Abstract
Background
Malnutrition is a prevalent and serious problem among community-dwelling older adults and is associated with adverse health outcomes, including functional limitations, reduced quality of life, and increased mortality. Awareness of malnutrition and its early signs is essential for timely recognition and prevention. However, quantitative evidence on malnutrition awareness and its social factors among older adults remains limited. This study aimed to determine the degree of malnutrition awareness in community-dwelling older adults and to explore its association with social, demographic, and health factors.
Methods
A cross-sectional study was conducted among community-dwelling adults aged ≥ 65 years in the Northern Netherlands. Participants completed a paper or digital questionnaire. The questionnaire consisted of the MAS and additional items assessing social, demographic and health factors. Malnutrition awareness was assessed with the Malnutrition Awareness Scale (MAS; scale from 0 to 22). Associations with social, demographic, and health factors were examined using univariate and multivariate linear regression analyses.
Results
A total of 216 participants were included (57% female). The median [interquartile range] MAS score was 16 [14–18], indicating that approximately half of participants demonstrated relatively high malnutrition awareness. In multivariate analyses, a larger social network (B = 0.12; p = 0.004) and female gender (B = 0.99; p = 0.036) were significantly associated with higher MAS scores.
Conclusion
The majority of the community-dwelling older adults scored in the upper range of the scale of malnutrition awareness. Larger social networks and female gender are associated with higher malnutrition awareness scores, underscoring the importance of social context in shaping malnutrition awareness. Public health initiatives are advised to focus on strengthening social networks and also to increase knowledge and cues about malnutrition, as these factors may be associated to the degree of malnutrition awareness.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s40795-026-01407-z.
Keywords: Malnutrition, Older adults, Aging, Social factors, Social network, Awareness, Health literacy, MAS
Introduction
Malnutrition is an important problem among older adults. In Europe, the prevalence of risk of malnutrition is reported to be 9% among older adults living at home, and 28% among hospitalized older adults [1]. Older adults are at increased risk of malnutrition due to age-related changes, such as unfavorable changes in body composition (e.g., loss of muscle mass), oral health, and appetite [2]. Malnutrition is associated with severe health problems and adverse outcomes for those affected, such as functional limitations, physical complications, longer hospital stays, reduced quality of life, and increased mortality [1, 3–5]. Besides having consequences on the personal level, malnutrition also has impact on societal level, for example by contributing to pressure on healthcare systems. In addition, malnourished older adults often require more medical care, such as nutritional treatment or hospitalization, which contributes to higher healthcare costs [6].
The growing prevalence of malnutrition among older adults together with demographic changes, such as an aging population and governmental policies that encourage older adults to live independently as long as possible, has increased the urgency of addressing malnutrition in this population [4, 7–10]. Many older adults fail to recognize their own weight loss or do not perceive it as a concern [11, 12]. In addition, qualitative studies suggest older adults are often unaware of the signs of malnutrition [4, 13–15]. The lack of malnutrition awareness among older adults combined with the fact that not all older adults have regular contact with healthcare professionals [16], may result in late recognition of risk factors for and signs of malnutrition. Thus, appropriate nutritional interventions may be delayed. Despite the negative health impact and high prevalence of malnutrition, quantitative insights regarding malnutrition awareness among older adults remain scarce. Quantification of malnutrition awareness may enable tailored policies for at risk groups and provide tools to evaluate effectiveness of public health interventions.
The Malnutrition Awareness Scale (MAS) was recently developed to assess malnutrition awareness in older adults [17]. More insight is needed into the role of social factors, such as socio-economic status, social network, and health literacy, since these factors are associated with risk factors for malnutrition. For instance, studies show that the likelihood of malnutrition is higher among socially isolated older adults, that social contacts positively influence nutritional intake, and that social contacts play an important role in promoting healthy eating habits [18, 19]. A low income and a lower education level have also been associated with an increased risk of malnutrition [6, 8, 20, 21]. However, up till now it is unclear to which extent social factors and malnutrition awareness relate to each other. Considering social factors, demographic factors such as age and gender, and health factors such as the general perceived health, body mass index (BMI), alcohol consumption, and smoking, may provide a more holistic understanding of malnutrition awareness and how it could be improved. Therefore, in this study we aimed to determine the degree of malnutrition awareness among community-dwelling older adults and to explore the association of malnutrition awareness with social, demographic and health factors.
Methods
Participants
The study targeted community dwelling adults aged ≥ 65 years in the Northern Netherlands. The following inclusion criteria were applied: (1) aged ≥ 65 years, (2) living independently at home, (3) living in the Northern Netherlands, and (4) being able to complete the questionnaire in Dutch.
Data were collected through both paper and digital questionnaires to ensure accessibility for a broad range of participants. Given the Netherlands’ high internet coverage (99% in 2024) [22], digital distribution was suitable, but paper versions were offered to older adults with lower digital literacy. Paper questionnaires were distributed through purposeful sampling and convenience, i.e., participants were selected based on their presence at locations that were selected for recruitment of participants, or because participants were easily accessible. The researchers have carefully considered the locations from which participants were recruited, to obtain a sample as varied as possible. The recruitment took place at various social initiatives and community centers in Groningen, such as the Salvation Army and a neighborhood initiative that cleans up litter. In addition, contacts of the research group members were approached to complete the questionnaire. Although a random sample was not drawn, the advantage of the purposeful and convenience sampling method is that a relatively large sample was obtained in a short period of data collection.
The digital questionnaire has been distributed in various Facebook groups in the Dutch provinces of Friesland, Groningen and Drenthe, elderly associations in the Northern Netherlands, and singing choirs in Drenthe and Friesland. Extensive details about the digitally completed questionnaires are provided in Additional file 1.
Procedure
Participants were recruited between April 12, 2024, and 22 July, 2024. Participants received written study information and provided informed consent. The Hanze Ethical Advisory Committee (HEAC) approved the study (reference heac.T2024.020). The manuscript follows STROBE guidelines recommended by the EQUATOR Network [Additional file 2] [23].
The questionnaire included 43 items covering demographics, health status and behavior (including self-reported health, BMI, smoking, and alcohol use), social network, health literacy, and malnutrition awareness. Data were collected by the researchers and trained Nutrition and Dietetics students at the Hanze University of Applied Sciences.
