Risk Factors Physical Inactivity Research Articles

Trend and projection of non-communicable diseases risk factors in Iran from 2001 to 2030

This study aims to investigate the trends and project the major risk factors of Non-communicable Diseases (NCDs) in Iran. We obtained the trend of prevalence of main risk factors related to NCDs in 30 to 70-year-old-individuals. The data were extracted from WHO STEP wise approach to NCDs risk factor surveillance (STEPS) survey. Also,the previous studies conducted at national and subnational levels from 2001 to 2016 were employed. The prevalence of risk factors was projected by 2030 using Bayesian Model Averaging (BMA) and Spatio-temporal model stratified by sex and province. The percent change for the age-standardized prevalence of smoking in men between 2001 and 2016 was calculated to be − 27.0. Also, the corresponding values for the risk factors of diabetes, hypertension, obesity and overweight, physical inactivity (PI), and mean of salt intake were − 26.1, 29.0, 70.0, 96.8, 116.6, and 7.5, respectively. It is predicted that smoking and these risk factors will undergo a change to show values of − 1.26, 38.7, 43.7, 2.36, and 15.3 by 2030, respectively. The corresponding values in women for the time interval of 2001–2016 were − 27.3, 26.3, 82.8, 1.88, 75.2, and 4.2, respectively. Plus, projections indicate that the 2030 variation values are expected to be − 25.0, 16.7, 37.5, 28.7, 26.7, and 10.9 respectively. This study showed that the prevalence of four risk factors of PI, overweight and obesity, hypertension, and diabetes is increasing in Iran. Therefor, it is necessary to carry out effective interventions to adopt a healthy lifestyle and reduce the risk factors.

Physical activity patterns among obese adults attending rural primary health care units, Ismailia Governorate, Egypt: A case-control study.

Obesity is an ignored health problem in all countries; there are a lot of health problems related directly or indirectly to overweight and obesity. The incidence of COVID-19 with social isolation and technological development in recent years strongly contributed to a progressive increase in obesity. . Assess the pattern of the 3 divisions of physical activity and sedentary behaviors in obese patients. Physical inactivity is a significant concern, especially among individuals with obesity and certain demographic characteristics. Addressing these factors and promoting physical activity interventions tailored to specific populations is essential in combating sedentary behavior and its associated health implications.This case-control study included 350 adult obese patients (BMI ≥ 30) and 75 people with normal BMI (18.5-24.9). Their sociodemographic data were analyzed and their pattern of physical activity related to work, movement to and from places for 10 minutes, and pattern of recreational activity were assessed, in addition to the assessment of the sedentary behaviors. The mean age of the study group was 34 years, the majority were females, educated, and working. Forty five percent of the total sample were physically inactive; the pattern of activity during travel to and from places (10 min) was lower in obese patients. Recreational activities were low in the studied population, in the present study the time spent sitting or reclining (except sleeping) was significantly higher among obese participants than controls (P ≤ .001). Obesity, urban residence, unemployment and illiteracy were independent risk factors for physical inactivity.

Long-term ambient air pollution exposure and prospective change in sedentary behaviour and physical activity in individuals at risk of type 2 diabetes in the UK.

Air pollution may be a risk factor for physical inactivity and sedentary behaviour (SED) through discouraging active lifestyles, impairing fitness and contributing to chronic diseases with potentially important consequences for population health. Using generalized estimating equations, we examined the associations between long-term particulate matter with diameter≤2.5μm (PM2.5), ≤10μm (PM10) and nitrogen dioxide (NO2) and annual change in accelerometer-measured SED, moderate-to-vigorous physical activity (MVPA) and steps in adults at risk of type 2 diabetes within the Walking Away from Type 2 Diabetes trial. We adjusted for important confounders including social deprivation and measures of the built environment. From 808 participants, 644 had complete data (1605 observations; 64.7% men; mean age 63.86years). PM2.5, NO2 and PM10 were not associated with change in MVPA/steps but were associated with change in SED, with a 1ugm-3 increase associated with 6.38 (95% confidence interval: 0.77, 12.00), 1.52 (0.49, 2.54) and 4.48 (0.63, 8.34) adjusted annual change in daily minutes, respectively. Long-term PM2.5, NO2 and PM10 exposures were associated with an annual increase in SED: ~11-22min/day per year across the sample range of exposure (three standard deviations). Future research should investigate whether interventions to reduce pollution may influence SED.

