Predictors Of Physical Inactivity Research Articles

Predictors of physical inactivity among pregnant women.

It is recommended that pregnant women be physically active to promote maternal and child health. This study aimed to explore the prevalence of physical inactivity and its modifiable predictors in the three trimesters in Chinese pregnant women. Four hundred forty-four pregnant women completed the Pregnant Physical Activity Questionnaire in the first, second, and third trimesters. The prevalence of physical inactivity reached its highest (66.2%) in the first trimester and lowest (19.4%) in the second trimester. Pregnant women with inadequate physical activity knowledge and low self-efficacy were at higher risk for physical inactivity. Monitoring physical inactivity could be incorporated into antenatal care and start from the first trimester. Prenatal care professionals should take action to increase pregnant women's physical activity self-efficacy and knowledge to enhance their physical activity.

Incidence and Predictors of Physical Inactivity Among Malaysian Community-Dwelling Older Persons.

The aim of this study was to determine the incidence of physical inactivity and identify the predictors for low physical activity among community-dwelling older persons living in Malaysia in 3 years follow-up. In this prospective study, physical activity levels were measured using the Physical Activity Scale for the Elderly. The arbitrary cutoff for Physical Activity Scale for the Elderly in this study was ≤70.9 for low and >141 for high physical activity levels. Out of the 955 physically active participants at baseline, 555 of them (mean [SD] age 68.82 [4.92] years) were successfully followed up to 3 years. Cumulative incidence of being physically inactive was 21% with rate of 7 per 100 person-years. It was found that being older (<.001), from Malay ethnic group (<.05), smokers (<.01), having lower gait speed (<.001), and lower cognitive status (<.05) were predictors for physical inactivity among Malaysian community-dwelling older persons in 3 years follow-up. These factors should be taken into consideration when planning for intervention and promotion strategies to increase physical activity levels among Malaysian older persons.

Pattern of physical activity and associated sociodemographic factors among the adult population of Tumkur city

Introduction: Non-communicable diseases (NCDs) account for nearly half of the overall global burden of disease. Physical inactivity has been reported to be a major independent “modifiable” risk factor for NCDs. Increase in physical activity decreases the incidence of cardiovascular diseases, Type 2 diabetes, stroke, and improves psychological wellbeing. Aim: To estimate the prevalence and predictors of physical inactivity among the adults in Tumkur. Methodology: This cross-sectional study was conducted among 374 adult participants from an urban area of Tumkur. The level of physical inactivity was measured by using WHO standard Global Physical Activity Questionnaire (GPAQ). Results: Overall prevalence of physical inactivity in our study was 52%. Among the study participants, contribution of physical activity in work domain was more with a median of 3720 minutes. The leisure time activities and transport domain contribute a median of 2520 and 1260 minutes, respectively. Both men and women were equally inactive, women – 56.2% and men - 48.8%. Females were 1.9 times more inactive than males. Literates were more inactive as compared to illiterates. Tobacco users were 2.2 times more inactive than non-tobacco users. Conclusion: Physical activity was observed to be inadequate among women, literates and current tobacco users. To counteract the morbidities brought on by physical inactivity, physical activity promotion is required.

Prevalence, Patterns, and Predictors of Physical Inactivity in an Urban Population of India.

Physical inactivity (PI) is a risk factor for mortality and morbidity. PI and its predictors among the urban population in Bhubaneswar, India, were unknown. Finding out the contribution of PI as a cause of existing noncommunicable diseases (NCD) is difficult without following up with a cohort. The study was hence done to find out the prevalence, patterns, and predictors of physical inactivity in an urban population, and simultaneously investigate its causal relationship with NCD from this cross-sectional study. Cluster random sampling was used with a sample size of 1203 with a design effect of three. Socio-demographic, health profile, physical activity levels, and stage of change for physical activity behavior were collected. Logistic regression and marginal structural model analysis (by inverse probability of treatment weighting {IPTW} using a generalized estimating equation {GEE} to investigate the relationship between physical activity and prevalence of NCDs) were done using IBM SPSS v20 software (Armonk, NY: IBM Corp.). Statistical significance was tested at p=0.05. A total of 1221 subjects participated. The mean age was 35.25 years and 71.9% were physically inactive. General caste, presence of NCD, and being in a static stage of change influenced physical activity positively. PI was found to be a risk factor for NCD with 1.54 times higher odds in this population. The study concluded that the prevalence of physical activity was low and PI was a causative factor for NCD.

