The Pattern Your Endocrinologist Can't See From One Reading

A fasting glucose of 147 mg/dL at the 8 AM lab draw tells your endocrinologist something. A 90-day log showing that number is consistently 30 points lower when the previous evening included 45 minutes of walking and a dinner under 40g carbohydrates tells them something actionable. The difference between those two clinical pictures is whether the data was captured between appointments.

Most self-monitoring stops at the meter reading. The number goes into the glucometer's memory, maybe into a paper log, maybe nowhere. The meal that preceded it, the exercise that morning, the sleep quality, the stress of an unusual workday — none of that context is recorded. Without context, individual readings are noise. With consistent context, they become signal, and the endocrinologist can make dosage and lifestyle recommendations based on cause-and-effect patterns rather than point-in-time snapshots.

What the Entry Type Field Forces You to Log

The Entry Type field — Glucose, Food, Exercise, Weight, Blood Panel, Vitals — structures each log entry around a single measurement type rather than cramming everything into a single daily record. This creates a queryable timeline where you can filter for all Blood Sugar readings after dinner, or all Exercise entries that precede a morning Fasting reading, and see the relationship between those events as a data series rather than reconstructing it from narrative notes.

Blood Sugar paired with Reading Context is the most operationally important combination. The Reading Context multi-select — Fasting, Before Breakfast, After Breakfast, Before Lunch, After Lunch, Before Dinner, After Dinner, Before Exercise, After Exercise — ties each glucose reading to its physiological moment. A postprandial reading 90 minutes after dinner has a different clinical interpretation than a fasting reading at 7 AM. Without the context field, both are just numbers. The context is what makes them comparable within a category.

HbA1c as a separate field captures the quarterly lab result that summarizes the preceding three months of glucose control. Logging it in the same database as the daily readings creates the vertical slice: on the day your HbA1c came back at 7.2, you can look at the prior 90 days of daily readings and see what drove that number. If your next quarterly result is 6.8, the same comparison shows what changed.

The lipid panel fields — Cholesterol Total, HDL, LDL, Triglycerides — alongside Free Testosterone represent the full cardiometabolic picture that comes from a comprehensive metabolic panel. These are quarterly or annual entries, not daily, but having them in the same database as daily glucose and exercise data creates a longitudinal record that most patients have scattered across multiple lab reports in a folder somewhere.

The 6 AM Log Before Breakfast

Tuesday morning. Fasting glucose: 118. Yesterday included 40 minutes of biking logged at 380 calories burned and a dinner recorded at 52g carbohydrates. Monday's fasting glucose was 134. The difference between the two days is visible in the prior evening's entries: Monday's dinner logged at 89g carbohydrates, no exercise.

That two-record comparison takes fifteen seconds to pull up and answers the question that otherwise takes a full appointment conversation to reconstruct.

Systolic and Diastolic as separate integer fields handle blood pressure as a Vitals entry type — not merged into a text cell, not approximate. The clinical distinction between 138/88 and 142/94 matters for medication thresholds, and that distinction is lost if blood pressure is entered as "138 over 88" in a notes field that can't be numerically filtered.

The Calories Consumed and Carbs Consumed fields don't require food diary precision. An estimate of dinner carbs logged within 15 minutes of eating is useful data. The alternative — logging nothing because perfect tracking is too time-consuming — produces nothing useful. The database accepts rough input and still produces trends.