Variables
Malnutrition awareness scale
Malnutrition awareness was assessed with the validated Malnutrition Awareness Scale (MAS), based on the Integrated Change (I-Change) model [17]. This model assumes that behavioral change occurs in phases. These phases describe a potential process through which individuals can move, from being unaware of their behavior to actually taking action to change health behavior [24]. The I-Change model distinguishes a pre-motivational phase (awareness), motivational phase (motivation), and post-motivational phase (action). The MAS focuses on the pre-motivational phase. In this phase four domains are assumed to be relevant to the degree in which people are aware, i.e., knowledge, risk perception, cues, and cognizance [24].
Knowledge was interpreted as understanding the factual information about malnutrition. Eight true or false statements were developed to test malnutrition knowledge. Examples of a true and false statement are: ‘Poor appetite increases the chance of unintentional weight loss’ and ‘It is normal to experience unintentional weight loss, when over 70 years old’. Participants were asked to indicate whether the statement was true, false or whether they did not know [17].
Cues refer to signals that a person perceives from the environment (external cues) as well as from own experiences (internal cues). Three items were developed referring to external cues and one item referring to internal cues. Examples of an external cue and an internal cue are ‘Do you know anyone in your environment who had to eat more due to unintentional weight loss?’ and ‘Would you notice if you did not eat enough for one or two weeks?’. Response options were yes and no [17].
Risk perception was interpreted as realizing the negative consequences of having malnutrition, being aware of important factors that could increase your risk of malnutrition, as well as considering it bad to have malnutrition symptoms. Examples of the six questionnaire items developed in this domain are: ‘Imagine not eating enough. How big would you estimate your chance that your muscles would weaken?’ (5-point response option ranging from very small to very big) and ‘How badly would you mind losing weight unintentionally?’ (5-point response option ranging from very bad to not bad at all) [17].
Cognizance, or being able to correctly estimate the extent to which one’s behavior corresponds to the desired behavior, has been positively associated with motivational factors such as attitude and self-efficacy, and is considered important for nutrition-related behavioral change. Examples of the five developed items are: ‘Do you make sure that you eat enough and healthy to stay healthy?’ and ‘Do you regularly weigh yourself to check if you are losing weight unintentionally?’ with response options yes and no [17].
In the questionnaire, awareness of the Global Leadership Initiative on Malnutrition (GLIM) criteria was integrated in the items [25]. Phenotypic criteria such as weight loss, low BMI, and reduced muscle mass were reframed as being “too thin” and “too skinny” or “to lose muscle” and “muscles would weaken”. Etiological factors such as reduced food intake and disease burden were referred to as “not eating enough” or “illness”. The MAS consists of 22 scored items and 1 priming item, with the total score ranging from 0 to 22. A higher score indicates better malnutrition awareness. The answering options include three types of answering categories: true, incorrect, or I don’t know; yes or no; or a 5-point Likert scale.
Social factors
Social networks were measured using the 6-item Lubben Social Network Scale (LSNS-6). The LSNS-6 is a brief instrument designed to measure social isolation among older adults, which assesses network size, density, and contact frequency. The scale includes three items on family ties and three on friendships, scored on a 6-point scale from none to nine or more. The total score ranges from 0 to 30, with scores ≤ 12 indicating social isolation [26].
Health literacy was assessed using three validated self-report screening items [27]. Responses were scored on a 5-point scale, ranging from never to always or from very much to not at all. The score ranges from 3 to 15, whereby higher scores indicate higher health literacy.
The level of education had seven response options. To improve interpretability, data of some variables were structured. Education level was operationalized as: (1) Lower education level: No education, primary school, primary or (pre-) vocational education; (2) Medium education level: secondary vocational education, higher general or preparatory scientific education; (3) Higher education level: higher vocational education or bachelor’s or master’s degree in scientific education. Income was assessed as below, average, or above the Dutch net average monthly income in 2024 (€2692) [28].
Co-variables
Co-variables included gender, age, general perceived health, height, weight, smoking behavior, and alcohol consumption. Gender was reported as male, female, or other/I don’t want to report. Age was reported in categories: 65–74 years; 75–84 years; 85–94 years, and 95 years or older. General perceived health was assessed with five response options: (1) very good; (2) good; (3) fair; (4) poor; (5) very poor. This variable was also structured into two categories: (1) (very) good and fair, and (2) (very) poor. Height was self-reported in centimeters (cm) and weight was self-reported in kilograms (kg). BMI was calculated using self-reported height and weight by dividing body weight by body height squared. BMI is divided into three categories: underweight (BMI < 22 kg/m2), normal weight (BMI 22–28 kg/m2) and overweight (BMI > 28 kg/m2) [25]. These cut-off values were established for persons aged 70 years and over. However, this study focuses on older adults aged ≥ 65 years. Due to privacy considerations, we have asked for age in broader categories, starting with 65–74 years. Therefore, it was not possible to distinguish between persons aged 65–69 years and persons aged 70 years. As result, we applied the BMI cut-off values of the GLIM [25] to the entire study population aged ≥ 65 years. Smoking behavior in the past year was reported as yes or no, and alcohol consumption was reported in number of alcoholic drinks consumed per week.
Statistical analyses
Descriptive statistics were used to provide an overview of the participant characteristics, presenting ordinal and dichotomous variables as absolute numbers and percentages. The distribution of the MAS score was assessed by visual inspection of a histogram and a QQ-plot. Continuous variables were reported as means with standard deviations (SD) when normally distributed, and as medians with interquartile ranges (IQR) when not normally distributed. After checking for a normal distribution, the median is the preferred measure for the results of the MAS questionnaire. Participants who completed at least 20 of the 22 MAS items were included in the analyses. Questionnaires with more than two missing MAS items were excluded. Data imputation was used only to a small number of questionnaire. For participants with one or two missing MAS items, data imputation was applied using a donor-based approach. Specifically, participants with similar total MAS score (based on completed items) were identified, and the most frequent response to the missing item among these participants was used for imputation. For example, if a participant had one missing answer and a total score of 14 for the remaining 21 items, responses from participants with a score of 14 or 15 were considered, and the most frequent answer was imputed. This approach was chosen to preserve the internal consistency of the MAS and to minimize distortion of individual total scores, while avoiding listwise deletion of otherwise valid cases. The scores on the four domains, which together form the MAS, were stratified for gender.
