Designing self-tracking experiences: A qualitative study of the perceptions of barriers and facilitators to adopting digital health technology for automatic urine analysis at home.

Autor:	Motta M; EPFL+ECAL Lab, EPFL, Lausanne, Switzerland., Groves E; EPFL+ECAL Lab, EPFL, Lausanne, Switzerland., Schneider A; EPFL+ECAL Lab, EPFL, Lausanne, Switzerland., Paoletti S; Life Science Technologies, CSEM SA, Landquart, Switzerland., Henchoz N; EPFL+ECAL Lab, EPFL, Lausanne, Switzerland., Ribes Lemay D; EPFL+ECAL Lab, EPFL, Lausanne, Switzerland.
Jazyk:	angličtina
Zdroj:	PLOS digital health [PLOS Digit Health] 2023 Sep 15; Vol. 2 (9), pp. e0000319. Date of Electronic Publication: 2023 Sep 15 (Print Publication: 2023).
DOI:	10.1371/journal.pdig.0000319
Abstrakt:	Self-tracking technologies open new doors to previously unimaginable scenarios. The diagnosis of diseases years in advance, or supporting the health of astronauts on missions to Mars are just some of many example applications. During the COVID-19 pandemic, a wide range of self-monitoring protocols emerged, revealing opportunities but also challenges including difficulties in understanding how to self-use monitoring systems, struggling to recognize the benefit of such systems and a high likelihood of abandonment. In this paper, we explore the role that design plays in the creation of a user experience of self-tracking, with a focus on urine analysis at home. We investigate adoption factors and forms of data expression to overcome the presented challenges. By combining insights from related work, semi-structured interviews and indicative user-tests, we show the potential of pairing a traditional numerical data representation (data quantification) with a qualitative expression of the data (data qualification). Indeed, qualitative expressions have the potential to convey the complexity of the phenomena tracked, enabling deep meaning-making and emotional connection to personal data. At the same time, we also identify issues with this approach, which can require a longer learning curve and lead to rejection by users more accustomed to traditional, numerical approaches. Based on the results, several recommendations have been converted into an experimental proposition, which also presents future plans for the continuation of the project. This article presents the first fundamental step in creating a meaningful experience of self-tracking, taking into consideration the needs and expectations of future users. Competing Interests: The authors have declared that no competing interests exist. (Copyright: © 2023 Motta et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu