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.

Photo Credit

Dawn on Clyde Street (Keith Lyons, CC BY 4.0)

Real-time monitoring (Firstbeat)

Gut reaction

I came across a 2015 paper written by David Zeevi and his colleagues this week.

The paper is titled Personalized Nutrition by Prediction of Glycemic Responses. Their graphical abstract is:

I had a profound, positive gut reaction to a paper about the gut. I think there is a lot to admire in this paper and I believe it has an important role to play in how we might present our research in sport as we explore how we might use analytics to transform performance.

Their brief abstract is:

People eating identical meals present high variability in post-meal blood glucose response. Personalized diets created with the help of an accurate predictor of blood glucose response that integrates parameters such as dietary habits, physical activity, and gut microbiota may successfully lower post-meal blood glucose and its long-term metabolic consequences.

I was particularly interested in how the research team analysed the data from their participant cohort:

We asked whether clinical and microbiome factors could be integrated into an algorithm that predicts individualized postprandial (post-meal) glycemic responses (PPGRs). To this end, we employed a two-phase approach. In the first, discovery phase, the algorithm was developed on the main cohort of 800 participants, and performance was evaluated using a standard leave-one-out cross validation scheme, whereby PPGRs of each participant were predicted using a model trained on the data of all other participants. In the second, validation phase, an independent cohort of 100 participants was recruited and profiled, and their PPGRs were predicted using the model trained only on the main cohort.

In their discussion, David and his colleagues note:

In this work we measured 46,898 PPGRs to meals in a population-based cohort of 800 participants. We demonstrate that PPGRs are highly variable across individuals even when they consume the same standardized meals. We further show that an algorithm that integrates clinical and microbiome features can accurately predict personalized PPGRs to complex, real-life meals even in a second independently collected validation cohort of 100 participants. Finally, personalized dietary interventions based on this algorithm induced lower PPGRs and were accompanied by consistent gut microbiota alterations.

The conclusion to the paper includes the following observation:

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 do think this is an exemplary paper. I found the language and transparency of their narrative very engaging. There were some powerful visualisations to add to my engagement. Their use of machine learning has sent me off thinking about the generalisability of their approach.

Above all, I was left thinking about “the utility of such personalized intervention over prolonged periods of several months and even years” in a wider sense of personal learning and performance journeys. David and his colleagues have shone a microbiota light on how I might do this.

Thinking about organisational change

Much more inventive than the average tour guide, Abbie illustrates his talks with photos of the area.

I have an opportunity to work with colleagues who are involved in organisational change processes.

I am keen to share Joanne Martin’s insights with them as we discuss the scope and pace of change efforts. In her discussions of mapping an organisational terrain, she observes:

If any cultural context is studied in sufficient depth, some things will seem to be consistent, clear, and indicative of collectivity-wide consensus. Simultaneously, other aspects of the culture will seem to coalesce into subcultures, enabling these subcultures to reinforce, be independent, or conflict with each other. At the same time, still other elements of the culture will seem fragmented, in a state of constant flux, and infused with confusion, doubt, and paradox. (2002:158)

A decade earlier (1992), Joanne proposed a three-perspective approach to understanding organisational change. These perspectives were: integration, differentiation and fragmentation. Each of these raises important strategic and operational issues for those championing change: how to address consensus, inconsistency and ambiguity.

Each of them underscores for me the importance of making time and taking time to discuss change. I am particularly interested in the ways in which organisational change becomes a bottom-up practice in an information rich culture.

Photo Credit

Abbie the Tour Guide (Gary Knight, CC BY 2.0)