Abstract
Objective
This study aimed to evaluate the relationship between attitudes towards artificial intelligence and adherence to the Mediterranean Diet (MD), carbon footprint awareness and environmental footprint awareness among adults.
Methods
This cross-sectional study was conducted in Ankara, Turkey, using snowball sampling and an online questionnaire. The study included 1350 adults aged 18–65 years. Data were collected through a questionnaire containing demographic information, self-report anthropometric measurements, and validated scales. Artificial Intelligence Attitude Scale, Ecological Footprint Awareness Scale, Carbon Footprint Awareness Scale, and Mediterranean Diet Adherence Scale (MEDAS) were used in the study.
Results
Of the participants, 76.7% were female and 23.3% were male. Participants with adequate MD adherence (MEDAS ≥ 7) were older, had higher BMI, and demonstrated higher ecological and carbon footprint awareness than those with lower adherence. AIAS-4 scores were similar between the MD adherence groups despite a weak negative correlation between AI attitudes and MEDAS scores (rho = −0.057, p = 0.037). AI attitudes were positively correlated with ecological (rho = 0.091, p < 0.001) and carbon footprint awareness (rho= 0.058, p = 0.032). In the multivariable linear regression model, ecological footprint awareness was independently associated with age (B = 0.229, p < 0.001), AIAS-4 (B = 1.151, p < 0.001), carbon footprint awareness (B = 1.331, p < 0.001), and male gender (B = − 4.186, p = 0.002), whereas MD adherence, education level, and AI use frequency were not significant predictors. The model explained 31.0% of the variance in ecological footprint awareness (R² = 0.310, p < 0.001).
Conclusions
Positive attitudes toward artificial intelligence were associated with greater ecological and carbon footprint awareness but with slightly lower adherence to the Mediterranean diet. In contrast, greater adherence to the Mediterranean diet was positively associated with environmental footprint awareness. Carbon footprint awareness emerged as the strongest independent predictor of ecological footprint awareness. These findings provide preliminary evidence that attitudes toward artificial intelligence, dietary behavior, and environmental sustainability awareness are interrelated, although the observed associations were generally small and should be interpreted cautiously.
Keywords: Artificial intelligence, Mediterranean diet, Ecological footprint, Carbon footprint
Introduction
Today, environmental problems such as climate change, depletion of natural resources, and loss of biodiversity are considered a significant public health and sustainability issue on a global scale [1]. Food systems contribute substantially to environmental pressures, including greenhouse gas emissions, land use, freshwater depletion, and ecosystem pollution. With increasing population and income levels, the environmental impacts of food systems are expected to rise further and may exceed planetary boundaries unless effective measures are taken [2]. Since individual dietary habits and food preferences are important components of food systems, sustainable dietary patterns have gained increasing attention as an approach that addresses both human health and environmental sustainability [1]. Sustainable healthy diets are defined as dietary patterns that promote health and well-being while having low environmental impact and being accessible, affordable, safe, and culturally acceptable [3].
The Mediterranean diet (MD) is widely recognized as one of the leading sustainable dietary patterns due to its high intake of plant-based foods, olive oil-based fat profile, moderate consumption of animal-source foods, and relatively low environmental impact [4]. Previous studies have shown that the MD is associated with a lower ecological and carbon footprint compared with Western-type dietary patterns [5–8]. Therefore, examining adherence to the MD together with indicators of environmental sustainability is important for developing sustainable nutrition strategies.
Environmental footprint indicators, particularly carbon footprint and ecological footprint, are commonly used to evaluate the environmental impacts of food production and consumption. Carbon footprint reflects the total greenhouse gas emissions generated throughout the life cycle of a product or activity, whereas ecological footprint estimates the biologically productive land and water area required to sustain consumption patterns [9–11]. Since dietary patterns are among the key determinants of these indicators, individuals’ awareness of their environmental footprint may play an important role in the adoption of sustainable eating habits [12]. Such awareness may be influenced not only by dietary behaviors but also by access to information, digital technologies, and next-generation information sources.
The rapid advancements in digital technologies in recent years have significantly transformed the way individuals access information and make decisions [13]. Artificial intelligence technologies, in particular, are playing an increasingly important role in the production and sharing of information in areas such as health, nutrition, and sustainability [14]. Indeed, in recent years, it has been stated that artificial intelligence can be an important tool in supporting sustainable development goals and has the potential to contribute to the solution of environmental problems [15]. However, individuals’ attitudes towards new technologies are among the important psychosocial factors affecting the adoption and use of these technologies in daily life. The successful adoption of artificial intelligence technologies in society largely depends on the level of acceptance of these technologies by users. Research shows that individuals’ attitudes towards artificial intelligence and their trust in these systems significantly influence the intention to use and adopt artificial intelligence technologies. In particular, factors such as perceived benefit, ease of use, trust, and individual attitudes are reported to be among the key determinants shaping the acceptance of artificial intelligence technologies [16, 17].
From a theoretical perspective, the relationship between attitudes toward artificial intelligence, dietary behavior, and environmental awareness can be interpreted through technology acceptance and behavior change frameworks. According to the Technology Acceptance Model, individuals’ acceptance and use of a technology are shaped by perceived usefulness, perceived ease of use, and attitudes toward that technology [18]. Similarly, behavior change frameworks such as the capability, opportunity, and motivation (COM)-B model suggest that behavior is influenced by capability, opportunity, and motivation [19]. In this context, AI-supported information sources may facilitate access to nutrition- and sustainability-related information, while attitudes toward AI may influence individuals’ motivation and willingness to engage with such information. Therefore, attitudes toward artificial intelligence may represent a relevant psychosocial factor associated with sustainable nutrition-related awareness and behaviors.
