Factors associated with fruit and vegetable intake and physical activity among reproductive age women in Gofa and Basketo Zones, Southern Ethiopia: a community based cross-sectional study

Factors associated with fruit and vegetable intake and physical activity among reproductive age women in Gofa and Basketo Zones, Southern Ethiopia: a community based cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Factors associated with fruit and vegetable intake and physical activity among reproductive age women in Gofa and Basketo Zones, Southern Ethiopia: a community based cross-sectional study Markos Manote Domba, Terefe Gelibo Argefa, Bahiru Mulatu Kebede This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5434074/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The escalation in burdens of non-communicable diseases coupled with unmet needs of sexual and reproductive health services progressively impacting women, and posing significant threats to forthcoming generations. Low fruits and vegetables intake, and insufficient physical activity are known risk factors of NCDs. This research endeavors to assess the level and factors associated with fruit and/or vegetable intake and physical activity among women of reproductive age in Gofa and Basketo Zones, Southern Ethiopia. Methods A community based cross-sectional study was employed among reproductive age women from September 9/2022 to December 6/2022. A multistage cluster sampling was used to select participants from the designated zones. A total of 1404 study participants were included in the analysis. Statistical analysis was conducted using Statistical Package for the Social Sciences software encompassing descriptive statistics, bivariate analysis, and multivariate logistic regression. Associations were deemed statistically significant if the p-value was < 0.05. Result Prevalence of adequate fruit and/or vegetable (FV) intake and physical activity were 48.4% and 78.3% respectively. Women who were from younger age groups, getting advice from health professionals, having primary school education, having > 4 family size, women from households with the lower wealth status, widowed/separated and Gofa zone residents were more likely to have adequate FV intake. Being married, single and Basketo zone residence were positively associated with physical activity. However, women from older age groups and having lesser educational status were less likely to be physically active. Women who were rural residents and having no family history of NCD were more likely to have both adequate FV intake and physical activity. Conclusion A substantial gap exists between the recommended level and actual FV consumption and physical activity. They are associated with different socio-economic, knowledge and health system factors. However, these preventive factors are more influenced by rural residence, having no family history of NCD, women’s occupation. Therefore, NCD prevention would be more effective if they account specific determinants in their design. Policy and socio-political factors influencing the rise of NCD risk factors should also be addressed. Fruit and vegetable intake physical activity reproductive age women Gofa Basketo South Ethiopia Introduction Consumption of fruit and vegetable are an important part of a healthy diet that can prevent all forms of malnutrition (under nutrition, micronutrient deficiency, overweight and obesity)[ 1 ]. Adequate amount is defined as being at least 400g per day which is considered to be equivalent to five servings of 80g of fruit and vegetable [ 2 – 4 ]. Physical activity (PA) is any bodily movement produced by skeletal muscles that require energy expenditure[ 5 ]. WHO recommends that adults should do at least 150–300 min of moderate-intensity aerobic physical activity, or at least 75–150 min of vigorous-intensity aerobic physical activity, or an equivalent combination of moderate- intensity and vigorous-intensity activity throughout the week for substantial health benefits[ 6 , 7 ]. Adequate consumption of fruit and vegetable and physical activity reduce the risk of non-communicable diseases and premature death [ 1 , 8 , 9 ]. Globally, women in reproductive age consume far less fruit and vegetables than the recommended minimum total of 400g[ 10 ], and women were less likely to be physically active compared with men[ 11 ]. About 7.6% of cardiovascular disease deaths were attributable to physical inactivity[ 12 ]. Women in sub-Saharan Africa consume only about one third of the recommended minimum dietary intake of fruit and vegetables [ 13 ]. In Ethiopia, consumption of fresh fruits is approximately 7 kg/person/year which is far below the recommended minimum level of dietary intake (146kg/person/year) [ 14 ] and majority of women in reproductive age consume monotonous plant based diet that may be inadequate to provide adequate nutrition[ 15 ]. Studies conducted among adults in eastern Ethiopia reported that 45% of adults did not achieve the WHO recommended level of physical activity[ 16 , 17 ]. NCDs are mostly linked with behavioral risk factors including low intake of fruits and vegetables, and insufficient physical activity[ 18 ]. The rising burden of NCDs is coupled with unmet needs of sexual and reproductive health services[ 19 ], and increased negative impact on reproductive health as well as fetal health[ 20 , 21 ]. Tackling NCDs in women needs a systematic understanding of major preventive factors and their predictors[ 22 , 23 ]. Evidence on the level and predictors of physical activity and fruit and vegetable intake among reproductive age women in low-income countries, like Ethiopia, is sparse and inconclusive. This evidentiary gap warrants studying the level of physical activity and fruit and vegetable intake among reproductive age women especially in the peripheral setup of the country including Gofa and Basketo. Therefore, this study aims to address the above gaps, and to advocate the policy makers and regional government in prevention and control of chronic disease among reproductive aged women. Materials and methods Study setting and period. The study was conducted in Gofa and Basketo Zones, Southern Ethiopia from Sept 9/2022 to Dec 6/2022. These two zones are adjacent and a good representative site for infra-structure limited areas of the region. Gofa and Basketo zones are administratively divided in to 13 districts (eight rural and five town administrations) having a total population of 720,864 (projected from 2007 Census) in the year 2021. The estimated number of women of reproductive age group is 167,961 (Gofa and Basketo Zone health departments’ bi-annual report, 2021). Study design A community based cross sectional study was conducted by the WHO a stepwise approach to the surveillance of NCD risk factors. Study population All women of reproductive age residing in the Gofa and Basketo zones during the data collection period were eligible for inclusion. This encompassed those who considered the study area their permanent residence for at least six months. Exclusions were made for non-permanent residents, pregnant women, individuals institutionalized in hospitals, prisons, nursing homes, or similar facilities, as well as those residing primarily in military camps or dormitories. Additionally, critically ill, mentally disabled, and physically disabled individuals unsuitable for physical participation were excluded. Sample size determination and sampling technique A mix of sampling approach: stratified, multi-stage cluster sampling, systematic random sampling and Kish method were employed to select the study population and the study participants. Gofa and Basketo zones have thirteen districts and each district stratified into rural (193 kebeles) and urban (62 kebeles) yielding 255 sampling strata. Kebeles are the lowest administrative structures in Ethiopia. The WHO regional office tools for assessing operational district health systems in Africa recommended that for the total number of districts between 10 to 19, sampling 50% of them could be enough [ 24 ]. Based on the above information, a total of seven districts were selected randomly by lottery method. Thirty percent of kebeles in the selected districts (120 rural and 36 urban kebeles) were sampled randomly. Then, forty eight kebeles were selected randomly by lottery method. Sample size was determined using a single proportion formula considering the Z-score = 1.96; Proportion = 50%; marginal error = 0.05; Design effect = 3.35; and non-response rate = 10%, making the total sample size of 1,416 respondents. All the households with eligible participants were listed and a total of 1416 households were selected based on equal probability systematic selection criteria to 48 clusters (40 rural and 8 urban kebeles). Taking into account the cost of the study and the level of precision, 30 households per “kebele” and along the residential household list, every 35th household is systematically selected. Finally, a single mother selected from every household using Kish method where more than one eligible woman exist. All a reproductive age women who were usual members of the selected households were eligible for the survey. Study variables and measurement The dependent variables : include adequate fruit and/or vegetable intake and physical activity. Independent variable : Socio-demographic variables include: age, place of residence, family history, family size, educational status, marital status, occupation of women, social support to NCD prevention and wealth status of household. Wealth Status was derived from the wealth index (five quintiles in the data set; poorest, poor, middle, rich and the richest) for the households. The variables included to calculate the index were main material of the walls, roofing, floor, separate room for cooking, type of fuel household mainly use for cooking, kind of toilet facility household use, household’s ownership of phone, radio, Television, mattress, bed, watch, stove, table, chair, beehive, ox, caw, hen, motorcycle and Generator [ 25 , 26 ]. Knowledge related factors/variables include getting advice from health professionals and using mass media. Structural factors include availability of safe recreational area and membership in a functional women’s development army. Definition of terms Adequate consumption of fruit and vegetable: is having ≥ 5 five serving of fruit and/or vegetable on an average day; which is based on the number of days the respondent ate fruit and/or vegetables in a typical week and the number of servings they ate on one of those day [ 27 ]. Physical activity: is meeting WHO recommendations on physical activity for health (adults doing at least 150 min of moderate-intensity or at least 75 vigorous-intensity physical activities per week, or equivalent). For adolescent respondents aged 15–17 years doing at least 60 minutes per day of moderate to vigorous intensity, mostly aerobic physical activity across a week or equivalents [ 27 ]. Data collection tool The data collection instrument is a questionnaire developed with adaption of the WHO Stepwise Surveillance questionnaire [ 28 ]. This questionnaire was translated into Gofatho and Basket languages and subsequently back-translated into English to ensure accuracy. Socio demographic and health system related data were collected. Data collection technique Data collectors were Health Extension Workers who had two days training on collecting data from all the three Steps before the survey. The training was imparted on STEPS instrument including interactive discussions and measurements. The interviewer-administered questionnaire covers socio-demographic information, knowledge and health system related questions. The assessment of variables was made according to suggestions by different scholars or standard guidelines such as measurement of socio-demographic variables [ 29 ], wealth index [ 25 , 26 ] and variables for evaluating physical activity [ 30 ], and fruits and vegetables consumption [ 31 ]. Data quality management To maintain data quality, the questionnaire underwent translation and pretesting as necessary. Following data collection, thorough checks for completeness and consistency were conducted, and coding was performed by both the supervisor and principal investigator. Pre-test of the data collection tool was conducted prior to the study on 70 participants in Zala district which is adjacent to the selected districts and having similar socio-demographic population. Based on the evaluation, the necessary adjustments were made and the final version of the questionnaire was developed. Data analysis SPSS software version 25 was used to conduct data analysis. Descriptive weighted analysis was done along with complex sample analysis. Binary logistic regression was used to see the association between the outcome and independent variables and variables with a p-value of less than 0.25 in the binary logistic regression were