Beyond age: understanding how mindful eating and food label reading shape generational differences in health outcomes – a cross-sectional study

Abstract

Background

The increasing prevalence of obesity and diet-related chronic diseases poses a considerable threat to public health. Consequently, understanding generational differences in dietary behaviors has gained growing importance. This study aims to examine the direct and indirect effects of generational cohort on anthropometric indicators and self-rated diet quality (SRDQ), mediated through mindful eating and food label reading attitudes.

Method

This cross-sectional design study involved 2725 participants from Generations X (n = 786), Y (n = 933), and Z (n = 1006) living in Istanbul, Türkiye. Participants’ sociodemographic characteristics and anthropometric measurements, including body mass index (BMI), waist circumference (WC), and waist–hip ratio (WHR), were recorded. Data on SRDQ were collected, alongside responses to the Mindful Eating Questionnaire and the Food Label Reading Attitude Scale. Descriptive statistics, one-way analysis of variance, and chi-square tests were applied to the data. The direct and indirect effects of generation groups were analyzed using structural equation modeling.

Results

Increasing mindful eating scores were associated with higher label reading attitude and SRDQ scores, while they were related to lower anthropometric risk indicators. Higher label reading attitudes showed an association with reduced WHR and increased SRDQ. Generation Z participants reported significantly lower scores for mindful eating, label reading attitude, anthropometric measures, and SRDQ than Generation X participants. Lower mindful eating scores were related to increased BMI among Generation Z, whereas lower label reading attitude scores were positively associated with WC and WHR. Furthermore, poor mindful eating and label reading attitudes and the combined effects of low mindful eating and label reading attitudes were linked to lower SRDQ.

Conclusion

Poor mindful eating and label reading attitudes in Generation Z may threaten long-term health sustainability and increase the risk of developing chronic diseases. Combining generation-specific public health strategies with interventions aimed at improving nutritional literacy and mindful eating habits from an early age could enhance individual and societal health outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-025-24997-y.

Background

Unhealthy shifts in nutritional behaviors worldwide adversely affect food intake, thereby contributing to obesity and other chronic diseases [1]. Individuals’ nutritional habits are influenced by biological drives and hunger–satiety mechanisms as well as various sociodemographic and environmental variables such as age, gender, cultural and religious beliefs, education level, economic status, technology use, and media exposure [2, 3]. Therefore, understanding the differences in nutritional behaviors specific to particular age groups is of critical importance for the development of public health strategies [4].

Generations are defined as age groups that are born in a certain time period and are affected by common social, political, and economic events. These shared experiences result in individuals having similar needs, behaviors, attitudes, perceptions, values, beliefs, and expectations [5]. Currently, five generational categories are widely recognized based on birth years: Silent Generation, Baby Boomer Generation, Generation X, Generation Y, and Generation Z [6]. Although some differences in nutritional behaviors and habits among generations have been demonstrated in previous studies [7, 8], research on this subject remains limited.

Mindful eating is characterized by being present during meals, recognizing hunger cues, distinguishing between emotional and physical hunger, and engaging in the eating experience through sensory perception [9–11]. This concept has been shown to be associated with healthier eating behaviors, lower body image dissatisfaction, and enhanced nutritional awareness, while offering an alternative to traditional dietary recommendations [12–15]. Likewise, food label reading behavior plays a significant role in shaping diet quality and nutritional awareness [16]. Food labels are designed to guide consumers toward healthier and more informed choices [17]. Previous studies have shown links between reading food labels and better health outcomes [16, 18]. However, the extent to which individuals read, interpret, and use food labels remains unclear [19], and the role of generational differences in label reading behavior has not yet been investigated.

Over the last six decades, trends in body composition have shifted unfavorably in young adults, with increased body fat and decreased muscle mass [20]. Negative changes in body composition have been attributed to poor diet quality, independent of total energy intake [21]. Self-rated diet quality (SRDQ), based on individuals’ subjective evaluations of the extent to which their dietary intake aligns with nutrition recommendations, emerges as a significant psychosocial determinant [22, 23]. SRDQ is regarded as a practical indicator of diet quality that can be employed in large-scale studies [24, 25].

Generational differences directly affect individuals’ lifestyles, nutritional habits, and therefore public health [26]. This situation necessitates that descriptive studies and intervention programs aimed at improving public health be designed in a way that considers the unique needs of different generations. Although previous studies have addressed the individual relationships between generation groups and mindful eating (ME) behaviors, anthropometric indicators, or certain nutritional attitudes [9, 27, 28], no study to date has simultaneously modeled these concepts together within the generational context. Addressing this gap is essential to understand how ME and label reading jointly shape SRDQ and anthropometric outcomes across generations. Therefore, the present study aimed to analyze the direct and indirect effects of generational groups on anthropometric variables and SRDQ through ME and label reading attitude (LRA) as mediating variables. The findings are expected to contribute to the development of effective, evidence-based intervention and education strategies for the unique needs of each generation.

