Towards Efficient Interaction for Personal Health Data Queries on Smartwatches

The smartwatch is rapidly becoming a go-to personal health tracking device, allowing for the collection of a broad range of personal health data. Yet, access to this data is often limited to discrete glanceable visualizations. This in part is due to a lack in our understanding of the queries desired to access such data. Thus, as practitioners and application designers, our ability to enable efficient exploratory interactions is limited. In this work, through analysis of a public dataset, we characterize personal health data queries desired for exploration on the smartwatch across multiple dimensions: (i) data requested and attributes of this data, (ii) aggregation methods, (iii) mechanisms for filtering, and (iv) interrogatives used. We conclude with discussion around our findings that can be utilized in future works aimed toward enabling efficient interaction with personal health data on the smartwatch.