fitted into a multivariable logistic regression model to control for confounding effects. The strength of the association was estimated by odds ratio and its 95% confidence interval. Associations with a p-value < 0.05 are considered statistically significant. Ethical considerations The survey protocol obtained ethical clearance from the institution review boards of the Southern Regional State Public Health Institute and Selinus University of Science and Literature. A paper-based written informed consent form was administered to eligible participants. At each stage of the process, consent was indicated by signing or making a mark on the consent form on a printed copy, which was retained by the participant and the data collector. A designated head of household provided written consent for the household to take part in the survey, after which individual members were rostered during a household interview. For minors ages 15–17, parents /guardians provided permission which was followed by assent by the participant. Results Socio-demographic characteristics of participants A total of 1404 women were responded to the interviewer-administered questionnaire with an overall response rate of 99.1%. The average age of participants was 28.9 years with a standard deviation of 7.5 years, and 41.5% fell within the 25–34 age range. A significant portion (39%) had no formal education, while the majorities (93.4%) were married. Predominantly, participants hailed from the Gofa zone (93.3%) and rural areas (83.3%). Furthermore, 84% resided in rural regions. In terms of wealth distribution, approximately 20.5%, 21.1%, and 20.6% of participants were categorized into the poorest, poorer, and middle wealth quintiles respectively. Housewives comprised the largest occupational group, accounting for 72% of participants. Additional socio-demographic details can be found in Table 1 . Table 1 Socio-demographic characteristics of participants Variable Categories Number Percentage Age, years 15–24 400 29.4 25–34 599 41.5 ≥ 35 405 29.1 Educational status Illiterate 519 38.6 Able to read and write 147 9.6 Primary education 400 28.7 Secondary education and above 338 23.1 Marital status Married 1297 93.4 Single 68 4.4 Widowed/Divorced 39 2.1 Residence 1. Urban 393 16.2 Rural 1011 83.8 Zone Gofa 1244 93.3 Basketo 160 6.7 Wealth index Poorest 288 20.5 Poorer 286 21.1 Middle 277 20.6 Richer 280 19.5 Richest 273 18.3 Occupational status Housewife 981 71.9 Merchant 172 12.7 Government employee 76 4.4 Other* 175 11.0 Family size ≤ 4 544 38.4 > 4 860 61.6 Family history of NCDs No 1292 92.7 Yes 112 7.3 Social support to prevent NCD risk factors No 846 60.8 Yes 558 39.2 *Others in occupational status: daily laborers, students and maidservants Regarding knowledge of NCD risk factors, only 34.6% of the study participants had good knowledge; but the vast majority had poor knowledge. Prevalence of fruit and vegetable intake and physical activity The prevalence of preventive factors (fruit and vegetable intake and physical activity) of non-communicable diseases varies significantly between the different socio-demographic sub-groups shown in Table 2 . Fruit and vegetable intake Adequate intake of fruits and vegetables among the study participants was 48.4%. Fruits and vegetables intake was significantly higher (P < .001) among rural residents (51.1%) compared with urban (34.4%). The prevalence was significantly higher (P < .001) among women from Gofa zone (49.4%) compared with Basketo (35.0%); and it was also higher among housewives (50.4%) compared to merchants (43.6%) and government employees. Adequate intake of fruits and vegetables was significantly higher (P 4 dwellers (51.0%) compared with women from households ≤ 4 dwellers (44.3%). Physical activity Prevalence of physical activity among reproductive age women was 78.3%. Physical activity was significantly higher (p < .001) among rural residents (80.3%) compared to urban (67.7%) and also significantly higher among Basketo (95.0%) zone residents compared to Gofa (77.1%). The prevalence was more common among women who have secondary school education (81.6%) compared to illiterate (78.1%) and women with primary school education (78.7%). It was also higher (p < .001) among married (78.7%) and single women (79.1%) compared to widowed/divorced (58.4%). Housewives (78.3%) had lower (< .001) physical activity compared to others such as maid servants, daily laborers and students (81.2%). Table 2 Prevalence of fruit and/or vegetable intake and physical activity by background characteristics of reproductive aged women Variable Categories Fruit and/or vegetable intake Physical activity N (%) N (%) Age, years 15–24 49.4% 84.9% 25–34 48.2% 74.3% ≥ 35 47.8% 77.4% P-value < .001* < .001* Educational status Illiterate 49.9% 78.1% Read and write 44.0% 69.7% Primary education 51.5% 78.7% Secondary education 43.9% 81.6% P-value < .001* < .001* Marital status Married 48.5% 78.7% Single 44.4% 79.1% Widowed/Divorced 52.3% 58.4% P-value < .001* < .001* 1. Residence 2. Urban 34.4% 67.7% 4. Rural 51.1% 80.3% P-value < .001* < .001* Zone Gofa 49.4% 77.1% Basketo 35.0% 95.0% P-value < .001* < .001* Wealth index Poorest 53.2% 79.5% 2. Poorer 51.2% 81.1% 4. Middle 38.5% 77.3% 6. Richer 55.7% 76.9% Richest 43.4% 76.3% P-value < .001* < .001* Occupational status Housewife 50.4% 78.3% Merchant 43.6% 77.4% Government employee 24.2% 73.6% Other 50.6% 81.2% P-value < .001* 4 51.0% 77.2% P-value .001 * < .001 * Factors association with adequate fruit and/or vegetable consumption and physical activity In a logistic regression model having adequate fruit and/or vegetable servings was significantly associated with several factors including age, marital status, residence, zone, educational status, wealth status, occupational status, family size, receiving advice from health professionals and family history of NCDs. Physical activity was associated with age, residence, zone, educational status, marital status, wealth index, occupational status, family size, family history of NCDs, and availability of safe recreational area during bivariate analysis. Variables with a p-value of less than 0.25 in binary logistic regression were included in a multivariable logistic regression model to control for confounding effects. Detailed factors associated with biological risk factors are presented in Tables 3 and 4 . Odds of adequate fruit and/or vegetable were more likely among women aged 15–24 (AOR: 1.09, 95% CI 1.06, 1.13) compared with those aged ≥ 35 years. Participants who are with primary school education (AOR: 1.22, 95% CI 1.18, 1.25) were more likely to use adequate fruit and/or vegetable compared to illiterate women. Conversely, women who are able to read and write (AOR: 0.73, 95% CI 0.71, 0.76) were less likely to have adequate fruit and/or vegetable intake compared to illiterates. Married women (AOR: 0.45, 95% CI 0.41, 0.48) were about 55% and single women (AOR: 0.33, 95% CI 0.29, 0.36) were about 67% less likely to use adequate fruit and/or vegetable compared with widowed/divorced. Rural residents were more likely to use adequate fruit and/or vegetable (AOR: 1.52, 95% CI 1.47, 1.57) than urban; and participants from Gofa were about two times more likely to use adequate fruit and/or vegetable (AOR: 1.83 95% CI 1.75, 1.92) than those from Basketo zone. Women from the poorest (AOR: 1.31, 95% CI 1.26, 1.36), poorer (AOR: 1.11, 95% CI 1.07, 1.15) and richer (AOR: 1.51, 95% CI 1.45, 1.56) households were more likely to use adequate fruit and/or vegetable compared to the richest. Conversely, women from middle wealth quartile were less likely to use adequate fruit and/or vegetable (AOR: 0.67, 95% CI 0.65, 0.69) compared to the richest. Regarding occupation housewives (AOR: 0.63, 95% CI 0.60, 0.65), merchants (AOR: 0.49, 95% CI 0.47, 0.51) and government employees (AOR: 0.52, 95% CI 0.48, 0.56) were less likely to use adequate fruit and/or vegetable compared to others such as maidservants, daily laborers and students. Women from households having family size > 4 were more likely to use adequate fruit and/or vegetable (AOR: 1.51, 95% CI 1.46, 1.54) than ≤ 4 family size. Women who get advice from health professionals were about two times more likely to use adequate fruit and/or vegetable (AOR: 2.36, 95% CI 2.30, 2.43) compared to those do not got professional advice. Women having no family history of NCDs were about four times more likely to use adequate fruit and/or vegetable (AOR: 4.30, 95% CI 4.07, 4.55) compared with those having family history of NCDs. Table 3 Bivariate and multivariate logistic regression of adequate fruit and/or vegetable intake and independent variables among reproductive age women Candidate Variables Categories Adequate fruit and/or vegetable consumption Inadequate fruit and/or vegetable consumption COR (95%CI) AOR (95%CI) Age, years 15–24 197(49.3) 203(50.7) 1.07 (1.04, 1.09) *** 1.09 (1.06, 1.13)*** 25–34 267(44.6) 332(55.4) 1.01 (.99, 1.04) * 1.06 (1.03, 1.09)* ≥ 35 192(47.4) 213(52.6) 1 1 Educational status Illiterate 252(48.6) 267(51.4) 1 1 Able to read and write 65(44.2) 82(55.8) 0.786 (0.76, 0.82)*** 0.73 (0.71, 0.76)*** Primary education 193(48.2) 207(51.8) 1.066 (1.04, 1.09)*** 1.22 (1.18, 1.25)*** Secondary education and above 146(43.2) 192(56.8) 0.783 (0.76, 0.80)*** 1.02 (0.98, 1.05) Marital status Married 611(47.1) 686(52.9) 0.86 (0.80, 0.92)*** 0.45 (0.41, 0.48)*** Single 26(38.2) 42(61.8) 0.73 (0.67, 0.79)*** 0.33 (0.29, 0.36)*** Widowed/Divorced 19(48.7) 20(51.3) 1 1 Residence Urban 142(36.1) 251(63.9) 1 1 Rural 514(50.8) 497(49.2) 2.0 (1.94, 2.05)*** 1.52 (1.47, 1.57)*** Zone Gofa 600(48.2) 644(51.8) 1.81 (1.74, 1.89)*** 1.83 (1.75, 1.92)*** Basketo 56(35.0) 104(65.0) 1 1 Wealth index Poorest 142(49.3) 146(50.7) 1.48 (1.43, 1.53)*** 1.31 (1.26, 1.36)*** Poorer 140(49) 146(51.0) 1.37 (1.32, 1.41)*** 1.11 (1.07, 1.15)*** Middle 105(37.9) 172(62.1) 0.82 (0.79, 0.85)*** 0.67 (0.65, 0.69)*** Richer 154(55) 126(45.0) 1.64 (1.59, 1.70)*** 1.51 (1.45, 1.56)*** Richest 115(42.1) 158(57.9) 1 1 Occupational status Housewife 473(48.2) 508(51.8) 0.99 (0.96, 1.03) 0.63 (0.60,0.65)*** Merchant 77(44.8) 95(55.2) 0.76 (0.72, 0.79)*** 0.49 (0.47,0 .51)*** Government employee 21(27.6) 55(72.4) 0.31 (0.29, 0.33)*** 0.52 (0.48, 0.56)*** Other 85(48.6) 90(51.4) 1 1 Family size ≤ 4 223(41) 321(59.0) 1 1 > 4 433(50.3) 427(49.7) 1.31 (1.28, 1.34)*** 1.51 (1.46, 1.54)*** Getting advice from health professionals No 157(32.3) 329(67.7) 1 1 Yes 499(54.4) 419(45.6) 2.61 (2.55, 2.68)*** 2.36 (2.30, 2.43)*** Family history of NCDs No 632(48.9) 660(51.1) 4.43 (4.22, 4.66)*** 4.30 (4.07, 4.55)*** Yes 24(21.4) 88(78.6) 1 1 COR-Crude Odds Ratio: odds ratio by bivariate analysis. 95% CI: confidence interval at the 95% level. AOR-Adjusted OR: odds ratio by multiple logistic regression *Candidate covariates at p-value < 0.25 in bi-variates and ** statically significant factors at p-value < 0.05, *** statically significant factors at p-value < 0.01. 1: Referent category Physical activity was less likely among women aged 25–34 (AOR: 0.54, 95% CI 0.52, 0 .56) and age ≥ 35 (AOR: 0.66, 95% CI 0.63, .68) 0.66 compared to those 15–24 years. Women who were illiterate (AOR: 0.67 95% CI 0.64, 0.71), able to read and write (AOR: 0.91, 95% CI 0.88, 0.94) were less likely to have physical activity compared to those with secondary school education. However, having primary education (AOR: 1.13, 95% CI 1.08, 1.18) were more likely to have physical activity. However, married (AOR: 2.13, 95% CI 1.97, 2.31) and single (AOR: 1.39, 95% CI 1.25, 1.54) women were more likely to have physical activity than widowed/divorced. Physical activity was about two times more likely among rural residents (AOR: 1.69, 95% CI 1.63, 1.74) than urban and over five times more likely among women from Basketo zone (AOR: 5.48, 95% CI 4.98, 6.02) than Gofa. Women from the poorer (AOR: 1.10, 95% CI 1.06, 1.15) 1.10 (1.06, 1.15) households were more likely to have physical activity; but those from middle wealth quartile (AOR: 0.95, 95% CI 0.91, 0.99) and richer households (AOR: 0.93, 95% CI 0.88, 0.97) were less likely to have physical activity compared to the richest. Government employees (AOR: 0.53, 95% CI (0.49, 0.58), merchants (AOR: 0.78, 95% CI 0.74, 0.83) and housewives (AOR: 0.79, 95% CI 0.75, 0.83) were less likely to have physical activity compared to others such as maidservants, daily laborers and students. Participants having no family history of NCDs (AOR: 1.24, 95% CI 1.18, 1.31) were more likely to have physical activity than those having a family history. Table 4 Bivariate and multivariate logistic regression of physical activity and independent variables among reproductive age women Candidate Variables Categories Physical activity Physical inactivity COR (95%CI) AOR (95%CI) Age, years 15–24 327(81.7) 73(18.3) 1 1 25–34 437(72.9) 162(27.1) 0.51 (0.50, 0.53)*** 0.54 (0.52,0 .56)*** ≥ 35 309(76.3) 96(23.7) 0.61 (0.59, 0.63)*** 0.66 (0.63, 0.68)*** Educational status Illiterate 395(76.1) 124(23.9) 0.80 (0.77, 0.83)*** 0.67 (0.64, 0.71)*** Able to read and write 97(66.0) 50(44.0) 0.52 (0.49, 0.54)*** 0.91 (0.88, 0.94)*** Primary education 314(78.5) 86(21.5) 0.83 (0.80, 0.86)*** 1.13 (1.08, 1.18)*** Secondary education and above 267(79.0) 71(21.0) 1 1 Marital status Married 994(76.6) 303(23.4) 2.64 (2.45, 2.84)*** 2.13 (1.97, 2.31)*** Single 54(79.4) 14(20.6) 2.69 (2.45, 2.96)*** 1.39 (1.25, 1.54)*** Widowed/Divorced 25(64.1) 14(35.9) 1 1 Residence Urban 263(66.9) 130(43.1) 1 1 Rural 810(80.1) 201(19.9) 1.95 (1.89, 2.01)*** 1.69 (1.63, 1.74)*** Zone Gofa 921(74.0) 323(26.0) 1 1 Basketo 152(95.0) 8(5.0) 5.64 (5.14, 6.19)*** 5.48 (4.98, 6.02)*** Wealth index Poorest 226(78.5) 62(21.5) 1.21 (1.16, 1.26) 1.01 (0.97, 1.06) Poorer 226(79.0) 60(21.0) 1.33 (1.28, 1.39)*** 1.10 (1.06, 1.15)*** Middle 210(75.8) 67(24.2) 1.06 (1.02, 1.10)*** 0.95 (0.91, 0.99)*** Richer 207(75.0) 73(25.0) 1.03 (.99, 1.08)* 0.93 (0.88, 0.97)*** Richest 204(74.7) 69(25.3) 1 1 Occupational status Housewife 754(76.9) 227(23.1) 083 (0.80,0 .87)*** 0.79 (0.75, 0.83)*** Merchant 132(76.7) 40(23.3) 0.79 (0.75, 0.84)*** 0.78 (0.74, 0.83)*** Government employee 55(72.4) 21(27.6) 0.65 (0.60,0.69)*** 0.53 (0.49, 0.58)*** Other 132(75.4) 43(24.6) 1 1 Family size ≤ 4 426(78.3) 118(21.7) 1.19 (1.15, 1.22)*** 1.00 (0.97, 1.03) > 4 647(75.2) 213(24.8) 1 1 Family history of NCDs No 987(76.4) 305(23.6) 4.43 (4.22, 4.66)*** 1.24 (1.18, 1.31)*** Yes 86(76.8) 26(23.2) 1 1 Availability of recreational area No 916(77.0) 273(33.0) 1.10 (1.07, 1.14)*** 0.980 (0.94, 1.02) Yes 157(73.0) 58(37.0) 1 1 COR-Crude Odds Ratio: odds ratio by bivariate analysis. 