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Zhu J, Wu Y, Lin S, Duan S, Wang X, Fang Y. Identifying and predicting physical limitation and cognitive decline trajectory group of older adults in China: A data-driven machine learning analysis. J Affect Disord 2024; 350:590-599. [PMID: 38218258 DOI: 10.1016/j.jad.2024.01.095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 11/24/2023] [Accepted: 01/07/2024] [Indexed: 01/15/2024]
Abstract
OBJECTIVE This study aimed to utilize data-driven machine learning methods to identify and predict potential physical and cognitive function trajectory groups of older adults and determine their crucial factors for promoting active ageing in China. METHODS Longitudinal data on 3026 older adults from the Chinese Longitudinal Healthy Longevity and Happy Family Survey was used to identify potential physical and cognitive function trajectory groups using a group-based multi-trajectory model (GBMTM). Predictors were selected from sociodemographic characteristics, lifestyle factors, and physical and mental conditions. The trajectory groups were predicted using data-driven machine learning models and dynamic nomogram. Model performance was evaluated by area under the receiver operating characteristics curve (AUROC), area under the precision-recall curve (PRAUC), and confusion matrix. RESULTS Two physical and cognitive function trajectory groups were determined, including a trajectory group with physical limitation and cognitive decline (14.18 %) and a normal trajectory group (85.82 %). Logistic regression performed well in predicting trajectory groups (AUROC = 0.881, PRAUC = 0.649). Older adults with lower baseline score of activities of daily living, older age, less frequent housework, and fewer actual teeth were more likely to experience physical limitation and cognitive decline trajectory group. LIMITATION This study didn't carry out external validation. CONCLUSIONS This study shows that GBMTM and machine learning models effectively identify and predict physical limitation and cognitive decline trajectory group. The identified predictors might be essential for developing targeted interventions to promote healthy ageing.
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Affiliation(s)
- Junmin Zhu
- School of Public Health, Xiamen University, Xiamen, Fujian, China; Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Yafei Wu
- School of Public Health, Xiamen University, Xiamen, Fujian, China; Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Shaowu Lin
- School of Public Health, Xiamen University, Xiamen, Fujian, China; Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, Fujian, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian, China
| | - Siyu Duan
- School of Public Health, Xiamen University, Xiamen, Fujian, China; Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Xing Wang
- School of Public Health, Xiamen University, Xiamen, Fujian, China; Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Ya Fang
- School of Public Health, Xiamen University, Xiamen, Fujian, China; Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, Fujian, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian, China.
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Khalili G, Zargoush M, Huang K, Ghazalbash S. Exploring trajectories of functional decline and recovery among older adults: a data-driven approach. Sci Rep 2024; 14:6340. [PMID: 38491130 PMCID: PMC10943109 DOI: 10.1038/s41598-024-56606-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 03/08/2024] [Indexed: 03/18/2024] Open
Abstract
Independently performing activities of daily living (ADLs) is vital for maintaining one's quality of life. Losing this ability can significantly impact an individual's overall health status, including their mental health and social well-being. Aging is an important factor contributing to the loss of ADL abilities, and our study focuses on investigating the trajectories of functional decline and recovery in older adults. Employing trajectory analytics methodologies, this research delves into the intricate dynamics of ADL pathways, unveiling their complexity, diversity, and inherent characteristics. The study leverages a substantial dataset encompassing ADL assessments of nursing home residents with diverse disability profiles in the United States. The investigation begins by transforming these assessments into sequences of disability combinations, followed by applying various statistical measures, indicators, and visual analytics. Valuable insights are gained into the typical disability states, transitions, and patterns over time. The results also indicate that while predicting the progression of ADL disabilities presents manageable challenges, the duration of these states proves more complicated. Our findings hold significant potential for improving healthcare decision-making by enabling clinicians to anticipate possible patterns, develop targeted and effective interventions that support older patients in preserving their independence, and enhance overall care quality.
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Affiliation(s)
- Ghazal Khalili
- DeGroote School of Business, McMaster University, Hamilton, ON, L8S 4L8, Canada
| | - Manaf Zargoush
- DeGroote School of Business, McMaster University, Hamilton, ON, L8S 4L8, Canada.
| | - Kai Huang
- DeGroote School of Business, McMaster University, Hamilton, ON, L8S 4L8, Canada
| | - Somayeh Ghazalbash
- Smith School of Business, Queen's University, Kingston, ON, K7L 2P3, Canada
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Wu Y, Xiang C, Jia M, Fang Y. Correction: Interpretable classifiers for prediction of disability trajectories using a nationwide longitudinal database. BMC Geriatr 2023; 23:190. [PMID: 36997852 PMCID: PMC10064752 DOI: 10.1186/s12877-023-03843-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/01/2023] Open
Affiliation(s)
- Yafei Wu
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102, Fujian, China
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, China
- School of Public Health, Xiamen University, Xiang'an Nan Road, Xiang'an District, Xiamen, 361102, Fujian, China
| | - Chaoyi Xiang
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, China
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, China
- School of Public Health, Xiamen University, Xiang'an Nan Road, Xiang'an District, Xiamen, 361102, Fujian, China
| | - Maoni Jia
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, China
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, China
- School of Public Health, Xiamen University, Xiang'an Nan Road, Xiang'an District, Xiamen, 361102, Fujian, China
| | - Ya Fang
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, China.
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102, Fujian, China.
- Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, China.
- School of Public Health, Xiamen University, Xiang'an Nan Road, Xiang'an District, Xiamen, 361102, Fujian, China.
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