Snapshots of Daily Life: Situations Investigated Through the Lens of Smartphone Sensing

Abstract

Daily life unfolds in a sequence of situational contexts, which are pivotal for explaining people’s thoughts, feelings, and behaviors. While situational data were previously difficult to collect, the ubiquity of smartphones now opens up new opportunities for assessing situations in situ, that is, while they occur. Seizing this development, the present study demonstrates how smartphones can help establish associations between the psychological perception and the physical reality of situations. We employed an intensive longitudinal sampling design and investigated 9,790 situational snapshots experienced by 455 participants for 14 consecutive days. These snapshots combined self-reported situation characteristics from experience samplings with their corresponding objective situation cues obtained via smartphone sensing. To account for the complexity of real-world situations, we extracted a total of 1,356 granular situation cues from different sensing modalities. We applied linear and nonlinear machine learning algorithms to examine how well these cues predicted the perceived characteristics in terms of the Situational Eight DIAMONDS, finding significant out-of-sample predictions for the five dimensions capturing the situations’ Duty, Intellect, Mating, pOsitivity, and Sociality. Analyses of (grouped) feature importance revealed that these predictions relied on complex constellations of cues representing various situational information about the Persons/Interactions and Objects present, the Events/Activities happening, and the current Location and Time. Furthermore, a nomological network analysis provided evidence for the construct validity of our cue-based DIAMONDS predictions. We conclude by discussing how smartphone-based situational snapshots, in general, and our prediction models, in particular, advance psychological research on situations.

Mobile Sensing Methods

“…Most importantly, mobile sensing will enable researchers to investigate person-situation interactions at both the objective and subjective level (Harari, Vaid, et al, 2020). Schoedel, Kunz et al (2022) provide a recent empirical illustration. Objective situation cues (e.g., being in a restaurant) have been conceptualized as having no psychological meaning per se (Rauthmann & Sherman, 2021).…”

“…However, they form the basis for situation perception (e.g., sociality) which in turn is linked to individuals' thoughts, feelings, and behaviors in a given situation (Rauthmann & Sherman, 2021). As comprehensively collecting objective situation cues has proven difficult so far, today relatively little is known about the objective reality of situations and consequently about the mapping of objective situation cues and subjective situation perception (Schoedel, Kunz, et al, 2022). Mobile sensing provides new opportunities in this regard.…”

“…Mobile sensing provides new opportunities in this regard. Accordingly, Schoedel, Kunz et al (2022) investigated how much information mobile sensing data contain about psychological situation experience and what kinds of information contained in mobile sensing data are informative for situation experience predictions. For this purpose, they extracted 1,356 objectively sensed mobile sensing cues (e.g., time of the day, current place, current activities and interactions) collected over a 14-days window.…”

Social dynamics and affect: Investigating within-person associations in daily life using experience sampling and mobile sensing

“…We formed indicators for the number of app usage sessions and app usage duration (in minutes) per ESM episode in the categories of communication (e.g., WhatsApp, email) and social media (e.g., Instagram, Twitter). For more details of this procedure, see (Schoedel, Kunz, et al, 2022).…”

“…Looking toward future research, models using machine learning algorithms would be able to incorporate all relevant variables from this dataset (and many more) to predict momentary affect (see Schoedel, Kunz, et al, 2022;Stachl, Au, et al, 2020, for prediction models of situation perception and personality traits). Such a data-driven approach could account for complex nonlinear and interaction effects in the large, multi-method variable space and shed light on the most important predictors of affect that, in turn, could be investigated in more depth (Stachl, Pargent, et al, 2020).…”