OBJECTIVES: Identifying the point of indifference in time-trade-off tasks is challenging. In this study, we test whether allowing response intervals rather than discrete indifference points can mitigate inconsistencies and the impacts of satisficing in terms of quick task completion, as well as how model precision is influenced.

METHODS: 100 participants were randomly assigned to either regular cTTO or interval TTO (iTTO) in an EQ-5D-5L valuation experiment. In iTTO, tasks were terminated after 15 moves or when respondents cycled over a 0.1-width interval. Tasks requiring fewer moves were tested by truncating empirical response paths. Task complexity was compared using task completion time, number of moves, and participants’ feedback. Satisficing in terms of earlier and faster task completion with experience was assessed by regressing the number of movements and task time on the task number. Inconsistencies were compared across arms. Model performance in terms of standard error, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) was investigated by combining cTTO and iTTO data with discrete choice experiment data from a previous study in the same population in 20-parameter hybrid model.

RESULTS: Demographic characteristics did not significantly differ between arms. iTTO participants experienced faster task completion (61.9 vs 59.7 seconds, p = 0.023), more movements (6.4 vs 10.5 movements, p < 0.001) and greater self-reported task comprehension (86% vs 94% participants reporting tasks were easy to understand, p = 0.007). Regression analysis did not indicate substantive differences in satisficing between arms. Value clustering (proportion of responses at -0.5, 0, 0.5, 1) was notably reduced in iTTO. Inconsistencies were not significantly different between arms. The iTTO hybrid model yielded similar mean standard errors but higher AIC and BIC.

CONCLUSIONS: The findings indicate that iTTO approach improves task comprehension and limits impacts of value clustering without compromising consistency or model precision.