Diet quality remains a central determinant of noncommunicable disease risk, operating alongside physical activity and other lifestyle practices across the life course (1–3). Contemporary public guidance emphasizes patterns rich in minimally processed foods, fruits and vegetables, whole grains, and appropriate energy balance, combined with regular moderate-to-vigorous activity (4). Aligning everyday eating with these recommendations is challenging because dietary choices are influenced by a combination of factors, including knowledge, values, motivation, self-regulation, habits, social context, and environmental constraints that affect time and cost (5–7).

National surveillance indicates that Türkiye faces a substantial burden of modifiable noncommunicable disease risk factors, providing a clear rationale for examining nutrition-related constructs in this context (8, 9). National monitoring data indicate that insufficient physical activity is common among adults and that excess body weight remains a widespread public-health concern (8). Official health statistics likewise show a continuing high prevalence of obesity among individuals aged 15 years and older (9). Consistent with these national indicators, recent evidence in Turkish adults suggests that nutrition-related attitudes vary across physical-activity and body-mass profiles, supporting the inclusion of lifestyle factors when examining evaluative nutrition constructs in Türkiye (10).

Health literacy and life-course perspectives underscore that capacities relevant to navigating food environments evolve with age, education, and accumulated experience, and they extend well beyond the possession of factual information (11, 12). Evidence across diverse adult populations shows that higher factual understanding is only inconsistently mirrored by healthier appraisals or intentions, pointing to gaps between what people know and how they evaluate or plan to act (13, 14). These gaps matter for intervention design because programs that privilege information alone may leave the motivational and regulatory ingredients of behavior change unaddressed.

Dual-process accounts and related social-cognition models position attitudes, perceived control, and habit strength as proximal to behavior, whereas declarative knowledge exerts influence chiefly when it is embedded in motivated, self-regulated action (15, 16). Meta-analytic syntheses consistently find that behavior-change techniques targeting self-regulatory processes goal setting, action and coping planning, self-monitoring, and feedback yield stronger effects than education alone, particularly when delivered in autonomy-supportive ways (17, 18). Randomized and digitally mediated interventions that incorporate motivational interviewing or self-determination theory–informed components further demonstrate added value beyond purely informational content (19–22).

Large population datasets reveal clustering of physical activity, smoking, alcohol use, and dietary behaviors within coherent profiles that appear to share determinants such as self-efficacy, health identity, and planning skill (23–27). Within this constellation, public-health anchors for weekly activity approximately 150 min of moderate intensity offer pragmatic thresholds for classifying exposure (4). Because attitudes index motivational readiness and regulatory capacity, they should align more closely with other health-promoting practices than factual knowledge alone (28).

Cohabitation and social ties can facilitate meal planning and reinforce norms that support healthier eating, whereas living alone is often associated with less favorable dietary patterns (29, 30). These sociodemographic gradients may strengthen evaluative orientations toward healthy eating even when factual knowledge has already reached a basic threshold through schooling or media exposure (11, 31, 32). Together, this literature suggests that attitudes may track lifestyle and social positioning more reliably than knowledge, with implications for the targeting of limited intervention resources.

This study (i) profiles adults’ nutrition knowledge and healthy-eating attitudes; (ii) quantifies their correspondence and any knowledge–attitude gap; and (iii) tests for differences across lifestyle factors physical activity, smoking, alcohol use, and family history of chronic illness as well as sociodemographic characteristics (education, age, gender, marital status). Guided by behavioral-clustering theory, the a priori expectation is that attitudes being closer to motivation and self-regulation will exhibit stronger and more consistent gradients across lifestyle factors than knowledge, whereas knowledge will show weaker or no gradients after accounting for sociodemographics. A further objective is to evaluate whether healthy-eating attitudes are independently associated with nutrition knowledge in multivariable models, anticipating only small associations if the constructs operate through distinct pathways.

The aim of the study is to determine the relationship between adults’ nutrition knowledge and their attitudes toward healthy eating and to assess how both vary by lifestyle and sociodemographic characteristics, thereby informing whether future programs should target informational, motivational, or contextual strategies for maximal impact.