95% CI: confidence interval at the 95% level. AOR-Adjusted OR: odds ratio by multiple logistic regression *Candidate covariates at p-value < 0.25 in bi-variates and ** statically significant factors at p-value < 0.05, *** statically significant factors at p-value < 0.01. 1: Referent category Discussion The current study revealed that the prevalence of adequate intake of fruits and/ vegetables and physical activity among women of reproductive age was 48.4% and 78.3% respectively. The distribution varies with socio-demographic differences among the study participants; and multivariable analysis shows that adequate intake of fruits and vegetables and physical activities were significantly associated with different independent variables. The prevalence of adequate intake of fruits and/ vegetables among the study population (48.4%) is inconsistent with a study conducted since 2017 in Ethiopia (1.8%) [ 32 ]. The possible explanation may be due to the difference in accessibility, availability and affordability of fruits and vegetables [ 33 ], and the difference in age and other social factors that could influence intake of fruit and vegetables. Individuals may also lack adequate information on the need to consume sufficient fruit and vegetables and the health consequences of insufficient intake. The multivariate analysis of the study also found that the prevalence of adequate intake of fruits and vegetables was associated with age, place of residence (rural vs. urban, location, occupation, family size and educational status. This finding is in line with previous study in India [ 34 ]. Consumption of fruit and/vegetable was negatively associated with age of women. This finding suggests that older women show less concern for fruit and vegetable consumption. It agrees with the findings of studies in Nigeria and Brazil [ 35 , 36 ]. Participants with primary school education were more likely to consume adequate levels of fruits and vegetables when compared with illiterate women, similar to findings of a previous study [ 33 ]. This indicates importance of education in promoting fruit and/vegetable consumption. Rural dwellers were more likely to consume the recommended amount of fruit and/or vegetable as compared to their counterparts. This could be related with possibility of higher availability and accessibility of a variety of fruit and vegetable and relatively lower nutrition transition to processed foods among rural areas in Africa compared to urban [ 37 ]. This is in line with another study in Ethiopia [ 32 ]. However, it is inconsistent with the result from studies in Addis Ababa, Ethiopia and Alfred Duma Municipality of Ladysmith [ 38 , 39 ], where daily consumption of fruits and vegetables among the study participants were 6.8% and 0.6% respectively. The difference might be due to variation in the setup. Majority of our study participants were from the rural setup. WHO recommendation to perform at least 150 minutes of moderate-intensity physical activity per week, or the equivalent [ 11 ]. The current study reports relatively low prevalence of physical activity (78.3%); and this is consistent with previous national and international surveys [ 40 ] and a study in sub-Saharan Africa which indicated 78% of the study participants were physically active [ 41 ]. However, a similar study at Kasese district of Uganda indicates fifty percent of female respondents were physically active [ 42 ]. The difference may be associated with the observed lack of awareness about physical activity, economic transitions and the change in modes of transportation. The prevalence of physical activity was lower among reproductive age women aged 25–34 and ≥ 35 years than women aged 15–24 years this may be as a result of the physical and psycho-social changes they undergo during the transition from single life to formation of family via marriage in Ethiopia. This might also be due to limited physical activity interventions for older adults. The finding is supported by a study in Eastern Ethiopia [ 43 ]. Socio-economic variations are determinates of NDCs, in context of study population, prevalence of physical activity was higher among women from households with the poorer wealth status compared with richest. This finding is in line with a study in Eastern Ethiopia [ 43 ]. This may be due to reduced sedentary lifestyle associated with occupations among this group of people [ 44 ] along with lower access to means of transportation, thereby increasing walking hours in a day. Physical activity was lower among participants with lower education. This finding is consistent with the national study [ 45 ]. This may be due to the importance of education in engaging in physical activity. Strengths and Limitations This pioneering study in the Gofa and Basketo zones examines the prevalence and factors linked to preventive factors of NCD among reproductive-aged women. Its strengths lie in its community-based approach covering both rural and urban residents, ensuring broader applicability. Additionally, rigorous measures were implemented, including pilot testing of instruments, comprehensive training of data collectors and supervisors, and high response rates (99%), enhancing data quality. The study's use of weighted analysis allows for extrapolation of findings to the entire study area. It delves into socioeconomic and knowledge-related factors associated with risk factors. However, the study's cross-sectional design limits its ability to establish causal relationships. Conclusion About half of the study participants consumed adequate fruits and/ vegetables and three fourth were physically active. Adequate fruit and/or vegetable and physical activity were more likely among rural residents and having no family history of NCDs. However, housewives, merchants and government employees were less likely to have both adequate intake of fruit and/or vegetable and physical activity. Having adequate fruit and/ vegetable intake was more likely among women who were younger age groups, having primary school education, Gofa zone residents, being from households with the lower wealth status, women with family size > 4 and getting health professional advice. However, it was less likely among married and single women. Having recommended level of physical activity was more likely among married and single women, and Basketo zone residents. However, it was less likely among older age group and those with lesser educational status. Therefore, it is necessary to minimize the burden of the growing non-communicable disease epidemic by promoting physical activity and adequate intake of fruit and/ vegetable through continuous awareness programs. The intervention should consider residential area, age, education, wealth and marital status. Lifestyle modifications are urgently required as preventive factors are alarmingly decreasing. Family-based changes and health system strengthening have to be fostered. The government and concerned bodies must promote the preventive factors among reproductive age women through a specific plausible plan drafted for the socioeconomic and cultural setting. Moreover, there is a need to consider further research with different study design to address other variables which were not included in this study. Declarations Ethical Approval This research study was approved on 14, June 2022 (pm 6.19/2199), by the Institutional Review board of South Ethiopia Region Public Health Institute. Competing interests We declare that no financial support received for the research authorship and/or publication of this article. Author Contribution MMD: conceived, drafted, did the analysis, and finalized the manuscript by incorporating the inputs from the other authors. TGA: Supervised the study ensured the quality of the data. BMK: assisted conceptualization, analysis and interpretation of the data. All authors made a substantial contribution to the local implementation of the study and critically reviewed the manuscript. Acknowledgment: I thank Selinus University Faculty of Natural Health Science for selecting and giving me a chance to research such public health topics and assigning a supervisor. My special heartfelt thanks go to my supervisor Prof. Salvatore Fava (PhD) for guiding me during the research process. I also extend my gratitude to data collectors and supervisors for supporting data collection. Data Availability Most of the data used to support the findings of this study are included within the article. More data are available upon reasonable request from the corresponding author. References Carvalho MVB, Costa L, Di C, Oliveira L, et al. Local food environment and fruit and vegetable consumption: An. Ecol study. 2017;5:13–20. Appleton KMKK, Smith E, Rooney C, McKinley MC et al. Low fruit and vegetable consumption is associated with low knowledge of the details of the 5-a-day fruit and vegetable message in the UK: findings from two cross-sectional questionnaire studies. J Hum Nutr Diet 2018:31121–30. Kjøllesdal MHA, Stigum H, Hla NY, Hlaing HH, et al. Consumption of fruits and vegetables and associations with risk factors for non-communicable diseases in the Yangon region of Myanmar: a cross-sectional study. BMJ Open. 2016;6:011649. J F. Fruit and Vegetables. Workshop WHO 2004:1–3. WHO. Physical activity [internet]. Available: https://www.who.int/news-room/fact-sheets/detail/physical-activity [Accessed updated 26 November 2020. Organization WH. WHO guidelines on physical activity and sedentary behaviour: web annex: evidence profiles. 2020. Bull FCA-AS, Biddle S, et al. World Health organization guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54:1451–62. Ponticelli CFE. Physical inactivity: a modifiable risk factor for morbidity and mortality in kidney transplantation. J Pers Med. 2021:11. Bellettiere JLM, Evenson KR et al. Sedentary behavior and cardiovascular disease in older women: the objective physical activity and cardiovascular health (OPACH) study. Circulation 2019:139:1036–46. Ronda-p E C-mJ JA, De, Gea T, Reid A et al. Differences in the Prevalence of Fruit and Vegetable Consumption in Spanish Workers. 2020: 1–14. Guthold RSG, Riley LM, et al. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1• 9 million participants. Lancet global health. 2018;6(10):e1077–86. Katzmarzyk PTFC, Shiroma EJ et al. Physical inactivity and non-communicable disease burden in low-income, middle-income and high-income countries. Br J Sports Med. 2022:56:101–6. Peltzer KPS. Fruits and vegetables consumption and associated factors among in-school adolescents in five Southeast Asian countries. Int J Environ Res Public Health. 2012;9:3575–87. Yazew TDA. Advanced Techniques in Biology & Medicine Health Bene ts of Fruit and Vegetables Consumption: Preventive Implications for Non- communicable Diseases in Ethiopia. 2020. Tefera WBT, Dereje M. Dietary diversity practice and associated factors among pregnant women attending ANC in Kolfe Keranyo sub city health centers, Addis Ababa. Ethiopia medRxiv. 2020. Mengesha MMRH, Ayele BH, et al. Level of physical activity among urban adults and the socio-demographic correlates: a population-based cross- sectional study using the global physical activity questionnaire. BMC Public Health. 