# Patterns of Glycemic Variability During a Diabetes Self-Management Educational Program

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Design

#### 2.2. Setting

#### 2.3. Participants

#### 2.4. Procedures

#### 2.5. Statistical Analysis

## 3. Results

## 4. Discussion

## 5. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Percentage change in glycemic variability indices. (

**a**) coefficient of variation (CV), mean amplitude of glycemic excursion (MAGE), mean of daily difference for inter-day variation (MODD), and continuous overlapping net glycemic action (CONGA). (

**b**) High blood glucose index (HBGI) and low blood glucose index (LBGI) from baseline among various glycemic control categories (risk plot).

**Figure 2.**Composite assessment of glycemic control and variability based on raw glucose values at the same time on two sequential days (lag plot).

GV Measure | Formula | Interpretation | |
---|---|---|---|

SD | $\sqrt{\frac{{\left({x}_{i}-\overline{x}\right)}^{2}}{k-1}}$ | where: x _{i} = individual observation$\overline{x}$ = mean of observation k = number of observations | Traditional measure of dispersion; Measures short-term, within-day variability; Easy to compute, used very often |

% CV | $\frac{s}{\overline{x}}$ × 100 | where: s = standard deviation $\overline{x}$ = mean of observation | Traditional measure of dispersion, standardized for mean; Measures short-term, within-day variability; Easy to compute using mean and standard deviation |

MAGE | $\sum}\frac{\mathsf{\lambda}}{n$ | if: λ > υ where: λ = each blood glucose increase or decrease n = number of observations υ = 1 SD of mean glucose for 24 hour period | Average of all glycemic excursions (except excursion having value <1 SD from mean glucose) in a 24 h time period; Captures short-term, within-day variability; Most commonly used |

CONGA | $\sqrt{\frac{{\sum}_{t={t}_{1}}^{{t}_{k*}}({D}_{t}-{\overline{D}}^{2}}{{k}^{*}-1}}$ | where: k* = No. of observations where, there is an observation m mins ago GR _{t} = glucose reading at time tm = n × 60 D _{t} = GR_{t} − GR_{t−m}$\overline{D}=\frac{{\sum}_{t={t}_{1}}^{{t}_{k*}}{D}_{t}}{{k}^{*}}$ | Standard deviation of summated difference between current observation and previous observation; Captures short-term, within-day variability; Complex calculation, specifically developed for CGM |

MODD | $\frac{{\sum}_{t={t}_{1}}^{{t}_{k*}}|G{R}_{1}-G{R}_{t-\widehat{t}}|}{{k}^{*}}$ | where: $\widehat{t}$ = 1440 (60 × 24); if reading taken every 1 min 96 (4 × 24); if reading taken every 15 min 24 (1 × 24); if reading taken every 60 min | 24 h mean absolute differences between two values measured at the same timepoint; short-term, inter-day variation; Needs additional computation |

HBGI | $\frac{1}{n}{\displaystyle {\displaystyle \sum}_{t=1}^{n}}rh\left(BGi\right)$ | where: $f\left(BG\right)=1.509\text{}\times [\left({\mathrm{log}}_{e}(BG){)}^{1.084}-5.381\right]\mathrm{if}\text{}\mathrm{BG}\text{}\mathrm{is}\text{}\mathrm{measured}\text{}\mathrm{in}\text{}mg/dL$ $f\left(BG\right)=1.509\text{}\times [\left({\mathrm{log}}_{e}(18\times BG){)}^{1.084}-5.381\right]\mathrm{if}\text{}\mathrm{BG}\text{}\mathrm{is}\text{}\mathrm{measured}\text{}\mathrm{in}\text{}mmol/dL$ $r\left(BG\right)=10\times f{\left(BG\right)}^{2}\text{}$ $rl\left(BG\right)=r\left(BG\right)\text{}\mathrm{if}\text{}f\left(BG\right)0\mathrm{and}\text{}0\text{}\mathrm{otherwise}$ | Log transformation of glucose values; Captures risk for predicting severe glycaemia; Complex calculation, easy to interpret |

LBGI | $\frac{1}{n}{\displaystyle {\displaystyle \sum}_{t=1}^{n}}rl\left(BGi\right)$ | where: $rl\left(BG\right)=r\left(BG\right)\text{}\mathrm{if}\text{}f\left(BG\right)0\mathrm{and}\text{}0\text{}\mathrm{otherwise}$ | Log transformation of glucose values; Captures risk for predicting severe hyperglycaemia (HGBI); Complex calculation, easy to interpret |

Domain | Activity | Days | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | ||

Knowledge | Understanding diabetes | ✓ | ✓ | ✓ | |||||||||||

Understanding therapies | ✓ | ✓ | |||||||||||||

Improving self-care | ✓ | ✓ | |||||||||||||

Foot care | ✓ | ||||||||||||||

Physical activity | A 30-min brisk walk | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |

Using activity trackers | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||||

Yoga/meditation | ✓ | ✓ | ✓ | ||||||||||||

Physical activity rewards | ✓ | ✓ | |||||||||||||

Nutrition | Meal planning | ✓ | ✓ | ✓ | ✓ | ||||||||||

Low-GI breakfast | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||||

Food diary feedback | ✓ | ✓ | ✓ | ✓ | |||||||||||

Meal planning rewards | ✓ | ✓ | |||||||||||||

Behavior | Stress reduction | ✓ | ✓ | ||||||||||||

Coping skills | ✓ | ✓ | |||||||||||||

Tobacco cessation | ✓ | ✓ | |||||||||||||

Disease management | CGM insertion/removal | ✓ | ✓ | ||||||||||||

CGM readings and feedback | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||

Screening for complications | ✓ | ||||||||||||||

Drug prescription review | ✓ | ✓ | |||||||||||||

Activity count | 4 | 4 | 5 | 5 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 7 |

**Table 3.**Baseline characteristics: Age, gender, years since diagnosis, complications, etc., by glycemic control status groups.

