research-article

Leveraging Mobile Technology to Increase the Permanent Adoption of Shelter Dogs

Authors:

Joelle Alcaidinho,

Giancarlo Valentin,

Krista Sanders,

Melody Jackson,

Thad StarnerAuthors Info & Claims

MobileHCI '15: Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services

Pages 463 - 469

https://doi.org/10.1145/2785830.2785861

Published: 24 August 2015 Publication History

Abstract

We present the results of an 8-week pilot study with 55 dogs investigating whether using quantimetric monitors and a companion smartphone application can reduce returns and increase the perceived strength of bonds between newly adopted dogs from the Humane Society of Silicon Valley and their adopters. Through this pilot study, we developed guidelines for future research and discovered promising results indicating that providing dog quantimetric data to adopters through the use of a smartphone application could yield reduced rates of re-relinquishment. Additionally, respondents indicated that they felt using the smartphone application helped them to better meet the activity needs of their dog and increased the bond between themselves and their newly adopted dog.

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Cited By

Verbeek PMajure CQuattrochi LTurner S(2024)The Welfare of Dogs as an Aspect of the Human–Dog Bond: A Scoping ReviewAnimals10.3390/ani1413198514:13(1985)Online publication date: 5-Jul-2024
https://doi.org/10.3390/ani14131985
Ramey C(2023)Wearable and embedded sensor systems for animal welfare monitoringProceedings of the Tenth International Conference on Animal-Computer Interaction10.1145/3637882.3637899(1-5)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1145/3637882.3637899
Hazan YHirsch-Matsioulas Ovan der Linden DZamansky A(2022)How Can Technology Support Dog Shelters in Behavioral Assessment: an Exploratory StudyProceedings of the Ninth International Conference on Animal-Computer Interaction10.1145/3565995.3566023(1-4)Online publication date: 5-Dec-2022
https://dl.acm.org/doi/10.1145/3565995.3566023
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Index Terms

Leveraging Mobile Technology to Increase the Permanent Adoption of Shelter Dogs
1. Human-centered computing

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cover image ACM Conferences

MobileHCI '15: Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services

August 2015

611 pages

ISBN:9781450336529

DOI:10.1145/2785830

General Chairs:
Sebastian Boring
University of Copenhagen, Denmark
,
Enrico Rukzio
University of Ulm, Germany
,
Program Chairs:
Hans Gellersen
Lancaster University, UK
,
Ken Hinckley
Microsoft Research Redmond, USA

Copyright © 2015 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 the author(s) 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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Published: 24 August 2015

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MobileHCI '15

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MobileHCI '15: 17th International Conference on Human-Computer Interaction with Mobile Devices and Services

August 24 - 27, 2015

Copenhagen, Denmark

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Overall Acceptance Rate 202 of 906 submissions, 22%

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Cited By

Verbeek PMajure CQuattrochi LTurner S(2024)The Welfare of Dogs as an Aspect of the Human–Dog Bond: A Scoping ReviewAnimals10.3390/ani1413198514:13(1985)Online publication date: 5-Jul-2024
https://doi.org/10.3390/ani14131985
Ramey C(2023)Wearable and embedded sensor systems for animal welfare monitoringProceedings of the Tenth International Conference on Animal-Computer Interaction10.1145/3637882.3637899(1-5)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1145/3637882.3637899
Hazan YHirsch-Matsioulas Ovan der Linden DZamansky A(2022)How Can Technology Support Dog Shelters in Behavioral Assessment: an Exploratory StudyProceedings of the Ninth International Conference on Animal-Computer Interaction10.1145/3565995.3566023(1-4)Online publication date: 5-Dec-2022
https://dl.acm.org/doi/10.1145/3565995.3566023
Lyanova AGrigarevichius KSinitca AKaplun DSysoev YBogachev M(2022)Animal Movement Pattern Model Identification based on Detrended Fluctuation Analysis2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON)10.1109/SIBIRCON56155.2022.10016985(2010-2014)Online publication date: 11-Nov-2022
https://doi.org/10.1109/SIBIRCON56155.2022.10016985
Farhat N(2021)Objective Assessment of Movement for Canine NeurologyProceedings of the Eight International Conference on Animal-Computer Interaction10.1145/3493842.3493898(1-4)Online publication date: 8-Nov-2021
https://dl.acm.org/doi/10.1145/3493842.3493898
Coghlan SWebber SCarter M(2021)Improving ethical attitudes to animals with digital technologies: the case of apes and zoosEthics and Information Technology10.1007/s10676-021-09618-7Online publication date: 21-Oct-2021
https://doi.org/10.1007/s10676-021-09618-7
Menaker TZamansky Avan der Linden DKaplun DSinitica AKarl SHuber L(2020)Towards a Methodology for Data-Driven Automatic Analysis of Animal Behavioral PatternsProceedings of the Seventh International Conference on Animal-Computer Interaction10.1145/3446002.3446126(1-6)Online publication date: 10-Nov-2020
https://dl.acm.org/doi/10.1145/3446002.3446126
Menaker T(2020)Data-Driven Analysis of Animal Behavioral PatternsProceedings of the Seventh International Conference on Animal-Computer Interaction10.1145/3446002.3446004(1-6)Online publication date: 10-Nov-2020
https://dl.acm.org/doi/10.1145/3446002.3446004
Zamansky Avan der Linden DHadar IBleuer-Elsner S(2019)Log My Dog: Perceived Impact of Dog Activity TrackingComputer10.1109/MC.2018.288963752:9(35-43)Online publication date: Sep-2019
https://doi.org/10.1109/MC.2018.2889637
Zamansky ASinitca AKaplun DPlazner MSchork IYoung Rde Azevedo C(2019)Analysis of Dogs’ Sleep Patterns Using Convolutional Neural NetworksArtificial Neural Networks and Machine Learning – ICANN 2019: Image Processing10.1007/978-3-030-30508-6_38(472-483)Online publication date: 9-Sep-2019
https://doi.org/10.1007/978-3-030-30508-6_38
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