Although the potential contributions of artificial intelligence technologies to sustainable development goals, environmental sustainability, and health are increasingly being investigated, studies examining the relationship between individuals’ attitudes toward artificial intelligence, sustainable dietary behaviors, and environmental footprint awareness remain limited. Understanding how attitudes toward AI-based information sources are associated with sustainable dietary preferences and environmental sustainability awareness may be important for developing digital and sustainable nutrition strategies. Therefore, this study aimed to evaluate the relationship between attitudes toward artificial intelligence, adherence to the Mediterranean diet, and environmental footprint awareness among adults.
Methods
This cross-sectional study was conducted between August 2025 and April 2026 among 1350 adults (1036 women, 314 men) aged 18–65 years. Research data were collected by using a web-based survey form (Google Forms) developed by the researchers. Participants were recruited using snowball sampling through an online questionnaire distributed via social media platforms. Initially, 300 primary participants were recruited through the researchers’ personal networks and invited to complete the survey. These participants were then asked to share the questionnaire with other eligible individuals within their networks, allowing the sample to expand through a snowball sampling process. Survey data were collected via the social media platforms Twitter, Facebook, WhatsApp, and Instagram. To prevent the same individual from completing the survey more than once, the “only answer once” feature of Google Forms was activated. Thus, each participant was allowed to respond only once using a single Google account. All questionnaire items were required to be completed before submission; therefore, no missing data were observed in the study. Consequently, no additional procedures were required for handling missing data in the statistical analyses.
The inclusion criteria were as follows: being between 18 and 65 years of age, having internet access, agreeing to participate voluntarily by marking the statement “I voluntarily agree to participate” after reading the information text on the homepage of the survey, and completing the questionnaire in full. No financial or non-material incentives were provided to the participants, and participation was entirely voluntary. Participants were excluded if they were younger than 18 years, did not provide informed consent, or submitted duplicate or inconsistent/implausible responses. Duplicate responses were prevented by activating the “only answer once” option in Google Forms, which restricted each Google account to a single submission. Implausible responses were defined as biologically implausible self-reported anthropometric values or logically inconsistent questionnaire responses identified during data screening. Because the questionnaire was distributed through an open online snowball sampling approach, the number of individuals who received or viewed the survey invitation could not be determined. Therefore, a response rate could not be calculated.
Prior to the commencement of the study, ethical approval was obtained from the University of Health Sciences Gülhane Scientific Research Ethics Committee (Decision No: 2025/357). Demographic characteristics of the participants (gender, age, education status, marital status, and income level) were collected through the survey form. Anthropometric data (body weight and height) were obtained based on self-reports. Participants’ attitudes toward artificial intelligence, adherence to the MD, and awareness levels of ecological and carbon footprints were evaluated using validated and reliable scales. Detailed information regarding these scales is provided below.
Anthropometric measurements
Anthropometric measurements, including body weight and height, were obtained through self-report provided by the participants. Instructions on how to accurately measure and report anthropometric data were included in the questionnaire. Body Mass Index (BMI) was calculated by dividing body weight (kg) by the square of height (m²). A BMI of less than 18.50 kg/m² was classified as underweight, 18.50–24.99 kg/m² as normal weight, 25.00–29.99 kg/m² as overweight, and 30.00 kg/m² or higher as obese [20].
Artificial intelligence attitude scale
Attitudes toward artificial intelligence were assessed using the Artificial Intelligence Attitude Scale. The original scale was developed and validated by Grassini [21], and its Turkish validity and reliability were established by Arslan et al. [22]. The scale is a short self-report instrument consisting of four items and has a single-factor structure. Participants rate their agreement with each item using a 10-point Likert scale (1 = Not at all; 10 = Completely Agree). The total score is calculated as the mean of all item scores. The total score is calculated by averaging the scores of the four items; therefore, the final AIAS score ranges from 1 to 10. Higher scores indicate more positive attitudes and greater acceptance of artificial intelligence technologies. The Cronbach’s alpha coefficient of the AIAS was reported as 0.90 in the original study, while it was found to be 0.890 in the present study.
Mediterranean Diet Adherence Scale (MEDAS)
Adherence to the MD was assessed using the 14-item Mediterranean Diet Adherence Screener (MEDAS). The instrument was originally developed by Martínez-González et al. [23] and its Turkish validity and reliability were established by Pehlivanoğlu et al. [24] The MEDAS consists of 14 items assessing key components of the MD pattern, including olive oil, fruits, vegetables, legumes, fish, red and processed meats, and wine consumption. Each item is scored as 0 or 1 based on compliance with the recommended intake criteria. The total score ranges from 0 to 14, with higher scores reflecting greater adherence to the MD. In line with previous studies and the Turkish validation of the MEDAS [24], a score of ≥ 7 was used as the cut-off point to indicate adequate adherence to the Mediterranean diet [25]. The Turkish validation study demonstrated good internal consistency, with a Cronbach’s alpha coefficient of 0.829 [24]. In the present study, the Cronbach’s alpha coefficient was 0.763.
