Evidence map›Paper›PMID 40519669›Full record

ArticleFrontiers in aging2025

Research on intrinsic capacity as a predictor of falls and disability in community-dwelling elderly.

Aihong Liu, Yanjie You, Yumei Wang, Ling Li, Jinrong Yuan

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Article in Frontiers in aging, 2025. 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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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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

5 authors.

Aihong LiuWuhan Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Yanjie YouWuhan Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Yumei WangWuhan Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Ling LiWuhan Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Jinrong YuanWuhan Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: With aging, a decline in intrinsic capacity can lead to functional impairments, thereby increasing the risk of adverse health outcomes. Objectives: This study aims to explore the prediction of intrinsic capacity decline on adverse health outcomes, such as falls and disability, over the course of 1 year. By addressing the gap in longitudinal research on community populations in China, the study seeks to deepen the local understanding of healthy aging theory, providing theoretical support for the development of elderly health intervention strategies tailored to Chinese contexts. Methods: A convenience sampling method was employed to select 248 community-dwelling elderly participants. Over a 1-year follow-up period, the outcomes of falls and disability were monitored. Logistic regression analysis was used to evaluate the relationship between intrinsic capacity and these outcomes. Results: Among the 248 participants, 46 (19%) experienced falls, and 31 (12.8%) became disabled during the follow-up year. The locomotive dimension (OR = 25.87, 95% CI: 2.95-227.03), psychological dimension (OR = 25.29, 95% CI: 6.45-99.28), and sensory dimensions (OR = 10.75, 95% CI: 2.92-39.56) were identified as significant risk factors for falls. For disability, the locomotive dimension (OR = 4.15, 95% CI: 0.97-17.72), cognitive dimension (OR = 11.27, 95% CI: 3.51-36.18), and psychological dimension (OR = 4.58, 95% CI: 1.69-12.40) were significant risk factors. Conclusion: Decline in intrinsic capacity serves as an independent predictor of both falls and disability among community-dwelling elderly individuals over the course of 1 year. Early identification of elderly individuals with decreased intrinsic capacity, along with targeted interventions based on different intrinsic capacity levels, can effectively reduce the incidence of falls and disability.

Indexed as

community elderly caredisabilityelderlyfallsintrinsic capacity

Identifiers

PMID40519669
PMCPMC12162613

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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.