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Guillermo Beltrán

dblp:220/4960 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Health and well-being technologies · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Health and well-being technologies
personal informatics
0.412020
SleepBandits: Guided Flexible Self-Experiments for Sleep · CHI 2020
Health and well-being technologies › personal informatics
self-experimentation
0.412020
SleepBandits: Guided Flexible Self-Experiments for Sleep · CHI 2020
Health and well-being technologies › personal informatics
self-tracking
0.412020
SleepBandits: Guided Flexible Self-Experiments for Sleep · CHI 2020
Health and well-being technologies
sleep
0.412020
SleepBandits: Guided Flexible Self-Experiments for Sleep · CHI 2020

Methods — techniques the papers use, named apart from their topics

sensor data collection · 0.4in-the-wild study · 0.4
YearPublicationVenuePosition
2020 SleepBandits: Guided Flexible Self-Experiments for Sleep
abstract
Self-experiments allow people to explore what behavioral changes lead to improved health and wellness. However, it is challenging to run such experiments in a scientifically valid way that is also flexible and able to accommodate the realities of daily life. We present a set of design principles for guided self-experiments that aim to lower this barrier to self-experimentation. We demonstrate the value of the principles by implementing them in SleepBandits, an integrated system that includes a smartphone application for sleep experiments. SleepBandits guides users through the steps of a single-case experiment, automatically collecting data from the built-in sensors and user input and calculating and presenting results in real-time. We released SleepBandits to the Google Play Store and people voluntarily downloaded and used it. Based on the data from 365 active users from this in-the-wild study, we discuss opportunities and challenges with the design principles and the SleepBandits system.
Nediyana Daskalova, Jina Yoon, Cintia Araújo, Guillermo Beltrán, Nicole Nugent, John McGeary, Joseph Jay Williams, Jeff Huang 0002
CHI5