Items within each domain were dichotomized as true/yes/(very) big (1) or false/no/(very) small (0). The scoring procedure followed the method described by Visser et al. for the MAS [17]. For each domain, proportional scores were calculated representing the proportion of correct responses. To ensure comparability across domains with different numbers of items, these proportional scores were standardized, resulting in domain scores ranging from 0.00 (lowest possible score) to 1.00 (highest possible score). Differences between domains were assessed using Chi-square tests, as the analysis focused on comparing proportions of correct responses. After establishing an overall significant difference, pairwise Chi-square tests were conducted to identify which specific domains differed significantly from each other. After the Chi-square test confirmed a significant difference between the proportions of correct answers between domains, standardized residuals were analyzed to determine which domain scored more correct items. Positive values indicated higher than expected scores and negative values indicated lower than expected scores. These domain analyses were performed to explore whether specific components of malnutrition awareness may require greater emphasis in public health awareness strategies.
Univariate and multivariate linear regression analyses using enter modus, examined associations between MAS scores and gender, age category, three levels of educational level, income, general perceived health, BMI, smoking behavior, social network, and health literacy. Categorical variables with more than two categories (age, educational level, income, and BMI) were included as dummy variables, using the following reference categories: 65–74 years (age group), middle education (educational level), average income (income), and normal weight (BMI). This approach allows comparison between meaningful subgroups (e.g., low, medium, and high education levels) without assuming a linear relationship between categories, thereby improving interpretability of the regression coefficients. Binary variables (gender, general perceived health, and smoking behavior) were coded as 0/1 indicators, with male, (very) good, and non-smoking as reference categories respectively. Continuous variables (age, social network, health literacy, and alcohol consumption) were entered as continuous predictors. All variables were entered into the multivariate regression analyses simultaneously. Significance was set at p < 0.05, and regression coefficients (B) with 95% confidence intervals (CI 95%) were calculated. Model fit was assessed using the coefficient of determination (R²) and adjusted R² for the multivariable regression models. In addition, for each MAS domain, knowledge, cues, cognizance, and risk perception, four multivariate domain specific analyses were performed. These four domain specific analyses are performed on the complete sample of older adults. Therefore, a Bonferroni correction was used to correct for the multiple comparisons. If a p-value of 0.05 is considered statistically significant for a single analysis, the Bonferroni correction for four comparisons results in an adjusted significance threshold of p < 0.013 (0.05/4). Accordingly, CI 99% for B were calculated to reflect this corrected significance. All statistical analyses were performed with IBM SPSS version: 28.0.1.0.
Results
A total of 231 participants aged ≥ 65 years completed the questionnaire. Fifteen (6%) participants were excluded for not living in the Northern Netherlands or for having more than two missing MAS items. Hence, 216 participants were included in the analysis, of whom 210 (97%) completed all MAS items. Data imputation was used for 6 participants (3%) with two or less missing MAS items. Given the small proportion of imputed data, the impact on the study findings is expected to be minimal. The majority of participants, 122 (57%), were aged 65–74 years. Of the 216 participants, 123 identified as female and 92 as male, one participant did not report their gender. In total, 35 (16%) of the participants were identified as socially isolated. The descriptive characteristics of the participants are presented in Table 1.
Table 1.
Characteristics of community-dwelling older adults aged ≥ 65 years in theNorthern Netherlands
| Variables | n (%); mean (SD); median [IQR] | |
|---|---|---|
| Type of questionnaire (n=216) | Paper | 39 (18) |
| Digital | 177 (82) | |
| Gender (n=216) | Male | 92 (43) |
| Female | 123 (57) | |
| Other/Unknown | 1 (1) | |
| Age group (n=216) | 65-74 years | 122 (57) |
| 75-84 years | 85 (40) | |
| 85-94 years | 7 (3) | |
| 95 years or older | 2 (1) | |
| Highest educational attainment (n=216) | Lower | 35 (16) |
| Medium | 55 (26) | |
| Higher | 126 (58) | |
| Income (n=216) | Below average <€2692 | 99 (46) |
| Average €2692 | 18 (8) | |
| Above average >€2692 | 94 (44) | |
| Unknown | 5 (2) | |
| Social networka (mean (SD); n=216) | 17 (5.8) | |
| Health literacyb (median [IQR]; n=216) | 14 [13-15] | |
| Alcohol (glasses/week; median [IQR]; n=210) | 2 [0-6] | |
| General perceived health score (n=216)c | (Very) good and fair | 153 (71) |
| (Very) bad | 63 (29) | |
| Body mass index, kg/m2 (n=216) | <22 (underweight) | 40 (19) |
| 22-28 (normal weight) | 129 (60) | |
| >28 (overweight) | 46 (21) | |
| Unknown | 1 (1) | |
| Smoking (n=216) | Yes | 13 (6) |
| No | 203 (94) |
SD Standard deviation, IQR Interquartile range
a Lubben Social Network Scale. Score can range from 0-30. Score ≤12: participants are socially isolated [26]
b Scores can range from 3 to 15. A higher score indicates a higher level of health literacy
c General perceived health dichotomized into 1) (very) good and fair and 2) (very) bad
MAS characteristics
The overall median MAS score was 16 (IQR: 14–18), ranging from 5 (n = 1) to 22 (n = 1). Table 2 presents characteristics of the MAS scores and percentage of correctly answered items per domain. The cognizance domain had the highest percentage of correctly answered questions.
Table 2.
Median total and domain-specific Malnutrition Awareness Scale (MAS) scores by gender (females n=123, males n=92)
| MAS component | Median | IQR | Percentage of correctly answered items* | |
|---|---|---|---|---|
| Total MAS (22 items) | Total | 16 | 14-18 | |
| Female | 17 | 14-18 | ||
| Male | 16 | 13-18 | ||
| Knowledge (8 items) | Total | 6 | 5-7 | 21 |
| Female | 7 | 6-8 | 26 | |
| Male | 6 | 5-7 | 13 | |
| Cues (3 items) | Total | 2 | 1-2 | 14 |
| Female | 2 | 1-2 | 18 | |
| Male | 1 | 1-2 | 10 | |
| Risk perception (6 items) | Total | 4 | 3-5 | 6 |
| Female | 4 | 3-5 | 7 | |
| Male | 4 | 3-5 | 3 | |
| Cognizance (5 items) | Total | 5 | 4-5 | 50 |
| Female | 4 | 4-5 | 47 | |
| Male | 5 | 4-5 | 54 |
*Percentage of correctly answered items within each MAS domain
Table 3 shows differences between proportional domain scores. Significant differences were observed between risk perception and the other domains, as well as between cognizance and cues.
