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Dataset of mHealth event logs

Version 2 2022-05-01, 12:49
Version 1 2022-05-01, 12:43
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posted on 2022-05-01, 12:49 authored by Raoul NuijtenRaoul Nuijten, Pieter Van GorpPieter Van Gorp

How does Facebook always seems to know what the next funny video should be to sustain your attention with the platform? Facebook has not asked you whether you like videos of cats doing something funny: They just seem to know. In fact, FaceBook learns through your behavior on the platform (e.g., how long have you engaged with similar movies, what posts have you previously liked or commented on, etc.). As a result, Facebook is able to sustain the attention of their user for a long time.

On the other hand, the typical mHealth apps suffer from rapidly collapsing user engagement levels. To sustain engagement levels, mHealth apps nowadays employ all sorts of intervention strategies. Of course, it would be powerful to know—like Facebook knows—what strategy should be presented to what individual to sustain their engagement.

To be able to do that, the first step could be to be able to cluster similar users (and then derive intervention strategies from there).

This dataset was collected through a single mHealth app over 8 different mHealth campaigns (i.e., scientific studies). Using this dataset, one could derive clusters from app user event data. One approach could be to differentiate between two phases: a process mining phase and a clustering phase. In the process mining phase one may derive from the dataset the processes (i.e., sequences of app actions) that users undertake. In the clustering phase, based on the processes different users engaged in, one may cluster similar users (i.e., users that perform similar sequences of app actions).


List of files

  • `0-list-of-variables.pdf` includes an overview of different variables within the dataset.
  • `1-description-of-endpoints.pdf` includes a description of the unique endpoints that appear in the dataset.
  • `2-requests.csv` includes the dataset with actual app user event data.
  • `2-requests-by-session.csv` includes the dataset with actual app user event data with a session variable, to differentiate between user requests that were made in the same session.


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