Evidence map›Paper›PMID 28459617›Full record

Trial reportDiabetes technology & therapeutics2017

Outpatient Closed-Loop Control with Unannounced Moderate Exercise in Adolescents Using Zone Model Predictive Control.

Lauren M Huyett, Trang T Ly, Gregory P Forlenza, Suzette Reuschel-DiVirgilio, Laurel H Messer, R Paul Wadwa, Ravi Gondhalekar, Francis J Doyle, Jordan E Pinsker, David M Maahs and 2 more

Abstract readClinical Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Diabetes technology & therapeutics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 1 pooled it
7.4field-weighted citation impact, top 2% 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

32 citing papers in PubMed, 1 synthesis or guideline pooled it, 66 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Trial
  4. Trial
  5. Trial
  6. Trial
  7. Trial
  8. Trial
  9. Metabolic Models, in Silico Trials, and Algorithms.Journal of diabetes science and technology · 2025
    Review
  10. Early Stages of Automated Insulin Delivery.Journal of diabetes science and technology · 2025
    Review
  11. Early Stages of Automated Insulin Delivery.Diabetes technology & therapeutics · 2025
    Review
  12. Metabolic Models, in Silico Trials, and Algorithms.Diabetes technology & therapeutics · 2025
    Review
  13. Article
  14. Review
  15. Article
  16. Article
  17. Review
  18. Time in range centered diabetes care.Clinical pediatric endocrinology : case reports and clinical investigations : official journal of the Japanese Society for Pediatric Endocrinology · 2021
    Review
  19. Article
  20. Article
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

12 authors at 3 institutions in 1 country.

Lauren M Huyett1 Department of Chemical Engineering, University of California Santa Barbara , Santa Barbara, California.
Trang T Ly3 Department of Pediatrics, Division of Pediatric Endocrinology and Diabetes, Stanford University , Stanford, California.
Gregory P Forlenza4 Barbara Davis Center for Childhood Diabetes, University of Colorado Anschutz Medical Campus , Aurora, Colorado.
Suzette Reuschel-DiVirgilio3 Department of Pediatrics, Division of Pediatric Endocrinology and Diabetes, Stanford University , Stanford, California.
Laurel H Messer4 Barbara Davis Center for Childhood Diabetes, University of Colorado Anschutz Medical Campus , Aurora, Colorado.
R Paul Wadwa4 Barbara Davis Center for Childhood Diabetes, University of Colorado Anschutz Medical Campus , Aurora, Colorado.
Ravi Gondhalekar2 William Sansum Diabetes Center , Santa Barbara, California.
Francis J Doyle1 Department of Chemical Engineering, University of California Santa Barbara , Santa Barbara, California.
Jordan E Pinsker2 William Sansum Diabetes Center , Santa Barbara, California.
David M Maahs3 Department of Pediatrics, Division of Pediatric Endocrinology and Diabetes, Stanford University , Stanford, California.
Bruce A Buckingham3 Department of Pediatrics, Division of Pediatric Endocrinology and Diabetes, Stanford University , Stanford, California.
Eyal Dassau1 Department of Chemical Engineering, University of California Santa Barbara , Santa Barbara, California.
Sansum Diabetes Research Institute · USUniversity of Colorado Anschutz Medical Campus · USUniversity of California, Santa Barbara · US

Funding

Ambulatory Artificial Pancreas: merging physiology, behavior, and control designDP3DK094331 · NIDDK · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI BASU, ANANDA, DOYLE, FRANCIS J · 2011 to 2014
$4.6M
K12 Career DevelopmentK12DK094712 · NIDDK · UNIVERSITY OF COLORADO DENVER · PI STECK, ANDREA · 2011 to 2021
$4.5M
Pediatric Artificial Pancreas System for Enhanced Diabetes Management in Young Children with Type 1 DiabetesDP3DK104057 · NIDDK · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI DASSAU, EYAL, DOYLE, FRANCIS J · 2014 to 2014
$1.8M
NIDDK NIH HHS DP3 DK104057NIDDK NIH HHS K12 DK094712
6 · The paper itself

Abstract

backgroundThe artificial pancreas (AP) has the potential to improve glycemic control in adolescents. This article presents the first evaluation in adolescents of the Zone Model Predictive Control and Health Monitoring System (ZMPC+HMS) AP algorithms, and their first evaluation in a supervised outpatient setting with frequent exercise. MATERIALS AND

methodsAdolescents with type 1 diabetes underwent 3 days of closed-loop control (CLC) in a hotel setting with the ZMPC+HMS algorithms on the Diabetes Assistant platform. Subjects engaged in twice-daily exercise, including soccer, tennis, and bicycling. Meal size (unrestricted) was estimated and entered into the system by subjects to trigger a bolus, but exercise was not announced.

resultsTen adolescents (11.9-17.7 years) completed 72 h of CLC, with data on 95 ± 14 h of sensor-augmented pump (SAP) therapy before CLC as a comparison to usual therapy. The percentage of time with continuous glucose monitor (CGM) 70-180 mg/dL was 71% ± 10% during CLC, compared to 57% ± 16% during SAP (P = 0.012). Nocturnal control during CLC was safe, with 0% (0%, 0.6%) of time with CGM <70 mg/dL compared to 1.1% (0.0%, 14%) during SAP. Despite large meals (estimated up to 120 g carbohydrate), only 8.0% ± 6.9% of time during CLC was spent with CGM >250 mg/dL (16% ± 14% during SAP). The system remained connected in CLC for 97% ± 2% of the total study time. No adverse events or severe hypoglycemia occurred.

conclusionsThe use of the ZMPC+HMS algorithms is feasible in the adolescent outpatient environment and achieved significantly more time in the desired glycemic range than SAP in the face of unannounced exercise and large announced meal challenges.

Indexed as

Adolescent BehaviorAdolescent Nutritional Physiological PhenomenaExercisePancreas, ArtificialActivities of Daily LivingAdolescentAlgorithmsChildChild BehaviorChild Nutritional Physiological PhenomenaCohort StudiesCombined Modality TherapyDiabetes Mellitus, Type 1Feasibility StudiesFemaleFood PreferencesAdolescentAlgorithmsArtificial pancreasExerciseType 1 diabetes

Identifiers

PMID28459617
PMCPMC5510043
OpenAlexW2610632420

What Socratic holds

Textmetadata
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 Socratic graph.