Experience Sampling Methodology: Conceptual and Technological Advances for Understanding and Assessing Variability in Well-being Research

Experience sampling methods (ESM) have enabled researchers to capture intensive longitudinal data and how worker well-being changes over time. The conceptual advances in understanding the variability of well-being are discussed. These emerging forms in the literature include affective inertia, affective variability, affective reactivity, and density distributions. While most ESM research has relied on the active provision of data by participants (i.e., self-reports), technological advances have enabled different forms of passive sensing that are useful for assessing and tracking well-being and its contextual factors. These include accelerometer data, location data, and physiological data. The strengths and weaknesses of passively sensed data and future ways forward are discussed, where the use of both active and passive forms of ESM data in the assessment and promotion of worker well-being is expected.