scholarly journals The Current Status of Health Promotion Lifestyle and Its Related Factors in Chinese Residents

2021 ◽  
Author(s):  
Qianqian Liu ◽  
Shusheng Huang ◽  
Xiaoyuan Qu ◽  
Aitian Yin
Keyword(s):  
Health Promotion ◽  
Logistic Regression ◽  
Regression Model ◽  
Behavioral Intention ◽  
Logistic Regression Model ◽  
Pearson Correlation ◽  
Current Status ◽  
Related Factors ◽  
Affective Factors ◽  
Factors Influencing

Abstract Objectives: This study aimed to explore the current status of Chinese residents' health promotion lifestyle and its influencing factors, especially explore how health attitudes affect health promotion lifestyle, thus can make targeted recommendations for health promotion in China and similar areas.Study design: This study was based on a household survey of 1769 adults aged 18 and over from Shandong Province of China conducted in 2018.Methods: A cross-sectional, face-to-face survey design was used. The between-group measured data were compared by One-way ANOVA or t-tests. The correlation between the health attitude and health promotion lifestyle was examined by Pearson correlation. Logistic regression model was used to examine the related factors influencing Chinese residents’ health promotion lifestyle.Results: The health promotion lifestyle of residents in Chinese residents was not good. Significant differences existed in health promotion lifestyle among different genders, education levels, income levels, marital status, and health attitude (Ps <0.001). Multivariable Logistic regression model found that gender, education level, annual family per capita income, affection and behavioral intention in health attitude were key factors influencing Chinese residents’ health promotion lifestyle (Ps<0.001). Conclusions: Individual health responsibilities need to be further strengthened. More affective factors and operable measures should be added to enhance health affection and health behavioral intention, so as to enhance health promotion lifestyle.

scholarly journals The status of health promotion lifestyle and its related factors in Shandong Province, China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qianqian Liu ◽  
Shusheng Huang ◽  
Xiaoyuan Qu ◽  
Aitian Yin
Keyword(s):  
Health Promotion ◽  
Logistic Regression ◽  
Behavioral Intention ◽  
Logistic Regression Model ◽  
Education Level ◽  
Shandong Province ◽  
Related Factors ◽  
Gender Education ◽  
The Status ◽  
Per Capita

Abstract Objectives This study aims to explore the status of Shandong Province, China residents’ health promotion lifestyle and its influencing factors, especially to explore how health attitude affects health promotion lifestyle, thus can make targeted recommendations for health promotion in China and similar areas. Methods 1800 adults were selected from urban and rural areas of Shandong Province, China, using multistage stratified, cluster random sampling method. A survey was conducted face-to-face from March to May, 2018, using Health Promotion Lifestyle Profile and Health Attitude Questionnaire. The between-group measured data were compared by One-way ANOVA or t-tests. The correlation between the health attitude and health promotion lifestyle was examined by Pearson correlation. Logistic regression model was used to examine the related factors influencing health promotion lifestyle. Health promotion lifestyle is the dependent variable, and gender, education level, annual family per capita income and health attitude are the independent variables. Results The mean (SD) of HPLP-IICR total score of the participants was 82.12(16.63). 54.50% of the participants had poor or average health promotion lifestyle, while 45.50% had good or excellent health promotion lifestyle. Significant differences existed in health promotion lifestyle among different gender, education level, income level, marital status, and health attitude (Ps < 0.001). Multivariable Logistic regression model found that male (OR = 0.35, 95% CI: 0.12–0.34), high school education level (OR = 0.57, 95% CI:0.17–0.41), junior middle school & below (OR = 0.42; 95% CI:0.12–0.33), annual family per capita income with < 10,000 CNY (OR = 2.53, 95% CI:1.24–2.06; OR = 2.14, 95% CI:1.08–3.12), low health affection (OR = 0.39, 95% CI:2.15–4.22), and low health behavioral intention (OR = 0.21; 95% CI: 2.33–5.29) were statistically significant correlates of average or poor health promotion lifestyle. Conclusions The health lifestyle needs to be further promoted in Shandong Province, China. The government and social sectors are encouraged to make more efforts to improve the accessibility and quality of health services. Meanwhile, individual responsibility cannot be ignored as well. More affective factors and operable measures should be added to enhance health affection and health behavioral intention, so as to enhance health promotion lifestyle.

