Predicting restriction of life-space mobility: a machine learning analysis of the IMIAS study

• Published in: Aging clinical and experimental research 2022-11, Vol.34 (11), p.2761-2768
• Main Authors: Pérez-Trujillo, Manuel, Curcio, Carmen-Lucía, Duque-Méndez, Néstor, Delgado, Alejandra, Cano, Laura, Gomez, Fernando
• Format: Article
• Language: English
• Subjects: Algorithms; Geriatrics/Gerontology; Machine learning; Medicine; Medicine & Public Health; Mobility; Older people; Original Article; Risk factors
• ISSN: 1720-8319; 1594-0667; 1720-8319
• DOI: 10.1007/s40520-022-02227-4

Background Some studies have employed machine learning (ML) methods for mobility prediction modeling in older adults. ML methods could be a helpful tool for life-space mobility (LSM) data analysis. Aim This study aimed to evaluate the predictive value of ML algorithms for the restriction of life-space mobility (LSM) among elderly people and to identify the most important risk factors for that prediction model. Methods A 2-year LSM reduction prediction model was developed using the ML-based algorithms decision tree, random forest, and eXtreme gradient boosting (XGBoost), and tested on an independent validation cohort. The data were collected from the International Mobility in Aging Study (IMIAS) from 2012 to 2014, comprising 372 older patients (≥ 65 years of age). LSM was measured by the Life-Space Assessment questionnaire (LSA) with five levels of living space during the month before assessment. Results According to the XGBoost algorithm, the best model reached a mean absolute error (MAE) of 10.28 and root-mean-square error (RMSE) of 12.91 in the testing portion. The variables frailty (39.4%), mobility disability (25.4%), depression (21.9%), and female sex (13.3%) had the highest importance. Conclusion The model identified risk factors through ML algorithms that could be used to predict LSM restriction; these risk factors could be used by practitioners to identify older adults with an increased risk of LSM reduction in the future. The XGBoost model offers benefits as a complementary method of traditional statistical approaches to understand the complexity of mobility.