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Predicting interruptibility for manual data collection: a cluster-based user model

Published: 04 September 2017 Publication History

Abstract

Previous work suggests that Quantified-Self applications can retain long-term usage with motivational methods. These methods often require intermittent attention requests with manual data input. This may cause unnecessary burden to the user, leading to annoyance, frustration and possible application abandonment. We designed a novel method that uses on-screen alert dialogs to transform recurrent smartphone usage sessions into moments of data contributions and evaluate how accurately machine learning can reduce unintended interruptions. We collected sensor data from 48 participants during a 4-week long deployment and analysed how personal device usage can be considered in scheduling data inputs. We show that up to 81.7% of user interactions with the alert dialogs can be accurately predicted using user clusters, and up to 75.5% of unintended interruptions can be prevented and rescheduled. Our approach can be leveraged by applications that require self-reports on a frequent basis and may provide a better longitudinal QS experience.

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    cover image ACM Conferences
    MobileHCI '17: Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services
    September 2017
    874 pages
    ISBN:9781450350754
    DOI:10.1145/3098279
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    Published: 04 September 2017

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    Author Tags

    1. interruptibility
    2. quantified-self
    3. self-reports
    4. smartphones

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    MobileHCI '17 Paper Acceptance Rate 45 of 224 submissions, 20%;
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    • (2023)A Meta-Synthesis of the Barriers and Facilitators for Personal Informatics SystemsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36108937:3(1-35)Online publication date: 27-Sep-2023
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