This article is a condensed version of a post on the ProofPilot blog on ProofPilot’s approach to study protocol organization and automation. You can read the whole article here.
Micro-interactions are unique moments that revolve around the completion of a single distinct activity. ProofPilot studies are made up of many micro-interactions or individual single distinct activities. We call them “study tasks.” Each of these tasks is tied together by activation and expiration rules.
The concept evolves from the world of service marketing and product design. Each individual study task completes a specific component of the study protocol. Each study task is a unique participant/research study interaction.
As a researcher designs their study, they break down their protocol into it’s unique interaction components. A pain assessment could be one task. The pain assessment might be followed by a treatment instruction task. Afterward, the next task might be a report detailing the participant’s progress, followed by another task, a reward to thank participant for their involvement in the study. To improve visual comprehension, a color and shape represent each broad category of task (measurements vs. treatments vs. rewards, etc.)
Each study task is visualized in a unique shape and color. Clicking on the diamonds allows you to see how each task is related to others and what arm the action takes place in. Those relations and placements are defined by rules.
Beyond the actual study task itself, each interaction has several supporting components.
- A type and template, allowing the user to choose from predefined elements and functionality to speed the design process and maintain consistency.
- An introduction, orienting the user to what this interaction is.
- A selection of who will conduct the task and where
- And a set of automation rules to determine when the task activates and expires.
Creating your automation rules that link together study tasks.
These individual study tasks are organized into study arms and ordered based on the automation rules created for each task. These rules are natural language IF/THEN statements. For example “Activate this task if a prior task is completed for three days.” As the participant completes tasks, rules automatically activate to show the next task based on rules and wait times.
As the research designer adds tasks, they see the study come together on the study flow page. They can click the diamonds to the right of every individual study task and see how it is related to others. They can group tasks to create a series of tasks (or a study visit) presented to participants one after another. They can invite others to review and edit the study design. Most importantly they can click preview and see the study from the participant’s perspective.
See exactly what the participant will experience while you are designing. Then, click a button and launch, and start collecting data.
The result is a clearer more consistent visual representation of the study that can be activated and launched to participate with a click of the button. It means the individual who conceived of the study can quickly design and launch that study without resources. The automation means studies have fewer deviations. And the reduced budget means studies can be run that were never feasible before — leading to knowledge and breakthroughs that could dramatically improve the human condition.