2019;19:1160. Abdeta CTZ, Seyoum B. Prevalence of physical inactivity and associated factors among adults in Harar town, eastern Ethiopia. Balt J Health Phys Act. 2018;10:72–80. WHO. Global status report on noncommunicable diseases 2010. Collaborators GRF. Global, regional, and national comparative risk assessment of 79 behaviournvironmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet (London, England). 2016;388(10053):1659. Bombard JM, Dietz PM, Galavotti ea. Chronic diseases and related risk factors among low-income mothers. Matern Child Health J. 2012;16:60–71. Arendas KQQ, Gruslin A. Obesity in pregnancy: pre-conceptional to postpartum consequences. J Obstet Gynaecol Can. 2008;30(6):477–88. Zenu S, Abebe E, Dessie ea. Co-occurrence of behavioral risk factors of non-communicable diseases and social determinants among adults in urban centers of Southwestern Ethiopia in 2020: a community-based cross-sectional study. J Multidisciplinary Healthc. 2021:1561–70. Marmot M, Bell R. Social determinants and non-communicable diseases: time for integrated action. BMJ. 2019;364. Sambo L, Chatora R, Goosen E. Tools for assessing the operationality of district health systems. Brazzaville: World Health Organization, Regional Office for Africa; 2003. Somefun OD, Ibisomi L. Determinants of postnatal care non-utilization among women in Nigeria. BMC Res Notes. 2016;9(1):1–11. Gebremedhin MMaT. Determinants of postnatal care non-utilization among women in Demba Gofa rural district, southern Ethiopia: a community-based unmatched case-control study. 2020. Bhagyalaxmi A, Atul T, Shikha J. Prevalence of risk factors of non-communicable diseases in a District of Gujarat, India. J Health Popul Nutr. 2013;31(1):78. WHO. WHO STEPS surveillance manual: the WHO STEPwise approach to chronic disease risk factor surveillance /. Noncommunicable Dis Mental Health 13 June 2008(ISBN 92 4 159383 0). Yaya S, Uthman OA. Ekholuenetale ea. Socioeconomic inequalities in the risk factors of noncommunicable diseases among women of reproductive age in sub-saharan Africa: a multi-country analysis of survey data. Front public health. 2018;6:412663. Ji C. Knowledge, attitudes and practices of community health workers regarding noncommunicable diseases in the eastern region of São Paulo, Brazil. 2020. Zammit NMJ, Bhiri S, et al. Three years community-based intervention program to prevent non-communicable disease risk factors in Sousse, Tunisia. J Health Sci. 2015;3(95):102. Terefe Gelibo KA, Tefera Taddele G, Taye et al. Low fruit and vegetable intake and its associated factors in Ethiopia: A community based cross sectional NCD steps surveyLow fruit and vegetable intake and its associated factors in Ethiopia: A community based cross sectional NCD steps survey. 2017. Nepali SRA, Olsen MH, et al. Factors affecting the fruit and vegetable intake in Nepal and its association with history of self-reported major cardiovascular events. BMC Cardiovasc Disord. 2020;20(1):1–10. Mahajan M, Naik N, Jain ea. Study of Knowledge, Attitudes, and Practices Toward Risk Factors and Early Detection of Noncommunicable Diseases Among Rural Women in India. J Glob Oncol. 2019;5:1–10. Kehinde Paul Adeosun NAC, Chukwuma O, Umea, et al. Factors Influencing Fruits and Vegetable Consumption among Pregnant Women. Nigeria: Evidence from Enugu State; 2022. Sarah Liduário Rocha Silva RdDM ACSL. Factors associated with inadequate consumption of fruit and vegetables among users of. Primary Health Care in Brazil; 2022. Keding G. Nutrition transition in rural Tanzania and Kenya. World Rev Nutr Diet. 2016;115:68–81. 10.1159/000442073 . Epub 2016 May 19. PMID: 27198465. 2016. Dlamini TXaS. Factors associated with consumption offruits and vegetables amongst adults in the Alfred Duma Local Municipality, Ladysmith 2021. 2021. Mulugeta Debelo Bortola SGBaDDA. Fruit and Vegetable Consumption and Associated Factors among Women of Reproductive Age Attending Maternal. and Child Health Department in Public Hospitals in Addis Ababa, Ethiopia.; 2021. EPHI F. WHO. Ethiopia STEPS report on risk factors for non-communicable diseaes and prevalence of selected NCDs. Addis Ababa: Ethiopia Public Health Institute; 2016. Regina Guthold GAS, Leanne M, Riley, Fiona C, Bull. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1•9 million participants. 2018. 2018. Mondo CKOM, Musoke R, Orem J, et al. The prevalence and distribution of non-communicable diseases and their risk factors in Kasese district, Uganda. South Afr J Diabetes Vascular Disease. 2016;13(1):31–6. Chalchisa Abdeta ZT, Berhanu Seyoum. Prevalence of physical inactivity and associated factors among adults in Harar town, Eastern Ethiopia adults in Harar town, Eastern Ethiopia,. 2018. 2018. Bista B. Dhungana ea. Prevalence and determinants of non-communicable diseases risk factors among reproductive aged women of Nepal: Results from Nepal Demographic Health Survey 2016. PLoS ONE. 2020;15(3):e0218840. Amenu KGT, Getnet M et al. Magnitude and determinants of physical inactivity in Ethiopia: evidence form 2015 Ethiopia national Ncd survey. Ethiopian Journal of Health Development. 2017. 2017. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5434074","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":379165474,"identity":"0f4b983b-1da7-4b58-94f2-daaaf680fac6","order_by":0,"name":"Markos Manote Domba","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYHACZgTr7z8bIMXYeIBILUCahy0NpKWBJC2HwUy8WnTbzz42+PBnm7w5e//BBxI85+3Wth8G2lJjE41Li9mZdOPEmW23DXf2HGY2MJC4nbztTCJQy7G03AZcWg6kMR/mbbjNuOFGMptEgsHtZLMDQC2MDYdxazn/jPkwz5/b9kAt7D8OJJxLNjv/kICWG2nMyTxstxNBtgCD6oCd2Q1Cttx4xmwI9EvyhjOHjaUZG5ITzG4AbUnA55fzacwSH/7ctt1wvPHhZ8YGO3uz8+kPH3yoscGpBQMkglUmEKscBOxJUTwKRsEoGAUjAwAA+nJnmGeaDCEAAAAASUVORK5CYII=","orcid":"","institution":"Public Health, Gofa Zone health Department, South Ethiopia Regional State Health Bureau, Sawla, Ethiopia","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Markos","middleName":"Manote","lastName":"Domba","suffix":""},{"id":379165475,"identity":"bd545f58-f9d6-4f59-954a-8d2555d5a70b","order_by":1,"name":"Terefe Gelibo Argefa","email":"","orcid":"","institution":"Public Health, Colombia University, Mailman School of Public Health, Addis Ababa, Ethiopia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Terefe","middleName":"Gelibo","lastName":"Argefa","suffix":""},{"id":379165476,"identity":"ed290b8d-96a5-4b8a-b050-2c89b077dcb1","order_by":2,"name":"Bahiru Mulatu Kebede","email":"","orcid":"","institution":"Arba Minch Collage of Health Science, Arba Minch, Ethiopia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bahiru","middleName":"Mulatu","lastName":"Kebede","suffix":""}],"badges":[],"createdAt":"2024-11-11 18:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5434074/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5434074/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72103395,"identity":"520e82f6-6da3-4fcf-b0b6-0a6eecfbe172","added_by":"auto","created_at":"2024-12-22 13:46:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":991286,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5434074/v1/0ba04000-4372-43b1-915e-8cab32f0854a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors associated with fruit and vegetable intake and physical activity among reproductive age women in Gofa and Basketo Zones, Southern Ethiopia: a community based cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eConsumption of fruit and vegetable are an important part of a healthy diet that can prevent all forms of malnutrition (under nutrition, micronutrient deficiency, overweight and obesity)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Adequate amount is defined as being at least 400g per day which is considered to be equivalent to five servings of 80g of fruit and vegetable [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Physical activity (PA) is any bodily movement produced by skeletal muscles that require energy expenditure[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. WHO recommends that adults should do at least 150\u0026ndash;300 min of moderate-intensity aerobic physical activity, or at least 75\u0026ndash;150 min of vigorous-intensity aerobic physical activity, or an equivalent combination of moderate- intensity and vigorous-intensity activity throughout the week for substantial health benefits[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Adequate consumption of fruit and vegetable and physical activity reduce the risk of non-communicable diseases and premature death [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGlobally, women in reproductive age consume far less fruit and vegetables than the recommended minimum total of 400g[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and women were less likely to be physically active compared with men[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. About 7.6% of cardiovascular disease deaths were attributable to physical inactivity[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Women in sub-Saharan Africa consume only about one third of the recommended minimum dietary intake of fruit and vegetables [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In Ethiopia, consumption of fresh fruits is approximately 7 kg/person/year which is far below the recommended minimum level of dietary intake (146kg/person/year) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and majority of women in reproductive age consume monotonous plant based diet that may be inadequate to provide adequate nutrition[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Studies conducted among adults in eastern Ethiopia reported that 45% of adults did not achieve the WHO recommended level of physical activity[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNCDs are mostly linked with behavioral risk factors including low intake of fruits and vegetables, and insufficient physical activity[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The rising burden of NCDs is coupled with unmet needs of sexual and reproductive health services[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and increased negative impact on reproductive health as well as fetal health[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Tackling NCDs in women needs a systematic understanding of major preventive factors and their predictors[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Evidence on the level and predictors of physical activity and fruit and vegetable intake among reproductive age women in low-income countries, like Ethiopia, is sparse and inconclusive. This evidentiary gap warrants studying the level of physical activity and fruit and vegetable intake among reproductive age women especially in the peripheral setup of the country including Gofa and Basketo. Therefore, this study aims to address the above gaps, and to advocate the policy makers and regional government in prevention and control of chronic disease among reproductive aged women.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e \u003cb\u003eStudy setting and period.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe study was conducted in Gofa and Basketo Zones, Southern Ethiopia from Sept 9/2022 to Dec 6/2022. These two zones are adjacent and a good representative site for infra-structure limited areas of the region. Gofa and Basketo zones are administratively divided in to 13 districts (eight rural and five town administrations) having a total population of 720,864 (projected from 2007 Census) in the year 2021. The estimated number of women of reproductive age group is 167,961 (Gofa and Basketo Zone health departments\u0026rsquo; bi-annual report, 2021).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eA community based cross sectional study was conducted by the WHO a stepwise approach to the surveillance of NCD risk factors.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eAll women of reproductive age residing in the Gofa and Basketo zones during the data collection period were eligible for inclusion. This encompassed those who considered the study area their permanent residence for at least six months. Exclusions were made for non-permanent residents, pregnant women, individuals institutionalized in hospitals, prisons, nursing homes, or similar facilities, as well as those residing primarily in military camps or dormitories. Additionally, critically ill, mentally disabled, and physically disabled individuals unsuitable for physical participation were excluded.