scholarly journals Non-participation modestly increased with distance to the examination clinic among adults in Finnish health examination surveys

2017 ◽  
Vol 46 (7) ◽  
pp. 752-754
Author(s):  
Jaakko Reinikainen ◽  
Perttu Saarsalmi ◽  
Tommi Härkänen ◽  
Pekka Jousilahti ◽  
Juha Karvanen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Population Health ◽  
Logistic Regression Model ◽  
Additional Analysis ◽  
Health Examination ◽  
Participation Rates ◽  
Related Factors ◽  
Health Related ◽  
At Home

Aims: Health examination surveys (HES) provide important information about population health and health-related factors, but declining participation rates threaten the representativeness of collected data. It is hard to conduct national HESs at examination clinics near to every sampled individual. Thus, it is interesting to look into the possible association between the distance from home to the examination clinic and non-participation, and whether there is a certain distance after which the participation activity decreases considerably. Methods: Data from two national HESs conducted in Finland in 2011 and 2012 were used and a logistic regression model was fitted to investigate how distance was related to non-participation. Results: We found out that non-participation modestly increased with distance to the examination clinic. An additional analysis indicated that the option of having an examination at home may decrease the effect of distance to participation. Conclusions: Long distances from home to the examination clinic are one reason for low participation activity. Possible bias caused by these differences in participation could be decreased by providing the option of a home examination.

scholarly journals Association between small dense low-density lipoprotein cholesterol and neuroimaging markers of cerebral small vessel disease in middle-aged and elderly Chinese populations

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaorong Yu ◽  
Yanhua Yu ◽  
Cunsheng Wei ◽  
Lin Wang ◽  
Junying Jiang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Pearson Correlation ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Lipoprotein Cholesterol ◽  
Low Density ◽  
Low Density Lipoprotein Cholesterol ◽  
Cutoff Value

Abstract Background Cerebral small vascular disease (CSVD) is one of the leading causes of death in the aged population and is closely related to abnormalities in low-density lipoprotein cholesterol (LDL-C). Our study aims to clarify the relationship between small and dense low-density lipoprotein cholesterol (sdLDL-C) (a subcomponent of LDL-C) and neuroimaging markers of CSVD. Methods In total, 1211 Chinese adults aged ≥45 years with cranial magnetic resonance imaging (MRI) were recruited in this retrospective study from January 2018 to May 2021. Serum lipids and other baseline characteristics were investigated in relation to the occurrence of CSVD. A logistic regression model was performed to analyze the relationships between LDL subtypes and CSVD risk, and the Pearson correlation coefficient was used to analyze the correlation between clinical characteristics and CSVD risk. ROC curves and AUCs were created and depicted to predict the best cutoff value of LDL-C subtypes for CSVD risk. Based on these data, we performed comprehensive analyses to investigate the risk factors for CSVD. Results Ultimately, 623 eligible patients were included in the present study. Of the 623 eligible patients, 487 were included in the CSVD group, and 136 were included in the group without CSVD (control group). We adjusted for confounders in the multivariate logistic regression model, and LDL-C3 was still higher in the CSVD patients than in the group of those without CSVD (OR (95% CI), 1.22(1.08–1.38), P < 0.05). Pearson correlation showed that there was a positive correlation between the levels of LDL-C3, LDL-C4, LDL-C5, glucose, age, hypertension, previous ischemic stroke and CSVD risk (r > 0.15, P < 0.01). Moreover, the best cutoff value of LDL-C3 to predict CSVD was 9.5 mg/dL with 68.4% sensitivity and 72.8% specificity, and the best cutoff value of LDL-C4 to predict CSVD was 5.5 mg/dL with 50.5% sensitivity and 90.4% specificity. Conclusion The results indicate that LDL-C3 is an independent risk factor for CSVD. A new prediction model based on LDL-C3 and LDL-C4 can help clinicians identify high-risk CSVD, even in people with normal LDL-C levels. The levels of sdLDL-C should be considered in the assessment and management of CSVD.

