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This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (5430) Neurology and Neurosciences (401) mHealth for Data Collection and Research (1794) Fitness Trackers and Smart Pedometers/Accelerometers (815) Falls and Emergency Detection with Sensors and other Technologies (83) Neurotech Innovations, Diagnostic Tools and Techniques (77)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58864, first published .
Fit senior man with headphones and water bottle checks phone after workout

Gait Disturbances in Older Adults With Cerebral Small Vessel Disease: Mixed Methods Study Using Smartphone Sensors and Video Analysis

Gait Disturbances in Older Adults With Cerebral Small Vessel Disease: Mixed Methods Study Using Smartphone Sensors and Video Analysis

Authors of this article:

Xiaojun Lai1 Author Orcid Image ;   Li-Yan Qiao2 Author Orcid Image ;   Pei-Luen Patrick Rau1 Author Orcid Image ;   Yankuan Liu1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Funayama T, Uchida Y, Asano Y, Soma D. MediaPipe-Based Activity Analysis by Healthcare Professionals: Method Development and Technology Acceptance—A Pilot Study. Sensors 2026;26(17):5681 View
  2. Cheng Y, Wang Q, Wang X, Wu X, Duan A, Liu W, QIAO M. Development and validation of a fall risk prediction model integrating objective gait metrics for patients with cerebral small vessel disease: protocol for a two-phase study. BMJ Open 2026;16(10):e119421 View