Table 3.
Pairwise comparisons between proportional Malnutrition Awareness Scale (MAS) domain scores
| Knowledge | Knowledge | Cues | Risk perception | Cognizance |
|---|---|---|---|---|
| Cues | 0.133 | |||
| Risk perception | 0.001* | 0.006* | ||
| Cognizance | 0.351 | 0.008* | 0.001* |
Proportional scores represent the proportion of correctly answered items within each domain
P-values were obtained using pairwise Chi-square tests
*Significance set at p<0.05
The domain analyses showed that higher risk perception was consistently associated with higher knowledge and cognizance scores, whereas lower cues scores were associated with lower risk perception. The strongest overall association was observed between risk perception and cognizance (χ²=23.58; p < 0.001) [Additional file 3].
Analyses of social factors and co-variables potentially associated with the MAS
The results of the univariate and multivariate linear regression analyses are presented in Table 4. In the univariate analyses, several factors were significantly associated with the MAS score. A lower education level was associated with lower MAS scores compared to both middle (p = 0.037) and higher education levels (p = 0.008). An income below average was associated with lower MAS scores compared to an average income (p = 0.012). Higher general perceived health was associated with higher MAS scores (p = 0.018). BMI was categorized into three groups (low, normal, and high). Participants with a high BMI had significantly lower MAS scores compared with those with a low BMI (p = 0.002) and those with a normal BMI (p = 0.033). These associations were not significant in the multivariate analysis. In the multivariate analysis, social network (p = 0.004) and gender (p = 0.036) remained significantly associated with the MAS score. Every increase of eight people in the social network corresponded to a one-point rise in MAS score. Women scored on average one point higher on the MAS (p = 0.036) than men. The association between social network and MAS did not differ by gender and was significant in both females (p = 0.002) and males (p = 0.049). The results of the analysis stratified for gender are presented in Additional file 4.
Table 4.
Linear regression analyses of associations between demographic, social, and health variables andMAS scores (n=216)
| Univariate analysis | Multivariate analysis | |||
|---|---|---|---|---|
| Variables | B (CI 95%) | p-value | B (CI 95%) | p-value |
| Gender (reference=male) | 1.01 (0.14-1.88) | 0.023* | 0.99 (0.07-1.90) | 0.036* |
| Age group (65-74 years) | ||||
| 75-84 years | -0.76 (-1.66-0.14) | 0.096 | -0.14 (-1.01-0.79) | 0.771 |
| 85-94 years | 1.73 (-0.73-4.20) | 0.168 | 1.47 (-1.13-4.06) | 0.267 |
| 95 or 95+ years | -1.48 (-6.00-3.04) | 0.518 | -0.96 (-5.44-3.51) | 0.672 |
| Educational level | ||||
| Lower vs middle** | -1.46 (-2.82--0.09) | 0.037* | -1.32 (-2.72-0.09) | 0.066 |
| Higher vs middle** | 0.19 (-0.83-1.20) | 0.721 | -0.15 (-1.21-0.92) | 0.788 |
| Lower vs higher** | -1.64 (-2.84--0.44) | 0.008* | -1.17 (-2.50-0.16) | 0.085 |
| Income | ||||
| Below average vs average** | -2.08 (-3.69--0.46) | 0.012* | -1.33 (-2.97-0.31) | 0.110 |
| Average vs above average** | 1.23 (2.85--0.40) | 0.138 | 0.49 (2.15--1.18) | 0.564 |
| Below average vs above average** | -0.85 (-1.76--0.06) | 0.066 | -0.85 (-1.82-0.13) | 0.090 |
| General perceived health (reference=(very) good and fair)a | -1.14 (-2.08--0.19) | 0.018* | -0.67 (-1.67-0.33) | 0.188 |
| BMI | ||||
| Low vs normal** | 1.02 (-0.12-2.15) | 0.079 | 0.70 (-0.44-1.83) | 0.227 |
| Low vs high** | 2.19 (0.84-3.55) | 0.002* | 1.70 (-0.11-2.11) | 0.077 |
| Normal vs high** | 1.17 (0.10-2.25) | 0.033* | 1.00 (-0.14-3.48) | 0.071 |
| Smoking (reference=yes) | 0.86 (-0.96-2.69) | 0.352 | 1.67 (-0.14-3.48) | 0.071 |
| Alcohol (n glasses/week) | 0.00 (-0.00-0.00) | 0.799 | 0.00 (-0.00-0.00) | 0.640 |
| Social networkb | 0.15 (0.07-0.22) | 0.001* | 0.12 (0.04-0.19) | 0.004* |
| Health literacyc | 0.16 (-0.10-0.41) | 0.225 | 0.14 (-0.15-0.43) | 0.337 |
*Significance set at p<0.05; **reference group
B Regression coefficient, CI Confidence interval
Multivariable model: R² = 0.21; adjusted R² = 0.15
a General perceived health dichotomized into (very) good and fair versus
b Lubben Social Network Scale. Score can range from 0-30. A higher score indicates a stronger social network [26]
c Scores can range from 3 to 15. A higher score indicates a higher level of health literacy
Subanalyses of the MAS scores
In the multivariate analysis of the four MAS domains, female gender was significantly and positively associated with higher scores for knowledge (p = 0.007) and cues (p = 0.013) [Additional file 5]. In addition, a larger social network was significantly associated with higher scores for cues (p = 0.002).
Discussion
This study aimed to assess malnutrition awareness among community-dwelling older adults and its association with demographic, social, and health factors. The majority of participants scored in the upper range of the scale, with a median MAS score of 16 (IQR: 14–18) out of 22, indicating that half of the participants scored 16 or higher. Domain analysis revealed the highest awareness in the cognizance domain, where half of participants answered all items correctly, whereas the lowest scores were observed in the risk perception domain, with only 6% of the participants answering all items correctly. Social network was positively associated with MAS scores: every increase of eight people was associated with a one-point higher MAS score. A larger social network was also associated with higher scores in the cues domain. Gender was associated with MAS scores: women scored on average one point higher and showed higher scores in both knowledge and cues domains.
