Envisioning Mobile Data Visualization Libraries for Digital Health
For developers of mHealth apps, this work highlights a tool gap and offers design considerations, but is primarily a position paper without empirical results.
The paper identifies that mobile health apps lack high-quality visualizations due to inadequate developer tools, and proposes dedicated mobile visualization libraries with health-specific features like normal ranges and thresholds to improve consistency and interpretability.
Mobile health (mHealth) applications support health management through rich data collection and self-reflection, yet the quality of their visualizations varies widely. A key limitation is the suboptimal design of visualizations for small-screen devices. We argue that this gap is partly driven by a lack of specialized developer tools. Existing libraries primarily target desktop or general-purpose mobile use, providing limited support for health-specific semantics such as normal ranges, thresholds, and goals. As a result, developers often resort to custom solutions that are inconsistent or hard to interpret. We therefore advocate for dedicated mobile visualization libraries tailored to personal health data and mobile contexts, and discuss key design considerations including intelligent defaults, built-in health annotations, and fluid interactions. Such libraries can lower barriers, promote consistency, and enable more accessible and interpretable mHealth applications.