Boosting Mobile In-Situ Data Collection with OmniTrack for Research

Introducing OmniTrack for Research, an open-source research platform for streamlining mobile-based, in situ data collection


A plan builder view of OmniTrack for Research and the participant app.

Collecting in situ data from participants’ mobile devices in their personal context has recently gained tremendous interest among researchers in many fields. In situ data range from low-level sensing data, such as GPS position, to subjective self-report data, such as sleep quality. To collect such data, researchers must distribute their study participants a proper recording method (e.g., mobile app) and find ways to gather the recorded data from participants.

We designed OmniTrack for Research (O4R) to help researchers to implement, conduct, and manage in situ data collection studies without any coding. With O4R, researchers can implement a data collection protocol on a website and deploy an Android reporting app to study participants remotely. The recorded data are synchronized with the server in real-time so that the researcher can monitor the participants’ progress and gain preliminary insights on the present dataset. Currently, O4R supports diary studies and experience sampling, which are two exemplary in situ data collection methods.

Implementing a Study with OmniTrack for Research

OmniTrack for Research is built on top of OmniTrack (, a flexible self-tracking app. We designed OmniTrack to enable people to build personalized trackers from scratch by combining both manual and automated tracking methods. As OmniTrack can produce diverse trackers ranging from a simple diary to a fully-automated location logger, this project inspired us to envision an ecosystem where researchers deploy a predefined OmniTrack app to study participants.

On a web-based researcher dashboard, researchers can author a study protocol, designing a tracking plan (a unit corresponding to a data table), reminders, and automated logging triggers. Depending on the configuration of the tracking plan and reminder, the study can become a diary study or experience sampling study. If needed, these configurations can be customized for each participant.

When authoring a tracking plan, researchers leverage various types of data fields. The field values can be fed by existing tracking services such as Fitbit. For example, Fitbit’s step count can be attached to a number field then the value is automatically loaded from the Fitbit server when the data capture is initiated, by manual inputs or the background logging triggers. By combining these components, researchers can produce various alternatives of the tracking system for capturing the same behavior. Let’s see an example:

The above image shows three different designs of sleep trackers. The leftmost one (a) is a manual sleep diary: It requires participants to insert date, sleep time range, and subjective sleep quality. If a researcher wants to lower the data capture burden, he or she may attach Fitbit to the sleep time field (b). When the participant opens the diary, the sleep time field will automatically fetch the last night’s sleep time from the Fitbit server. If the study aims to capture only the sleep time ranges, the entire tracker recording may be automated by attaching a background logging trigger (c). According to the settings in (c), the sleep log will be automatically recorded every day at midnight.

What Can We Do with OmniTrack for Research?

We have tested the feasibility of O4R by applying it in real-world studies. Here, we introduce several published projects:

  1. A diary study (by Young-Ho Kim and colleagues) to understand how knowledge workers conceptualize personal productivity: O4R helped produce rich qualitative diary entries about diverse work- and nonwork-related activities that participants related to their productivity. See this Medium article for more detail.

The following projects leveraged the customizability of O4R: The participants built their own customized trackers (diaries) on-site and performed self-tracking.

  1. A co-design habit tracking study (by Sung-In Kim and colleagues) for supporting autistic adolescents’ habit formation: Autistic children and their parents collaborated to design a customized habit tracker from scratch. They used the OmniTrack app on-site to implement their tracker as well as record the data. See the paper for more detail.
  2. A co-design diary study (by Eunkyung Jo and colleagues) for understanding the role of self-tracking in parenting stress of new parents: Young mothers used OmniTrack to design personalized trackers regarding their parenting. See the paper for more detail.

Learn and Use OmniTrack for Research

If you are interested in OmniTrack for Research and want to dig more deeply, please check these resources:

  1. Official website
  2. Documentation for installing and using O4R
  3. Source code (OmniTrack for Research is completely free open-sourced!)



Young-Ho Kim
Sparks of Innovation: Stories from the HCIL

Postdoctoral researcher @ University of Maryland, College Park