"My Doctor is Keeping an Eye on Me!"

Kim, Yoojung; Ji, Sookyoung; Lee, Hyun-Jeong; Kim, Jeong‐Whun; Yoo, Sooyoung; Lee, Joongseek

doi:10.1145/2858036.2858145

Cited by 34 publications

(38 citation statements)

References 29 publications

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“…To address barriers of data use and interpretation, a first obvious approach may be for self-tracking tools to support clinical standards of information structure and representation, effectively addressing the problem of structure unfamiliarity and interpretability under time constraints (B4.1: unfamiliar structure, B4.2: unhelpful structure, and B3.1: insufficient time). Prior studies have observed that summarised forms of information, such as tables and charts, are most appropriate when decisions need to be made quickly, such as in acute settings [27,45]. The interview findings reflect this; participants preferred data which was presented in a familiar summarised structure.…”

Section: Implications For Design: Data Use and Interpretationmentioning

confidence: 61%

“…For example, within the management of irritable bowel syndrome, one study identified that lack of standardisation within patient-generated data makes it difficult for clinicians to interpret the data [13]. Another study, which focused on using consumer apps for managing diet, found that clinicians were concerned that data from these apps were unreliable [27]. While these studies yield insights about the needs of particular clinical settings, identifying barriers common across such clinical settings promises to advance designs to improve the use of patient-generated data across clinical contexts.…”

Section: Introductionmentioning

confidence: 99%

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Common Barriers to the Use of Patient-Generated Data Across Clinical Settings

West

Kleek

Giordano

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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Patient-generated data, such as data from wearable fitness trackers and smartphone apps, are viewed as a valuable information source towards personalised healthcare. However, studies in specific clinical settings have revealed diverse barriers to their effective use. In this paper, we address the following question: are there barriers prevalent across distinct workflows in clinical settings to using patient-generated data? We conducted a twopart investigation: a literature review of studies identifying such barriers; and interviews with clinical specialists across multiple roles, including emergency care, cardiology, mental health, and general practice. We identify common barriers in a six-stage workflow model of aligning patient and clinician objectives, judging data quality, evaluating data utility, rearranging data into a clinical format, interpreting data, and deciding on a plan or action. This workflow establishes common ground for HCI practitioners and researchers to explore solutions to improving the use of patient-generated data in clinical practices.

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Section: Implications For Design: Data Use and Interpretationmentioning

confidence: 61%

Section: Introductionmentioning

confidence: 99%

Common Barriers to the Use of Patient-Generated Data Across Clinical Settings

West

Kleek

Giordano

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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show abstract

“…The smartphone provides the most flexible user input possibilities and already significantly reduces the effort in comparison to traditional pen and paper methods [15]. A smartwatch can supplement a smartphone since it is always in reach and can be used in a less conspicuous manner.…”

Section: Introductionmentioning

confidence: 99%

“…An entry with missing data like the amount is better than no record at all since the user still can edit and complete the entry with the smartphone afterwards. Already incomplete entries can provide medical relevant information about the time and frequency of nutrition intake [15].…”

Section: Introductionmentioning

confidence: 99%

Development of a mobile multi-device nutrition logger

Seiderer

Flutura

André

2017

Proceedings of the 2nd ACM SIGCHI International Workshop on Multisensory Approaches to Human-Food Interaction

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In this paper we present a mobile system for nutrition logging which integrates multiple devices and modalities to facilitate food and drink tracking. The user is free to decide in each situation to use the most appropriate device combination out of a smartphone, smartwatch and smartscale. We describe the design and implementation of our system which is based on a requirements analysis. Finally, first results of a preliminary in-situ study with the prototype are reported giving first hints about the benefits and challenges of this multi-device approach in daily life scenarios.

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“…Not for redistribution. The definitive Version of Record can be found at DOI: h p://dx.doi.org/10.1145/3079452.3079486 possibilities and already signi cantly reduces the e ort in comparison to traditional pen and paper methods [3]. A smartwatch can supplement a smartphone since it is always in reach and can be used in a less conspicuous manner.…”

Section: Introductionmentioning

confidence: 99%

Development of a Multi-Device Nutrition Logging Prototype Including a Smartscale

Seiderer

André

2017

Proceedings of the 2017 International Conference on Digital Health

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In this work we present a portable system for nutrition logging that integrates multiple devices and modalities in order to facilitate food and drink tracking. e system consists of a smartphone, a smartwatch and a smartscale that can be combined exibly by users depending on the current situation and their personal needs. Based on a requirement analysis, we present the rationale behind the design and implementation of our food and drink logger. We also report the preliminary results of an in-situ study we conducted in order to explore the potential bene ts and challenges of a multidevice approach to nutrition tracking in daily life se ings.

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"My Doctor is Keeping an Eye on Me!"

Cited by 34 publications

References 29 publications

Common Barriers to the Use of Patient-Generated Data Across Clinical Settings

Common Barriers to the Use of Patient-Generated Data Across Clinical Settings

Development of a mobile multi-device nutrition logger

Development of a Multi-Device Nutrition Logging Prototype Including a Smartscale

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