Evidence map›Paper›PMID 40996442›Full record

ArticleInternational urogynecology journal2025

Association of Handgrip Strength With Urinary Incontinence in Postmenopausal Korean Women: A Machine Learning Study.

Sung Jin Kim, Young Goo Lee, Ohseong Kwon, Sahyun Pak, Sung Gon Park, Sung Tae Cho

Abstract read
PubMed Publisher
In one paragraph

Article in International urogynecology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Sung Jin KimDepartment of Urology, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, 1, Singil-ro, Yeongdeungpo-gu, Seoul, 07441, South Korea.
Young Goo LeeDepartment of Urology, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, 1, Singil-ro, Yeongdeungpo-gu, Seoul, 07441, South Korea.
Ohseong KwonDepartment of Urology, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, 1, Singil-ro, Yeongdeungpo-gu, Seoul, 07441, South Korea.
Sahyun PakDepartment of Urology, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, 1, Singil-ro, Yeongdeungpo-gu, Seoul, 07441, South Korea.
Sung Gon ParkDepartment of Urology, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, 1, Singil-ro, Yeongdeungpo-gu, Seoul, 07441, South Korea.
Sung Tae ChoDepartment of Urology, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, 1, Singil-ro, Yeongdeungpo-gu, Seoul, 07441, South Korea. cst326@paran.com.ORCID 0000-0002-4691-6159

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introduction and hypothesisUrinary incontinence (UI) is a common condition in older women and is associated with frailty and functional decline. Handgrip strength is a simple, noninvasive marker of muscle function that may help identify women at risk for UI. This study aimed to examine the association between handgrip strength and UI in postmenopausal Korean women and evaluate its predictive utility using machine learning models.

methodsWe analyzed data from 4213 postmenopausal women aged ≥45 years who participated in the Korean Longitudinal Study of Aging (2006 -2022). UI was defined as reporting urine leakage on ≥5 days during the past month. Handgrip strength was measured using a standardized digital dynamometer protocol. Five machine learning models-logistic regression, random forest (RF), support vector machine, extreme gradient boosting (XGB), and deep neural network-were used to predict UI. Model performance was evaluated using fivefold cross-validation. Logistic regression and generalized additive models were used to validate findings.

resultsAmong postmenopausal women, the prevalence of UI was 26.0%. Compared to the continent group, individuals with UI were older (73.5 ± 6.7 vs. 68.3 ± 6.1 years, p < 0.001), exhibited lower handgrip strength (20.4 ± 4.8 vs. 22.5 ± 4.9 kg, p < 0.001), and had a higher burden of comorbidities and functional impairments. In multivariable logistic regression models adjusting for age, BMI, and other demographic, clinical, lifestyle, and frailty-related symptom variables, lower handgrip strength remained independently associated with UI (adjusted OR 0.971, 95% CI 0.951-0.992 per 1 kg decrease). RF and XGB models demonstrated robust predictive performance, with AUCs of 0.887 and 0.860, respectively. A handgrip strength threshold of 15.75 kg was identified as a clinically relevant cutoff below which UI risk increased.

conclusionHandgrip strength is independently associated with UI in postmenopausal women. As a practical and noninvasive measure, it may be useful in identifying individuals at increased risk of incontinence and guiding early interventions.

Indexed as

Hand StrengthMachine LearningPostmenopauseUrinary IncontinenceAgedFemaleHumansLongitudinal StudiesMiddle AgedPrevalenceRepublic of KoreaFrailtyHandgrip strengthMachine learningPostmenopausal womenPredictive modelingUrinary incontinence

Identifiers

What OpenQuestion holds

Textmetadata
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.