Ecological footprint awareness scale
Ecological footprint awareness was assessed using the Ecological Footprint Awareness Scale developed by Tekindal et al. [26]. The scale consists of 30 items rated on a 5-point Likert scale ranging from 1 (“Strongly disagree”) to 5 (“Strongly agree”). Scores are calculated by summing all item responses without dividing by the number of items. The total score ranges from 30 to 150, with higher scores indicating greater ecological footprint awareness. Similarly, higher scores obtained from the subdimensions reflect higher levels of awareness in the respective domains.
The Ecological Footprint Awareness Scale demonstrated excellent internal consistency, with a Cronbach’s alpha coefficient of 0.960 in the original study and 0.966 in the present study.
Carbon footprint awareness scale
Carbon footprint awareness was assessed using the Carbon Footprint Awareness Scale developed by Üstgörül et al. in 2024 [27]. The scale consists of 19 items distributed across five subdimensions: transportation (3 items), fuel consumption (3 items), electricity use (5 items), food consumption (5 items), and waste management (3 items). All items are rated on a 5-point Likert scale. The maximum possible score is 95, with higher scores indicating greater carbon footprint awareness. The cut-off point of the scale is 57, and scores above this threshold indicate higher levels of carbon footprint awareness. The Carbon Footprint Awareness Scale demonstrated good internal consistency in the original study, with a Cronbach’s alpha coefficient of 0.86. In the present study, the Cronbach’s alpha coefficient was 0.775, indicating acceptable internal consistency.
Statistical analysis
All statistical analyses were performed using IBM SPSS Statistics software (IBM Corp., Armonk, NY, USA). Prior to analysis, data were checked for completeness, coding accuracy, and distributional assumptions. Continuous variables were assessed for normality using visual methods (histograms and Q–Q plots) and analytical tests (Kolmogorov–Smirnov/Shapiro–Wilk tests, as appropriate). As most continuous variables were not normally distributed, results were summarized as median and interquartile range (IQR), whereas categorical variables were presented as number (n) and percentage (%). Descriptive statistics were used to characterize the study population, including age, body mass index (BMI), Artificial Intelligence Attitude Scale (AIAS) scores, Mediterranean Diet Adherence Screener (MEDAS) scores, ecological footprint awareness, carbon footprint awareness, sex, educational level, marital status, economic status, BMI classification, and frequency of artificial intelligence use. Comparisons between two groups were conducted using the Mann–Whitney U test for continuous variables and the chi-square test for categorical variables. Associations between variables were evaluated using Spearman’s rank correlation coefficient (rho), due to the non-normal distribution of variables. To identify independent factors associated with ecological footprint awareness, a multivariable linear regression model was constructed. Based on the study objectives, age, gender, education level, AIAS-4 score, MEDAS score, AI usage frequency, and carbon footprint awareness were prespecified as theoretically relevant variables and entered simultaneously into the model using the enter method. Education level and AI usage frequency were entered as ordinal variables because their categories represented ordered levels. Ecological footprint awareness score was defined as the dependent variable. BMI was not included because it was not considered one of the primary study variables and showed only a negligible borderline association with ecological footprint awareness. Education level and AI usage frequency were entered as ordinal variables because their categories represented increasing ordered levels. Education level was coded as 1 = literate, 2 = primary school, 3 = secondary school, 4 = high school, and 5 = bachelor’s degree. AI usage frequency was coded as 1 = rarely, 2 = occasionally, 3 = frequently and 4 = daily. Multicollinearity was assessed using tolerance and variance inflation factor (VIF) values. Regression coefficients (B), 95% confidence intervals (CI), and p-values were reported. Model fit was evaluated using the coefficient of determination (R²). Before interpreting the multiple linear regression model, the main assumptions of linear regression were evaluated. The normality of residuals was assessed using a histogram and normal P–P plot of standardized residuals. Linearity and homoscedasticity were examined using the scatterplot of standardized predicted values and standardized residuals. Potential influential observations were assessed using standardized residuals and Cook’s distance.
Results
A total of 1,350 individuals were included in the study. The median age was 21.00 years (IQR = 7.00), and the median BMI was 22.92 kg/m² (IQR = 5.75) The median AIAS score was 7.00 (IQR = 2.75). Median ecological footprint awareness and carbon footprint awareness scores were 118.00 (IQR = 30.00) and 68.00 (IQR = 12.00), respectively. The median MEDAS score was 6.00 (IQR = 2.00). Most participants were female (76.7%) and held a bachelor’s degree (74.9%). The majority were single (78.9%) and reported that their income was equal to their expenses (60.9%). According to BMI classification, 58.4% had normal weight, 23.9% were overweight, 8.5% were obese and 9.2% were underweight. Regarding the frequency of artificial intelligence use, 36.0% reported occasional use, 32.4% frequent use, 15.9% daily use, and 15.8% rare use (Table 1).
Table 1.
