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

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Abstract

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.

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Acknowledgements

We are grateful to all the older adults who have given their time and confidence to this research. We would also like to thank our universities and research groups for providing continuous support.

Funding

This work was supported by The Canadian Institutes of Health Research (Grant No. AAM 108751).

Author information

Authors and Affiliations

  1. Departamento de Informática y Computación, Facultad de Administración, Grupo GAIA, Universidad Nacional de Colombia, Manizales, Colombia

    Manuel Pérez-Trujillo & Néstor Duque-Méndez

  2. Research Group in Geriatrics and Gerontology, Faculty of Health Sciences, Universidad de Caldas, Manizales, Colombia

    Carmen-Lucía Curcio, Alejandra Delgado, Laura Cano & Fernando Gomez

Authors
  1. Manuel Pérez-Trujillo
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  2. Carmen-Lucía Curcio
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  3. Néstor Duque-Méndez
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  4. Alejandra Delgado
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  5. Laura Cano
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  6. Fernando Gomez
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Corresponding author

Correspondence to Carmen-Lucía Curcio.

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The authors declare that there are no conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study was approved by the ethics committees of each site.

Human and animal rights statement

The study including human participants has been performed in accordance with the ethical standards of the declaration of Helsinki and its later ammendments. Institutional review board statement this study was approved by the ethical committee of Universidad de Caldas.

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Pérez-Trujillo, M., Curcio, CL., Duque-Méndez, N. et al. Predicting restriction of life-space mobility: a machine learning analysis of the IMIAS study. Aging Clin Exp Res 34, 2761–2768 (2022). https://doi.org/10.1007/s40520-022-02227-4

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