Evidence map›Paper›PMID 41820891›Full record

ArticleBMC geriatrics2026

Validation of World Guideline gait speed and TUG cut-offs for fall risk stratification in ambulatory nursing home residents aged ≥ 80 years: a multicenter cross-sectional diagnostic study.

Shuqi Jia, Lin Wang, Fei Li, Yue Shi, Cong Liu, Songya Gang, Xing Wang

Abstract readMulticenter StudyValidation Study
In one paragraph

Article in BMC geriatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Shuqi JiaSchool of Athletic Performance, Shanghai University of Sport, Shanghai, China.
Lin WangSports Medicine and Rehabilitation Center, Shanghai University of Sport, Shanghai, China.
Fei LiSchool of Athletic Performance, Shanghai University of Sport, Shanghai, China.
Yue ShiSchool of Athletic Performance, Shanghai University of Sport, Shanghai, China.
Cong LiuSchool of Physical Education, Shanghai University of Sport, Shanghai, China.
Songya GangDepartment of Physical Education Teaching, Shanghai Sanda University, Shanghai, China.
Xing WangSchool of Physical Education, Shanghai University of Sport, Shanghai, China. wangxing1933@sus.edu.cn.

Funding

National Social Science Fund of China 22BTY076Shanghai University of Sport, the Shanghai Key Laboratory of Human Performance 11DZ2261100
6 · The paper itself

Abstract

backgroundFalls are highly prevalent among nursing home residents, particularly among adults aged ≥ 80 years. The World Guidelines for Falls Prevention and Management recommend gait speed ≤ 0.8 m/s and Timed Up and Go (TUG) > 15 s as risk thresholds; however, their discriminatory validity in ambulatory institutionalized populations remains insufficiently examined.

objectiveTo validate the discriminatory performance and clinical utility of guideline-recommended gait speed (≤ 0.8 m/s) and TUG (> 15 s) thresholds for fall risk stratification among ambulatory nursing home residents aged ≥ 80 years.

methodsIn this multicenter cross-sectional diagnostic accuracy study, 132 ambulatory nursing home residents aged ≥ 80 years were recruited from four care homes in Shanghai. Gait speed, TUG performance, and 12-month retrospective fall history were assessed by trained staff. Logistic regression models were developed using a 7:3 derivation–validation split. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration statistics, Brier scores, and decision curve analysis (DCA).

resultsForty-eight participants (36.4%) reported at least one fall in the past year. In the gait speed model, slower gait speed was significantly associated with higher fall risk (OR = 0.078), with moderate discrimination in both the derivation (AUC = 0.799) and validation cohorts (AUC = 0.794). The TUG model demonstrated comparable discrimination (AUC = 0.771 and 0.783 in derivation and validation cohorts, respectively), with prolonged TUG time associated with increased fall risk (OR = 1.096). Both models showed good calibration (Brier scores = 0.168–0.195). Decision curve analysis indicated positive net clinical benefit across clinically relevant threshold ranges.

conclusionsWorld Guideline gait speed and TUG thresholds demonstrated acceptable discriminatory performance among ambulatory nursing home residents aged ≥ 80 years. These simple mobility assessments may support fall risk stratification within this ambulatory institutional subgroup; however, findings should not be generalized to residents with advanced frailty or dependence on assistive devices.

Indexed as

Accidental FallsGeriatric AssessmentNursing Home ResidentsNursing HomesWalking SpeedAged, 80 and overCross-Sectional StudiesFemaleHumansMaleRetrospective StudiesRisk AssessmentDiagnostic accuracyFallsGait speedGeriatric assessmentNursing home residentsRisk stratificationTimed up and go

Identifiers

PMID41820891
PMCPMC13094102

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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.