Methods

Study design

This descriptive, cross-sectional study investigated the direct and indirect effects of generational groups on anthropometric variables and SRDQ through ME and LRA. Ethical permission was obtained from the Fenerbahçe University Non-Interventional Clinical Research Ethics Committee (Protocol no: 82.2024fbu). The study model was created using the structural equation modeling (SEM) to evaluate the moderating effects. The hypothesized models are presented in Fig. 1.

Fig. 1.

The hypothesized models. Hypothesized models for the effects of generation groups on (a) waist circumference (WC), (b) waist–hip ratio (WHR), (c) body mass index (BMI), and (d) self-rated diet quality (SRDQ). Gen: generation, ME: mindful eating, LRA: label reading attitude.

Research population

The study included a total of 2725 participants residing in Istanbul, Türkiye, comprising individuals from Generation X (n = 786), Generation Y (n = 933), and Generation Z (n = 1006). Inclusion criteria were being aged 18–65 years, residing in Istanbul, voluntary agreement to participate, and completion of both the questionnaire and anthropometric measurements. The exclusion criteria of the study were defined as pregnancy, the presence of cognitive impairments that could hinder participation, and incomplete data in either the questionnaire forms or anthropometric measurements. Individuals were classified into generations based on their birth years as follows: Generation X between 1965 and 1976, Generation Y between 1977 and 1994, and Generation Z between 1995 and 2009 [29].

This study conducted an a priori power analysis for SEM. Power analysis was performed to determine the minimum sample size required to test the reliability of structural parameters of the model. The analysis was conducted using the R package, based on the root mean square error of approximation (RMSEA). Diagonally weighted least squares was used as the fit function, f₀ = 0.0075 based on RMSEA difference and alpha = 0.05 and beta = 0.05 as effect sizes [30]. The model structure with a degree of freedom (df) of 3 required a sample of 2295 participants to detect small effects (RMSEA = 0.05) with 95% power.

Data collection

All data were collected through face-to-face interviews with participants. The interview form developed for this study is available in Supplementary File 1. Data collection was conducted between September and December 2024 within the framework of a convenience sampling approach. Sociodemographic characteristics, SRDQ, anthropometric measurements, levels of ME, and LRA were determined.

Sociodemographic characteristics

Demographic characteristics of the participants, including gender, age, education level, employment status, smoking, alcohol consumption, and the presence of any chronic disease history, were recorded.

Self-rated diet quality

Participants were asked to self-rate their dietary habits and diet quality on a three-point Likert scale (“healthy,” “not healthy,” and “I don’t know”) using the question, “How would you describe your current dietary habits and diet quality?”.

Anthropometric measurements

Participants’ body weights, heights, and waist and hip circumferences were determined by the researchers. Height was measured using a stadiometer, and body weight was measured with a portable weighing scale. Participants were classified according to their body mass index (BMI, kg/m²) values as follows: <18.50, underweight; between 18.50 and 24.99, normal; between 25.00 and 29.99, overweight; and above 29.99, obese. Waist circumference (WC) was assessed at the midpoint between the inferior edge of the least palpable rib and the superior aspect of the iliac crest, using a stretch-resistant tape. Hip circumference was assessed at the broadest part of the buttocks, with the measuring tape positioned parallel to the ground. Each measurement was taken twice; if the measurements were 1 cm apart, the average was calculated. If the difference between the two measurements exceeded 1 cm, two measurements were repeated. For WC measurements, ≥ 80 was considered risky and ≥ 88 was considered high risk in women, and ≥ 94 was considered risky and ≥ 102 was considered high risk in men. A waist–hip ratio (WHR) of ≥ 0.90 cm in men and ≥ 0.85 cm in women was expressed as high risk [31].

Mindful eating questionnaire (MEQ)

The MEQ is a 30-item, five-point Likert-type scale originally developed by Framson et al. [32]. It was adapted into Turkish by Köse et al., who reported that the scale consists of seven subdimensions and has a Cronbach’s alpha internal consistency coefficient of 0.73 [33]. The validity and reliability analyses of the scale were repeated for the current study group, and since the item factor loadings of four items were found to be markedly low, these items were omitted from the scale. Confirmatory factor analysis results (χ² = 1912.424, df = 264, p < 0.01; RMSEA = 0.048; comparative fit index [CFI] = 0.912; Tucker–Lewis index [TLI] = 0.90; standardized root mean square residual [SRMR] = 0.052) confirmed the validity of the 26-item scale and six subscales. Cronbach’s alpha internal consistency coefficients revealed the reliability of the MEQ, with 0.82 for the total scale, 0.69 for disinhibition, 0.82 for emotional eating, 0.69 for control of eating, 0.68 for eating discipline, 0.61 for focusing on food, and 0.62 for interference subscales.