The sample included 408 adults (46.3% women, n = 189; 53.7% men, n = 219; mean age = 28.60 years). By highest educational attainment, 48.5% (n = 198) had completed high school, 17.2% (n = 70) held an associate degree, 24.0% (n = 98) held a bachelor’s degree, and 10.3% (n = 42) held a master’s degree. Regarding marital status, 65.2% (n = 266) were single and 34.8% (n = 142) were married (see Table 2).

As shown in Table 3, men scored higher on NKS than women, t(406) = −3.70, p < 0.001, mean difference (Male − Female) = 8.98 [95% CI (4.21, 13.75)], d = 0.36. ASHN Total and its subscales did not differ by gender (e.g., mean difference (Male − Female) = −0.63, 95% CI [−3.01, 1.75] for ASHN Total; all p > 0.05).

As shown in Table 4, married participants scored higher on ASHN than single participants, t(406) = 3.31, p < 0.001, mean difference (Single − Married) = −4.15 [95% CI (−6.61, −1.69)], d = 0.34. Subscales showed a similar pattern for EN, t(406) = 5.06, p < 0.001, mean difference (Single − Married) = −2.42 [95% CI (−3.36, −1.48)], d = 0.52; for PN, t(406) = 2.64, p = 0.009, mean difference (Single − Married) = −1.15 [95% CI (−2.01, −0.29)], d = 0.27; and for MP, t(406) = 3.06, p = 0.002, mean difference (Single − Married) = −1.35 [95% CI (−2.22, −0.48)], d = 0.31. IN did not differ by marital status, t(406) = −1.83, p = 0.068, mean difference (Single − Married) = 0.82 [95% CI (−0.06, 1.70)], d = −0.19. NKS Total did not differ, t(406) = 0.68, p = 0.490, mean difference (Single − Married) = −1.76 [95% CI (−6.85, 3.33)].

As shown in Table 5, only IN differed by family chronic illness, t(406) = −2.41, p = 0.016, mean difference (No − Yes) = 1.01 [95% CI (0.19, 1.83)], d = 0.24. NKS Total, ASHN Total, EN, PN, and MP did not differ between groups [all p > 0.05; e.g., ASHN Total mean difference (No-Yes) = 1.11, 95% CI (−1.34, 3.56)].

As shown in Table 6, ASHN was higher among non-users than users, t(406) = 2.48, p = 0.013, mean difference (Users − Non-users) = −3.13 [95% CI (−5.61, −0.65)], d = 0.26. MP was lower among non-users, t(406) = 3.65, p < 0.001, mean difference (Users − Non-users) = −1.61 [95% CI (−2.48, −0.74)], d = 0.38. NKS Total, IN, EN, and PN did not differ by alcohol use [all p > 0.05; e.g., NKS mean difference (Users − Non-users) = −1.30, 95% CI (−6.41, 3.81)].

As shown in Table 7, Educational attainment was not associated with NKS, F(3,404) = 0.52, p = 0.660, η² < 0.01. In contrast, healthy-eating attitudes differed substantially by education, ASHN Total: F(3,404) = 18.97, p < 0.001, η² = 0.12, with master’s degree reporting higher attitudes (M = 86.23, SD = 12.03) than high-school graduates (M = 72.84, SD = 10.81). The same pattern was observed for EN (F(3,404) = 19.26, p < 0.001, η² = 0.12; MD M = 22.61 vs. HS M = 17.63) and for IN, PN, and MP (all p ≤ 0.006; η² = 0.03–0.06), indicating an education gradient primarily for attitudinal domains rather than knowledge.

As shown in Table 8, physical activity frequency showed no statistically significant differences in NKS (F(3,404) = 2.14, p = 0.090, η² = 0.01). However, physical activity was associated with more favorable healthy-nutrition attitudes, ASHN Total: F(3,404) = 10.10, p < 0.001, η² = 0.07, with the high-activity group scoring higher (M = 82.45, SD = 9.88) than the sedentary group (M = 71.22, SD = 13.20). Differences were also evident for IN (F(3,404) = 8.10, p < 0.001, η² = 0.05) and PN (F(3,404) = 11.11, p < 0.001, η² = 0.07), whereas EN did not differ across activity levels (F(3,404) = 1.74, p = 0.150). MP varied modestly (F(3,404) = 3.29, p = 0.020, η² = 0.02), suggesting that activity frequency aligned more with information/positive nutrition orientations than with emotion-related attitudes.