Baseline characteristics of participants
| Total | |
|---|---|
| Median(IQR) | |
| Age | 21.00(7.00) |
| BMI | 22.92(5.75) |
| AIAS | 7.00(2.75) |
| Ecological footprint awareness | 118.00(30.00) |
| Carbon footprint awareness | 68.00(12.00) |
| MEDAS | 6.0(2.0) |
| n (%) | |
| Gender | |
| Female | 1036(76.7) |
| Male | 314(23.3) |
| Education Status | |
| Literate | 16(1.2) |
| Primary school | 23(1.7) |
| Middle school | 40(3.0) |
| High school | 260(19.3) |
| Bachelor degree | 1011(74.9) |
| Marital Status | |
| Single | 1065(78.9) |
| Married | 285(21.1) |
| Economical status | |
| Income more than expenses | 225(16.7) |
| Income equal to expenses | 822(60.9) |
| Income less than expenses | 303(22.4) |
| BMI Classification | |
| Underweight (< 18.50 kg/m2) | 124(9.2) |
| Normal (18.50–24.99 kg/m2) | 789(58.4) |
| Overweight (25.00–29.99 kg/m2) | 322(23.9) |
| Obese (≥ 30.0 kg/m2) | 115(8.5) |
| Frequency of artificial intelligence use | |
| Rarely | 213(15.8) |
| Occasionally | 486(36.0) |
| Frequently | 437(32.4) |
| Daily | 214(15.9) |
Continuous variables are presented as median (IQR), and categorical variables are presented as number (percentage). AIAS-4 Artificial Intelligence Attitude Scale-4, MEDAS Mediterranean Diet Adherence Screener. Higher scores indicate greater attitudes toward artificial intelligence, Mediterranean diet adherence, and environmental footprint awareness
Table 2 shows characteristics of the participants according to MD adherence. Participants with adequate adherence to the MD (MEDAS ≥ 7) were older (median: 22.0 vs. 20.0 years, p < 0.001), had higher BMI (23.58 vs. 22.37 kg/m², p < 0.001), ecological and carbon footprint awareness scores (p < 0.001). However, the groups had similar AIAS-4 levels (median (IQR): 7.0(2.75) vs. 7.0(3.75), p = 0.047). Gender distribution did not differ significantly between the groups (p = 0.906). Education level was also significantly associated with MD adherence (p = 0.004). Married individuals were more prevalent in the MD adherence group (MEDAS ≥ 7) (29.5% vs. 15.8%, p < 0.001). Economic status showed a significant association (p = 0.025), with a higher proportion of individuals reporting income greater than expenses in the adequate Mediterranean diet adherence group. BMI classification differed significantly between groups (p = 0.019), with a higher proportion of overweight and obese individuals in the MD adherence group (MEDAS ≥ 7). However, frequency of artificial intelligence use did not differ significantly between groups (p = 0.103). Similarly, categorical carbon footprint awareness (< 57 vs. ≥57) was not significantly associated with MD adherence (p = 0.093).
Table 2.
Comparison of variables according to mediterranean diet adherence
| Low Mediterranean Diet Adherence (MEDAS < 7) n = 825 |
Adequate Mediterranean Diet Adherence (MEDAS ≥ 7) n = 525 |
P | |
|---|---|---|---|
| Median(IQR) | Median(IQR) | ||
| Age | 20.00(5) | 22.0(14.0) | < 0.001 |
| BMI | 22.37(5.88) | 23.58(5.29) | < 0.001 |
| AIAS | 7.0(2.75) | 7.0(3.75) | 0.047 |
| Ecological footprint awareness | 117.0(31.0) | 120.0(30.50) | < 0.001 |
| Carbon footprint awareness | 67.0(13.0) | 69.0(14.0) | < 0.001 |
| n (%) | n (%) | ||
| Gender | |||
| Female | 634(76.8) | 402(76.6) | 0.906 |
| Male | 191(23.2) | 123(23.4) | |
| Education Status | |||
| Literate | 5(0.6) | 11(2.1) | |
| Primary school | 11(1.3) | 12(2.3) | 0.004 |
| Middle school | 20 (2.4) | 20(3.8) | |
| High school | 144(17.5) | 116(22.1) | |
| Bachelor degree | 645(78.2) | 366(69.7) | |
| Marital Status | |||
| Single | 695(84.2) | 370(70.5) | < 0.001 |
| Married | 130(15.8) | 155(29.5) | |
| Economical status | |||
| Income more than expenses | 124(15.0) | 101(19.2) | 0.025 |
| Income equal to expenses | 499(60.5) | 323(61.5) | |
| Income less than expenses | 202(24.5) | 101(19.2) | |
| Carbon footprint awareness | |||
| < 57 | 116(14.1) | 60(11.4) | 0.093 |
| ≥ 57 | 709(85.9) | 465(88.6) | |
| BMI Classification | |||
| Underweight (< 18.50 kg/m2) | 85(10.3) | 39(7.4) | |
| Normal (18.50–24.99 kg/m2) | 495(60.0) | 294(56.0) | 0.019 |
| Overweight (25.00–29.99 kg/m2) | 186(22.5) | 136(25.9) | |
| Obese (≥ 30.0 kg/m2) | 59(7.2) | 56(10.7) | |
| Frequency of artificial intelligence use | |||
| Rarely | 116(14.1) | 97(18.5) | |
| Occasionally | 298(36.1) | 188(35.8) | 0.103 |
| Frequently | 282(34.2) | 155(29.5) | |
| Daily | 129(15.6) | 85(16.2) | |
Continuous variables are presented as median (IQR), and categorical variables are presented as number (percentage). Differences between groups were evaluated using the Mann–Whitney U test for continuous variables and the chi-square test for categorical variables. AIAS-4: Artificial Intelligence Attitude Scale-4
Table 3 shows correlation between study variables. Age was positively correlated with BMI (rho = 0.365, p < 0.001), MEDAS score (rho = 0.158, p < 0.001), carbon footprint awareness (rho = 0.092, p < 0.001), and ecological footprint awareness (rho = 0.140, p < 0.001), but negatively correlated with AIAS scores (rho = − 0.085, p = 0.002). BMI was positively correlated with MEDAS score (rho = 0.129, p < 0.001) and showed weak borderline correlation with ecological footprint awareness (rho = 0.053, p = 0.050). AIAS score was negatively correlated with MEDAS score (rho = − 0.057, p = 0.037), but positively correlated with carbon footprint awareness (rho = 0.058, p = 0.032) and ecological footprint awareness (rho = 0.091, p < 0.001).