Baseline Characteristic | Optimal Control (n = 12) | Acceptable Control (n = 12) | Poor Control (n = 22) | Overall (n = 46) |
---|---|---|---|---|

Mean (SD) or N (%) | ||||

Male | 8 (66.7%) | 6 (50%) | 10 (45.5%) | 24 (52.2%) |

Female | 4 (33.3%) | 6 (50%) | 12 (54.5%) | 22 (47.8%) |

Age | 56.7 (13.2) | 54.5 (10.6) | 52.2 (11.2) | 54.0 (11.5) |

BMI | 26.5 (2.6) | 25.8 (4.4) | 26.5 (5.1) | 26.3 (4.3) |

Waist Circumference (cm) | 97.8 (7.8) | 93.4 (7.8) | 97.7 (10.2) | 96.59 (9.1) |

Hip Circumference (cm) | 100 (7.3) | 99.3 (7.7) | 103 (11.5) | 101.4 (9.6) |

WHR | 0.98 (0.1) | 0.94 (0.1) | 0.95 (0.1) | 0.96 (0.1) |

SBP | 126 (22) | 130 (9.9) | 132 (17.8) | 129.8 (17.2) |

DBP | 82.5 (10.4) | 77.8 (10.3) | 83.4 (10.5) | 81.7 (10.45) |

Body Fat Percentage ^{‡} | 26 (4.1) | 24.8 (5.1) | 28.1 (4.2) | 26.6 (4.6) |

HbA1c | 6.6 (0.3) | 7.5 (0.3) | 9.5 (1.2) | 8.21 (1.6) |

Duration of diabetes (years) | 6.8 (8.8) | 10.1 (6.9) | 8.4 (7.8) | 8.4 (7.8) |

Hypertension | 5 (41.7%) | 7 (58.3%) | 8 (36.4%) | 20 (43.5%) |

IHD | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) |

Hypothyroidism | 3 (25.0%) | 2 (16.7%) | 1 (4.5%) | 6 (13.0%) |

Stroke | 1 (8.3%) | 0 (0%) | 0 (0%) | 1 (2.2%) |

PVD | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) |

Retinopathy | 0 (0%) | 0 (0%) | 1 (4.5%) | 1 (2.2%) |

Neuropathy | 1 (8.3%) | 2 (16.7%) | 3 (13.6%) | 6 (13.0%) |

Nephropathy | 1 (8.3%) | 2 (16.7%) | 0 (0%) | 3 (6.5%) |

^{‡}—Two missing from poor control group.

Measure | Baseline (Day 2) | Mid (Day 7) | End (Day 13) |
---|---|---|---|

Optimal Control (n = 11) | |||

Mean Glucose | 115.90 | 107.96 | 98.21 |

SD Glucose | 34.15 | 34.15 | 34.86 |

Coefficient of variation | 29.46 | 31.64 | 35.49 |

MODD | 24.56 | 24.44 | 23.14 |

MAGE | 126.15 | 117.27 | 111.55 |

CONGA | 8.67 | 8.24 | 7.61 |

HBGI | 3.13 | 2.93 | 3.05 |

LBGI | 2.30 | 3.57 | 5.95 |

Acceptable Control (n = 10) | |||

Mean Glucose | 127.22 | 104.29 | 102.21 |

SD Glucose | 50.02 | 27.62 | 25.50 |

Coefficient of variation | 39.32 | 26.48 | 24.94 |

MODD | 30.79 | 21.53 | 18.90 |

MAGE | 138.01 | 109.43 | 105.65 |

CONGA | 8.87 | 6.46 | 5.69 |

HBGI | 5.56 | 1.41 | 1.34 |

LBGI | 4.08 | 4.00 | 3.47 |

Poor Control (n = 20) | |||

Mean Glucose | 203.67 | 176.81 | 144.62 |

SD Glucose | 77.43 | 62.22 | 53.26 |

Coefficient of variation | 38.02 | 35.19 | 36.83 |

MODD | 45.82 | 37.04 | 32.28 |

MAGE | 216.63 | 186.93 | 154.13 |

CONGA | 14.10 | 9.62 | 8.38 |

HBGI | 16.62 | 10.99 | 7.16 |

LBGI | 3.41 | 2.14 | 4.06 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Joshi, A.; Mitra, A.; Anjum, N.; Shrivastava, N.; Khadanga, S.; Pakhare, A.; Joshi, R.
Patterns of Glycemic Variability During a Diabetes Self-Management Educational Program. *Med. Sci.* **2019**, *7*, 52.
https://doi.org/10.3390/medsci7030052

**AMA Style**

Joshi A, Mitra A, Anjum N, Shrivastava N, Khadanga S, Pakhare A, Joshi R.
Patterns of Glycemic Variability During a Diabetes Self-Management Educational Program. *Medical Sciences*. 2019; 7(3):52.
https://doi.org/10.3390/medsci7030052

**Chicago/Turabian Style**

Joshi, Ankur, Arun Mitra, Nikhat Anjum, Neelesh Shrivastava, Sagar Khadanga, Abhijit Pakhare, and Rajnish Joshi.
2019. "Patterns of Glycemic Variability During a Diabetes Self-Management Educational Program" *Medical Sciences* 7, no. 3: 52.
https://doi.org/10.3390/medsci7030052