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Do smartphone usage scales predict behavior?
Open accessPublished version of a preprint
Abstract
Understanding how people use technology remains important, particularly when measuring the impact this might have on individuals and society. However, despite a growing body of resources that can quantify smartphone use, research within psychology and social science overwhelmingly relies on self-reported assessments. These have yet to convincingly demonstrate an ability to predict objective behavior. Here, and for the first time, we compare a variety of smartphone use and ‘addiction’ scales with objective behaviors derived from Apple's Screen Time application. While correlations between psychometric scales and objective behavior are generally poor, single estimates and measures that attempt to frame technology use as habitual rather than ‘addictive’ correlate more favorably with subsequent behavior. We conclude that existing self-report instruments are unlikely to be sensitive enough to accurately predict basic technology use related behaviors. As a result, conclusions regarding the psychological impact of technology are unreliable when relying solely on these measures to quantify typical usage.
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“even though self-reported smartphone assessments have been shown to perform poorly when attempting to predict objective smartphone behaviours.29”
From Smartphone use as a possible risk factor for myopia (2021) · cites this paper as [29]
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“Therefore the use of smartphone data as a surrogate indicator of phone use provides a better indicator of smartphone behaviour than self-reported usage data.29”
From Smartphone use as a possible risk factor for myopia (2021) · cites this paper as [29]
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“Second, although retrospective self-reports of adolescents' social media use are most commonly employed, they may be biased and suffer from underestimation (48).”
From Does Social Media Use Increase Depressive Symptoms? A Reverse Causation Perspective (2021) · cites this paper as [48]
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Metadata
- Authors
- Ellis, D. A., Davidson, B. I., Shaw, H., & Geyer, K.
- Affiliations
- Lancaster University (GB)
- University of Bath (GB)
- University of Lincoln (GB)
- Corresponding author
- Ellis, David A.
- Venue
- International Journal of Human-Computer Studies
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 130
- Pages
- 86-92
- ISSN
- 1071-5819 (ISSN-L)
- OpenAlex
- W2942805235
- Semantic Scholar
- 0d1a5d44d54de6b5dce0722f1880e3e00a6ce4cd
- Cited by
- 326
- References
- 59
- Citations by year
- 2026: 34 · 2025: 32 · 2024: 26 · 2023: 44 · 2022: 41 · 2021: 62 · 2020: 68 · 2019: 18
- FWCI
- 95.08
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- International Journal of Human-Computer Studies (publishedVersion)
- Bristol Research (University of Bristol) (publishedVersion, other-oa)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Pure (University of Bath) (submittedVersion, cc-by-nc-nd)
- PsyArXiv (OSF Preprints) (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- Topics
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Social Sciences › Education › Child Development and Digital Technology
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- smartphone use, self-report measures, technology addiction, habit, scale validity
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