Barriers to fruit and vegetable consumption among migrant workers in Bangkok: a mixed-methods study

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Myanmar migrants in Thailand are vulnerable to poverty and poor diets, but their food environment and behavior have not been studied. The objective of this study was to describe and analyze the fruit and vegetable intake of Myanmar migrant workers in Bangkok. Methods The study combined a quantitative survey of 199 Myanmar migrants working in factories, construction sites, and service industries with in-depth qualitative interviews of 10 migrants. The study analyzed psychological and food environment factors. Results The average fruit and vegetable consumption was 195 g/day, about half the WHO-recommended amount. About a quarter of meals were purchased, and the rest were home-cooked. Quantitative results revealed that home cooking, number of market visits, self-efficacy, and intention are statistically significant predictors of intake. While fresh fruits and vegetables are generally available, key constraints identified in the qualitative analysis included limited mobility, the high cost of fruits and vegetables relative to earned incomes, and long working hours that compel people to prioritize convenience over healthy eating. Conclusions Fruit and vegetable intake is low among migrant workers in Bangkok, putting them at risk of non-communicable disease. There is a need for more targeted strategies to improve migrants’ access to healthy food options. healthy diet diet quality food choice food environment urban Thailand Background Micronutrient deficiencies are a major global health concern. Over 2 billion people are estimated to be affected by iron deficiency alone ( 1 ). It has been estimated that the diets of over 5 billion people, or 62% of the world’s population, are deficient in one or several micronutrients ( 2 ). Low consumption of micronutrient-rich foods, such as fruits and vegetables, is a key driver. In developing countries, an estimated 78% of adults consume fewer than five portions or 400 grams of fruits and vegetables daily ( 3 , 4 ), the minimum amount recommended by the World Health Organization ( 5 ). While much research focuses on vulnerable groups such as pregnant or lactating women and children, other groups, including working-age migrants, are also at risk of micronutrient-poor diets but are often overlooked. The International Convention on the Protection of the Rights of All Migrant Workers and Members of Their Families defines a migrant worker as “a person who is to be engaged, is engaged or has been engaged in a remunerated activity in a State of which he or she is not a national” ( 6 ) The group includes both documented and undocumented workers, particularly those who engage in elementary occupations in low-skilled, low-paid jobs such as crop harvesting, factory work, or cleaning services. Existing studies on migrant dietary intake focus on people from low- and middle-income countries working in high-income countries. These studies often compare migrant and host population diets ( 7 , 8 ) or analyze migrants’ food environments and habits. Research on African farmworkers in Spain reported diets with little variety and too many obesogenic products ( 9 ). Latino migrant farmworkers in North Carolina faced structural constraints that contributed to poor dietary habits, such as overreliance on superstores, limited mobility, and a lack of refrigerator space ( 10 ). Latino migrant workers were also studied in the United States using qualitative research methods ( 11 ). It found that migrants dramatically reduced their consumption of fresh fruits and vegetables, as they perceived these foods to be of poor quality and high price. Another study compared the diets of Puerto Rican migrants living in Massachusetts with those of Puerto Ricans living in Puerto Rico and found that 57% of migrants had poor diet quality while only 20% of Puerto Ricans in Puerto Rico had poor diet quality ( 12 ). A study with ethnic minorities in California found that 67% and 78% of Asian children consumed less than 200 grams of fruit and vegetables per day, compared to 34% and 56% of Caucasian children ( 13 ). Moreover, a study of Myanmar migrants in Malaysia found that 96% were mildly or moderately food insecure ( 14 ). In Thailand, about 2.3 million Myanmar nationals were officially registered as migrant workers in 2024, constituting 75% of migrant workers in elementary occupations ( 15 ). The actual number is probably higher because many migrants are not registered. Most migrants occupied low-paying jobs in agriculture, construction, factories, and service industries ( 15 ). They contribute considerably to the Thai economy but often face low incomes, limited access to education, restricted access to public health services, and disadvantaged housing and employment conditions, making them vulnerable to poverty and suboptimal diets. Although there have been studies on food behavior and the environment in low and middle-income countries, only a few have explored the mechanisms underlying food behavior among migrants. The Health Action Process Approach (HAPA) proposes factors can influence a health behavior, such as belief in one’s own capability (self-efficacy), a plan to achieve the action goal and to cope with possible setbacks (planning), and intention to perform the behavior ( 16 ). Existing studies applying HAPA to fruit and vegetable intake consistently highlighted the role of these factors in shaping diet behavior across population groups ( 17 – 19 ). While these psychological factors could offer valuable insights into fruit and vegetable intake, an individual’s diet can also be shaped by other contextual factors. The HAPA model allowed for the inclusion of contextual factors to better capture barriers or resources related to a health behavior, which can have stronger effects than the cognitive predictors ( 16 ). In the broader literature, several factors have been widely incorporated as key determinants of eating behavior. Firstly, habit reflects automatic routines that repeat frequently over time and often require less effort to execute. Therefore, habit could fundamentally affect eating behavior, as it can determine how much effort and information are needed to choose what a person eats ( 20 , 21 ). Secondly, a large body of research shows that social support can positively influence fruit and vegetable intake and diet quality ( 22 – 24 ). Lastly, studies have shown that greater nutritional knowledge is often associated with increased fruit and vegetable intake ( 25 , 26 ). Including these commonly studied factors alongside HAPA constructs provides a more comprehensive view of the drivers of fruit and vegetable intake. In Thailand, previous studies found that low fruit and vegetable consumption was a key dietary risk factor among adults, with three-quarters of Thai nationals consuming too few vegetables ( 27 , 28 ). To better understand the factors that lead to suboptimal diets, it is essential to complement studies based on nationally representative samples with in-depth research on vulnerable groups. This study is one of three that examines urban populations at risk of low fruit and vegetable consumption, specifically seniors ( 29 ), Buddhist monks, and migrants. Methods The objective of this study is to describe and analyze the fruit and vegetable intake of Myanmar migrant workers in Bangkok. Myanmar migrants are a population group at risk of poor diets, including low fruit and vegetable consumption. A mixed-methods approach is applied to examine how psychological and contextual factors jointly shape fruit and vegetable intake. The quantitative component compares the relative influence of psychological variables, including dietary self-efficacy, intention, and planning, and contextual variables such as habit, social influence, nutritional knowledge, demographic factors, and environmental factors such as food sources. In addition, the study described the overall meal quality using the diet quality score. By analyzing psychological and contextual factors within a single model, we identify which factors produce stronger effects on dietary behavior. To complement and extend these findings, the qualitative analysis provides deeper insight into how participants interpret their food environments, navigate constraints, and assign meaning to their eating practices. This mixed-method study combined a structured survey with 199 samples and in-depth interviews with ten participants, providing a comprehensive understanding of dietary behavior. The quantitative component assessed dietary behavior and its correlates across personal, social, and environmental factors through structured surveys. The study combined fruit and vegetable intake as a single outcome to reflect the way fruit and vegetables are commonly promoted together in nutrition research, dietary guidelines, and public health policy. Since fruit and vegetable intake is typically treated as a single behavioral target, our analysis focuses on identifying influences on combined fruit and vegetable intake rather than separating fruit and vegetables into two models. The qualitative component explored participants’ experiences through in-depth interviews and was based on the science of food choice ( 30 ). The study plan was approved by the World Vegetable Center’s Research Ethics Committee (registration ID 2023-17). Participation was virtually risk-free. Verbal and written consent were obtained, and interviewers clarified participants’ right to withdraw at any time. The participant was informed of the anonymity and confidentiality. All personal identifiers were removed prior to analysis. Quantitative data and analysis A structured questionnaire was used to collect data on intake, food sources, psychological factors, and demographic information of respondents. The tool was developed in English, translated into Burmese, and back-translated. The translated questionnaire and interview guide were reviewed by two native Myanmar professionals familiar with the context. They checked for linguistic accuracy, cultural appropriateness, and clarity, and minor adjustments were made accordingly. Outcome Fruit and vegetable intake was assessed using a short dietary measure adapted from the Health Examination Survey of Thailand ( 27 ). Participants reported the days per week they consumed vegetables and the number of servings they ate on those days. The same questions were asked about fruit intake. Photos of 80-gram serving sizes of various raw and cooked vegetables and fruits assisted the reporting ( 31 ). Daily servings were converted into grams to estimate daily fruit and vegetable intake. Predictors Food sources , including cooking frequency, number of market visits, and kitchen access were recorded and included as predictors. Demographic data, including age, household size, gender, income, and work sector, were also included as predictors. Other characteristics were collected to provide context, including marital status, employment status, home gardening, chronic illness, dental issues, and residence characteristics. Psychological drivers were derived from the HAPA framework and included intention, dietary self-efficacy, and planning ( 16 ). All responses were rated on a 5-point Likert scale from “strongly disagree” ( 1 ) to “strongly agree” ( 5 ). Combined scores were used, and higher scores reflect a higher level of each construct. Intention to eat fruit and vegetables was measured separately by two items, “I intend to eat fruit every day” and “I intend to eat vegetables every day.” The items were adapted from Pandey et al., which were originally specific to vegetables ( 32 ). The composite score was used, with higher scores indicating a greater intention to eat fruit and vegetables daily (α = 0.67). Dietary self-efficacy , adapted from Pandey et al., was used ( 32 ). Four items measure participants' confidence in their ability to consume sufficient fruit, and four parallel items measure their confidence in their ability to consume sufficient vegetables (α = 0.85). Planning was measured using an adapted scale that includes action planning and coping planning ( 17 ). Three items assessed action planning, and two assessed coping planning (α = 0.91). Social influences on fruit and vegetable consumption were examined using a scale adapted from Pandey et al. ( 32 ). The scale included four items assessing whether participants’ close contacts consumed fruit and encouraged them to do the same, with four parallel items for vegetables (α = 0.88). Automatic habit was assessed using two items adapted from Pandey et al. ( 32 ). The items were, or example, “Eating two servings of fruit and vegetables is something I do automatically” (α = 0.72). Nutritional knowledge was assessed using a 10-item scale previously adopted in ( 29 ). Participants were asked to identify each statement with “Correct”, “Incorrect”, or “I don’t know”. The content was targeting nutritional knowledge specific to fruit and vegetables for people of all ages, for example, “Carrots, pumpkins, and orange sweet potatoes are all sources of vitamin A.” Diet quality assessment The Burmese version of the Diet Quality Questionnaire (DQQ) was used ( 33 ). It contains 29 food groups in a yes/no format to describe food consumed the previous day. Various DDQ indicators were calculated following the indicator guide ( 34 ). The Dietary Diversity Score (DDS) represents the diversity of food groups consumed from a list of 10 standard food groups aggregated from the 29 food groups. The All-5 Score is a binary indicator. It takes a value of one if all five recommended food groups are consumed, meaning at least one vegetable, one fruit, one pulse, nut, or seed, one animal-source food, and at least one starchy staple. The Non-Communicable Disease (NCD)-Risk score is a proxy for ultra-processed food (UPF) intake (0–9), with a higher value indicating more UPF consumption. NCD-Protect is an indicator (0–9) of dietary factors protective against NCDs, based on consumption of nine food groups associated with meeting WHO recommendations on fruits, vegetables, whole