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. 2023 Dec 6;23(1):2431.
doi: 10.1186/s12889-023-17343-7.

Physical activity, sleep disorders, and type of work in the prevention of cognitive function decline in patients with hypertension

Affiliations

Physical activity, sleep disorders, and type of work in the prevention of cognitive function decline in patients with hypertension

Mengdi Zhang et al. BMC Public Health. .

Abstract

Background: Hypertensive patients are likelier to have cognitive function decline (CFD). This study aimed to explore physical activity level, sleep disorders, and type of work that influenced intervention effects on cognitive function decline in hypertensive patients and to establish a decision tree model to analyze their predictive significance on the incidence of CFD in hypertensive patients.

Methods: This cross-sectional study recruited patients with essential hypertension from several hospitals in Shandong Province from May 2022 to December 2022. Subject exclusion criteria included individuals diagnosed with congestive heart failure, valvular heart disease, cardiac surgery, hepatic and renal dysfunction, and malignancy. Recruitment is through multiple channels such as hospital medical and surgical outpatient clinics, wards, and health examination centers. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), and sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Moreover, we obtained information on the patients' type of work through a questionnaire and their level of physical activity through the International Physical Activity Questionnaire (IPAQ).

Results: The logistic regression analysis results indicate that sleep disorder is a significant risk factor for CFD in hypertension patients(OR:1.85, 95%CI:[1.16,2.94]), mental workers(OR:0.12, 95%CI: [0.04,0.37]) and those who perform both manual and mental workers(OR: 0.5, 95%CI: [0.29,0.86]) exhibit protective effects against CFD. Compared to low-intensity, moderate physical activity(OR: 0.53, 95%CI: [0.32,0.87]) and high-intensity physical activity(OR: 0.26, 95%CI: [0.12,0.58]) protects against CFD in hypertension patients. The importance of predictors in the decision tree model was ranked as follows: physical activity level (54%), type of work (27%), and sleep disorders (19%). The area under the ROC curves the decision tree model predicted was 0.72 [95% CI: 0.68 to 0.76].

Conclusion: Moderate and high-intensity physical activity may reduce the risk of developing CFD in hypertensive patients. Sleep disorders is a risk factor for CFD in hypertensive patients. Hypertensive patients who engage in mental work and high-intensity physical activity effectively mitigate the onset of CFD in hypertensive patients.

Keywords: Cognitive function decline; Hypertension; Physical activity; Sleep disorders; Type of work.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flow chart of the study population
Fig. 2
Fig. 2
The decision tree model illustrates the impact of lifestyles on CFD and has been generated by the software using ten cross-validations. The boxes are numbered sequentially, with the blue icon representing NCI and the red icon representing CFD. Each box has a specific percentage and number of examples. The influence factor of the following classification is indicated below each box, while the corresponding category of influence factor is located at the top of each box
Fig. 3
Fig. 3
The significance of predictive variables in the decision tree model is a crucial aspect to consider. We included work type, sleep disorders, and physical activity levels in the decision tree model to assess their impact of CFD in hypertensive patients. The figure illustrates the relative importance of each influencing factor in predicting the outcome within the decision tree model. Notably, physical activity level is the root node variable deemed the most critical predictive variable
Fig. 4
Fig. 4
We utilized the decision tree prediction probability model to forecast the ROC curve of CFD. The dotted lines in the figure demonstrate the predictive capacity of the decision tree model in acknowledging the incidence of CFD. The AUC was 0.72, P < 0.001

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