As shown in Table 9, smoking status was associated with small but significant differences in NKS (F(3,404) = 3.47, p = 0.010, η² = 0.02), with heavier smokers reporting higher knowledge (≥11/day: M = 54.39, SD = 23.04) than former smokers (M = 41.73, SD = 22.44). In contrast, healthy-nutrition attitudes were less favorable among heavier smokers: ASHN Total: F(3,404) = 6.47, p < 0.001, η² = 0.04, with never smokers (M = 78.53, SD = 10.86) and former smokers (M = 78.37, SD = 13.32) scoring higher than ≥11/day smokers (M = 72.30, SD = 12.65). Similar differences were observed for IN (F(3,404) = 4.94, p < 0.001, η² = 0.03), PN (F(3,404) = 5.86, p < 0.001, η² = 0.04), and MP (F(3,404) = 6.41, p < 0.001, η² = 0.04), while EN did not differ (F(3,404) = 1.98, p = 0.110). Overall, smoking status related more consistently to nutrition attitudes than to knowledge.

As shown in Table 10, age was positively associated with ASHN Total (r = 0.27, p < 0.001) and with the EN (r = 0.31), PN (r = 0.19), and MP (r = 0.23) subscales, indicating small-to-moderate increases in healthier attitudes with greater age. By contrast, no association was observed between age and NKS Total (r = −0.07, ns) or IN (r = 0.01, ns).

As shown in Table 11, higher NKS scores were modestly associated with lower healthy-eating attitudes overall (ASHN Total: r = −0.18, p < 0.001). Negative associations were also observed for IN (r = −0.21, p < 0.001), PN (r = −0.11, p = 0.020), and MP (r = −0.17, p < 0.001), whereas EN showed no association (r = −0.01, p = 0.760). Collectively, these small effects suggest that greater nutrition knowledge does not necessarily translate into more favorable attitudes across all domains, indicating a possible knowledge–attitude gap.

As shown in Table 12, a simultaneous multiple linear regression examined whether ASHN subscales were independently associated with NKS Total. The overall model was significant but explained a small proportion of variance (R² = 0.063; Adj. R² = 0.054). Controlling for the other subscales, IN (β = −0.17, p = 0.001) and MP (β = −0.16, p = 0.006) were negative predictors of NKS, whereas EN (β = 0.06, p = 0.270) and PN (β = 0.02, p = 0.620) were not significant. Diagnostics indicated no multicollinearity concerns (Tolerance = 0.64–0.79; VIF = 1.25–1.55) and approximately independent errors (DW ≈ 2.00). Residual-versus-fitted plots indicated approximate linearity and homoscedasticity, Cook’s distance and leverage did not identify influential cases, and the Normal P–P plot showed standardized residuals closely approximating normality (Figure 1). Higher scores on the IN and MP attitude facets were associated with lower nutrition-knowledge scores after adjustment, whereas affective and positive-nutrition attitudes (EN, PN) were unrelated to knowledge. Overall, ASHN subscales accounted for approximately 5–6% of variability in NKS, indicating that nutrition-related attitudes and nutrition knowledge were only weakly linked in this sample.

This study clarified how adults’ NKS relates to their ASHN and how these constructs vary across physical activity, smoking, alcohol use, family chronic illness, and sociodemographic factors. Correlations between NKS and ASHN indices were small and frequently negative (e.g., ASHN Total r ≈ −0.18; IN r ≈ −0.21; PN r ≈ −0.11; MP r ≈ −0.17), and multiple regression showed small negative coefficients for IN and MP when predicting NKS after mutual adjustment. This pattern reflects construct distinctness rather than measurement failure: NKS captures declarative facts, whereas ASHN reflects evaluative orientation agreement with positive nutrition practices (PN/IN) and lower endorsement of malnutrition-congruent statements (MP). People with higher knowledge may also be more risk-attentive and thus more likely to recognize pitfalls that map onto MP items, producing modest inverse links. In some subgroups most visibly heavier smokers greater exposure to health information may not translate into more favorable attitudes, suggesting cognitive–affective misalignment or reactance (7). Unmeasured variables such as time scarcity, cost salience, diet restraint, self-regulation, or food environment plausibly shape knowledge acquisition and attitudes in different ways, further attenuating alignment in cross-section. The modest explained variance and marginal associations are consistent with two complementary explanations: measurement error in brief self-report indicators and construct mismatch, because declarative knowledge does not necessarily translate into motivation, self-regulation, or environmental opportunity. In future work, incorporating psychosocial determinants (e.g., autonomous motivation, self-efficacy, planning, habit strength) and contextual measures (e.g., time pressure, affordability, food availability) alongside richer behavioral indicators (diet quality indices and device-based activity) may improve model fit and clarify pathways.

