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

Human-Computer Interaction