Table 4.

Representative quotes characterizing TREC transdisciplinary outcomes (January–February 2014)

Outcome		Representative quotes
1	New transdisciplinary team and consortia formation	It is important to showcase the contributions of transdisciplinary projects to other investigators. I’ve been in so many different venues where I present on the really consistent evidence that we are finding in TREC around sleep and obesity. There are so many different ways that it then triggers another investigator in the room to come up to me and say, ‘I’m just beginning a cohort. I hadn’t thought about including sleep or including questions about sleep.’ And I see that as a direct result of presenting in these national forums and kind of getting the word out, to then have someone in the room, or other researchers in the room, consider adding sleep into their studies. (Participant 4220)
2	Integrated theoretical framework development	[The] unified framework […] is bridging basic science, epidemiology, and clinical research. That’s [Investigator]’s project on animal models for healthy aging. […] We have also developed statistical approaches to analyze predictors of healthy aging in large cohort studies. We wanted to identify dietary factors, lifestyle factors and genetic factors for healthy aging. For a clinical component we have a project to look at the effects of physical activity and metformin among cancer survivors, […] We want to prolong the life of the cancer survivors and improve both the quality and the quantity of their lifespan. […] This is a relatively novel area that we have developed. (Participant 4200) Sometimes in pediatrics we borrow a lot from adult medicine. […] But I am interested in innovating from within pediatrics. There’s a lot of work showing that insufficient sleep in adults is associated with obesity or adiposity gained through different mechanisms, such as hormonal influences, physical activity, or lack of physical activity. But what’s never discussed in adults is that insufficient sleep might actually affect executive function. In children, behavior and impulsivity [are] things that as pediatricians, we know. We know behavior. We know cognition. It’s our language. It doesn’t come up as often in some of the adult mechanisms, but in children it’s incredibly plausible that sleep deprivation in a child affects their impulsivity, their behavior, their ability to make appropriate decisions. That kind of executive function and control has never been studied or proposed as a mechanism for why sleep is associated with obesity. There are many different examples where I also bring in that conceptual framework to broaden our sense of what are some mechanistic pathways [between sleep and obesity]. (Participant 4220)
3	Multi-Level intervention model development and testing	The statistical work that was done on our main TREC project [an exercise/weight loss intervention] data previously was done by exercise scientists [with participants] in the lab. […] As an interventionist, a behavior scientist, I think about the data in such a different way […] How might GPS data-the data that puts you in an environment-affect the algorithms and the whole picture? […] So in that way, the neighborhood piece comes into it. [We] have applied machine learning algorithms to free living data, not lab data. The computer scientists [are] not public health people so they haven’t thought about collecting free living data either. […] They might have developed algorithms to be able to detect X, Y, Z, but not with a purpose in mind. [We think about how] this algorithm can help somebody do X, Y. (Participant 4330)
4	Development and adaptation of relevant statistical models	You have a surrogate measure and gold standard measure of diet, and you have a biomarker, the method of triads. There’s a mathematical way of using those three pieces of data to come up with a measurement error correction, even if there is correlated error… So what we’ve done is take that general concept of the method of triads and find new applications for them. So the fact that it has an application with nutritional epidemiology doesn’t mean that there’s other areas where it cannot be used. And as long as the statistical principles of the method applies to any other problem, this can be used for any other problem. (Participant 4220)
5	Translation of findings across levels of science	Transdisciplinary teams ‘should be mentoring basic scientists in a way that helps us to translate to humans…mentoring human researchers in a way that helps them to understand animal models…-training both of those sets of scientists in understanding health policy and changing policy, dissemination science.’ (Participant 4400)
6	Public policy influence	It is a dilemma. When I first started talking about [engaging in policy work], I had very traditional researchers in this university saying, ‘that’s not for you to do. You’re too junior to be thinking about affecting policy. You should be very focused and that’s a distraction.
7 and 8	Transdisciplinary manuscript publication and transdisciplinary grant awards	We’ve already talked with NIH and NCI about looking at the community, neighborhood characteristics, and cancer outcomes and how they might be related. We’re applying for a cross-TREC pilot grant [to gather] preliminary information to look at this. […] The luxury of working with the two different sites [is that] we can look in Philadelphia and in St. Louis and compare across populations to see if there are similarities or not, then move forward into intervention work. (Participant 4440)
9	Training the next generation of transdisciplinary researchers	My expectations were to learn about other objective measures of energetics. [I’ve learned] a lot about accelerometry and GPS from other TREC center [investigators]. I don’t know if that was my goal at the outset, but through TREC, my goals have become to build that expertise. It’s definitely new frameworks. It’s new technology, it’s new kind of analytical approaches, too. And it’s not kind of the traditional Epi that I was taught here. So I’m trying to incorporate what I know in this new technology, new approach. (Focus Group 4200) Most innovation happens at the intersections—not in the center—of disciplines. In the center of disciplines is repetition. You do what you know, what your mentor did. If you really want innovation, you have to be at an intersection, an interface. These days, doing what you did just isn’t sufficient. You just cannot come in with incremental science anymore. You’ve got to come in with something new. […] There’s not a good future for very, very generally unidisciplinary research, especially in human research. (Participant 4300)