Objective:The purpose of this study was to determine the effectiveness of Mobile-Stroke Risk Scale and Life Style Guidance(M-SRSguide)in promoting a healthy lifestyle and reducing stroke risk factors in atrisk persons...Objective:The purpose of this study was to determine the effectiveness of Mobile-Stroke Risk Scale and Life Style Guidance(M-SRSguide)in promoting a healthy lifestyle and reducing stroke risk factors in atrisk persons.Methods:This research was an clinical trial with a pre-test and post-test control group design.The accessible population is persons at risk of stroke in the community(West and East Kalimantan Province,Indonesia).Thirty-two participants in the intervention group and 32 participants in the control group participated in this study.The sampling method was systematic random sampling.We allocate the sample into the intervention and control groups using a randomized block design.The intervention group used the M-SRSguide.The control group used manual book for a self-assessment of stroke risk.The measurement of a healthy lifestyle and the stroke risk factors was performed before and six months after the intervention.Results:There are no significant differences in healthy lifestyle and stroke risk factors between the two groups after the intervention(P>0.05).Analysis of healthy lifestyle behavior assessment items in the intervention group showed an increase in healthy diets,activity patterns,and stress control after the use of the M-SRSguide(P<0.01).Conclusion:The use of M-SRSguide is effective in promoting a healthy lifestyle.展开更多
BACKGROUND Data on non-drug related risk-factors for gastrointestinal bleeding(GIB)in the general population are limited,especially for life-style factors,clinical measurements and laboratory parameters.AIM To identif...BACKGROUND Data on non-drug related risk-factors for gastrointestinal bleeding(GIB)in the general population are limited,especially for life-style factors,clinical measurements and laboratory parameters.AIM To identify and investigate non-drug risk factors for major GIB in the general population of Finland.METHODS We performed a retrospective cohort study using data from the FINRISK health examination surveys,which have been conducted every 5 years across Finland from 1987 to 2007.Participants were adults aged 25 years to 74 years,excluding those with a previous hospitalization for GIB.Follow-up from enrollment was performed through linkage to national electronic health registers and ended at an event of GIB that led to hospitalization/death,death due to any other cause,or after 10 years.Covariates included demographics,socioeconomic and lifestyle factors,clinical measurements,laboratory parameters and comorbidities.Variable selection was undertaken using Least Absolute Shrinkage and Selection Operator(LASSO)and factors associated with GIB were identified using Cox regression.RESULTS Among 33,508 participants,403(1.2%)experienced GIB[256 men(63.5%);mean age,56.0 years(standard deviation(SD)±12.1)]and 33105 who did not experience GIB[15768 men(47.6%);mean age,46.8(SD±13)years],within 10 years of follow-up.Factors associated with a significantly increased risk of GIB were baseline age[per 10-year increase;hazard ratio(HR)1.62,95%confidence interval(CI):1.42-1.86],unemployment(HR:1.70,95%CI:1.11-2.59),body mass index(BMI)(HR:1.15,95%CI:1.01-1.32),gamma-glutamyl transferase(GGT)(HR:1.05,95%CI:1.02-1.09),precursors of GIB(HR:1.90,95%CI:1.37-2.63),cancer(HR:1.47,95%CI:1.10-1.97),psychiatric disorders(HR:1.32,95%CI:1.01-1.71),heart failure(HR:1.46,95%CI:1.04-2.05),and liver disorders(HR:3.20,95%CI:2.06-4.97).Factors associated with a significantly decreased risk of GIB were systolic blood pressure(SBP)(HR:0.78,95%CI:0.64-0.96),6-10 cups of coffee a day(HR:0.67,95%CI:0.46-0.99),or>10 cups(HR:0.43,95%CI:0.23-0.81).CONCLUSION Our study confirms established risk-factors for GIB and identifies potential risk-factors not previously reported such as unemployment,BMI,GGT,SBP and coffee consumption.展开更多
