Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
Using patient-generated health data (PGHD) in depression care can provide valuable insights into patients' health. Due to the technical possibilities, patients can collect many PGHD types. However, these are not necessarily highly relevant to depression and are not considered relevant by all users. We, therefore, examined the relevance of various PGHD types for the treatment of depression and identified different types of users based on their data preferences. We surveyed 170 participants with depression and created a ranking for the most relevant data types. With subsequent cluster analysis, we identified four different user types: "Track-it-alls", "Medical Trackers", "Psychological Trackers," and "Untrackables". Based on these clusters, we show different possibilities for which user group and which types of PGHD are most suitable. With the results of this paper, we underline the need for tailored PGHD apps to improve personalized care in depression treatment.
Recommended Citation
Reindl-Spanner, Philipp; Prommegger, Barbara; Gensichen, Jochen; and Krcmar, Helmut, "Trackable Me: Relevant Data and User Types for the Tracking of Patient-Generated Health Data in Depression Care" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 4.
https://aisel.aisnet.org/hicss-58/hc/wellness_management/4
Trackable Me: Relevant Data and User Types for the Tracking of Patient-Generated Health Data in Depression Care
Hilton Waikoloa Village, Hawaii
Using patient-generated health data (PGHD) in depression care can provide valuable insights into patients' health. Due to the technical possibilities, patients can collect many PGHD types. However, these are not necessarily highly relevant to depression and are not considered relevant by all users. We, therefore, examined the relevance of various PGHD types for the treatment of depression and identified different types of users based on their data preferences. We surveyed 170 participants with depression and created a ranking for the most relevant data types. With subsequent cluster analysis, we identified four different user types: "Track-it-alls", "Medical Trackers", "Psychological Trackers," and "Untrackables". Based on these clusters, we show different possibilities for which user group and which types of PGHD are most suitable. With the results of this paper, we underline the need for tailored PGHD apps to improve personalized care in depression treatment.
https://aisel.aisnet.org/hicss-58/hc/wellness_management/4