BrisT1D Dataset: Young Adults with Type 1 Diabetes in the UK using Smartwatches
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915407402631168 |
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| author | James, Sam Gordon Armstrong, Miranda Elaine Glynis O'Kane, Aisling Ann Emerson, Harry Abdallah, Zahraa S. |
| author_facet | James, Sam Gordon Armstrong, Miranda Elaine Glynis O'Kane, Aisling Ann Emerson, Harry Abdallah, Zahraa S. |
| contents | Background: Type 1 diabetes (T1D) has seen a rapid evolution in management technology and forms a useful case study for the future management of other chronic conditions. Further development of this management technology requires an exploration of its real-world use and the potential of additional data streams. To facilitate this, we contribute the BrisT1D Dataset to the growing number of public T1D management datasets. The dataset was developed from a longitudinal study of 24 young adults in the UK who used a smartwatch alongside their usual T1D management. Findings: The BrisT1D dataset features both device data from the T1D management systems and smartwatches used by participants, as well as transcripts of monthly interviews and focus groups conducted during the study. The device data is provided in a processed state, for usability and more rapid analysis, and in a raw state, for in-depth exploration of novel insights captured in the study. Conclusions: This dataset has a range of potential applications. The quantitative elements can support blood glucose prediction, hypoglycaemia prediction, and closed-loop algorithm development. The qualitative elements enable the exploration of user experiences and opinions, as well as broader mixed-methods research into the role of smartwatches in T1D management. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_17757 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BrisT1D Dataset: Young Adults with Type 1 Diabetes in the UK using Smartwatches James, Sam Gordon Armstrong, Miranda Elaine Glynis O'Kane, Aisling Ann Emerson, Harry Abdallah, Zahraa S. Human-Computer Interaction Machine Learning Background: Type 1 diabetes (T1D) has seen a rapid evolution in management technology and forms a useful case study for the future management of other chronic conditions. Further development of this management technology requires an exploration of its real-world use and the potential of additional data streams. To facilitate this, we contribute the BrisT1D Dataset to the growing number of public T1D management datasets. The dataset was developed from a longitudinal study of 24 young adults in the UK who used a smartwatch alongside their usual T1D management. Findings: The BrisT1D dataset features both device data from the T1D management systems and smartwatches used by participants, as well as transcripts of monthly interviews and focus groups conducted during the study. The device data is provided in a processed state, for usability and more rapid analysis, and in a raw state, for in-depth exploration of novel insights captured in the study. Conclusions: This dataset has a range of potential applications. The quantitative elements can support blood glucose prediction, hypoglycaemia prediction, and closed-loop algorithm development. The qualitative elements enable the exploration of user experiences and opinions, as well as broader mixed-methods research into the role of smartwatches in T1D management. |
| title | BrisT1D Dataset: Young Adults with Type 1 Diabetes in the UK using Smartwatches |
| topic | Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2507.17757 |