MAS scores in this study were somewhat higher than those reported in the study in which the MAS was developed and tested among 216 community-dwelling individuals aged > 60 years in the Netherlands (p = 0.004), where the mean MAS score was 14.8 (± 3.2) [17]. A possible explanation for this difference is that 58% of participants in the present study reported being highly educated, compared to 35% in the original MAS study [17]. Education level is a key indicator of socio-economic status and has been linked to greater health consciousness and more health-related knowledge [29, 30]. Specifically, a higher education level has been associated with better nutrition awareness in older adults [31–34], as well as increased awareness of healthy lifestyle behaviors in general [35]. It is unlikely that today’s older adults were extensively educated about nutrition during their formal schooling, since much of the current knowledge on healthy eating has only become widely available in recent decades. Nevertheless, the literature suggests that college and university graduates tend to show more interest in health and nutrition, and are more capable of acquiring and retaining new information compared to their less-educated peers [32]. This suggests that older adults with a higher education level may be more aware of malnutrition and its risks due to greater engagement with health-related information. This is in line with recent evidence from a cross-sectional study among Chinese older adults, which also identified education level as an important factor associated with malnutrition awareness, further supporting the association between educational attainment and malnutrition awareness across different cultural contexts [36]. However, unlike previous studies, the present study additionally examined domain-specific malnutrition awareness using the MAS. These domain analyses help identify which components of malnutrition awareness may require greater emphasis in public health awareness strategies.
Domain analysis showed highest awareness in the cognizance domain, with half of participants answering all items correctly. This may reflect a learning effect, as these were the final five items. In addition, the cognition domain items were relatively straightforward, such as ‘Do you skip a warm meal more than twice a week?‘. This may also explain the higher proportion of correct responses. The lowest scores were observed in the risk perception domain, with only one in 17 participants answering all items correctly. This suggests that older adults may underestimate their vulnerability to malnutrition, consistent with previous research showing that many older adults fail to recognize their own weight loss or do not perceive it as a concern [11, 12]. Low risk perception may hinder early recognition and prevention. When weight loss and reduced food intake are normalized as part of aging, the perceived urgency to seek professional support or to implement preventive measures may decrease [11].
The observed associations between cognizance and both cues and risk perception suggest that a better understanding of signs of malnutrition may support greater attention to cues and risk perception. According to the Health Belief Model, an individual’s likelihood of adopting health-promoting behaviors is influenced by perceived susceptibility and perceived severity, which are shaped by factual knowledge and exposure to cues to action [37]. Similarly, higher knowledge scores were associated with higher risk perception, suggesting that these constructs are related in this population. Since risk perception showed the lowest scores and was associated with both knowledge and cues, interventions should focus on strengthening of these domains to improve overall malnutrition awareness.
Importantly, a larger social network is significantly associated with higher malnutrition awareness. We observed that an increase of eight people in the social network was associated with a one-point higher MAS score. This finding is consistent with the theory of Umberson & Montez, which proposes that social relationships may be linked to health behaviors through mechanisms such as social influence, social control, and access to information [19]. Through these mechanisms, individuals may be exposed to information that contributes to health knowledge and awareness of malnutrition risks [19]. This is further supported by the positive association between social network and the cues domain, suggesting that more frequent social interactions may be associated with greater exposure to cues related to nutrition and health. Similarly, findings from a recent Chinese study among older adults suggest that social and living-related factors, such as cohabitation status, are associated with malnutrition awareness, highlighting the relevance of social context in shaping awareness across different populations [36]. Previous studies have additionally shown that social support and social engagement are associated with healthier dietary behaviors and improved nutritional outcomes in older adults [38]. This may partly explain the observed association between larger social networks and higher MAS scores in the present study, as social interactions can facilitate the exchange of health-related information and reinforce awareness of nutrition-related risks. Since cues refer to external environmental signals [37], it is plausible that individuals with larger social networks have more opportunities to encounter such cues, which may contribute to higher scores in this domain.
Female gender was associated with higher scores in the knowledge and cues domains. This gender difference may reflect broader patterns in health information behavior, where women show greater engagement with health-related information compared to men. A Finnish population-based study on health information behavior found that men are generally less inclined and motivated to seek health-related information, while women show greater interest and are more proactive and attentive to global health issues and daily health choices [39]. Women also receive more informal health information from their networks [39].
In the present study, education and income were significantly associated with MAS scores in univariate analysis; however, these associations were not significant in the multivariate model. This may be due to interactions with other variables. For example, higher education may be associated with larger social networks [40], which in turn may be associated with greater exposure to health related information an higher MAS scores [19, 41]. Although previous research has shown that socio-economic status, including education and income, correlates with greater health awareness [29, 30], the present findings suggest that this relationship may diminish when broader social variables are taken into account.
Strengths and limitations
This is the first study using the MAS to examine the association between social factors and malnutrition awareness in older adults. A strength is the low amount of missing data, as 97% completed all MAS items, indicating high compliance. Thus, there was limited need for data imputation, which is unlikely to have influenced the study findings. Moreover, the data were collected both on paper and digitally, increasing the likelihood of participation among both digitally skilled older adults and those with limited digital skills. If the data collection had been exclusively digital, older adults with limited digital skills may have been less likely to participate. By using both methods, the study population may be more representative of the entire older adult population.
This study also has some limitations. Firstly, participants were recruited through purposeful and convenience sampling, which increases the risk of selection bias. The sample included a relatively high proportion of highly educated and digitally reachable participants, which may have positively influenced results, although this may not be representative of the general population of community-dwelling older adults. The higher proportion of highly educated participants may have resulted in the higher MAS scores, as they are more aware of malnutrition [31–34]. Although efforts were made to recruit a diverse sample by including both paper and digital questionnaires and by approaching participants in various community settings, certain groups may still be underrepresented. For example, older adults with digital skills who were able to complete the digital questionnaire may be overrepresented, while those who are less digitally skilled or less mobile and therefore less likely to visit a community center or complete the questionnaire digitally may be underrepresented. Therefore, the findings should be interpreted with caution, and generalizability to the broader population of older adults may be limited. Secondly, not all social variables, such as cultural norms and living conditions, were included due to questionnaire length. Thirdly, the health literacy instrument used may have lacked sensitivity, as the median score was very high. The instrument was originally developed for clinical patients and mainly focused on understanding hospital-related information, which may have been less applicable for community-dwelling older adults in this study, as they are not typically patients and may not interact with hospital information or completing medical forms. Further research among community dwelling older adults could consider alternative instruments, such as the European Health Literacy Survey Questionnaire (HLS-EU-Q) [42] or the Health Literacy Questionnaire (HLQ) [43], which were developed for the general population rather than clinical population. Consequently, MAS scores in the present study may have been higher than would be expected in the broader older adult population.