Intersecting risk factors for physical inactivity among Canadian adults.

On average, 45% of Canadian adults meet the recommended 150 minutes per week of moderate-to-vigorous physical activity. This singular statistic masks a wide range of adherence levels among different groups within the population. The purpose of this paper is to determine how sex, age, and family arrangement intersect with known risk factors for physical inactivity to identify groups within the Canadian population most at risk of not meeting the physical activity recommendation. Using six combined cycles of the Canadian Health Measures Survey (from 2007 to 2019), this study examines how the percentage of Canadian males and females aged 18 to 79 years meeting the physical activity recommendation differs across sociodemographic, family arrangement, and health factors. Logistic regression was used to examine whether the association between specific factors and adherence to the physical activity recommendation differed by sex. Latent class analysis was used to identify sex-specific combinations of sociodemographic, family arrangement and health-related factors within the Canadian population that are associated with varying levels of adherence to the physical activity recommendation. More males met the physical activity recommendation compared with females (49% versus 38%). Latent classes with the lowest adherence to the physical activity recommendation (19% among females and 29% among males) primarily included those who were single or married with no children and who had a high probability of having many risk factors for physical inactivity, including being older, having a lower education, having lower income, smoking, having central adiposity, and having poor or fair self-rated general health. Latent classes with the highest adherence to the physical activity recommendation (61% among females and 67% among males) primarily included individuals with no spouse and no children and who had a low probability of having any risk factors for physical inactivity. For females, an additional class (32% of which met the physical activity recommendation) comprised young single mothers who had several risk factors for physical inactivity, including having low income, smoking and having central adiposity. Understanding how risk factors for physical inactivity intersect with sex, age, and family arrangement may inform strategies aimed at increasing physical activity among those who are most vulnerable.

Tıp fakültesi öğrencilerinde azalmış kor enduransı, yorgunluk ve fiziksel inaktivite için risk faktörleri

Aim: In medical faculty students’ investigation of risk factors for physical inactivity and fatigue and basic resilience is important to identify individuals at risk. The primary aim of this study was to determine the factors affecting core endurance, fatigue, and physical inactivity in medical faculty students. The secondary aim is to evaluate the relationship between core endurance and fatigue, physical activity, and low back pain. Additionally, to investigate the relationship between fatigue and physical activity level. Methods: This quantitative cross-sectional study was conducted with 201 healthy volunteer medical faculty students. Demographic data and the history of low back pain were recorded. The trunk flexors endurance test (FLET), Modified Biering-Sorensen test (MBST), and lateral bridge test (LBT) were used to measure trunk muscle core endurance. The physical activity levels of the participants were measured using the “International Physical Activity Scale” (IPAQ). The fatigue Severity Scale was used for the evaluation of fatigue. Results: FLET (p=0.021), MBST (p=0.004), LBT-Right (<0.001), LBT-Left (<0.001) tests were significantly higher in the group with FSS <2.3. A significant correlation was found between female gender and FLET (p<0.001), MBST (p<0.001), LBT-Right (p<0.001), LBT-Left (p<0.001). Gender (p=0.049), MBST (p=0.003) and MET 3 (p=0.025) were determined as factors affecting fatigue in the regression model. Female gender (OR= 0.376; p= 0.049) and MBST (OR= 0.986; p= 0.003) was determined as a risk factor for fatigue. Conclusion: Female gender, decreased core endurance, and physical inactivity are protective factors affecting fatigue in medical school students. Age, gender, body mass index, smoking, alcohol, and low back pain history were not found to be risk factors for physical inactivity. Improving core endurance is protective for physical inactivity.

Physical Inactivity and Sedentary Behaviour among Panamanian Adults: Results from the National Health Survey of Panama (ENSPA) 2019.

