Published on in Vol 8 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/85173, first published .
Correction: Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity: Model Development and Validation Study

Correction: Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity: Model Development and Validation Study

Correction: Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity: Model Development and Validation Study

Corrigenda and Addenda

1Department of Sports Science and Physical Education, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)

2Neuroscience Research Australia, University of New South Wales, Sydney, Australia

3School of Population Health, University of New South Wales, Sydney, Australia

4CUHK Jockey Club Institute of Ageing, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)

*these authors contributed equally

Corresponding Author:

Yijian Yang, MD, PhD

Department of Sports Science and Physical Education

The Chinese University of Hong Kong

G07 Kwok Sports Building. University Ave. The Chinese University of Hong Kong. N.T.

Hong Kong

China (Hong Kong)

Phone: 852 39434001

Fax:852 26035781

Email: yyang@cuhk.edu.hk



In “Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity: Model Development and Validation Study” (JMIR Aging 2025;1(8) e77140) the authors made a few corrections.

In the Results section of the Abstract, the sentence:

Explainable artificial intelligence analysis revealed that older adults with frailty exhibited more variable, complex, and asymmetric gait patterns, which were characterized by higher stride length variability, increased sample entropy, and a higher gait symmetry score.

Has been revised to:

Explainable artificial intelligence analysis revealed that older adults with frailty exhibited more variable, complex, and asymmetric gait patterns, which were characterized by higher stride length variability, increased sample entropy, and a lower gait symmetry score.

In the Results section, the sentence:

Higher gait symmetry score (indicating less symmetry; Multimedia Appendix 5) in the vertical direction contributes to the frail group, while lower values are associated with a nonfrailty classification.

Has been revised to:

Higher gait symmetry score (indicating more symmetry; Multimedia Appendix 5) in the vertical direction contributes to the frail group, while lower values are associated with a nonfrailty classification.

In the Results section, the sentence:

Compared with the nonfrail group, the frail group exhibited higher stride length variability, greater sample entropy in the vertical direction, and increased gait symmetry score in the vertical direction.

Has been revised to:

Compared with the nonfrail group, the frail group exhibited higher stride length variability, greater sample entropy in the vertical direction, and decreased gait symmetry score in the vertical direction.
In the Multimedia Appendix 5, the formula:

Has been revised to:

The correction will appear in the online version of the paper on the JMIR Publications website, together with the publication of this correction notice. Because this was made after submission to full-text repositories, the corrected article has also been resubmitted to those repositories.

Multimedia Appendix 1

Gait symmetry score calculation (Revised).

DOCX File , 14 KB

This is a non–peer-reviewed article. submitted 02.Oct.2025; accepted 06.Oct.2025; published 29.Oct.2025.

Copyright

©Xiaoping Zheng, Ziwei Zeng, Kimberley S van Schooten, Yijian Yang. Originally published in JMIR Aging (https://aging.jmir.org), 29.Oct.2025.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Aging, is properly cited. The complete bibliographic information, a link to the original publication on https://aging.jmir.org, as well as this copyright and license information must be included.