It has been a feast of delight this week on Clyde Street.
I have been following up on the ideas shared by David Zeevi and his colleagues about personalisation and prediction. One part of the paper has stayed with me:
Dietary interventions based on our predictor showed significant improvements in multiple aspects of glucose metabolism, including lower PPGRs and lower fluctuations in blood glucose levels within a short 1-week intervention period. It will be interesting to evaluate the utility of such personalized intervention over prolonged periods of several months and even years. (My emphasis)
I have been thinking about the implications of this for the learning and performance environments we build and maintain in sport. A week of investigating research and practice in precision medicine has encouraged me to contemplate the skills we might need to be engaged with long-term athlete and coach personal flourishing.
Leroy Hood and his colleagues have championed a systems biology approach to predictive, preventative, personalised and participatory healthcare. They noted:
Systems biology is a scientific discipline that endeavours to quantify all of the molecular elements of a biological system to assess their interactions and to integrate that information into graphical network models that serve as predictive hypotheses to explain emergent behaviours. (2004:640)
Leroy and Andrea Watson predicted:
a paradigm shift in medicine will take place within the next two decades replacing the current approach, which is predominantly reactive, to one that can increasingly predict and prevent cellular dysfunction and disease. (2004:179)
A decade later, researchers and practitioners are embedded in precision medicine and deliver treatments:
targeted to the needs of individual patients on the basis of genetic, biomarker, phenotypic, or psychosocial characteristics that distinguish a given patient from other patients with similar clinical presentations. (Larry Jameson and Dan Longo, 2015)
This shift requires a fundamental rethink of how to deliver personal care. Reza Mirnezami, Jeremy Nicholson and Ara Darzi (2012), for example observe:
Precision medicine will require handling of multi-parametric data and some proficiency in interpreting “-omics” data, placing new demands on medical professionals, who may be ill equipped to deal with the anticipated complexity and volume of new information. Addressing these challenges will require effective clinical decision support tools and new educational models.
These new educational models fascinate me, particularly in the context of understanding how analytics are embedded in coaches’ learning pathways in formal accreditation and in continuing learning.
Two posts this week added to my reflections about precision.
In the first post, Ricardo Tavares considered Why We Need Positional Data in conversations about football analysis. I really enjoyed the way Ricardo shared the process of analysis using a single example. I delight in n=1 studies and their resonance with other performances.
What made Ricardo’s post of particular interest to me was the open sharing he demonstrated. His post concludes:
You can download the the csv file with the data here (x and y coordinates on a scale of 0 to 100). The player data is here (player numbers and names aren’t filled yet, but they should be up soon).
If you know Python, you can also view (and download) the Jupyter Notebook that made the animations here (or here, for a more browser friendly version).
The second post, Protecting an NHL Player’s Greatest Asset is an interview with the San Jose Sharks’ trainer, Mike Potenza. In the interview, Mike notes:
The NHL is one of the longest seasons in professional sports. Each team will commonly play 13-16 games per month with no consistency to the format of days they play. West coast teams will travel more than the east coast teams due to the proximity of franchise locations. Given the compressed game schedule, travel schedule and requirements for mandatory days off per league rules, practice time is limited but still a valuable commodity.
In San Jose our goal is to monitor every practice during the season, which includes pre-season training camp. Team workload/intensity and duration of monitored practice times are shared with the coaching staff so they have useful information when planning the next workday and the yearly work to rest schedule.
Mike uses these data to monitor
- Accumulative work load
- Training effect
- % of max HR
- High intensity duration
Mike discussed the absence of HR monitoring in games (an NHL stipulation):
It is a major missing piece of the puzzle that we do not have game data from HR monitors or GPS units because we do not know the cost of a NHL hockey game and the stresses that go along with that. The frequency and physical component of games per week is very high both in the regular season and in the playoffs. This being the case, missing game data forces performance coaches to only draw conclusions from sub-maximal practice data that only can be compared to practice and not the main show! To further dissect the issue, by not monitoring games, performance coaches do not have a reference for the metabolic specific zones achieved in games. These are extremely useful pieces of data that would be used to assign HR training zones for players who earn substantially different time on ice (T.O.I.) accumulations.
This brought me back to think about the precision we bring to the observation about each player’s performance and the decision-support we use to modulate training, whilst having some clarity about long-term flourishing.
These are active debates in healthcare and I see a similar need to have these conversations in sport.
Ultimately, precision medicine should ensure that patients get the right treatment at the right dose at the right time, with minimum ill consequences and maximum efficacy. (Reza Mirnezami, Jeremy Nicholson and Ara Darzi, (2012)
Ricardo’s use of a single event to raise fundamental questions about what we observe and analyse in sport took me back to Ference Marton (1994:7) and the idea of phenomenography that aims “at a very specific level of description, corresponding to a level of experience believed to be critical as far as our capabilities for experiencing certain phenomena in certain ways are concerned”.
Considerations about precision in sport contexts require us, I believe, to make sense of digital records of performance in our everyday practice. As in medicine, we have immense opportunities to explore new paradigms. Phenomenography encourages us to reflect on “the question of people being capable, or not, of experiencing and acting in certain ways” (Ference Marton (1994:7).
This seems to me to be the start of an agenda to discuss our educational models.
Dawn on Clyde Street (Keith Lyons, CC BY 4.0)
Real-time monitoring (Firstbeat)