Prevalence and Predictors of Physical Inactivity among Adults - A Cross-Sectional Study.

Background:Physical inactivity is detrimental to health. It is a risk factor for noncommunicable diseases.Aim and Objective:The aim of this study was to find the prevalence and predictors of physical inactivity among adults.Methodology:A cross-sectional community-based study was conducted among 374 adults aged 20–50 years. Trained field investigators administered the Global Physical Activity Questionnaire. Data were analyzed using Epi Info software for Windows. Logistic regression model was used to study the factors associated with physical inactivity.Results:The prevalence of physical inactivity among the study participants was 52.1%. Females (OR=1.9[1.2-3.1]), literate individuals (OR = 1.8 [1.04–3.2]), and current tobacco users (OR = 2.2 [1.8–4.1]) were significantly more physically inactive; as compared to their counterparts.Conclusions:Nearly half of the study participants were physically inactive, according to the World Health Organization guidelines. This highlights the need for strengthening the existing interventions for promoting physical activity among adults.

Physical inactivity and perceived environmental factors: a cross-sectional study among civil servants in Abia State, Southeastern Nigeria.

Introductionphysical inactivity has been identified as the fourth leading risk factor for global mortality due to non-communicable diseases. Prevalence rates of 91.0% and 62.2%, have been documented among civil servants in the northern and southern parts of Nigeria, respectively. There is a paucity of data regarding the relationship between physical inactivity and environmental factors among civil servants in the State. This study assessed the prevalence and perceived environmental factors associated with physical inactivity among civil servants in Abia State Nigeria.Methodswe conducted a cross-sectional study in which we recruited 440 civil servants using a multistage sampling technique. We used an interviewer-administered structured questionnaire to collect data on sociodemographic, physical activity, and neighbourhood environmental attributes. Descriptive, bivariate and multivariate analysis were done. The level of significance was set at 5%.Resultsthe mean age of the respondents was 39.0±9.2 years, and 61% were females. The prevalence of physical inactivity was 48.4% (95%CI: 43.7%-53.2%). The putative environmental factors included perceptions of low residential density areas, perceived absence of neighbourhood sidewalks, perceived unavailability of bicycling facilities and the perception of an unsafe neighbourhood due to night crimes. The predictor of physical inactivity was the perceived absence of neighbourhood sidewalks (aOR=2.02, 95%CI: 1.10-3.73).Conclusionin this study, physical inactivity is prevalent among civil servants in Abia State. The need for the stakeholders in collaboration with the Ministry of Environment to focus on the provision of sidewalk facilities, layout of residential areas and limit security risks in the State to enhance physical activity is highlighted.

Physical inactivity in older adults with cognitive impairment without dementia: room for improvement.

