Can digital gait biomarkers complement the WHO intrinsic capacity framework in fall risk stratification? A cross-sectional exploratory analysis

The World Health Organization (WHO) Intrinsic Capacity (IC) framework serves as a holistic construct for healthy aging and fall risk screening. However, current IC assessments predominantly rely on subjective reports or macroscopic physical tests, which may lack the sensitivity to detect subtle kinematic deficits. This study aimed to determine whether integrating objective, wearable-derived digital gait biomarkers into the WHO IC framework provides measurable incremental value for identifying older adults with a history of falls. We conducted a cross-sectional analysis of 843 community-dwelling older adults in Beijing, China. Fall history and IC domains were assessed using standard protocols. Gait performance was quantified via a wearable system during a 12-m walk, yielding 45 spatiotemporal and kinetic features. LASSO regression was employed to handle multicollinearity and identify the most discriminative gait markers to construct a composite Gait Score. The incremental discriminative ability of adding the Gait Score to the standard IC model was evaluated using the Area Under the Curve (AUC), Net Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI). Among participants, 105 (12.5%) reported falls. LASSO regression identified five gait variables included in the Gait Score: step length, landing control force, toe-off angle, step-duration asymmetry, and initial support time. In fully adjusted multivariable models, both IC impairment (OR = 1.330, 95% CI 1.082–1.634) and the composite Gait Score (OR = 2.583, 95% CI 1.123–5.945) were independently associated with fall status. Adding the Gait Score to the IC-based model yielded a statistically detectable but modest improvement in discrimination (IDI = 0.007, P = 0.044). In a sensitivity analysis that repeated feature selection and score derivation within the bootstrap procedure, the AUC for Model 3 decreased from 0.676 (apparent) to 0.607 (optimism-corrected), underscoring the exploratory nature of the incremental finding. In this cross-sectional sample, wearable-derived gait characteristics were associated with past-year fall status and provided limited, exploratory complementary discrimination beyond the IC impairment score. These findings require prospective external validation before the approach can be considered for clinical risk stratification.

Authors

Institutions

Publication Details

Journal
BMC Geriatrics
Published
2026-09-04
DOI
https://doi.org/10.1186/s12877-026-08214-y
Primary Topic
Balance, Gait, and Falls Prevention
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Can digital gait biomarkers complement the WHO intrinsic capacity framework in fall risk stratification? A cross-sectional exploratory analysis

Liyun Sang, Huixiu Hu, Jie Zhang, Ji Shen et al.
BMC Geriatrics
Balance, Gait, and Falls Prevention
article

Can digital gait biomarkers complement the WHO intrinsic capacity framework in fall risk stratification? A cross-sectional exploratory analysis

Liyun Sang, Huixiu Hu, Jie Zhang, Ji Shen, Chao Sun
article en

Abstract

The World Health Organization (WHO) Intrinsic Capacity (IC) framework serves as a holistic construct for healthy aging and fall risk screening. However, current IC assessments predominantly rely on subjective reports or macroscopic physical tests, which may lack the sensitivity to detect subtle kinematic deficits. This study aimed to determine whether integrating objective, wearable-derived digital gait biomarkers into the WHO IC framework provides measurable incremental value for identifying older adults with a history of falls. We conducted a cross-sectional analysis of 843 community-dwelling older adults in Beijing, China. Fall history and IC domains were assessed using standard protocols. Gait performance was quantified via a wearable system during a 12-m walk, yielding 45 spatiotemporal and kinetic features. LASSO regression was employed to handle multicollinearity and identify the most discriminative gait markers to construct a composite Gait Score. The incremental discriminative ability of adding the Gait Score to the standard IC model was evaluated using the Area Under the Curve (AUC), Net Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI). Among participants, 105 (12.5%) reported falls. LASSO regression identified five gait variables included in the Gait Score: step length, landing control force, toe-off angle, step-duration asymmetry, and initial support time. In fully adjusted multivariable models, both IC impairment (OR = 1.330, 95% CI 1.082–1.634) and the composite Gait Score (OR = 2.583, 95% CI 1.123–5.945) were independently associated with fall status. Adding the Gait Score to the IC-based model yielded a statistically detectable but modest improvement in discrimination (IDI = 0.007, P = 0.044). In a sensitivity analysis that repeated feature selection and score derivation within the bootstrap procedure, the AUC for Model 3 decreased from 0.676 (apparent) to 0.607 (optimism-corrected), underscoring the exploratory nature of the incremental finding. In this cross-sectional sample, wearable-derived gait characteristics were associated with past-year fall status and provided limited, exploratory complementary discrimination beyond the IC impairment score. These findings require prospective external validation before the approach can be considered for clinical risk stratification.

BMC Geriatrics
Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Shanxi Province Hospital of Traditional Chinese Medicine (CN), National Center for Clinical Laboratories (CN), Shanxi Cardiovascular Hospital (CN)
National Key Research and Development Program of China
Reduced inequalities
Openalex Percentile: Top 5%
Balance, Gait, and Falls Prevention
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.