grains, pulses, nuts and seeds, and fiber. The Global Dietary Recommendations (GDR) score (0–18) combines the NCD-Protect and NCD-Risk. Sample size The minimum sample size for the regression analysis was calculated using G*Power. Based on 16 predictors, a medium effect size of 0.15, a power level of 0.8, and an alpha of 0.05, the minimum sample size was 143 participants. Researchers added 10% to account for potential errors, such as missing or inconsistent data, aiming to recruit at least 158 adult Myanmar workers in low-skill jobs. With guidance from a Myanmar field supervisor, we recruited 199 participants from the construction, factory, and service sectors across 19 districts and 31 subdistricts. We employed purposive and snowball sampling, starting in areas with high concentrations of Myanmar workers and expanding through referrals, aiming for 10–15 participants per area. Procedures Questionnaires were programmed in KoboToolbox. The interviews took approximately 15 minutes to complete. Participants were given 80 baht (approximately USD 2.40) as a token of appreciation after completing the survey. All participants read and signed an informed consent. No identifying information, such as addresses, was collected, as some participants may be undocumented. Where necessary, we requested permission to conduct interviews with the supervisors. Data collection took place from July to August 2024. Qualitative data and analysis We applied the science of food choice to conceptualize food behavior, food environment, and decision processes ( 30 ). This framework has three main interrelated questions: “WHAT do people eat?”, “HOW do people acquire, prepare, distribute, and consume the food they eat?”, and “WHY do people make the food choices that they do?” ( 30 ). The interview was semi-structured based on these three questions. We conducted semi-structured interviews guided by prompts. Interviews explore a range of available food options, providing an overview of what people eat and the options available to them. Questions also revolve around how people acquire, prepare, share, and consume their food. Interview questions asked participants where they usually get fruit and vegetables, and how they consume them. The reasons behind each person’s food choices were also addressed. A pilot coding was carried out on two transcripts. The codes address the WHAT, HOW, and WHY questions ( 30 ). An independent researcher conducted both inductive and deductive coding to create the study codebook. For the first WHAT questions, example codes are ‘food options’, ‘quantity’, and ‘quality’. The HOW questions included codes such as ‘acquisition’, ‘storing’, ‘serve’, and ‘consume’. We then applied a typology of food environments to differentiate specific ways people acquire food, such as ‘purchasing’, ‘wild’, cultivated’, and ‘social food exchange’ ( 35 ). For the WHY question, we took Blake et al.'s ( 30 ) concepts on identifying why and how the decision-making process is carried out. However, this framework only provides a broad scope of the decision-making process. Therefore, we further categorize the emerging themes into individual, social, physical, and macro-level factors, following ( 36 ). This framework provides sub-themes and categorizes levels of influence on food choice. Individual-level factors include, for example, health considerations, attitudes, and taste preferences. Social-level factors encompass household composition and meal-sharing practices, while physical-level factors address the physical environment. Macro-level factors encompass broader influences, including urbanization and cultural norms. By integrating these multiple levels of influence, this framework provides a comprehensive lens for examining the complex interactions that influence dietary behaviors. The main analysis of this study was based on the integrated codebook. Two other researchers verified the codebook. Interviews were conducted in Burmese with ten migrants from three areas of Bangkok, identified with the assistance of a Myanmar field coordinator based on high concentrations of migrants working in each sector. The sample for the in-depth interviews did not overlap with the survey. The first area was at a fresh market in Bangkapi, located near several construction sites. The second was an area with many factories in Sathu Pradit. The third area targeted service workers from several malls in the Pratunam district. A field coordinator contacted 3–4 participants in each area and scheduled interviews in advance. All interviews took place near participants’ workplaces and were conducted in English and Burmese by a Thai researcher and a Burmese translator, lasting about 40 minutes. Eight men and two women were interviewed. Despite differences in work sectors, migrants shared similar socioeconomic backgrounds and living conditions. The researchers noted that thematic saturation was reached after the eighth interview, as no new themes arose. Data collection continued as planned until ten interviews were completed. Participants received 200 baht as a token of appreciation. Interview transcripts were transcribed in Burmese and translated into English. Data was analyzed thematically using Atlas.ti (version 25.0.1.32924). We identified recurring patterns and developed sub-themes, which were based on commonalities observed across multiple cases and were initially organized within the structure of our codebook. Co-occurrence analysis was used to determine which codes appeared together most frequently, thereby understanding the relationships between codes. The analysis complied with the Standards for Reporting Qualitative Research (SRQR) ( 37 ). Results Sample characteristics Table 1 summarizes the sample characteristics by work sector. Among the entire sample, 22% worked in construction, 33% in factories, 25% in services, and 23% in other sectors. Approximately half of the participants are female (51%), with an average age of 31 years. Women are less present in construction compared to other sectors. The samples have resided in Thailand for approximately 6.2 years. The majority (92%) work more than 40 hours a week. Table 1 Mean characteristics of the sample of migrant workers in Bangkok, Thailand Construc-tion ( n = 43) Factories ( n = 61) Services ( n = 50) Other (n = 45) All (n = 199) Demographics: Gender (1 = female) 0.26 0.56 0.56 0.62 0.51 Age (years) 30.93 30.03 30.00 34.33 31.12 Education (1 = above primary) 1.00 1.00 0.98 1.00 0.99 Years in Thailand 4.56 6.07 6.30 7.89 6.21 Working > 40hrs/week (= 1) 0.93 0.96 0.92 0.86 0.92 Married/cohabiting (= 1) 0.37 0.36 0.34 0.53 0.40 Living with family members (= 1) 0.4 0.43 0.42 0.58 0.46 Household size (persons) 4.30 3.21 4.30 3.60 3.81 Persons needing care (persons) 0.77 0.74 0.70 0.64 0.71 Chronic illness (= 1) 0.02 0.00 0.20 0.00 0.01 Dental issues (= 1) 0.07 0.11 0.14 0.20 0.13 Building type (proportions): − Apartment 0.51 0.67 0.62 0.62 0.61 − House or townhouse 0.12 0.19 0.34 0.20 0.22 − Worker dormitory 0.37 0.11 0.02 0.02 0.16 − Other 0.00 0.02 0.02 0.02 0.02 Residence characteristics (proportions): − Running water 0.98 1.00 0.98 1.00 0.98 − Home garden 0.07 0.07 0.10 0.07 0.08 − Kitchen 1.00 1.00 0.74 0.93 0.92 − Refrigerator 0.77 0.70 0.70 0.71 0.72 Food sources: Cooking frequency/week (count) 10.05 10.72 6.58 12.02 9.83 Bought meals/week (count) 4.77 3.75 3.80 4.78 4.22 Usually eats alone (= 1) 0.33 0.38 0.54 0.51 0.44 Psychological factors: Dietary self-efficacy score ( 1 – 5 ) 3.45 3.46 3.54 3.43 3.47 Intention score ( 1 – 5 ) 3.51 3.56 3.47 3.49 3.51 Planning score ( 1 – 5 ) 2.53 2.53 2.75 2.66 2.61 Contextual factors: Automatic habits score ( 1 – 5 ) 3.60 3.66 3.79 3.79 3.71 Social influence score ( 1 – 5 ) 3.23 3.14 3.38 3.13 3.22 Nutritional knowledge score ( 1 – 10 ) 5.09 5.57 6.16 6.07 5.73 Regarding health conditions, 1% of the sample reported having a chronic illness, while 13% reported dental issues. It was relatively common to share housing with non-family members (59%) to split the rent. Nearly all migrants had access to running water (98%) and a functional kitchen (92%). A lower percentage had access to a refrigerator (72%), vital for keeping fresh produce. Home gardening was relatively uncommon, with only 8% of people growing their own fruit or vegetables. Nutrition knowledge was low, as only 57% of the knowledge questions were answered correctly. The knowledge test used binary choice questions, and random guessing would likely yield 50% correct answers; hence, this is just marginally better than random guessing. The nutrition knowledge was lower among construction workers. About 44% of respondents indicated that they usually eat alone, with the highest percentage among service workers. Across all sectors, psychological scores related to fruit and vegetable intake were moderate. Automatic habits scored the highest on average ( M = 3.71, SD = 0.81), with slightly higher means in the service ( M = 3.79, SD = 0.90) and other ( M = 3.79, SD = 0.80) sectors, suggesting that incorporating fruits and vegetables may be a somewhat routine behavior for many participants. Dietary self-efficacy was also relatively high and consistent across sectors ( M = 3.47, SD = 0.51), indicating moderate confidence in the ability to maintain healthy eating. Similarly, intention to consume fruits and vegetables was moderate ( M = 3.51, SD = 0.96), with only slight variation between groups. Social influence was moderate overall ( M = 3.22, SD = 0.60), with slightly higher scores in the service sector ( M = 3.38, SD = 0.61), indicating that social norms or encouragement may be limited in this sample. In contrast, planning showed the lowest mean scores (overall M = 2.61, SD = 0.73), especially in the construction and factory sectors (both M = 2.53), suggesting that while participants may be motivated, few take concrete steps to plan their intake. The results of the diet quality questionnaire reveal poor diet quality across all work sectors (Table 2 ). The mean DDS was 6.9 on a 10-point scale, the NCD-Protect score was 4.6 on a 9-point scale, the mean NCD-Risk score was 2.7 on a 9-point scale, and the mean GDR score was 10.9 on an 18-point scale. Only about half (53%) of the respondents consume all five recommended food groups. Table 2 Mean diet quality and intake for the sample of migrant workers in Bangkok, Thailand Construc-tion ( n = 43) Factories ( n = 61) Services ( n = 50) Other ( n = 45) All ( n = 199) Dietary Quality Questionnaire (score): Global Dietary Recommendation (0–18) 10.77 10.79 10.84 11.44 10.94 Dietary Diversity Score (0–10) 6.93 6.51 7.10 7.18 6.90 ALL5 (% yes) 0.58 0.44 0.54 0.60 0.53 NCD-Protect (0–9) 4.42 4.34 4.74 4.98 4.60 NCD-Risk (0–9) 2.65 2.56 2.90 2.53 2.66 Fruit intake: Fruit intake (days/week) 3.23 3.59 3.68 3.58 3.53 Fruit intake (portions/day) 1.42 1.49 1.66 1.87 1.60 Fruit intake (grams/day) 60.33 63.89 83.20 76.19 70.75 Vegetable intake: Vegetable intake (days/week) 5.00 4.72 5.30 5.36 5.07 Vegetable intake (portions/day) 1.95 2.08 1.94 2.33 2.08 Vegetable intake (grams/day) 113.22 120.28 122.97 141.97 124.33 Combined fruit and vegetable intake (g/capita/day) 173.55 184.17 206.17 218.16 195.09 Notes: GDR = Global Dietary Recommendation score; DDS = dietary diversity score; ALL5 = consumption of all five food groups; NCD-Protect = consumption of nine food groups associated with meeting WHO recommendations on fruits, vegetables, whole grains, pulses, nuts and seeds, and fiber; NCD-Risk: proxy for ultra-processed food (UPF) intake (0–9), with a higher value indicating more UPF consumption. Fruits were available at home for about 2.2 days per week, and vegetables were available for 2.6 days per week. Fruit was consumed for approximately 3.5 days per week, and vegetables were consumed 5.1 days per week. The mean daily intake was 70.7 grams of fruit and 124.3 grams of vegetables. The combined daily intake of 195.1 grams is less than half the WHO recommendation, indicating significant underconsumption of fruit and vegetables. Regression results Regression diagnostics were conducted to assess model assumptions, including multicollinearity, residual normality, linearity, and homoscedasticity. No issues were detected. The regression model was statistically significant, explaining approximately 25% of the variance in average daily fruit and vegetable intake ( F (17, 166) = 3.46, p < 0.001). However, contrary to expectations, only cooking frequency (β = 0.16, p < 0.05), number of market visits (β = 0.19, p < 0.05), self-efficacy (β = 0.15, p < 0.10), and intention (β = 0.25, p < 0.01) emerged as statistically significant predictors (Table 3 ). Specifically, participants who cooked at home more frequently and visited fresh markets more often reported higher combined fruit and vegetable intake. Higher self-efficacy, which reflected greater confidence in one’s ability to consume fruits and vegetables, and stronger intention to do so, were both positively associated with intake. In contrast, socio-demographic factors (gender, household size, income), social support, and nutritional knowledge showed no significant associations. Table 3 Determinants of fruit and vegetable intake, in grams/capita/day, among migrant workers in Bangkok, Thailand as based on a linear regression model (n = 199) Covariate Unstandardized coefficient ( B ) Standard error p-value Standardized coefficient (β) Demographic factors: Gender (1 = female) -12.96 20.11 0.520 -0.05 Age (years) 0.35 1.24 0.779 0.02 Household size (persons) -3.03 3.38 0.371 -0.06 Income 0.00 0.00 0.900 0.01 Work sector (Other = 1): − Construction -11.84 29.43 0.688 -0.04 − Factory -9.29 26.17 0.723 -0.03 − Service 19.20 29.57 0.517 0.06 Food sources: Cooking frequency/week 3.48 1.72 0.044 0.16 Market visit/week 11.65 4.66 0.013 0.19 Kitchen (= 1) -67.97 44.96 0.132 -0.12 Psychological factors (1–5 pts): Dietary self-efficacy 37.57 22.47 0.096 0.15 Intention 34.04 12.29 0.006 0.25 Planning -7.88 13.96 0.573 -0.04 Contextual factors (1–5 pts): Automatic habit 20.97 13.19 0.114 0.13 Social influence -12.55 17.55 0.476 -0.06 Nutritional knowledge (0–10) -0.88 6.05 0.885 -0.01 Constant -63.19 90.96 0.488 . R 2 0.25 F-statistic 