Understanding behaviours in context using mobile sensing
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Mobile sensing, that is, using quantitative data from smartphones or wearables as tracked by onboard sensors, could provide an important add-on to currently applied methods.
“mobile sensing, that is, using quantitative data from smartphones or wearables as tracked by onboards sensors could provide an important add-on to currently applied methods (for an overview, see Harari & Gosling, 2023).”
From Understanding Autobiographical Memory in the Digital Age: The AMEDIA-Model (2024) · cites this paper as Harari & Gosling, 2023
Cites
Diary Methods: Capturing Life as it is Lived
Bolger, N., Davis, A., & Rafaeli, E. (2003) · Annual Review of Psychology · doi:10.1146/annurev.psych.54.101601.145030 · Cited by 3,843 · cited 1× · see citing works
“…of researchers to follow individuals with notepads 13 or video cameras 14 and asking individuals to use portable devices (such as personal digital assistants, beepers, wristwatches and smartphones) that prompt them to complete daily diaries or in-the-moment ‘experience sampling’ surveys 15–19 .”
BackgroundA Survey Method for Characterizing Daily Life Experience: The Day Reconstruction Method
Kahneman, D., Krueger, A. B., Schkade, D. A., Schwarz, N., & Stone, A. A. (2004) · Science · doi:10.1126/science.1103572 · Cited by 3,422 · see citing works
Activity recognition using cell phone accelerometers
Kwapisz, J. R., Weiss, G. M., & Moore, S. A. (2011) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1964897.1964918 · Cited by 2,912 · see citing works
Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018) · Annals of Behavioral Medicine · doi:10.1007/s12160-016-9830-8 · Cited by 2,171 · see citing works
Unique in the Crowd: The privacy bounds of human mobility
de Montjoye, Y.-A., Hidalgo, C. A., Verleysen, M., & Blondel, V. D. (2013) · Scientific Reports · doi:10.1038/srep01376 · Cited by 1,645 · cited 2× · see citing works
“Findings from this study and others like it 133,134 point to the potential for using data about activities occurring in digital contexts to automate assessment of personality and other psychological constructs.”
BackgroundCanzian, L., & Musolesi, M. (2015) · Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2750858.2805845 · Cited by 477 · see citing works
Smartphone usage in the 21st century: who is active on WhatsApp?
Montag, C., Błaszkiewicz, K., Sariyska, R., Lachmann, B., Andone, I., Trendafilov, B., Eibes, M., & Markowetz, A. (2015) · BMC Research Notes · doi:10.1186/s13104-015-1280-z · Cited by 408 · see citing works
The relationship between mobile phone location sensor data and depressive symptom severity
Saeb, S., Lattie, E. G., Schueller, S. M., Kording, K. P., & Mohr, D. C. (2016) · PeerJ · doi:10.7717/peerj.2537 · Cited by 313 · see citing works
Jacobson, N. C., & Chung, Y. J. (2020) · Sensors · doi:10.3390/s20123572 · Cited by 139 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Understanding Autobiographical Memory in the Digital Age: The AMEDIA-Model
Hutmacher, F., Appel, M., & Schwan, S. (2024) · Psychological Inquiry · doi:10.1080/1047840x.2024.2384125 · Cited by 12 · see citing works
- Authors
- Harari, G. M., & Gosling, S. D.
- Affiliations
- Stanford University (US)
- The University of Melbourne (AU)
- Corresponding author
- Harari, Gabriella M.
- Venue
- Nature Reviews Psychology
- Published
- 2023-10-23
- Volume
- 2
- Issue
- 12
- Pages
- 767-779
- ISSN
- 2731-0574 (ISSN-L)
- OpenAlex
- W4387879519
- Cited by
- 56
- References
- 9
- Citations by year
- 2026: 22 · 2025: 24 · 2024: 10
- FWCI
- 2.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Reviews Psychology (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Keywords
- mobile sensing, daily behavior, naturalistic observation, psychological constructs, research agenda, smartphone data
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
