BACKGROUND: While smartphone digital phenotyping smartphone apps today can collect vast amounts of information on participants, less is known about how this data can be shared back with participants. Effective data visualization is critical to ensuring applications of digital signals are more informed, ethical, and impactful. But little is known about how sharing of this data, especially at different levels from raw data to analyzed data, impacts patients’ perceptions.
METHODS: We compared five different visualizations strategies, each a graph, generated from data created by the open source mindLAMP app, that reflected different ways to share data from simple amount of data captured to more complex clinical correlations. All graphs were shown to 28 participants during individual video interviews, and the graphs usability was measured via the System Usability Scale (SUS). Additionally, participants were asked about their comfort sharing different kinds of data, administered the Digital Working Alliance Inventory (D-WAI), and if they would want to use these visualizations with care providers.
RESULTS: Of the five graphs shown to participants, the graph visualizing change in survey responses over the course of a week, received the highest usability score, with the graph showing multiple metrics changing over a week receiving the lowest usability score. Participants were significantly more likely to be willing to share geolocation data after viewing the graphs, and 25 of 28 participants agreed that they would like to use these graphs to communicate with their clinician.
CONCLUSIONS: Data visualization can help participants and patients understand digitally-sourced data and increase trust in how they are sampled and used to create visualizations. As data sourced from digital technology becomes more complex, simple visualizations may fail to capture their multiple dimensions and new interactive data visualizations may be necessary to help realize their full value.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/46319 |
| Date | 09 June 2023 |
| Creators | Scheuer, Luke Sanders |
| Contributors | Fulford, Daniel, Torous, John |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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