Table 3.
Associations between age, BMI, artificial intelligence attitudes, Mediterranean diet adherence, and environmental footprints
| Age | BMI | AIAS-4 | MEDAS | Carbon footprint | Ecological footprint | ||
|---|---|---|---|---|---|---|---|
| Age | rho | 1 | 0.365 | -0.085 | 0.158 | 0.092 | 0.140 |
| p | < 0.001 | 0.002 | < 0.001 | < 0.001 | < 0.001 | ||
| BMI | rho | 0.365 | 1 | 0.018 | 0.129 | 0.008 | 0.053 |
| p | < 0.001 | 0.500 | < 0.001 | 0.762 | 0.050 | ||
| AIAS-4 | rho | -0.085 | 0.018 | 1 | -0.057 | 0.058 | 0.091 |
| p | 0.002 | 0.500 | 0.037 | 0.032 | < 0.001 | ||
| MEDAS | rho | 0.158 | 0.129 | -0.057 | 1 | 0.122 | 0.098 |
| p | < 0.001 | < 0.001 | 0.037 | < 0.001 | < 0.001 | ||
| Carbon footprint awareness | rho | 0.092 | 0.008 | 0.058 | 0.122 | 1 | 0.543 |
| p | < 0.001 | 0.762 | 0.032 | < 0.001 | < 0.001 | ||
| Ecological footprint awareness | rho | 0.140 | 0.053 | 0.091 | 0.098 | 0.543 | 1 |
| p | < 0.001 | 0.050 | < 0.001 | < 0.001 | < 0.001 |
Spearman’s rank correlation coefficients (rho) and corresponding p-values are presented. AIAS-4: Artificial Intelligence Attitude Scale-4; MEDAS: Mediterranean Diet Adherence Screener. Statistical significance was set at p < 0.05
Regression diagnostics indicated no evidence of problematic multicollinearity, with VIF values ranging from 1.045 to 1.463 and tolerance values ranging from 0.684 to 0.957. The maximum Cook’s distance was 0.030, indicating no highly influential observations. Standardized residuals ranged from − 4.424 to 2.958, with 11 observations (0.8% of the sample) exceeding an absolute value of 3. Visual inspection of the histogram and normal P–P plot indicated an approximately normal distribution of residuals, with only minor deviations. The plot of standardized residuals against standardized predicted values showed no marked nonlinear pattern, although some variation in residual dispersion was observed. Overall, the diagnostic findings were considered acceptable for interpretation of the linear regression model.
Multivariable linear regression analysis showed that age (B = 0.229, p < 0.001), attitudes toward artificial intelligence (B = 1.151, p < 0.001), and carbon footprint awareness (B = 1.331, p < 0.001) were positively associated with ecological footprint awareness. In contrast, male gender was negatively associated with ecological footprint awareness (B = − 4.186, p = 0.002). Mediterranean Diet adherence (p = 0.659), AI usage frequency (p = 0.930), and education level (p = 0.736) were not significantly associated with ecological footprint awareness. The overall model explained 31.0% of the variance in ecological footprint awareness (R² = 0.310, p < 0.001). Collinearity diagnostics indicated no multicollinearity problems (VIF values close to 1) (Table 4).
Table 4.
Multivariable regression analysis examining factors associated with environmental footprint awareness
| Unstandardized Coefficients | t | Sig. | 95.0% Confidence Interval for B | Collinearity Statistics | VIF | |||
|---|---|---|---|---|---|---|---|---|
| B | Std. Error | Lower Bound | Upper Bound | Tolerance | ||||
| (Constant) | 18.852 | 5.663 | 3.329 | < 0.001 | 7.743 | 29.961 | ||
| Age | 0.229 | 0.058 | 3.972 | < 0.001 | 0.116 | 0.342 | 0.789 | 1.267 |
| AIAS-4 | 1.151 | 0.338 | 3.408 | < 0.001 | 0.489 | 1.814 | 0.720 | 1.389 |
| Carbon footprint | 1.331 | 0.058 | 22.755 | < 0.001 | 1.216 | 1.446 | 0.957 | 1.045 |
| MEDAS | -0.120 | 0.271 | − 0.441 | 0.659 | − 0.651 | 0.412 | 0.951 | 1.051 |
| AI Usage Frequency | 0.062 | 0.707 | 0.088 | 0.930 | -1.324 | 1.449 | 0.684 | 1.463 |
| Gender | -4.186 | 1.349 | -3.104 | 0.002 | -6.832 | -1.541 | 0.928 | 1.077 |
| Education Level | -0.266 | 0.790 | − 0.337 | 0.736 | -1.816 | 1.283 | 0.909 | 1.100 |
| Dependent variable: Ecological foot print R square: 0.310 p < 0.001 | ||||||||
Multivariable linear regression analysis was performed to identify independent factors associated with ecological footprint awareness. Unstandardized regression coefficients (B), standard errors (SE), 95% confidence intervals (CI), and collinearity statistics are presented. Gender was coded as 0 = female and 1 = male; Education level was coded as 1 = literate, 2 = primary school, 3 = middle school, 4 = high school, and 5 = bachelor’s degree. p < 0.05
Collinearity diagnostics indicated no problematic multicollinearity (tolerance: 0.684–0.957; VIF: 1.045–1.463). The maximum Cook’s distance was 0.030. Standardized residuals ranged from − 4.424 to 2.958, with 11 observations (0.8%) exceeding |3|
Discussion
This study aimed to evaluate the relationship between attitudes towards artificial intelligence and adherence to the MD and awareness of environmental footprint in adult individuals. The main findings indicated that more positive attitudes toward artificial intelligence were very weakly associated with lower adherence to the Mediterranean diet (MD), while being positively associated with ecological and carbon footprint awareness. Furthermore, ecological footprint awareness was significantly predicted by age, gender, attitudes toward artificial intelligence, and particularly carbon footprint awareness. Finally, greater adherence to the MD was associated with higher levels of ecological and carbon footprint awareness.
