The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Activity sensing in the wild: a field trial of ubifit garden
Abstract
Recent advances in small inexpensive sensors, low-power processing, and activity modeling have enabled applications that use on-body sensing and machine learning to infer people's activities throughout everyday life. To address the growing rate of sedentary lifestyles, we have developed a system, UbiFit Garden, which uses these technologies and a personal, mobile display to encourage physical activity. We conducted a 3-week field trial in which 12 participants used the system and report findings focusing on their experiences with the sensing and activity inference. We discuss key implications for systems that use on-body sensing and activity inference to encourage physical activity.
Overview
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
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
In search of how people change: Applications to addictive behaviors.
Prochaska, J. O., DiClemente, C. C., & Norcross, J. C. (1992) · American Psychologist · doi:10.1037/0003-066x.47.9.1102 · Cited by 8,596
Design requirements for technologies that encourage physical activity
Consolvo, S., Everitt, K., Smith, I., & Landay, J. A. (2006) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1124772.1124840 · Cited by 801
Self-citationChick clique: persuasive technology to motivate teenage girls to exercise
Toscos, T., Faber, A., An, S., & Gandhi, M. P. (2006) · CHI '06 Extended Abstracts on Human Factors in Computing Systems · doi:10.1145/1125451.1125805 · Cited by 280
Self-citationAcceptance and usability of a relational agent interface by urban older adults
Bickmore, T. W., Caruso, L., & Clough-Gorr, K. (2005) · CHI '05 Extended Abstracts on Human Factors in Computing Systems · doi:10.1145/1056808.1056879 · Cited by 142 · cited 1×
“Laura [3] is an animated conversational agent, displayed on a wallmounted touch-screen display, who acts as a virtual exercise advisor.”
Background
Cited by
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
A survey of mobile phone sensing
Lane, N., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. (2010) · IEEE Communications Magazine · doi:10.1109/mcom.2010.5560598 · Cited by 2,292
Healthcare in the pocket: Mapping the space of mobile-phone health interventions
Klasnja, P., & Pratt, W. (2012) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2011.08.017 · Cited by 970
Self-citationThe design of eco-feedback technology
Froehlich, J., Findlater, L., & Landay, J. (2010) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1753326.1753629 · Cited by 813
Self-citationUnderstanding quantified-selfers' practices in collecting and exploring personal data
Choe, E. K., Lee, N. B., Lee, B., Pratt, W., & Kientz, J. A. (2014) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557372 · Cited by 766
Metadata
- Authors
- Consolvo, S., McDonald, D. W., Toscos, T., Chen, M. Y., Froehlich, J., Harrison, B., Klasnja, P., LaMarca, A., LeGrand, L., Libby, R., Smith, I., & Landay, J. A.
- Venue
- Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2008-04-06
- Pages
- 1797-1806
- OpenAlex
- W2156221064
- Cited by
- 1,131
- References
- 4
- FWCI
- 44.61
- Type
- conference paper
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Activity sensing in the wild: a field trial of ubifit garden. https://app.cochat.ai/papers/10.1145/1357054.1357335
- The paper
- Consolvo, S., McDonald, D. W., Toscos, T., Chen, M. Y., Froehlich, J., Harrison, B., Klasnja, P., LaMarca, A., LeGrand, L., Libby, R., Smith, I., & Landay, J. A. (2008). Activity sensing in the wild: a field trial of ubifit garden. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/1357054.1357335
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
