Evidence map›Paper›PMID 37590491›Full record

ArticleJMIR diabetes2023

Impact of a Combined Continuous Glucose Monitoring-Digital Health Solution on Glucose Metrics and Self-Management Behavior for Adults With Type 2 Diabetes: Real-World, Observational Study.

Abhimanyu B Kumbara, Anand K Iyer, Courtney R Green, Lauren H Jepson, Keri Leone, Jennifer E Layne, Mansur Shomali

Open access · goldAbstract read
In one paragraph

Article in JMIR diabetes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
4.6field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Observational
  5. Observational
  6. Review
  7. Review
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Observational
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Abhimanyu B KumbaraWelldoc, Inc, Columbia, MD, United States.ORCID https://orcid.org/0000-0003-3356-9001
Anand K IyerWelldoc, Inc, Columbia, MD, United States.ORCID https://orcid.org/0009-0002-6558-5532
Courtney R GreenDexcom, Inc, San Diego, CA, United States.ORCID https://orcid.org/0000-0002-9279-0649
Lauren H JepsonDexcom, Inc, San Diego, CA, United States.ORCID https://orcid.org/0000-0003-2773-5157
Keri LeoneDexcom, Inc, San Diego, CA, United States.ORCID https://orcid.org/0000-0003-3749-3577
Jennifer E LayneDexcom, Inc, San Diego, CA, United States.ORCID https://orcid.org/0000-0001-7144-7051
Mansur ShomaliWelldoc, Inc, Columbia, MD, United States.ORCID https://orcid.org/0000-0002-7127-6683
Dexcom (United States) · USCASI Pharmaceuticals (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe BlueStar (Welldoc) digital health solution for people with diabetes incorporates data from multiple devices and generates coaching messages using artificial intelligence. The BlueStar app syncs glucose data from the G6 (Dexcom) real-time continuous glucose monitoring (RT-CGM) system, which provides a glucose measurement every 5 minutes.

objectiveThe objective of this real-world study of people with type 2 diabetes (T2D) using the digital health solution and RT-CGM was to evaluate change in glycemic control and engagement with the program over 3 months.

methodsParticipants were current or former enrollees in an employer-sponsored health plan, were aged 18 years or older, had a T2D diagnosis, and were not using prandial insulin. Outcomes included CGM-based glycemic metrics and engagement with the BlueStar app, including logging medications taken, exercise, food details, blood pressure, weight, and hours of sleep.

resultsParticipants in the program that met our analysis criteria (n=52) were aged a mean of 53 (SD 9) years; 37% (19/52) were female and approximately 50% (25/52) were taking diabetes medications. The RT-CGM system was worn 90% (SD 8%) of the time over 3 months. Among individuals with suboptimal glycemic control at baseline, defined as mean glucose >180 mg/dL, clinically meaningful improvements in glycemic control were observed, including reductions in a glucose management indicator (-0.8 percentage points), time above range 181-250 mg/dL (-4.4 percentage points) and time above range >250 mg/dL (-14 percentage points; all P<.05). Time in range 70-180 mg/dL also increased by 15 percentage points (P=.016) in this population, which corresponds to an increase of approximately 3.5 hours per day in the target range. Over the 3-month study, 29% (15/52) of participants completed at least one engagement activity per week. Medication logging was completed most often by participants (23/52, 44%) at a rate of 12.1 (SD 0.8) events/week, and this was closely followed by exercise and food logging.

conclusionsThe combination of an artificial intelligence-powered digital health solution and RT-CGM helped people with T2D improve their glycemic outcomes and diabetes self-management behaviors.

Indexed as

artificial intelligencecontinuous glucose monitoringdiabetes managementdigital healthdigital health interventionengagementglycemic outcomesmHealthtype 2 diabetes

Identifiers

PMID37590491
PMCPMC10520761
OpenAlexW4385852268

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.