Digital Disadvantage in Nutrition Services: How Environmental Literacy Gaps Limit Elderly Chinese with Chronic Diseases

Digital Disadvantage in Nutrition Services: How Environmental Literacy Gaps Limit Elderly Chinese with Chronic Diseases | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Digital Disadvantage in Nutrition Services: How Environmental Literacy Gaps Limit Elderly Chinese with Chronic Diseases Ya Shi, Yu Zhang, Laixi Zhang, Huiyi Zhang, Manoj Sharma, Lei Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6653933/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Purpose Given the high prevalence of nutrition-related chronic diseases (NRCDs) among the elderly population in China, this study explores the association between digital and non-digital health Nutrition Health Service Support (NHSS) and the Level of Environmental and Health Literacy (LEHL) to foster healthy aging. Methods In this cross-sectional study, the demographic profiles of 752 Chinese older adults were detailed and analyzed, and the level of NHSS and LEHL was determined for those with NRCDs. The linear regression was employed to explore the impact of NHSS and scores across dimensions on the residents’ LEHL. Subgroup analyses were also performed to examine the potential roles of demographic characteristics, disease status, and health indicators in the association between NHSS and LEHL. Results Among the 752 elderly participants, 48.7% were affected by one or more NRCDs. This cross-sectional study involving 752 older adults in China found that the number of NRCDs and the NHSS were positively associated with LEHLQ scores. With low NHSS as a reference, moderate and high NHSS levels were associated with increased LEHLQ scores among Chinese older adults. Furthermore, Stratified analyses revealed that Social environmental support (SES) and Online environmental support (OES)demonstrated the strongest positive association with LEHLQ scores, with the high-support group showing effect sizes 1.3-fold greater than Family environmental support (FES) and 2.5-fold greater than Online environmental support (OES). A linear trend was found for the NHSS and LEHLQ scores between different groups such as different sex, Han population, rural and urban residents, those with an income below 5000 RMB, smokers and non-smokers, drinkers and non-drinkers, normal BMI, older adults with or without NRCD, and older adults with primary school education or below. Conclusion Improving LEHL among Chinese older adults is essential for narrowing health disparities and promoting healthy aging. To address the digital disadvantage in this population, strengthening the NHSS system is critical to ensure equitable access to nutrition-related information and services nutrition-related chronic diseases social cognitive theory nutrition health service support level of environmental and health literacy older adults Figures Figure 1 Figure 2 1. Introduction Global health data show that 26% of individuals have hypertension, 463 million have diabetes, and 523 million suffer from cardiovascular diseases [ 1 , 2 ]. Therefore, nutrition-related chronic diseases (NRCDs) pose a significant threat to global health [ 3 ]. In China, the incidence of such diseases is on the rise, particularly among the elderly [ 4 – 6 ]. The prolonged nature and cooccurrence of these conditions in many patients place a substantial burden on China’s healthcare system and severely influence the quality of life of the country’s elderly population [ 7 , 8 ]. As early as 1986, the Ottawa Charter for Health Promotion highlighted the significance of environmental support for individuals, enabling them to enhance their control over their health and improve their health status [ 9 ]. Social Cognitive Theory posits that an individual’s behaviour is influenced by the external environment, with social, familial, and digital support playing critical roles [ 10 ]. In Turkey, school-based nutrition education—an important form of institutional social support—has been associated with significantly higher nutrition knowledge and literacy levels among students [ 11 ]. Enhancing mothers’ educational attainment and providing appropriate employment opportunities as part of social support strategies can improve their nutritional awareness [ 12 ]. Previous studies indicated that the health behaviours of Chinese college students can be enhanced through health information delivered via information technology [ 13 ]. Supportive nutritional care for older adults has improved their quality of life and health outcomes [ 14 ]. The consumption of specific nutritious diets by community-dwelling older adults can significantly reduce their malnutrition risk [ 15 ]. Health education campaigns, which provide support, are one of the key determinants in fostering health behaviors among older individuals in rural China [ 16 ]. Based on the aforementioned research phenomena and guided by SCT, we define “Nutrition Health Service Support” (NHSS) as the provision of nutrition-related support within external environments, aiming to enhance individuals’ overall health status [ 17 ]. The Healthy China Initiative (2019–2030) for a Healthy China (2019–2030) outlines that by 2030, the level of environmental and health literacy (LEHL) among Chinese residents will reach at least 25% [ 18 ]. This targeted plan aims to enhance the ability of older Chinese individuals to comprehend basic environmental and health knowledge and to apply this understanding to make informed decisions about common issues with support from NHSS. In China, LEHL is a crucial indicator for assessing the comprehensive knowledge, attitude, and behaviour of Chinese residents regarding environmental health [ 19 ]. Previous studies have found that the health literacy of Chinese students can be significantly improved through health education [ 20 ]. Sun et al. found that health education reading materials can significantly improve health literacy among older adults [ 21 ]. Additionally, in patients with long-term conditions in Denmark, low levels of social support were associated with lower health literacy [ 22 ]. A cross-sectional survey indicated that the family was considered a fundamental mechanism for enhancing health literacy among university students [ 23 ]. In terms of Online environmental support, a cross-sectional study found that although most Turkish women accessed health information through digital media, their health literacy remained low [ 24 ]. Existing research on the impact of NHSS on LEHL has not reached a consensus. Based on the environmental support dimension of SCT, this study aims to systematically explore the association between NHSS and LEHL in the Chinese older population with NRCDs from multidimensional perspectives, including social, family, and online environmental support. The findings of this study can provide empirical evidence for improving health literacy among older adults in China, thereby contributing to the implementation of the healthy aging strategy. 2. Methods 2.1 Study sample This cross-sectional study was conducted between March 2023 and December 2023. Survey volunteers were recruited to increase the reliability of measures, and centralized training was conducted in February 2023. The trained investigators went to families and communities in Chongqing to conduct one-on-one on-site surveys, focusing on face-to-face interviews with elderly individuals. Each participant was interviewed for a minimum of 15–20 minutes to ensure that he/she understood the questions and provided accurate responses. Instructed-response items were set to reduce the illogical recall of the subjects and identify and exclude inattentive participants. The inclusion criteria were as follows: a. permanent residents over 60 years old in Chongqing, b. ability to communicate independently, and c. those who can independently complete the investigation and give informed consent. The exclusion criteria were as follows: a. severe hearing impairment, language disorder, or inability to communicate normally; b. with severe cognitive impairment or mental illness; and c. unable to participate due to poor health status. A total of 814 subjects were included, most of whom (94%) were aged ≥ 60 years. Height and weight indices were collected in 99% of participants. However, 12 individuals were excluded from the study because of the abnormal index, and 50 individuals were excluded from the study due to having an age under 60 years. Thus, 752 individuals were available for this study. To make each interviewee feel as comfortable as possible, the interviewer asked for a written informed consent. The study protocols were approved by the Ethics Committee of Chongqing Medical University in China. 2.2 Health and demographic data The participants completed detailed questionnaires on lifestyle, demographics, and health status. Smoking history and alcohol intake were self-reported as status (current and former, never). Height and weight were measured by trained personnel. Body mass index (BMI) was calculated by self-reported height and weight (weight/height 2 , kg/m 2 ) and categorized as underweight, normal, overweight, or obese. The classification of human BMI referred to the appropriate criteria of BMI for the elderly mentioned in The Guidelines of Physical Fitness Index and Weight Management in China, Adult Weight Evaluation and Dietary Guidelines for Chinese Residents (2022): the appropriate range of BMI for the elderly in China is a. 22.0 kg/m 2 BMI < 26.9 kg/m 2 for those aged 80 years; b. 20.0 kg/m2 BMI < 26.9 kg/m2 for those aged 65–79 years; and c. 18.5 kg/m2 BMI < 23.9 kg/m2 for those aged 60–64 years. BMIs lower than the appropriate range were divided according to wasting, and those above the appropriate range were divided into categories according to overweight and obesity [ 25 – 27 ]. Basic demographic data included sex, age, ethnicity, residence, monthly average income (including child support), and level of education (LE), which was classified into primary school or below, junior high school, high school (including vocational school/ technical school), and college degree or above. The health status of the subjects was assessed, and the number of NRCDs was obtained from their clinical history records. The diseases were categorized into three groups: no NRCD (0), single-type NRCD (1), and multiple NRCDs (2). 