Characterizing visualization insights from quantified selfers' personal data presentations
- Submitting institution
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University of Southampton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 20749816
- Type
- D - Journal article
- DOI
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10.1109/MCG.2015.51
- Title of journal
- IEEE Computer Graphics and Applications
- Article number
- -
- First page
- 28
- Volume
- 35
- Issue
- 4
- ISSN
- 0272-1716
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2015
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
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-
- Citation count
- 30
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- “Quantified Selfers” (QS) run their own personal experiments to better understand what factors affect their wellbeing. They often have poor statistical knowledge. In analysis of experiments of 30 QS, we identified 8 points where interaction design could help produce better quality information for self-reflection and decision support. This work led to new models for self-study like in5 (https://dl.acm.org/doi/10.1145/3290607.3312977) based on schraefel’s Inbodied Interaction work (https://dl.acm.org/toc/interactions/2020/27/2#sec10) . Results of one 6-week trial of in5 with international creative agency Ogilvy Mayer (https://tinyurl.com/in5ogilvy) lead to redesign of their 2016 workspace at Oxo Towers London offices and worker wellbeing policies (Dan Bennet, daniel.bennett@ogilvy.com, https://ogilvy.co.uk/agency/behaviour-change).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -