QuantifyMe: An Automated Single-Case Experimental Design Platform
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018. We designed, developed, and evaluated a novel system, QuantifyMe, for novice self-experimenters to conduct proper-methodology single-case self-experiments in an automated and scientific manner using t...
| Main Authors: | , , , , |
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| Format: | Book |
| Language: | English |
| Published: |
Springer International Publishing
2021
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| Online Access: | https://hdl.handle.net/1721.1/137052 |
| _version_ | 1826196147410042880 |
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| author | Sano, Akane Taylor, Sara Ferguson, Craig Mohan, Akshay Picard, Rosalind W. |
| author_facet | Sano, Akane Taylor, Sara Ferguson, Craig Mohan, Akshay Picard, Rosalind W. |
| author_sort | Sano, Akane |
| collection | MIT |
| description | © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018. We designed, developed, and evaluated a novel system, QuantifyMe, for novice self-experimenters to conduct proper-methodology single-case self-experiments in an automated and scientific manner using their smartphones. In this work we evaluate its use with four different kinds of personalized investigations, examining how variables such as sleep duration and regularity, activity, and leisure time affect personal happiness, stress, productivity, and sleep efficiency. We describe lessons learned developing the system, and recommendations for its improvement, as well as its potential for enabling personalized insights to be scientifically evaluated in many individuals. |
| first_indexed | 2024-09-23T10:21:43Z |
| format | Book |
| id | mit-1721.1/137052 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:21:43Z |
| publishDate | 2021 |
| publisher | Springer International Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1370522021-11-02T03:34:04Z QuantifyMe: An Automated Single-Case Experimental Design Platform Sano, Akane Taylor, Sara Ferguson, Craig Mohan, Akshay Picard, Rosalind W. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018. We designed, developed, and evaluated a novel system, QuantifyMe, for novice self-experimenters to conduct proper-methodology single-case self-experiments in an automated and scientific manner using their smartphones. In this work we evaluate its use with four different kinds of personalized investigations, examining how variables such as sleep duration and regularity, activity, and leisure time affect personal happiness, stress, productivity, and sleep efficiency. We describe lessons learned developing the system, and recommendations for its improvement, as well as its potential for enabling personalized insights to be scientifically evaluated in many individuals. 2021-11-01T19:16:03Z 2021-11-01T19:16:03Z 2018 2019-08-02T11:11:18Z Book http://purl.org/eprint/type/ConferencePaper 1867-8211 1867-822X https://hdl.handle.net/1721.1/137052 Sano, Akane, Taylor, Sara, Ferguson, Craig, Mohan, Akshay and Picard, Rosalind W. 2018. "QuantifyMe: An Automated Single-Case Experimental Design Platform." en 10.1007/978-3-319-98551-0_23 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Springer International Publishing MIT web domain |
| spellingShingle | Sano, Akane Taylor, Sara Ferguson, Craig Mohan, Akshay Picard, Rosalind W. QuantifyMe: An Automated Single-Case Experimental Design Platform |
| title | QuantifyMe: An Automated Single-Case Experimental Design Platform |
| title_full | QuantifyMe: An Automated Single-Case Experimental Design Platform |
| title_fullStr | QuantifyMe: An Automated Single-Case Experimental Design Platform |
| title_full_unstemmed | QuantifyMe: An Automated Single-Case Experimental Design Platform |
| title_short | QuantifyMe: An Automated Single-Case Experimental Design Platform |
| title_sort | quantifyme an automated single case experimental design platform |
| url | https://hdl.handle.net/1721.1/137052 |
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