Physical inactivity (PI) has been described as an independent risk factor for a large number of major non-communicable diseases and is associated with an increased risk of premature death. Additionally, sedentary behaviour has been associated with increased overall mortality. We estimated the national prevalence of PI and sedentary behaviour using the Global Physical Activity Questionnaire version 2. Using unconditional logistic regressions, the possible risk factors for PI were assessed. Over half of the people included in this study (54.9%; 95% CI: 54.1-57.3%) were physically inactive, with the median time spent engaged in sedentary behaviour being 120 min per day. Statistically significant associations with PI were observed with regard to sex, living area, and alcohol consumption. PI prevalence in Panama was elevated and showed a sex difference (women: 64.7%, 95% CI: 63.7-66.7%; men: 43.4%, 95% CI: 41.5-47.5%). According to our analysis of three-domain-related physical activities, the main contribution to the total estimated energy expenditure of physical activity/week came from the transport domain, followed by the work/household domain, and the least significant contributor was consistently the domain of exercise- and sports-related physical activities.

Promoting Effects of the Exercise Behavioral Ecological Model on Physical Activity Behaviors of Students.

Objectives: In this study, we explored the promoting effects of the Exercise Behavioral Ecological Model on the physical activity behaviors of middle school students, and relevant risk factors for physical inactivity. Methods: A total of 291 middle school students (junior and senior high school students) were enrolled as the research participants. The sedentary behavior of middle school students was assessed using the Adolescent Sedentary Behavior Scale. We used the Exercise Behavioral Ecological Model to influence the exercise behaviors of middle school students from the perspectives of environment, physiology, and psychology. Then the independent risk factors for physical inactivity behaviors of middle school students were analyzed by means of multivariate logistic regression analysis. Results: The change strategy, positive effect on decision-making balance, and self- efficacy scores were higher and the negative effect on decision-making balance score was lower than those before intervention (p<.05). Multivariate logistic regression analysis denoted that grade (senior high school students), sedentary time (> 4 hours), daily TV watching time (>2 hours) and change stage (pre-contemplation stage) were associated risk factors for physical inactivity among middle school students (p <.05). Conclusions: The Exercise Behavioral Ecological Model can facilitate physical activity and reduce sedentary behavior.

INVESTIGATION ON THE PREVALENCE AND RISK FACTORS OF TYPE 2 DIABETES MELLITUS AMONG THE RURAL POPULATION IN STATE OF TAMILNADU

Background: Diabetes mellitus is a common health problem in this world, causing a huge burden for individuals, families, and communities. It is a clinical syndrome characterized by hyperglycaemia due to absolute or relative deciency of Insulin. Methods: A cross-sectional study carried out among 530 type 2 diabetic mellitus patients in rural eld practice area of Tamil Nadu. The objectives of the study were to estimate the prevalence of risk factors for Type 2 diabetes mellitus among the population in rural areas of Tamil Nadu. Data was analyzed using the SPSS version 20.0 software. Results: Among the 530 people were studied in age group of 20–70 (aged 45 ± 18.7 years). Majority (48%) were more than 50-70 years of age. In this study, 272 of the participants were female and 258 were male. In relation to residence area, 412 (78%) were Rural people and 118 (22%) were Urban people. The data result was expressed as mean and standard deviation. A probability value is less than 0.05 and it was considered statistically signicant. The risk factors of physical inactivity, Illiteracy, Mixed diet, Stress, hypertension, overweight/obesity, Tobacco smoking, Alcohol intake, Family history of diabetes and Sedentary workers were investigated. Conclusions: It concluded that the prevalence and risk factors of type 2 diabetic mellitus is high. It is required to reduce the trouble for rising type 2 diabetic mellitus in the society. The risk factors were signicantly associated with the high rate of complications of diabetes. It is suggests that on focusing of interventions to prevent and control the type 2 diabetic mellitus.

Prevalence and associated factors of physical inactivity among middle-aged and older adults in India: results of a national cross-sectional community survey

ObjectiveThis study aimed to determine the prevalence and associated factors of physical inactivity in middle-aged and older adults in India.DesignPopulation-based cross-sectional study.SettingNationally representative sample of general community-dwelling middle-aged and older...

Association between Objectively Measured Physical Activity and Arterial Stiffness in Children with Congenital Heart Disease.

