Technostress and Insulin Resistance Risk: A Cross-Sectional Analysis of 104,175 Spanish Workers Using TyG, TyG-BMI, METS-IR, and SPISE Indices

Abstract

Background: Technostress has emerged as a growing occupational health concern in increasingly digitalized workplaces. Although its psychological consequences have been extensively investigated, little is known about its potential association with metabolic health and insulin resistance. This study aimed to evaluate the relationship between technostress and insulin resistance risk using four validated surrogate markers in a large cohort of Spanish workers. Methods: A cross-sectional study was conducted among 104,175 Spanish workers who underwent routine occupational health assessments. Technostress was assessed using a 15-item questionnaire covering five technostress dimensions and was analyzed using four operational categories (low, moderate, high, and very high). Insulin resistance risk was assessed using the triglyceride–glucose (TyG) index, TyG-body mass index (TyG-BMI), the metabolic score for insulin resistance (METS-IR), and the single-point insulin sensitivity estimator (SPISE). Sociodemographic characteristics, lifestyle habits, anthropometric measurements, and biochemical parameters were also recorded. Modified Poisson regression with robust variance estimation was used to estimate crude and adjusted prevalence ratios (PRs) for increased insulin resistance risk according to technostress level. Firth penalized logistic regression was additionally performed as a sensitivity analysis to address sparse-data bias and separation. Results: Higher technostress levels were associated with progressively less favorable anthropometric and metabolic profiles. The prevalence of increased insulin resistance risk rose significantly across technostress categories for all markers evaluated (p < 0.001). For TyG, prevalence increased from 3.8% in the low technostress group to 54.3% in the very high technostress group. Corresponding increases were observed for TyG-BMI (0.2% to 78.2%), METS-IR (0.0% to 45.2%), and elevated SPISE-IR values (0.0% to 57.5%). After adjustment for age, sex, educational level, physical activity, adherence to the Mediterranean diet, and smoking status, higher technostress remained associated with a higher prevalence of increased insulin resistance risk. A clear graded cross-sectional pattern was observed, with the strongest associations found among workers reporting very high technostress. Conclusions: Higher technostress was associated with a higher prevalence of increased insulin resistance risk according to TyG, the primary outcome, with a clear graded cross-sectional pattern across technostress categories. Associations were also observed for the secondary BMI-containing indices (TyG-BMI, METS-IR, and SPISE-IR), which should be interpreted as complementary rather than independent evidence. Given the cross-sectional design and the pronounced clustering of technostress with adiposity and lifestyle characteristics, residual confounding cannot be excluded and causal inference is not possible.