Planned

:bulb:

Support Long-Term Biomarkers (e.g. Bloodwork) in Experiments and Insights

Currently the experiments interface doesn’t nicely support infrequent measurements of things like bloodwork and body composition, for example if these are measured once at the end of baseline period and the end of the treatment phase. The following need to be changed:

  1. How predictions are displayed - don’t say “decreasing calories decreases body fat percentage the same day”
  2. How experiment results, statistics, confidence and effect size are displayed - currently a dependent variable with only a single measurement in each phase will show up with “no confidence” tag

Changes also need to be made in order to provide meaningful correlations in insights, as these long-term markers are lagging behind lifestyle changes that may affect them, so same-day correlations are not as meaningful.

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  • 25 October 2024 NTL created this task

  • avatar
    NTL

    Also add support for bloodwork reference ranges and unit conversions

    06 March 2025
  • avatar
    Blaze Kwaymullina

    You need to be able to put all the data from a blood test in test type test name reference range, result, if result was outside of reference range ect.

    06 March 2025

  • 07 March 2025 NTL edited this task

  • 28 July 2025 NTL edited this task

  • avatar
    NTL

    Related task: https://changemap.co/ntl/reflect/task/10574-long-term-experiment-predictions/

    28 July 2025
  • avatar
    NTL

    Reference range support is complete: https://changemap.co/ntl/reflect/task/10748-support-for-metric-specific-reference-ranges/

    23 September 2025
  • avatar
    Larry

    I’m not sure long-term biomarkers should be a major priority for experiments/insights. Because they’re sparse, lagging, and often reflect different biological time windows, they seem better treated as a separate long-horizon outcome layer rather than as normal experiment variables. 🤷🏼‍♂️

    For individual health tracking, I think the higher-value focus is on daily or weekly markers like blood pressure, blood sugar, ketones, SpO2, weight, and body fat %, since those are more actionable and much easier to relate to behavior (and changes in long-term biomarkers). Trying to model infrequent biomarkers accurately starts to move into research/lab territory more than personal tracking.

    Just my thoughts.

    17 March