These additional analyses indicated that adherence to the MD was higher among older individuals, those with higher educational attainment, individuals with higher BMI, married participants, and those with higher income levels. These findings are consistent with previous literature suggesting that healthier dietary patterns are associated with older age and more favorable educational and socioeconomic conditions [28–34].
The decline in positive attitudes toward artificial intelligence with increasing age, alongside higher adherence to the MD, may reflect generational differences in the adoption and use of digital technologies [35, 36]. Older individuals tend to prefer more traditional and healthier dietary patterns [28], while also exhibiting more cautious attitudes toward emerging technologies, partly due to lower digital literacy and technology adaptation [37]. Conversely, increasing age has been associated with greater environmental awareness. Previous studies have reported higher ecological footprint awareness among older adults compared with younger groups [38, 39]. Consistent with these findings, age was identified as a significant predictor of ecological footprint awareness in the present study, and carbon footprint awareness also increased with age. This relationship may be explained by accumulated life experience, stronger environmental responsibility, and a greater tendency toward sustainable lifestyle practices among older individuals [28].
Attitudes toward artificial intelligence are an important factor influencing individuals’ health- and nutrition-related information sources and behaviors. Although positive attitudes have been reported to increase the use of personalized nutrition tools [22], our findings revealed that higher levels of positive attitudes toward artificial intelligence were associated with lower adherence to the MD. This finding suggests that favorable perceptions of artificial intelligence may not always be accompanied by healthier dietary behaviors [22, 40]. One possible explanation, based on previous literature, is that individuals may adopt nutrition-related information obtained from artificial intelligence and digital platforms without critically evaluating its validity, potentially leading them toward popular or restrictive dietary approaches [41]. Similarly, automation bias has been proposed as a potential mechanism whereby individuals may accept technology-based recommendations without sufficient critical evaluation [42]. However, these mechanisms were not directly assessed in the present study and should therefore be considered plausible hypotheses rather than explanations established by our findings. Further studies directly assessing these mechanisms are needed to clarify the observed association between attitudes toward artificial intelligence and MD adherence.
In recent years, the widespread adoption of artificial intelligence technologies in the field of sustainability has led to significant changes in how individuals understand and evaluate environmental impacts. artificial intelligence powered systems that optimize energy use, support waste management practices, and provide predictive analyses to reduce carbon emissions help individuals make more informed decisions regarding environmental issues [43]. Within the framework of the Sustainable Development Goals (SDG 13), artificial intelligence has been reported to make substantial contributions to understanding climate change, integrating renewable energy systems, and developing low-carbon technologies. Furthermore, its ability to provide real-time information on energy efficiency and carbon emissions may encourage the adoption of environmentally friendly behaviors and support the transformation of social norms related to environmental sustainability [15, 44, 45]. Indeed, studies integrating artificial intelligence capabilities into behavior change models have demonstrated that digital communication and environmental information provided by artificial intelligence can reshape social norms through online learning networks [15]. In this context, positive attitudes toward artificial intelligence strengthen data-driven decision-making processes, optimize resource management, improve the effectiveness of recycling practices, and facilitate the monitoring of environmental impacts. Machine learning and artificial intelligence -based predictive analytics contribute to more efficient resource planning and the reduction of environmental burdens [42]. Therefore, when these mechanisms are considered collectively, positive attitudes toward artificial intelligence may be associated with greater ecological and carbon footprint awareness by helping individuals better understand environmental impacts. Accordingly, the positive relationship observed in our study may be explained by the increased visibility of information, decision-support mechanisms, and the promotion of environmentally friendly behaviors that artificial intelligence provides within sustainability processes.
The findings may be interpreted through the Technology Acceptance Model and the COM-B framework, although their specific constructs were not directly measured [18, 19]. Favorable attitudes toward AI may increase individuals’ willingness and motivation to engage with technology-supported nutrition and sustainability information [15, 22], which may partly explain the positive associations with ecological and carbon footprint awareness. At the same time, both ecological and carbon footprint awareness were positively associated with Mediterranean diet adherence, whereas the direct association between AI attitudes and Mediterranean diet adherence was weak and negative. This pattern suggests that greater engagement with AI-supported information may increase awareness without necessarily translating into healthier dietary behaviors, highlighting a possible awareness–behavior gap. However, these potential pathways were not formally examined in the present study and should be investigated in future research.