Food label reading attitude scale

The Food Label Reading Attitude Scale is a 20-item, five-point Likert-type scale developed by Sığırcı and Sarp [34]. The Cronbach alpha internal consistency coefficient reported by Sığırcı and Sarp was 0.93, and the factor analysis results revealed that one factor scale was valid and reliable. The current study group (n = 2725), with a reliability coefficient of 0.93 and confirmatory factor analysis results (χ² = 512.927, df = 106, p < 0.01; RMSEA = 0.038; CFI = 0.987; TLI = 0.976; SRMR = 0.028), demonstrated that the scale was valid and reliable.

Statistical analysis

The data were analyzed using SPSS (IBM SPSS Statistics, version 25) and the R package. The distributional properties of the data were evaluated using the Kolmogorov–Smirnov test. For descriptive analysis, means, standard deviation (SD) values, and frequencies of the groups were calculated. Values were presented as the mean ± SD. A one-way ANOVA test was used for comparisons among Generations X, Y, and Z. A chi-square test was performed to compare SRDQ among generations. In the second phase of analysis, SEM was used to assess the direct and indirect relationships between generational group, anthropometric measurements, and SRDQ, with ME and LRA included as mediators. A p-value of < 0.05 was considered statistically significant. Although the model was reported using chi-square statistic (χ2), it was not utilized to evaluate the model fit due to its sensitivity to sample size [35] and the risk of erroneous conclusions in large samples [36], as is the case here. Instead, the TLI, CFI, RMSEA with its 90% confidence interval, and SRMR were employed.

Results

Descriptive statistics of the participants

Information on the demographic characteristics of the participants is presented in Table 1. Overall, 52.5% of participants were female and 47.5% were male. Significant differences were observed between generations in terms of education level and employment status (p < 0.01). The proportion of participants with undergraduate or graduate-level education was highest in Generation Z (83.8%) and lowest in Generation X (31%). Conversely, the proportion of participants with elementary or lower education was highest in Generation X (39.1%). The employment rates of Generations X, Y, and Z were 48.6%, 78%, and 38.88%, respectively. Smoking was most prevalent in Generation Y (45.2%) and least in Generation X (38.4%). Alcohol consumption was highest in Generation Z (40.2%) and lowest in Generation X (36.2%). Significant differences were found between generations for both behaviors (p < 0.01). Chronic disease prevalence decreased across generations: Generations X (36.2%), Y (17.1%), and Z (10.4%). A significant difference was found between generations in terms of chronic disease history (p < 0.01).

Table 1.

Demographic characteristics of participants

	Generation X	Generation Y	Generation Z	p
	n (%)	n (%)	n (%)
Gender
Female	383 (48.70)	505 (54.10)	543 (54.00)
Male	403 (51.30)	428 (45.90)	463 (46.00)
Education level
Elementary school or lower	307 (39.10)	163 (17.50)	12 (1.20)	< 0.01**
High school	235 (29.90)	233 (25.00)	151 (15)
Undergraduate	207 (26.40)	450 (48.20)	805 (80.10)
Graduate	36 (4.60)	87 (9.30)	37 (3.70)
Employment status
Non-Employee	404 (51.40)	205 (22)	616 (61.20)	< 0.01**
Employee	383 (48.60)	728 (78)	390 (38.80)
Smoking
Yes	302 (38.40)	422 (45.20)	423 (42.00)	< 0.05*
No	484 (61.60)	511 (54.80)	583 (58.00)
Alcohol consumption
Yes	193 (24.60)	305 (32.70)	404 (40.20)	< 0.01**
No	593 (75.40)	628 (67.30)	602 (59.80)
Chronic disease history
Yes	284 (36.20)	159 (17.10)	105 (10.40)	< 0.01**
No	501 (63.80)	772 (82.90)	900 (89.60)

*: p < 0.05, **: p < 0.01

The highest scores for ME and LRA were observed in Generations X and Y, respectively, and the lowest in Generation Z. A significant difference in ME and LRA was found between generations (Table 2, p < 0.01).

Table 2.

ME and LRA scores of participants

	Generation X		Generation Y		Generation Z		p
	Mean	SD	Mean	SD	Mean	SD
Mindful Eating Questionnaire	3.48	0.57	3.39	0.54	3.25	0.52	< 0.01*
Food Label Reading Attitude Scale	72.84	17.53	74.37	16.48	68.33	16.71	< 0.01*

*: p < 0.01

Differential analysis of anthropometric measurements and SRDQ between generations

Significant generational differences were observed in BMI, WC, and WHR (Table 3, p < 0.01). The rate of normal BMI was determined as 57.5% in Generation Z, 42.8% in Generation Y, and 29.9% in Generation X. The rate of WC being within normal limits was most common in Generation Z (69.3%) and least common in Generation X (32.2%). Similarly, normal WHR was most common in Generation Z (63.9%) and least common in Generation X (43.4%).

Table 3.

Comparison of anthropometric measurements across generations

	Generation X			Generation Y				Generation Z			p
	%	Mean	SD	%	Mean	SD		%	Mean	SD
BMI
Underweight	0.80	27.56	4.82	1.70	26.22		4.66	8.30	23.70	4.66	< 0.01*
Normal	29.90			42.80				57.50
Overweight	43.80			38.70				26.70
Obese	24.40			15.90				6.90
Morbid obese	1.10			1.00				0.60
WC
Normal	32.20	93.45	13.99	45.70	88.25	16.21		69.30	80.89	14.90	< 0.01*
At risk	23.00			23.00				17.80
At high risk	44.80			31.30				12.90
WHR
Normal	43.40	0.88	0.12	54.90	0.86	0.26		63.90	0.81	0.11	< 0.01*
At risk	56.60			45.10				36.10

BMI body mass index, WC waist circumference, WHR waist-hip ratio 

*: p < 0.01

SRDQ levels of the generations are presented in Table 4. Significant differences were revealed among generations in terms of SRDQ (χ² = 52.83, sd = 4, p < 0.001). While 43% of Generation X reported that their dietary habits and diet quality were healthy, this rate decreased to 38.2% in Generation Y and 30% in Generation Z. Conversely, the rate of participants who described their dietary habits as unhealthy was the highest in Generation Z at 51.6%.

Table 4.

Comparison of SRDQ across generations

Generation		f (%)	χ²	df	p
X	Healthy	338 (43.00%)	52.83	4	< 0.001*
	Not healthy	275 (35.00%)
	I don’t know	173 (22.00%)
Y	Healthy	356 (38.20%)
	Not healthy	405 (43.40%)
	I don’t know	172 (18.40%)
Z	Healthy	302 (30.00%)
	Not healthy	519 (51.60%)
	I don’t know	185 (18.40%)

*: p < 0.001

Structural model evaluation

The SEM included one exogenous variable (generations), two mediators (ME and LRA), and one endogenous variable (either an anthropometric variable or SRDQ). Generation X was considered the reference group, whereas Generations Y and Z were treated as dummy variables. Table 5 presents the SEM fit indices used in this study. For all models, χ2 = 22.627 (p < 0.01). Model 2 in this study met the criteria for an acceptable fit, and the other models had an excellent fit with a CFI of ≥ 0.95, TLI of ≥ 0.95, RMSEA of ≤ 0.05, and SRMR of ≤ 0.05. The consistency of the fit indices indicated that the hypothesized relationships were compatible with the data and that the model was statistically valid [35, 37].

Table 5.

Values related to the fit criteria of the established structural equation modeling

Fit Indexes	Excellent Fit	Acceptable Fit	Model 1 Fit Values	Model 2 Fit Values	Model 3 Fit Values	Model 4 Fit Values
RMSEA	0 ≤ RMSEA ≤ 0.05	0.05 < RMSEA ≤ 0.08	0.05	0.06	0.04	0.05
SRMR	0.00 ≤ SRMR ≤ 0.05	0.05 ≤ SRMR ≤ 0.10	0.013	0.013	0.013	0.013
CFI	0.95 ≤ CFI ≤ 1.00	0.95 ≤ CFI < 0.90	0.95	0.94	0.96	0.97
TLI	TLI ≥ 0.95	TLI ≥ 0.90	0.95	0.91	0.97	0.97

RMSEA Root Mean Square Error of Approximation, SRMR Standardized Root Mean Square Residual, CFI Comparative Fit Index, TLI Tucker-Lewis Index

Pathways between generations, anthropometric variables, and SRDQ

Table 6; Fig. 2 present the direct regression coefficients between variables in the hypothesized models. The ME levels of Generations Y and Z were significantly lower than those of Generation X (β = −0.171 and β = −0.423, respectively; p < 0.001). ME positively and significantly affected LRA (β = 0.314, p < 0.001). The LRA of Generation Z was significantly lower than that of Generation X (β = −0.132, p = 0.003), whereas this difference was significantly higher in Generation Y (β = 0.144, p = 0.001).

Table 6.