Persons with cognitive impairment without dementia are at high risk of adverse health outcomes. Tailored intervention targeting moderate-vigorous physical activity (MVPA) may reduce these risks. To identify the prevalence and predictors of physical inactivity among older adults with cognitive impairment, no dementia (CIND); and estimate the proportion of inactive people with CIND who are capable of greater MVPA. We studied 1875 community dwelling participants (over age 65) with CIND in the Health and Retirement Study. Physical inactivity was defined as MVPA ≤ 1x/week. Associations of physical inactivity with sociodemographic, health, and physical function were examined using chi-square and modified Poisson regression. We considered physically inactive participants capable of greater MVPA if they reported MVPA at least 1-3x/month, no difficulty walking several blocks, or no difficulty climbing several flights of stairs. Fifty-six percent of participants with CIND were physically inactive. Variables with the highest age, sex, and race/ethnicity adjusted risk ratio (ARR) for physical inactivity were self-rated health (poor [76.9%]vs. excellent [34.2%]; ARR [95% CI] 2.27 [1.56-3.30]), difficulty walking (across the room [86.5%] vs. none [40.5%]; ARR [95% CI] 2.09 [1.87-2.35]), total assets (lowest quartile [62.6%] vs. highest quartile [43.1%]; ARR [95% CI] 1.54 [1.29-1.83]), and lower education attainment (less than high school [59.6%] vs college graduate [42.8%]; ARR [95% CI] 1.46 [1.17-1.83]). Among physically inactive older adults with CIND, 61% were estimated to be capable of greater MVPA. Although physical inactivity is prevalent among older adults with CIND, many are capable of greater MVPA. Developing tailored physical activity interventions for this vulnerable population may improve cognitive, health, and quality of life outcomes.

A Statistical Study on the Prevalence of Physical inactivity among Cardiovascular Diseases patients: The Predictive role of Demographic and Socioeconomic Factors

Physical inactivity (PI) is an established modifiable risk factor of cardiovascular diseases (CVDs) which is the leading cause of global mortality. Researchers and practitioners have been trying to reduce the surge of PI in the population but still, a substantial chunk of the world population is struggling with the issues of PI. This study is aimed at determining the prevalence and associated background factors of PI among CVDs patients. Further, profiles of potentially physically inactive people will also be identified for the future. A cross-sectional study was conducted at Punjab Institute of Cardiology (PIC) Lahore, Pakistan spanning the duration of September 2018 to February 2019. A sample of 230 CVDs patients, using 95% confidence interval (CI), 80% power of test and 5% margin of error was selected in the study. The data on PI was collected using standardized international physical activity questionnaire. In addition to descriptive statistics, bivariate analysis, multiple logistic regression analysis and odds ratios (OR) were also used. The study included 230 participants, in which 156 (68%) were males. The average age of the CVDs patients was 50.11±11.15 years. One hundred thirteen (49%) of the patients were physically inactive. Forward stepwise logistic regression estimated that good subjective financial well-being (OR= 0.560; 95% CI: 0.439-0.714), high years of schooling (OR = 0.932; 95% CI: 0.871-0.998), male gender (OR = 0.336, 95% CI: 0.162-0.698) and advanced ages (OR = 1.041, 95% CI: 1.011-1.072) were the strong factors in determining the likelihood of PI. This study concludes that the prevalence of PI in CVDs patients is alarming and background factors are the strong predictors of PI. These factors can be used to design customized strategies for the reduction of PI which would ultimately help in reducing the incidence of CVDs in the population.

Physical inactivity after stroke: Incidence and early predictors based on 190 individuals in a 1-year follow-up of the Fall Study of Gothenburg.

ObjectiveTo determine the incidence of physical inactivity and factors prior to stroke and in acute stroke that are associated with physical inactivity 1 year after strokeDesignProspective longitudinal cohortPatientsA total of 190 consecutively included individuals with acute strokeMethodsA follow-up questionnaire, relating to physical activity level using the Saltin-Grimby Physical Activity Scale, was sent to participants in the Fall Study of Gothenburg 1 year after stroke. Predictors of physical inactivity at baseline were identified using univariable and multivariable logistic regression analyses.ResultsPhysical inactivity 1 year after stroke was reported by 70 (37%) of the 190 patients who answered the questionnaire and was associated with physical inactivity before the stroke, odds ratio (OR) 4.07 (95% confidence interval (95% CI) 1.69–9.80, p = 0.002); stroke severity (assessed by National Institutes of Health Stroke Scale (NIHSS), score 1–4), OR 2.65 (95% CI) 1.04–6.80, p = 0.042) and fear of falling in acute stroke, OR 2.37 (95% CI 1.01–5.60, p = 0.048).ConclusionAlmost 4 in 10 participants reported physical inactivity 1 year after stroke. Physical inactivity before the stroke, stroke severity and fear of falling in acute stroke are the 3 main factors that predict physical inactivity 1 year after stroke.LAY ABSTRACTAfter a stroke, there is a risk of becoming physically inactive, which could affect one’s health. In order to create a rehabilitation plan with individualized preventive actions and interventions against physical inactivity, it is important, early after stroke onset, to identify patients who are at risk of becoming physically inactive. Previous studies of the early prediction of physical inactivity are few in number, with small sample sizes. This research on 190 individuals describes which factors prior to and in acute stroke (which is the first week after stroke) are associated with physical inactivity 1 year after stroke. The results showed that physical inactivity before stroke, stroke severity, and a fear of falling in acute stroke are associated with physical inactivity 1 year after stroke.