Ecological footprint awareness stands out as an important determinant of healthy and sustainable eating behaviors [46]. Increasing environmental awareness and promoting healthy eating attitudes are considered important strategies for encouraging adherence to sustainable dietary models such as the MD [47]. The literature suggests that greater adherence to the MD contributes to environmentally friendly dietary practices [48]. Owing to its high consumption of plant-based foods and relatively low intake of animal products, the MD is associated with lower greenhouse gas emissions and reduced environmental burden compared with Western dietary patterns [47, 49]. This relationship has been consistently reported across different populations [5, 49–51]. In particular, a large-scale study demonstrated that individuals with higher MD adherence exhibited more sustainable eating behaviors and greater awareness of reducing their ecological footprint [52]. Consistent with these findings, our study identified a positive association between ecological footprint awareness and MD adherence, suggesting that increasing environmental awareness may support the adoption of sustainable and healthy dietary behaviors.
Similarly, a positive association was observed between carbon footprint awareness and adherence to the Mediterranean Diet (MD) in the present study. Carbon footprint is an important indicator of the environmental impact of human activities and is widely recognized as a key measure of sustainable development. Accordingly, the United Nations Sustainable Development Goals emphasize reducing carbon footprints and promoting sustainable lifestyles as priority objectives [53]. Since human activities are the primary source of carbon emissions, increasing individuals’ awareness of their environmental impact is essential for emission reduction efforts [26]. Environmental awareness and positive environmental attitudes play a crucial role in encouraging environmentally responsible behaviors and reducing carbon emissions [54–56]. Individuals with higher carbon footprint awareness may make more conscious decisions regarding daily behaviors, including energy consumption, transportation, and particularly food choices, thereby reducing their environmental impact [57]. Dietary habits represent a major determinant of carbon footprint. Dietary patterns characterized by higher consumption of plant-based foods and lower consumption of red and processed meats are generally associated with lower greenhouse gas emissions [58]. Supporting this evidence, a study involving more than 6,000 participants reported that individuals with greater adherence to the MD had lower diet-related CO₂ emissions compared with those with lower adherence [59]. Therefore, individuals with higher carbon footprint awareness may be more inclined to adopt dietary patterns with lower environmental impacts, which could, in turn, contribute to greater adherence to the MD.
The regression analysis conducted in the present study revealed that ecological footprint awareness was significantly predicted by carbon footprint awareness. Carbon footprint awareness emerged as the strongest predictor in the model, indicating a close association between these two constructs. This finding is not unexpected, given that carbon footprint is widely recognized as one of the principal components of the broader ecological footprint concept and accounts for a substantial proportion of total ecological footprint estimates [60, 61] Therefore, the strong association observed between carbon footprint awareness and ecological footprint awareness may partly reflect conceptual overlap between the two measures rather than entirely independent dimensions of environmental awareness. Nevertheless, ecological footprint awareness encompasses a broader perspective, including dimensions such as natural resource consumption, land use, and pressures exerted on ecosystems, whereas carbon footprint awareness primarily focuses on greenhouse gas emissions and climate-related impacts. Accordingly, individuals with higher levels of carbon footprint awareness may also exhibit greater ecological footprint awareness through a more comprehensive evaluation of environmental consequences. However, the extent to which these constructs represent distinct versus overlapping dimensions of sustainability awareness remains unclear. Future research should further examine the shared and unique components of ecological and carbon footprint awareness in order to better understand their independent contributions and to facilitate a more accurate interpretation of associations observed in regression models.
In addition to age, gender was also identified as a significant predictor of ecological footprint awareness in the present study. Male gender was negatively associated with ecological footprint awareness, indicating that men had lower ecological footprint awareness scores than women after adjustment for other variables (p < 0.001). This finding is consistent with previous studies reporting that women generally demonstrate higher environmental concern, stronger pro-environmental attitudes, and greater sensitivity to environmental risks than men [62, 63]. Compared to men, women might be more cautious and worried about environmental issues, as well as more accountable for their ecological carbon footprint [63]. This difference may be partly explained by gender-related socialization processes, care-oriented roles, and women’s greater involvement in household-level sustainable practices, such as resource saving and environmentally conscious consumption. However, this finding should be interpreted with caution because women constituted 76.7% of the study sample, whereas men represented only 23.3%. This imbalance may have reduced the representativeness of the gender-specific estimates and limits the generalizability of the observed association between gender and ecological footprint awareness. Therefore, future studies including more balanced samples are needed to confirm this finding.
No significant association was observed between BMI and attitudes toward artificial intelligence (rho = 0.018, p = 0.500). Likewise, BMI showed only a negligible borderline association with ecological footprint awareness (rho = 0.053, p = 0.050) and no association with carbon footprint awareness (rho = 0.008, p = 0.762). These findings suggest that BMI was not meaningfully related to the primary constructs examined in this study. Although previous studies have reported associations between weight status and the intention to use AI-assisted health applications [64], this construct differs from general attitudes toward AI assessed in the present study. Given the very small effect size of the ecological footprint association, these findings should be interpreted cautiously and confirmed in future studies.
