LiveLoadingtotal visitors

Open source
Prediction & risk

A personal risk dashboard: an educational concept

Explore risk-related variables without generating a diagnostic score, medical recommendation, or unsupported individual probability.

2 min readMedium read3 sourcesChecked 30 Sept 2026Limited Evidence

By The Diabetes Guide editorial project · Updated 30 Sept 2026

On this page
In simple words

A personal dashboard can show useful facts, but it cannot diagnose you. The page here explains what each signal can and cannot tell you.

  1. Step 1: Strong and weak signals

    Lab glucose has a defined role. Sleep scores or resting heart rate are weak proxies on their own.

    For example: A speedometer is precise; “the car feels fast” is not.

  2. Step 2: Try it

    Tick signals to read what each one can and cannot show.

    For example: Flipping flashcards.

  3. Step 3: A safe pipeline

    Collect, check units, handle missing data, use a validated model, show uncertainty, then see a clinician.

    For example: A recipe with checks at every step.

Remember: This is a learning tool. It does not produce a risk score.

The full story

Want more? Below is the detailed version with the real science words. It is fine to skip it.

Interact with the signals

Learning demo

Explore the signals. Understand their limits.

This is not a real risk calculator. It gives no probability, no diagnosis and no treatment advice — just an explanation of what each signal can and cannot tell you. Your choices stay on this page and are never sent anywhere.

Tick a signal to learn about it

Tick a signal to see what it can — and cannot — tell you.

Follow the screening steps →

A scientifically responsible pipeline

Data collection → provenance and units → missingness checks → validated model appropriate to the population → calibrated estimate with uncertainty → laboratory assessment when indicated → shared clinical decision. A safer product stores source timestamps and distinguishes measured values from inferred ones. 12

Strong signals and weak proxies

Laboratory glucose has a defined clinical role. Waist and family history add context. Resting heart rate, consumer sleep scores and unstandardized CGM features can reflect many processes and should not independently label disease. A machine-learning model needs prospective validation; more fields do not automatically create a better model. 3

Trace the evidence

Sources and further reading

2.Evaluation of the Indian Diabetes Risk Score in ICMR-INDIAB (opens in a new tab)

Deepa et al.; Indian Journal of Medical Research · 2023 · Population study

Moderate Evidence
Who was studied, limits and source check

Population: Urban and rural India

Sample: 113,043 surveyed individuals

Limitations: Screening for undiagnosed diabetes is distinct from predicting incident disease; external calibration remains important.

Source checked 2026-09-30. See the original publication for full methods.

Source checking is an editorial literature check, not independent medical review. This page is for learning. It cannot diagnose you or make a treatment plan. Evidence labels describe the cited claims, not the whole topic.

What does “Limited Evidence” mean?

Only a few small studies, and they do not fully agree. Like asking three friends and getting three answers.

Keep reading

The body

The one-page mental model

Diabetes means there is too much sugar in the blood for too long, because the body does not have enough insulin or cannot use it well.

4 minEasy read

Type 1

Type 1 diabetes: the complete journey

Type 1 diabetes is usually an autoimmune condition. The body’s defence system harms the beta cells, so less and less insulin is made.

4 minEasy read