To develop and validate a short questionnaire to estimate physical activity (PA) practice and sedentary behavior for the adult population. The short questionnaire was developed using data from a cross-sectional population-based survey (n = 6352) that included the Minnesota leisure-time PA questionnaire. Activities that explained a significant proportion of the variability of population PA practice were identified. Validation of the short questionnaire included a cross-sectional component to assess validity with respect to the data collected by accelerometers and a longitudinal component to assess reliability and sensitivity to detect changes (n = 114, aged 35 to 74 years). Six types of activities that accounted for 87% of population variability in PA estimated with the Minnesota questionnaire were selected. The short questionnaire estimates energy expenditure in total PA and by intensity (light, moderate, vigorous), and includes 2 questions about sedentary behavior and a question about occupational PA. The short questionnaire showed high reliability, with intraclass correlation coefficients ranging between 0.79 to 0.95. The Spearman correlation coefficients between estimated energy expenditure obtained with the questionnaire and the number of steps detected by the accelerometer were as follows: 0.36 for total PA, 0.40 for moderate intensity, and 0.26 for vigorous intensity. The questionnaire was sensitive to detect changes in moderate and vigorous PA (correlation coefficients ranging from 0.26 to 0.34). The REGICOR short questionnaire is reliable, valid, and sensitive to detect changes in moderate and vigorous PA. This questionnaire could be used in daily clinical practice and epidemiological studies.

Background. Behavioral procrastination has traditionally been considered a self-regulatory and psychological phenomenon; however, growing evidence suggests that chronic procrastination may also contribute to adverse physical health outcomes through persistent stress exposure, unhealthy lifestyle behaviors, and impaired long-term behavioral regulation. Nevertheless, its relationship with insulin resistance and visceral adiposity phenotypes remains poorly understood. Objective. To evaluate the association between behavioral procrastination and multiple validated surrogate indices of insulin resistance and visceral adiposity in a large occupational cohort of Spanish workers. Methods. A cross-sectional study was conducted among 92,184 Spanish workers aged 18–69 years who underwent standardized occupational health examinations between January 2021 and December 2024. Behavioral procrastination was assessed using a nine-item Spanish adaptation of the Pure Procrastination Scale (PPS-9) that has not been formally validated in the Spanish population; participants were classified into four operational, non-clinical categories. These categories were based on an unvalidated 9-item Spanish adaptation and operational, non-clinical cutoffs and should therefore not be interpreted as validated clinical levels of procrastination. Differences in the prevalence of adverse metabolic phenotypes across operational PPS-9 categories were assessed using Pearson’s chi-square test. Multivariable linear regression models with heteroscedasticity-consistent (HC3) robust standard errors were used to evaluate adjusted associations between operational PPS-9 categories and continuous insulin resistance and visceral adiposity phenotypes, with the very low/absent PPS-9 category as the reference group. Sex-stratified and sensitivity analyses were also performed. Results. The unadjusted prevalence of adverse metabolic phenotypes differed significantly across operational PPS-9 categories (all Pearson’s chi-square p < 0.001). In fully adjusted models, compared with participants in the very low/absent procrastination category, those in the very high/chronic category showed higher METS-IR (adjusted difference 19.478; 95% CI 19.193–19.763), LAP (47.504; 95% CI 45.518–49.490), VAI (4.174; 95% CI 3.813–4.534), METS-VF (1.310; 95% CI 1.290–1.330), and TyG index (0.230; 95% CI 0.203–0.257), together with lower SPISE (−3.725; 95% CI −3.775 to −3.674). Sex-stratified analyses showed adverse associations in both men and women, although their magnitude varied by metabolic phenotype. Sensitivity analyses excluding participants with diabetes mellitus yielded consistent findings. Sequential adjustment substantially attenuated some associations, particularly for the TyG index (70.6%) and VAI (50.7%), indicating that demographic and lifestyle factors accounted for a substantial proportion of the observed between-category differences, although residual associations remained after adjustment. Conclusions. Higher operational PPS-9 categories were associated with adverse insulin resistance and visceral adiposity phenotypes; however, substantial attenuation after demographic and lifestyle adjustment indicates that an important proportion of the observed differences is accounted for by covariate structure. Because the exposure was derived from an unvalidated 9-item Spanish adaptation of the PPS using operational, non-clinical cutoffs, these categories should not be interpreted as established clinical levels of procrastination. Given this measurement limitation, the cross-sectional design, and the marked age differences across categories, the findings should be interpreted cautiously and should not be considered evidence of an independent or causal metabolic effect of procrastination.

