CGM
Sections
GlucoDex — Technical Reference

Continuous Glucose
& Glycemic Variability

Reference for every metric GlucoDex computes from a continuous glucose monitor (CGM) trace — definitions, formulas, target ranges, and the evidence base behind each. The headline figures are the international CGM-consensus metrics; on them GlucoDex layers the established variability and risk indices, a few emerging patterns, and cross-node fusion composites. Companion to the trace; not a substitute for clinical evaluation or your diabetes care team. All values and ranges here are in mg/dL — the CGM-consensus unit GlucoDex computes and exports in; the dashboard adds an optional mg/dL ⇄ mmol/L display switch (display-only, compute stays mg/dL).

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Important: CGM sensors read interstitial glucose with a lag and a calibration error band — they are not equivalent to a lab venous draw, and GMI is an estimate that can differ from a measured HbA1c. These readings are for personal tracking and to inform a conversation with your clinician, not to self-direct insulin or medication dosing.
Evidence Measured Validated Emerging Experimental Heuristic fill = trust · hover a badge for source
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Glucose & Quality
Raw sensor statistics and the coverage gauges that gate everything else
ℹ️
Pipeline. GlucoDex parses a CGM export (timestamped glucose, typically 5-minute samples), holds positional compression artifact out of the hypoglycemia accounting, and computes the consensus metrics over the active period. Vendor timestamps are parsed by explicit format per the Clock Contract (MDY for CGM exports), never a locale guess.
Mean glucoseAverage Sensor Glucose
Core

The mean of all valid sensor readings — the level around which everything else varies, and the input to GMI.

Formula
mean = Σ glucose[t] / N
Mean (mg/dL)Approx. control
< 140Near-normal average
140–180Elevated
> 180High — review with clinician
SDStandard Deviation
Advanced

Absolute glucose variability. Read alongside CV (consensus prefers CV because SD scales with the mean).

Formula
SD = √( Σ(glucose − mean)² / (N−1) )
No fixed target — interpret via CV (<36% stable). Lower is better at a given mean.
MAGMean Absolute Glucose Change
Research

Average absolute rate of change per hour — how briskly glucose moves between samples.

Formula
MAG = Σ|glucose[t] − glucose[t−1]| / total hours
Relative measure — compare across your own recordings; lower indicates steadier glucose.
% Sensor activeCoverage
Core

Share of the reporting period with valid sensor data. Consensus guidance wants ≥70% over 14 days for the metrics to be representative.

Formula
% Active = valid samples / expected samples × 100
% ActiveConfidence
≥ 70%Representative (consensus)
50–70%Partial — interpret with care
< 50%Insufficient
Active durationRecording Span
Advanced

Total days of active sensor wear in the analysis. Some indices (ADRR, MODD) require at least two days; the consensus AGP window is 14 days.

Formula
duration = last sample time − first sample time
DaysAdequacy
≥ 14Full consensus window
3–14Partial window
< 3Too short for inter-day indices
Compression flaggedPositional Artifact
Research

Minutes of suspected nocturnal compression (lying on the sensor), held out of the time-below-range accounting so a false low does not inflate hypoglycemia. A genuine sharp insulin hypo (sustained sub-70, gradual descent + Somogyi rebound) is now disambiguated from a positional artifact and survives into the nocturnal-hypo count; only a near-vertical, single-cell drop-and-recover is flagged here.

Formula
sum of minutes in slope+level compression pattern overnight
Diagnostic flag, not a target — high values mean review sleeping position / sensor site.
Data confidenceRecording Quality
Research

A direct data-quality confidence multiplier reflecting coverage, gaps and flagged artifact.

Formula
composite of coverage × gap penalty × artifact penalty
Quality gauge — higher is better; low values widen the uncertainty on every other metric.
Nocturnal hypoNighttime Lows
Advanced

Count of detected nocturnal sub-70 mg/dL episodes — the most clinically consequential lows, and an input to the hypo↔QTc fusion signal.

Formula
count of distinct episodes < 70 mg/dL during 00:00–06:00
EpisodesRead
0None detected
1–2Occasional — review
≥ 3Recurrent — discuss with clinician
ExcursionsExcursion Count
Research

Number of slope-detected glucose excursions exceeding one SD — the raw count feeding MAGE.

Formula
count of peaks/troughs with amplitude > 1 SD
Relative — compare across recordings; lower indicates fewer large swings.
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Core Glycemic
The international CGM-consensus headline metrics
Time in RangeTIR 70–180 mg/dL
Core

The primary CGM target: percentage of time glucose sits in 70–180 mg/dL. Each 5-point gain in TIR is clinically meaningful; the consensus goal for most adults is >70%.