Direct standardized regression coefficients between variables in all models

Dependent variable	Independent variable	β	SE	z	p	%95 CI (Lower - Upper)
ME	Gen Y (vs. X)	−0.171	0.047	−3.673	< 0.001*	−0.263	−0.079
	Gen Z (vs. X)	−0.423	0.047	−9.047	< 0.001*	−0.515	−0.331
LRA	ME	0.314	0.021	14.802	< 0.001*	0.273	0.355
	Gen Y (vs. X)	0.144	0.044	3.241	0.001*	0.058	0.230
	Gen Z (vs. X)	−0.132	0.044	−2.995	0.003*	−0.218	−0.046
WC	ME	−0.170	0.022	−7.619	< 0.001*	−0.213	−0.127
	LRA	0.042	0.021	2.045	0.04*	0.001	0.083
	Gen Y (vs. X)	−0.389	0.055	−7.054	< 0.001*	−0.497	−0.281
	Gen Z (vs. X)	−1.039	0.058	−17.992	< 0.001*	−1.153	−0.925
WHR	ME	−0.041	0.025	−1.682	0.093	−0.090	0.008
	LRA	−0.061	0.022	−2.743	0.006*	−0.104	−0.018
	Gen Y (vs. X)	−0.291	0.061	−4.772	< 0.001*	−0.411	−0.171
	Gen Z (vs. X)	−0.556	0.061	−9.111	< 0.001*	−0.676	−0.436
BMI	ME	−0.185	0.021	−8.967	< 0.001*	−0.463	−0.247
	LRA	0.021	0.018	1.141	0.254	−0.014	0.056
	Gen Y (vs. X)	−0.35	0.053	−6.557	< 0.001*	−0.491	−0.263
	Gen Z (vs. X)	−0.947	0.054	−17.525	< 0.001*	−1.065	−0.849
SRDQ	ME	0.317	0.022	14.717	< 0.001*	0.274	0.360
	LRA	0.064	0.02	3.278	0.001*	0.017	0.111
	Gen Y (vs. X)	−0.123	0.054	−2.279	0.023*	−0.229	−0.017
	Gen Z (vs. X)	−0.235	0.054	−4.369	< 0.001*	−0.341	−0.129

Gen generation, ME mindful eating, LRA label reading attitude, WC waist circumference, WHR waist-hip ratio, BMI body mass index, SRDQ self-rated diet quality

*: p < 0.05 indicates statistical significance

Fig. 2.

Test results of the hypothesis model. The effects of the generation groups on (a) waist circumference (WC), (b) waist–hip ratio (WHR), (c) body mass index (BMI), and (d) self-rated diet quality (SRDQ) directly and indirectly through mindful eating (ME) and label reading attitude (LRA). Gen: generation. *: p < 0.05 indicates statistical significance

WC was significantly associated with ME (β = −0.170, p < 0.001) and LRA (β = 0.042, p = 0.042). Compared with Generation X, Generations Y and Z exhibited significantly lower WC (β = −0.389, β = −1.039, respectively; p < 0.001). The LRA was found to reduce WHR (β = −0.61, p = 0.006), whereas ME had no significant affect WHR (p = 0.093). Further, the WHR of individuals from Generations Y and Z was statistically significantly lower than that of Generation X (β = −0.291, β = −0.556, respectively; p < 0.001). BMI was significantly affected by both ME and generation. ME was negatively correlated with BMI (β = −0.185, p < 0.001). Moreover, BMI was significantly lower for Generations Y and Z than for Generation X (β = −0.35, β = −0.947, p < 0.001, respectively). LRA did not have a significant effect on BMI (p = 0.254).

SRDQ was significantly and positively associated with LRA (β = 0.064, p = 0.001) and ME (β = 0.317, p = < 0.001). SRDQ levels were lower in Generations Y (β = −0.123, p = 0.023) and Z (β = −0.235, p < 0.001) than in Generation X.

In terms of indirect effects, WC was significantly influenced by LRA in Generations Y (β = 0.029, p = 0.001) and Z (β = 0.072, p < 0.001). Generation Z also showed a significant and negative indirect effect on WC through ME and LRA (β = −0.006, p = 0.048). WHR was indirectly affected by Generation Y (β = −0.009, p = 0.036) and Generation Z (β = 0.008, p = 0.046) via LRA. In addition, the effects of Generations Y and Z on WHR via ME and LRA were positive and significant (respectively, β = 0.003; β = 0.008, p < 0.05). The effect of ME on BMI was positive for Generations Y (β = 0.032, p = 0.001) and Z (β = 0.078, p < 0.001), though LRA had no indirect effects.

The effects of Generations Y and Z on SRDQ via ME (β = −0.054, β = −0.134, respectively; p < 0.001) and via ME and LRA (β = −0.003, p = 0.016; β = −0.009, p = 0.002, respectively) were negative. Generation Y showed a positive indirect effect on SRDQ through LRA (β = 0.009, p = 0.022), whereas Generation Z exhibited a negative effect (β = −0.008, p = 0.027). The indirect regression coefficients between the variables in the hypothesized models are presented in Table 7.

Table 7.

Indirect effects between variables in all models.