2.3 Exposure variables: NHSS scores The social cognitive theory (SCT) was proposed by Bandura [ 28 ] and is a widely accepted theory for explaining individual behaviour, particularly in the fields of health education and promotion, psychology, and education [ 29 ]. SCT encompasses three key components: personal cognition, environment, and personal behaviour. It effectively predicts health behaviour by focusing on the motivations that drive individuals to engage in health-related actions and translating these motivations into behavioral intentions [ 28 , 30 ]. With SCT as the basis, the environmental factors influencing the health of elderly individuals with NRCDs in this study were defined as NHSS. In particular, NHSS refers to the guidance, counselling, and assistance provided to elderly individuals through various channels and resources within their family, social, and online environments to enhance their nutritional health status [ 31 – 35 ]. The NHSS scale (see Additional file 2) assessed three dimensions of perceived support: Social environmental support (SES), Family environmental support (FES), and Online environmental support (OES). All items were positively framed using a 5-point Likert scale ranging from 0 ("strongly disagree") to 4 ("strongly agree"). The scale demonstrated good internal consistency, with Cronbach's α coefficients of 0.853 for SES (3 items), 0.736 for FES (3 items), and 0.867 for OES (4 items). Confirmatory Factor Analysis (CFA) was conducted using Amos 24.0 to assess the structural validity of the scale comprising three latent variables (WSP, FES, SES). The model demonstrated acceptable fit: Absolute fit indices: χ²/df = 3.718 (acceptable if < 5), RMSEA = 0.058 (good fit 0.90, indicating excellent fit). Parsimonious fit indices: PGFI, PCFI, and PNFI all exceeded 0.50. These results support the scale’s robust structural validity and discriminant validity. A high score indicates a great level of support for nutrition and health services. The NHSS scores were divided into three levels: low, middle, and high. 2.4 Outcome variables: LEHL questionnaire scores The LEHL among residents is a crucial indicator for assessing the effectiveness of the “Healthy China Initiative (2019–2030)” in promoting a healthy environment [ 18 ]. It refers to an individual’s ability to understand basic environmental and health knowledge and use it to make informed decisions about common issues. In this study, the Level of Environmental and Health Literacy Questionnaire (LEHLQ) was independently designed “The Chinese Dietary Guidelines” [ 26 ]. The LEHLQ (see Additional file 2) includes a five-item dimension. The first three items are positively worded and scored on a 5-point Likert scale from 0 ("strongly disagree") to 4 ("strongly agree"). The last two items are reverse-worded (e.g., "Hypertension has nothing to do with salt intake”; “Patients with hyperlipidemia do not need to reduce consumption of animal offal"); their scores are assigned in reverse order to ensure that higher scores indicate higher literacy levels. In this study, Cronbach’s α was 0.87, and the questionnaire demonstrated good construct validity (KMO 0.91, Bartlett’s test p < 0.001). 2.5 Statistical analysis Statistical analyses were conducted using Stata 18.0 and R 4.3.2. The basic demographic characteristics of elderly individuals were summarized using means, standard deviations, frequencies, and proportions. Bar charts depicted the level of nutrition and health service support among elderly individuals with NRCDs and the residents’ nutritional environmental health literacy levels. Correlations between variables were analyzed using the R “corrplot” function, and linear regression was employed to explore the impact of NHSS and scores across dimensions on the residents’ LEHL. Subgroup analyses were also performed to examine the potential roles of demographic characteristics, disease status, and health indicators in the relationship between NHSS and the residents’ LEHL. A significance level of α = 0.05 was set for all tests, and a two-tailed approach was applied. 3. Results 3.1 Basic demographic characteristics The characteristics of the participants are provided in Table 1 . Among the 752 elderly participants, approximately 53.6% were female, the average age was 69.60 ± 6.58 years, and most of them were of Han ethnicity. The proportions of participants with an education level below junior high, living in urban areas, and earning less than 3,000 RMB per month were 85.1%, 71.4%, and 76.3%, respectively. Regarding health status, over half of the elderly participants reported a history of smoking or drinking, the majority had a normal BMI, and more than half were not suffering from NRDs. Table 1 Sample characteristics. Variables Total Number N = 752 Sex male 349 (46.4%) female 403 (53.6%) Age 69.60(6.58) Ethnicity Han 713 (94.8%) Other 39 (5.2%) Education level Primary school or below 425 (56.5%) Junior high school 215 (28.6%) High school 81 (10.8%) College degree or above 31 (4.1%) Residence Cities 537 (71.4%) Rural 215 (28.6%) Monthly household income ≤ 1000 RMB 188 (25.0%) 1001–3000 RMB 386 (51.3%) 3001–5000 RMB 120 (16.0%) ≥ 5001 RMB 58 (7.7%) Alcohol intake Never 414 (55.1%) Current and former 338 (44.9%) Smoking history Never 508 (67.6%) Current and former 244 (32.4%) BMI Normal 498 (66.2%) Thin 125 (16.6%) Obesity and overweight 129 (17.2%) Number of chronic disease types no nutrition-related chronic diseases 386 (51.3%) one type of nutrition-related chronic diseases 211 (28.1%) multiple nutrition-related chronic diseases 155 (20.6%) Nutrition health service support Low 251 (33.4%) Middle 259 (34.4%) High 242 (32.2%) LEHLQ scores 13.33 (2.60) 3.2 Analysis of the differences in NHSS and LEHLQ scores among elderly individuals with varying numbers of NRCDs The LEHLQ scores showed statistically significant differences in distribution across different disease numbers ( P < 0.05). However, the distribution of NHSS did not show statistically significant differences (Fig. 1 ). 3.3 Analysis of the correlations among NHSS, LEHLQ, and NRCDs in elderly individuals Positive correlations with the LEHLQ scores were observed for the number of NRCDs and NHSS scores (correlation = 0.171, 0.324, p < 0.01, Fig. 2 ). Heatmap showing Spearman’s correlations of LEHLQ scores with sociodemographic characteristics, disease status, and nutrition health service support. Negative correlations are depicted in blue, and positive correlations are shown in red. The intensity of the colors represents the strength of the correlation, with dark shades indicating strong positive or negative correlations. Symbols ** and * represent the correlation between two dietary food factors with p-values < 0.01 and < 0.05, respectively. 3.4 Linear regression analysis between NHSS and LEHLQ scores With low NHSS as a reference, moderate NHSS was associated with a 0.67-point increase in LEHLQ (95% CI: 0.24–1.11, p = 0.002); High NHSS showed stronger benefits (β = 1.21, 95% CI: 0.74–1.68, p < 0.001). This dose-response relationship persisted after adjusting for sociodemographic and behavioural confounders (Table 2 ). Stratified analyses of NHSS subdimensions revealed differential associations with LEHLQ scores (Additional file 1): SES demonstrated the strongest positive association with LEHLQ scores, with the high-support group showing effect sizes 1.3-fold greater than FES and 2.5-fold greater than OES. Table 2 The associations between NHSS and LEHLQ scores. Crude model a Model 1 b Model 2 c β (95%Cl) P -value β (95%Cl) P -value β (95%Cl) P -value Middle vs. Low 0.805(0 .365, 1.244) < 0.001 0.696(0.263, 1.128) 0.002 0.674(0.242, 1.107) 0.002 High vs. Low 1.500(1.053, 1.947) < 0.001 1.197(0.729, 1.665) < 0.001 1.208(0.738, 1.678) < 0.001 a Crude model: no covariates were adjusted. b Model 1: age, sex, education level, residence, and monthly household income were adjusted. c Model 2: additionally add alcohol intake, smoking history, BMI, and types of nutritional chronic diseases were adjusted. 3.5 Subgroup analysis according to lifestyle, demographics, and health status As shown in Table 3 , with low NHSS as a reference, a linear trend was observed between NHSS and LEHLQ scores across various groups: different sex, Han population, rural and urban residents, those with an income below 5000 RMB, smokers and non-smokers, drinkers and non-drinkers, normal BMI, older adults with or without NRCD, and older adults with primary school education or below. Table 3 Subgroup analyses of the relationship between NHSS and LEHLQ scores. Nutrition health service support (NHSS) low middle high P for trend P for Interaction Sex 0.764 male 0.00 0.85 (0.18–1.53) 1.32 (0.57–2.07) 0.001 female 0.00 0.46 (-0.11 -1.02) 0.96 (0.36–1.55) 0.002 Ethnicity 0.230 Han 0.00 0.67 (0.23–1.11) 1.12 (0.65–1.58) 0.000 other 0.00 0.01 (-2.37-2.39) 1.47 (-4.58-7.53) 0.819 Residence 0.338 Cities 0.00 0.55 (0.02–1.08) 0.77 (0.22–1.33) 0.007 Rural 0.00 0.82 (0.10–1.54) 2.19 (1.31–3.07) 0.000 Monthly household income 0.652 ≤ 1000 RMB 0.00 1.08 (0.40–1.76) 1.91 (0.98–2.85) 0.000 1001–3000 RMB 0.00 0.56 (-0.05-1.18) 1.06 (0.42–1.70) 0.001 3001–5000 RMB 0.00 0.29 (-1.01-1.59) 1.16 (-0.05-2.38) 0.032 ≥ 5001 RMB 0.00 0.55 (-2.02- 3.11) 0.00 (-2.42-2.41) 0.832 Alcohol intake 0.463 None 0.00 0.78 (0.21–1.36) 1.19 (0.58–1.80) 0.000 Current and former 0.00 0.46 (-0.21-1.13) 0.95 (0.21–1.69) 0.012 Smoking history 0.543 None 0.00 0.52 (0.00–1.04) 1.10 (0.54–1.65) 0.000 Current and former 0.00 0.84 (0.05–1.63) 1.00 (0.14–1.87) 0.021 BMI 0.605 Normal 0.00 0.69 (0.16–1.21) 1.14 (0.58–1.71) 0.000 Thinness 0.00 0.09 (-0.98-1.16) 0.69 (-0.55-1.93) 0.305 Obesity 0.00 1.22 (0.10–2.33) 1.11 (-0.07-2.29) 0.065 Number of chronic disease types 0.908 No nutrition-related chronic diseases 0.00 0.82 (0.21–1.43) 1.43 (0.79–2.06) 0.000 One type nutrition-related chronic diseases 0.00 0.48 (-0.31-1.27) 0.54 (-0.36-1.44) 0.205 Multiple nutrition-related chronic diseases 0.00 0.38 (-0.66-1.43) 1.39 (0.25–2.54) 0.018 Education level 0.274 Primary school or below 0.78 (0.27–1.28) 1.26 (0.63–1.88) 0.000 Junior high school 0.87 (-0.02-1.76) 0.90 (0.06–1.74) 0.056 High school -0.02 (-2.11- 2.07) 1.29 (-0.54-3.12) 0.091 College degree or above -1.99 (-5.39 1.41) -0.98 (-4.11 -2.15) 0.946 4. Discussion This cross-sectional study involving 752 older adults in China found that the number of NRCDs and the NHSS were positively associated with LEHLQ scores. With low NHSS as a reference, both moderate and high NHSS levels were associated with increased LEHLQ scores among Chinese older adults. Furthermore, Stratified