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  • Review Article
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Understanding behaviours in context using mobile sensing

Nature Reviews Psychology volume 2, pages 767–779 (2023)Cite this article

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Abstract

Mobile sensing refers to the collection of methods by which researchers derive measures of human behaviours and contexts from the onboard sensors and logs found in smartphones, wearables and smart home devices. By tracking real-world behaviours in their natural contexts automatically, unobtrusively, continuously and in detail over extended periods of time, mobile sensing can help researchers to realize the potential of ecological approaches to psychology. In this Review, we consider how mobile sensing presents new opportunities for understanding behaviours in context and review illustrative findings from mobile sensing studies in psychology in three areas of research: social behaviours in physical and digital contexts, mobility behaviours in spatial contexts, and activities in digital contexts. In doing so, we highlight themes in the existing research and demonstrate the capabilities of mobile sensing, while evaluating how far mobile sensing has come in delivering on the promise of ecological approaches. To guide future mobile sensing research in psychology, we conclude with a research agenda focused on conceptual and measurement issues, pursuing explanatory and predictive research, and overcoming technical and practical barriers.

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Fig. 1: Sensing behaviours in context.

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Acknowledgements

The authors thank J. Bunting, K. Chen, D. Jordan and E. Stogianni for their research assistance with this project.

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Authors and Affiliations

  1. Department of Communication, Stanford University, Stanford, CA, USA

    Gabriella M. Harari

  2. Department of Psychology, The University of Texas at Austin, Austin, TX, USA

    Samuel D. Gosling

  3. School of Psychological Science, University of Melbourne, Melbourne, Victoria, Australia

    Samuel D. Gosling

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G.M.H. researched and wrote the manuscript. G.M.H. and S.D.G. contributed equally to the discussion of content and reviewing and editing the manuscript.

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Correspondence to Gabriella M. Harari.

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Glossary

Application programming interface

(API). A type of software that enables two pieces of software to connect and communicate with one another.

Big Five traits

A model of five primary dimensions of individual differences in personality (extraversion, agreeableness, conscientiousness, neuroticism and openness).

Classification models

A subset of supervised machine learning models that classify data into different categories.

Depression

A negative affective state that interferes with daily life, ranging from unhappiness and discontent to an extreme feeling of sadness, pessimism and despondency.

Machine learning

A set of methods that detect and predict patterns in data.

Personality traits

Relatively stable, consistent and enduring characteristic patterns of thinking, feeling and behaving that describe an individual.

Subjective well-being

The happiness and life satisfaction appraisal of an individual, typically assessed using self-reports of affective well-being (for example, negative and positive affect) or cognitive well-being (for example, satisfaction in different life domains).

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Harari, G.M., Gosling, S.D. Understanding behaviours in context using mobile sensing. Nat Rev Psychol 2, 767–779 (2023). https://doi.org/10.1038/s44159-023-00235-3

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