A stranger! From the outside! Oo-o-o-o-o-o…
Greetings!
So yeah, I don’t get alot of visitors here. But welcome to my personal (work?) website.
Add a clean table of contents? But for now…
- You can download a .pdf copy of my CV.
- The Publications page has direct links to refereed publications.
- Coming soon :
- Blog (in progress)
- Code for methods?
- Teaching stuff?
- Other fun stuff?
I am Greg Petrucci! I come in peace!
Other than being a Toy Story superfan, I’m a PhD. student at the University of Massachusetts in the Department of Kinesiology. My advisor is John R. Sirard, PhD.. I also work closely with John Staudenmayer. My research expertise is in the area of using accelerometers to estimate aspects of physical behaviors (e.g., physical activity, sedentary behavior and sleep). To that end, I’ve worked extensively on projects aimed at calibration and validation of accelerometer data processing methods (see MOCA for more details).
Recently I’ve been keen on making accelerometer data processing methods (i.e., existing and new, simple cut-points and complex machine learning techniques, etc.) easily accessible for use in intervention and cohort studies.
Why do the methods have to be accessible?
Why can’t someone (e.g., intervention scientist, epidemiologist, fill in the blank with your favorite field, my mother, etc.) just find the orignal paper where some reseachers came up with some method to process the data, and follow that reciepe?
Short answer:
It would take forever.
Picture answer:
Science answer:
In 2022 Pffeiffer et al., published a scoping review in Physiol Meas, 43(09) summarizing the (in)accessibility of novel acceleormeter data processing methods to estimate physical activity or energy expenditure. Here are the results they report in their abstract:
Main Results: Studies (N = 168) included adults (n = 143), and/or children (n = 38). Model use ranged from 0 to 27 uses/year (average 0.83) with 101 models that have never been used. Approximately half of uses occurred in a free-living setting (52%) and/or by other authors (56%). Over half of included articles (n = 107) did not provide complete access to their model. Sixty-one articles provided access to their method by including equations, coefficients, cut-points, or decision trees in the paper (n = 48) and/or by providing access to code (n = 13).
What’s being done to fix this problem?
In the same issue of Physiol Meas (43(09)), Kimberly Clevenger et al. published a note that includes a very helpful Accelerometer Repository, framework and method reporting guidelines for estimating physical activity or energy expenditure from accelerometer data. This should (hopefully) make it easier to find and use the good methods we have worked hard to devlop.
RainDrop.io- Links to other socials I use often
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- Thanks to Dean Attali for making the template.