\u003c/p\u003e\n\u003ch3\u003eSample size determination and sampling technique\u003c/h3\u003e\n\u003cp\u003eA mix of sampling approach: stratified, multi-stage cluster sampling, systematic random sampling and Kish method were employed to select the study population and the study participants. Gofa and Basketo zones have thirteen districts and each district stratified into rural (193 kebeles) and urban (62 kebeles) yielding 255 sampling strata. Kebeles are the lowest administrative structures in Ethiopia. The WHO regional office tools for assessing operational district health systems in Africa recommended that for the total number of districts between 10 to 19, sampling 50% of them could be enough [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Based on the above information, a total of seven districts were selected randomly by lottery method. Thirty percent of kebeles in the selected districts (120 rural and 36 urban kebeles) were sampled randomly. Then, forty eight kebeles were selected randomly by lottery method.\u003c/p\u003e \u003cp\u003eSample size was determined using a single proportion formula considering the Z-score\u0026thinsp;=\u0026thinsp;1.96; Proportion\u0026thinsp;=\u0026thinsp;50%; marginal error\u0026thinsp;=\u0026thinsp;0.05; Design effect\u0026thinsp;=\u0026thinsp;3.35; and non-response rate\u0026thinsp;=\u0026thinsp;10%, making the total sample size of 1,416 respondents. All the households with eligible participants were listed and a total of 1416 households were selected based on equal probability systematic selection criteria to 48 clusters (40 rural and 8 urban kebeles). Taking into account the cost of the study and the level of precision, 30 households per \u0026ldquo;kebele\u0026rdquo; and along the residential household list, every 35th household is systematically selected. Finally, a single mother selected from every household using Kish method where more than one eligible woman exist. All a reproductive age women who were usual members of the selected households were eligible for the survey.\u003c/p\u003e\n\u003ch3\u003eStudy variables and measurement\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eThe dependent variables\u003c/strong\u003e: include adequate fruit and/or vegetable intake and physical activity.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eIndependent variable\u003c/em\u003e: Socio-demographic variables include: age, place of residence, family history, family size, educational status, marital status, occupation of women, social support to NCD prevention and wealth status of household. Wealth Status was derived from the wealth index (five quintiles in the data set; poorest, poor, middle, rich and the richest) for the households. The variables included to calculate the index were main material of the walls, roofing, floor, separate room for cooking, type of fuel household mainly use for cooking, kind of toilet facility household use, household\u0026rsquo;s ownership of phone, radio, Television, mattress, bed, watch, stove, table, chair, beehive, ox, caw, hen, motorcycle and Generator [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Knowledge related factors/variables include getting advice from health professionals and using mass media. Structural factors include availability of safe recreational area and membership in a functional women\u0026rsquo;s development army.\u003c/p\u003e\n\u003ch3\u003eDefinition of terms\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAdequate consumption of fruit and vegetable: is having\u0026thinsp;\u0026ge;\u0026thinsp;5 five serving of fruit and/or vegetable on an average day; which is based on the number of days the respondent ate fruit and/or vegetables in a typical week and the number of servings they ate on one of those day [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePhysical activity: is meeting WHO recommendations on physical activity for health (adults doing at least 150 min of moderate-intensity or at least 75 vigorous-intensity physical activities per week, or equivalent). For adolescent respondents aged 15\u0026ndash;17 years doing at least 60 minutes per day of moderate to vigorous intensity, mostly aerobic physical activity across a week or equivalents [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData collection tool\u003c/h2\u003e \u003cp\u003eThe data collection instrument is a questionnaire developed with adaption of the WHO Stepwise Surveillance questionnaire [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This questionnaire was translated into Gofatho and Basket languages and subsequently back-translated into English to ensure accuracy. Socio demographic and health system related data were collected.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection technique\u003c/h3\u003e\n\u003cp\u003eData collectors were Health Extension Workers who had two days training on collecting data from all the three Steps before the survey. The training was imparted on STEPS instrument including interactive discussions and measurements. The interviewer-administered questionnaire covers socio-demographic information, knowledge and health system related questions. The assessment of variables was made according to suggestions by different scholars or standard guidelines such as measurement of socio-demographic variables [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], wealth index [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and variables for evaluating physical activity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and fruits and vegetables consumption [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eData quality management\u003c/h3\u003e\n\u003cp\u003eTo maintain data quality, the questionnaire underwent translation and pretesting as necessary. Following data collection, thorough checks for completeness and consistency were conducted, and coding was performed by both the supervisor and principal investigator. Pre-test of the data collection tool was conducted prior to the study on 70 participants in Zala district which is adjacent to the selected districts and having similar socio-demographic population. Based on the evaluation, the necessary adjustments were made and the final version of the questionnaire was developed.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eSPSS software version 25 was used to conduct data analysis. Descriptive weighted analysis was done along with complex sample analysis. Binary logistic regression was used to see the association between the outcome and independent variables and variables with a p-value of less than 0.25 in the binary logistic regression were fitted into a multivariable logistic regression model to control for confounding effects. The strength of the association was estimated by odds ratio and its 95% confidence interval. Associations with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEthical considerations\u003c/h2\u003e \u003cp\u003e The survey protocol obtained ethical clearance from the institution review boards of the Southern Regional State Public Health Institute and Selinus University of Science and Literature. A paper-based written informed consent form was administered to eligible participants. At each stage of the process, consent was indicated by signing or making a mark on the consent form on a printed copy, which was retained by the participant and the data collector. A designated head of household provided written consent for the household to take part in the survey, after which individual members were rostered during a household interview. For minors ages 15\u0026ndash;17, parents /guardians provided permission which was followed by assent by the participant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eSocio-demographic characteristics of participants\u003c/p\u003e \u003cp\u003eA total of 1404 women were responded to the interviewer-administered questionnaire with an overall response rate of 99.1%. The average age of participants was 28.9 years with a standard deviation of 7.5 years, and 41.5% fell within the 25\u0026ndash;34 age range. A significant portion (39%) had no formal education, while the majorities (93.4%) were married. Predominantly, participants hailed from the Gofa zone (93.3%) and rural areas (83.3%). Furthermore, 84% resided in rural regions. In terms of wealth distribution, approximately 20.5%, 21.1%, and 20.6% of participants were categorized into the poorest, poorer, and middle wealth quintiles respectively. Housewives comprised the largest occupational group, accounting for 72% of participants. Additional socio-demographic details can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics of participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducational status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAble to read and write\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary education and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidowed/Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. Urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGofa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasketo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eOccupational status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMerchant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily history of NCDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSocial support to prevent NCD risk factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Others in occupational status: daily laborers, students and maidservants\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding knowledge of NCD risk factors, only 34.6% of the study participants had good knowledge; but the vast majority had poor knowledge.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of fruit and vegetable intake and physical activity\u003c/h2\u003e \u003cp\u003eThe prevalence of preventive factors (fruit and vegetable intake and physical activity) of non-communicable diseases varies significantly between the different socio-demographic sub-groups shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFruit and vegetable intake\u003c/h2\u003e \u003cp\u003eAdequate intake of fruits and vegetables among the study participants was 48.4%. Fruits and vegetables intake was significantly higher (P\u0026thinsp;\u0026lt;\u0026thinsp;.001) among rural residents (51.1%) compared with urban (34.4%). The prevalence was significantly higher (P\u0026thinsp;\u0026lt;\u0026thinsp;.001) among women from Gofa zone (49.4%) compared with Basketo (35.0%); and it was also higher among housewives (50.4%) compared to merchants (43.6%) and government employees. Adequate intake of fruits and vegetables was significantly higher (P\u0026thinsp;\u0026lt;\u0026thinsp;.001) among widowed/separated (52.3%) compared to married (48.5%) and single women (44.4%); and it was also significantly higher (.001\u003csup\u003e*\u003c/sup\u003e) among women from households with \u0026gt;\u0026thinsp;4 dwellers (51.0%) compared with women from households\u0026thinsp;\u0026le;\u0026thinsp;4 dwellers (44.3%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePhysical activity\u003c/h2\u003e \u003cp\u003ePrevalence of physical activity among reproductive age women was 78.3%. Physical activity was significantly higher (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) among rural residents (80.3%) compared to urban (67.7%) and also significantly higher among Basketo (95.0%) zone residents compared to Gofa (77.1%). The prevalence was more common among women who have secondary school education (81.6%) compared to illiterate (78.1%) and women with primary school education (78.7%). It was also higher (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) among married (78.7%) and single women (79.1%) compared to widowed/divorced (58.4%). Housewives (78.3%) had lower (\u0026lt;\u0026thinsp;.001) physical activity compared to others such as maid servants, daily laborers and students (81.2%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence of fruit and/or vegetable intake and physical activity by background characteristics of reproductive aged women\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFruit and/or vegetable intake\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEducational status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRead and write\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidowed/Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1. Residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2. Urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4. Rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eZone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGofa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasketo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2. Poorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4. Middle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6. Richer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eOccupational status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMerchant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFamily size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. \u0026le;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFactors association with adequate fruit and/or vegetable consumption and physical activity\u003c/h2\u003e \u003cp\u003eIn a logistic regression model having adequate fruit and/or vegetable servings was significantly associated with several factors including age, marital status, residence, zone, educational status, wealth status, occupational status, family size, receiving advice from health professionals and family history of NCDs. Physical activity was associated with age, residence, zone, educational status, marital status, wealth index, occupational status, family size, family history of NCDs, and availability of safe recreational area during bivariate analysis. Variables with a p-value of less than 0.25 in binary logistic regression were included in a multivariable logistic regression model to control for confounding effects. Detailed factors associated with biological risk factors are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eOdds of adequate fruit and/or vegetable were more likely among women aged 15\u0026ndash;24 (AOR: 1.09, 95% CI 1.06, 1.13) compared with those aged\u0026thinsp;\u0026ge;\u0026thinsp;35 years. Participants who are with primary school education (AOR: 1.22, 95% CI 1.18, 1.25) were more likely to use adequate fruit and/or vegetable compared to illiterate women. Conversely, women who are able to read and write (AOR: 0.73, 95% CI 0.71, 0.76) were less likely to have adequate fruit and/or vegetable intake compared to illiterates. Married women (AOR: 0.45, 95% CI 0.41, 0.48) were about 55% and single women (AOR: 0.33, 95% CI 0.29, 0.36) were about 67% less likely to use adequate fruit and/or vegetable compared with widowed/divorced. Rural residents were more likely to use adequate fruit and/or vegetable (AOR: 1.52, 95% CI 1.47, 1.57) than urban; and participants from Gofa were about two times more likely to use adequate fruit and/or vegetable (AOR: 1.83 95% CI 1.75, 1.92) than those from Basketo zone. Women from the poorest (AOR: 1.31, 95% CI 1.26, 1.36), poorer (AOR: 1.11, 95% CI 1.07, 1.15) and richer (AOR: 1.51, 95% CI 1.45, 1.56) households were more likely to use adequate fruit and/or vegetable compared to the richest. Conversely, women from middle wealth quartile were less likely to use adequate fruit and/or vegetable (AOR: 0.67, 95% CI 0.65, 0.69) compared to the richest. Regarding occupation housewives (AOR: 0.63, 95% CI 0.60, 0.65), merchants (AOR: 0.49, 95% CI 0.47, 0.51) and government employees (AOR: 0.52, 95% CI 0.48, 0.56) were less likely to use adequate fruit and/or vegetable compared to others such as maidservants, daily laborers and students. Women from households having family size\u0026thinsp;\u0026gt;\u0026thinsp;4 were more likely to use adequate fruit and/or vegetable (AOR: 1.51, 95% CI 1.46, 1.54) than \u0026le;\u0026thinsp;4 family size. Women who get advice from health professionals were about two times more likely to use adequate fruit and/or vegetable (AOR: 2.36, 95% CI 2.30, 2.43) compared to those do not got professional advice. Women having no family history of NCDs were about four times more likely to use adequate fruit and/or vegetable (AOR: 4.30, 95% CI 4.07, 4.55) compared with those having family history of NCDs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate and multivariate logistic regression of adequate fruit and/or vegetable intake and independent variables among reproductive age women\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCandidate Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdequate fruit and/or vegetable consumption\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eInadequate fruit and/or vegetable consumption\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e197(49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e203(50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.07 (1.04, 1.09) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.09 (1.06, 1.13)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e267(44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e332(55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.01 (.99, 1.04) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.06 (1.03, 1.09)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e192(47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e213(52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducational status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e252(48.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e267(51.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAble to read and write\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e65(44.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82(55.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.786 (0.76, 0.82)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.73 (0.71, 0.76)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e193(48.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e207(51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.066 (1.04, 1.09)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.22 (1.18, 1.25)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary education and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e146(43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e192(56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.783 (0.76, 0.80)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.02 (0.98, 1.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e611(47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e686(52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.86 (0.80, 0.92)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.45 (0.41, 0.48)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e26(38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42(61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.73 (0.67, 0.79)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.33 (0.29, 0.36)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidowed/Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e19(48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20(51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e142(36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e251(63.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e514(50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e497(49.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.0 (1.94, 2.05)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.52 (1.47, 1.57)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGofa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e600(48.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e644(51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.81 (1.74, 1.89)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.83 (1.75, 1.92)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasketo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e56(35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104(65.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e142(49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e146(50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.48 (1.43, 1.53)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.31 (1.26, 1.36)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e140(49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e146(51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.37 (1.32, 1.41)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.11 (1.07, 1.15)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e105(37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e172(62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.82 (0.79, 0.85)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.67 (0.65, 0.69)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e154(55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126(45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.64 (1.59, 1.70)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.51 (1.45, 1.56)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e115(42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e158(57.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eOccupational status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e473(48.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e508(51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.99 (0.96, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.63 (0.60,0.65)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMerchant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e77(44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95(55.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.76 (0.72, 0.79)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.49 (0.47,0 .51)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e21(27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55(72.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.31 (0.29, 0.33)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.52 (0.48, 0.56)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e85(48.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90(51.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e223(41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e321(59.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e433(50.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e427(49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.31 (1.28, 1.34)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.51 (1.46, 1.54)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGetting advice from health professionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e157(32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e329(67.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e499(54.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e419(45.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.61 (2.55, 2.68)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.36 (2.30, 2.43)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily history of NCDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e632(48.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e660(51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.43 (4.22, 4.66)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.30 (4.07, 4.55)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e24(21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88(78.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCOR-Crude Odds Ratio: odds ratio by bivariate analysis. 95% CI: confidence interval at the 95% level. AOR-Adjusted OR: odds ratio by multiple logistic regression\u003c/p\u003e \u003cp\u003e*Candidate covariates at p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.25 in bi-variates and ** statically significant factors at p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** statically significant factors at p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01. 1: Referent category\u003c/p\u003e \u003cp\u003ePhysical activity was less likely among women aged 25\u0026ndash;34 (AOR: 0.54, 95% CI 0.52, 0 .56) and age\u0026thinsp;\u0026ge;\u0026thinsp;35 (AOR: 0.66, 95% CI 0.63, .68) 0.66 compared to those 15\u0026ndash;24 years. Women who were illiterate (AOR: 0.67 95% CI 0.64, 0.71), able to read and write (AOR: 0.91, 95% CI 0.88, 0.94) were less likely to have physical activity compared to those with secondary school education. However, having primary education (AOR: 1.13, 95% CI 1.08, 1.18) were more likely to have physical activity. However, married (AOR: 2.13, 95% CI 1.97, 2.31) and single (AOR: 1.39, 95% CI 1.25, 1.54) women were more likely to have physical activity than widowed/divorced. Physical activity was about two times more likely among rural residents (AOR: 1.69, 95% CI 1.63, 1.74) than urban and over five times more likely among women from Basketo zone (AOR: 5.48, 95% CI 4.98, 6.02) than Gofa.\u003c/p\u003e \u003cp\u003eWomen from the poorer (AOR: 1.10, 95% CI 1.06, 1.15) 1.10 (1.06, 1.15) households were more likely to have physical activity; but those from middle wealth quartile (AOR: 0.95, 95% CI 0.91, 0.99) and richer households (AOR: 0.93, 95% CI 0.88, 0.97) were less likely to have physical activity compared to the richest. Government employees (AOR: 0.53, 95% CI (0.49, 0.58), merchants (AOR: 0.78, 95% CI 0.74, 0.83) and housewives (AOR: 0.79, 95% CI 0.75, 0.83) were less likely to have physical activity compared to others such as maidservants, daily laborers and students. Participants having no family history of NCDs (AOR: 1.24, 95% CI 1.18, 1.31) were more likely to have physical activity than those having a family history.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate and multivariate logistic regression of physical activity and independent variables among reproductive age women\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCandidate Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ePhysical inactivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e327(81.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73(18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e437(72.