Formula
TIR = time in 70–180 mg/dL / valid time × 100
TIRRead
> 70%At target (consensus)
50–70%Below target
< 50%Well below target
GMIGlucose Management Indicator
Core

An estimated HbA1c-equivalent computed from mean glucose. A modelled estimate that can differ from a lab HbA1c by ±0.3% or more — see GMI vs Lab.

Formula
GMI (%) = 3.31 + 0.02392 × mean glucose (mg/dL)
GMIRead
< 6.5%At/near target
6.5–7.5%Above target
> 7.5%High — review
Est. HbA1cADAG Estimated A1c
Advanced

The ADAG study’s mean-glucose-to-A1c regression — an alternative estimate to GMI using a different coefficient set.

Formula
eA1c (%) = (mean glucose + 46.7) / 28.7
eA1cRead
< 6.5%At/near target
6.5–7.5%Above target
> 7.5%High
CVCoefficient of Variation
Core

Glucose variability normalised to the mean — the consensus stability metric. Above 36%, hypoglycemia risk rises sharply.

Formula
CV (%) = SD / mean × 100
CVRead
< 36%Stable (consensus)
≥ 36%Unstable — elevated hypo risk
Time BelowTime <70 mg/dL
Core

Total time below 70 mg/dL — overall hypoglycemia exposure (TBR level 1 + 2 combined).

Formula
time < 70 mg/dL / valid time × 100
Time BelowRead
< 4%At target (consensus)
4–10%Above target
> 10%High hypo exposure
Time AboveTime >180 mg/dL
Core

Total time above 180 mg/dL — overall hyperglycemia exposure (TAR level 1 + 2 combined).

Formula
time > 180 mg/dL / valid time × 100
Time AboveRead
< 25%At target (consensus)
25–50%Above target
> 50%High hyper exposure
Tight RangeTITR 70–140 mg/dL
Advanced

Time in the tighter 70–140 mg/dL band — a more demanding target introduced in the 2023 trial-metrics consensus, relevant for near-normal glycemia goals.

Formula
TITR = time in 70–140 mg/dL / valid time × 100
TITRRead
> 50%Strong tight control
30–50%Moderate
< 30%Limited tight control
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Time-in-Range Breakdown
The consensus exposure bands above and below target
TBR 54–69Time Below Range (Low)
Advanced

Time in 54–69 mg/dL — level-1 hypoglycemia.

Formula
time in 54–69 mg/dL / valid time × 100
TBR 54–69Read
< 4%At target
≥ 4%Above target
TBR <54Time Below Range (Very Low)
Advanced

Time below 54 mg/dL — clinically significant level-2 hypoglycemia.

Formula
time < 54 mg/dL / valid time × 100
TBR <54Read
< 1%At target
≥ 1%Above target — act
TAR 181–250Time Above Range (High)
Advanced

Time in 181–250 mg/dL — level-1 hyperglycemia.

Formula
time in 181–250 mg/dL / valid time × 100
Read within the <25% total Time-Above target; lower is better.
TAR >250Time Above Range (Very High)
Advanced

Time above 250 mg/dL — level-2 hyperglycemia.

Formula
time > 250 mg/dL / valid time × 100
TAR >250Read
< 5%At target
≥ 5%Above target
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Variability & Risk Indices
Established glycemic-variability and risk equations
MAGEMean Amplitude of Glycemic Excursions
Advanced

Average size of glucose swings exceeding one SD — the classic variability index, sensitive to large meal/treatment excursions.

Formula
MAGE = mean amplitude of excursions > 1 SD
Relative — lower is steadier; commonly < 60 mg/dL indicates low variability.
MODDMean of Daily Differences
Research

Average absolute difference between glucose at the same clock time on consecutive days — day-to-day reproducibility. Needs ≥2 days.

Formula
MODD = mean |glucose(t,day) − glucose(t,day−1)|
Relative — lower means more reproducible daily patterns.
CONGAContinuous Overlapping Net Glycemic Action
Research

SD of the differences between readings a fixed interval apart (commonly 1, 2 or 4 h) — intra-day variability at a chosen timescale.

Formula
CONGA-n = SD( glucose[t] − glucose[t−n h] )
Relative — lower indicates steadier glucose over the chosen interval.
J-indexLevel + Variability Index
Research

A single index combining mean level and variability into one number.

Formula
J = 0.001 × (mean + SD)² [mg/dL]
J-indexRead
< 20Good control
20–40Fair
> 40Poor
GRADEGlycaemic Risk Assessment
Research

A risk score weighting each reading by its log-scaled deviation from euglycemia — penalises both highs and lows.