Path	β	SE	z	p	%95 CI (Lower - Upper)
Gen Y → ME → WC	0.006	0.003	1.749	0.08	0.000	0.012
Gen Z → ME → WC	−0.006	0.003	−1.67	0.095	−0.012	−0.000
Gen Y → LRA → WC	0.029	0.009	3.321	0.001*	0.011	0.047
Gen Z → LRA → WC	0.072	0.012	5.811	< 0.001*	0.048	0.096
Gen Y → ME → LRA → WC	−0.002	0.001	−1.798	0.072	−0.004	−0.000
Gen Z → ME → LRA → WC	−0.006	0.003	−1.973	0.048*	−0.012	−0.000
Gen Y → ME → WHR	0.007	0.005	1.527	0.127	−0.003	0.017
Gen Z → ME → WHR	0.017	0.011	1.649	0.099	−0.005	0.039
Gen Y → LRA → WHR	−0.009	0.004	−2.101	0.036*	−0.017	−0.001
Gen Z → LRA → WHR	0.008	0.004	1.999	0.046*	0.000	0.016
Gen Y → ME → LRA → WHR	0.003	0.001	2.177	0.03*	0.001	0.005
Gen Z → ME → LRA → WHR	0.008	0.003	2.593	0.01*	−0.003	0.009
Gen Y → ME → BMI	0.032	0.009	3.391	0.001*	−0.050	−0.014
Gen Z → ME → BMI	0.078	0.012	6.308	< 0.001*	0.054	0.102
Gen Y → LRA → BMI	0.003	0.003	1.075	0.282	−0.003	0.009
Gen Z → LRA → BMI	−0.003	0.003	−1.064	0.287	−0.009	0.003
Gen Y → ME → LRA → BMI	−0.001	0.001	−1.081	0.28	−0.003	0.001
Gen Z → ME → LRA → BMI	−0.003	0.002	−1.122	0.262	−0.007	0.001
Gen Y → ME → SRDQ	−0.054	0.015	−3.557	< 0.001*	−0.083	−0.025
Gen Z → ME → SRDQ	−0.134	0.018	−7.569	< 0.001*	−0.169	−0.099
Gen Y → LRA → SRDQ	0.009	0.004	2.297	0.022*	0.001	0.017
Gen Z → LRA → SRDQ	−0.008	0.004	−2.204	0.027*	−0.016	−0.000
Gen Y → ME → LRA → SRDQ	−0.003	0.001	−2.41	0.016*	−0.005	−0.001
Gen Z → ME → LRA → SRDQ	−0.009	0.003	−3.05	0.002*	−0.015	−0.003

Gen generation, ME mindful eating, LRA label reading attitude, WC waist circumference, WHR waist-hip ratio, BMI body mass index, SRDQ self-rated diet quality

*: p < 0.05 indicates statistical significance

Discussion

This is a pioneering investigation of the effects of generational differences in anthropometric risk factors and SRDQ, mediated by ME and LRA. The findings revealed significant generational differences, with both direct and indirect effects of the aforementioned risk factors. All anthropometric measurements, including BMI, WC, and WHR, were significantly higher in Generation X than in Generations Y and Z. This increase is consistent with evidence showing that aging is characterized by significant negative changes in body composition [38]. Aging is associated with psychosocial [39] and physiological changes [40], including lifestyle factors, such as nutrition and physical activity. Pontzer et al. demonstrated decreased resting energy expenditure starting at 46.5 years of age, revealing that energy metabolism declines with age [41]. Furthermore, the total fat mass tends to increase with age, and its localization in the body changes [38]. WC and abdominal visceral fat frequently increase with age [42]. Yıldız and Çetinkaya reported that the obesity rates were highest among individuals aged 50–65 years [43]. From a psychosocial perspective, the encounter of Generation X with the European market and their subsequent familiarity with modern consumption culture, combined with the development of new and luxurious eating and drinking habits, may explain certain anthropometric measurements [44]. Conversely, the levels of anthropometric variables reflecting health risks in the study were lowest in Generation Z. Consistent with previous evidence, younger individuals generally exhibit lower age-related anthropometric risks [38, 42]. In addition, Frayn and Knäuper emphasized that young individuals achieve weight control in the short term by unconsciously restricting their calorie intake; however, their risk of emotional eating increases in the long term [45]. Öztürk and Tekeli reported that Generation Z places greater importance on weight control than Generations X and Y, equating it with overall health [28].

The ME levels of Generation Z individuals were found to be lower than those of Generations X and Y. Similarly, another study demonstrated that ME scores were highest in the oldest generation and lowest in the youngest generation [9]. Bayram et al. reported a negative relationship between social media addiction and ME, suggesting that the high digital exposure of Generation Z (e.g., smartphone use) may be associated with greater automaticity in eating and reduced sensory awareness [46]. Similarly, Seslikaya and Arslan reported a significant positive relationship between social media use and emotional eating, which emphasizes the possible impact of digital exposure on unhealthy eating patterns [47].

ME has previously been associated with lower BMI, reduced eating disorder symptoms, and successful weight management [48]. In this study, ME was significantly associated with higher LRA and lower BMI, WHR, and WC. Generations Y and Z exhibited significantly lower ME levels than Generation X, which in the model were statistically associated with higher BMI through indirect pathways. No studies have examined the relationship between ME and LRA. Our findings suggest that higher ME levels may be linked to greater nutritional awareness and a tendency to evaluate product labels before purchase more critically. This is consistent with the study conducted by Miller et al., who reported that ME interventions increased scores on eating-related self-efficacy and cognitive control overeating [49].

