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 opportunity, the present study demonstrates how smartphones can help establish associations between the psychological perception and 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 cues obtained via smartphone sensing. More precisely, we extracted a total of 1,356 granular cues from different sensing modalities to account for the complexity of real-world situations. We applied linear and nonlinear machine learning algorithms to examine how well these cues predicted the perceived characteristics in terms of the Situational Eight Duty, Intellect, Adversity, Mating, pOsitivity, Negativity, Deception, Sociality (DIAMONDS), finding significant out-of-sample predictions for the five dimensions reflecting the situations’ Duty, Intellect, Mating, pOsitivity, and Sociality. In a series of follow-up analyses, we further explored the data patterns captured by our models, revealing, for example, that those cues related to time and location were particularly informative of the respective situation characteristics. We conclude by interpreting the mapping between cues and characteristics in real-world situations and discussing how smartphone-based situational snapshots may push the boundaries of psychological research on situations.

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

“…Results showed that having more conversations than usual—as detected by the audio-sampling algorithm—was associated with higher PA (for happiness, see Sun et al, 2020). Still, at this point, it remains unclear to what extent this effect is because of the medium of contact itself or because of contextual differences in the types of conversation topics, places, or communication partners chiefly associated with different mediums of contact (Matz & Harari, 2021; Schoedel et al, 2023; Vogel et al, 2017).…”

“…We formed indicators for the number of app usage sessions and app usage duration (in min) 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 et al (2023).…”

Does Your Smartphone “Know” Your Social Life? A Methodological Comparison of Day Reconstruction, Experience Sampling, and Mobile Sensing

“…4. The PhoneStudy app (Schoedel et al, 2023) allows the assessment of different features of mobile-phone usage and sensors and runs on the Android operating system, which 65% of German mobile-phone owners use (Keusch, 2023). We provide additional information about the different data logging modes of the PhoneStudy app in Appendix C at https://osf.io/82mu6.…”

“…The study started for all participants on a Thursday with a video call. Participants received information about the study, gave informed consent, and installed the Phone-Study research app (Schoedel et al, 2023). 4 Participants answered a baseline questionnaire on their demographics, personality traits, and social network.…”

Never miss a beep: Using mobile sensing to investigate (non-)compliance in experience sampling studies

“…With smartphones being omnipresent in our daily lives, they are not only perfectly suited to supersede devices previously used for sending beeps in ESM studies such as paper-and-pencil diaries or personal digital assistants (e.g., PALM). They also offer the possibility of continuously collecting a variety of data types without the active engagement or interruption of participants’ day-to-day behavior, which in turn can be used to derive contextual and behavioral information, even if participants miss certain beeps (Harari et al., 2016 ; Elmer et al., 2022 ; Schoedel et al., 2023 ). Accordingly, scholars have recently pointed out the huge potential of using mobile sensing as a toolbox to gather further insight into compliance in ESM studies (Murray et al., 2023 ; Sun et al., 2020 ), for example, by using GPS data instead of self-reported information on locations (Sokolovsky et al., 2014 ).…”