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How carat affects user behavior: implications for mobile battery awareness applications

Authors:

Kumaripaba Athukorala,

Eemil Lagerspetz,

Maria von Kügelgen,

Adam J. Oliner,

Giulio JacucciAuthors Info & Claims

CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 1029 - 1038

https://doi.org/10.1145/2556288.2557271

Published: 26 April 2014 Publication History

Abstract

Mobile devices have limited battery life, and numerous battery management applications are available that aim to improve it. This paper examines a large-scale mobile battery awareness application, called Carat, to see how it changes user behavior with long-term use. We conducted a survey of current Carat Android users and analyzed their interaction logs. The results show that long-term Carat users save more battery, charge their devices less often, learn to manage their battery with less help from Carat, have a better understanding of how Carat works, and may enjoy competing against other users. Based on these findings, we propose a set of guidelines for mobile battery awareness applications: battery awareness applications should make the reasoning behind their recommendations understandable to the user, be tailored to retain long-term users, take the audience into account when formulating feedback, and distinguish third-party and system applications.

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Cited By

Nguyen NZubair MZuniga ATarkoma SHui PLee HPerrault SAmmar MFlores HNurmi P(2024)The Price is Right? The Economic Value of Sharing SensorsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.333007111:3(3468-3482)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3330071
Vaid SHarari G(2022)Smartphones in Personal Informatics: A Framework for Self-Tracking Research with Mobile SensingDigital Phenotyping and Mobile Sensing10.1007/978-3-030-98546-2_6(77-104)Online publication date: 23-Jul-2022
https://doi.org/10.1007/978-3-030-98546-2_6
Opoku Asare KTerhorst YVega JPeltonen ELagerspetz EFerreira D(2021)Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory StudyJMIR mHealth and uHealth10.2196/265409:7(e26540)Online publication date: 12-Jul-2021
https://doi.org/10.2196/26540
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Index Terms

How carat affects user behavior: implications for mobile battery awareness applications
1. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Conferences

CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2014

4206 pages

ISBN:9781450324731

DOI:10.1145/2556288

General Chairs:
Matt Jones
Swansea University, Wales, UK
,
Philippe Palanque
Université Paul Sabatier, France
,
Program Chairs:
Albrecht Schmidt
University of Stuttgart, Germany
,
Tovi Grossman
Autodesk Research, Canada

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 April 2014

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CHI '14: CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2014

Ontario, Toronto, Canada

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CHI '14 Paper Acceptance Rate 465 of 2,043 submissions, 23%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Cited By

Nguyen NZubair MZuniga ATarkoma SHui PLee HPerrault SAmmar MFlores HNurmi P(2024)The Price is Right? The Economic Value of Sharing SensorsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.333007111:3(3468-3482)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3330071
Vaid SHarari G(2022)Smartphones in Personal Informatics: A Framework for Self-Tracking Research with Mobile SensingDigital Phenotyping and Mobile Sensing10.1007/978-3-030-98546-2_6(77-104)Online publication date: 23-Jul-2022
https://doi.org/10.1007/978-3-030-98546-2_6
Opoku Asare KTerhorst YVega JPeltonen ELagerspetz EFerreira D(2021)Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory StudyJMIR mHealth and uHealth10.2196/265409:7(e26540)Online publication date: 12-Jul-2021
https://doi.org/10.2196/26540
Welber SZhao VDolin CMorkved OHoffmann HUr B(2021)Do Users Have Contextual Preferencesfor Smartphone Power Management?Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450613.3456813(44-54)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3450613.3456813
Tonetto LLagerspetz EYi Ding AOtt JTarkoma SNurmi P(2021)The mobility laws of location-based gamesEPJ Data Science10.1140/epjds/s13688-021-00266-x10:1Online publication date: 15-Feb-2021
https://doi.org/10.1140/epjds/s13688-021-00266-x
Visuri Avan Berkel NGoncalves JRawassizadeh RFerreira DKostakos V(2021)Understanding usage style transformation during long-term smartwatch usePersonal and Ubiquitous Computing10.1007/s00779-020-01511-2Online publication date: 19-Jan-2021
https://doi.org/10.1007/s00779-020-01511-2
Min CLee YYoo CHwang IJu YSong JKang S(2020)Scalable Power Impact Prediction of Mobile Sensing Applications at Pre-Installation TimeIEEE Transactions on Mobile Computing10.1109/TMC.2019.290989719:6(1448-1464)Online publication date: 1-Jun-2020
https://doi.org/10.1109/TMC.2019.2909897
Zuniga AFlores HLagerspetz ENurmi PTarkoma SHui PManner J(2019)Tortoise or Hare? Quantifying the Effects of Performance on Mobile App RetentionThe World Wide Web Conference10.1145/3308558.3313428(2517-2528)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313428
Vaid SHarari G(2019)Smartphones in Personal Informatics: A Framework for Self-Tracking Research with Mobile SensingDigital Phenotyping and Mobile Sensing10.1007/978-3-030-31620-4_5(65-92)Online publication date: 1-Nov-2019
https://doi.org/10.1007/978-3-030-31620-4_5
Peltonen ELagerspetz EHamberg JMehrotra AMusolesi MNurmi PTarkoma SBailie LOliver N(2018)The hidden image of mobile appsProceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services10.1145/3229434.3229474(1-12)Online publication date: 3-Sep-2018
https://dl.acm.org/doi/10.1145/3229434.3229474
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