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e162(27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.51 (0.50, 0.53)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.54 (0.52,0 .56)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e309(76.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96(23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.61 (0.59, 0.63)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.66 (0.63, 0.68)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducational status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e395(76.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124(23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.80 (0.77, 0.83)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.67 (0.64, 0.71)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAble to read and write\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e97(66.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50(44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.52 (0.49, 0.54)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.91 (0.88, 0.94)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e314(78.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86(21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.83 (0.80, 0.86)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.13 (1.08, 1.18)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary education and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e267(79.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71(21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e994(76.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e303(23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.64 (2.45, 2.84)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.13 (1.97, 2.31)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e54(79.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14(20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.69 (2.45, 2.96)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.39 (1.25, 1.54)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWidowed/Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e25(64.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14(35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e263(66.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130(43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e810(80.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e201(19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.95 (1.89, 2.01)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.69 (1.63, 1.74)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGofa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e921(74.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e323(26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasketo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e152(95.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8(5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e5.64 (5.14, 6.19)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.48 (4.98, 6.02)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e226(78.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62(21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.21 (1.16, 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01 (0.97, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e226(79.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60(21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.33 (1.28, 1.39)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.10 (1.06, 1.15)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e210(75.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67(24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.06 (1.02, 1.10)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.95 (0.91, 0.99)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRicher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e207(75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73(25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.03 (.99, 1.08)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.93 (0.88, 0.97)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRichest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e204(74.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69(25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eOccupational status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e754(76.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e227(23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e083 (0.80,0 .87)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.79 (0.75, 0.83)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMerchant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e132(76.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40(23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.79 (0.75, 0.84)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.78 (0.74, 0.83)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e55(72.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21(27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.65 (0.60,0.69)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.53 (0.49, 0.58)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e132(75.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43(24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e426(78.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118(21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.19 (1.15, 1.22)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (0.97, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e647(75.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e213(24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily history of NCDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e987(76.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e305(23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.43 (4.22, 4.66)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.24 (1.18, 1.31)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e86(76.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26(23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAvailability of recreational area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e916(77.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e273(33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.10 (1.07, 1.14)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.980 (0.94, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e157(73.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58(37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCOR-Crude Odds Ratio: odds ratio by bivariate analysis. 95% CI: confidence interval at the 95% level. AOR-Adjusted OR: odds ratio by multiple logistic regression\u003c/p\u003e \u003cp\u003e*Candidate covariates at p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.25 in bi-variates and ** statically significant factors at p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** statically significant factors at p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01. 1: Referent category\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study revealed that the prevalence of adequate intake of fruits and/ vegetables and physical activity among women of reproductive age was 48.4% and 78.3% respectively. The distribution varies with socio-demographic differences among the study participants; and multivariable analysis shows that adequate intake of fruits and vegetables and physical activities were significantly associated with different independent variables.\u003c/p\u003e \u003cp\u003eThe prevalence of adequate intake of fruits and/ vegetables among the study population (48.4%) is inconsistent with a study conducted since 2017 in Ethiopia (1.8%) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The possible explanation may be due to the difference in accessibility, availability and affordability of fruits and vegetables [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and the difference in age and other social factors that could influence intake of fruit and vegetables. Individuals may also lack adequate information on the need to consume sufficient fruit and vegetables and the health consequences of insufficient intake. The multivariate analysis of the study also found that the prevalence of adequate intake of fruits and vegetables was associated with age, place of residence (rural vs. urban, location, occupation, family size and educational status. This finding is in line with previous study in India [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Consumption of fruit and/vegetable was negatively associated with age of women. This finding suggests that older women show less concern for fruit and vegetable consumption. It agrees with the findings of studies in Nigeria and Brazil [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Participants with primary school education were more likely to consume adequate levels of fruits and vegetables when compared with illiterate women, similar to findings of a previous study [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This indicates importance of education in promoting fruit and/vegetable consumption. Rural dwellers were more likely to consume the recommended amount of fruit and/or vegetable as compared to their counterparts. This could be related with possibility of higher availability and accessibility of a variety of fruit and vegetable and relatively lower nutrition transition to processed foods among rural areas in Africa compared to urban [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This is in line with another study in Ethiopia [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, it is inconsistent with the result from studies in Addis Ababa, Ethiopia and Alfred Duma Municipality of Ladysmith [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], where daily consumption of fruits and vegetables among the study participants were 6.8% and 0.6% respectively. The difference might be due to variation in the setup. Majority of our study participants were from the rural setup.\u003c/p\u003e \u003cp\u003eWHO recommendation to perform at least 150 minutes of moderate-intensity physical activity per week, or the equivalent [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The current study reports relatively low prevalence of physical activity (78.3%); and this is consistent with previous national and international surveys [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and a study in sub-Saharan Africa which indicated 78% of the study participants were physically active [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, a similar study at Kasese district of Uganda indicates fifty percent of female respondents were physically active [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The difference may be associated with the observed lack of awareness about physical activity, economic transitions and the change in modes of transportation. The prevalence of physical activity was lower among reproductive age women aged 25\u0026ndash;34 and \u0026ge;\u0026thinsp;35 years than women aged 15\u0026ndash;24 years this may be as a result of the physical and psycho-social changes they undergo during the transition from single life to formation of family via marriage in Ethiopia. This might also be due to limited physical activity interventions for older adults. The finding is supported by a study in Eastern Ethiopia [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSocio-economic variations are determinates of NDCs, in context of study population, prevalence of physical activity was higher among women from households with the poorer wealth status compared with richest. This finding is in line with a study in Eastern Ethiopia [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This may be due to reduced sedentary lifestyle associated with occupations among this group of people [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] along with lower access to means of transportation, thereby increasing walking hours in a day. Physical activity was lower among participants with lower education. This finding is consistent with the national study [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This may be due to the importance of education in engaging in physical activity.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eThis pioneering study in the Gofa and Basketo zones examines the prevalence and factors linked to preventive factors of NCD among reproductive-aged women. Its strengths lie in its community-based approach covering both rural and urban residents, ensuring broader applicability. Additionally, rigorous measures were implemented, including pilot testing of instruments, comprehensive training of data collectors and supervisors, and high response rates (99%), enhancing data quality. The study's use of weighted analysis allows for extrapolation of findings to the entire study area. It delves into socioeconomic and knowledge-related factors associated with risk factors. However, the study's cross-sectional design limits its ability to establish causal relationships.