Attitudes clustered with lifestyle, whereas knowledge did not. ASHN Total and its cognitively evaluative facets (IN and PN) increased monotonically from sedentary to high physical-activity categories, yet NKS did not vary by activity. Parallel gradients appeared for smoking and alcohol: never- and former-smokers and non-drinkers showed more favorable attitudes (higher PN, lower MP), while NKS was comparable across alcohol groups and paradoxically higher among heavier smokers. These converging gradients are consistent with behavioral clustering (23–25): individuals oriented toward one health domain (activity, non-smoking) also endorse more favorable nutrition attitudes, likely via shared determinants such as self-efficacy, planning, goal setting, and health identity (16, 26, 27, 41). Knowledge, by contrast, may accumulate through schooling, media, or clinical encounters and thus remains orthogonal to day-to-day lifestyle patterns (42). Notably, EN was the least discriminating subdomain across activity and lifestyle strata, whereas IN and PN differentiated groups clearly; interventions that emphasize beliefs and concrete practices may therefore tap into the same self-regulatory capacities that sustain physical activity and non-smoking, while purely affective appeals may be less sensitive to existing lifestyle orientation (16, 17).

Sociodemographic gradients favored attitudes over knowledge. Educational attainment displayed a robust stepwise association with ASHN (Master’s > Bachelor’s/Associate > High school) without a corresponding gradient in NKS, consistent with the idea that formal education shapes values, norms, and executive capacities that support favorable appraisals of healthy eating (43, 44) more than it augments domain-specific facts beyond a general threshold. Married participants showed more favorable ASHN, plausibly reflecting shared household routines, meal planning, and diffusion of health priorities within families (10, 29, 30). Age correlated positively with EN, PN, and MP, but not with NKS, suggesting that life-course prioritization of health may gradually strengthen attitudes even when factual knowledge remains relatively stable (11, 13, 45). Taken together, these gradients support a view in which attitudes are embedded in roles and contexts, whereas knowledge is comparatively context-insensitive once basic literacy is achieved (12).

Within a planned-behavior perspective, IN/PN align more closely with attitudinal and control beliefs that feed intention and action than NKS does (20, 46, 47). Dual-process accounts similarly predict that affective dispositions (EN) and cognitive evaluations (IN/PN) need not rise with knowledge unless interventions also engage motivation, identity, and habit (14, 15). Durable improvements likely require pairing informational content with self-regulatory tools (goal setting, implementation intentions, self-monitoring, environmental prompts), motivational interviewing or values-consistent framing, and social scaffolding (family/peer support) (18, 19, 22, 48). The paradoxical combination of relatively higher NKS and less favorable ASHN among heavier smokers exemplifies where alignment strategies clarifying goals, addressing ambivalence, building coping plans, and translating knowledge into feasible micro-behaviors should supersede additional didactic content (49, 50).

In this adult sample, nutrition knowledge and healthy-eating attitudes were only weakly aligned, sometimes showing small negative associations. Attitudes—but not knowledge—varied systematically across physical activity and lifestyle factors, and age and education related more consistently to attitudes than to knowledge; heavier smoking also illustrated knowledge–attitude misalignment. These patterns suggest that information alone is unlikely to produce sustained change without motivation- and self-regulation–focused supports and reductions in contextual barriers. Accordingly, practice and policy may benefit from embedding attitude-focused components within broader health-behavior platforms, particularly physical-activity promotion and tobacco-control programs. In clinical care, brief screening for attitudinal barriers and readiness can guide counselling beyond information provision (e.g., motivational interviewing and implementation intention planning), while policy implementation through primary care, community programs, and workplace wellness may enable scalable, resource-efficient gains.