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DOI 10.1136/bmjopen-2023-072170

Медицинская библиотека · BMJ open · 2023

Kinesiophobia and its associated factors in patients with coronary heart disease: a cross-sectional study based on latent feature analysis.

Wang Zijiao, Zhang Yifan, Wang Yanping, Liu Lamei, Zhang Junmei

СтатьяПеревод пока не подготовлен

Аннотация

OBJECTIVES

The aim of this study was to explore the current situation of kinesiophobia in patients with coronary heart disease, classify it based on potential profile analysis and explore the relevant factors of kinesiophobia in different categories of patients with coronary heart disease.

DESIGN

Cross-sectional study.

SETTING

Patients with coronary heart disease in China.

PARTICIPANTS

Adult (aged >18 years) patients with coronary heart disease in China; 252 participants in this study answered the questionnaire.

PRIMARY AND SECONDARY OUTCOME MEASURES: The study investigated the scores of Tampa Scale for Kinesiophobia Heart, and collected information on the patient's age, gender, monthly household income, education level, place of residence, marital status, occupational status, hypertension, diabetes, heart failure and body mass index (BMI).

RESULTS

Kinesiophobia in patients with coronary heart disease can be divided into low fear type (C1), intermediate fear type (C2) and high fear type (C3). Elderly patients were classified as type C3. Women and patients with a normal BMI were classified as type C1; patients with a normal BMI and patients with an overweight BMI were classified as type C2.

CONCLUSION

Kinesiophobia of patients with coronary heart disease can be divided into three categories, and intervention measures are implemented according to their different demographic characteristics to reduce kinesiophobia of patients and promote the participation of patients in exercise rehabilitation.

Для цитирования: Wang Zijiao, Zhang Yifan, Wang Yanping, Liu Lamei, Zhang Junmei. Kinesiophobia and its associated factors in patients with coronary heart disease: a cross-sectional study based on latent feature analysis.. BMJ open. 2023. DOI: 10.1136/bmjopen-2023-072170.

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