analyses revealed that SES demonstrated the strongest positive association with LEHLQ scores, with the high-support group showing effect sizes 1.3-fold greater than FES and 2.5-fold greater than OES. A linear trend was found for the NHSS and LEHLQ scores between different groups such as different sex, Han population, rural and urban residents, those with an income below 5000 RMB, smokers and non-smokers, drinkers and non-drinkers, normal BMI, older adults with or without NRCD, and older adults with primary school education or below. These findings are crucial for Chinese older adults, as LEHL is linked to their ability to cope with the complex health demands of modern society [ 36 ]. In China, chronic diseases affect over 180 million elderly individuals, with 75% of them having at least one such condition [ 37 ]. As the incidence of NRCD escalates among the aging Chinese population, concern for these health issues arises. Our study found that individuals with a high burden of NRCD exhibit great LEHL. Elderly individuals with NRCD often opt for the Mediterranean diet, resulting in healthy eating habits and potentially enhancing their LEHL [ 38 , 39 ]. Therefore, their LEHL is enhanced. Owing to their health conditions, these older adults have become proactive in seeking health knowledge from various sources, including social, familial, and digital platforms [ 40 ], and learning about these health issues to bolster their self-care capabilities and manage the progression of NRCDs. The pervasive issue of nutrition-related chronic diseases worldwide has, to a significant degree, heightened public health consciousness [ 41 ]. The Chinese government has also actively issued policy documents such as "Healthy China 2030," aiming to promote the multidimensional dissemination of nutritional and health knowledge across society, families, and digital networks, thereby enhancing the LEHL of older adults in China. The extent of NHSS is directly linked to LEHL among China’s older adults. By imparting scientific nutritional health knowledge through these services, we can empower older adults to adopt healthy lifestyles [ 16 ]. Nutritional education is pivotal in preventing NRCDs and enhancing overall health [ 42 ]. In the social environment support dimension of environmental support, we found that FES significantly increased the LEHL of older adults in China. This finding is consistent with that of Nielsen et al. [ 22 ], but contrasts with the results of Klinger et al. [ 43 ], who reported that social support had minimal impact on mitigating low health literacy among older adults in Germany. These differences may be attributed to variations in regional and cultural contexts. In the dimension of family environment support, we also observed similar associations. A cross-sectional study found that family health among patients with chronic diseases had a significant impact on self-efficacy [ 44 ]. Family support not only helps build and strengthen patients’ confidence in coping with illness but also facilitates the maintenance of health-promoting behaviours [ 45 ]. In the dimension of online environmental support, online nutrition education is an effective channel for disseminating educational content, particularly in grassroots areas where medical education resources are scarce [ 46 ]. These resources cater to the nutritional education needs of patients with NRCDs. Survey data revealed that over 50% of patients with chronic diseases in China turn to online settings for nutritional health information [ 47 ]. This online quest for health information equips patients with NHSS and fosters self-efficacy in managing their health [ 48 ]. It consequently contributes to an improved quality of life in their later years. Subgroup analysis examining the correlation between NHSS and LEHLQ scores has uncovered their linear relationship, particularly within the Han ethnic group. Compared with the Han ethnic group, ethnic minority groups tend to exhibit lower health literacy levels [ 49 , 50 ]. The disparity in access and utilization of NHSS among ethnic minorities may be attributed to the uneven distribution of medical and educational resources [ 49 , 51 ]. In this research, ethnic minorities constituted a mere 5.2% of the participants, resulting in a substantial sample size disparity between Han ethnic and ethnic minorities. This imbalance is significant enough to potentially skew the accurate representation of nutritional literacy levels between these two groups. In addition, a significant linear trend between NHSS and LEHLQ scores was observed among Chinese elderly with monthly incomes below 5,000 RMB. This result suggests that individuals with low incomes are likely to face barriers to accessing adequate NHSS due to financial limitations, consequently affecting their LEHL [ 52 ]. A study conducted in Iran supported this finding, indicating that individuals with sufficient monthly salaries make healthy food choices and possess high nutritional knowledge, thereby influencing their LEHL [ 53 ]. Moreover, a significant association between NHSS and LEHL was observed among individuals with normal body weight. Previous studies have found that underweight and obese older adults generally exhibit poorer physical function and overall health status compared to those with normal weight, which in turn negatively affects their quality of life in later years [ 54 ]. This may be attributed to the possibility that individuals with a normal BMI tend to have higher overall health literacy [ 55 ]. Additionally, they may place greater emphasis on preventive nutritional service support and demonstrate higher responsiveness to such services. A significant association between NHSS and LEHL was also identified among individuals with primary school education or below. Numerous studies have demonstrated a strong positive correlation between educational level and health literacy [ 56 , 57 ]. This association may stem from two primary factors: first, older adults with lower education levels are often at a disadvantage in terms of accessing, understanding, and evaluating health information [ 58 ]; second, education level is closely linked to socioeconomic status, and lower educational level is frequently associated with limited economic resources, which may hinder access to nutritional and health services [ 59 ]. For older adults in China, achieving a healthy later life is of vital importance. To attain this goal, they need to effectively utilize NHSS available within their social, family, and online environments to enhance their LEHL. This study contributes to the design of more targeted nutritional and health services, aiming to improve health literacy among the older adults. With improved LEHL, older adults in China may be better able to make independent choices regarding healthy diets and living environments, thereby delaying the onset of disability and reducing the caregiving burden on families and society. This study has several limitations. First, the cross-sectional design restricts the ability to infer causal relationships between NHSS and LEHL. Further cohort studies are needed to explore these associations in greater depth. Future research should adopt longitudinal designs, such as prospective cohort studies, to clarify the potential causal mechanisms among these variables. Second, although data were collected through face-to-face field surveys conducted by trained professionals in both home and community settings, the reliance on self-reported responses may have introduced recall bias. To reduce bias arising from subjective reporting and enhance the accuracy and reliability of the data, future studies could integrate objective assessment indicators. Finally, the study sample was limited to residents aged 60 and above in Chongqing, China, which may restrict the generalizability of the findings to older adults in other regions. To improve external validity, future research should include elderly populations from diverse regions across the country and conduct nationally representative surveys to verify the universality of the findings. 5. Conclusions This cross-sectional study found that NRCDs and the NHSS were positively associated with LEHLQ scores. With low NHSS as a reference, both moderate and high NHSS levels were associated with increased LEHLQ scores among Chinese older adults. Furthermore, Stratified analyses revealed that SES demonstrated the strongest positive association with LEHLQ scores. The NHSS and LEHLQ scores exhibited a linear trend across different subgroups of older adults. Enhancing the LEHL of China’s elderly is essential for mitigating health disparities and effectively tackling the challenges posed by an aging population. Declarations Ethics approval and consent to participate This study was carried out in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Chongqing Medical University, 2023081, 7 November 2023. Informed consent was given by all participants before the beginning of the survey. Consent for publication Informed consent for publication was obtained from all authors. Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due to funding requirements but are available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests. Funding Open access funding provided by High-Quality Development Research on Health Management Service Industry in Chongqing. This research was funded by Think Tank Research Project of Chongqing Association for Science and Technology in 2023, grant number: 2023KXKT12. Authors' contributions Conceptualization: Ya Shi, Yu Zhang ; Methodology: Ya Shi, Yu Zhang ; Formal analysis and investigation: Ya Shi, Yu Zhang and Laixi Zhang ; Writing - original draft preparation: Ya Shi, Yu Zhang ; Writing - review and editing: Laixi Zhang, Huiyi Zhang, Manoj Sharma, Lei Zhang and Yong Zhao ; Funding acquisition:Yong Zhao ; Supervision:Yong Zhao Acknowledgements We extend our sincere gratitude to all the interviewees and researchers for their valuable contributions to the project. Authors' information School of Public Health, Chongqing Medical University, Chongqing 400016, China Ya Shi, Yu Zhang, Huiyi Zhang and Yong Zhao Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing 400016, China Ya Shi, Yu Zhang, Huiyi Zhang and Yong Zhao Research Center for Public Health Security, Chongqing Medical University, Chongqing 400016, China Ya Shi, Yu Zhang, Huiyi Zhang and Yong Zhao Nutrition Innovation Platform-Sichuan and Chongqing, School of Public Health, Chongqing Medical University, Chongqing 400016, China Ya Shi, Yu Zhang, Huiyi Zhang and Yong Zhao Health Management Centre, First Affiliated Hospital of Army Medical University, Chongqing 400038, China Laixi Zhang Department of Social and Behavioral Health, School of Public Health, University of Nevada, Las Vegas (UNLV), Las Vegas, NV, United States Manoj Sharma Department of Internal Medicine, Kirk Kerkorian School of Medicine, University of Nevada, Las Vegas (UNLV), Las Vegas, NV, United States Manoj Sharma China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shanxi, 710061, PR China Lei Zhang Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia Lei Zhang School of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia Lei Zhang Chongqing Key Laboratory of Child Nutrition and Health, Children’s Hospital of Chongqing Medical University, Chongqing 400014, China Yong Zhao References Global burden. of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. 