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In this paper, we present our system design for conducting longitudinaldaily-task studies with the same workers throughout on Amazon Mechanical Turk.We implement this system to conduct a study into touch dynamics, and presentour experiences, challenges and lessons learned from doing so. Studyparticipants installed our application on their Apple iOS phones and completedtwo tasks daily for 31 days. Each task involves performing a series ofscrolling or swiping gestures, from which behavioral information such asmovement speed or pressure is extracted.  The completion of the daily tasks did not require extra interaction with theMechanical Turk platform, yet paid workers through it. This differs somewhatfrom the typical rapid completion of one-off tasks that workers are used to onAmazon Mechanical Turk.  This atypical use of the platform prompted us to evaluate aspects related tolong-term worker retention and engagement over the study period, in particularthe impacts of payment schedule (amount and structure over time) and remindernotifications. We also investigate the specific concern of reconciling informedconsent with workers' desire to complete tasks quickly.  We find that using the Mechanical Turk platform for conducting longitudinaldaily task studies is a viable method to augment or replace traditional labstudies.

Daily Turking: Designing Longitudinal Daily-task Studies on Mechanical Turk