Capturing the Dynamics of the Social Environment Through Experience Sampling Methods, Passive Sensing, and Egocentric Networks: Scoping Review

Abstract

Background Social interactions are important for well-being, and therefore, researchers are increasingly attempting to capture people’s social environment. Many different disciplines have developed tools to measure the social environment, which can be highly variable over time. The experience sampling method (ESM) is often used in psychology to study the dynamics within a person and the social environment. In addition, passive sensing is often used to capture social behavior via sensors from smartphones or other wearable devices. Furthermore, sociologists use egocentric networks to track how social relationships are changing. Each of these methods is likely to tap into different but important parts of people’s social environment. Thus far, the development and implementation of these methods have occurred mostly separately from each other. Objective Our aim was to synthesize the literature on how these methods are currently used to capture the changing social environment in relation to well-being and assess how to best combine these methods to study well-being. Methods We conducted a scoping review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Results We included 275 studies. In total, 3 important points follow from our review. First, each method captures a different but important part of the social environment at a different temporal resolution. Second, measures are rarely validated (>70% of ESM studies and 50% of passive sensing studies were not validated), which undermines the robustness of the conclusions drawn. Third, a combination of methods is currently lacking (only 15/275, 5.5% of the studies combined ESM and passive sensing, and no studies combined all 3 methods) but is essential in understanding well-being. Conclusions We highlight that the practice of using poorly validated measures hampers progress in understanding the relationship between the changing social environment and well-being. We conclude that different methods should be combined more often to reduce the participants’ burden and form a holistic perspective on the social environment.

Computational Social Science is growing up: Why puberty consists of embracing measurement validation, theory development, and open science practices

“…They found that some individual characteristics, such as age and partisanship, are predictive of over-self-reporting on Facebook, but not on Twitter. Recently, Langener et al [54]publishedareviewonthe combined application of passive-measurement methods and daily-life surveys (Experience Sampling Methods; e.g., Csikszentmihalyi and Larson [16]; Trull and Ebner-Priemer [90]) to capture participant's social context. While highlighting the importance of combining these data sources, they conclude that there is a strong need for better-validated measurement methods.…”

“…Second, there are ethical and methodological issues -such as the external validity of online-behavior for understanding offline behavior (e.g., Jürgens et al [48]). Third, the lack of conceptual and theoretical frameworks incorporating both types of data, such as the processing of data types with different temporal granularity (Langener et al [54]; Stier et al [86]). Despite these challenges, the combination of data sources holds great potential for the joint study of digital social behavior.…”

Computational social science is growing up: why puberty consists of embracing measurement validation, theory development, and open science practices

“…Recently, Langener et al. [ 54 ] published a review on the combined application of passive-measurement methods and daily-life surveys (Experience Sampling Methods; e.g., Csikszentmihalyi and Larson [ 16 ]; Trull and Ebner-Priemer [ 90 ]) to capture participant’s social context. While highlighting the importance of combining these data sources, they conclude that there is a strong need for better-validated measurement methods.…”

It’s All About Timing: Exploring Different Temporal Resolutions for Analyzing Digital-Phenotyping Data

“…Conceptual clarity is critical in all psychological studies because poorly defined concepts make it challenging to build theories and interpret results (Bringmann et al, 2022). Nevertheless, there has been a lack of clarity regarding which construct passive smartphone measures aim to capture (Langener et al, 2023). For example, passive measures like GPS and accelerometer data are frequently used to measure social behavior; however, it is not yet clear which social behaviors these measures capture and how accurately these measures capture the intended construct (Langener et al, 2023; Tsapeli & Musolesi, 2015).…”

“…Nevertheless, there has been a lack of clarity regarding which construct passive smartphone measures aim to capture (Langener et al, 2023). For example, passive measures like GPS and accelerometer data are frequently used to measure social behavior; however, it is not yet clear which social behaviors these measures capture and how accurately these measures capture the intended construct (Langener et al, 2023; Tsapeli & Musolesi, 2015). Thus, researchers are advised to carefully consider the meaning of each variable they collect.…”