Background: Behavioral procrastination has traditionally been considered a psychological construct related to self-regulatory failure and maladaptive coping behaviors. However, growing evidence suggests that procrastination may also influence physical health through unhealthy lifestyle patterns, chronic stress exposure, and impaired behavioral regulation. Nevertheless, the relationship between procrastination and objectively assessed cardiometabolic disease remains insufficiently explored. Objective: To evaluate the association between behavioral procrastination and metabolic syndrome in a large occupational cohort of Spanish workers.Methods: A cross-sectional study was conducted in 92,184 active workers undergoing routine occupational health assessments between 2021 and 2024. Behavioral procrastination was evaluated using the PPS-9 questionnaire and categorized into very low, moderate, high, and very high/chronic procrastination levels. Metabolic syndrome was defined according to harmonized international criteria. Anthropometric, clinical, biochemical, and lifestyle-related variables were analyzed. Logistic regression models were constructed to assess the independent association between procrastination and metabolic syndrome after adjustment for demographic, behavioral, and adiposity-related variables.Results: Higher behavioral procrastination levels were consistently associated with a markedly less favorable cardiometabolic profile, including higher body mass index, waist circumference, blood pressure, fasting glucose, triglycerides, TyG index, and estimated heart age (all p < 0.001). The prevalence of metabolic syndrome increased progressively across procrastination categories, ranging from 0.1% in participants with very low procrastination to 34.6% in those with very high/chronic procrastination according to IDF criteria. In fully adjusted models including age, sex, smoking status, physical activity, dietary habits, educational level, and BMI, behavioral procrastination remained independently associated with metabolic syndrome. Compared with participants with very low procrastination, adjusted odds ratios were 5.87 (95% CI 3.99–8.63) for moderate procrastination, 12.35 (95% CI 8.34–18.29) for high procrastination, and 22.97 (95% CI 15.29–34.52) for very high/chronic procrastination (all p < 0.001). A significant dose-response relationship was observed across the full spectrum of procrastination scores.Conclusions: Behavioral procrastination was strongly and independently associated with metabolic syndrome and adverse cardiometabolic profiles in this large occupational cohort. The observed dose-response relationship and the persistence of the association after extensive adjustment suggest that procrastination may represent a relevant behavioral marker of cardiometabolic vulnerability. Future longitudinal studies are needed to clarify potential causal mechanisms and evaluate whether behavioral interventions targeting procrastination may contribute to cardiometabolic prevention strategies.