Background: The association between physical activity (PA) and arterial stiffness is particularly important in children with congenital heart disease (CHD) who are at risk for arterial stiffening. The aim of this study was to examine the association between objectively measured PA and arterial stiffness in children and adolescents with CHD. Methods: In 387 children and adolescents with various CHD (12.2 ± 3.3 years; 162 girls) moderate-to-vigorous PA (MVPA) was assessed with the “Garmin vivofit jr.” for 7 consecutive days. Arterial stiffness parameters including pulse wave velocity (PWV) and central systolic blood pressure (cSBP) were non-invasively assessed by oscillometric measurement via Mobil-O-Graph®. Results: MVPA was not associated with PWV (ß = −0.025, p = 0.446) and cSBP (ß = −0.020, p = 0.552) in children with CHD after adjusting for age, sex, BMI z-score, peripheral systolic blood pressure, heart rate and hypertensive agents. Children with CHD were remarkably active with 80% of the study population reaching the WHO recommendation of average 60 min of MVPA per day. Arterial stiffness did not differ between low-active and high-active CHD group after adjusting for age, sex, BMI z-score, peripheral systolic blood pressure, heart rate and hypertensive agents (PWV: F = 0.530, p = 0.467; cSBP: F = 0.843, p = 0.359). Conclusion: In this active cohort, no association between PA and arterial stiffness was found. Longer exposure to the respective risk factors of physical inactivity might be necessary to determine an impact of PA on the vascular system.

A Cumulative Muscle Index and Its Parameters for Predicting Future Cognitive Decline: Longitudinal Outcomes of the ASPRA Cohort.

Sarcopenia and cognitive decline share the major risk factors of physical inactivity; previous studies have shown inconsistent associations. We aimed to identify the association of sarcopenia and its parameters with cognitive decline. The 3-year longitudinal outcomes of 1327 participants from the Aging Study of the Pyeongchang Rural Area (ASPRA) cohort were analyzed. Cognitive performance was evaluated using the Mini-Mental State Examination (MMSE), and sarcopenia was defined by the following: the original and revised Asian Working Group for Sarcopenia (AWGS), the original and revised European Working Group on Sarcopenia in Older People (EWGSOP), and the Cumulative Muscle Index (CMI), a novel index based on the number of impaired domains of sarcopenia. Approximately half of the participants showed meaningful cognitive decline. Sarcopenia by the original EWGSOP and the CMI were associated with cognitive decline. Only the CMI showed consistent predictability for cognitive impairment even with different criteria of the MMSE score (OR 1.23 [1.04–1.46]; OR 1.34 [1.12–1.59]; OR 1.22 [1.01–1.49], using the 1, 2, and 3 cut-off value, respectively). Of the CMI parameters, gait speed was satisfactorily predictive of 3-year cognitive impairment (OR 0.54 [0.30–0.97]). In conclusion, sarcopenia based on the CMI may be predictive of future cognitive impairment. Gait speed was the single most important indicator of cognitive decline.

Abstract 047: Using Geospatial Data To Eliminate Disparities In Covid-19 Testing

Introduction: The first case of COVID-19 in Chattanooga/Hamilton County, Tennessee (CHC) was identified on March 13, 2020. By early April, 51 RT-PCR confirmed cases were identified, with white, non-Hispanic males and females representing 82% (41/51) of positive cases and remaining cases representing black residents (18%; 9/51). That few people from racial/ethnic minorities were being tested became a key public health concern. We hypothesized that local mapping of health-related data would identify regions where individuals at greater risk for COVID-19 live and work and have limited access to testing and healthcare services. Methods: The CDC 500 Cities data was used to generate layered maps of prevalence estimates for cardiovascular disease, type 2 diabetes mellitus, chronic lung disease, and the behavioral risk factors of physical inactivity and obesity. Layers also included the CDC Social Vulnerability Index, age distribution, gender, race, ethnicity, and zip codes. Maps were shared with intersectoral collaborators representing the black and Hispanic/Latinx communities who provided specific neighborhood information to the maps. Collaborators included hospital systems, the local health department, community health centers, the private sector, and non-profit organizations. Maps were used to identify geographic sites for mobile and strategic testing within communities at higher risk for the spread of the coronavirus. Specific diverse neighborhoods along with worksites were then provided with testing beginning in early May and ongoing. Results: Strategic and mobile testing beginning in early May increased three-fold the number of identified new cases of COVID-19. Seventy percent (652/932) of these positive tests were among ethnically Hispanic/Latinx and 16% (149/932) among black residents. Positive tests continued to increase at a greater rate among Hispanic/Latinx and black residents compared with white residents through the months of May-July (68/10K vs. 2.6/10K, OR = 4.85, 95% CI 2.66, 9.02). Conclusions: This example of inter-sectoral collaboration, data sharing, and data use through strategic mapping of vulnerable populations for COVID-19 was an effective means to enhance COVID-19 testing and identification of positive cases throughout CHC. This expanded partnering resulting in targeted testing may be a useful approach among similar communities and subsequent outbreaks.