Early prediction of physical inactivity in 215 individuals 6 months after a mild stroke: The fall study of Gothenburg.

To determine the frequency of, and factors associated with, physical inactivity 6 months post-stroke. Prospective longitudinal cohort. A total of 215 consecutively included individuals with mild acute stroke from the Fall Study of Gothenburg. Physical activity level was assessed using the 4-level Saltin-Grimby Physical Activity Scale. Associations between independent variables and the dependent variable physical inactivity 6 months post-stroke were identified using univariable and multivariable logistic regression analyses. At 6 months post-stroke, 81 individuals (37.7%) reported physical inactivity. Physical inactivity at 6 months after a mild stroke were associated with: stroke severity, odds ratio (OR) 1.29 (95% confidence interval (95% CI) 1.01-1.66, p = 0.043); physical inactivity prior to stroke, OR 2.89 (95% CI 1.39-6.04, p = 0.0046); moderate postural control, OR 6.71 (95% CI 2.48-18.16, p = 0.0002); poor postural control, OR 2.55 (95% CI 1.19-5.48, p = 0.016); and number of drugs in acute stroke, OR 1.20 (95% CI 1.06-1.35, p = 0.0046). In this exploratory study, almost 40% of subjects reported physical inactivity 6 months after an acute mild stroke. Predictors of physical inactivity were physical inactivity before stroke and greater stroke severity, impaired postural control and a higher number of drugs in acute stroke.

Prevalence and Predictors of Physical Inactivity in a Rural Poulation in Nigeria

Prevalence and Predictors of Physical Inactivity in a Rural Poulation in Nigeria

Screening for physical inactivity among adults: the value of distance walked in the six-minute walk test. A cross-sectional diagnostic study.

Accelerometry provides objective measurement of physical activity levels, but is unfeasible in clinical practice. Thus, we aimed to identify physical fitness tests capable of predicting physical inactivity among adults. Diagnostic test study developed at a university laboratory and a diagnostic clinic. 188 asymptomatic subjects underwent assessment of physical activity levels through accelerometry, ergospirometry on treadmill, body composition from bioelectrical impedance, isokinetic muscle function, postural balance on a force platform and six-minute walk test. We conducted descriptive analysis and multiple logistic regression including age, sex, oxygen uptake, body fat, center of pressure, quadriceps peak torque, distance covered in six-minute walk test and steps/day in the model, as predictors of physical inactivity. We also determined sensitivity (S), specificity (Sp) and area under the curve of the main predictors by means of receiver operating characteristic curves. The prevalence of physical inactivity was 14%. The mean number of steps/day (≤ 5357) was the best predictor of physical inactivity (S = 99%; Sp = 82%). The best physical fitness test was a distance in the six-minute walk test and ≤ 96% of predicted values (S = 70%; Sp = 80%). Body fat > 25% was also significant (S = 83%; Sp = 51%). After logistic regression, steps/day and distance in the six-minute walk test remained predictors of physical inactivity. The six-minute walk test should be included in epidemiological studies as a simple and cheap tool for screening for physical inactivity.

