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Exploring outdoor activity limitation (OAL) factors among older adults using interpretable machine learning

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Abstract

Background

The occurrence of outdoor activity limitation (OAL) among older adults is influenced by multidimensional and confounding factors associated with aging.

Aim

The aim of this study was to apply interpretable machine learning (ML) to develop models for multidimensional aging constraints on OAL and identify the most predictive constraints and dimensions across multidimensional aging data.

Methods

This study involved 6794 community-dwelling participants older than 65 from the National Health and Aging Trends Study (NHATS). Predictors included related to six dimensions: sociodemographics, health condition, physical capacity, neurological manifestation, daily living habits and abilities, and environmental conditions. Multidimensional interpretable machine learning models were assembled for model construction and analysis.

Results

The multidimensional model demonstrated the best predictive performance (AUC: 0.918) compared to the six sub-dimensional models. Among the six dimensions, physical capacity had the most remarkable prediction (AUC: physical capacity: 0.895, daily habits and abilities: 0.828, physical health: 0.826, neurological performance: 0.789, sociodemographic: 0.773, and environment condition: 0.623). The top-ranked predictors were SPPB score, lifting ability, leg strength, free kneeling, laundry mode, self-rated health, age, attitude toward outdoor recreation, standing time on one foot with eyes open, and fear of falling.

Discussion

Reversible and variable factors, which are higher in the set of high-contribution constraints, should be prioritized as the main contributing group in terms of interventions.

Conclusion

The integration of potentially reversible factors, such as neurological performance in addition to physical function into ML models, yields a more accurate assessment of OAL risk, which provides insights for targeted, sequential interventions for older adults with OAL.

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Data availability

The NHATS dataset can be obtained from www.nhats.org.

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Acknowledgements

The authors are grateful to the NHATS participants for their contribution of time and energy to the study.

Funding

This work was supported by the National Key Research and Development Program of China (Grant Nos. 2020YFC2008500 and 2020YFC2008502).

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Authors and Affiliations

  1. College of Computer Science, Sichuan University, Chengdu, Sichuan, China

    Lingjie Fan, Junjie Zhang & Tao Lin

  2. Department of Rehabilitation Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China

    Fengyi Wang

  3. School of Medicine, Mianyang Central Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

    Shuang Liu

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Contributions

LF and JZ planned and designed the study, interpreted the results, and drafted the initial version of the manuscript. FW and JZ conducted the data analysis and interpreted the results, and SL contributed to the preparation of the methods and results. This study was completed under the supervision of LT. All the authors have reviewed, edited, and approved the final version.

Corresponding author

Correspondence to Tao Lin.

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Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethical approval and consent to participate

NHATS was approved by the Johns Hopkins Bloomberg School of Public Health Institutional Review Board. Informed consent was obtained from the NHATS participants. This study was exempt from an institutional ethical review because it involved a publicly available dataset.

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Written informed consent was obtained from all the participants.

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Fan, L., Zhang, J., Wang, F. et al. Exploring outdoor activity limitation (OAL) factors among older adults using interpretable machine learning. Aging Clin Exp Res 35, 1955–1966 (2023). https://doi.org/10.1007/s40520-023-02461-4

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