) and were not associated with either total falls (aRR = 1.21, P = 0.129) or explained falls (aRR = 1.07, P = 0.656). Compared with individuals without limitations in either the FINEA or GROSSA, individuals with limitations in both indices had a higher risk of falls, including total falls (aRR = 1.35, P = 0.002), explained falls (aRR = 1.31, P = 0.033) and unexplained falls (aRR = 1.62, P = 0.004).

conclusionFINEA scores were positively associated with accidental falls, while GROSSA scores were positively associated with unexplained falls. The group for whom both measures were impaired showed a significantly higher risk of both explained and unexplained falls. FINEA or GROSSA scores should be investigated further as possible tools to screen for and identify community-dwelling adults at high risk of falling.

Indexed as

Accidental FallsIndependent LivingAgedCross-Sectional StudiesHumansIncidenceLongitudinal StudiesProspective StudiesRisk FactorsFallMotor indexTILDA

Identifiers

PMID36585625
PMCPMC9805168
OpenAlexW4313365026

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.