Obesity and overweight are complex phenomena due to causes and consequences as these are the predisposing factors for developing lifestyle-related diseases. In the recent times, obesity and chronic diseases are being ...Obesity and overweight are complex phenomena due to causes and consequences as these are the predisposing factors for developing lifestyle-related diseases. In the recent times, obesity and chronic diseases are being taken very seriously than ever before. Once upon a time, obesity was considered as a problem only in developed countries, now it is of a serious concern in low- and middle-income countries. In Papua New Guinea (PNG), a few studies have been conducted on obesity-related issues. However, there is not enough data on obesity to clearly understand about the leading contributing factors. Our research group designed this study to assess the influencing factors of obesity and as well as to identify the principal cause. It is a descriptive study, used structured and semi-structured interview and survey questionnaire. This study was conducted in the capital city of PNG, Port Moresby, and recruited 87 adults aged 30 - 50 years. According to interview and analysis findings, the contributing factors of obesity are less physical movement, unhealthy diet, lifestyle, cultural value system, low education and social system. Government strategies and initiatives are not strong enough to educate and motivate the community people. This study suggests developing an effective coordination among all stakeholders to ensure quality education and increase awareness to prevent obesity and its complications. The standard health policies and strategies are also recommended at the local, national and international level with strong commitment.展开更多
目的本研究旨在确定影响老年人视力障碍的危险因素,建立和评价视力障碍风险预测模型。方法采用非匹配病例对照设计研究,2020年6一12月,从辽宁省某教学医院的眼科门诊和体检中心招募了586名研究参与者(411名被纳人到建模组,175名被纳人...目的本研究旨在确定影响老年人视力障碍的危险因素,建立和评价视力障碍风险预测模型。方法采用非匹配病例对照设计研究,2020年6一12月,从辽宁省某教学医院的眼科门诊和体检中心招募了586名研究参与者(411名被纳人到建模组,175名被纳人内部验证组)。视力障碍定义为最佳矫正视力<6/18(世界卫生组织定义)。调查了患者的基本情况和可能的预测因素,包括人口统计学资料、疾病和药物使用情况及生活方式。利用二元logistic回归分析建立视力障碍风险预测模型。以受试者工作特征曲线下面积(area under curve,AUC)评价预测模型的有效性。结果老年人视力障碍的6个独立影响因素为年龄、血管收缩压、体力活动评分、糖尿病、自我报告的眼部疾病史和文化程度。建立了老年人视力障碍风险预测模型,在建模组和内部验证组均显示出较强的预测能力,AUC分别为0.87(95%CI 0.83~0.90)和0.81(95%CI 0.74~0.88)。结论老年人视力障碍风险预测模型具有较高的预测能力。识别有视力障碍风险的老年人,有助于医护人员制订适当、有针对性的早期教育和干预计划,以预防和延缓老年人视力障碍的发生和发展,预防因视力障碍造成的伤害。展开更多
基金We thank the Politeknik Kesehatan Kementerian Kesehatan Indonesia,LB.01.01/I.1/2657/2019 for funding this study,the respondents for participating in this study,and Marshall Godwin for granting permission to use the Simple Lifestyle Indicator Questionnaire(SLIQ).
文摘Objective:The purpose of this study was to determine the effectiveness of Mobile-Stroke Risk Scale and Life Style Guidance(M-SRSguide)in promoting a healthy lifestyle and reducing stroke risk factors in atrisk persons.Methods:This research was an clinical trial with a pre-test and post-test control group design.The accessible population is persons at risk of stroke in the community(West and East Kalimantan Province,Indonesia).Thirty-two participants in the intervention group and 32 participants in the control group participated in this study.The sampling method was systematic random sampling.We allocate the sample into the intervention and control groups using a randomized block design.The intervention group used the M-SRSguide.The control group used manual book for a self-assessment of stroke risk.The measurement of a healthy lifestyle and the stroke risk factors was performed before and six months after the intervention.Results:There are no significant differences in healthy lifestyle and stroke risk factors between the two groups after the intervention(P>0.05).Analysis of healthy lifestyle behavior assessment items in the intervention group showed an increase in healthy diets,activity patterns,and stress control after the use of the M-SRSguide(P<0.01).Conclusion:The use of M-SRSguide is effective in promoting a healthy lifestyle.