3.46 < 0.001 Qualitative results The thematic analysis revealed key interactions between the physical, social, and personal drivers of food choice. The themes described here include: ( 1 ) responses and strategies to limited food options, ( 2 ) positive and negative effects of food sharing on intake, ( 3 ) demanding work conditions on eating habits, and ( 4 ) health considerations and taste preferences undermined by practical constraints. While this structure reflects the analytical frameworks used, it does not imply a hierarchy or chronological order of influences. Interactions between different levels of factors provide a comprehensive understanding of the unique context of the study participant. Responses and strategies to limited healthy food options Study participants described multiple accessible food outlets near their residence or workplace. Bangkok has an abundance of food suppliers selling prepared meals, snacks, and fresh produce. These include mobile food and fresh produce vendors, fresh produce markets, supermarkets, and convenience stores, resulting in a wide range of food options from cheap to expensive. Many migrants, therefore, opt to buy ready-made meals instead of cooking, as this is more convenient. However, unhealthy foods were more prevalent than options with fruit or vegetables. Most of the food mentioned by the participants was fresh produce, snacks, and cooked meals. The cooked meals were mostly Thai foods, featuring various curries, stews, and stir-fry dishes that contain alternating meat, vegetables, and spices. Burmese meals were only occasionally available near factories or construction sites. Participants rarely mentioned healthy snack options besides fruit, particularly during work time. Typical snacks from convenience stores and street food vendors near the workplace are packaged cakes, fried potatoes, pa tong ko (Chinese donuts), and roti (Indian paratha). Although a variety of choices exist, based on their descriptions, relatively few were healthy. Participants in the service sector mentioned challenges accessing fresh fruit. Cut fruits sold by small mobile vendors were expensive and sold in small amounts (around 1–2 servings). Small quantities of easy-access vegetables from grocery trucks were also mentioned. Grocery trucks usually sell small quantities of vegetables in plastic bags, enough for one meal. Several participants noted that fresh markets and fruit trucks (modified pick-up trucks with open backs to display fruit, usually selling it by the kilogram) offer lower prices and fresher produce than those sold by small mobile vendors (two- to three-wheeled modified motorcycles selling chopped fruits in small quantities). Therefore, migrants prefer to buy cheaper fruit and vegetables from fresh markets or fruit trucks. [Respondent 9; Female factory workerเ] They buy from the mobile food vendor at the front of the factory. But for me, as the fruits from the vendor seem to be insufficient, I do not buy from the vendor. Buying from the fruit-selling truck, I feel like I can get more. I also eat more fruits than others, so I buy them in kilos and I keep them in the fridge for the next day. While physical access to food was not the main issue, several participants expressed dissatisfaction with the quality of meal options, which lacked vegetables. They said that restaurant food and street food have meat as the main ingredient, not vegetables, but they felt that vegetables should be cheaper than meat. However, meat options are more popular in restaurants due to their taste and versatility. [Respondent 1; Male service worker] I don’t exactly know their aim. But if I have to guess, maybe the price of the meals. The prices of a lunch box with meat and a lunch box with vegetables are the same. And the shop from the mall, where we buy our lunch, is not good enough while they are cooking the vegetables. But for the meat curry, they do well, and they can cook well, and the taste is really nice. But for the vegetables, they were only cooked as a side dish. After they put the meat curry, they put the fried vegetables next to the curry as a side dish, but they were not delicious either. That’s why people are not eating the vegetable menus so much. Although food was plentiful, access to affordable, healthy options was limited, contributing to dietary patterns that lacked sufficient fruit and vegetables. The interaction between the physical environment and individual factors came into play, requiring migrants to dedicate more time and effort to obtaining fruit and vegetables. Positive and negative effects of food sharing on intake Food sharing among roommates and in the workplace emerged in several interviews across sectors. Migrant workers in Bangkok often share their accommodations to split rent. Food sharing and exchange are common practices among roommates, regardless of whether they are family. People living together typically take turns buying fruit and vegetables to share, as purchasing in bulk is more economical. Food preparation responsibility is typically shared, with individuals taking turns cooking and sharing meals. Although not always fixed, this role is primarily assigned to a woman. Since the kitchen is usually part of the living space, individuals can use the cooking facilities at any time throughout the day. [Respondent 4; Male construction worker] If two people are living in a room together, while one person cooks rice, the other can cook the curry at that time. Two people per room isn’t a problem. Others will be couples. As a couple, the woman does the cooking. If there are two men in a room, one cooks the rice, and the other the curry. Food sharing is common among many participants, particularly when roommates share similar views on health and lifestyle, as well as food habits. These social influences can have positive or negative effects. The cook or sharer may enjoy something that the other typically does not like, yet they will still share the same food. One person may adopt the other’s eating habits, such as consuming a lot of fruit daily. Conversely, if one prefers cooking with unhealthy ingredients or simply has different tastes, the other might struggle to change habits. Employees also mentioned the food shared by employers. For instance, a shop owner or factory manager occasionally provides workers with free fruit. This was not a stable food source, relying on the manager’s generosity and budget. One might be swayed by a friend’s suggestion to eat more fruit, especially when they share similar health concerns. At the same time, individuals may be tempted to consume foods they would not typically choose for themselves due to convenience and workplace norms. A service worker mentioned how it is common for them to cook and eat together using ingredients purchased by the owner. Otherwise, they would need to buy their own ingredients. [Respondent 10; Male service worker] Usually, I don’t really get to choose. My boss will be the one to buy it, and we usually make curries. Yes, but I will make my own if I don’t like it. If they make only one meal of one type of curry and we don’t like it, we can go out or make our own. All of us know how to cook. Consequently, food sharing plays a significant role, sometimes reinforcing health-conscious behavior and, at other times, compromising healthy food choices due to convenience, preferences, or social dynamics. Demanding work conditions on eating habits Participants from various sectors commented that demanding work conditions heavily shaped their eating habits, making it difficult to cook with vegetables. A typical workday runs from 7:30 a.m. to 5:00 p.m., with unpaid overtime, fixed schedules, and limited breaks. Some are allowed to leave the sites only once every two weeks. Most migrants work six days a week, often with side jobs. With everyone in the household employed, cooking becomes difficult. Some opt for simple dishes, such as fried eggs on rice, rather than preparing vegetable soup or stir-fried vegetables, which require more time to prepare. The workers can only cook in the evening after work or on their day off. Despite wanting to eat more vegetables, exhaustion and limited time are major barriers. [Respondent 7; Male factory worker] (…I cook…) mainly in the evening because when I return to my house after finishing my job, I have more time and can cook by myself. I am also in a rush and in a hurry to arrive at the job during the day. That’s why I can only stir-fry water spinach at that time. Sometimes, I can only fry an egg. Some construction and factory workers cook their own meals, bring lunch to work, or come home to eat, as they usually live nearby. While cooking is more convenient and affordable, most still struggle to buy, store, and prepare food with their limited free time. [Respondent 4; Male construction worker] Yes, I go out and buy groceries every 15 days, so I don't have to eat meat all the time. I also cut up vegetables. It's not easy to put them in the fridge for 15 days, that kind of thing. Across all groups, respondents reported purchasing fresh ingredients as their main method of acquiring food. Only service workers relied more on purchased meals than home-cooked meals, since purchasing ready-made meals in the food court where they work is more convenient. Their accommodation is typically farther from their work than that of those working in construction or factories. Due to their work and time constraints, many rely on stalls along their commute to buy fruits. [Respondent 10; Male service worker] I can get apples and oranges easily on my way. We are not the same from one place to another, but whoever buys the fruits from wherever we bring them to our living place…They (other working migrants) also don’t have much free time. On weekends, when they have music performances from 17:00 to 22:00, they won’t have any time to go buy it. On weekends when they are off, they will be in their rooms and don’t really go anywhere, but there are times when they go out to buy vegetables. Workplace demands create significant time and physical barriers for migrants, preventing them from cooking or choosing healthy meals. As a result, convenience heavily influences food choices, often limiting the intake of enough fruits and vegetables. Practical constraints and taste preferences undermine health considerations Health was a major concern among all participants. Interviewees were well aware of the health benefits of consuming fruits and vegetables. They cited various advantages, such as improved eyesight, digestion, liver function, brain function, blood circulation, kidney health, and skin health. They noted that fruits and vegetables provide essential vitamins and fiber, leaving them feeling refreshed after eating. They considered the consumption of fruits and vegetables essential for overall health and a way to counteract ‘bad stuff’. A participant viewed their food choice as necessary only to meet work demands. [Respondent 8; Male factory worker] I don’t feel too good about my meals. I know what I should eat, but I just have to keep following the tasks that I do to be ok with my working nature. I know that not eating anything and drinking only a cup of coffee isn’t good for me. That’s why I eat extra food for my other meals. While participants acknowledged that rice and meat provide strength, they found fruits and vegetables refreshing and preferred them for both health and taste. However, despite this, many struggled to adjust their eating habits accordingly. They still encounter physical, time, and financial challenges, even with support from close connections and larger social circles. For instance, participants have to deviate from their usual commuting routes and budget limits to purchase affordable produce at the fresh market. Some expressed that they would need more time and money to eat as much fruit and cook as many vegetables as they desire. [Respondent 8; Male factory worker] Because of many reasons, according to my health and the requirements of my body. But these are just needs, not my wants, because I have to eat the fruits based on my budget. Although participants valued the health benefits and taste of fruits and vegetables, they are often limited by practical barriers, such as limited time, budget, and convenience, highlighting the gap between health intentions and actual eating habits. Discussion Our quantitative data revealed that Myanmar migrants in Bangkok have low fruit and vegetable consumption, less than half of the WHO-recommended 400 grams, consistent with previous findings in Thailand ( 27 , 38 ). The findings confirm previous studies that documented the relatively poor quality of diets of migrant workers in North America ( 10 – 12 ), Europe ( 8 , 9 ), Australia ( 39 ), and Asia ( 14 ). While many studies have focused on farmworkers in high-income countries, our study highlights that similar dietary challenges persist among migrants in an urban setting in a middle-income country. There was an overall prevalence of suboptimal diets among migrants across work sectors and genders. While prior research in high-income countries often shows higher fruit and vegetable intake among women than men ( 40 – 42 ), our data did not reveal gender differences in intake. This finding is parallel with a study that found no significant gender gap in fruit and vegetable intake across 28 low- and middle-income countries ( 3 ). One plausible explanation, supported by our qualitative findings, is that men and women eat the same meals out of convenience. These shared practices may reduce typical gender-based differences in dietary behavior seen in other settings. Despite the apparent low quality of diets, self-reported dietary diseases were minimal. The respondents were relatively young, whereas chronic diet-related diseases are more likely to occur at an older age. It is also possible that diseases are under-reported due to limited access to health services. The absence of immediate health problems may lead people to give less priority to healthy eating. Urban life in Bangkok may also encourage the adoption of a less healthy food culture, given the abundance of highly processed food