Supported on Android & iOS.
This data source records the time that the smartphone's screen turns on or off. It does not record the content of the screen or for what reason it was turned on or off. It only captures whether it was turned on or off, and the time of the event.
Each screen state record includes the following:
The time the new state of the phone has started. Internally stored as
The time the new state of the phone has ended. Internally stored as
Whether the screen turned on or off. Internally stored as
A given record of this data source looks like the following:
This means user ID 2 on device ID
2bae23410b7063ec, turned on the device
true) at 1606939056984 (
2020-12-02 14:57:36.984-05:00) and the
device was on until 1606939060036 (
Supported in Android.
This data source records how often participants are using which app. The captured data only includes the name of the app and the aggregate amount of time it was used over a certain period. It does not include any content of the application.
Each app usage record includes the following:
The unique code name of the app used, for example, com.ethica.logger for Avicenna
or com.facebook.kanata for Facebook. Internally stored as
The beginning of the time period over which this report was collected. Note that
this does NOT refer to the time the application was started. Internally stored as
The end of the time period over which this report was collected. Note that this does
NOT refer to the time the application was closed. Internally stored as
Last time the application was used (the time the app was closed), in the time window
specified by the
start_time and the
end_time. Internally stored as
Foreground Time (MS):\
The amount of time, in milliseconds, that the application was in the foreground during
the time window specified by the
start_time and the
end_time. An app is considered
foreground if one of its screens is the currently active screen and the user is looking
at it (i.e. the screen is on) or interacting with it. Internally stored as
As an example, if a record is shown as follow:
"start_time": "May 9th 2018, 09:30:45.537+0000",
"end_time": "May 10th 2018, 09:30:45.536+0000",
"last_used": "May 10th 2018, 09:03:54.098+0000",
It means during the 24 hour period from May 9th 2018, 09:30:45.537+0000 to May 10th 2018, 09:30:45.536+0000, the participant was using YouTube (com.google.android.youtube) for approximately 206 minutes (12,379,944 milliseconds).
Configuration in Android
Monitoring app usage statistics has been integrated into the core functionalities of the Avicenna app.
In studies where app usage data monitoring is required, Avicenna handles this internally without the need for any additional installations. Upon registration, participants will receive a notification through the Avicenna app to grant permission to access app usage data. This notification provides participants with an easy way to navigate to the required settings and grant permission.
By eliminating the need for a separate extension app, we have streamlined the process, making it more straightforward for participants. Now, they need to download and install the Avicenna app from the Google Play Store, provide the necessary permissions, and they're all set.
Alternatively, participants can be instructed to follow the steps below to grant permission via phone settings, before they receive the notification from the Avicenna app:
- Open Android's Settings.
- Click on
- Scroll down to find and then select
Apps with access to usage data.
- From the list of applications shown, select
Allow usage trackingto
This will provide the required permission for Avicenna to access statistics on app usage.
Android Apps with 0 ms Usage
This data source captures any app that is running on the participant's Android
phone, even if they don't interact with that app directly. An example is Google
Mobile Services which usually runs behind the scenes and performs support
tasks. But as it's running, the collected data will include references to it
com.google.android.gms), indicating that it's been used for 0 milliseconds.
If you are focused on the apps the participant has directly interacted with
them, you can exclude the results with 0 ms foreground time.