Individuals often do not use food labels effectively or at all [18, 50]. This may be due to difficulties in understanding label information or a reluctance to invest time and effort required to interpret this information [51, 52]. Sociodemographic factors, such as age and education, significantly affect the search, understanding, and use of food label information [53]. Older individuals are more likely to read labels than younger individuals due to their sensitivity regarding their health [54, 55]. In this study we found that Generation Z had a significantly lower LRA than Generation X. This finding can be explained by the fact that young individuals tend to misinterpret complex label information, as emphasized by Talagala and Arambepola [56]. In addition, the Food Standards Agency report indicated that 67% of young individuals underestimate the calorie content of products labeled as “light” [57]. Empirical evidence suggests that higher exposure to social media advertisements in younger generations is associated with reduced reliance on nutrition labels [58]. Similarly, reports indicate that the increasing use of digital platforms among youth decreases attention to traditional, evidence-based sources such as food labels [59]. Furthermore, the study found that Generation Y had the highest LRA scores. This may be related to this generation’s increasing interest in the food production process, functionality, and sustainability [60]. The findings reveal the necessity of structuring food label education using generation-specific approaches.

The study found significant relationships between LRA and anthropometric variables; a positive correlation with WC and a negative correlation with WHR were observed. The literature has reported that individuals who regularly read and understand food labels have healthier body weights and lower obesity risks [61]. This protective effect is due to mechanisms such as increased quality food choices, improved portion control, and increased awareness of energy and nutrient content [62]. In addition, improving label reading skills through food literacy and nutrition education interventions results in significantly improved diet quality and anthropometric indicators [63]. In this context, the current study found that low LRA levels in Generation Z were associated with higher risk of WC and WHR. In Generation Y, label reading attitudes were associated with higher WC, while they were inversely associated with WHR. This paradox may indicate a limitation arising from the assessment of label reading solely at the attitudinal level, which does not adequately reflect actual behavior or label literacy. As highlighted in previous conceptual frameworks [59], label reading literacy requires individuals to integrate multiple skills, including accessing nutritional information on packages, accurately comprehending this information, comparing different labels, critically evaluating the obtained data, and ultimately applying these insights to their dietary choices. Indeed, Sharf et al. reported that 43.9% of young adults thought that they understood labels very well, but only 27.2% had high comprehension scores [64]. Similarly, a study conducted with Malaysian adolescents aged 13–16 years indicated that 89.7% of them read labels but only exhibited moderate label literacy [65]. These findings demonstrate that considering label reading not merely at the attitudinal level but as a multifaceted construct encompassing comprehension, comparison, evaluation, and application is critical for understanding health outcomes.

The study found that Generation X had the highest SRDQ, whereas Generation Z had the lowest SRDQ compared with other generations. Akşit and Aşık reported that Generation X tends to value health in food choices and adopts more health-oriented behaviors, whereas Generation Z exhibits a hedonistic approach, giving more importance to taste, convenience, and ease of preparation [66]. Previous studies have linked digital media addiction, early brand loyalty, and a higher preference for convenience foods to less health-conscious dietary behaviors in Generation Z, which have been empirically associated with their lower SRDQ scores [67, 68]. Sánchez–Sánchez et al. reported that the use of digital devices during meals increases the consumption of ultra-processed foods and decreases the compliance with the Mediterranean diet [40]. These findings collectively provide empirical support that digital media exposure and the preference for convenience foods represent key behavioral mechanisms underlying the lower SRDQ levels observed in Generation Z.

Food labels are effective in changing consumer behaviors and purchasing decisions [53]. Food label use is the main determinant of diet quality [69]. Food label use has been associated with healthy dietary patterns, eating practices, and lifestyle behaviors. It increases fruit and vegetable consumption and reduces fat intake [70]. In older adults, the habit of reading food labels has been reported to be associated with better health status and healthier dietary behaviors [71]. The study revealed a positive and significant relationship between SRDQ and LRA. In Generation Y, high LRA indirectly increased SRDQ. However, in Generation Z, SRDQ was associated with low LRA, which indirectly decreased through the combined effects of ME and LRA in Generations Y and Z. The decline in SRDQ levels in younger generations may be associated with decreased nutrition and health awareness, changing lifestyle norms, and economic constraints. In this context, the findings of Kırbıyık et al., who reported a negative association between food label reading attitudes and food addiction in adults [72], provide valuable evidence linking LRA not only to nutritional awareness but also to problematic eating behaviors. This supports a more comprehensive interpretation of the indirect effects of LRA in the present model. Beyond attitudinal and behavioral mechanisms, generational differences in SRDQ may also reflect economic priorities. Makowska et al. reported that older generations prioritized food quality in their dietary choices, whereas younger generations placed greater emphasis on price [73]. This suggests that structural and economic determinants should also be considered when interpreting intergenerational differences in diet quality.