Physical inactivity in healthy, obese, and diabetic adults in Germany: An analysis of related socio-demographic variables.

BackgroundAdults with diabetes or obesity are more likely to be physically inactive than healthy adults. Physical activity is essential in the management of both diseases, necessitating targeted interventions in these groups. This study analysed physical inactivity (defined as not taking part in leisure-time physical activity) in over 100,000 adults in Germany considering their body mass index and the presence of diabetes. Furthermore, the relationship between specific socio-demographic factors with physical inactivity was investigated, particularly focussing diabetic and obese people, to refine the identification of risk-groups for targeted interventions on physical activity promotion.MethodsData from 13 population-based health surveys conducted in Germany from 1997 to 2018 were used. The relevant variables extracted from these datasets were merged and employed in the analyses. We included data from 129,886 individuals in the BMI analyses and 58,311 individuals in the diabetes analyses. Logistic regression analyses were performed to identify the importance of six socio-demographic variables (age, sex/gender, education, income, employment, and migration) for the risk of physical inactivity.ResultsObese and diabetic people reported a higher prevalence of physical inactivity than those who were not affected. Logistic regression analyses revealed advanced age, low education level, and low household income as risk factors for physical inactivity in all groups. A two-sided migration background and unemployment also indicated a higher probability of physical inactivity.ConclusionSimilar socio-demographic barriers appear to be important determinants of physical inactivity, regardless of BMI status or the presence of diabetes. However, physical activity promoting interventions in obese and diabetic adults should consider the specific disease-related characteristics of these groups. A special need for target group specific physical activity programmes in adults from ethnic minorities or of advanced age was further identified.

Physical Activity and Outdoor Leisure Time Physical Exercise: A Population Study of Correlates and Hindrances in a Resource-Constrained African Setting.

PurposeThe study evaluated the burden of physical inactivity, its correlates, and the self-reported hindrances to outdoor leisure-time physical exercises in Enugu Nigeria. It also evaluated the prevalence of leisure-time outdoor physical exercise and its correlates in Enugu Nigeria.Patient and MethodsThis is a cross-sectional household survey involving 6628 individuals aged 20 to 60 years from 2848 households in Enugu Nigeria. Binary logistic regression and multinomial regression analyses were carried out as appropriate. Estimates were weighted to account for the actual population distribution of important sociodemographic variables and reported with the 95% confidence interval.ResultsThe burden of physical inactivity was 32.68% (95% CI: 31.24–34.12%). Urban dwellers were less likely to be physically active than rural dwellers (AOR = 0.477; 95% CI = 0.410–0.555). For each year increase in age, the odds of being physically active decreases by a factor of 0.993 (AOR = 0.993; 95% CI= 0.988–0.998). Gender, income level and education did not predict physical inactivity. Physical inactivity significantly increases the odds of being obese by a factor of 1.428 (AOR: 1.428; 95% CI: 1.190–1.714). Only 6.45% (95% CI: 5.82%-7.09%) participants reported at least once a week outdoor leisure-time physical exercise. The major barriers include lack of time and lack of interest in outdoor leisure-time physical exercise.ConclusionThe burden of physical inactivity is high, while the level of outdoor physical exercise is low in Enugu, Nigeria. Urban dwelling and increasing age are risk factors for physical inactivity. Living in urban areas, being less than 40 years of age, having a university education, and a high personal income are factors that positively drive outdoor leisure-time physical exercises. Policies that will promote awareness of the health benefits of physical activity and outdoor physical exercise are needed if Nigeria is to achieve the global mandate of reducing physical inactivity by 10% in the year 2025.