It is important to distinguish statistical significance from practical relevance when interpreting these findings. Given the large sample size, some associations reached statistical significance despite their small magnitude. Therefore, particularly weak correlations should be interpreted cautiously and should not necessarily be considered clinically or public health meaningful. Future studies are needed to determine the practical relevance of these associations.
One of the main strengths of this study is its holistic approach to examining the relationships between attitudes towards artificial intelligence, adherence to the MD, and awareness of both ecological and carbon footprints, all within the same model. In particular, the combined assessment of nutritional behaviors and environmental sustainability indicators with attitudes towards artificial intelligence fills a gap in the literature that has been addressed in only a limited number of studies. Furthermore, the use of previously validated and reliable measurement tools enhances the reliability of the findings. The large sample size increases statistical power, contributing to more robust and interpretable results. Moreover, examining different variables within the same model allows for a more comprehensive assessment of the multidimensional interactions between digital health, nutritional behaviors, and environmental awareness. However, there are some limitations to consider when interpreting the findings of this study. First, due to the cross-sectional design of the study, the relationships between attitudes toward artificial intelligence, adherence to the Mediterranean Diet, and environmental footprint awareness cannot be evaluated within a causal framework. Therefore, the direction of the observed relationships cannot be clearly determined, and future longitudinal or experimental studies are needed to better understand these associations.
Second, data were collected using an online survey method and a non-probability snowball sampling technique, which may have introduced selection bias. In particular, the sample was predominantly composed of younger, highly educated, and female participants, which limits the generalizability of the findings to the broader adult population. Moreover, the use of online snowball sampling may have introduced self-selection bias, as individuals with higher levels of digital literacy or a particular interest in health, nutrition, environmental sustainability, or artificial intelligence may have been more likely to participate in the study. This may have resulted in the overrepresentation of individuals who were more engaged with these topics. Since participation required internet access and engagement with online platforms, individuals with lower digital engagement may have been underrepresented. Consequently, the attitudes toward artificial intelligence and the levels of environmental and dietary awareness observed in this study may differ from those of individuals with lower digital literacy or less interest in these topics. Therefore, the findings should be interpreted with caution when generalizing to the general population.
Another limitation is that the data were obtained through self-report. Assessing variables such as BMI, dietary habits, and environmental awareness via self-report may be susceptible to recall bias, social desirability bias, and measurement error. In particular, anthropometric data obtained through self-report may differ from actual measurements. In addition, although the study examined the multidimensional relationships between attitudes toward artificial intelligence, adherence to the Mediterranean Diet, and environmental footprint awareness, some potentially relevant variables that may influence dietary behaviors and environmental awareness, such as stress level, emotional eating behavior, lifestyle characteristics, socioeconomic status, and access to digital tools, were not evaluated. This may limit the contextual interpretation of the findings. Furthermore, although some variables demonstrated significant associations in univariate analyses, not all of them were included in the final regression model. Therefore, the possibility of residual confounding and omitted variable bias cannot be completely excluded. Future studies should consider a broader range of demographic, behavioral, and dietary variables when examining predictors of ecological footprint awareness. In addition, a relatively large number of statistical tests were performed in the present study. Although these analyses were conducted within an exploratory framework, multiple comparisons may have increased the risk of Type I error. Therefore, some statistically significant findings should be interpreted with caution and confirmed in future studies. Finally, the study was conducted within a specific geographical and cultural context, which may limit the representation of cultural, environmental, and socioeconomic differences observed in other societies. Therefore, multicenter studies with larger and more diverse samples are recommended.
Conclusion
This study demonstrated significant associations between attitudes toward artificial intelligence, adherence to the MD, and environmental footprint awareness among adults. More positive attitudes toward artificial intelligence were associated with younger age, higher ecological and carbon footprint awareness but lower adherence to the MD. In contrast, greater adherence to the MD was positively associated with ecological and carbon footprint awareness, supporting the link between sustainable dietary patterns and environmental consciousness. Furthermore, carbon footprint awareness emerged as the strongest predictor of ecological footprint awareness, while age, gender, and attitudes toward artificial intelligence were also independently associated with ecological footprint awareness. Given the cross-sectional nature of the study, these findings should be considered exploratory, and longitudinal studies are needed to clarify the direction and practical relevance of the observed associations.
Acknowledgements
We are grateful to all the study respondents for giving their time to participate in the surveys.
Abbreviations
- BMI
Body mass index
- MD
Mediterranean diet
- AIAS-4
Artificial Intelligence Attitude Scale
- SDG
Sustainable development goals
Author contributions
NA conceptualized and designed the study. NA and ÖMÇ contributed to data collection. NA and EME performed the statistical analysis and interpreted the data. NA, ömç, EME, ÜGG drafted the manuscript. NA, ÖMÇ, EME, and ÜGG critically revised the manuscript. All authors read and approved the final version of the manuscript.
Funding
There was no funding or sponsoring organisation for this paper.
Data availability
The datasets analyzed during the current study are available from the corresponding author on reasonable request. The data are not publicly available due to ethical restrictions and protection of participant confidentiality.
Declarations
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Ethics approval and consent to participate
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study was conducted with the approval of the University of Health Sciences Gulhane Scientific Research Ethics Committee, numbered 2025/357. Informed consent was obtained from all participants included in the study
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets analyzed during the current study are available from the corresponding author on reasonable request. The data are not publicly available due to ethical restrictions and protection of participant confidentiality.