options ( 43 , 44 ). Few demographic or psychosocial variables explained differences in intake. This may reflect the homogeneity of the sample in terms of age, income, education, and exposure to similar food environments. A study on the food security of migrants in Malaysia also reported a lack of significant associations in their data, where uniform constraints may obscure associations with dietary behavior ( 14 ). Nutritional knowledge was generally low. Most participants scored only half of the maximum score. The most frequently misunderstood facts pertained to the nutritional value of rice and meat compared to vegetables, as well as the benefits of dark leafy greens. This mirrors a recent study with pregnant Myanmar migrants in Bangkok, highlighting the challenges to accessing nutritional information ( 45 ). Despite the lack of factual information, participants expressed concern about their health and demonstrated a preference for fruits and vegetables. However, nutritional knowledge was not associated with intake, likely due to limited variability in the sample. A higher frequency of market visits was associated with higher intake, as participants preferred buying fresh produce in bulk at fresh markets for better value. Grocery trucks and small vendors were less ideal. These patterns reflect both environmental and economic influences on food choice. This supports food environment frameworks emphasizing both physical and economic access ( 46 ). Price, storage limitations, and value for money shape what people buy, even when they express a preference for healthy options. Home cooking was associated with higher fruit and vegetable consumption, as confirmed by both the quantitative and qualitative results. While home-cooking was associated with better diets ( 38 ), long working hours and physically demanding jobs limit migrants’ time and energy for shopping and cooking. Even with awareness and intent to eat healthily, convenience often takes precedence. This mirrors broader urban trends, though migrants are further constrained by low income and limited mobility. Food access in Bangkok was not a primary barrier to fruit and vegetable consumption, which contrasts with migrant studies on rural areas in high-income countries. For instance, Latino farmworkers in North Carolina relied heavily on supermarkets, which were difficult to access ( 10 ). However, in urban areas, there is a greater choice of affordable, yet unhealthy foods, which creates a difficult challenge. Choosing healthy options can be challenging when inexpensive, convenient foods are readily available. We observed a moderate but positive effect of intention and a small positive effect of self-efficacy on intake. The in-depth interviews revealed that, although participants were aware of the benefits and had positive attitudes, taking action required time and motivation. Only those with a stronger intention and self-efficacy were able to invest the necessary effort in healthy eating. Previous studies yielded similar results, suggesting that interventions designed to enhance dietary self-efficacy and intention can be utilized as a simple yet powerful tool to promote healthier diets ( 47 – 49 ). Social support did not show a significant relationship with intake, contrasting with previous studies ( 19 ). Interview data revealed that roommates sometimes promoted intake, but also shared quick meals that lacked vegetables. This illustrates the variety of mixed messages one can receive simultaneously ( 50 ). Social support may be too inconsistent or ambivalent to reliably promote healthy eating in this population, particularly when physical constraints are present. This study is without its limitations. Certain non-significant results may stem from the limited conceptual variability in the samples, since migrants generally occupy similar socioeconomic status and lifestyle. Another limitation was the challenges of reaching the target population, resulting in a non-random, relatively small sample. It is also important to note the bi-directional nature of certain variables in the model, such as automatic habit and self-efficacy. Where behavior is successfully enacted, it can also reinforce habitual behavior and self-efficacy. Therefore, the mechanism underlying food behavior and change could benefit from advanced statistical modelling techniques that allow examination of multiple pathways of influence. Conclusions This study showed that fruit and vegetable consumption is low among migrant workers in Bangkok, independent of work sector and gender. This low consumption increases their risk of diet-related non-communicable disease. There is a need for targeted interventions to address the systemic barriers this group faces. Dietary interventions should prioritize addressing practical and environmental barriers, rather than relying solely on behavioral change. Nutritional training may help improve food behavior, given the low levels of nutritional knowledge observed, but structural constraints must also be addressed. Migrants need access to a kitchen and a refrigerator to store perishable foods. Local authorities, factory owners, or worksite managers could arrange for fruit and vegetable vendors to visit the workplace regularly, improving access to affordable, fresh produce. These measures would require minimal to no infrastructure. Most importantly, workers should not routinely work overtime, as this limits their ability to shop, cook, and care for their health. Many work overtime or hold second jobs due to low earnings. While affordable food is abundant in Bangkok, healthy food is rarely convenient or cheap. Addressing income poverty and economic justice is crucial for improving the quality of diets among disadvantaged populations. Declarations Ethics approval and consent to participate The study plan was approved by the World Vegetable Center’s Research Ethics Committee (registration ID 2023-17). Participation was virtually risk-free. Verbal and written consent were obtained before the data collection. Consent for publication Not applicable Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by a grant from the International Institute of Tropical Agriculture (IITA) under the CGIAR Food Frontiers & Security Science Program, as supported by contributors to the CGIAR Trust Fund (https://www.cgiar.org/funders). Open access funding was provided by the Foreign, Commonwealth & Development Office (FCDO) of the United Kingdom. Authors' contributions Piraorn Suvanbenjakule: conceptualization, methodology, validation, formal analysis, investigation, resources, data curation, writing - original draft, writing - review & editing, visualization, project administration; Pepijn Schreinemachers: conceptualization, methodology, writing - review & editing, visualization, supervision, funding acquisition; Ee Von Goh: methodology, validation, writing - review & editing, supervision Acknowledgements The authors acknowledge the support from the International Institute of Tropical Agriculture (IITA) under the CGIAR Food Frontiers & Security Science Program, as supported by contributors to the CGIAR Trust Fund (https://www.cgiar.org/funders). 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Gender differences in the roles for social support in ensuring adequate fruit and vegetable consumption among older adult Canadians. Appetite 2015 Sept 1;92:102–9. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviewers invited by journal 08 Jan, 2026 Editor invited by journal 08 Jan, 2026 Editor assigned by journal 07 Jan, 2026 Submission checks completed at journal 07 Jan, 2026 First submitted to journal 06 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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global health concern. Over 2\u0026nbsp;billion people are estimated to be affected by iron deficiency alone (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It has been estimated that the diets of over 5\u0026nbsp;billion people, or 62% of the world’s population, are deficient in one or several micronutrients (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Low consumption of micronutrient-rich foods, such as fruits and vegetables, is a key driver. In developing countries, an estimated 78% of adults consume fewer than five portions or 400 grams of fruits and vegetables daily (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), the minimum amount recommended by the World Health Organization (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). While much research focuses on vulnerable groups such as pregnant or lactating women and children, other groups, including working-age migrants, are also at risk of micronutrient-poor diets but are often overlooked.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eInternational Convention on the Protection of the Rights of All Migrant Workers and Members of Their Families\u003c/em\u003e defines a migrant worker as “a person who is to be engaged, is engaged or has been engaged in a remunerated activity in a State of which he or she is not a national” (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) The group includes both documented and undocumented workers, particularly those who engage in elementary occupations in low-skilled, low-paid jobs such as crop harvesting, factory work, or cleaning services.\u003c/p\u003e \u003cp\u003eExisting studies on migrant dietary intake focus on people from low- and middle-income countries working in high-income countries. These studies often compare migrant and host population diets (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) or analyze migrants’ food environments and habits. Research on African farmworkers in Spain reported diets with little variety and too many obesogenic products (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Latino migrant farmworkers in North Carolina faced structural constraints that contributed to poor dietary habits, such as overreliance on superstores, limited mobility, and a lack of refrigerator space (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Latino migrant workers were also studied in the United States using qualitative research methods (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). It found that migrants dramatically reduced their consumption of fresh fruits and vegetables, as they perceived these foods to be of poor quality and high price. Another study compared the diets of Puerto Rican migrants living in Massachusetts with those of Puerto Ricans living in Puerto Rico and found that 57% of migrants had poor diet quality while only 20% of Puerto Ricans in Puerto Rico had poor diet quality (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). A study with ethnic minorities in California found that 67% and 78% of Asian children consumed less than 200 grams of fruit and vegetables per day, compared to 34% and 56% of Caucasian children (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Moreover, a study of Myanmar migrants in Malaysia found that 96% were mildly or moderately food insecure (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Thailand, about 2.3\u0026nbsp;million Myanmar nationals were officially registered as migrant workers in 2024, constituting 75% of migrant workers in elementary occupations (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The actual number is probably higher because many migrants are not registered. Most migrants occupied low-paying jobs in agriculture, construction, factories, and service industries (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). They contribute considerably to the Thai economy but often face low incomes, limited access to education, restricted access to public health services, and disadvantaged housing and employment conditions, making them vulnerable to poverty and suboptimal diets.\u003c/p\u003e \u003cp\u003eAlthough there have been studies on food behavior and the environment in low and middle-income countries, only a few have explored the mechanisms underlying food behavior among migrants. The Health Action Process Approach (HAPA) proposes factors can influence a health behavior, such as belief in one’s own capability (self-efficacy), a plan to achieve the action goal and to cope with possible setbacks (planning), and intention to perform the behavior (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Existing studies applying HAPA to fruit and vegetable intake consistently highlighted the role of these factors in shaping diet behavior across population groups (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e–\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). While these psychological factors could offer valuable insights into fruit and vegetable intake, an individual’s diet can also be shaped by other contextual factors. The HAPA model allowed for the inclusion of contextual factors to better capture barriers or resources related to a health behavior, which can have stronger effects than the cognitive predictors (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the broader literature, several factors have been widely incorporated as key determinants of eating behavior. Firstly, habit reflects automatic routines that repeat frequently over time and often require less effort to execute. Therefore, habit could fundamentally affect eating behavior, as it can determine how much effort and information are needed to choose what a person eats (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Secondly, a large body of research shows that social support can positively influence fruit and vegetable intake and diet quality (\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e–\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Lastly, studies have shown that greater nutritional knowledge is often associated with increased fruit and vegetable intake (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Including these commonly studied factors alongside HAPA constructs provides a more comprehensive view of the drivers of fruit and vegetable intake.