Level of physical activity among middle-aged and older Chinese people: evidence from the China health and retirement longitudinal study

BackgroundWith data from different regions accumulated, physical inactivity (PI) was found to be pandemic worldwide. Using China Health and Retirement Longitudinal Study (CHARLS), a nationwide longitudinal survey data, we aimed to delineate the prevalence, incidence and risk factors of physical inactivity (PI) among Chinese people aged 45 years and older.MethodsThe CHARLS covered nearly all provinces, autonomous regions, municipalities of mainland China. With data from CHARLS, three cross-sectional analyses and a cohort analysis were conducted. In cross-sectional studies, we used surveys at 2011, 2013 and 2015 to examine the prevalence and its trend of PI. Multivariate generalized linear model was conducted in survey at 2011 to examine the risk factors for prevalent PI. Multiple imputation of missing values was used and results before and after imputation were compared. In cohort analysis, we identified people free of PI at 2011 and followed them up until 2015 to estimate the incidence of PI. Generalized estimating equation was used to examine the risk factors associated with incidence PI. In all analyses, PI was defined as insufficient physical activity according to the International Physical Activity Questionnaire (IPAQ) criterion.Results6650, 5946 and 9389 participants were eligible for cross-sectional analyses, and 4525 participants were included for cohort analysis. The weighted prevalence of PI was 22.25% (95% CI: 20.63–23.95%) in 2011, 20.64% (95% CI: 19.22–22.14%) in 2013 and 19.31% (95% CI: 18.28–20.38%) in 2015. In multivariate analysis, PI was associated with older age, higher education, overweight, obesity and difficulties in daily living, and was negatively associated with working and higher level of expenditure. No material change was detected in results after multiple imputation. In cohort analysis, older age, abundant public facilities, difficulties in daily living were identified as risk factors of incidence PI, while urban areas, college and above education, and working were protective factors.ConclusionsPI is pandemic in 45 years and older people in China. People with older age, difficulties in daily living and people who are not working are at higher risk. More efforts should be paid in estimating and promoting leisure-time physical activities.

Risk Factors for Physical Inactivity Among Children With and Without Asthma Living in Peri-Urban Communities of Lima, Peru.

The authors sought to examine physical activity patterns among children with and without asthma in 2 peri-urban communities in Lima, Peru, to identify socioeconomic and demographic risk factors for physical inactivity and examine the relationship between asthma and physical activity. The authors measured mean steps per day in 114 children (49 with asthma and 65 without) using pedometers worn over a 1-week period. They also used the 3-day physical activity recall to determine the most common activities carried out by children. The authors found that 84.2% of the children did not meet the daily international physical activity recommendations. Girls took significantly fewer mean steps per day as compared with boys (2258 fewer steps, 95% confidence interval, 1042-3474), but no other factors, including asthma status, showed significant differences in the mean daily steps. Mean daily steps were positively associated with higher socioeconomic status among girls, and current asthma had a larger inverse effect on daily steps in boys when compared with girls. Physical activity levels were below recommended guidelines in all children. There is a need for policy and neighborhood-level interventions to address low physical activity levels among Peruvian youth. Special focus should be given to increasing the physical activity levels in girls.

The Influence of Cognitive Impairment on Health Behaviors among Older Adults.

Objectives: Previous research on cognitive impairment and health behaviors has focused largely on how health behaviors affect cognition; rarely has it examined whether cognitive impairment affects health behaviors. The purpose of this study was to examine the impact of cognitive impairment on engagement in health behaviors among older adults. Methods: The study sample included 19,644 adults aged 50 or older from the Health and Retirement Study 1995-2012 surveys. We used mixed-effects logistic regression to analyze the influence of cognitive impairment, measured using the Telephone Interview for Cognitive Status, on the engagement of health behaviors including physical activity, smoking, and drinking. Results: Cognitive impairment without dementia [CIND] (OR = .84, 95% CI = .80-.89) and dementia (OR = .68, 95% CI = .61-.75) were associated with a lower likelihood of engaging in regular vigorous physical activity during longitudinal follow-up, after adjusting for covariates. CIND or dementia was not significantly associated with the likelihood of smoking or alcohol consumption. Conclusions: CIND and dementia are risk factors for physical inactivity among older adults. Promotion of regular physical activity should be an essential component of health promotion programs for persons with cognitive impairment.