Implications for research and practice
The findings of the present study may contribute to the development of policy measures aimed at increasing malnutrition awareness among older adults. Given the high prevalence of malnutrition, its negative health effects, and increased healthcare costs [3], early recognition and prevention are essential [4, 17, 20]. Early recognition and prevention may contribute to improved nutritional status and may help reduce health complications and additional healthcare expenses [3, 4, 17, 20]. Higher malnutrition awareness may also be associated with greater engagement in preventive behaviors and timely help-seeking. Targeted interventions may therefore benefit from focusing on knowledge and cue recognition, as these domains were associated with risk perception and overall awareness in the present study. Strengthening social networks may also be relevant, as larger networks were associated with greater exposure to health cues and higher MAS scores [19]. These findings also align with the social network theory which suggests that social networks influence health through mechanisms such as social support, social influence, social engagement, and access to resources. These mechanisms shape health-related behaviors, such as dietary habits, which in turn influence the risk of malnutrition [39]. Social networks can be strengthened through community programs and social activities that foster connection [44–46]. Further research into social factors and malnutrition awareness can help design interventions that reach not only individuals, but also the broader social context in which they live. Encouraging community participation and support networks may contribute to earlier recognition and prevention of malnutrition, ultimately improving health outcomes and reducing healthcare costs.
Conclusion
This study provides insights into the degree of malnutrition awareness among older adults aged ≥ 65 years in the Northern Netherlands. With a median MAS score of 16 out of 22, the majority of participants had scores in the upper range of the scale. Malnutrition awareness is higher among women and individuals with larger social networks. These findings suggest that demographic and social factors are associated with malnutrition awareness in older adults and may be relevant to consider in public health strategies. Interventions targeting social networks and knowledge may also be relevant, given their observed associations with malnutrition awareness.
Supplementary Information
Additional file 1: Extensive details about the digital questionnaires.
Additional file 2: Reporting checklist for cross sectional study.
Additional file 3: Standardized residuals (±1.96) for associations between cognizance, risk perception, cues, and knowledge domains of the Malnutrition Awareness Scale (MAS).
Additional file 4: Univariate analysis between social network and Malnutrition Awareness Scale (MAS) score, stratified for gender.
Additional file 5: Multivariate analyses of variables potentially associated with the Malnutrition Awareness Scale (MAS) domains.
Acknowledgements
The authors would like to thank the older volunteers who participated in our studies, Nutrition and Dietetics students Elma de Wolf and Quirine Beijers of the Hanze University of Applied Sciences Groningen and the staff of the Research Group Healthy Ageing, Allied Health Care and Nursing of the Hanze University of Applied Sciences Groningen who contributed to the data collection.
Authors’ contributions
The research idea and design were developed by LH, HJW and MS. LH and MS contributed to data collection. The statistical analysis was the responsibility of LH with support from MS. LH wrote the manuscript draft. MS and HJW critically reviewed the manuscript. LH, MS, and HJW revised the manuscript. All authors contributed to the article and approved the submitted version.
Funding
This research received no external funding.
Data availability
The dataset generated and analyzed during the current study is available upon reasonable request.
Declarations
Ethics approval and consent to participate
All participants provided written informed consent prior to their participation. The Hanze Ethical Advisory Committee (HEAC) approved the study protocol (reference heac.T2024.020). Participation was voluntary, and participants were informed about the study’s purpose and procedures. They were assured of the confidentiality of their data and their right to withdraw from the study at any time without any consequences.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Leij-Halfwerk S, Verwijs MH, Van Houdt S, Borkent JW, Guaitoli PR, Pelgrim T, Heymans MW, Power L, Visser M, Corish C, De van Der Schueren MA. Prevalence of protein-energy malnutrition risk in European older adults in community, residential and hospital settings, according to 22 malnutrition screening tools validated for use in adults ≥ 65 years. Maturitas. 2019;126:80–9. 10.1016/j.maturitas.2019.05.006. [DOI] [PubMed] [Google Scholar]
- 2.National Institute on Aging (NIA). (2020). The National Institute on Aging: Strategic Directions for Research, 2020–2025. In https://www.nia.nih.gov/about/aging-strategic-directions-research. Geraadpleegd op 19 maart 2024, van https://www.nia.nih.gov/sites/default/files/2020-05/nia-strategic-directions-2020-2025.pdf.