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAbout half of the study participants consumed adequate fruits and/ vegetables and three fourth were physically active. Adequate fruit and/or vegetable and physical activity were more likely among rural residents and having no family history of NCDs. However, housewives, merchants and government employees were less likely to have both adequate intake of fruit and/or vegetable and physical activity. Having adequate fruit and/ vegetable intake was more likely among women who were younger age groups, having primary school education, Gofa zone residents, being from households with the lower wealth status, women with family size\u0026thinsp;\u0026gt;\u0026thinsp;4 and getting health professional advice. However, it was less likely among married and single women. Having recommended level of physical activity was more likely among married and single women, and Basketo zone residents. However, it was less likely among older age group and those with lesser educational status.\u003c/p\u003e \u003cp\u003eTherefore, it is necessary to minimize the burden of the growing non-communicable disease epidemic by promoting physical activity and adequate intake of fruit and/ vegetable through continuous awareness programs. The intervention should consider residential area, age, education, wealth and marital status. Lifestyle modifications are urgently required as preventive factors are alarmingly decreasing. Family-based changes and health system strengthening have to be fostered. The government and concerned bodies must promote the preventive factors among reproductive age women through a specific plausible plan drafted for the socioeconomic and cultural setting. Moreover, there is a need to consider further research with different study design to address other variables which were not included in this study.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eEthical Approval\u003c/h2\u003e\n\u003cp\u003eThis research study was approved on 14, June 2022 (pm 6.19/2199), by the Institutional Review board of South Ethiopia Region Public Health Institute.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that no financial support received for the research authorship and/or publication of this article.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eMMD: conceived, drafted, did the analysis, and finalized the manuscript by incorporating the inputs from the other authors. TGA: Supervised the study ensured the quality of the data. BMK: assisted conceptualization, analysis and interpretation of the data. All authors made a substantial contribution to the local implementation of the study and critically reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgment:\u003c/h2\u003e\n\u003cp\u003eI thank Selinus University Faculty of Natural Health Science for selecting and giving me a chance to research such public health topics and assigning a supervisor. My special heartfelt thanks go to my supervisor Prof. Salvatore Fava (PhD) for guiding me during the research process. I also extend my gratitude to data collectors and supervisors for supporting data collection.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eMost of the data used to support the findings of this study are included within the article. More data are available upon reasonable request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCarvalho MVB, Costa L, Di C, Oliveira L, et al. 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J Multidisciplinary Healthc. 2021:1561\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarmot M, Bell R. Social determinants and non-communicable diseases: time for integrated action. BMJ. 2019;364.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSambo L, Chatora R, Goosen E. Tools for assessing the operationality of district health systems. Brazzaville: World Health Organization, Regional Office for Africa; 2003.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSomefun OD, Ibisomi L. Determinants of postnatal care non-utilization among women in Nigeria. BMC Res Notes. 2016;9(1):1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGebremedhin MMaT. Determinants of postnatal care non-utilization among women in Demba Gofa rural district, southern Ethiopia: a community-based unmatched case-control study. 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhagyalaxmi A, Atul T, Shikha J. Prevalence of risk factors of non-communicable diseases in a District of Gujarat, India. J Health Popul Nutr. 2013;31(1):78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. WHO STEPS surveillance manual: the WHO STEPwise approach to chronic disease risk factor surveillance /. Noncommunicable Dis Mental Health 13 June 2008(ISBN 92 4 159383 0).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYaya S, Uthman OA. Ekholuenetale ea. Socioeconomic inequalities in the risk factors of noncommunicable diseases among women of reproductive age in sub-saharan Africa: a multi-country analysis of survey data. Front public health. 2018;6:412663.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi C. Knowledge, attitudes and practices of community health workers regarding noncommunicable diseases in the eastern region of S\u0026atilde;o Paulo, Brazil. 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZammit NMJ, Bhiri S, et al. Three years community-based intervention program to prevent non-communicable disease risk factors in Sousse, Tunisia. J Health Sci. 2015;3(95):102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerefe Gelibo KA, Tefera Taddele G, Taye et al. Low fruit and vegetable intake and its associated factors in Ethiopia: A community based cross sectional NCD steps surveyLow fruit and vegetable intake and its associated factors in Ethiopia: A community based cross sectional NCD steps survey. 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNepali SRA, Olsen MH, et al. Factors affecting the fruit and vegetable intake in Nepal and its association with history of self-reported major cardiovascular events. BMC Cardiovasc Disord. 2020;20(1):1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahajan M, Naik N, Jain ea. Study of Knowledge, Attitudes, and Practices Toward Risk Factors and Early Detection of Noncommunicable Diseases Among Rural Women in India. J Glob Oncol. 2019;5:1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKehinde Paul Adeosun NAC, Chukwuma O, Umea, et al. Factors Influencing Fruits and Vegetable Consumption among Pregnant Women. Nigeria: Evidence from Enugu State; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarah Lidu\u0026aacute;rio Rocha Silva RdDM ACSL. Factors associated with inadequate consumption of fruit and vegetables among users of. Primary Health Care in Brazil; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeding G. Nutrition transition in rural Tanzania and Kenya. World Rev Nutr Diet. 2016;115:68\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1159/000442073\u003c/span\u003e\u003cspan address=\"10.1159/000442073\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2016 May 19. PMID: 27198465. 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDlamini TXaS. Factors associated with consumption offruits and vegetables amongst adults in the Alfred Duma Local Municipality, Ladysmith 2021. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMulugeta Debelo Bortola SGBaDDA. Fruit and Vegetable Consumption and Associated Factors among Women of Reproductive Age Attending Maternal. and Child Health Department in Public Hospitals in Addis Ababa, Ethiopia.; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEPHI F. WHO. Ethiopia STEPS report on risk factors for non-communicable diseaes and prevalence of selected NCDs. Addis Ababa: Ethiopia Public Health Institute; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRegina Guthold GAS, Leanne M, Riley, Fiona C, Bull. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1\u0026bull;9 million participants. 2018. 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMondo CKOM, Musoke R, Orem J, et al. The prevalence and distribution of non-communicable diseases and their risk factors in Kasese district, Uganda. South Afr J Diabetes Vascular Disease. 2016;13(1):31\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChalchisa Abdeta ZT, Berhanu Seyoum. Prevalence of physical inactivity and associated factors among adults in Harar town, Eastern Ethiopia adults in Harar town, Eastern Ethiopia,. 2018. 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBista B. Dhungana ea. Prevalence and determinants of non-communicable diseases risk factors among reproductive aged women of Nepal: Results from Nepal Demographic Health Survey 2016. PLoS ONE. 2020;15(3):e0218840.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmenu KGT, Getnet M et al. Magnitude and determinants of physical inactivity in Ethiopia: evidence form 2015 Ethiopia national Ncd survey. Ethiopian Journal of Health Development. 2017. 2017.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fruit and vegetable intake, physical activity, reproductive age women, Gofa, Basketo, South Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-5434074/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5434074/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe escalation in burdens of non-communicable diseases coupled with unmet needs of sexual and reproductive health services progressively impacting women, and posing significant threats to forthcoming generations. Low fruits and vegetables intake, and insufficient physical activity are known risk factors of NCDs. This research endeavors to assess the level and factors associated with fruit and/or vegetable intake and physical activity among women of reproductive age in Gofa and Basketo Zones, Southern Ethiopia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA community based cross-sectional study was employed among reproductive age women from September 9/2022 to December 6/2022. A multistage cluster sampling was used to select participants from the designated zones. A total of 1404 study participants were included in the analysis. Statistical analysis was conducted using Statistical Package for the Social Sciences software encompassing descriptive statistics, bivariate analysis, and multivariate logistic regression. Associations were deemed statistically significant if the p-value was \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003ePrevalence of adequate fruit and/or vegetable (FV) intake and physical activity were 48.4% and 78.3% respectively. Women who were from younger age groups, getting advice from health professionals, having primary school education, having\u0026thinsp;\u0026gt;\u0026thinsp;4 family size, women from households with the lower wealth status, widowed/separated and Gofa zone residents were more likely to have adequate FV intake. Being married, single and Basketo zone residence were positively associated with physical activity. However, women from older age groups and having lesser educational status were less likely to be physically active. Women who were rural residents and having no family history of NCD were more likely to have both adequate FV intake and physical activity.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eA substantial gap exists between the recommended level and actual FV consumption and physical activity. They are associated with different socio-economic, knowledge and health system factors. However, these preventive factors are more influenced by rural residence, having no family history of NCD, women\u0026rsquo;s occupation. Therefore, NCD prevention would be more effective if they account specific determinants in their design. Policy and socio-political factors influencing the rise of NCD risk factors should also be addressed.\u003c/p\u003e","manuscriptTitle":"Factors associated with fruit and vegetable intake and physical activity among reproductive age women in Gofa and Basketo Zones, Southern Ethiopia: a community based cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-17 11:16:43","doi":"10.21203/rs.3.rs-5434074/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9a3d0f49-4c49-4ff5-9f81-4554aabd15bb","owner":[],"postedDate":"December 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-22T13:38:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-17 11:16:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5434074","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5434074","identity":"rs-5434074","version":["v1"]},"buildId":"zQwnuV7TCBrMSSSToR1PI","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}