On Being an Early-Career Researcher in Digital Health: Part 2

Recap. Research in digital health (eHealth, mHealth, big data in healthcare) is a hot area with considerable room for growth. But working in this field has some potential pitfalls that early-career researchers (ECRs) need to consider before jumping in. Last time, I described some of the expenses associated with this work and some ways that ECRs can plan for these. I also noted that this work is time-consuming, which is today's topic.

Time is a commodity in academia, and the stress of effectively managing one's time is especially high for ECRs - we're just getting the hang of what works and what doesn't, after all. Of course, any research project* can be time- and effort-consuming, and perhaps more so for ECRs. We're less likely to have large collaborative networks, and in our networks, we have less clout than more established researchers. We're also just establishing our labs and identifying responsible RAs. So projects aren't always completed on our intended schedules, and we end up doing much of the work ourselves. This stalls our progress and creates undue worry about achieving tenure. 

In digital health, it's possible that you're actually unable to do all of the work yourself. For example, if I wanted to design a web program to deliver a new intervention, I would need a computer programmer's assistance. Same goes for collecting data in real time via smartphone app. As with reducing costs and/or accessing technology, maximizing the benefits of collaborations is essential for time management. If you're collaborating with non-academics, there is a good chance that your ideas about "complete" and "timely" work will not align with theirs. This could lead to a great deal of time and effort spent chasing down the products you need. See here for some signs that your collaborations are toxic.

Two other considerations about time management in digital health research. It's intuitive to expect that technology will decrease the time and effort that you need to spend managing data collection. Indeed, something like switching from in-person to internet survey administration frees up many hours that would have been devoted to participant supervision. But this exchange is not equal across all devices, platforms, and methods.

Take the aforementioned real-time data collection as an example. Once the app is set up, you just let it run and the data come rolling in, right? Anyone who has ever worked with this type of ecological momentary assessment procedure is laughing right now. Bugs in your app, device problems, and most of all, participant error will take more time to address than you can imagine. And managing the resulting data is a job I would never, ever want.

Similarly, take a web-based behavior change program. Once the platform is set up, even if it's been plot-tested, monitoring use, responding to problems, and managing any participant interaction takes more time than you expect. Students are fantastic and helpful in some respects, but they have myriad other responsibilities and will never treat the project with the same care you will. One more reason to pursue funding as early and often as possible: if you have professional staff, you don't have to bear this burden yourself. (It is my goal in life to get a grant that allows me to hire a professional research coordinator.) 

Finally, keeping up with advancements in the field presents unique challenges in digital health. As noted, the field is exploding, with new papers and journals appearing every week. Moreover, technology evolves, especially in commercial industry, at an alarming rate. By the time you hear about a device or platform, design a study, learn how to use the technology, hire/train others, and collect the data, your technology is out of date. Then you have to write papers, wrangle any collaborators, deal with peer review and rejection.... by the time you actually publish your findings, the current technology is much more sophisticated than what you used. If behavioral science is your field, then the technology matters less than the way it's used. But if not, have a plan for how you'll deal with criticisms about living in the digital past.

What other challenges do you see for digital health ECRs, or ECRs more broadly? Post your comments below.

*I'm currently reading Joel Cooper's Cognitive Dissonance: Fifty Years of Classic Theory, which describes many of the key studies in this social psychology area. It's reconnected me with my incredible respect for social psychology research. You want time-consuming? They use rigorous experimental methods to test complex theories, using multiple moderators, confederates, and elaborate cover stories. Impressive.