Understanding User Contexts and Coping Strategies for Context-aware Phone Distraction Management ...
summary
A mobile ESM app collects 9,000 in‑situ responses from 34 participants over three weeks and an online survey yields 231 user‑written if‑then rules; analysis uses multilevel linear regression and state‑diagramming. Results show individual differences predict perceived distraction more than location or activity, different users get distracted in different contexts, and users want selective handling of interruptions. Design components required are trigger conditions, filtering conditions, actions, and release conditions, and an end‑user programming interface supports personalized context‑aware distraction management.
AI-written summary · about
related talks
05mA Multi-perspective Analysis of Social Context and Personal Factors in Office Settings for the ...
06mA Multi-Sensor Approach to Automatically Recognize Breaks and Work Activities of Knowledge ...
06mEnd-User Development of Experience Sampling Smartphone Apps - Recommendations and Requirements
11mEvaluating the Usability of Privacy Choice Mechanisms
20mExploring End User Programming Needs In Home Automation
18mAlways On(line)? User Experience of Smartwatches and their Role within Multi-Device Ecologies
21mBehavior Change in a Mobile World
19mCrowdsourcing GO: Effect of Worker Situation on Mobile Crowdsourcing Performance