\u003c/p\u003e \u003cp\u003eIn Thailand, previous studies found that low fruit and vegetable consumption was a key dietary risk factor among adults, with three-quarters of Thai nationals consuming too few vegetables (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). To better understand the factors that lead to suboptimal diets, it is essential to complement studies based on nationally representative samples with in-depth research on vulnerable groups. This study is one of three that examines urban populations at risk of low fruit and vegetable consumption, specifically seniors (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), Buddhist monks, and migrants.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe objective of this study is to describe and analyze the fruit and vegetable intake of Myanmar migrant workers in Bangkok. Myanmar migrants are a population group at risk of poor diets, including low fruit and vegetable consumption. A mixed-methods approach is applied to examine how psychological and contextual factors jointly shape fruit and vegetable intake. The quantitative component compares the relative influence of psychological variables, including dietary self-efficacy, intention, and planning, and contextual variables such as habit, social influence, nutritional knowledge, demographic factors, and environmental factors such as food sources. In addition, the study described the overall meal quality using the diet quality score. By analyzing psychological and contextual factors within a single model, we identify which factors produce stronger effects on dietary behavior. To complement and extend these findings, the qualitative analysis provides deeper insight into how participants interpret their food environments, navigate constraints, and assign meaning to their eating practices.\u003c/p\u003e\u003cp\u003eThis mixed-method study combined a structured survey with 199 samples and in-depth interviews with ten participants, providing a comprehensive understanding of dietary behavior. The quantitative component assessed dietary behavior and its correlates across personal, social, and environmental factors through structured surveys. The study combined fruit and vegetable intake as a single outcome to reflect the way fruit and vegetables are commonly promoted together in nutrition research, dietary guidelines, and public health policy. Since fruit and vegetable intake is typically treated as a single behavioral target, our analysis focuses on identifying influences on combined fruit and vegetable intake rather than separating fruit and vegetables into two models. The qualitative component explored participants’ experiences through in-depth interviews and was based on the science of food choice (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe study plan was approved by the World Vegetable Center’s Research Ethics Committee (registration ID 2023-17). Participation was virtually risk-free. Verbal and written consent were obtained, and interviewers clarified participants’ right to withdraw at any time. The participant was informed of the anonymity and confidentiality. All personal identifiers were removed prior to analysis.\u003c/p\u003e\u003ch3\u003eQuantitative data and analysis\u003c/h3\u003e\u003cp\u003eA structured questionnaire was used to collect data on intake, food sources, psychological factors, and demographic information of respondents. The tool was developed in English, translated into Burmese, and back-translated. The translated questionnaire and interview guide were reviewed by two native Myanmar professionals familiar with the context. They checked for linguistic accuracy, cultural appropriateness, and clarity, and minor adjustments were made accordingly.\u003c/p\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003cp\u003e \u003cem\u003eFruit and vegetable intake\u003c/em\u003e was assessed using a short dietary measure adapted from the Health Examination Survey of Thailand (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Participants reported the days per week they consumed vegetables and the number of servings they ate on those days. The same questions were asked about fruit intake. Photos of 80-gram serving sizes of various raw and cooked vegetables and fruits assisted the reporting (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Daily servings were converted into grams to estimate daily fruit and vegetable intake.\u003c/p\u003e\u003cp\u003ePredictors\u003c/p\u003e\u003cp\u003e \u003cem\u003eFood sources\u003c/em\u003e, including cooking frequency, number of market visits, and kitchen access were recorded and included as predictors. \u003cem\u003eDemographic\u003c/em\u003e data, including age, household size, gender, income, and work sector, were also included as predictors. Other characteristics were collected to provide context, including marital status, employment status, home gardening, chronic illness, dental issues, and residence characteristics.\u003c/p\u003e\u003cp\u003ePsychological drivers were derived from the HAPA framework and included intention, dietary self-efficacy, and planning (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). All responses were rated on a 5-point Likert scale from “strongly disagree” (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) to “strongly agree” (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Combined scores were used, and higher scores reflect a higher level of each construct.\u003c/p\u003e\u003cp\u003e \u003cem\u003eIntention\u003c/em\u003e to eat fruit and vegetables was measured separately by two items, “I intend to eat fruit every day” and “I intend to eat vegetables every day.” The items were adapted from Pandey et al., which were originally specific to vegetables (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The composite score was used, with higher scores indicating a greater intention to eat fruit and vegetables daily (α = 0.67).\u003c/p\u003e\u003cp\u003e \u003cem\u003eDietary self-efficacy\u003c/em\u003e, adapted from Pandey et al., was used (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Four items measure participants' confidence in their ability to consume sufficient fruit, and four parallel items measure their confidence in their ability to consume sufficient vegetables (α = 0.85).\u003c/p\u003e\u003cp\u003e \u003cem\u003ePlanning\u003c/em\u003e was measured using an adapted scale that includes action planning and coping planning (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Three items assessed action planning, and two assessed coping planning (α = 0.91).\u003c/p\u003e\u003cp\u003e \u003cem\u003eSocial influences\u003c/em\u003e on fruit and vegetable consumption were examined using a scale adapted from Pandey et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The scale included four items assessing whether participants’ close contacts consumed fruit and encouraged them to do the same, with four parallel items for vegetables (α = 0.88).\u003c/p\u003e\u003cp\u003e \u003cem\u003eAutomatic habit\u003c/em\u003e was assessed using two items adapted from Pandey et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The items were, or example, “Eating two servings of fruit and vegetables is something I do automatically” (α = 0.72).\u003c/p\u003e\u003cp\u003e \u003cem\u003eNutritional knowledge\u003c/em\u003e was assessed using a 10-item scale previously adopted in (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Participants were asked to identify each statement with “Correct”, “Incorrect”, or “I don’t know”. The content was targeting nutritional knowledge specific to fruit and vegetables for people of all ages, for example, “Carrots, pumpkins, and orange sweet potatoes are all sources of vitamin A.”\u003c/p\u003e\u003cp\u003eDiet quality assessment\u003c/p\u003e\u003cp\u003eThe Burmese version of the Diet Quality Questionnaire (DQQ) was used (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). It contains 29 food groups in a yes/no format to describe food consumed the previous day. Various DDQ indicators were calculated following the indicator guide (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). The Dietary Diversity Score (DDS) represents the diversity of food groups consumed from a list of 10 standard food groups aggregated from the 29 food groups. The All-5 Score is a binary indicator. It takes a value of one if all five recommended food groups are consumed, meaning at least one vegetable, one fruit, one pulse, nut, or seed, one animal-source food, and at least one starchy staple. The Non-Communicable Disease (NCD)-Risk score is a proxy for ultra-processed food (UPF) intake (0–9), with a higher value indicating more UPF consumption. NCD-Protect is an indicator (0–9) of dietary factors protective against NCDs, based on consumption of nine food groups associated with meeting WHO recommendations on fruits, vegetables, whole grains, pulses, nuts and seeds, and fiber. The Global Dietary Recommendations (GDR) score (0–18) combines the NCD-Protect and NCD-Risk.\u003c/p\u003e\u003cp\u003eSample size\u003c/p\u003e\u003cp\u003eThe minimum sample size for the regression analysis was calculated using G*Power. Based on 16 predictors, a medium effect size of 0.15, a power level of 0.8, and an alpha of 0.05, the minimum sample size was 143 participants. Researchers added 10% to account for potential errors, such as missing or inconsistent data, aiming to recruit at least 158 adult Myanmar workers in low-skill jobs. With guidance from a Myanmar field supervisor, we recruited 199 participants from the construction, factory, and service sectors across 19 districts and 31 subdistricts. We employed purposive and snowball sampling, starting in areas with high concentrations of Myanmar workers and expanding through referrals, aiming for 10–15 participants per area.\u003c/p\u003e\u003cp\u003eProcedures\u003c/p\u003e\u003cp\u003eQuestionnaires were programmed in KoboToolbox. The interviews took approximately 15 minutes to complete. Participants were given 80 baht (approximately USD 2.40) as a token of appreciation after completing the survey. All participants read and signed an informed consent. No identifying information, such as addresses, was collected, as some participants may be undocumented. Where necessary, we requested permission to conduct interviews with the supervisors. Data collection took place from July to August 2024.\u003c/p\u003e\u003ch2\u003eQualitative data and analysis\u003c/h2\u003e\u003cp\u003eWe applied the science of food choice to conceptualize food behavior, food environment, and decision processes (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This framework has three main interrelated questions: “WHAT do people eat?”, “HOW do people acquire, prepare, distribute, and consume the food they eat?”, and “WHY do people make the food choices that they do?” (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The interview was semi-structured based on these three questions.\u003c/p\u003e\u003cp\u003eWe conducted semi-structured interviews guided by prompts. Interviews explore a range of available food options, providing an overview of what people eat and the options available to them. Questions also revolve around how people acquire, prepare, share, and consume their food. Interview questions asked participants where they usually get fruit and vegetables, and how they consume them. The reasons behind each person’s food choices were also addressed.\u003c/p\u003e\u003cp\u003eA pilot coding was carried out on two transcripts. The codes address the WHAT, HOW, and WHY questions (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). An independent researcher conducted both inductive and deductive coding to create the study codebook. For the first WHAT questions, example codes are ‘food options’, ‘quantity’, and ‘quality’. The HOW questions included codes such as ‘acquisition’, ‘storing’, ‘serve’, and ‘consume’. We then applied a typology of food environments to differentiate specific ways people acquire food, such as ‘purchasing’, ‘wild’, cultivated’, and ‘social food exchange’ (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). For the WHY question, we took Blake et al.'s (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) concepts on identifying why and how the decision-making process is carried out. However, this framework only provides a broad scope of the decision-making process.\u003c/p\u003e\u003cp\u003eTherefore, we further categorize the emerging themes into individual, social, physical, and macro-level factors, following (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). This framework provides sub-themes and categorizes levels of influence on food choice. Individual-level factors include, for example, health considerations, attitudes, and taste preferences. Social-level factors encompass household composition and meal-sharing practices, while physical-level factors address the physical environment. Macro-level factors encompass broader influences, including urbanization and cultural norms. By integrating these multiple levels of influence, this framework provides a comprehensive lens for examining the complex interactions that influence dietary behaviors. The main analysis of this study was based on the integrated codebook. Two other researchers verified the codebook.\u003c/p\u003e\u003cp\u003eInterviews were conducted in Burmese with ten migrants from three areas of Bangkok, identified with the assistance of a Myanmar field coordinator based on high concentrations of migrants working in each sector. The sample for the in-depth interviews did not overlap with the survey. The first area was at a fresh market in Bangkapi, located near several construction sites. The second was an area with many factories in Sathu Pradit. The third area targeted service workers from several malls in the Pratunam district. A field coordinator contacted 3–4 participants in each area and scheduled interviews in advance. All interviews took place near participants’ workplaces and were conducted in English and Burmese by a Thai researcher and a Burmese translator, lasting about 40 minutes. Eight men and two women were interviewed. Despite differences in work sectors, migrants shared similar socioeconomic backgrounds and living conditions. The researchers noted that thematic saturation was reached after the eighth interview, as no new themes arose. Data collection continued as planned until ten interviews were completed. Participants received 200 baht as a token of appreciation.