That-variation in German and Spanish L2 English

2014 ◽  
Vol 6 (2) ◽  
pp. 271-299 ◽  
Author(s):  
STEFANIE WULFF ◽  
NICHOLAS LESTER ◽  
MARIA T. MARTINEZ-GARCIA
Keyword(s):  
Logistic Regression ◽  
Second Language ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Native Speakers ◽  
Related Factors ◽  
Cue Validity ◽  
L2 Learners ◽  
Complement Clauses

abstractIn certain English finite complement clauses, inclusion of the complementizer that is optional. Previous research has identified various factors that influence when native speakers tend to produce or omit the complementizer, including syntactic weight, clause juncture constraints, and predicate frequency. The present study addresses the question to what extent German and Spanish learners of English as a second language (L2) produce and omit the complementizer under similar conditions. 3,622 instances of English adjectival, object, and subject complement constructions were retrieved from the International Corpus of English and the German and Spanish components of the International Corpus of Learner English. A logistic regression model suggests that L2 learners’ and natives’ production is largely governed by the same factors. However, in comparison with native speakers, L2 learners display a lower rate of complementizer omission. They are more impacted by processing-related factors such as complexity and clause juncture, and less sensitive to verb-construction cue validity.

scholarly journals Evaluation of Workload and Human Errors in Nurses

2019 ◽  
Author(s):  
Mahshid Naseri Karimvand ◽  
Naser Hasheminejad ◽  
Ali Faghihi Zarandi ◽  
Yones Jahani
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Human Error ◽  
Logistic Regression Model ◽  
Control Strategies ◽  
Statistical Significance ◽  
Medical Sciences ◽  
Related Factors ◽  
Human Errors ◽  
Significance Level

Background: Identification of human errors and their related factors in nurses dealing with the health of humans is important. Considering that much workload can increase the risk of human error, this study aimed to investigate the relationship between workload and human errors among nurses working in educational hospitals of Kerman University of Medical Sciences. Methods: This descriptive-analytical study was performed on 145 nurses from educational hospitals affiliated to Kerman University of Medical Sciences in 20. The workload was evaluated using the NASA-TLX questionnaire and human errors with SHERPA technique. The statistical test used was logistic regression model and the statistical significance level was considered <0.05 and the samples were selected randomly. The sample size was selected based on the percentage of functional errors reported by a study on human error assessment related to the duties of nurses in Semnan. Results: A total of 138 probable errors were detected in the nursing staff of these hospitals, 74% of nurses committed errors in seven main duties during their service. Patient medication with the highest frequency (34%) followed by the injection of the drug to the patient with a frequency of 23% were the most frequently committed errors by nurses. The findings of the research showed that workload in 53.1% of the nurses was very high and in 43.1% of the nurses was high, the results of the logistic regression model showed that there was no significant relationship between errors and workload in nurses. Conclusion: The results of the study showed that the average workload and human error in the nurses were high. Therefore, control strategies such as holding training sessions, implementation of clinical governance program in all wards, recruitment of adequate nurses, reduction of workload, reduction of work hours and the appropriate patient/nurse ratio should be given attention by the hospital managers depending on conditions; as well as the prevention of the two reported errors should be given top priority in corrective measures.

scholarly journals Analysis of comparable academic performance: panel logistic regression model

