Mobile Sensing at the Service of Mental Well-being: a Large-scale Longitudinal Study
Open accessAbstract
Measuring mental well-being with mobile sensing has been an increasingly active research topic. Pervasiveness of smartphones combined with the convenience of mobile app distribution platforms (e.g., Google Play) provide a tremendous opportunity to reach out to millions of users. However, the studies at the confluence of mental health and mobile sensing have been longitudinally limited, controlled, or confined to a small number of participants. In this paper we report on what we believe is the largest longitudinal in-the-wild study of mood through smartphones. We describe an Android app to collect participants' self-reported moods and system triggered experience sampling data while passively measuring their physical activity, sociability, and mobility via their device's sensors. We report the results of a large-scale analysis of the data collected for about three years from 18,000 users.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Binary prediction is quite common in the mood prediction literature, where mood is simplified to a binary state, so that extreme depression is binned in the same class as moderate unhappiness.
“Further, binary prediction is quite common in the mood prediction literature, where mood is simplified to a binary state [21], so that, for instance, extreme depression is binned in the same class as moderate unhappiness.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [21]
Utsureko and EmotionSense, two independent smartphone applications for mood monitoring through self-reports, were used by more than 24 000 and 17 000 users, respectively.
“On a larger scale, Utsureko [20] and EmotionSense [21], two independent smartphone applications for mood monitoring through self-reports, were used by more than 24 000 and 17 000 users, respectively.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [21]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 50 · Cited by 134
Research works printed but not linked: 5
- [11][11] Carter, S., Mankoff, J., and Heer, J. Momento: Support for situated ubicomp experimentation. In CHI '07 , ACM (2007). -
- [22][22] Girardello, A., and Michahelles, F. Appaware: Which mobile applications are hot? In MobileHCI '10 , ACM (2010). -
- [24][24] Hernandez, J., Hoque, M. E., Drevo, W., and Picard, R. W. Mood meter: Counting smiles in the wild. In UbiComp '12 , ACM (2012). -
- [36][36] Lu, H., Frauendorfer, D., Rabbi, M., Mast, M. S., Chittaranjan, G. T., Campbell, A. T., Gatica-Perez, D., and Choudhury, T. Stresssense: Detecting stress in unconstrained acoustic environments using smartphones. In UbiComp '12 , ACM (2012). -
- [43][43] Parate, A., Chiu, M.-C., Chadowitz, C., Ganesan, D., and Kalogerakis, E. Risq: Recognizing smoking gestures with inertial sensors on a wristband. In MobiSys '14 , ACM (2014). -
- Authors
- Servia-Rodríguez, S., Rachuri, K. K., Mascolo, C., Rentfrow, P. J., Lathia, N., & Sandstrom, G. M.
- Affiliations
- University of Cambridge (GB)
- Samsung Research America, San Jose, USA
- University of Cambridge, Cambridge, United Kingdom
- University of Essex (GB)
- Venue
- Proceedings of the 26th International Conference on World Wide Web
- Published
- 2017-04-03
- Pages
- 103-112
- OpenAlex
- W2604757304
- Cited by
- 134
- References
- 50
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 14 · 2023: 16 · 2022: 21 · 2021: 14 · 2020: 16 · 2019: 20 · 2018: 14 · 2017: 6
- FWCI
- 47.95
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International World Wide Web Conference (WWW) (publishedVersion, cc-by)
- Open Access at Essex (University of Essex) (acceptedVersion)
- Funders
- EPSRC (EP/K019392, EP/I032673/1)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- mobile sensing, psychological well-being, mood measurement, ecological momentary assessment, smartphones, physical activity, sociability, mobility, Android application, self-reported mood
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
