Love the colors and graphs. They transport a lot of data without looking bloated-by-design.

Also, considering the number of such workouts he completed and the fact that it seems he was aware of his deteriorating heart, he carried on? Even despite reported fatigue? Mad respect for his determination.

Perhaps because statistics education attunes us to average over individual differences, any mention of "data" unfortunately makes people instinctively jump to this 'cross-sectional' corner... textbooks explain how the typical human body behaves, and standard education and medical practice respectively emphasize averaging and population-level guidelines.

I think this approach is limited in some fundamental ways, and forces a dilemma: either accept wide and unactionable bands for what counts as normal, or use narrow bands that over-pathologize most individuals as diseased. The natural consequence of this is to start "fixing" deviations, and this is the territory of dangerous over-steering. This is why the medical community is afraid of "incidentalomas" and over-intervention. Tests which are point-in-time and compare one human to others are problematic in this respect. This is especially acute when the body is measured along many axes: like a full body scan. Much like the US Air Force Study of pilots discovered that there is no single pilot compatible with the "average" pilot measurements, comparing metrics from an individual's full body scan to a cohort average is likely to drastically overestimate problems.

In some ways, learned systems (like our physiology which is constantly adapting to our individual environment and experiences) follow the opposite of the Anna Karenina principle: every healthy individual is healthy in their own way. So the real diagnostic leverage isn't comparing an individual to an aggregate human template, but measuring change against their own homeostasis, and this requires high temporal resolution, not wider population comparisons.

I'm currently exploring how to bring this kind of longitudinal tracking to higher-fidelity modalities like wearable imaging. If any clinicians or researchers here are thinking about how to operationalize personal baselines in practice, I'd love to chat (email in profile).

A 2020 systematic review by Fuller et al. found that consumer wearables often perform poorly at estimating energy expenditure, even when they measure steps and heart rate reasonably well. Across brands like Apple, Garmin, Polar, and Withings, calorie burn estimates frequently showed substantial error, often in the 15–40% range and sometimes exceeding 50%. Wearables can still be useful for tracking trends, but not for precise calorie calculations.

protip: if you actually want to do better, look into "adaptive TDEE".