- 3.Van der Pols-Vijlbrief R, Wijnhoven HAH, Visser M. Perspectives on the causes of undernutrition of community-dwelling Older Adults: a Qualitative study. The J Nutr Health Aging. 2017;21(10):1200–9. 10.1007/s12603-017-0872-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Visser M, Volkert D, Corish C, Geisler C, De Groot LCPGM, Cruz-Jentoft AJ, Lohrmann C, O’Connor EM, Schindler K, De Van Der Schueren MA. Tackling the increasing problem of malnutrition in older persons: The Malnutrition in the Elderly (MANUEL) Knowledge Hub. Nutr Bull. 2017;42(2):178–86. [Google Scholar]
- 5.Zügül Y, Van Rossum C, Visser M. Prevalence of Undernutrition in Community-Dwelling Older Adults in The Netherlands: Application of the SNAQ65 + Screening Tool and GLIM Consensus Criteria. Nutrients. 2023;15(18):3917. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Donini LM, Scardella P, Piombo L, Neri B, Asprino R, Proietti A, Carcaterra S, Cava E, Cataldi S, Cucinotta D, Di Bella G, Barbagallo M, Morrone A. Malnutrition in elderly: social and economic determinants. The J Nutr Health Aging. 2013;17(1):9–15. 10.1007/s12603-012-0374-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.De van Der Schueren MA, Jager-Wittenaar H. Malnutrition risk screening: New insights in a new era. Clin Nutr. 2022;41(10):2163–8. [DOI] [PubMed] [Google Scholar]
- 8.Patil DJ, Shindhe MM. Effect of socio-economic, demographic and environmental factors on nutritional status of elderly: a cross sectional study. Int J Community Med Public Health. 2019;6(8):3483. 10.18203/2394-6040.ijcmph20193476. [DOI] [Google Scholar]
- 9.Jiang W, Zhao Y, Wu X, Du Y, Zhou W. Health inequalities of global protein-energy malnutrition from 1990 to 2019 and forecast prevalence for 2044: data from the Global Burden of Disease Study 2019. Public Health. 2023;225:102–9. 10.1016/j.puhe.2023.10.003. [DOI] [PubMed] [Google Scholar]
- 10.Liang, S., Xi, S., Liu, J., Tang, G., Zhang, W., Guo, X., Yang, C., Zhang, C., & Cai, G. (2025). Global burden and cross-country inequalities of nutritional deficiencies in adults aged 65 years and older, 1990–2021: population-based study using the GBD 2021. BMC Geriatrics, 25(1). 10.1186/s12877-025-05728-9. [DOI] [PMC free article] [PubMed]
- 11.Avgerinou C, Bhanu C, Walters K, Croker H, Liljas A, Rea J, Bauernfreund Y, Kirby-Barr M, Hopkins J, Appleton A, Kharicha K. Exploring the Views and Dietary Practices of Older People at Risk of Malnutrition and Their Carers: A Qualitative Study. Nutrients. 2019;11(6):1281. 10.3390/nu11061281. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Reynolds CME, Castro PD, Geraghty AA, Browne S, Bardon LA, Corish CA. It takes a village’: a qualitative study on malnutrition and oral nutritional supplements with older adults in Ireland. Eur J Pub Health. 2021;31(6):1284–90. 10.1093/eurpub/ckab099. [DOI] [PubMed] [Google Scholar]
- 13.Castro PD, Reynolds C, Kennelly S, Geraghty AA, Finnigan K, McCullagh L, Gibney ER, Perrotta C, Corish C. An investigation of community-dwelling older adults’ opinions about their nutritional needs and risk of malnutrition; a scoping review. Clin Nutr. 2021;40(5):2936–45. 10.1016/j.clnu.2020.12.024. [DOI] [PubMed] [Google Scholar]
- 14.Craven D, Lovell GP, Pelly FE, Isenring E. Community-Living older adults’ perceptions of body weight, signs of malnutrition and sources of information: A descriptive analysis of survey data. J Nutr Health Aging. 2017;22(3):393–9. 10.1007/s12603-017-0942-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Linschooten JO, Verwijs MH, Beelen J, De Van Der Schueren M, Roodenburg AJC. Low awareness of community-dwelling older adults on the importance of dietary protein: new insights from four qualitative studies. J Nutritional Sci. 2021;10. 10.1017/jns.2021.92. [DOI] [PMC free article] [PubMed]
- 16.Leyva B, Taber JM, Trivedi AN. Medical care avoidance among older adults. J Appl Gerontol. 2017;39(1):74–85. 10.1177/0733464817747415. [DOI] [PubMed] [Google Scholar]
- 17.Visser M, Sealy MJ, Leistra E, Naumann E, De van der Schueren MA, Jager-Wittenaar H. The Malnutrition Awareness Scale for community-dwelling older adults: Development and psychometric properties. Clin Nutr. 2024;43(2):446–52. 10.1016/j.clnu.2023.12.023. [DOI] [PubMed] [Google Scholar]
- 18.Boulos C, Salameh P, Barberger-Gateau. Social isolation and risk for malnutrition among older people. Geriatr Gerontol Int. 2017;17:286–94. 10.1111/ggi.12711. [DOI] [PubMed] [Google Scholar]
- 19.Umberson D, Montez JK. Social Relationships and Health: A Flashpoint for Health Policy. J Health Soc Behav. 2010;51(1suppl):S54–66. 10.1177/0022146510383501. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Krzymińska-Siemaszko R, Deskur-Śmielecka E, Kaluźniak-Szymanowska A, Kaczmarek B, Kujawska-Danecka H, Klich-Rączka A, Mossakowska M, Małgorzewicz S, B. Dworak L, Kostka T, Chudek J, ‐ Wieczorowska-Tobis K. Socioeconomic risk factors of poor nutritional status in Polish elderly population: The results of PolSenior2 study. Nutrients. 2021;13(12). 10.3390/nu13124388. [DOI] [PMC free article] [PubMed]
- 21.Nazri NSM, Vanoh D, Leng SK. Malnutrition, low diet quality and its risk factors among older adults with low socio-economic status: a scoping review. Nutr Res Rev. 2020b;34(1):107–16. 10.1017/s0954422420000189. [DOI] [PubMed] [Google Scholar]
- 22.Statistics | Eurostat. (17-12-2024). https://ec.europa.eu/eurostat/databrowser/view/ISOC_CI_IN_H/default/table?lang=en
- 23.Von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies. PLoS Med. 2007;4(10):e296. 10.1371/journal.pmed.0040296. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Kasten S, Van Osch L, Candel MJJM, De Vries H. The influence of pre-motivational factors on behavior via motivational factors: a test of the I-Change model. BMC Psychol. 2019;7(1). 10.1186/s40359-019-0283-2. [DOI] [PMC free article] [PubMed]
- 25.Cederholm T, Jensen G, Correia M, Gonzalez M, Fukushima R, Higashiguchi T, Baptista G, Barazzoni R, Blaauw R, Coats A, Crivelli A, Evans D, Gramlich L, Fuchs-Tarlovsky V, Keller H, Llido L, Malone A, Mogensen K, Morley J, Yu J. GLIM criteria for the diagnosis of malnutrition – A consensus report from the global clinical nutrition community. Clin Nutr. 2019;38(1):1–9. 10.1016/j.clnu.2018.08.002. [DOI] [PubMed] [Google Scholar]