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An online diabetes nutrition education programme for american indian and alaska native adults with type 2 diabetes: perspectives from key stakeholders. Public Health Nutr. 2021 2021;24(6):1449-59. Fleischhacker SE, Woteki CE, Coates PM, Hubbard VS, Flaherty GE, Glickman DR et al. Strengthening national nutrition research: rationale and options for a new coordinated federal research effort and authority. Am J Clin Nutr. 2020 2020;112(3):721 – 69. Chaves C, Camargo JT, Zandonadi RP, Nakano EY, Ginani VC. Nutrition literacy level in bank employees: the case of a large brazilian company. Nutrients. 2023 2023;15(10). Klinger J, Berens EM, Schaeffer D. Health literacy and the role of social support in different age groups: results of a german cross-sectional survey. Bmc Public Health. 2023 2023;23(1):2259. Luo ZN, Li K, Chen AQ, Qiu YC, Yang XX, Lin ZW et al. The influence of family health on self-efficacy in patients with chronic diseases: the mediating role of perceived social support and the moderating role of health literacy. Bmc Public Health. 2024 2024;24(1):3398. Kline KS, Scott LD, Britton AS. The use of supportive-educative and mutual goal-setting strategies to improve self-management for patients with heart failure. Home Healthc Nurse. 2007 2007;25(8):502 – 10. Zhang Z, Monro J, Venn BJ. Development and evaluation of an internet-based diabetes nutrition education resource. Nutrients. 2019 2019;11(6). Opinions of the General Office of the State Council of the People'. s Republic of China on Promoting the Development of Internet + Medical Health. Contemp Rural Finance Econ. 2018(06):42–5. Zhao YC, Zhao M, Song S. Online health information seeking among patients with chronic conditions: integrating the health belief model and social support theory. J Med Internet Res. 2022 2022;24(11):e42447. Bergman L, Nilsson U, Dahlberg K, Jaensson M, Wångdahl J. Health literacy and e-health literacy among arabic-speaking migrants in sweden: a cross-sectional study. Bmc Public Health. 2021 2021;21(1):2165. Cheng L, Chen Q, Zhang FY, Wu W, Cui W, Hu X. Functional health literacy among left-behind students in senior high schools in an ethnic minority area: a cross-sectional study. Medicine (Baltimore). 2020 2020;99(8):e19167. van der Gaag M, van der Heide I, Spreeuwenberg P, Brabers A, Rademakers J. Health literacy and primary health care use of ethnic minorities in the netherlands. Bmc Health Serv Res 2017. 2017;17(1):350. Yang Q, Yu S, Wang C, Gu G, Yang Z, Liu H et al. Health literacy and its socio-demographic risk factors in hebei: a cross-sectional survey. Medicine (Baltimore). 2021 2021;100(21):e25975. Yarmohammadi P, Morowatisharifabad MA, Rahaei Z, Khayyatzadeh SS, Madadizadeh F. Nutrition literacy and its related demographic factors among workers of taraz steel company, chaharmahal and bakhtiari, iran. Front Public Health. 2022 2022;10:911619. Yan LL, Daviglus ML, Liu K, Pirzada A, Garside DB, Schiffer L, et al. Bmi and health-related quality of life in adults 65 years and older. Obes Res 2004. 2004;12(1):69–76. Geboers B, Reijneveld SA, Jansen CJ, de Winter AF. Health literacy is associated with health behaviors and social factors among older adults: results from the lifelines cohort study. J Health Commun 2016. 2016;21(sup2):45–53. Prel JD, Rohrbacher M, Schröder CC, Breckenkamp J. Do health literacy, physical health and past rehabilitation utilization explain educational differences in the subjective need for medical rehabilitation? Results of the lida cohort study. Bmc Public Health. 2024 2024;24(1):1622. Meier C, Vilpert S, Borrat-Besson C, Jox RJ, Maurer J. Health literacy among older adults in switzerland: cross-sectional evidence from a nationally representative population-based observational study. Swiss Med Wkly. 2022 2022;152:w30158. Hochhauser M, Brusovansky M, Sirotin M, Bronfman K. Health literacy in an israeli elderly population. Isr J Health Policy Res. 2019 2019;8(1):61. Tokal P, Sart G, Danilina M, Ta'Amnha MA. The impact of education level and economic freedom on gender inequality: panel evidence from emerging markets. Front Psychol. 2023 2023;14:1202014. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Additionalfile2.ChongqingOlderadultsNutritionSupportandEnvironmentalHealthLiteracySurvey.docx Additionalfile3.zip Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 Jul, 2025 Reviewers agreed at journal 05 Jul, 2025 Reviews received at journal 04 Jul, 2025 Reviewers agreed at journal 02 Jul, 2025 Reviewers agreed at journal 27 Jun, 2025 Reviewers invited by journal 13 Jun, 2025 Editor invited by journal 23 May, 2025 Editor assigned by journal 15 May, 2025 Submission checks completed at journal 15 May, 2025 First submitted to journal 15 May, 2025 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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Negative correlations are depicted in blue, and positive correlations are shown in red. The intensity of the colors represents the strength of the correlation, with dark shades indicating strong positive or negative correlations. Symbols\u003csup\u003e ** \u003c/sup\u003eand \u003csup\u003e*\u003c/sup\u003e represent the correlation between two dietary food factors with p-values \u0026lt; 0.01 and \u0026lt; 0.05, respectively.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6653933/v1/3dab77ca8a6da4186a0f8015.png"},{"id":84815162,"identity":"eb58f1b5-3dbc-4a29-bf86-b9c22420b215","added_by":"auto","created_at":"2025-06-17 15:28:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1697635,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6653933/v1/5740b6d1-1638-4abf-ba8c-aad736133769.pdf"},{"id":84812547,"identity":"f4cf4025-30dc-4499-b493-a1250d22f771","added_by":"auto","created_at":"2025-06-17 15:04:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18468,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6653933/v1/5f780fd172461b70547ef197.docx"},{"id":84811928,"identity":"485af02e-994e-4eb5-8be0-2962a7f1c394","added_by":"auto","created_at":"2025-06-17 14:56:16","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":34323,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.ChongqingOlderadultsNutritionSupportandEnvironmentalHealthLiteracySurvey.docx","url":"https://assets-eu.researchsquare.com/files/rs-6653933/v1/d3b091dedf6e8376df773a6c.docx"},{"id":84811937,"identity":"c98129b9-7a4e-4c38-be33-ae54bd4a8f51","added_by":"auto","created_at":"2025-06-17 14:56:16","extension":"zip","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":338308,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3.zip","url":"https://assets-eu.researchsquare.com/files/rs-6653933/v1/a2a3b8ea1c513b31e11e34ea.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Digital Disadvantage in Nutrition Services: How Environmental Literacy Gaps Limit Elderly Chinese with Chronic Diseases","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobal health data show that 26% of individuals have hypertension, 463\u0026nbsp;million have diabetes, and 523\u0026nbsp;million suffer from cardiovascular diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Therefore, nutrition-related chronic diseases (NRCDs) pose a significant threat to global health [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In China, the incidence of such diseases is on the rise, particularly among the elderly [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The prolonged nature and cooccurrence of these conditions in many patients place a substantial burden on China\u0026rsquo;s healthcare system and severely influence the quality of life of the country\u0026rsquo;s elderly population [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs early as 1986, the Ottawa Charter for Health Promotion highlighted the significance of environmental support for individuals, enabling them to enhance their control over their health and improve their health status [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Social Cognitive Theory posits that an individual\u0026rsquo;s behaviour is influenced by the external environment, with social, familial, and digital support playing critical roles [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In Turkey, school-based nutrition education\u0026mdash;an important form of institutional social support\u0026mdash;has been associated with significantly higher nutrition knowledge and literacy levels among students [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Enhancing mothers\u0026rsquo; educational attainment and providing appropriate employment opportunities as part of social support strategies can improve their nutritional awareness [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Previous studies indicated that the health behaviours of Chinese college students can be enhanced through health information delivered via information technology [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Supportive nutritional care for older adults has improved their quality of life and health outcomes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The consumption of specific nutritious diets by community-dwelling older adults can significantly reduce their malnutrition risk [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Health education campaigns, which provide support, are one of the key determinants in fostering health behaviors among older individuals in rural China [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Based on the aforementioned research