Are you left with questions when you are reviewing blood glucose logs of your patients with gestational diabetes (GDM)? Like:
- What are glucose levels doing overnight?
- How about after an unlogged snack??
- When is the long-acting insulin wearing off?
And that’s assuming your patients are providing thorough, accurate logs. The truth is, we can always use more, better information. Fortunately, continuous glucose monitoring (CGM) use for gestational diabetes can reflect the total picture.
Here are seven key insights that CGM data can reveal that fingersticks often miss.
1. Overnight highs or lows
If your patient’s last fingerstick was one or two hours after dinner, you’re likely missing what it’s doing overnight. Yes, the fasting can be an indication, but it isn’t uncommon to have spikes and lows that aren’t necessarily reflected in the morning reading.
When the fasting levels are high, it can be a guessing game to figure out why. Is it because there wasn’t enough protein at dinner? Do they need a nighttime snack? Does the glucose stay high overnight? CGMs typically read data every 1-5 minutes, so you can easily establish an overnight pattern and make adjustments accordingly.
2. Glucose variability throughout a 24-hour period
Similar to the unknowns overnight, the inability of a few fingersticks to show the range of glucose levels throughout the day means that the data is incomplete.
Glucose variability, or the measurement of the fluctuations throughout a 24-hour period is a metric (often reported as coefficient of variation or CV) that you can see with CGM data. A greater degree of glucose variability can lead to adverse maternal and fetal outcomes.1 CGM data will better reveal the extent of the glucose variability, so you can determine the appropriate course of action to reduce the risk of complications.
3. Post-meal peaks
With the one- or two-hour postprandial readings, you are getting a clue about how effectively your patient is reaching target levels. But you may be missing the peak, especially if you’re looking at two-hour data.
Ideally, the glucose levels of your patients would rarely exceed 140 mg/dL.2 After a high-carbohydrate meal with low protein and fat, an immediate glucose spike and drop may occur. A fingerstick two hours later may not capture the glucose excursion, but CGM data will convey the whole postprandial story.
4. Post-snack numbers
Let’s face it, your patients already feel overwhelmed by post-meal fingersticks. It’s not likely that they’ll be checking blood sugars after snacks. That means the impact of a snack on blood sugar is often unknown.
Again, CGM data shows the impact of an entire day of eating, not just the few times that the fingersticks are performed. Not only is this useful for determining patterns, but the data can be used to inform your diabetes and nutrition education.
5. Trending numbers
Spotting a trend in glucose is difficult with limited data points given by fingersticks. Whether glucose is dropping or on its way up, one snapshot isn’t enough to demonstrate trends.
Not only does the CGM provide alerts to the user to notify them of an upward or downward trend, but providers can see potential problem areas when reviewing data. Knowing the trend can prevent over- or under-correction of glucose levels.
6. Impact of exercise
A daily workout may reduce fasting or post-meal glucose levels, but it may be difficult for patients to make a direct link to the benefit of performing the activity.
Seeing the real-time glucose data after physical activity has been shown to reinforce positive behavior changes.3 It can also be helpful to guide when exercise would be most beneficial. In addition, treatment can be more fine-tuned to support physical activity.
7. Time in range
Lastly, fingersticks give you four or five snapshots of what the glucose is doing in 24 hours. That’s it. Meanwhile, CGM data captured every 5 minutes provides a robust and full picture. You get an exact measurement of how much time your patient spends in range.
There is a caveat to this metric. Typical data reports show time in range (TIR) as 70-180 mg/dL, which does not match the recommendations for the range in pregnancy. Research supports a target range closer to glucose levels not complicated by diabetes, which is between 63-140 mg/dL.4
While your patient can adjust their target in most CGM apps, the provider-facing comprehensive reports still show the target as 70-180 mg/dL. This makes data reviews more time-intensive.
In the end, while finger sticks serve as a valuable baseline, they also lack a significant amount of data that modern CGMs tools can offer, which can help paint a more complete picture of patient health for OB/GYNs.
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LilyLink simplifies gestational diabetes care by providing data only for pregnancy-specific targets. We have designed a patient app that automates postprandial logging, has AI carbohydrate calculations, and provides educational nudges.
Our provider portal distills the data for you to quickly see the accurate TIR for pregnancy. No more spotty paper logs or trying to decipher postprandials on tiny, illegible graphs.
If you’d like a demo to see how LilyLink can elevate your gestational diabetes management, contact us!
References:
- Tano S, Kotani T, Inamura T, et al. Glucose variability as a key mediator in the relationship between pre-pregnancy overweight/obesity and late-onset hypertensive disorders of pregnancy. Sci Rep. 2025;15(1):18123. Published 2025 May 24. doi:10.1038/s41598-025-02965-1
- Durnwald C, Beck RW, Li Z, et al. Continuous Glucose Monitoring-Derived Differences in Pregnancies With and Without Adverse Perinatal Outcomes. Obstet Gynecol. 2024;144(5):684-696. doi:10.1097/AOG.0000000000005668
- Liao Y, Basen-Engquist KM, Urbauer DL, Bevers TB, Hawk E, Schembre SM. Using Continuous Glucose Monitoring to Motivate Physical Activity in Overweight and Obese Adults: A Pilot Study. Cancer Epidemiol Biomarkers Prev. 2020;29(4):761-768. doi:10.1158/1055-9965.EPI-19-0906
- Castorino K, Durnwald C, Ehrenberg S, et al. Practical Considerations for Using Continuous Glucose Monitoring in Patients with Gestational Diabetes Mellitus. J Womens Health (Larchmt). 2025;34(1):10-20. doi:10.1089/jwh.2023.0864