- 26.Lubben JE, Blozik E, Gillmann G, Iliffe S, Von Renteln Kruse W, Beck JC, Stuck AE. Performance of an abbreviated version of the Lubben Social Network Scale among three European community-dwelling older adult populations. Gerontologist. 2006;46(4):503–13. 10.1093/geront/46.4.503. [DOI] [PubMed] [Google Scholar]
- 27.Chew, L. D., Bradley, K. A., & Boyko, E. J. Brief questions to identify patients with inadequate health literacy. Family Medicine. 2004;36(8):588–594. [PubMed]
- 28.De Graaf E. (2024, 8 december). Dit is wat de Nederlander gemiddeld verdient. Gulden Courant. https://www.guldencourant.nl/economie/dit-is-wat-de-nederlander-gemiddeld-verdient?utm_source=chatgpt.com
- 29.Colón-Ramos U, Kabagambe EK, Baylin A, Ascherio A, Campos H, Peterson KE. Socio-economic status and health awareness are associated with choice of cooking oil in Costa Rica. Public Health Nutr. 2007;10(11):1214–22. 10.1017/s1368980007699571. [DOI] [PubMed] [Google Scholar]
- 30.Wang J, Geng L. Effects of socioeconomic status on physical and psychological health: lifestyle as a mediator. Int J Environ Res Public Health. 2019;16(2):281. 10.3390/ijerph16020281. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.De Vriendt T, Matthys C, Verbeke W, Pynaert I, De Henauw S. Determinants of nutrition knowledge in young and middle-aged Belgian women and the association with their dietary behaviour. Appetite. 2009;52(3):788–92. 10.1016/j.appet.2009.02.014. [DOI] [PubMed] [Google Scholar]
- 32.Jeruszka-Bielak M, Kołłajtis-Dołowy A, Santoro A, Ostan R, Berendsen AM, Jennings A, Meunier N, Marseglia A, Caumon E, Gillings R, De Groot LCPGM, Franceschi C, Hieke S, Pietruszka B. Are nutrition-related knowledge and attitudes reflected in lifestyle and health among elderly people? A study across five European countries. Front Physiol. 2018;9. 10.3389/fphys.2018.00994. [DOI] [PMC free article] [PubMed]
- 33.Lin W, Lee Y. Nutrition knowledge, attitudes and dietary restriction behaviour of Taiwanese elderly. Asia Pac J Clin Nutr. 2005;14(3):221–9. [PubMed] [Google Scholar]
- 34.Sorasun R, Serena S. Correlation between nutritional awareness and food consumption behavior of the elderly in Samut Prakarn Province. Thail Journal Public Health Epidemiology. 2023;15(2):55–63. 10.5897/jphe2022.1427. [DOI] [Google Scholar]
- 35.Taheri M, Mohammadi M, Paknia B, Mohammadbeigi A. Elderly awareness on healthy lifestyle during aging. Tropical Medicine Surgery. 2013;1(5). 10.4172/2329-9088.1000139. [DOI]
- 36.Li F, Zhang J, Wu D, Yu X, Chen X. Malnutrition awareness and its determinants among Chinese older adults: findings from a cross-sectional survey. Front Public Health. 2025;13:1638285. 10.3389/fpubh.2025.1638285. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Alyafei A, Easton-Carr R. (2024, 19 mei). The Health Belief Model of Behavior Change. StatPearls - NCBI Bookshelf. https://www.ncbi.nlm.nih.gov/books/NBK606120/ [PubMed]
- 38.Bardon LA, Corish CA, Lane M, Bizzaro MG, Villarroel KL, Clarke M, Power LC, Gibney ER, Castro PD. Ageing rate of older adults affects the factors associated with, and the determinants of malnutrition in the community: a systematic review and narrative synthesis. BMC Geriatr. 2021;21(1):676. 10.1186/s12877-021-02583-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Ek S. Gender differences in health information behaviour: a Finnish population-based survey. Health Promot Int. 2013;30(3):736–45. 10.1093/heapro/dat063. [DOI] [PubMed] [Google Scholar]
- 40.Völker B, Hofstra B, Corten R, Van Tubergen F. Who’s in your extended network? Analysing the size and homogeneity of acquaintanceship networks in the Netherlands. Social Networks. 2025;83:173–85. 10.1016/j.socnet.2025.05.007. [DOI] [Google Scholar]
- 41.Berkman LF, Glass T, Brissette I, Seeman TE. From social integration to health: Durkheim in the new millennium. Social Science Medicine. 2000;51(6):843–57. 10.1016/s0277-9536(00)00065-4. [DOI] [PubMed] [Google Scholar]
- 42.Fullam J, Doyle G, Sorensen K, Van den Broucke S, Kondilis B. The development and validation of the European health literacy survey (hls.eu). Ir J Med Sci. 2011;108:225–6. [Google Scholar]
- 43.Osborne RH, Batterham RW, Elsworth GR, Hawkins M, Buchbinder R. The grounded psychometric development and initial validation of the Health Literacy Questionnaire (HLQ). BMC Public Health. 2013;13:658. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Coll-Planas L, Del Valle Gómez G, Bonilla P, Masat T, Puig T, Monteserin R. Promoting social capital to alleviate loneliness and improve health among older people in Spain. Health Social Care Community. 2015;25(1):145–57. 10.1111/hsc.12284. [DOI] [PubMed] [Google Scholar]
- 45.Harada K, Masumoto K, Katagiri K, Fukuzawa A, Chogahara M, Kondo N, Okada S. Community intervention to increase neighborhood social network among Japanese older adults. Geriatrics Gerontol International/Geriatrics Gerontology International. 2017;18(3):462–9. 10.1111/ggi.13208. [DOI] [PubMed] [Google Scholar]
- 46.Mills CM, Keller HH, DePaul VG, Donnelly C. Nutrition risk varies according to social network type: data from the Canadian Longitudinal Study on Aging. Family Med Community Health. 2023;11(1):e002112. 10.1136/fmch-2022-002112. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Additional file 1: Extensive details about the digital questionnaires.
Additional file 2: Reporting checklist for cross sectional study.
Additional file 3: Standardized residuals (±1.96) for associations between cognizance, risk perception, cues, and knowledge domains of the Malnutrition Awareness Scale (MAS).
Additional file 4: Univariate analysis between social network and Malnutrition Awareness Scale (MAS) score, stratified for gender.
Additional file 5: Multivariate analyses of variables potentially associated with the Malnutrition Awareness Scale (MAS) domains.
Data Availability Statement
The dataset generated and analyzed during the current study is available upon reasonable request.