Background: Behavioral procrastination has been increasingly recognized as a maladaptive self-regulatory pattern associated with unhealthy lifestyle behaviors, psychological stress, and adverse cardiometabolic profiles. However, its relationship with accelerated cardiovascular aging remains poorly understood. This study aimed to evaluate the association between behavioral procrastination and heart age acceleration in a large occupational cohort of Spanish workers. Methods: A multicenter cross-sectional study was conducted including 92,184 actively employed Spanish workers undergoing routine occupational health examinations between 2021 and 2024. Behavioral procrastination was assessed using the Pure Procrastination Scale-9 (PPS-9). Estimated heart age and heart age acceleration were calculated using a cardiovascular risk-factor-based algorithm. Multivariable linear and logistic regression analyses were performed to evaluate associations between procrastination score, continuous heart age acceleration, and accelerated cardiovascular aging phenotypes after adjustment for demographic, lifestyle, anthropometric, and cardiometabolic variables. Restricted cubic spline analyses and sex-stratified analyses were additionally conducted. Results: Higher procrastination levels were associated with progressively worse cardiometabolic and cardiovascular aging profiles. Mean heart age acceleration increased from −3.1 ± 6.0 years in participants with very low procrastination to 14.0 ± 6.4 years in those with very high/chronic procrastination (p < 0.001). The prevalence of accelerated cardiovascular aging (>0 years) increased from 27.2% to 94.2% across increasing procrastination categories, whereas severe accelerated cardiovascular aging (≥10 years) increased from 1.7% to 75.6% (both p < 0.001). In fully adjusted multivariable analyses, each 5-point increase in PPS-9 score was associated with a 0.50-year increase in heart age acceleration (B = 0.50; 95% CI 0.48–0.52; p < 0.001). Participants with very high/chronic procrastination exhibited significantly higher odds of accelerated cardiovascular aging (OR 1.89; 95% CI 1.65–2.18) and severe accelerated cardiovascular aging (OR 2.51; 95% CI 2.16–2.92). Associations were significantly stronger among women (p-interaction < 0.001). Findings remained robust in sensitivity analyses excluding participants with diabetes mellitus. Conclusions: Behavioral procrastination was associated with higher estimated heart age acceleration and less favorable cardiovascular aging profiles in this large occupational cohort. Higher procrastination severity was consistently related to greater estimated heart age acceleration and a higher prevalence of cardiovascular aging phenotypes, even after extensive multivariable adjustment. These findings indicate that higher procrastination levels were associated with less favorable cardiovascular aging profiles beyond traditional biomedical risk factors. However, given the cross-sectional design, no conclusions regarding causality or temporality can be drawn.

Background: Cardiovascular risk prediction models are central to primary prevention strategies, yet substantial variability exists between contemporary and traditional equations used in clinical practice. In Spain, SCORE2 and REGICOR currently coexist as major cardiovascular risk assessment tools despite important methodological differences. However, evidence regarding their concordance in large occupational populations remains limited. Objective: To evaluate the agreement between SCORE2 and REGICOR in cardiovascular risk stratification among Spanish workers and to quantify the extent of cardiovascular risk reclassification associated with SCORE2 implementation. Methods: A multicenter cross-sectional study was conducted in 216,310 Spanish workers aged 40–64 years undergoing routine occupational health examinations between 2019 and 2024. Cardiovascular risk was estimated using SCORE2, REGICOR, ERICE, DORICA, Globorisk, and Framingham-based equations. High-risk categories were defined according to the thresholds recommended for each model. Agreement between categorical classifications was assessed using Cohen’s kappa coefficient, whereas Pearson correlation coefficients were calculated for continuous risk estimates. Results: SCORE2 classified 15,617 workers (7.22%) as high cardiovascular risk, whereas REGICOR identified only 4409 individuals (2.04%). Among workers classified as high risk by SCORE2, 14,387 (92.1%) were not identified as high risk by REGICOR. Agreement between SCORE2 and REGICOR was slight (kappa = 0.094), indicating minimal concordance in high-risk classification. By contrast, SCORE2 demonstrated higher agreement with Framingham hard coronary events (kappa = 0.567) and Globorisk (kappa = 0.534). Correlation analyses showed strong associations between SCORE2 and several continuous cardiovascular risk estimates, including the Framingham categorical score (r = 0.768), Framingham hard coronary events (r = 0.758), and Globorisk (r = 0.739), whereas the correlation between SCORE2 and REGICOR was substantially lower (r = 0.251). These findings indicate that strong statistical correlation does not necessarily translate into clinically meaningful agreement in cardiovascular risk categorization. Conclusions: Substantial discordance exists between SCORE2 and REGICOR in the identification of high cardiovascular risk among Spanish workers. SCORE2 consistently classified a considerably larger proportion of individuals as high risk, whereas REGICOR showed limited concordance with contemporary cardiovascular prediction models. Continued reliance on REGICOR instead of SCORE2 may lead to under-identification of workers who could currently be considered candidates for intensified primary cardiovascular prevention according to contemporary European prevention strategies. Nevertheless, the present study does not establish which model provides superior prediction of future cardiovascular events, and prospective outcome-based validation studies remain necessary.