Predictors of physical inactivity in men and women with type 2 diabetes from the Detection of Ischemia in Asymptomatic Diabetics (DIAD) study.

The purpose of this secondary analysis was to determine changes in physical inactivity from baseline to 5 years and to identify factors associated with and predictive of physical inactivity among individuals with type 2 diabetes enrolled in the Detection of Ischemia in Asymptomatic Diabetics (DIAD) study. DIAD was a prospective randomized screening trial that assessed the prevalence of silent ischemia in asymptomatic patients with type 2 diabetes. Subjects were recruited from diabetes and primary care practices at 14 centers throughout the United States and Canada. This is a secondary data analysis of the physical activity data (type and hours/week) collected. No intervention was conducted. In all subjects, physical inactivity rose from 24% at baseline to 33% at 5 years (S = 28.93; P < .0001). This change was significant in both men (S = 11.44; P < .0001), increasing from 23% to 31%, and women (S = 18.05; P < .0001), increasing from 25% to 36%. Gender differences were noted in several factors associated with baseline physical inactivity as well as in factors predictive of physical inactivity at 5 years. Important factors associated at both time points included lower level of education, current employment, presence of peripheral and autonomic neuropathy, and indicators of overweight/obesity. Baseline physical inactivity was strongly predictive of physical inactivity at 5 years (odds ratio, 3.27; 95% confidence interval, 2.36-4.54; P < .0001). Gender-related differences were noted in factors associated with and predictive of physical inactivity.

Predictors of Physical Inactivity Among Elderly Malaysians

Physical inactivity is the fourth leading risk factor for global mortality. Regular moderate-intensity physical activity has significant benefits for health. To determine the socioeconomic predictors of physical inactivity among elderly Malaysian population. A nationwide community-based survey was conducted among 4831 respondents aged ≥60 years with a face-to-face questionnaire. The prevalence of physical inactivity among the elderly was 88.0%, highest in respondents aged older than 80 years (95.4%), females (90.1%), other Bumiputra (92.2%), earning household income less than RM1000 (87.9%), and residing in urban locality (88.4%). In the multivariate model, the predictors of physical inactivity were only sex, ethnicity, locality, and age group (adjusted odds ratio = 1.3-3.6). The predictors of physical inactivity can identify the risk factors to develop policies that will reduce the public health burden of noncommunicable diseases.

Individual and school level correlates and predictors of physical inactivity among adolescents from low socioeconomic backgrounds

Introduction: Epidemiological research has indicated that the socioeconomic environment beyond the family may influence children's physical activity. We examined the influence of the school environment (economic deprivation, social fragmentation, and school climate) on students’ level of physical activity.

Prevalence and Predictors of Physical Inactivity in a Slum in Brazil

The aim of this study was to ascertain the prevalence of physical inactivity and examine the role of potential predictors in a very low-income adult population in a slum located in Recife city, northeast of Brazil. A cross-sectional study was conducted with 1,176 subjects aged 20-60 years residing in a slum. Using the short version of the International Physical Activity Questionnaire, 307 (26.1%) study participants-97 (23.8%) men and 210 (27.3%) women-have a low physical activity score (MET-minutes per week). Increased age was associated with physical inactivity only in people without overweight/obesity. Low physical activity was less common (i.e., respondents were more active) than in other Brazilian population-based studies. These results suggest that the relationship between physical activity and socioeconomic level is more complex and depends on the internal characteristics of the community.