\u003c/p\u003e\u003cp\u003eInterview transcripts were transcribed in Burmese and translated into English. Data was analyzed thematically using \u003cem\u003eAtlas.ti\u003c/em\u003e (version 25.0.1.32924). We identified recurring patterns and developed sub-themes, which were based on commonalities observed across multiple cases and were initially organized within the structure of our codebook. Co-occurrence analysis was used to determine which codes appeared together most frequently, thereby understanding the relationships between codes. The analysis complied with the Standards for Reporting Qualitative Research (SRQR) (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSample characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the sample characteristics by work sector. Among the entire sample, 22% worked in construction, 33% in factories, 25% in services, and 23% in other sectors. Approximately half of the participants are female (51%), with an average age of 31 years. Women are less present in construction compared to other sectors. The samples have resided in Thailand for approximately 6.2 years. The majority (92%) work more than 40 hours a week.\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\u003eMean characteristics of the sample of migrant workers in Bangkok, Thailand\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstruc-tion (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactories \u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eServices\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther \u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;45)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;199)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographics:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (1\u0026thinsp;=\u0026thinsp;female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (1\u0026thinsp;=\u0026thinsp;above primary)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears in Thailand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking\u0026thinsp;\u0026gt;\u0026thinsp;40hrs/week (=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/cohabiting (=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with family members (=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size (persons)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersons needing care (persons)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic illness (=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDental issues (=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilding type (proportions):\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026minus; Apartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026minus; House or townhouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026minus; Worker dormitory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026minus; Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence characteristics (proportions):\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026minus; Running water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026minus; Home garden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026minus; Kitchen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026minus; Refrigerator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood sources:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCooking frequency/week (count)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBought meals/week (count)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsually eats alone (=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological factors:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary self-efficacy score (\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntention score (\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlanning score (\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContextual factors:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutomatic habits score (\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial influence score (\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutritional knowledge score (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.73\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\u003eRegarding health conditions, 1% of the sample reported having a chronic illness, while 13% reported dental issues. It was relatively common to share housing with non-family members (59%) to split the rent. Nearly all migrants had access to running water (98%) and a functional kitchen (92%). A lower percentage had access to a refrigerator (72%), vital for keeping fresh produce. Home gardening was relatively uncommon, with only 8% of people growing their own fruit or vegetables.\u003c/p\u003e \u003cp\u003eNutrition knowledge was low, as only 57% of the knowledge questions were answered correctly. The knowledge test used binary choice questions, and random guessing would likely yield 50% correct answers; hence, this is just marginally better than random guessing. The nutrition knowledge was lower among construction workers. About 44% of respondents indicated that they usually eat alone, with the highest percentage among service workers.\u003c/p\u003e \u003cp\u003eAcross all sectors, psychological scores related to fruit and vegetable intake were moderate. Automatic habits scored the highest on average (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.71, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.81), with slightly higher means in the service (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.79, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.90) and other (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.79, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.80) sectors, suggesting that incorporating fruits and vegetables may be a somewhat routine behavior for many participants. Dietary self-efficacy was also relatively high and consistent across sectors (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.47, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.51), indicating moderate confidence in the ability to maintain healthy eating. Similarly, intention to consume fruits and vegetables was moderate (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.51, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.96), with only slight variation between groups. Social influence was moderate overall (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.22, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.60), with slightly higher scores in the service sector (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.38, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.61), indicating that social norms or encouragement may be limited in this sample. In contrast, planning showed the lowest mean scores (overall \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.61, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.73), especially in the construction and factory sectors (both \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.53), suggesting that while participants may be motivated, few take concrete steps to plan their intake.\u003c/p\u003e \u003cp\u003eThe results of the diet quality questionnaire reveal poor diet quality across all work sectors (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The mean DDS was 6.9 on a 10-point scale, the NCD-Protect score was 4.6 on a 9-point scale, the mean NCD-Risk score was 2.7 on a 9-point scale, and the mean GDR score was 10.9 on an 18-point scale. Only about half (53%) of the respondents consume all five recommended food groups.\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\u003eMean diet quality and intake for the sample of migrant workers in Bangkok, Thailand\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstruc-tion (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactories \u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eServices\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther \u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;45)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;199)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary Quality Questionnaire (score):\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal Dietary Recommendation (0\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary Diversity Score (0\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALL5 (% yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCD-Protect (0\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCD-Risk (0\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFruit intake:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFruit intake (days/week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFruit intake (portions/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFruit intake (grams/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetable intake:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetable intake (days/week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetable intake (portions/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetable intake (grams/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e141.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e124.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined fruit and vegetable intake (g/capita/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e173.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e184.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e206.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e218.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e195.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes: GDR\u0026thinsp;=\u0026thinsp;Global Dietary Recommendation score; DDS\u0026thinsp;=\u0026thinsp;dietary diversity score; ALL5\u0026thinsp;=\u0026thinsp;consumption of all five food groups; NCD-Protect\u0026thinsp;=\u0026thinsp;consumption of nine food groups associated with meeting WHO recommendations on fruits, vegetables, whole grains, pulses, nuts and seeds, and fiber; NCD-Risk: proxy for ultra-processed food (UPF) intake (0\u0026ndash;9), with a higher value indicating more UPF consumption.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFruits were available at home for about 2.2 days per week, and vegetables were available for 2.6 days per week. Fruit was consumed for approximately 3.5 days per week, and vegetables were consumed 5.1 days per week. The mean daily intake was 70.7 grams of fruit and 124.3 grams of vegetables. The combined daily intake of 195.1 grams is less than half the WHO recommendation, indicating significant underconsumption of fruit and vegetables.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRegression results\u003c/h3\u003e\n\u003cp\u003eRegression diagnostics were conducted to assess model assumptions, including multicollinearity, residual normality, linearity, and homoscedasticity. No issues were detected. The regression model was statistically significant, explaining approximately 25% of the variance in average daily fruit and vegetable intake (\u003cem\u003eF\u003c/em\u003e(17, 166)\u0026thinsp;=\u0026thinsp;3.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, contrary to expectations, only cooking frequency (β\u0026thinsp;=\u0026thinsp;0.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), number of market visits (β\u0026thinsp;=\u0026thinsp;0.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), self-efficacy (β\u0026thinsp;=\u0026thinsp;0.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10), and intention (β\u0026thinsp;=\u0026thinsp;0.25, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) emerged as statistically significant predictors (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Specifically, participants who cooked at home more frequently and visited fresh markets more often reported higher combined fruit and vegetable intake. Higher self-efficacy, which reflected greater confidence in one\u0026rsquo;s ability to consume fruits and vegetables, and stronger intention to do so, were both positively associated with intake. In contrast, socio-demographic factors (gender, household size, income), social support, and nutritional knowledge showed no significant associations.\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\u003eDeterminants of fruit and vegetable intake, in grams/capita/day, among migrant workers in Bangkok, Thailand as based on a linear regression model (n\u0026thinsp;=\u0026thinsp;199)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnstandardized coefficient (\u003cem\u003eB\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandardized coefficient (β)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic factors:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (1\u0026thinsp;=\u0026thinsp;female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-12.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size (persons)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork sector (Other\u0026thinsp;=\u0026thinsp;1):\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026minus; Construction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-11.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026minus; Factory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026minus; Service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood sources:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCooking frequency/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket visit/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKitchen (=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-67.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological factors (1\u0026ndash;5 pts):\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary self-efficacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlanning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContextual factors (1\u0026ndash;5 pts):\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutomatic habit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial influence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-12.