Evidence map›Paper›PMID 42104462›Full record

ArticleArchives of public health = Archives belges de sante publique2026

Frailty trajectories and mortality prediction among Japanese community-dwelling older adults: a cohort study comparing time-based and age-based models.

Mengjiao Yang, Yang Liu, Mingyu Cui, Dandan Jiao, Yuko Sawada, Akihiro Kakuda, Shuanghong Li, Jinrui Zhang, Meiling Qian, Lujiao Huang and 1 more

Abstract read
In one paragraph

Article in Archives of public health = Archives belges de sante publique, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Mengjiao YangDepartment of Cardiovascular Surgery, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.
Yang LiuKey Laboratory of Digital-Intelligent Disease Surveillance and Health Governance, North Sichuan Medical College, Nanchong, China.
Mingyu CuiDepartment of Nutrition and Food Hygiene, School of Public Health, Peking University, Beijing, China.
Dandan JiaoDepartment of Nursing, The First Affiliated Hospital and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China.
Yuko SawadaDepartment of Physical Therapy, Morinomiya University of Medical Sciences, Osaka, Japan.
Akihiro KakudaDepartment of Physical Therapy, Morinomiya University of Medical Sciences, Osaka, Japan.
Shuanghong LiGraduate School of Comprehensive Human Science, University of Tsukuba, Tsukuba, Japan.
Jinrui ZhangGraduate School of Comprehensive Human Science, University of Tsukuba, Tsukuba, Japan.
Meiling QianGraduate School of Comprehensive Human Science, University of Tsukuba, Tsukuba, Japan.
Lujiao HuangGraduate School of Comprehensive Human Science, University of Tsukuba, Tsukuba, Japan.
Tokie AnmeFaculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki, 3058577, Japan. tokieanme@gmail.com.

Funding

Key Laboratory of Digital-Intelligent Disease Surveillance and Health Governance JBSZ25-Y-01Medical Science and Technology Project of Sichuan Provincial Health Commission 25LCYJ26Research Development Program of the Affiliated Hospital of North Sichuan Medical College 2025GC020
6 · The paper itself

Abstract

backgroundGroup-based trajectory modeling (GBTM) can be applied using either time-based or age-based approaches to identify frailty trajectories; however, the comparative effectiveness of the methods for mortality prediction remains unclear. This study aimed to compare time-based and age-based trajectory modeling in identifying frailty trajectories and their predictive value for mortality among Japanese community-dwelling older adults.

methodsOf 1085 community-dwelling older adults aged ≥ 65 years, 512 participants with at least two frailty assessments who remained independent during 2011-2017 were included. Frailty was assessed using the Kihon Checklist. Both time-based and age-based GBTM used data from 2011, 2014, and 2017 to identify trajectories. Agreement between methods was assessed using Cohen's Kappa coefficient. Kaplan-Meier survival analysis and Cox proportional hazards models were used to evaluate mortality prediction during follow-up (May 2017-March 2021).

resultsAmong the 512 participants (mean age 72 ± 6 years; 54.7% female), both models identified two frailty trajectories: low increasing (88.3% vs. 83.8%) and high increasing (11.7% vs. 16.2%) groups for the time-based and age-based models, respectively. The methods showed substantial agreement (Kappa = 0.63, P < 0.001). During follow-up, 48 participants (9.4%) died, with a median survival time of 27 (IQR 18-33) months. The high increasing group showed a higher mortality risk than the low increasing group in the time-based (adjusted HR = 3.0, 95% CI = 1.4-6.5, P = 0.006) and age-based models (adjusted HR = 2.5, 95% CI = 1.2-5.1, P = 0.01). Both models showed comparable discriminative ability (C-index: 0.77 vs. 0.77) and model fit (AIC: 451 vs. 453).

conclusionsIn this three-wave Japanese community-based older adult study, both time-based and age-based GBTM identified similar two-group frailty trajectories with substantial agreement and effectively identified high-risk populations for mortality. These findings suggest that both modeling methods are suitable for frailty trajectory analysis and mortality risk prediction in this population; however, their generalizability to other populations requires further validation.

Indexed as

FrailtyFrailty trajectoryLongitudinal studiesMortality, Aging

Identifiers

PMID42104462
PMCPMC13326487

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.