Time and Motion (T&M) studies quantify time-related outcomes. Any given intervention process can be broken down into a set of pre-defined tasks for repeated observations, allowing estimation of the mean task durations in support of health economic analyses. While aiming to achieve robust estimates, variability in time measurements remains a main methodological challenge. OBJECTIVES: To discuss the importance of handling variability and confounding in T&M studies. METHODS: Investigation of the impact of variability on process duration begins with the analysis of process flow predictors and particularly the identification of potential confounders of process duration. Process-related variability can result from differences between countries or centers (e.g., geography, institution type) or within centers (e.g., patient characteristics, process specifics). Additional variability in time measurements can be due to insufficient delineation of tasks and inter-rater differences.  RESULTS: Once potential sources of variability are identified, it must be decided whether a variable is to be minimized or accounted for in the study design relating back to the health economics objective of the T&M study. For instance, clear delineation of processes to be observed and thorough training help limit inter-rater variability. On the other hand, limiting data collection to a homogenous sample of centers and patients (i.e., specific patient and process characteristics), while minimizing variability in study setting and population, can compromise generalizability of the results. In situations where a medical intervention can be used to treat a broad range of patient populations with distinct clinical characteristics, limiting data collection to a certain subgroup means generating results applicable to these patients only. CONCLUSIONS: Variability can be controlled through thoughtful study design. However, significant confounders should be identified and accounted for to produce valid process time estimation. Proper handling of variability in time measurement will improve precision of the duration estimates in support of health economic analyses.