Preferences and Effectiveness of Sleep Data Visualizations for Smartwatches and Fitness Bands

Alaul Islam (1) , Ranjini Aravind (2) , Tanja Blascheck (3) , Anastasia Bezerianos (4) , Petra Isenberg (1)

1 AVIZ - Analysis and Visualization
2 Université Paris-Saclay
3 University of Stuttgart
4 ILDA - Interacting with Large Data - LISN

We present the fndings of four studies related to the visualization of sleep data on wearables with two form factors: smartwatches and ftness bands. Our goal was to understand the interests, preferences, and efectiveness of diferent sleep visualizations by form factor. In a survey, we showed that wearers were mostly interested in weekly sleep duration, and nightly sleep phase data. Visualizations of this data were generally preferred over purely text-based representations, and the preferred chart type for ftness bands, and smartwatches was often the same. In one in-person pilot study, and two crowdsourced studies, we then tested the efectiveness of the most preferred representations for diferent tasks, and found that participants performed simple tasks efectively on both form factors but more complex tasks benefted from the larger smartwatch size. Lastly, we refect on our crowdsourced study methodology for testing the efectiveness of visualizations for wearables. Supplementary material is available at https://osf.io/yz8ar/.

Mots clés: glanceable visualization; smartphones; smartwatch; fitness trackers

Domaines: Interface homme-machine [cs.HC]

Alaul Islam, Ranjini Aravind, Tanja Blascheck, Anastasia Bezerianos, Petra Isenberg. Preferences and Effectiveness of Sleep Data Visualizations for Smartwatches and Fitness Bands. CHI 2022 - Conference on Human Factors in Computing Systems, Apr 2022, New Orleans, LA, United States. ⟨10.1145/3491102.3501921⟩. ⟨hal-03587029⟩