Health Mashups

Bentley, Frank; Tollmar, Konrad; Stephenson, Peter; Levy, Laura; Jones, Brian; Robertson, Scott; Price, Ed; Catrambone, Richard; Wilson, Jeff

doi:10.1145/2503823

Cited by 203 publications

(33 citation statements)

References 30 publications

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“…Increased self-consciousness helps to focus on certain behaviour and can improve well-being [25]. Earlier studies about migraine patients' willingness to wear sensors to detect migraine pre-symptoms and their pre-symptoms revealed that 88.8% of the respondents wanted a device to detect an upcoming migraine attack.…”

Section: Related Workmentioning

confidence: 99%

Preferred Biosignals to Predict Migraine Attack

Huttunen

Halonen

2018

Well-Being in the Information Society. Fighting Inequalities

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Abstract. Migraine is classified to two classes, with aura and without aura, and migraine seizures last usually several hours. The goal of this study was to identify the most important symptoms of migraine to be monitored by wearable sensors to predict the migraine attack. The purpose of wearable sensors is to guide patients to take the migraine medication in time, and to support their own care. Self-measurement is a growing trend worldwide and sensor technology has been used for several years in activity wristbands, smartphones, rings, mobile phones, and mobile applications. The study was conducted as an operational study, randomised for those who had been diagnosed with migraine by a doctor. The study was divided into two parts, at first a questionnaire was sent to 17 people in social media. On the basis of the questionnaire, a qualitative interview was conducted for 12 persons with migraine. Responses to the questionnaire were compared to the results of the interview, and the answers to the research questions were sought. Migraine patients considered important that device reports quality of sleep, pulse, blood pressure, stress levels, sleep apnea, and energy consumption.

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Section: Related Workmentioning

confidence: 99%

Preferred Biosignals to Predict Migraine Attack

Huttunen

Halonen

2018

Well-Being in the Information Society. Fighting Inequalities

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“…Moreover, the data made available to the user from PI tools are usually visualizations of the monitored phenomena, with, at most, simple correlations among user's activities which are displayed using natural language [2]. EUM, and especially its cognitive part, would provide information one step ahead: it will allow to correlate not only the activities among themselves and with the user's attitudes, but also with the user's cognitive states and skills involved in those activities.…”

Section: Enriching the Qs With The Eummentioning

confidence: 99%

Quantified self and modeling of human cognition

Cena¹,

Likavec²,

Rapp³

2015

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 AC

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“…Epstein et al visualized subsets of collected location and physical activity data using a variety of presentations [16]. Bentley et al developed a system that aggregates multiple aspects of personal well-being data and provides people with insights based on complex relations using natural language [17]. They showed that users were able to understand complex relationships and change their behavior to improve their well-being.…”

Section: Introductionmentioning

confidence: 99%

Exploring Genetic Data Across Individuals: Design and Evaluation of a Novel Comparative Report Tool

Westendorf¹,

Shaer²,

Pollalis³

et al. 2018

J Med Internet Res

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BackgroundThe growth in the availability of personal genomic data to nonexperts poses multiple challenges to human-computer interaction research; data are highly sensitive, complex, and have health implications for individuals and families. However, there has been little research on how nonexpert users explore their genomic data.ObjectiveWe focus on how to support nonexperts in exploring and comparing their own personal genomic report with those of other people. We designed and evaluated CrossGenomics, a novel tool for comparing personal genetic reports, which enables exploration of shared and unshared genetic variants. Focusing on communicating comparative impact, rarity, and certainty, we evaluated alternative novel interactive prototypes.MethodsWe conducted 3 user studies. The first focuses on assessing the usability and understandability of a prototype that facilitates the comparison of reports from 2 family members. Following a design iteration, we studied how various prototypes support the comparison of genetic reports of a 4-person family. Finally, we evaluated the needs of early adopters—people who share their genetic reports publicly for comparing their genetic reports with that of others.ResultsIn the first study, sunburst- and Venn-based comparisons of two genomes led to significantly higher domain comprehension, compared with the linear comparison and with the commonly used tabular format. However, results show gaps between objective and subjective comprehension, as sunburst users reported significantly lower perceived understanding and higher levels of confusion than the users of the tabular report. In the second study, users who were allowed to switch between the different comparison views presented higher comprehension levels, as well as more complex reasoning than users who were limited to a single comparison view. In the third study, 35% (17/49) reported learning something new from comparing their own data with another person’s data. Users indicated that filtering and toggling between comparison views were the most useful features.ConclusionsOur findings (1) highlight features and visualizations that show strengths in facilitating user comprehension of genomic data, (2) demonstrate the value of affording users the flexibility to examine the same report using multiple views, and (3) emphasize users’ needs in comparison of genomic data. We conclude with design implications for engaging nonexperts with complex multidimensional genomic data.

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Health Mashups

Cited by 203 publications

References 30 publications

Preferred Biosignals to Predict Migraine Attack

Preferred Biosignals to Predict Migraine Attack

Quantified self and modeling of human cognition

Exploring Genetic Data Across Individuals: Design and Evaluation of a Novel Comparative Report Tool

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