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutritional knowledge (0\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-63.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eQualitative results\u003c/h3\u003e\n\u003cp\u003eThe thematic analysis revealed key interactions between the physical, social, and personal drivers of food choice. The themes described here include: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) responses and strategies to limited food options, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) positive and negative effects of food sharing on intake, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) demanding work conditions on eating habits, and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) health considerations and taste preferences undermined by practical constraints. While this structure reflects the analytical frameworks used, it does not imply a hierarchy or chronological order of influences. Interactions between different levels of factors provide a comprehensive understanding of the unique context of the study participant.\u003c/p\u003e \u003cp\u003eResponses and strategies to limited healthy food options\u003c/p\u003e \u003cp\u003eStudy participants described multiple accessible food outlets near their residence or workplace. Bangkok has an abundance of food suppliers selling prepared meals, snacks, and fresh produce. These include mobile food and fresh produce vendors, fresh produce markets, supermarkets, and convenience stores, resulting in a wide range of food options from cheap to expensive. Many migrants, therefore, opt to buy ready-made meals instead of cooking, as this is more convenient.\u003c/p\u003e \u003cp\u003eHowever, unhealthy foods were more prevalent than options with fruit or vegetables. Most of the food mentioned by the participants was fresh produce, snacks, and cooked meals. The cooked meals were mostly Thai foods, featuring various curries, stews, and stir-fry dishes that contain alternating meat, vegetables, and spices. Burmese meals were only occasionally available near factories or construction sites. Participants rarely mentioned healthy snack options besides fruit, particularly during work time. Typical snacks from convenience stores and street food vendors near the workplace are packaged cakes, fried potatoes, \u003cem\u003epa tong ko\u003c/em\u003e (Chinese donuts), and \u003cem\u003eroti\u003c/em\u003e (Indian paratha). Although a variety of choices exist, based on their descriptions, relatively few were healthy.\u003c/p\u003e \u003cp\u003eParticipants in the service sector mentioned challenges accessing fresh fruit. Cut fruits sold by small mobile vendors were expensive and sold in small amounts (around 1\u0026ndash;2 servings). Small quantities of easy-access vegetables from grocery trucks were also mentioned. Grocery trucks usually sell small quantities of vegetables in plastic bags, enough for one meal. Several participants noted that fresh markets and fruit trucks (modified pick-up trucks with open backs to display fruit, usually selling it by the kilogram) offer lower prices and fresher produce than those sold by small mobile vendors (two- to three-wheeled modified motorcycles selling chopped fruits in small quantities). Therefore, migrants prefer to buy cheaper fruit and vegetables from fresh markets or fruit trucks.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e[Respondent 9; Female factory workerเ] They buy from the mobile food vendor at the front of the factory. But for me, as the fruits from the vendor seem to be insufficient, I do not buy from the vendor. Buying from the fruit-selling truck, I feel like I can get more. I also eat more fruits than others, so I buy them in kilos and I keep them in the fridge for the next day.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhile physical access to food was not the main issue, several participants expressed dissatisfaction with the quality of meal options, which lacked vegetables. They said that restaurant food and street food have meat as the main ingredient, not vegetables, but they felt that vegetables should be cheaper than meat. However, meat options are more popular in restaurants due to their taste and versatility.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e[Respondent 1; Male service worker] I don\u0026rsquo;t exactly know their aim. But if I have to guess, maybe the price of the meals. The prices of a lunch box with meat and a lunch box with vegetables are the same. And the shop from the mall, where we buy our lunch, is not good enough while they are cooking the vegetables. But for the meat curry, they do well, and they can cook well, and the taste is really nice. But for the vegetables, they were only cooked as a side dish. After they put the meat curry, they put the fried vegetables next to the curry as a side dish, but they were not delicious either. That\u0026rsquo;s why people are not eating the vegetable menus so much.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAlthough food was plentiful, access to affordable, healthy options was limited, contributing to dietary patterns that lacked sufficient fruit and vegetables. The interaction between the physical environment and individual factors came into play, requiring migrants to dedicate more time and effort to obtaining fruit and vegetables.\u003c/p\u003e \u003cp\u003ePositive and negative effects of food sharing on intake\u003c/p\u003e \u003cp\u003eFood sharing among roommates and in the workplace emerged in several interviews across sectors. Migrant workers in Bangkok often share their accommodations to split rent. Food sharing and exchange are common practices among roommates, regardless of whether they are family. People living together typically take turns buying fruit and vegetables to share, as purchasing in bulk is more economical. Food preparation responsibility is typically shared, with individuals taking turns cooking and sharing meals. Although not always fixed, this role is primarily assigned to a woman. Since the kitchen is usually part of the living space, individuals can use the cooking facilities at any time throughout the day.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e[Respondent 4; Male construction worker] If two people are living in a room together, while one person cooks rice, the other can cook the curry at that time. Two people per room isn\u0026rsquo;t a problem. Others will be couples. As a couple, the woman does the cooking. If there are two men in a room, one cooks the rice, and the other the curry.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFood sharing is common among many participants, particularly when roommates share similar views on health and lifestyle, as well as food habits. These social influences can have positive or negative effects. The cook or sharer may enjoy something that the other typically does not like, yet they will still share the same food. One person may adopt the other\u0026rsquo;s eating habits, such as consuming a lot of fruit daily. Conversely, if one prefers cooking with unhealthy ingredients or simply has different tastes, the other might struggle to change habits.\u003c/p\u003e \u003cp\u003eEmployees also mentioned the food shared by employers. For instance, a shop owner or factory manager occasionally provides workers with free fruit. This was not a stable food source, relying on the manager\u0026rsquo;s generosity and budget. One might be swayed by a friend\u0026rsquo;s suggestion to eat more fruit, especially when they share similar health concerns. At the same time, individuals may be tempted to consume foods they would not typically choose for themselves due to convenience and workplace norms. A service worker mentioned how it is common for them to cook and eat together using ingredients purchased by the owner. Otherwise, they would need to buy their own ingredients.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e[Respondent 10; Male service worker] Usually, I don\u0026rsquo;t really get to choose. My boss will be the one to buy it, and we usually make curries. Yes, but I will make my own if I don\u0026rsquo;t like it. If they make only one meal of one type of curry and we don\u0026rsquo;t like it, we can go out or make our own. All of us know how to cook.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eConsequently, food sharing plays a significant role, sometimes reinforcing health-conscious behavior and, at other times, compromising healthy food choices due to convenience, preferences, or social dynamics.\u003c/p\u003e \u003cp\u003eDemanding work conditions on eating habits\u003c/p\u003e \u003cp\u003eParticipants from various sectors commented that demanding work conditions heavily shaped their eating habits, making it difficult to cook with vegetables. A typical workday runs from 7:30 a.m. to 5:00 p.m., with unpaid overtime, fixed schedules, and limited breaks. Some are allowed to leave the sites only once every two weeks. Most migrants work six days a week, often with side jobs. With everyone in the household employed, cooking becomes difficult. Some opt for simple dishes, such as fried eggs on rice, rather than preparing vegetable soup or stir-fried vegetables, which require more time to prepare. The workers can only cook in the evening after work or on their day off. Despite wanting to eat more vegetables, exhaustion and limited time are major barriers.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e[Respondent 7; Male factory worker] (\u0026hellip;I cook\u0026hellip;) mainly in the evening because when I return to my house after finishing my job, I have more time and can cook by myself. I am also in a rush and in a hurry to arrive at the job during the day. That\u0026rsquo;s why I can only stir-fry water spinach at that time. Sometimes, I can only fry an egg.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSome construction and factory workers cook their own meals, bring lunch to work, or come home to eat, as they usually live nearby. While cooking is more convenient and affordable, most still struggle to buy, store, and prepare food with their limited free time.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e[Respondent 4; Male construction worker] Yes, I go out and buy groceries every 15 days, so I don't have to eat meat all the time. I also cut up vegetables. It's not easy to put them in the fridge for 15 days, that kind of thing.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAcross all groups, respondents reported purchasing fresh ingredients as their main method of acquiring food. Only service workers relied more on purchased meals than home-cooked meals, since purchasing ready-made meals in the food court where they work is more convenient. Their accommodation is typically farther from their work than that of those working in construction or factories. Due to their work and time constraints, many rely on stalls along their commute to buy fruits.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e[Respondent 10; Male service worker] I can get apples and oranges easily on my way. We are not the same from one place to another, but whoever buys the fruits from wherever we bring them to our living place\u0026hellip;They (other working migrants) also don\u0026rsquo;t have much free time. On weekends, when they have music performances from 17:00 to 22:00, they won\u0026rsquo;t have any time to go buy it. On weekends when they are off, they will be in their rooms and don\u0026rsquo;t really go anywhere, but there are times when they go out to buy vegetables.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWorkplace demands create significant time and physical barriers for migrants, preventing them from cooking or choosing healthy meals. As a result, convenience heavily influences food choices, often limiting the intake of enough fruits and vegetables.\u003c/p\u003e \u003cp\u003ePractical constraints and taste preferences undermine health considerations\u003c/p\u003e \u003cp\u003eHealth was a major concern among all participants. Interviewees were well aware of the health benefits of consuming fruits and vegetables. They cited various advantages, such as improved eyesight, digestion, liver function, brain function, blood circulation, kidney health, and skin health. They noted that fruits and vegetables provide essential vitamins and fiber, leaving them feeling refreshed after eating. They considered the consumption of fruits and vegetables essential for overall health and a way to counteract \u0026lsquo;bad stuff\u0026rsquo;. A participant viewed their food choice as necessary only to meet work demands.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e[Respondent 8; Male factory worker] I don\u0026rsquo;t feel too good about my meals. I know what I should eat, but I just have to keep following the tasks that I do to be ok with my working nature. I know that not eating anything and drinking only a cup of coffee isn\u0026rsquo;t good for me. That\u0026rsquo;s why I eat extra food for my other meals.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhile participants acknowledged that rice and meat provide strength, they found fruits and vegetables refreshing and preferred them for both health and taste. However, despite this, many struggled to adjust their eating habits accordingly. They still encounter physical, time, and financial challenges, even with support from close connections and larger social circles. For instance, participants have to deviate from their usual commuting routes and budget limits to purchase affordable produce at the fresh market. Some expressed that they would need more time and money to eat as much fruit and cook as many vegetables as they desire.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e[Respondent 8; Male factory worker] Because of many reasons, according to my health and the requirements of my body. But these are just needs, not my wants, because I have to eat the fruits based on my budget.