Formula
GRADE = mean of 425·[log₁₀(log₁₀(glucose/18)) + 0.16]²
Relative — lower is better; a GRADE breakdown attributes risk to hypo vs hyper.
ADRRAverage Daily Risk Range
Research

A symmetric daily risk index capturing both hypo- and hyperglycemic excursions equally. Needs ≥2 days of data.

Formula
ADRR = mean over days of ( max LR + max HR ) per day
ADRRRisk
< 20Low
20–40Moderate
> 40High
LBGILow Blood-Glucose Index
Advanced

A risk-weighted measure of hypoglycemia exposure — emphasises how far and how often glucose drops low.

Formula
LBGI = mean of 10·f(glucose)² over readings where f<0
LBGIHypo risk
< 1.1Minimal
1.1–2.5Low–moderate
> 2.5Moderate–high
HBGIHigh Blood-Glucose Index
Advanced

A risk-weighted measure of hyperglycemia exposure — the upper-range counterpart of LBGI.

Formula
HBGI = mean of 10·f(glucose)² over readings where f>0
HBGIHyper risk
< 4.5Low
4.5–9Moderate
> 9High
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🌅
Emerging Patterns
Less-standardized variability and circadian signals
GVPGlucose Variability Percentage
Research

Variability as the extra path-length the glucose trace travels versus a flat line — a geometric variability measure that captures both amplitude and frequency.

Formula
GVP = ( trace path length / flat-line length − 1 ) × 100
Relative — lower means a flatter, steadier trace; less standardized than CV.
Dawn riseDawn Phenomenon
Advanced

The pre-breakfast glucose rise from the 03–06h nadir — a circadian counter-regulatory pattern that can drive morning highs.

Formula
Dawn rise = pre-breakfast glucose − nadir(03–06h)
Dawn riseRead
< 20 mg/dLMinimal
20–40 mg/dLModerate
> 40 mg/dLMarked dawn effect
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Cross-Node & Composites
Fusion values from other nodes and GlucoDex internal composites
ℹ️
Fusion. One value here (QTc) is imported from ECGDex when both nodes run, to surface the nocturnal-hypoglycemia↔QT-prolongation pattern. The composites blend glycemic and autonomic inputs — directional risk signals internal to the suite, never clinical diagnoses.
QTc (ECGDex)Rate-Corrected QT (Fusion)
Research

The rate-corrected QT interval imported from ECGDex when available — paired with nocturnal hypoglycemia to flag the hypo↔QTc pattern below.

Formula
QTc = QT / √RR (Bazett) — computed by ECGDex
QTc (ms)Read
< 450Normal
450–480Borderline
> 480Prolonged
Stability ScoreGlycemic Stability Composite
Core

A 0–100 stability headline blending time-in-range, CV and hypoglycemia exposure.

Method
_m:true, blend of TIR, CV and hypo-exposure (internal weights)
Internal composite — no external source. Directional, baseline-relative.
IR-risk bandInsulin-Resistance Risk
Advanced

A directional insulin-resistance risk band from glycemic level/variability and autonomic inputs.

Method
_m:true, glycemic load × autonomic markers (fusion)
Internal directional band — not a diagnosis; no external source.
Autonomic riskAutonomic Risk Composite
Research

A composite autonomic-risk signal combining ECGDex overnight slope, surges and coupling with glycemic context.

Method
_m:true, ECGDex slope + surge + coupling (fusion)
Internal fusion composite — directional; no external source.
Glycemic variabilityVariability Composite
Research

A composite variability index blending CV, MAGE and the dawn rise — a single fusion input for the Integrator.

Method
_m:true, blend of CV, MAGE and dawn rise
Internal composite — no external source.
Hypo⟷QTc riskNocturnal Hypo × QTc
Research

A directional composite flagging the coincidence of nocturnal hypoglycemia and prolonged QTc — the “dead-in-bed” risk pattern. A screening signal, not a diagnosis.

Method
_m:true, nocturnal-hypo count × QTc-prolongation overlap
Internal fusion screen — no external source; corroborate clinically.
GMI vs LabGMI−Lab A1c Delta
Advanced

The gap between GMI and a lab HbA1c when one is entered. GMI reflects recent mean glucose; lab A1c reflects red-cell glycation over months — a delta is expected, not an error.

Formula
GMI vs Lab = GMI − lab HbA1c
Internal comparison — a persistent large delta warrants a calibration / haematology discussion.
Sensor bias vs lab A1cCalibration Check
Research

Sensor mean glucose versus the estimated average glucose implied by a lab A1c — a sensor-calibration sanity check.

