The research was conducted between July 10 and September 15, 2024, as part of the Psychology and Behavior Investigation of Chinese Residents survey. Researchers used quota-based convenience sampling with predefined age quotas meant to approximate the adult age distribution of Shanghai. Of 1,245 questionnaires returned, 1,011 were retained after age-quota matching, meaning 81.2 percent of returned questionnaires entered the final analysis. The 234 excluded questionnaires were not a random subsample, and the authors note that age matching alone cannot correct for selection on education, health status, internet access, or health-related interest.

The participants were 52.0 percent female and 48.0 percent male, with a median age of 42 years. Most were employed (59.6 percent), lived in urban areas (76.7 percent), and held a bachelor’s degree (55.9 percent). The largest income group reported monthly per capita household income above 9,000 CNY (41.3 percent). Notably, more than 95 percent of participants reported no history of hypertension, diabetes, or hyperlipidemia, and only 1.2 percent smoked. The authors acknowledge that this profile suggests the sample may be healthier and more highly educated than the wider Shanghai adult population, which limits how far the findings can be generalized.

Four instruments anchored the study. Dietary self-regulation was measured with the five-item Self-regulation of Eating Behavior Questionnaire, which scores from 5 to 25 and showed a Cronbach’s alpha of 0.888 in this sample. E-health literacy was assessed with the eight-item eHEALS scale (alpha 0.932), perceived stress with the four-item PSS-4 (alpha 0.905), and rest intolerance with the eight-item Rest Intolerance Scale, or RIS-8 (alpha 0.932). Rest intolerance refers to negative affective and cognitive reactions—guilt, anxiety, self-blame, shame, or persistent task-related thoughts—that arise when people rest, engage in leisure, or temporarily disengage from work or study. Higher RIS-8 scores indicate stronger negative reactions to rest, not greater tolerance.

The bivariate picture was striking. E-health literacy was positively correlated with dietary self-regulation (r = 0.482), positively correlated with rest intolerance (r = 0.395), and negatively correlated with perceived stress (r = −0.508). Perceived stress was negatively correlated with dietary self-regulation (r = −0.446) and, unexpectedly, negatively correlated with rest intolerance (r = −0.146). Rest intolerance itself was positively correlated with dietary self-regulation (r = 0.569), the strongest bivariate link in the study. All correlations reached statistical significance at p < 0.01.

Because several of these patterns ran counter to intuition, the researchers also performed an exploratory latent profile analysis using the five dietary self-regulation items as indicators. Models with one to five profiles were estimated, and fit indices including AIC, BIC, aBIC, entropy, the LMR test, and the bootstrap likelihood ratio test pointed to a three-class solution. The three profiles—roughly 301 participants (29.8 percent), 197 participants (19.5 percent), and 512 participants (50.7 percent)—distinguished low, moderate, and high overall dietary self-regulation. Crucially, the item-response curves were largely parallel across classes, differing mainly in overall elevation. The authors therefore describe the classes as level-based strata rather than qualitatively distinct behavioral subtypes, and caution that the profiles should not be treated as stable subgroups until replicated longitudinally and shown to predict meaningful outcomes.

The centerpiece of the analysis was a serial indirect-association model using PROCESS Model 6 with 5,000 bootstrap resamples, adjusted for gender, age, education, employment, residence, and income. In this model, e-health literacy was the independent variable, perceived stress the first mediator, rest intolerance the second, and dietary self-regulation the outcome—an ordering informed by the transactional model of stress and coping, in which people first appraise demands against coping resources and then develop cognitive and behavioral responses. The total association between e-health literacy and dietary self-regulation was 0.615 (95 percent CI, 0.564–0.666). The direct association conditional on both mediators was 0.211 (95 percent CI, 0.147–0.274), and the total indirect association was 0.404 (95 percent CI, 0.344–0.466). The specific indirect association through perceived stress was 0.214 (95 percent CI, 0.166–0.261), and through rest intolerance 0.205 (95 percent CI, 0.161–0.254).

The serial pathway—e-health literacy through perceived stress and then rest intolerance to dietary self-regulation—was small and negative: −0.015 (95 percent CI, −0.031 to −0.001, p = 0.048), with a confidence interval that only narrowly excluded zero. Its interpretation was further complicated by a direction reversal. In bivariate analysis, perceived stress and rest intolerance were weakly negatively correlated, but after covariate adjustment the standardized coefficient flipped to a weak positive value (β = 0.068, p = 0.046). A staged sensitivity analysis traced this shift: unadjusted r = −0.146; after adding e-health literacy, β = 0.097; after adding age and gender, β = 0.102; and fully adjusted, β = 0.068. The authors interpret this as model dependence—possibly statistical suppression or shared variance—rather than a robust directional relationship. Variance inflation factors for the four variables were all below 5, so serious multicollinearity was considered unlikely.

Two findings deserve particular caution. First, the positive association between rest intolerance and dietary self-regulation was unexpected, because higher RIS-8 scores indicate stronger negative reactions to rest, not adaptive self-control. The authors explicitly state this result should not be read as evidence that rest intolerance enhances eating behavior; it may reflect residual confounding, self-report response tendencies, unmeasured motivational or health-related factors, or model-specific adjustment effects. They also note the RIS-8 was developed and initially evaluated in student samples, so its construct validity in a general adult population remains uncertain. Second, Harman’s single-factor test suggested the first unrotated factor accounted for 44.674 percent of the total variance, and although VIF values were below 5, the authors treat common method variance as a possibility that cannot be ruled out, since all key variables were self-reported at one time point.

The overall conclusion is deliberately restrained. The variables were interrelated within the prespecified cross-sectional model, but the small serial estimate and the directional instability of the stress–rest intolerance coefficient do not support a clear sequential psychological mechanism. Because everything was measured at one time point, temporal ordering and causal mediation cannot be established, and reverse causality—people with stronger dietary self-regulation reporting greater e-health literacy, for instance—remains plausible. The authors frame the findings as hypothesis-generating: future community-based research could test whether combining digital health-literacy training, stress-management support, and concrete dietary skills such as goal setting, meal planning, and self-monitoring improves eating behavior, and whether helping people manage guilt and task-related thinking during rest affects engagement with dietary education. Such possibilities, they emphasize, require longitudinal designs, experimental interventions, and further psychometric validation of the RIS-8 before any causal or clinical recommendations can be justified.

Subject of Research: Cross-sectional associations among e-health literacy, perceived stress, rest intolerance, and dietary self-regulation in Shanghai adults

Article Title: Dietary self-regulation levels among adults in Shanghai: cross-sectional associations with e-health literacy, perceived stress, and rest intolerance

Article References: Tian, F., Du, L., Yang, H., & Zhang, Y. (2026). Dietary self-regulation levels among adults in Shanghai: cross-sectional associations with e-health literacy, perceived stress, and rest intolerance. Frontiers in Public Health, 14, Article 1928922. https://doi.org/10.3389/fpubh.2026.1928922

Image Credits: AI Generated

DOI: 10.3389/fpubh.2026.1928922

Keywords: dietary self-regulation, e-health literacy, perceived stress, rest intolerance, cross-sectional study, latent profile analysis, Shanghai adults, serial mediation, bootstrap analysis, public health nutrition, self-report survey, psychological correlates