phenomena and guided by SCT, we define \u0026ldquo;Nutrition Health Service Support\u0026rdquo; (NHSS) as the provision of nutrition-related support within external environments, aiming to enhance individuals\u0026rsquo; overall health status [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Healthy China Initiative (2019\u0026ndash;2030) for a Healthy China (2019\u0026ndash;2030) outlines that by 2030, the level of environmental and health literacy (LEHL) among Chinese residents will reach at least 25% [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This targeted plan aims to enhance the ability of older Chinese individuals to comprehend basic environmental and health knowledge and to apply this understanding to make informed decisions about common issues with support from NHSS. In China, LEHL is a crucial indicator for assessing the comprehensive knowledge, attitude, and behaviour of Chinese residents regarding environmental health [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Previous studies have found that the health literacy of Chinese students can be significantly improved through health education [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Sun et al. found that health education reading materials can significantly improve health literacy among older adults [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, in patients with long-term conditions in Denmark, low levels of social support were associated with lower health literacy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A cross-sectional survey indicated that the family was considered a fundamental mechanism for enhancing health literacy among university students [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In terms of Online environmental support, a cross-sectional study found that although most Turkish women accessed health information through digital media, their health literacy remained low [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExisting research on the impact of NHSS on LEHL has not reached a consensus. Based on the environmental support dimension of SCT, this study aims to systematically explore the association between NHSS and LEHL in the Chinese older population with NRCDs from multidimensional perspectives, including social, family, and online environmental support. The findings of this study can provide empirical evidence for improving health literacy among older adults in China, thereby contributing to the implementation of the healthy aging strategy.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study sample\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted between March 2023 and December 2023. Survey volunteers were recruited to increase the reliability of measures, and centralized training was conducted in February 2023. The trained investigators went to families and communities in Chongqing to conduct one-on-one on-site surveys, focusing on face-to-face interviews with elderly individuals. Each participant was interviewed for a minimum of 15\u0026ndash;20 minutes to ensure that he/she understood the questions and provided accurate responses. Instructed-response items were set to reduce the illogical recall of the subjects and identify and exclude inattentive participants.\u003c/p\u003e \u003cp\u003eThe inclusion criteria were as follows: a. permanent residents over 60 years old in Chongqing, b. ability to communicate independently, and c. those who can independently complete the investigation and give informed consent. The exclusion criteria were as follows: a. severe hearing impairment, language disorder, or inability to communicate normally; b. with severe cognitive impairment or mental illness; and c. unable to participate due to poor health status.\u003c/p\u003e \u003cp\u003eA total of 814 subjects were included, most of whom (94%) were aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years. Height and weight indices were collected in 99% of participants. However, 12 individuals were excluded from the study because of the abnormal index, and 50 individuals were excluded from the study due to having an age under 60 years. Thus, 752 individuals were available for this study.\u003c/p\u003e \u003cp\u003e To make each interviewee feel as comfortable as possible, the interviewer asked for a written informed consent. The study protocols were approved by the Ethics Committee of Chongqing Medical University in China.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Health and demographic data\u003c/h2\u003e \u003cp\u003eThe participants completed detailed questionnaires on lifestyle, demographics, and health status. Smoking history and alcohol intake were self-reported as status (current and former, never). Height and weight were measured by trained personnel. Body mass index (BMI) was calculated by self-reported height and weight (weight/height\u003csup\u003e2\u003c/sup\u003e, kg/m\u003csup\u003e2\u003c/sup\u003e) and categorized as underweight, normal, overweight, or obese. The classification of human BMI referred to the appropriate criteria of BMI for the elderly mentioned in The Guidelines of Physical Fitness Index and Weight Management in China, Adult Weight Evaluation and Dietary Guidelines for Chinese Residents (2022): the appropriate range of BMI for the elderly in China is a. 22.0 kg/m\u003csup\u003e2\u003c/sup\u003e BMI\u0026thinsp;\u0026lt;\u0026thinsp;26.9 kg/m\u003csup\u003e2\u003c/sup\u003e for those aged 80 years; b. 20.0 kg/m2 BMI\u0026thinsp;\u0026lt;\u0026thinsp;26.9 kg/m2 for those aged 65\u0026ndash;79 years; and c. 18.5 kg/m2 BMI\u0026thinsp;\u0026lt;\u0026thinsp;23.9 kg/m2 for those aged 60\u0026ndash;64 years. BMIs lower than the appropriate range were divided according to wasting, and those above the appropriate range were divided into categories according to overweight and obesity [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBasic demographic data included sex, age, ethnicity, residence, monthly average income (including child support), and level of education (LE), which was classified into primary school or below, junior high school, high school (including vocational school/ technical school), and college degree or above.\u003c/p\u003e \u003cp\u003eThe health status of the subjects was assessed, and the number of NRCDs was obtained from their clinical history records. The diseases were categorized into three groups: no NRCD (0), single-type NRCD (1), and multiple NRCDs (2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Exposure variables: NHSS scores\u003c/h2\u003e \u003cp\u003eThe social cognitive theory (SCT) was proposed by Bandura [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and is a widely accepted theory for explaining individual behaviour, particularly in the fields of health education and promotion, psychology, and education [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. SCT encompasses three key components: personal cognition, environment, and personal behaviour. It effectively predicts health behaviour by focusing on the motivations that drive individuals to engage in health-related actions and translating these motivations into behavioral intentions [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. With SCT as the basis, the environmental factors influencing the health of elderly individuals with NRCDs in this study were defined as NHSS. In particular, NHSS refers to the guidance, counselling, and assistance provided to elderly individuals through various channels and resources within their family, social, and online environments to enhance their nutritional health status [\u003cspan additionalcitationids=\"CR32 CR33 CR34\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The NHSS scale (see Additional file 2) assessed three dimensions of perceived support: Social environmental support (SES), Family environmental support (FES), and Online environmental support (OES). All items were positively framed using a 5-point Likert scale ranging from 0 (\"strongly disagree\") to 4 (\"strongly agree\"). The scale demonstrated good internal consistency, with Cronbach's α coefficients of 0.853 for SES (3 items), 0.736 for FES (3 items), and 0.867 for OES (4 items). Confirmatory Factor Analysis (CFA) was conducted using Amos 24.0 to assess the structural validity of the scale comprising three latent variables (WSP, FES, SES). The model demonstrated acceptable fit: Absolute fit indices: χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;3.718 (acceptable if\u0026thinsp;\u0026lt;\u0026thinsp;5), RMSEA\u0026thinsp;=\u0026thinsp;0.058 (good fit\u0026thinsp;\u0026lt;\u0026thinsp;0.08), GFI\u0026thinsp;=\u0026thinsp;0.914 (good fit\u0026thinsp;\u0026ge;\u0026thinsp;0.90). Incremental fit indices: IFI\u0026thinsp;=\u0026thinsp;0.925, CFI\u0026thinsp;=\u0026thinsp;0.924, NFI\u0026thinsp;=\u0026thinsp;0.900 (all \u0026gt;\u0026thinsp;0.90, indicating excellent fit). Parsimonious fit indices: PGFI, PCFI, and PNFI all exceeded 0.50. These results support the scale\u0026rsquo;s robust structural validity and discriminant validity. A high score indicates a great level of support for nutrition and health services. The NHSS scores were divided into three levels: low, middle, and high.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Outcome variables: LEHL questionnaire scores\u003c/h2\u003e \u003cp\u003eThe LEHL among residents is a crucial indicator for assessing the effectiveness of the \u0026ldquo;Healthy China Initiative (2019\u0026ndash;2030)\u0026rdquo; in promoting a healthy environment [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. It refers to an individual\u0026rsquo;s ability to understand basic environmental and health knowledge and use it to make informed decisions about common issues. In this study, the Level of Environmental and Health Literacy Questionnaire (LEHLQ) was independently designed \u0026ldquo;The Chinese Dietary Guidelines\u0026rdquo; [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The LEHLQ (see Additional file 2) includes a five-item dimension. The first three items are positively worded and scored on a 5-point Likert scale from 0 (\"strongly disagree\") to 4 (\"strongly agree\"). The last two items are reverse-worded (e.g., \"Hypertension has nothing to do with salt intake\u0026rdquo;; \u0026ldquo;Patients with hyperlipidemia do not need to reduce consumption of animal offal\"); their scores are assigned in reverse order to ensure that higher scores indicate higher literacy levels. In this study, Cronbach\u0026rsquo;s α was 0.87, and the questionnaire demonstrated good construct validity (KMO 0.91, Bartlett\u0026rsquo;s test p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using Stata 18.0 and R 4.3.2. The basic demographic characteristics of elderly individuals were summarized using means, standard deviations, frequencies, and proportions. Bar charts depicted the level of nutrition and health service support among elderly individuals with NRCDs and the residents\u0026rsquo; nutritional environmental health literacy levels. Correlations between variables were analyzed using the R \u0026ldquo;corrplot\u0026rdquo; function, and linear regression was employed to explore the impact of NHSS and scores across dimensions on the residents\u0026rsquo; LEHL. Subgroup analyses were also performed to examine the potential roles of demographic characteristics, disease status, and health indicators in the relationship between NHSS and the residents\u0026rsquo; LEHL. A significance level of α\u0026thinsp;=\u0026thinsp;0.05 was set for all tests, and a two-tailed approach was applied.