Formula
bias = sensor mean − eAG(lab A1c), eAG = 28.7×A1c − 46.7
Internal check — large persistent bias suggests sensor calibration drift.
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Projection
Population-norm projection — directional, not a measurement
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✔️
Validation Status Matrix
What is consensus/literature-validated versus experimentally derived — the key provenance table for research use

Validation refers to the underlying metric’s validation in published literature/consensus, and does not imply validation of the GlucoDex implementation against a gold-standard laboratory dataset.

Metric CategoryStatusBasis
TIR / TITR / TBR / TAR / CV targets● ConsensusInternational CGM consensus; Battelino 2019 / 2023
GMI · Estimated A1c● Literature-basedBergenstal 2018; Nathan/ADAG 2008
Mean / SD / MAG / coverage● Direct measurementRaw sensor statistics
MAGE · MODD · CONGA · J-index● Literature-basedService 1970; McDonnell 2005; Wójcicki 1995
GRADE · ADRR · LBGI · HBGI● Literature-basedHill 2007; Kovatchev 2006
GVP · Dawn phenomenon◐ EmergingPublished but less standardized / device-dependent
QTc (fusion)● Literature-basedBazett rate correction, computed by ECGDex
Stability · IR-risk · Autonomic · Hypo↔QTc · GMI-vs-lab○ Experimental compositeGlucoDex / fusion internal algorithms; no independent validation
Metric Tier Definitions
TierMeaningExamples
CoreConsensus-supported, universally interpretableTIR, GMI, CV, Time Below/Above
AdvancedPublished support, less common in routine reviewTITR, MAGE, LBGI/HBGI, dawn rise
ResearchExploratory / emerging / composites — interpret with cautionGRADE, ADRR, fusion composites
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Formula → Citation Map
Every computed metric mapped to its primary source
Metric / FormulaPrimary CitationCategory
TIR / TBR / TAR / CV targetsBattelino 2019 (consensus)Consensus
TITR (tight range)Battelino 2023 (trial metrics)Consensus
GMI = 3.31 + 0.02392·meanBergenstal 2018Index
eA1c = (mean+46.7)/28.7Nathan / ADAG 2008Index
MAGEService 1970Variability
CONGAMcDonnell 2005Variability
J-index = 0.001·(mean+SD)²Wójcicki 1995Variability
GRADEHill 2007Risk
ADRR / LBGI / HBGIKovatchev 2006Risk
QTc (Bazett)ECGDex (cross-node)Fusion
Stability / IR-risk / Autonomic / Hypo↔QTcGlucoDex internal — no external sourceInternal
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𝑓
Formula Provenance Index
Compact audit index — every formula mapped to its primary source
FormulaSource / AuthorYearReference
GMI = 3.31 + 0.02392·meanBergenstal RM et al.2018Diabetes Care. 41(11):2275–80
eA1c = (mean+46.7)/28.7Nathan DM et al. (ADAG)2008Diabetes Care. 31(8):1473–8
TIR/TBR/TAR/CV targetsBattelino T et al.2019Diabetes Care. 42(8):1593–1603
TITR 70–140Battelino T et al.2023Lancet Diabetes Endocrinol. 11(1):42–57
MAGEService FJ et al.1970Diabetes. 19(9):644–55
CONGAMcDonnell CM et al.2005Diabetes Technol Ther. 7(2):253–63
J-index = 0.001·(mean+SD)²Wójcicki JM1995Horm Metab Res. 27(1):41–2
GRADEHill NR et al.2007Diabet Med. 24(7):753–8
ADRR / LBGI / HBGIKovatchev BP et al.2006Diabetes Care. 29(11):2433–8
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⚠️
Known Limitations
The interpretation context for consumer CGM analysis
ℹ️
These limitations are inherent to continuous glucose monitoring. They do not invalidate GlucoDex outputs but define the appropriate interpretation context.
🩸 Sensor & Sampling
  • Interstitial glucose lags blood glucose by ~5–15 min
  • Factory-calibrated sensors carry a MARD error band (~8–10%)
  • Compression (lying on the sensor) can create false nocturnal lows
  • Metrics need ≥70% coverage over 14 days to be representative
🧪 Estimates vs Lab
  • GMI / eA1c are models of mean glucose, not lab HbA1c
  • Lab A1c reflects months of red-cell glycation; GMI reflects ~14 days
  • A GMI–lab delta is expected, not an error
  • Anaemia / haemoglobinopathy can shift lab A1c independent of glucose
📊 Algorithmic
  • Stability / IR-risk / Autonomic / Hypo↔QTc are internal composites
  • Fusion values depend on ECGDex being present and time-aligned
  • Variability bands (GVP, MAGE) are relative, not universal cut-points
