ArticleJMIR mental health2023
Capturing the Dynamics of the Social Environment Through Experience Sampling Methods, Passive Sensing, and Egocentric Networks: Scoping Review.
Article in JMIR mental health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Examining the use of experience sampling methods in sport psychology research: a scoping review.Frontiers in psychology · 2026Pooled it
- Day-to-day Social Interactions Online and Offline: The Interplay Between Interaction Mode, Interaction Quality, and Momentary Well-being.Communication research · 2026Article
- Real-world conversations across the schizophrenia spectrum: Implementing passive audio sensing to examine linguistic style matching.Journal of psychopathology and clinical science · 2025Article
- A Dual In-Person and Remote Assessment Approach to Developing Digital End Points Relevant to Autism and Co-Occurring Conditions: Protocol for a Multisite Observational Study.JMIR research protocols · 2025Article
- Families being supportive together: A multimethod and multi-informant intensive longitudinal study of family protective mechanisms for adolescent depression.Psychological assessment · 2025Article
- ESM-Q: A consensus-based quality assessment tool for experience sampling method items.Behavior research methods · 2025Article
- A template and tutorial for preregistering studies using passive smartphone measures.Behavior research methods · 2024Article
- Leveraging Experience Sampling/Ecological Momentary Assessment for Sociological Investigations of Everyday Life.Annual review of sociology · 2024Article
- Predicting Mood Based on the Social Context Measured Through the Experience Sampling Method, Digital Phenotyping, and Social Networks.Administration and policy in mental health · 2024Article
- Feedback About a Person's Social Context - Personal Networks and Daily Social Interactions.Administration and policy in mental health · 2024Article
- Computational social science is growing up: why puberty consists of embracing measurement validation, theory development, and open science practices.EPJ data science · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSocial 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.
objectiveOur 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.
methodsWe conducted a scoping review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.
resultsWe 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.
conclusionsWe 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.
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