Intrinsic capacity and its association with predictors among Chinese empty nest older adults in communities: a latent class analysis

Abstract Objectives This study aimed to examine Intrinsic Capacity (IC) subgroups and the association of IC subgroups with IC predictors in Chinese urban empty nesters. Methods A convenient sample of 385 older adults aged 60 and above in Community Health Service Center was recruited from Hei Longjia...

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Main Authors: Wenxin Guo, Lina Meng, Junzhe Han, Bin Yang, Jiayu Sun, Yuting Guo, Jiawei Wu, Yang Liu
Format: Article
Language:English
Published: BMC 2024-12-01
Series:BMC Geriatrics
Subjects:
Online Access:https://doi.org/10.1186/s12877-024-05583-0
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author Wenxin Guo
Lina Meng
Junzhe Han
Bin Yang
Jiayu Sun
Yuting Guo
Jiawei Wu
Yang Liu
author_facet Wenxin Guo
Lina Meng
Junzhe Han
Bin Yang
Jiayu Sun
Yuting Guo
Jiawei Wu
Yang Liu
author_sort Wenxin Guo
collection DOAJ
description Abstract Objectives This study aimed to examine Intrinsic Capacity (IC) subgroups and the association of IC subgroups with IC predictors in Chinese urban empty nesters. Methods A convenient sample of 385 older adults aged 60 and above in Community Health Service Center was recruited from Hei Longjiang Province, China, between June 2023 and December 2023. Latent class Analysis (LCA) was conducted to explore IC subgroups using the sensory, cognition, locomotion, psychological, and vitality domains of IC as input variables. Multinomial logistic regression was performed to explore the association between latent subgroups and the IC predictors. Results We identified three IC subgroups: "Low IC level—Low locomotion domain"(33.5%), "Medium IC level—Low sensory domain" (16.9%) and "High IC level" (49.6%). Being young, married, without multimorbidity, receiving visits from children ≥ 1 time per week, a low score of self-neglect, a high score of social networking, and a low score of loneliness were closely correlated to the "High IC level" subgroup of empty-nest older adults in communities. Conclusion The potential subgroups of the IC of empty-nest older adults in communities can be identified through five IC domains. The older empty-nesters should pay extra attention to their critical IC predictors. Community medical staff and other workers should provide intervention measures for different subgroups of older adults to improve their IC in an effective and individualized manner.
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series BMC Geriatrics
spelling doaj-art-6d02b524111c4ac79c250560e8c1b6d42024-12-22T12:46:33ZengBMCBMC Geriatrics1471-23182024-12-0124111110.1186/s12877-024-05583-0Intrinsic capacity and its association with predictors among Chinese empty nest older adults in communities: a latent class analysisWenxin Guo0Lina Meng1Junzhe Han2Bin Yang3Jiayu Sun4Yuting Guo5Jiawei Wu6Yang Liu7Department of Nursing, Daqing Campus, Harbin Medical UniversityDepartment of Nursing, Daqing Campus, Harbin Medical UniversityDepartment of Nursing, Daqing Campus, Harbin Medical UniversityDepartment of Nursing, Daqing Campus, Harbin Medical UniversityDepartment of Nursing, Daqing Campus, Harbin Medical UniversitySartu District Dongfeng Street Community Health Service CenterDepartment of Basic Medicine, Harbin Medical UniversityDepartment of Nursing, Daqing Campus, Harbin Medical UniversityAbstract Objectives This study aimed to examine Intrinsic Capacity (IC) subgroups and the association of IC subgroups with IC predictors in Chinese urban empty nesters. Methods A convenient sample of 385 older adults aged 60 and above in Community Health Service Center was recruited from Hei Longjiang Province, China, between June 2023 and December 2023. Latent class Analysis (LCA) was conducted to explore IC subgroups using the sensory, cognition, locomotion, psychological, and vitality domains of IC as input variables. Multinomial logistic regression was performed to explore the association between latent subgroups and the IC predictors. Results We identified three IC subgroups: "Low IC level—Low locomotion domain"(33.5%), "Medium IC level—Low sensory domain" (16.9%) and "High IC level" (49.6%). Being young, married, without multimorbidity, receiving visits from children ≥ 1 time per week, a low score of self-neglect, a high score of social networking, and a low score of loneliness were closely correlated to the "High IC level" subgroup of empty-nest older adults in communities. Conclusion The potential subgroups of the IC of empty-nest older adults in communities can be identified through five IC domains. The older empty-nesters should pay extra attention to their critical IC predictors. Community medical staff and other workers should provide intervention measures for different subgroups of older adults to improve their IC in an effective and individualized manner.https://doi.org/10.1186/s12877-024-05583-0Older adultsIntrinsic capacityHealth ecology modelInfluencing factorsLatent class analysis
spellingShingle Wenxin Guo
Lina Meng
Junzhe Han
Bin Yang
Jiayu Sun
Yuting Guo
Jiawei Wu
Yang Liu
Intrinsic capacity and its association with predictors among Chinese empty nest older adults in communities: a latent class analysis
BMC Geriatrics
Older adults
Intrinsic capacity
Health ecology model
Influencing factors
Latent class analysis
title Intrinsic capacity and its association with predictors among Chinese empty nest older adults in communities: a latent class analysis
title_full Intrinsic capacity and its association with predictors among Chinese empty nest older adults in communities: a latent class analysis
title_fullStr Intrinsic capacity and its association with predictors among Chinese empty nest older adults in communities: a latent class analysis
title_full_unstemmed Intrinsic capacity and its association with predictors among Chinese empty nest older adults in communities: a latent class analysis
title_short Intrinsic capacity and its association with predictors among Chinese empty nest older adults in communities: a latent class analysis
title_sort intrinsic capacity and its association with predictors among chinese empty nest older adults in communities a latent class analysis
topic Older adults
Intrinsic capacity
Health ecology model
Influencing factors
Latent class analysis
url https://doi.org/10.1186/s12877-024-05583-0
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