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Basic demographic characteristics\u003c/h2\u003e \u003cp\u003eThe characteristics of the participants are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among the 752 elderly participants, approximately 53.6% were female, the average age was 69.60\u0026thinsp;\u0026plusmn;\u0026thinsp;6.58 years, and most of them were of Han ethnicity. The proportions of participants with an education level below junior high, living in urban areas, and earning less than 3,000 RMB per month were 85.1%, 71.4%, and 76.3%, respectively. Regarding health status, over half of the elderly participants reported a history of smoking or drinking, the majority had a normal BMI, and more than half were not suffering from NRDs.\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\u003eSample characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e349 (46.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e403 (53.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.60(6.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e713 (94.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e425 (56.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege degree or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e537 (71.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly household income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1000 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1001\u0026ndash;3000 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e386 (51.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3001\u0026ndash;5000 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5001 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol intake\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e414 (55.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent and former\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e338 (44.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e508 (67.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent and former\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e244 (32.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e498 (66.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125 (16.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity and overweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of chronic disease types\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno nutrition-related chronic diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e386 (51.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eone type of nutrition-related chronic diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211 (28.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emultiple nutrition-related chronic diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155 (20.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNutrition health service support\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e251 (33.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e259 (34.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e242 (32.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLEHLQ scores\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.33 (2.60)\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\u003e \u003cb\u003e3.2 Analysis of the differences in NHSS and LEHLQ scores among elderly individuals with varying numbers of NRCDs\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe LEHLQ scores showed statistically significant differences in distribution across different disease numbers (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the distribution of NHSS did not show statistically significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Analysis of the correlations among NHSS, LEHLQ, and NRCDs in elderly individuals\u003c/h2\u003e \u003cp\u003ePositive correlations with the LEHLQ scores were observed for the number of NRCDs and NHSS scores (correlation\u0026thinsp;=\u0026thinsp;0.171, 0.324, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHeatmap showing Spearman\u0026rsquo;s correlations of LEHLQ scores with sociodemographic characteristics, disease status, and nutrition health service support. Negative correlations are depicted in blue, and positive correlations are shown in red. The intensity of the colors represents the strength of the correlation, with dark shades indicating strong positive or negative correlations. Symbols \u003csup\u003e**\u003c/sup\u003e and \u003csup\u003e*\u003c/sup\u003e represent the correlation between two dietary food factors with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and \u0026lt;\u0026thinsp;0.05, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Linear regression analysis between NHSS and LEHLQ scores\u003c/h2\u003e \u003cp\u003eWith low NHSS as a reference, moderate NHSS was associated with a 0.67-point increase in LEHLQ (95% CI: 0.24\u0026ndash;1.11, p\u0026thinsp;=\u0026thinsp;0.002); High NHSS showed stronger benefits (β\u0026thinsp;=\u0026thinsp;1.21, 95% CI: 0.74\u0026ndash;1.68, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This dose-response relationship persisted after adjusting for sociodemographic and behavioural confounders (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Stratified analyses of NHSS subdimensions revealed differential associations with LEHLQ scores (Additional file 1): SES demonstrated the strongest positive association with LEHLQ scores, with the high-support group showing effect sizes 1.3-fold greater than FES and 2.5-fold greater than OES.\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\u003eThe associations between NHSS and LEHLQ scores.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCrude model \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 1 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 2 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95%Cl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ (95%Cl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ (95%Cl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle vs. Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.805(0 .365, 1.244)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.696(0.263, 1.128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.674(0.242, 1.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh vs. Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.500(1.053, 1.947)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.197(0.729, 1.665)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.208(0.738, 1.678)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003e \u003csup\u003ea\u003c/sup\u003e Crude model: no covariates were adjusted.\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e Model 1: age, sex, education level, residence, and monthly household income were adjusted.\u003c/p\u003e \u003cp\u003e \u003csup\u003ec\u003c/sup\u003e Model 2: additionally add alcohol intake, smoking history, BMI, and types of nutritional chronic diseases were adjusted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Subgroup analysis according to lifestyle, demographics, and health status\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, with low NHSS as a reference, a linear trend was observed between NHSS and LEHLQ scores across various groups: different sex, Han population, rural and urban residents, those with an income below 5000 RMB, smokers and non-smokers, drinkers and non-drinkers, normal BMI, older adults with or without NRCD, and older adults with primary school education or below.\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\u003eSubgroup analyses of the relationship between NHSS and LEHLQ scores.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eNutrition health service support (NHSS)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003emiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP for Interaction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.18\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.57\u0026ndash;2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.11 -1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.36\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.23\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.65\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-2.37-2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-4.58-7.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.02\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.22\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.10\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.31\u0026ndash;3.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly household income\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1000 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.40\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.98\u0026ndash;2.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1001\u0026ndash;3000 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.05-1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.42\u0026ndash;1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3001\u0026ndash;5000 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-1.01-1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.05-2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5001 RMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-2.02- 3.