Comparative Evaluation of Touch-Based Input Techniques for Experience Sampling on Smartwatches

Smartwatches are emerging as an increasingly popular platform for longitudinal in situ data collection with methods often referred to as experience sampling and ecological momentary assessment. Their small size challenges designers of relevant applications to ensure usability and a positive user experience. This paper investigates the usability of different input techniques for responding to in situ surveys administered on smartwatches. In this paper, we classify different input techniques that can support this task. Then, we report on two user studies that compared different input techniques and their suitability at two levels of user activity: while sitting and while walking. A pilot study (N = 18) examined numeric input with three input techniques that utilize common features of smartwatches with a touchscreen: Multi-Step Tapping, Bezel Rotation, and Swiping. The main study (N = 80) examined numeric input and list selection including in the comparison two more techniques: Long-List Tapping and Virtual Buttons to scroll through options. Overall, we found that whether users are seated or walking did not affect the speed or accuracy of input. Bezel rotation was the slowest input technique but also the most accurate. Swiping resulted in most errors. Long-List Tapping yielded the shortest reaction times. Future research should examine different form factors for the smartwatch and diverse usage contexts.