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAlthough participants valued the health benefits and taste of fruits and vegetables, they are often limited by practical barriers, such as limited time, budget, and convenience, highlighting the gap between health intentions and actual eating habits.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur quantitative data revealed that Myanmar migrants in Bangkok have low fruit and vegetable consumption, less than half of the WHO-recommended 400 grams, consistent with previous findings in Thailand (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The findings confirm previous studies that documented the relatively poor quality of diets of migrant workers in North America (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), Europe (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), Australia (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), and Asia (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). While many studies have focused on farmworkers in high-income countries, our study highlights that similar dietary challenges persist among migrants in an urban setting in a middle-income country.\u003c/p\u003e \u003cp\u003eThere was an overall prevalence of suboptimal diets among migrants across work sectors and genders. While prior research in high-income countries often shows higher fruit and vegetable intake among women than men (\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), our data did not reveal gender differences in intake. This finding is parallel with a study that found no significant gender gap in fruit and vegetable intake across 28 low- and middle-income countries (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). One plausible explanation, supported by our qualitative findings, is that men and women eat the same meals out of convenience. These shared practices may reduce typical gender-based differences in dietary behavior seen in other settings.\u003c/p\u003e \u003cp\u003eDespite the apparent low quality of diets, self-reported dietary diseases were minimal. The respondents were relatively young, whereas chronic diet-related diseases are more likely to occur at an older age. It is also possible that diseases are under-reported due to limited access to health services. The absence of immediate health problems may lead people to give less priority to healthy eating. Urban life in Bangkok may also encourage the adoption of a less healthy food culture, given the abundance of highly processed food options (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFew demographic or psychosocial variables explained differences in intake. This may reflect the homogeneity of the sample in terms of age, income, education, and exposure to similar food environments. A study on the food security of migrants in Malaysia also reported a lack of significant associations in their data, where uniform constraints may obscure associations with dietary behavior (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNutritional knowledge was generally low. Most participants scored only half of the maximum score. The most frequently misunderstood facts pertained to the nutritional value of rice and meat compared to vegetables, as well as the benefits of dark leafy greens. This mirrors a recent study with pregnant Myanmar migrants in Bangkok, highlighting the challenges to accessing nutritional information (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Despite the lack of factual information, participants expressed concern about their health and demonstrated a preference for fruits and vegetables. However, nutritional knowledge was not associated with intake, likely due to limited variability in the sample.\u003c/p\u003e \u003cp\u003eA higher frequency of market visits was associated with higher intake, as participants preferred buying fresh produce in bulk at fresh markets for better value. Grocery trucks and small vendors were less ideal. These patterns reflect both environmental and economic influences on food choice. This supports food environment frameworks emphasizing both physical and economic access (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Price, storage limitations, and value for money shape what people buy, even when they express a preference for healthy options.\u003c/p\u003e \u003cp\u003eHome cooking was associated with higher fruit and vegetable consumption, as confirmed by both the quantitative and qualitative results. While home-cooking was associated with better diets (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), long working hours and physically demanding jobs limit migrants\u0026rsquo; time and energy for shopping and cooking. Even with awareness and intent to eat healthily, convenience often takes precedence. This mirrors broader urban trends, though migrants are further constrained by low income and limited mobility.\u003c/p\u003e \u003cp\u003eFood access in Bangkok was not a primary barrier to fruit and vegetable consumption, which contrasts with migrant studies on rural areas in high-income countries. For instance, Latino farmworkers in North Carolina relied heavily on supermarkets, which were difficult to access (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, in urban areas, there is a greater choice of affordable, yet unhealthy foods, which creates a difficult challenge. Choosing healthy options can be challenging when inexpensive, convenient foods are readily available.\u003c/p\u003e \u003cp\u003eWe observed a moderate but positive effect of intention and a small positive effect of self-efficacy on intake. The in-depth interviews revealed that, although participants were aware of the benefits and had positive attitudes, taking action required time and motivation. Only those with a stronger intention and self-efficacy were able to invest the necessary effort in healthy eating. Previous studies yielded similar results, suggesting that interventions designed to enhance dietary self-efficacy and intention can be utilized as a simple yet powerful tool to promote healthier diets (\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSocial support did not show a significant relationship with intake, contrasting with previous studies (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Interview data revealed that roommates sometimes promoted intake, but also shared quick meals that lacked vegetables. This illustrates the variety of mixed messages one can receive simultaneously (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Social support may be too inconsistent or ambivalent to reliably promote healthy eating in this population, particularly when physical constraints are present.\u003c/p\u003e \u003cp\u003eThis study is without its limitations. Certain non-significant results may stem from the limited conceptual variability in the samples, since migrants generally occupy similar socioeconomic status and lifestyle. Another limitation was the challenges of reaching the target population, resulting in a non-random, relatively small sample. It is also important to note the bi-directional nature of certain variables in the model, such as automatic habit and self-efficacy. Where behavior is successfully enacted, it can also reinforce habitual behavior and self-efficacy. Therefore, the mechanism underlying food behavior and change could benefit from advanced statistical modelling techniques that allow examination of multiple pathways of influence.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study showed that fruit and vegetable consumption is low among migrant workers in Bangkok, independent of work sector and gender. This low consumption increases their risk of diet-related non-communicable disease. There is a need for targeted interventions to address the systemic barriers this group faces. Dietary interventions should prioritize addressing practical and environmental barriers, rather than relying solely on behavioral change. Nutritional training may help improve food behavior, given the low levels of nutritional knowledge observed, but structural constraints must also be addressed. Migrants need access to a kitchen and a refrigerator to store perishable foods. Local authorities, factory owners, or worksite managers could arrange for fruit and vegetable vendors to visit the workplace regularly, improving access to affordable, fresh produce. These measures would require minimal to no infrastructure. Most importantly, workers should not routinely work overtime, as this limits their ability to shop, cook, and care for their health. Many work overtime or hold second jobs due to low earnings. While affordable food is abundant in Bangkok, healthy food is rarely convenient or cheap. Addressing income poverty and economic justice is crucial for improving the quality of diets among disadvantaged populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe study plan was approved by the World Vegetable Center\u0026rsquo;s Research Ethics Committee (registration ID 2023-17). Participation was virtually risk-free. Verbal and written consent were obtained before the data collection.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by a grant from the International Institute of Tropical Agriculture (IITA) under the CGIAR Food Frontiers \u0026amp; Security Science Program, as supported by contributors to the CGIAR Trust Fund (https://www.cgiar.org/funders). Open access funding was provided by the Foreign, Commonwealth \u0026amp; Development Office (FCDO) of the United Kingdom.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003ePiraorn Suvanbenjakule: conceptualization, methodology, validation, formal analysis, investigation, resources, data curation, writing - original draft, writing - review \u0026amp; editing, visualization, project administration; Pepijn Schreinemachers: conceptualization, methodology, writing - review \u0026amp; editing, visualization, supervision, funding acquisition; Ee Von Goh: methodology, validation, writing - review \u0026amp; editing, supervision\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors acknowledge the support from the International Institute of Tropical Agriculture (IITA) under the CGIAR Food Frontiers \u0026amp; Security Science Program, as supported by contributors to the CGIAR Trust Fund (https://www.cgiar.org/funders). We also acknowledge long-term strategic donors to the World Vegetable Center, including Taiwan, the United Kingdom, the United States, Australia, Germany, Thailand, South Korea, the Philippines, and Japan. Open access funding was provided by the Foreign, Commonwealth \u0026amp; Development Office (FCDO) of the United Kingdom.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFAO ILSI. Preventing micronutrient malnutrition a guide to food-based approaches - Why policy makers should give priority to food-based strategies [Internet]. 1999 [cited 2025 June 24]. 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Appetite 2015 Sept 1;92:102\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"healthy diet, diet quality, food choice, food environment, urban, Thailand","lastPublishedDoi":"10.21203/rs.3.rs-8528580/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8528580/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLow fruit and vegetable intake is a major public health concern, especially among marginalized populations. Myanmar migrants in Thailand are vulnerable to poverty and poor diets, but their food environment and behavior have not been studied. The objective of this study was to describe and analyze the fruit and vegetable intake of Myanmar migrant workers in Bangkok.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study combined a quantitative survey of 199 Myanmar migrants working in factories, construction sites, and service industries with in-depth qualitative interviews of 10 migrants. The study analyzed psychological and food environment factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe average fruit and vegetable consumption was 195 g/day, about half the WHO-recommended amount. About a quarter of meals were purchased, and the rest were home-cooked. Quantitative results revealed that home cooking, number of market visits, self-efficacy, and intention are statistically significant predictors of intake. While fresh fruits and vegetables are generally available, key constraints identified in the qualitative analysis included limited mobility, the high cost of fruits and vegetables relative to earned incomes, and long working hours that compel people to prioritize convenience over healthy eating.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFruit and vegetable intake is low among migrant workers in Bangkok, putting them at risk of non-communicable disease. There is a need for more targeted strategies to improve migrants\u0026rsquo; access to healthy food options.\u003c/p\u003e","manuscriptTitle":"Barriers to fruit and vegetable consumption among migrant workers in Bangkok: a mixed-methods study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 12:05:29","doi":"10.21203/rs.3.rs-8528580/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-23T10:43:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192603346840660462396485511601657763636","date":"2026-04-22T09:00:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211614508964832413251851878124980030113","date":"2026-01-09T16:17:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-08T05:25:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-08T05:19:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-07T10:56:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-07T10:55:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nutrition","date":"2026-01-06T08:16:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"79b7a79d-d7e4-43db-8779-82cd88879534","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T12:05:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 12:05:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8528580","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8528580","identity":"rs-8528580","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}