⚖️ Regulatory
  • Not FDA cleared or CE marked as a medical device
  • Not for diagnosis or self-directed insulin/medication dosing
  • Personal, research, and wellness use only
  • Discuss medication and dosing changes with your care team
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📚
Academic References
Primary sources for the CGM-consensus and variability metrics
⚠️
Provenance note. The consensus and variability indices use the verified sources below. Fusion composites and the metabolic-age projection are internal blends with no external source, labelled as such.
Method / MetricPrimary CitationCategory
TIR / TBR / TAR / CV targets (consensus)Battelino T, Danne T, Bergenstal RM, et al. Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care. 2019;42(8):1593–1603. doi: 10.2337/dci19-0028Consensus
TITR (tight range) & trial metricsBattelino T, Alexander CM, Amiel SA, et al. Continuous glucose monitoring and metrics for clinical trials: an international consensus statement. Lancet Diabetes Endocrinol. 2023;11(1):42–57. doi: 10.1016/S2213-8587(22)00319-9Consensus
GMIBergenstal RM, Beck RW, Close KL, et al. Glucose Management Indicator (GMI): a new term for estimating A1C from CGM. Diabetes Care. 2018;41(11):2275–2280. doi: 10.2337/dc18-1581Index
Estimated A1c (ADAG)Nathan DM, Kuenen J, Borg R, et al. Translating the A1C assay into estimated average glucose values. Diabetes Care. 2008;31(8):1473–8. doi: 10.2337/dc08-0545Index
MAGEService FJ, Molnar GD, Rosevear JW, et al. Mean amplitude of glycemic excursions, a measure of diabetic instability. Diabetes. 1970;19(9):644–55. doi: 10.2337/diab.19.9.644Variability
CONGAMcDonnell CM, Donath SM, Vidmar SI, et al. A novel approach to continuous glucose analysis utilizing glycemic variation. Diabetes Technol Ther. 2005;7(2):253–63. doi: 10.1089/dia.2005.7.253Variability
J-indexWójcicki JM. “J”-index. A new proposition of the assessment of current glucose control in diabetic patients. Horm Metab Res. 1995;27(1):41–2. doi: 10.1055/s-2007-979906Variability
GRADEHill NR, Hindmarsh PC, Stevens RJ, et al. A method for assessing quality of control from glucose profiles. Diabet Med. 2007;24(7):753–8. doi: 10.1111/j.1464-5491.2007.02119.xRisk
ADRR / LBGI / HBGIKovatchev BP, Otto E, Cox D, et al. Evaluation of a new measure of blood glucose variability in diabetes. Diabetes Care. 2006;29(11):2433–8. doi: 10.2337/dc06-1085Risk
QTc (rate-corrected QT)Imported from ECGDex (Bazett rate correction) — see the ECGDex reference guide.Fusion
Stability / IR-risk / Autonomic / Hypo↔QTcGlucoDex internal composites — no external source. Directional only.Internal
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Abbreviation Index
Every acronym used in this guide — searchable, jump to its section
terms
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📋
Project Credits
Authorship, contributions, and open-source provenance
Author
Michal Planicka
Concept · Architecture · Algorithms
Implementation · Validation · UI/UX
Assisted Development
AI-Assisted
Code review · Documentation
Literature synthesis · Reference formatting
Licence & Suggested Citation
Apache-2.0 Open-source
Planicka M. GlucoDex: Continuous Glucose Analysis Node. Version 1.0.0. 2026.
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Reference Guide Version: 1.0.0  ·  Node: GlucoDex — continuous glucose monitor  ·  Last Literature Review: June 2026  ·  Apache-2.0 Licence
Intended use & safety

Tepna computes biometric patterns from your wearable and sensor data to support personal self-quantification. It is not a medical device, does not diagnose, treat, cure, screen for, or prevent any disease or condition, and is not a substitute for professional clinical evaluation. It has not been reviewed or cleared by the FDA, CE, or any regulatory body. Always consult a qualified healthcare provider about your health. Use at your own risk. For research and personal use only. 100% local — no data leaves your device.

T Tepna physiological-signal suite
© 2026 Michal Planicka — Concept · Architecture · Algorithms Not a medical device · does not diagnose or treat · not FDA/CE cleared · research & personal use only · ◈ Made in Asheville, NC
licenceApache-2.0