Wellbeing Insights in a Data-Driven Future
Visuri, Aku; Van Berkel, Niels; Tag, Benjamin (2024-01-30)
IEEE
30.01.2024
A. Visuri, N. Van Berkel and B. Tag, "Wellbeing Insights in a Data-Driven Future," 2023 Fourteenth International Conference on Mobile Computing and Ubiquitous Network (ICMU), Kyoto, Japan, 2023, pp. 1-7, doi: 10.23919/ICMU58504.2023.10412212.
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© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202404232916
https://urn.fi/URN:NBN:fi:oulu-202404232916
Tiivistelmä
Abstract
This article explores meaningful insights users might be able to obtain at the intersection of wearable technology and user-generated data. While wearables have become ubiquitous in monitoring health and well-being, the utility of the data they collect remains limited for end-users. Our TypeAware case study delves into users' challenges in interpreting and deriving actionable insights from their wearable data. The TypeAware application aims to enhance user understanding of digital well-being and sleep quality data. Our results indicate that, despite engagement, participants encountered difficulties generating actionable insights from their data. Leveraging the capabilities of large language models, our results demonstrate the potential for automating insight generation: transforming raw data into meaningful, user-friendly understandings. Ulti-mately, this work calls for a shift in wearable technology design, advocating for more user-centric approaches that empower individuals to unlock the full potential of their wearable data for improved well-being.
This article explores meaningful insights users might be able to obtain at the intersection of wearable technology and user-generated data. While wearables have become ubiquitous in monitoring health and well-being, the utility of the data they collect remains limited for end-users. Our TypeAware case study delves into users' challenges in interpreting and deriving actionable insights from their wearable data. The TypeAware application aims to enhance user understanding of digital well-being and sleep quality data. Our results indicate that, despite engagement, participants encountered difficulties generating actionable insights from their data. Leveraging the capabilities of large language models, our results demonstrate the potential for automating insight generation: transforming raw data into meaningful, user-friendly understandings. Ulti-mately, this work calls for a shift in wearable technology design, advocating for more user-centric approaches that empower individuals to unlock the full potential of their wearable data for improved well-being.
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