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-2.42-2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol intake\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.21\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.58\u0026ndash;1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent and former\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.21-1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.21\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking history\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.00\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.54\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent and former\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.05\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.14\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.16\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.58\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThinness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.98-1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.55-1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.10\u0026ndash;2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.07-2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of chronic disease types\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo nutrition-related chronic diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.21\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.79\u0026ndash;2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne type nutrition-related chronic diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.31-1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.36-1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple nutrition-related chronic diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.66-1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.25\u0026ndash;2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.27\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.63\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-0.02-1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.06\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-2.11- 2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-0.54-3.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege degree or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(-5.39 1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(-4.11 -2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis cross-sectional study involving 752 older adults in China found that the number of NRCDs and the NHSS were positively associated with LEHLQ scores. With low NHSS as a reference, both moderate and high NHSS levels were associated with increased LEHLQ scores among Chinese older adults. Furthermore, Stratified analyses revealed that SES demonstrated the strongest positive association with LEHLQ scores, with the high-support group showing effect sizes 1.3-fold greater than FES and 2.5-fold greater than OES. A linear trend was found for the NHSS and LEHLQ scores between different groups such as different sex, Han population, rural and urban residents, those with an income below 5000 RMB, smokers and non-smokers, drinkers and non-drinkers, normal BMI, older adults with or without NRCD, and older adults with primary school education or below. These findings are crucial for Chinese older adults, as LEHL is linked to their ability to cope with the complex health demands of modern society [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn China, chronic diseases affect over 180\u0026nbsp;million elderly individuals, with 75% of them having at least one such condition [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. As the incidence of NRCD escalates among the aging Chinese population, concern for these health issues arises. Our study found that individuals with a high burden of NRCD exhibit great LEHL. Elderly individuals with NRCD often opt for the Mediterranean diet, resulting in healthy eating habits and potentially enhancing their LEHL [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Therefore, their LEHL is enhanced. Owing to their health conditions, these older adults have become proactive in seeking health knowledge from various sources, including social, familial, and digital platforms [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and learning about these health issues to bolster their self-care capabilities and manage the progression of NRCDs. The pervasive issue of nutrition-related chronic diseases worldwide has, to a significant degree, heightened public health consciousness [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The Chinese government has also actively issued policy documents such as \"Healthy China 2030,\" aiming to promote the multidimensional dissemination of nutritional and health knowledge across society, families, and digital networks, thereby enhancing the LEHL of older adults in China.\u003c/p\u003e \u003cp\u003eThe extent of NHSS is directly linked to LEHL among China\u0026rsquo;s older adults. By imparting scientific nutritional health knowledge through these services, we can empower older adults to adopt healthy lifestyles [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Nutritional education is pivotal in preventing NRCDs and enhancing overall health [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In the social environment support dimension of environmental support, we found that FES significantly increased the LEHL of older adults in China. This finding is consistent with that of Nielsen et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], but contrasts with the results of Klinger et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], who reported that social support had minimal impact on mitigating low health literacy among older adults in Germany. These differences may be attributed to variations in regional and cultural contexts. In the dimension of family environment support, we also observed similar associations. A cross-sectional study found that family health among patients with chronic diseases had a significant impact on self-efficacy [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Family support not only helps build and strengthen patients\u0026rsquo; confidence in coping with illness but also facilitates the maintenance of health-promoting behaviours [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In the dimension of online environmental support, online nutrition education is an effective channel for disseminating educational content, particularly in grassroots areas where medical education resources are scarce [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. These resources cater to the nutritional education needs of patients with NRCDs. Survey data revealed that over 50% of patients with chronic diseases in China turn to online settings for nutritional health information [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This online quest for health information equips patients with NHSS and fosters self-efficacy in managing their health [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. It consequently contributes to an improved quality of life in their later years.\u003c/p\u003e \u003cp\u003eSubgroup analysis examining the correlation between NHSS and LEHLQ scores has uncovered their linear relationship, particularly within the Han ethnic group. Compared with the Han ethnic group, ethnic minority groups tend to exhibit lower health literacy levels [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The disparity in access and utilization of NHSS among ethnic minorities may be attributed to the uneven distribution of medical and educational resources [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In this research, ethnic minorities constituted a mere 5.2% of the participants, resulting in a substantial sample size disparity between Han ethnic and ethnic minorities. This imbalance is significant enough to potentially skew the accurate representation of nutritional literacy levels between these two groups. In addition, a significant linear trend between NHSS and LEHLQ scores was observed among Chinese elderly with monthly incomes below 5,000 RMB. This result suggests that individuals with low incomes are likely to face barriers to accessing adequate NHSS due to financial limitations, consequently affecting their LEHL [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. A study conducted in Iran supported this finding, indicating that individuals with sufficient monthly salaries make healthy food choices and possess high nutritional knowledge, thereby influencing their LEHL [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Moreover, a significant association between NHSS and LEHL was observed among individuals with normal body weight. Previous studies have found that underweight and obese older adults generally exhibit poorer physical function and overall health status compared to those with normal weight, which in turn negatively affects their quality of life in later years [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. This may be attributed to the possibility that individuals with a normal BMI tend to have higher overall health literacy [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Additionally, they may place greater emphasis on preventive nutritional service support and demonstrate higher responsiveness to such services. A significant association between NHSS and LEHL was also identified among individuals with primary school education or below. Numerous studies have demonstrated a strong positive correlation between educational level and health literacy [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. This association may stem from two primary factors: first, older adults with lower education levels are often at a disadvantage in terms of accessing, understanding, and evaluating health information [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]; second, education level is closely linked to socioeconomic status, and lower educational level is frequently associated with limited economic resources, which may hinder access to nutritional and health services [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor older adults in China, achieving a healthy later life is of vital importance. To attain this goal, they need to effectively utilize NHSS available within their social, family, and online environments to enhance their LEHL. This study contributes to the design of more targeted nutritional and health services, aiming to improve health literacy among the older adults. With improved LEHL, older adults in China may be better able to make independent choices regarding healthy diets and living environments, thereby delaying the onset of disability and reducing the caregiving burden on families and society.