Predictors of changes in physical activity in a prospective cohort study of the Danish adult population

To investigate predictors of changes in physical activity, in a prospective population-based study. Data were from the Danish Health Interview Surveys in 1994 and 2000, and included persons between 16 and 64 years of age who answered the questions on physical activity and various covariates in 1994, and who were re-interviewed in 2000. In total 2,957 subjects participated (62% of the original sample). Data were analysed using logistic regression. Predictors of physical inactivity were, for men and women respectively, heavy smoking (odds ratio (OR) 2.07, 95% confidence interval (CI) 1.24-3.48, and OR 2.23, 95% CI 1.34-3.71), poor self-rated health (OR 2.11, 95% CI 1.25-3.58, and OR 1.75, 95% CI 1.10-2.80), and believing that one's own effort has no effect on health (OR 1.86, 95% CI 1.20-2.88, and OR 1.63, 95% CI 1.00-2.65). For men, further predictors for physical inactivity were obesity as compared to normal weight (OR 2.10, 95% CI 1.11-3.98), and being unmarried as compared to being married (OR 2.22, 95% CI 1.40-3.51). The only predictor for becoming physically active among initially sedentary respondents was meeting often with family (p=0.011). Our results show a number of strong predictors for physical inactivity. Variables of an unhealthy lifestyle tend to cluster in some individuals, and physical inactivity interventions may therefore be more successful if they also address smoking, self-belief, and obesity. Interventions tailored to raise inactive people's level of activity should focus on social support from the family.

The lack of social gradient of health behaviors and psychosocial factors in Northern Italy

To examine inequalities in health behaviors and psychosocial factors in Northern Italy. The study was based on a computer-assisted telephone interview (CATI) of 4 002 non-institutionalized adults living in the Veneto region of Italy. Cigarette smoking, binge drinking, fruit and vegetables consumption and stress failed to show a social gradient. Only physical activity was significantly associated with social class. Stress was a significant predictor of physical inactivity, smoking and low fruit and vegetable intake. Lack of emotional support was associated with smoking and physical inactivity among males, and low fruit and vegetable intake for both genders. Three proposed explanations may account for the lack of consistent social gradient of health behaviors in Northern Italy: a) socio-economic context; b) uncompleted epidemiological transition of behavioral risk factors across social classes; c) lack of systematic health promotion efforts. Future research is needed to examine the plausibility of such explanations.

Factors Related to Non-Participation in Physical Activity Among the Students in Hong Kong

The patterns and determinants of physical inactivity among the Chinese youths are unknown. This study assessed the prevalence and predictors of physical inactivity among students in a Hong Kong university. A cross-sectional survey of year 1 and year 3 Chinese undergraduate students in 1997, using a structured, self-administered questionnaire was carried out. Questionnaires were returned by 64 % (1189/1849) of the students. Thirty-one percent of the respondents did not participate in physical activity. Being female, not a university resident, studying in the Faculty of Arts and rating current health status as poor or very poor were predictors of physical inactivity. No time and no interest were the most common reasons for non-participation in physical activity among the students. This study showed that about one-third of Chinese university students are physically inactive and would gain health benefits from organized intervention. Awareness among the students about the risks of physical inactivity should be enhanced and efforts made to increase physical activity should be reinforced. Factors identified as predictors of physical inactivity should be taken into account in the design of intervention.

Physical activity and social status in adolescence as predictors of physical inactivity in adulthood

Background Physical inactivity is related to an increased risk of certain chronic diseases. The aim here was to evaluate how physical activity and social status in adolescence are associated with physical inactivity in adulthood. Methods The sample comprised 3664 males and 4130 females who answered questions on physical activity and social status at 14 and 31 years of age in follow-up surveys of the Northern Finland birth cohort of 1966. Associations between explanatory factors and physical inactivity in adulthood were analyzed using multivariable logistic regression. Results Infrequent participation in sports after school hours at 14 years of age and, in males, additionally a low grade in school sports, was associated with physical inactivity at the age of 31 years, independent of social circumstances in adulthood. Low social class of the childhood family was associated with physical inactivity in adolescence but not with inactivity at 31 years of age. Poor school achievement in adolescence was associated with adult inactivity independent of adolescent physical activity. Conclusion Infrequent participation in sports, a low grade in school sports, and poor school achievements in adolescence were associated with physical inactivity in adulthood. Participation in sports is to be strongly supported among all adolescents because of its long-term beneficial effects on adult health through its tendency to reduce the probability of adult inactivity.