2010 ◽  
Vol 51 ◽  
Author(s):  
Marijus Radavičius ◽  
Pavel Samusenko
Keyword(s):  
Logistic Regression ◽  
Academic Performance ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Conditional Logistic Regression ◽  
Related Factors ◽  
Conditional Logistic Regression Model ◽  
Student Academic Performance

Conditional logistic regression model is fitted to data of student academic performance. This enables one to compare difficulties of different examinations and to identify related factors.

scholarly journals An Assessment of Factors Influencing Forest Harvesting in Smallholder Tobacco Production in Hurungwe District, Zimbabwe: An Application of Binary Logistic Regression Model

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Chipo Chivuraise ◽  
Tafireyi Chamboko ◽  
Godfrey Chagwiza
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Forest Harvesting ◽  
Natural Forest ◽  
Selling Price ◽  
Binary Logistic Regression Model ◽  
Factors Influencing ◽  
Tobacco Production

Deforestation is one of the major effects posed by the smallholder tobacco farming as the farmers heavily depend on firewood sourced from natural forest for curing tobacco. The research aims at assessing the factors that influence the harvesting of natural forest in the production of tobacco. Data is collected through the structured questionnaire from 60 randomly selected farmers. Binary logistic regression model is used to explain the significance of factors influencing natural forest harvesting. Results show that farmer experience, tobacco selling price, and agricultural training level negatively affect the harvesting of natural forests (to obtain firewood) for curing tobacco significantly (p<0.05). However, gender, size of the household, tobacco yield, and level of education are insignificant (p>0.05) in influencing natural forest harvesting. Though farmers are exploiting the environment and at the same time increasing foreign currency earning through tobacco production, there is therefore a need to put in place policies that encourage sustainable forest product utilization such as gum plantations, subsidizing price of coal, and introducing fees, as well as penalties or taxes to the offenders.

scholarly journals Debt as a Source of Financial Energy of the Farm—What Causes the Use of External Capital in Financing Agricultural Activity? A Model Approach

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4124
Author(s):  
Danuta Zawadzka ◽  
Agnieszka Strzelecka ◽  
Ewa Szafraniec-Siluta
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Regression Tree ◽  
Agricultural Activity ◽  
Factors Influencing ◽  
External Capital ◽  
Farm Accountancy Data Network ◽  
Using Data ◽  
The One

The aim of this study was to identify and assess the factors influencing the increase in the financial energy of a farm through the use of external capital, taking into account the farmer’s and farm characteristics. For its implementation, a logistic regression model and a classification-regression tree analysis (CRT) were used. The study was conducted on a group of farms in Central Pomerania (Poland) participating in the system of collecting and using data from farms (Farm Accountancy Data Network—FADN). Data on 348 farms were used for the analyses, obtained through a survey conducted in 2020 with the use of a questionnaire. Based on the analysis of the research results presented in the literature to date, it was established that the use of external capital in a farm as a factor increasing financial energy is determined, on the one hand, by the socio-demographic characteristics of the farmer and the characteristics of the farm, and on the other hand, by the availability of external financing sources. Factors relating to the first of these aspects were taken into account in the study. Using the logistic regression model, it was established that the propensity to indebtedness of farms is promoted by the following factors: gender of the head of the household (male, GEND), younger age of the head of the household (AGE), having a successor who will take over the farm in the future (SUC), higher value of generated production (PROD_VALUE), larger farm area (AREA) and multi-directional production of the farm (production diversification), as opposed to targeting plant or animal production only (farm specialization—SPEC). The results of the analysis carried out with the use of classification and regression trees (CRT) showed that the key factors influencing the use of outside capital as a source of financial energy in the agricultural production process are, first of all, features relating to an agricultural holding: the value of generated production (PROD_VALUE), agricultural area (AREA) and production direction (SPEC). The age of the farm manager (AGE) turned out to be of key importance among the farmer’s features favoring the tendency to take debt in order to finance agricultural activity.

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