Food Behaviors, Health, and Bean Nutrition Awareness among Low-Income Men: A Pilot Study

Bean consumption is known to lower blood cholesterol and postprandial blood glucose. With higher chronic disease risk, low-income men could theoretically benefit from increased bean intakes. The study objective was to explore low-income men’s food behaviors, bean health benefit awareness, and bean consumption practices and preferences. Seventy-one low-income men aged 18–65 years (µ 41 ± 12.7; 53% white, 16% black, 31% Hispanic) completed a survey on health risks, food behaviors, bean health knowledge, attitudes toward dry and canned beans, and bean preferences. Eighty-seven percent had one or more health risk factors of physical inactivity, smoking, or obesity. Most men compared food prices, and thought about healthy food choices for their families, but few planned meals or read nutrition facts labels. White men had significantly higher bean health benefit knowledge than black or Hispanic men (p < 0.01). Most men liked the taste of beans, disagreed dry beans took too long to prepare, and 79% ate them at least 2–3 times per month. Forty-nine percent agreed beans caused intestinal gas. Improving men’s awareness of the health benefits of beans as well as leveraging existing positive attitudes may be useful approaches to increase bean consumption among low-income and minority male populations.

Shift work and physical inactivity: findings from the Finnish Public Sector Study with objective working hour data.

Objectives Shift work is a risk factor for chronic diseases, and physical inactivity can have an influence on this association. We examined whether intra-individual changes in working time characteristics were associated with changes in physical inactivity and examined the risk factors for physical inactivity among shift workers in a 17-year longitudinal study cohort. Methods Study participants were 95 177 employees from the Finnish public sector. Work schedule information was based on questionnaire responses and additional register-based working time characteristics for 26 042 employees. The associations between working time characteristics and physical inactivity were examined using a fixed-effects logistic model. To investigate the risk factors for physical inactivity among shift workers, the odds ratios (OR) of worktime control and having small children were calculated. Results Compared with day work, shift work without night shifts was associated with physical inactivity among men [OR 1.38, 95% confidence interval (CI) 1.09-1.74], whereas shift work with night shifts was negatively associated with physical inactivity among women (OR 0.85, 95% CI 0.76-0.96). Register-based working time data confirmed that workers with a higher percentage of night shifts had a lower risk of physical inactivity. Having small children was associated with physical inactivity among shift workers (OR 1.47, 95% CI 1.32-1.65). Conclusions Both survey and objective working hour data revealed that workers having work schedules with night shifts were more likely to be physically active. Having small children was a risk factor for physical inactivity among shift workers.

Prevalence and predictors of physical inactivity levels among Kenyan adults (18\u201369\xa0years): an analysis of STEPS survey 2015

BackgroundPhysical inactivity accounts for more than 3 million deaths worldwide, and is implicated in causing 6% of coronary heart diseases, 7% of diabetes, and 10% of colon or breast cancer. Globally, research has shown that modifying four commonly shared risky behaviours, including poor nutrition, tobacco use, harmful use of alcohol, and physical inactivity, can reduce occurrence of non-communicable diseases (NCDs). Risk factor surveillance through population-based periodic surveys, has been identified as an effective strategy to inform public health interventions in NCD control. The stepwise approach to surveillance (STEPS) survey is one such initiative, and Kenya carried out its first survey in 2015. This study sought to describe the physical inactivity risk factors from the findings of the Kenya STEPS survey.MethodsThis study employed countrywide representative survey administered between April and June 2015. A three stage cluster sampling design was used to select clusters, households and eligible individuals. All adults between 18 and 69 years in selected households were eligible. Data on demographic, behavioural, and biochemical characteristics were collected. Prevalence of physical inactivity was computed. Logistic regression used to explore factors associated with physical inactivity.ResultsA total of 4500 individuals consented to participate from eligible 6000 households. The mean age was 40.5 (39.9–41.1) years, with 51.3% of the respondents being female. Overall 346 (7.7%) of respondents were classified as physically inactive. Physical inactivity was associated with female gender, middle age (30–49 years), and increasing level of education, increasing wealth index and low levels of High Density Lipoproteins (HDL).ConclusionA modest prevalence of physical inactivity slightly higher than in neighbouring countries was found in this study. Gender, age, education level and wealth index are evident areas that predict physical inactivity which can be focused on to develop programs that would work towards reducing physical inactivity among adults in Kenya.