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Updated: Aug 5, 2026
07:51
Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study
Published on: March 14, 2017
Smartphone-Based Physical Performance and Multidimensional Determinants of Self-Reported Knee Pain in
Ui-Jae Hwang1, Peng Xia1, Tianxiang Fan1
1Department of Rehabilitation Sciences, Hong Kong Polytechnic University, 11 Yuk Choi Road, Hung Hom, Kowloon, Hong Kong, China (Hong Kong), (+852) 60580339.
JMIR Mhealth and Uhealth
|August 4, 2026
Summary
Knee pain in older adults is linked to pain and housing conditions, with sit-to-stand function acting as a key network hub. This study combined machine learning and network analysis to understand these complex associations.
Area of Science:
- Gerontology
- Biomedical Informatics
- Public Health
Background:
- Knee pain affects over 20% of adults globally, impacting daily life and function.
- Traditional studies often examined isolated risk factors for knee pain.
- The International Classification of Functioning, Disability and Health (ICF) model provides a framework for understanding these multifactorial influences.
Purpose of the Study:
- To investigate self-reported knee pain in community-dwelling older adults.
- To integrate machine learning and network analysis with smartphone-based physical performance measures.
- To identify key factors associated with knee pain in this population.
Main Methods:
- A cross-sectional study of 852 adults aged 60+ years.
- Utilized smartphone-based measurements for walking speed, sit-to-stand, and gait parameters.
- Employed machine learning algorithms and partial-correlation network analysis to model knee pain predictors.
Main Results:
- Knee pain prevalence was 36.9% in the study cohort.
- Top predictors included EuroQol 5-Dimension (EQ-5D) Pain/Discomfort and housing environment.
- Sit-to-stand performance emerged as a central network hub, linking various health domains.
Conclusions:
- Knee pain in older adults is associated with psychosocial and environmental factors, including housing.
- Sit-to-stand performance plays a crucial role in the network of factors influencing knee pain.
- The study highlights the potential of integrating advanced analytical methods for comprehensive health assessments.
