Dataset Information

Current status and influencing factors of self-management in knee joint discomfort among middle-aged and elderly people: a cross-sectional study.

ABSTRACT:

Background

This study aims to identify the current status and factors influencing self-management of knee discomfort in middle-aged and elderly people in China.

Methods

A stratified multistage cluster sampling method was used to select participants from communities in China from January 15 to May 31, 2020. A cross-sectional survey was conducted using the general information questionnaire and the Knee Joint Discomfort Self-management Scale. Univariate analysis and a generalized linear model were used to analyze the factors influencing self-management.

Results

The prevalence of knee discomfort was 77%. Moderate to severe discomfort accounted for 30.5%. The average item score of self-management in 9640 participants was 1.98 ± 0.76. The highest and lowest levels were: 'daily life management' and 'information management'. Gender, ethnicity, education level, economic source, chronic disease, knee pain in the past month, and the degree of self-reported knee discomfort were significant predictors of self-management.

Conclusion

The self-management of knee discomfort in middle-aged and elderly people is poor, and the degree of discomfort is a significant predictor. Healthcare providers should consider socioeconomic demographic and clinical characteristics to help these individuals improve their self-management skills. Attention should also be given to improving their ability to access health information and making them aware of disease risks.

SUBMITTER: Guo Y

PROVIDER: S-EPMC10541685 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Publications

Current status and influencing factors of self-management in knee joint discomfort among middle-aged and elderly people: a cross-sectional study.

Guo Yabin Y Zhao Peipei P Zeng Biyun B Su Manman M Zhou Yang Y Liu Xiaotong X Zhou Yang Y

BMC geriatrics 20230929 1

<h4>Background</h4>This study aims to identify the current status and factors influencing self-management of knee discomfort in middle-aged and elderly people in China.<h4>Methods</h4>A stratified multistage cluster sampling method was used to select participants from communities in China from January 15 to May 31, 2020. A cross-sectional survey was conducted using the general information questionnaire and the Knee Joint Discomfort Self-management Scale. Univariate analysis and a generalized lin ...[more]

PMID: 37773113

Similar Datasets

Project description:AimThis study used machine learning methods to develop a prediction model for knee pain in middle-aged and elderly individuals.MethodsA total of 5386 individuals above 45 years old were obtained from the National Health and Nutrition Examination Survey. Participants were randomly divided into a training set and a test set at a 7 : 3 ratio. The training set was used to create a prediction model, whereas the test set was used to validate the proposed model. We constructed multiple predictive models based on three machine learning methods: logistic regression, random forest, and Extreme Gradient Boosting. The model performance was evaluated by areas under the receiver (AUC), sensitivity, specificity, positive predictive value, and negative predictive value. Additionally, we created a simplified nomogram based on logistic regression for better clinical application.ResultsAbout 31.4% (1690) individuals were with self-reported knee pain. The logistic regression showed that female gender (odds ratio [OR] = 1.28), pain elsewhere (OR = 4.64), and body mass index (OR = 1.05) were significantly associated with increased risk of knee pain. In the test set, the logistic regression (AUC = 0.71) showed similar but slightly higher accuracy than the random forest (AUC = 0.70), while the performance of the Extreme Gradient Boosting model was less reliable (AUC = 0.59). Based on mean decrease accuracy, the most important first five predictions were pain elsewhere, waist circumference, body mass index, age, and gender. Additionally, the most important first five predictions with the highest mean decrease Gini index were pain elsewhere, body mass index, waist circumference, triglycerides, and age. The nomogram model showed good discrimination ability with an AUC of 0.75 (0.73-0.77), a sensitivity of 0.72, specificity of 0.71, a positive predictive value of 0.45, and a negative predictive value of 0.88.ConclusionThis study proposed a convenient nomogram tool to evaluate the risk of knee pain for the middle-aged and elderly US population in primary care. All the input variables can be easily obtained in a clinical setting, and no additional radiologic assessments were required.

Dataset Information

Current status and influencing factors of self-management in knee joint discomfort among middle-aged and elderly people: a cross-sectional study.

Background

Methods

Results

Conclusion

Publications

Current status and influencing factors of self-management in knee joint discomfort among middle-aged and elderly people: a cross-sectional study.

Similar Datasets

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