In this study, ME was positively associated with SRDQ, a finding consistent with those of previous studies revealing that it supports individuals to naturally regulate their energy intake, make more conscious food choices, and comply with evidence-based dietary recommendations [14, 74]. Large-scale observational studies have revealed a significant and positive relationship between ME behaviors and high diet quality scores. For example, the large-scale NutriNet–Santé study reported that ME was positively associated with Mediterranean diet scores and negatively associated with energy intake and ultra-processed food consumption [14]. Another study conducted with adults in Turkey reported that ME may help avoid excessive consumption of high-energy foods [75]. In addition, this study found that SRDQ was lower in Generations Y and Z compared with Generation X, and this difference was associated with lower ME levels in these generations. The findings highlight potential associations that warrant further investigation through intervention studies to determine whether ME-focused strategies can improve diet quality across generations.

The large sample size and the use of SEM, a robust analytical method that allows simultaneous examination of complex direct and indirect relationships, are the major strengths of this study. In addition, the multidimensional analysis of behavioral variables such as ME and LRA, both in general and at the generation level, puts this study in a unique position to contribute to the existing literature. Studies examining generations have generally focused on the last two generations [76]. In contrast, the broad generational scope of this study from Generation X to Z increases its value.

However, some limitations of the study should be considered. First, given its cross-sectional design, the findings should be interpreted as associations rather than causal relationships. Prospective longitudinal investigations are required to more clearly delineate the behavioral determinants underlying generational differences. While our SEM analyses accounted for the principal covariates included in the model, important lifestyle and contextual factors (e.g., physical activity, overall dietary intake, and socioeconomic status) were not comprehensively captured, and therefore the possibility of residual confounding cannot be excluded. Second, the reliance on self-reported evaluations of dietary quality and eating behaviors may have introduced systematic biases, including social desirability and recall errors. This limitation may have led to the underestimation of unhealthy behaviors and the overestimation of healthy practices. Furthermore, the extent to which individuals with higher levels of LRA accurately comprehend and apply label information remains uncertain. Some participants may misinterpret “healthy” labels and inadvertently overconsume energy-dense products. In future studies, reliance solely on attitudinal measures should be avoided, and more objective methods that go beyond self-reported assessments and are supported by validated label literacy tests and behavioral evaluations should be employed. This approach will contribute to distinguishing between LRA and actual label literacy. Finally, sociocultural factors may have influenced the findings, as the study was restricted to Turkish literates residing in a single metropolitan context (Istanbul, Türkiye), thereby limiting generalizability to diverse sociocultural contexts. Although the relatively large sample size enhanced statistical power, the use of convenience sampling and reliance on voluntary participation may have introduced self-selection bias, further constraining the external validity of the results.

Conclusion

This study found that Generation Z had the lowest levels of ME, LRA, BMI, WC, WHR, and SRDQ. Generational differences in ME and LRA appear to be associated with important public health outcomes. The fact that Generation Z has lower ME and LRA suggests that this group is likely influenced by contemporary factors such as fast-paced lifestyles, digitalization, and increased exposure to high stimuli. Low levels of SRDQ reported by Generation Z may be linked to potential challenges in long-term health sustainability. Considering that LRA and ME behavior were statistically associated with anthropometric variables and SRDQ, integrating nutrition education programs at an early age could yield positive results in the long term. Such interventions may improve individuals’ current nutritional behaviors and contribute to the prevention of obesity, diabetes, and cardiometabolic diseases. It may also enable the reduction of intergenerational health inequalities.

Abbreviations

BMI

Body mass index

χ2

Chi-square statistic

CFI

Comparative fit index

CI

Confidence interval

df

Degree of freedom

LRA

Label reading attitudes

ME

Mindful eating

MEQ

Mindful Eating Questionnaire

RMSEA

Root mean square error of approximation

SRDQ

Self-rated diet quality

SD

Standard deviation

SRMR

Standardized root mean square residual

SEM

Structural equation modeling

TLI

Tucker–Lewis index

WC

Waist circumference

WHR

Waist–hip ratio

Authors’ contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ö.A, B.Y and M.Ş.D. The initial and final drafts of the manuscript was written by Ö.A. All authors read, reviewed, and approved the final manuscript.

Funding

This research received no external funding.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

The study is in line with the ethical norms of human research. All methods of the study were carried out in accordance with the Declaration of Helsinki and relevant regulations. The study was approved by Fenerbahçe University Non-Interventional Clinical Research Ethics Committee (No: 82.2024fbu). Informed consent was obtained from all participants before inclusion in the study.

Consent for publication

Not applicable. This manuscript does not contain any individual person’s data (e.g., images, videos, or clinical details) that require consent for publication.

Competing interests

The authors declare no competing interests.