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

BACKGROUND: Carbohydrate 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 tha...

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Autores principales: Alfonsi, Jeffrey E, Choi, Elizabeth E Y, Arshad, Taha, Sammott, Stacie-Ann S, Pais, Vanita, Nguyen, Cynthia, Maguire, Bryan R, Stinson, Jennifer N, Palmert, Mark R
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7657721/
https://www.ncbi.nlm.nih.gov/pubmed/33112249
http://dx.doi.org/10.2196/22074
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author Alfonsi, Jeffrey E
Choi, Elizabeth E Y
Arshad, Taha
Sammott, Stacie-Ann S
Pais, Vanita
Nguyen, Cynthia
Maguire, Bryan R
Stinson, Jennifer N
Palmert, Mark R
author_facet Alfonsi, Jeffrey E
Choi, Elizabeth E Y
Arshad, Taha
Sammott, Stacie-Ann S
Pais, Vanita
Nguyen, Cynthia
Maguire, Bryan R
Stinson, Jennifer N
Palmert, Mark R
author_sort Alfonsi, Jeffrey E
collection PubMed
description BACKGROUND: Carbohydrate 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. OBJECTIVE: Our objective was to test the app's usability and potential impact on carbohydrate counting accuracy. METHODS: Iterative 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(1c) level was also assessed. RESULTS: Use 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(1c) levels (P=.03). Qualitative interviews and acceptability scale scores were positive. No major technical challenges were identified. Moreover, 43% (9/21) of iSpy participants were still engaged, with usage at least once every 2 weeks, at the end of the study. CONCLUSIONS: Our 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 REGISTRATION: ClinicalTrials.gov NCT04354142; https://clinicaltrials.gov/ct2/show/NCT04354142
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spelling pubmed-76577212020-11-13 Carbohydrate Counting App Using Image Recognition for Youth With Type 1 Diabetes: Pilot Randomized Control Trial Alfonsi, Jeffrey E Choi, Elizabeth E Y Arshad, Taha Sammott, Stacie-Ann S Pais, Vanita Nguyen, Cynthia Maguire, Bryan R Stinson, Jennifer N Palmert, Mark R JMIR Mhealth Uhealth Original Paper BACKGROUND: Carbohydrate 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. OBJECTIVE: Our objective was to test the app's usability and potential impact on carbohydrate counting accuracy. METHODS: Iterative 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(1c) level was also assessed. RESULTS: Use 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(1c) levels (P=.03). Qualitative interviews and acceptability scale scores were positive. No major technical challenges were identified. Moreover, 43% (9/21) of iSpy participants were still engaged, with usage at least once every 2 weeks, at the end of the study. CONCLUSIONS: Our 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 REGISTRATION: ClinicalTrials.gov NCT04354142; https://clinicaltrials.gov/ct2/show/NCT04354142 JMIR Publications 2020-10-28 /pmc/articles/PMC7657721/ /pubmed/33112249 http://dx.doi.org/10.2196/22074 Text en ©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. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 28.10.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Alfonsi, Jeffrey E
Choi, Elizabeth E Y
Arshad, Taha
Sammott, Stacie-Ann S
Pais, Vanita
Nguyen, Cynthia
Maguire, Bryan R
Stinson, Jennifer N
Palmert, Mark R
Carbohydrate Counting App Using Image Recognition for Youth With Type 1 Diabetes: Pilot Randomized Control Trial
title Carbohydrate Counting App Using Image Recognition for Youth With Type 1 Diabetes: Pilot Randomized Control Trial
title_full Carbohydrate Counting App Using Image Recognition for Youth With Type 1 Diabetes: Pilot Randomized Control Trial
title_fullStr Carbohydrate Counting App Using Image Recognition for Youth With Type 1 Diabetes: Pilot Randomized Control Trial
title_full_unstemmed Carbohydrate Counting App Using Image Recognition for Youth With Type 1 Diabetes: Pilot Randomized Control Trial
title_short Carbohydrate Counting App Using Image Recognition for Youth With Type 1 Diabetes: Pilot Randomized Control Trial
title_sort carbohydrate counting app using image recognition for youth with type 1 diabetes: pilot randomized control trial
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7657721/
https://www.ncbi.nlm.nih.gov/pubmed/33112249
http://dx.doi.org/10.2196/22074
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