文摘BACKGROUND Data on non-drug related risk-factors for gastrointestinal bleeding(GIB)in the general population are limited,especially for life-style factors,clinical measurements and laboratory parameters.AIM To identify and investigate non-drug risk factors for major GIB in the general population of Finland.METHODS We performed a retrospective cohort study using data from the FINRISK health examination surveys,which have been conducted every 5 years across Finland from 1987 to 2007.Participants were adults aged 25 years to 74 years,excluding those with a previous hospitalization for GIB.Follow-up from enrollment was performed through linkage to national electronic health registers and ended at an event of GIB that led to hospitalization/death,death due to any other cause,or after 10 years.Covariates included demographics,socioeconomic and lifestyle factors,clinical measurements,laboratory parameters and comorbidities.Variable selection was undertaken using Least Absolute Shrinkage and Selection Operator(LASSO)and factors associated with GIB were identified using Cox regression.RESULTS Among 33,508 participants,403(1.2%)experienced GIB[256 men(63.5%);mean age,56.0 years(standard deviation(SD)±12.1)]and 33105 who did not experience GIB[15768 men(47.6%);mean age,46.8(SD±13)years],within 10 years of follow-up.Factors associated with a significantly increased risk of GIB were baseline age[per 10-year increase;hazard ratio(HR)1.62,95%confidence interval(CI):1.42-1.86],unemployment(HR:1.70,95%CI:1.11-2.59),body mass index(BMI)(HR:1.15,95%CI:1.01-1.32),gamma-glutamyl transferase(GGT)(HR:1.05,95%CI:1.02-1.09),precursors of GIB(HR:1.90,95%CI:1.37-2.63),cancer(HR:1.47,95%CI:1.10-1.97),psychiatric disorders(HR:1.32,95%CI:1.01-1.71),heart failure(HR:1.46,95%CI:1.04-2.05),and liver disorders(HR:3.20,95%CI:2.06-4.97).Factors associated with a significantly decreased risk of GIB were systolic blood pressure(SBP)(HR:0.78,95%CI:0.64-0.96),6-10 cups of coffee a day(HR:0.67,95%CI:0.46-0.99),or>10 cups(HR:0.43,95%CI:0.23-0.81).CONCLUSION Our study confirms established risk-factors for GIB and identifies potential risk-factors not previously reported such as unemployment,BMI,GGT,SBP and coffee consumption.
文摘Obesity and overweight are complex phenomena due to causes and consequences as these are the predisposing factors for developing lifestyle-related diseases. In the recent times, obesity and chronic diseases are being taken very seriously than ever before. Once upon a time, obesity was considered as a problem only in developed countries, now it is of a serious concern in low- and middle-income countries. In Papua New Guinea (PNG), a few studies have been conducted on obesity-related issues. However, there is not enough data on obesity to clearly understand about the leading contributing factors. Our research group designed this study to assess the influencing factors of obesity and as well as to identify the principal cause. It is a descriptive study, used structured and semi-structured interview and survey questionnaire. This study was conducted in the capital city of PNG, Port Moresby, and recruited 87 adults aged 30 - 50 years. According to interview and analysis findings, the contributing factors of obesity are less physical movement, unhealthy diet, lifestyle, cultural value system, low education and social system. Government strategies and initiatives are not strong enough to educate and motivate the community people. This study suggests developing an effective coordination among all stakeholders to ensure quality education and increase awareness to prevent obesity and its complications. The standard health policies and strategies are also recommended at the local, national and international level with strong commitment.
基金funded by the National Natural Science Foundation of China(NO:71974198).
文摘目的本研究旨在确定影响老年人视力障碍的危险因素,建立和评价视力障碍风险预测模型。方法采用非匹配病例对照设计研究,2020年6一12月,从辽宁省某教学医院的眼科门诊和体检中心招募了586名研究参与者(411名被纳人到建模组,175名被纳人内部验证组)。视力障碍定义为最佳矫正视力<6/18(世界卫生组织定义)。调查了患者的基本情况和可能的预测因素,包括人口统计学资料、疾病和药物使用情况及生活方式。利用二元logistic回归分析建立视力障碍风险预测模型。以受试者工作特征曲线下面积(area under curve,AUC)评价预测模型的有效性。结果老年人视力障碍的6个独立影响因素为年龄、血管收缩压、体力活动评分、糖尿病、自我报告的眼部疾病史和文化程度。建立了老年人视力障碍风险预测模型,在建模组和内部验证组均显示出较强的预测能力,AUC分别为0.87(95%CI 0.83~0.90)和0.81(95%CI 0.74~0.88)。结论老年人视力障碍风险预测模型具有较高的预测能力。识别有视力障碍风险的老年人,有助于医护人员制订适当、有针对性的早期教育和干预计划,以预防和延缓老年人视力障碍的发生和发展,预防因视力障碍造成的伤害。
