Evidence map›Paper›PMID 33112249›Full record

Trial reportJMIR mHealth and uHealth2020

Carbohydrate Counting App Using Image Recognition for Youth With Type 1 Diabetes: Pilot Randomized Control Trial.

Jeffrey E Alfonsi, Elizabeth E Y Choi, Taha Arshad, Stacie-Ann S Sammott, Vanita Pais, Cynthia Nguyen, Bryan R Maguire, Jennifer N Stinson, Mark R Palmert

Registry-linked trialOpen access · goldAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04354142 (iSpy), which is not on this map. Cited by 45 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 7 pooled it
9.7field-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.

NCT04354142 nacompletednot on this map

iSpy: A Pilot Randomized Control Trial of a Novel Carbohydrate Counting Smartphone App for Youth With Type 1 Diabetes

TypeinterventionalSponsorThe Hospital for Sick ChildrenRan2018 to 2020Enrolled46ConditionsDiabetes Mellitus, Type 1ArmsiSpy
3 · Its place in the literature

Who cites it

45 citing papers in PubMed, 7 syntheses or guidelines pooled it, 82 citations in OpenAlex.

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  19. [Artificial intelligence in preventive medicine for children and adolescents-applications and acceptance].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025
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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

9 authors at 4 institutions in 1 country.

Jeffrey E AlfonsiDepartment of Medicine, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.ORCID 0000-0002-6305-9195
Elizabeth E Y ChoiInner Analytics Inc, Toronto, ON, Canada.ORCID 0000-0003-0531-8133
Taha ArshadDivision of Endocrinology, Hospital for Sick Children, Toronto, ON, Canada.ORCID 0000-0002-1673-301X
Stacie-Ann S SammottDivision of Endocrinology, Hospital for Sick Children, Toronto, ON, Canada.ORCID 0000-0001-9092-5231
Vanita PaisDivision of Endocrinology, Hospital for Sick Children, Toronto, ON, Canada.ORCID 0000-0002-7142-0972
Cynthia NguyenPeter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada.ORCID 0000-0001-9741-2463
Bryan R MaguireBiostatistics Design and Analysis Unit, SickKids Research Institute, Toronto, ON, Canada.ORCID 0000-0003-2855-2563
Jennifer N StinsonPeter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada.ORCID 0000-0002-9969-8052
Mark R PalmertDivision of Endocrinology, Hospital for Sick Children, Toronto, ON, Canada.ORCID 0000-0002-4096-0685
Hospital for Sick Children · CAUniversity of Toronto · CASickKids Foundation · CAWestern University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCarbohydrate counting is an important component of diabetes management, but it is challenging, often performed inaccurately, and can be a barrier to optimal diabetes management. iSpy is a novel mobile app that leverages machine learning to allow food identification through images and that was designed to assist youth with type 1 diabetes in counting carbohydrates.

objectiveOur objective was to test the app's usability and potential impact on carbohydrate counting accuracy.

methodsIterative usability testing (3 cycles) was conducted involving a total of 16 individuals aged 8.5-17.0 years with type 1 diabetes. Participants were provided a mobile device and asked to complete tasks using iSpy app features while thinking aloud. Errors were noted, acceptability was assessed, and refinement and retesting were performed across cycles. Subsequently, iSpy was evaluated in a pilot randomized controlled trial with 22 iSpy users and 22 usual care controls aged 10-17 years. Primary outcome was change in carbohydrate counting ability over 3 months. Secondary outcomes included levels of engagement and acceptability. Change in HbA

resultsUse of iSpy was associated with improved carbohydrate counting accuracy (total grams per meal, P=.008), reduced frequency of individual counting errors greater than 10 g (P=.047), and lower HbA

conclusionsOur results provide evidence of efficacy and high acceptability of a novel carbohydrate counting app, supporting the advancement of digital health apps for diabetes care among youth with type 1 diabetes. Further testing is needed, but iSpy may be a useful adjunct to traditional diabetes management.

trial registrationClinicalTrials.gov NCT04354142; https://clinicaltrials.gov/ct2/show/NCT04354142.

Indexed as

Diabetes Mellitus, Type 1Mobile ApplicationsNutrition TherapyAdolescentCarbohydratesChildHumansPilot ProjectsCarbohydratescarbohydrate countingdigital health applications (apps)image recognitionmHealthtype 1 diabetesyouth

Identifiers

PMID33112249
PMCPMC7657721
OpenAlexW3097655478

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.