The ProofPilot Blog - Design, Launch & Participant in Research Studies

How ProofPilot Uses Micro-Interactions To Make Designing A Research Study Easy And Visual

ProofPilot’s visual protocol design language makes it as easy to design and launch a research study as it is to manage a blog.

ProofPilot’s purpose is to make it as easy to design a research protocol and launch a study as it is to write a blog post. That means being able design a study easily, click a button to launch the study to participants that runs automatically. When we say “study” we mean randomized controlled trials, longitudinal outcome studies … If those kinds of studies sound complicated. It’s because they are.

To accomplish our goal, we had to create an online app that allowed non-technical individuals to create a study without prior research knowledge. That online design experience needed to accurately communicate what the study protocol design is to those who are reviewing it and at the same time be in a format that is easy to launch with one click. It needed to be possible without software developers, database experts or user interface designers. (And the Ph.D. is optional.)

Here’s how we applied a concept called micro-interactions to change the protocol design process and experience — and accomplish our goals.

How Research Studies Are Designed Today

Before going into our solution, let’s look at how study protocols are designed today.

Career researchers spend months designing study protocols. These plans describe exactly what process to follow for each participant. What data needs to be collected? What treatments are run? When, Where, How and Why? They even define who can be in a study and who can’t. They are repetitive by definition. In fact, deviations can render an otherwise perfect participant ineligible.

This prescribed repetition makes human subject research studies an ideal candidate for automation.

But most research studies are manual. The research world has not yet seen the benefits of technology. Today, most researchers communicate the study protocol via written DOC and PDF files. So, an army of graduate students or expensive clinical research consultants interprets the protocol document to implement each instruction. They repeat the protocol manually for each participant over months and years. Despite training on that protocol, misinterpretations and mistakes happen. Those protocol deviations can doom a study.

Ugh. Wading through a long highly technical PDF research protocol.

Automation can reduce deviations and increase efficiency.

When we think automation, we think computer code. Microsoft Word and PDF documents are not code, and not a format that translates easily to code with a click of a button.

So, in addition to study staff, researchers bring in a group of tech designers, developers, and data managers. The technology professionals review that protocol doc, create technical specifications and get to programming. Sometimes it’s a complex data model that gets programmed into an expensive enterprise class solution. Other times, software developers piece together open sourced solutions or program from scratch.

It’s a mess. It takes a year (or more) to design a typical research study. When things change, it’s easy to update that original word document. But the simplest protocol change can mean weeks of programming.

In all this, participants are almost an after-thought. So now recruitment and engagement becomes a problem. More people, more manual processes. More opportunities for mistakes. And, of course, you need a bigger budget.

The Solution: The Micro-Interaction as Study Tasks

We knew that was wanted the study design experience to be visual. Many researchers like visual representations of their protocols. Some Researchers add flow charts and tables to their DOC and PDF files. But, these black and white diagrams aren’t consistent. This lack of common structure makes automation impossible. It also means with every document, readers have to learn what the writer means for every representation.

Our solution is a new visual protocol design language takes the protocol flow chart and table a step further. We standardize it based around a concept called “Micro-Interactions.”

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 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.

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