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Symptom profiles and their influencing factors among people with subjective cognitive decline: a secondary analysis of cross-sectional data from the 2019 Korea Community Health Survey using latent class analysis
OBJECTIVE: Subjective cognitive decline, self-perceived cognitive deterioration without objective impairment, is becoming a vital health indicator, especially due to its intermediate stage between normal function and mild cognitive impairment. Cognitive decline often coexists with various symptoms t...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10450090/ https://www.ncbi.nlm.nih.gov/pubmed/37620262 http://dx.doi.org/10.1136/bmjopen-2023-072236 |
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author | Seong, Hohyun Park, Jongmin |
author_facet | Seong, Hohyun Park, Jongmin |
author_sort | Seong, Hohyun |
collection | PubMed |
description | OBJECTIVE: Subjective cognitive decline, self-perceived cognitive deterioration without objective impairment, is becoming a vital health indicator, especially due to its intermediate stage between normal function and mild cognitive impairment. Cognitive decline often coexists with various symptoms that may interact with each other, serve as prognostic indicators and influence the progression of cognitive decline. This study aims to identify symptom clusters among individuals with subjective cognitive decline, using latent class analysis, and to identify factors affecting these symptom clusters, offering insights into understanding and potentially preventing cognitive decline progression. DESIGN AND SETTING: A secondary data analysis of the nationally representative cross-sectional data obtained from the 2019 Korea Community Health Survey. OUTCOMES: We performed latent class analysis using six symptoms (pain, sleep deprivation, depression, fatigue, restlessness and apathy) to determine the distinct symptom clusters. After selecting the best latent class model, we identified factors influencing those symptom clusters using multinomial logistic regression analyses. RESULTS: We found that a three-latent-class model best fitted the data: a low symptom-burden group (39.9%), a moderate symptom-burden group (44.8%) and a high symptom-burden group (15.3%). Male gender, higher age, higher perceived health status and lower perceived stress status, were strongly associated with lesser odds of being in the moderate (OR: 0.37 (95% CI: 0.33 to 0.41)) to OR: 2.20 (95% CI: 2.03 to 2.39)) and high symptom-burden groups (OR: 0.18 (95% CI: 0.15 to 0.21)) to OR: 8.53 (95% CI: 7.68 to 9.49)) as compared with being in the low symptom-burden group. CONCLUSION: Findings may contribute to improving clinical practitioners’ understanding of the unique symptom patterns experienced by people with subjective cognitive decline and their influencing factors. Furthermore, we recommend that formal caregivers screen and manage prevalent symptoms considering the factors affecting the symptoms of people with subjective cognitive decline in clinical practice. |
format | Online Article Text |
id | pubmed-10450090 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-104500902023-08-26 Symptom profiles and their influencing factors among people with subjective cognitive decline: a secondary analysis of cross-sectional data from the 2019 Korea Community Health Survey using latent class analysis Seong, Hohyun Park, Jongmin BMJ Open Nursing OBJECTIVE: Subjective cognitive decline, self-perceived cognitive deterioration without objective impairment, is becoming a vital health indicator, especially due to its intermediate stage between normal function and mild cognitive impairment. Cognitive decline often coexists with various symptoms that may interact with each other, serve as prognostic indicators and influence the progression of cognitive decline. This study aims to identify symptom clusters among individuals with subjective cognitive decline, using latent class analysis, and to identify factors affecting these symptom clusters, offering insights into understanding and potentially preventing cognitive decline progression. DESIGN AND SETTING: A secondary data analysis of the nationally representative cross-sectional data obtained from the 2019 Korea Community Health Survey. OUTCOMES: We performed latent class analysis using six symptoms (pain, sleep deprivation, depression, fatigue, restlessness and apathy) to determine the distinct symptom clusters. After selecting the best latent class model, we identified factors influencing those symptom clusters using multinomial logistic regression analyses. RESULTS: We found that a three-latent-class model best fitted the data: a low symptom-burden group (39.9%), a moderate symptom-burden group (44.8%) and a high symptom-burden group (15.3%). Male gender, higher age, higher perceived health status and lower perceived stress status, were strongly associated with lesser odds of being in the moderate (OR: 0.37 (95% CI: 0.33 to 0.41)) to OR: 2.20 (95% CI: 2.03 to 2.39)) and high symptom-burden groups (OR: 0.18 (95% CI: 0.15 to 0.21)) to OR: 8.53 (95% CI: 7.68 to 9.49)) as compared with being in the low symptom-burden group. CONCLUSION: Findings may contribute to improving clinical practitioners’ understanding of the unique symptom patterns experienced by people with subjective cognitive decline and their influencing factors. Furthermore, we recommend that formal caregivers screen and manage prevalent symptoms considering the factors affecting the symptoms of people with subjective cognitive decline in clinical practice. BMJ Publishing Group 2023-08-24 /pmc/articles/PMC10450090/ /pubmed/37620262 http://dx.doi.org/10.1136/bmjopen-2023-072236 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Nursing Seong, Hohyun Park, Jongmin Symptom profiles and their influencing factors among people with subjective cognitive decline: a secondary analysis of cross-sectional data from the 2019 Korea Community Health Survey using latent class analysis |
title | Symptom profiles and their influencing factors among people with subjective cognitive decline: a secondary analysis of cross-sectional data from the 2019 Korea Community Health Survey using latent class analysis |
title_full | Symptom profiles and their influencing factors among people with subjective cognitive decline: a secondary analysis of cross-sectional data from the 2019 Korea Community Health Survey using latent class analysis |
title_fullStr | Symptom profiles and their influencing factors among people with subjective cognitive decline: a secondary analysis of cross-sectional data from the 2019 Korea Community Health Survey using latent class analysis |
title_full_unstemmed | Symptom profiles and their influencing factors among people with subjective cognitive decline: a secondary analysis of cross-sectional data from the 2019 Korea Community Health Survey using latent class analysis |
title_short | Symptom profiles and their influencing factors among people with subjective cognitive decline: a secondary analysis of cross-sectional data from the 2019 Korea Community Health Survey using latent class analysis |
title_sort | symptom profiles and their influencing factors among people with subjective cognitive decline: a secondary analysis of cross-sectional data from the 2019 korea community health survey using latent class analysis |
topic | Nursing |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10450090/ https://www.ncbi.nlm.nih.gov/pubmed/37620262 http://dx.doi.org/10.1136/bmjopen-2023-072236 |
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