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A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use
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Abstract
There is widespread public and academic interest in understanding the uses and effects of digital media. Scholars primarily use self-report measures of the quantity or duration of media use as proxies for more objective measures, but the validity of these self-reports remains unclear. Advancements in data collection techniques have produced a collection of studies indexing both self-reported and log-based measures. To assess the alignment between these measures, we conducted a pre-registered meta-analysis of this research. Based on 106 effect sizes, we found that self-reported media use correlates only moderately with logged measurements, that self-reports were rarely an accurate reflection of logged media use and that measures of problematic media use show an even weaker association with usage logs. These findings raise concerns about the validity of findings relying solely on self-reported measures of media use.
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“However, although log-based measures are often seen as the gold standard, they have their own validity threats, such as technical errors and the erroneous tracing of SM apps running in the background when the screen is turned off [42,43].”
From Social media use and its impact on adolescent mental health: An umbrella review of the evidence. (2022) · cites this paper as [42]
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In the citing paper
“Despite these encouraging findings, the uMARS is a self-report measure, which according to a recent meta-analysis (Parry et al., 2021) may not be a reliable indicator of actual usage.”
From Pilot study of a well-being app to support New Zealand young people during the COVID-19 pandemic (2021) · cites this paper as Parry et al., 2021
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“It was recently suggested that that device-based measures can provide a more accurate assessment of social media use than self-reported tools (Parry et al., 2021;Sewall et al., 2022).”
From Relation Between Social Media Use and Body Image Concerns and Disordered Eating in Adolescence: A Systematic Review and Meta-Analysis (2026) · cites this paper as Parry et al., 2021
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In the citing paper
“Indeed, self-report measures of time spent with SM correlate only moderately with similar log-based measures [42,43].”
From Social media use and its impact on adolescent mental health: An umbrella review of the evidence. (2022) · cites this paper as [42]
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In the citing paper
“a recent systematic review has evidenced that the correspondence between self-reported and logged social media use is far from perfect (Parry et al., 2021).”
From Do New Forms of Reading Pay Off? A Meta-Analysis on the Relationship Between Leisure Digital Reading Habits and Text Comprehension (2025) · cites this paper as Parry et al., 2021
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“a recent meta-analysis found only moderate correlations between self-reported and objective measures of use (Parry et al., 2021).”
From Time Spent on Social Media and Associations with Mental Health in Young Adults: Examining TikTok, Twitter, Instagram, Facebook, Youtube, Snapchat, and Reddit (2025) · cites this paper as Parry et al., 2021
In the citing paper
“A recent meta-analysis has found that self-reported media use correlates only moderately with logged measurements and that self-reports are a somewhat inaccurate reflection of logged (true) media use76.”
From The impact of digital media on children’s intelligence while controlling for genetic differences in cognition and socioeconomic background (2022) · cites this paper as [76]
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“It was recently suggested that that device-based measures can provide a more accurate assessment of social media use than self-reported tools (Parry et al., 2021 ; Sewall et al., 2022 ).”
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The Impact of Microbiota on Neurological Disorders: Mechanisms and Therapeutic Implications
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Metadata
- Authors
- Parry, D. A., Davidson, B. I., Sewall, C. J. R., Fisher, J. T., Mieczkowski, H., & Quintana, D. S.
- Affiliations
- Stellenbosch University (ZA)
- University of Bristol (GB)
- University of Pittsburgh (US)
- University of Illinois Urbana-Champaign (US)
- Stanford University (US)
- Oslo University Hospital (NO)
- Corresponding author
- Parry, Douglas A.
- ORCID
- Venue
- Nature Human Behaviour
- Published
- 2021-05-17
- Volume
- 5
- Issue
- 11
- Pages
- 1535-1547
- ISSN
- 2397-3374 (ISSN-L)
- OpenAlex
- W3162414724
- Semantic Scholar
- da0492d6a38ad15f97a61ede4cdb7cfa4d655571
- Cited by
- 793
- References
- 108
- Citations by year
- 2026: 152 · 2025: 150 · 2024: 145 · 2023: 165 · 2022: 117 · 2021: 56
- FWCI
- 23.13
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Human Behaviour (publishedVersion)
- PubMed (publishedVersion)
- VU Research Portal (publishedVersion)
- Pure (University of Bath) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- PsyArXiv (OSF Preprints) (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Social Sciences › Education › Child Development and Digital Technology
- Social Sciences › Communication › Media and Digital Communication
- Keywords
- digital media use, usage logs, measurement validity, self-report accuracy, problematic media use, self-assessment bias
- MeSH terms
- Screen Time, Humans, Reproducibility of Results, Self Report, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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