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, the cross-sectional design restricts the ability to infer causal relationships between NHSS and LEHL. Further cohort studies are needed to explore these associations in greater depth. Future research should adopt longitudinal designs, such as prospective cohort studies, to clarify the potential causal mechanisms among these variables. Second, although data were collected through face-to-face field surveys conducted by trained professionals in both home and community settings, the reliance on self-reported responses may have introduced recall bias. To reduce bias arising from subjective reporting and enhance the accuracy and reliability of the data, future studies could integrate objective assessment indicators. Finally, the study sample was limited to residents aged 60 and above in Chongqing, China, which may restrict the generalizability of the findings to older adults in other regions. To improve external validity, future research should include elderly populations from diverse regions across the country and conduct nationally representative surveys to verify the universality of the findings.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis cross-sectional study found that NRCDs and the NHSS were positively associated with LEHLQ scores. With low NHSS as a reference, both moderate and high NHSS levels were associated with increased LEHLQ scores among Chinese older adults. Furthermore, Stratified analyses revealed that SES demonstrated the strongest positive association with LEHLQ scores. The NHSS and LEHLQ scores exhibited a linear trend across different subgroups of older adults. Enhancing the LEHL of China\u0026rsquo;s elderly is essential for mitigating health disparities and effectively tackling the challenges posed by an aging population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was carried out in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Chongqing Medical University, 2023081, 7 November 2023. Informed consent was given by all participants before the beginning of the survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent for publication was obtained from all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to funding requirements but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOpen access funding provided by High-Quality Development Research on Health Management Service Industry in Chongqing. This research was funded by Think Tank Research Project of Chongqing Association for Science and Technology in 2023, grant number: 2023KXKT12.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization:\u0026nbsp;Ya Shi,\u0026nbsp;Yu Zhang\u0026nbsp;; Methodology:\u0026nbsp;Ya Shi,\u0026nbsp;Yu Zhang\u0026nbsp;; Formal analysis and investigation:\u0026nbsp;Ya Shi, Yu Zhang and Laixi Zhang\u0026nbsp;; Writing - original draft preparation:\u0026nbsp;Ya Shi, Yu Zhang\u0026nbsp;; Writing - review and editing:\u0026nbsp;Laixi Zhang, Huiyi Zhang, Manoj Sharma, Lei Zhang and Yong Zhao\u0026nbsp;; Funding acquisition:Yong Zhao\u0026nbsp;; Supervision:Yong Zhao\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our sincere gratitude to all the interviewees and researchers for their valuable contributions to the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSchool of Public Health, Chongqing Medical University, Chongqing 400016, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYa Shi, Yu Zhang,\u0026nbsp;Huiyi Zhang and Yong Zhao\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Center for Medicine and Social Development, Chongqing Medical University, Chongqing 400016, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYa Shi, Yu Zhang,\u0026nbsp;Huiyi Zhang and Yong Zhao\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Center for Public Health Security, Chongqing Medical University, Chongqing 400016, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYa Shi, Yu Zhang,\u0026nbsp;Huiyi Zhang and Yong Zhao\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNutrition Innovation Platform-Sichuan and Chongqing, School of Public Health, Chongqing Medical University, Chongqing 400016, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYa Shi, Yu Zhang,\u0026nbsp;Huiyi Zhang and Yong Zhao\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHealth Management Centre, First Affiliated Hospital of Army Medical University, Chongqing 400038, China\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLaixi Zhang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepartment of Social and Behavioral Health, School of Public Health, University of Nevada, Las Vegas (UNLV), Las Vegas, NV, United States\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eManoj Sharma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepartment of Internal Medicine, Kirk Kerkorian School of Medicine, University of Nevada, Las Vegas (UNLV), Las Vegas, NV, United States\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eManoj Sharma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChina-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, Shanxi, 710061, PR China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLei Zhang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArtificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLei Zhang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSchool of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLei Zhang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChongqing Key Laboratory of Child Nutrition and Health, Children’s Hospital of Chongqing Medical University, Chongqing 400014, China\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYong Zhao\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal burden. of 87 risk factors in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the global burden of disease study 2019. 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Front Psychol. 2023 2023;14:1202014.\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-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"nutrition-related chronic diseases, social cognitive theory, nutrition health service support, level of environmental and health literacy, older adults","lastPublishedDoi":"10.21203/rs.3.rs-6653933/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6653933/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eGiven the high prevalence of nutrition-related chronic diseases (NRCDs) among the elderly population in China, this study explores the association between digital and non-digital health Nutrition Health Service Support (NHSS) and the Level of Environmental and Health Literacy (LEHL) to foster healthy aging.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this cross-sectional study, the demographic profiles of 752 Chinese older adults were detailed and analyzed, and the level of NHSS and LEHL was determined for those with NRCDs. The linear regression was employed to explore the impact of NHSS and scores across dimensions on the residents\u0026rsquo; LEHL. Subgroup analyses were also performed to examine the potential roles of demographic characteristics, disease status, and health indicators in the association between NHSS and LEHL.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 752 elderly participants, 48.7% were affected by one or more NRCDs. This cross-sectional study involving 752 older adults in China found that the number of NRCDs and the NHSS were positively associated with LEHLQ scores. With low NHSS as a reference, moderate and high NHSS levels were associated with increased LEHLQ scores among Chinese older adults. Furthermore, Stratified analyses revealed that Social environmental support (SES) and Online environmental support (OES)demonstrated the strongest positive association with LEHLQ scores, with the high-support group showing effect sizes 1.3-fold greater than Family environmental support (FES) and 2.5-fold greater than Online environmental support (OES). A linear trend was found for the NHSS and LEHLQ scores between different groups such as different sex, Han population, rural and urban residents, those with an income below 5000 RMB, smokers and non-smokers, drinkers and non-drinkers, normal BMI, older adults with or without NRCD, and older adults with primary school education or below.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eImproving LEHL among Chinese older adults is essential for narrowing health disparities and promoting healthy aging. To address the digital disadvantage in this population, strengthening the NHSS system is critical to ensure equitable access to nutrition-related information and services\u003c/p\u003e","manuscriptTitle":"Digital Disadvantage in Nutrition Services: How Environmental Literacy Gaps Limit Elderly Chinese with Chronic Diseases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 14:56:11","doi":"10.21203/rs.3.rs-6653933/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-07-16T16:43:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123622358223711267853031907397537769782","date":"2025-07-06T03:24:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-04T19:46:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260111929351146814607644387556796422883","date":"2025-07-02T22:53:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"225147165310475612271987370766463411844","date":"2025-06-27T20:47:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-13T07:10:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-23T07:58:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-15T10:17:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-15T07:47:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-05-15T07:46:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fb67c772-86e2-475a-9c8b-7cf98a314bc0","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-17T14:56:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-17 14:56:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6653933","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6653933","identity":"rs-6653933","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}