Ignite Creation Date:
2026-03-26 @ 3:17 PM
Ignite Modification Date:
2026-03-30 @ 12:24 AM
Study NCT ID:
NCT07348432
Status:
RECRUITING
Last Update Posted:
2026-01-16
First Post:
2025-09-22
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
The diabEAT Study: Insulin dElivery Technologies And eaTing Behaviours in People With Type 1 Diabetes
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003922', 'term': 'Diabetes Mellitus, Type 1'}, {'id': 'D001068', 'term': 'Feeding and Eating Disorders'}, {'id': 'D005247', 'term': 'Feeding Behavior'}, {'id': 'D001327', 'term': 'Autoimmune Diseases'}, {'id': 'D004700', 'term': 'Endocrine System Diseases'}], 'ancestors': [{'id': 'D003920', 'term': 'Diabetes Mellitus'}, {'id': 'D044882', 'term': 'Glucose Metabolism Disorders'}, {'id': 'D008659', 'term': 'Metabolic Diseases'}, {'id': 'D009750', 'term': 'Nutritional and Metabolic Diseases'}, {'id': 'D007154', 'term': 'Immune System Diseases'}, {'id': 'D012817', 'term': 'Signs and Symptoms, Digestive'}, {'id': 'D012816', 'term': 'Signs and Symptoms'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}, {'id': 'D001523', 'term': 'Mental Disorders'}, {'id': 'D001522', 'term': 'Behavior, Animal'}, {'id': 'D001519', 'term': 'Behavior'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D007316', 'term': 'Insemination, Artificial, Heterologous'}, {'id': 'D016503', 'term': 'Drug Delivery Systems'}], 'ancestors': [{'id': 'D007315', 'term': 'Insemination, Artificial'}, {'id': 'D027724', 'term': 'Reproductive Techniques, Assisted'}, {'id': 'D012099', 'term': 'Reproductive Techniques'}, {'id': 'D013812', 'term': 'Therapeutics'}, {'id': 'D008919', 'term': 'Investigative Techniques'}, {'id': 'D007314', 'term': 'Insemination'}, {'id': 'D012098', 'term': 'Reproduction'}, {'id': 'D055703', 'term': 'Reproductive Physiological Phenomena'}, {'id': 'D012101', 'term': 'Reproductive and Urinary Physiological Phenomena'}, {'id': 'D004358', 'term': 'Drug Therapy'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'CROSS_SECTIONAL', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 106}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2024-07-29', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-01', 'completionDateStruct': {'date': '2026-05-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-01-12', 'studyFirstSubmitDate': '2025-09-22', 'studyFirstSubmitQcDate': '2026-01-12', 'lastUpdatePostDateStruct': {'date': '2026-01-16', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-01-16', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-01-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Disordered Eating Behaviours', 'timeFrame': 'Collected at one time point per participant at the time of survey completion. The study has an observational, cross-sectional design from April 2024 to estimated May 2026.', 'description': 'The primary outcome variable disordered eating behaviours will be measured through the Three Factor Eating Questionnaire-Revised 21 (TFEQ) in adults. The primary outcome for youth (12 to 17y) is the Child Three Factor Eating Questionnaire 17, which is an adapted questionnaire from the TFEQ.\n\nTFEQ refers to current dietary practice and measures 3 different aspects of eating behaviour: cognitive dietary restraint (conscious restriction of food intake) score ranges from 6 to 24, uncontrolled eating (tendency to eat more than usual due to loss of control over intake accompanied by subjective feelings of hunger) score ranges from 9 to 36, and emotional eating (inability to resist emotional cues) score ranges from 6 to 24. Higher scores in the respective scales indicate greater level of restrained, uncontrolled, or emotional eating.'}, {'measure': 'Diabetes Disordered Eating Behaviours', 'timeFrame': 'Collected at one time point per participant at the time of survey completion. The study has an observational, cross-sectional design from April 2024 to estimated May 2026', 'description': 'The Diabetes Eating Problem Survey (DEPS-R) is a 16-item questionnaire for youth and adults over the age of 12 years. It refers to disordered eating behaviours specific to people with type 1 diabetes. The score ranges from 0 to 80, with a higher score indicating higher risk of disordered eating.'}, {'measure': 'Orthorexia Eating Behaviours', 'timeFrame': 'Collected at one time point per participant at the time of survey completion. The study has an observational, cross-sectional design from April 2024 to estimated May 2026', 'description': "The Teruel Orthorexia Scale is a 17-item questionnaire, that is validated in both English and French. It measures 'healthy orthorexia' behaviours as well as behaviours related to 'orthorexia nervosa'. Healthy orthorexia describes individuals who have a high interest in healthy eating, but in a non-pathological dimension of orthorexia. This sub scales score ranges from 0 to 27. Orthorexia nervosa includes behaviours that are related to healthy eating that may cause malnutrition, emotional distress, and social impairment. This subscale ranges from 0 to 24. A higher score represents more orthorexia symptoms."}], 'secondaryOutcomes': [{'measure': 'Glucose Time In Range (TIR)', 'timeFrame': 'Collected at one time point per participant at the time of survey completion. The study has an observational, cross-sectional design from April 2024 to estimated May 2026.', 'description': 'Time in Range (TIR) (%): glucose TIR is the percentage of time that glucose levels are between 3.9 and 10.0 mmol/L. A higher TIR indicates improved glycemic levels. Guidelines recommend a TIR greater than 70% for most PwT1D.'}, {'measure': 'Coefficient of Variation (CV)', 'timeFrame': 'Collected at one time point per participant at the time of survey completion. The study has an observational, cross-sectional design from April 2024 to estimated May 2026.', 'description': 'Coefficient of Variation (CV) is determined by dividing the standard deviation of blood glucose levels over a 14-day period, by the total mean glucose and multiplying by 100 to obtain a percentage. This value reflects the variability of glucose levels. A higher CV value indicates more glycemic variability.'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Automated Insulin Delivery', 'Disordered Eating Behaviours', 'Nutrition', 'Type 1 Diabetes'], 'conditions': ['Insulin Dependent Diabetes Mellitus', 'Feeding and Eating Disorders', 'Eating Behavior', 'Type 1 Diabetes', 'Autoimmune Diseases', 'Endocrine System Diseases']}, 'referencesModule': {'references': [{'pmid': '34368518', 'type': 'BACKGROUND', 'citation': 'Aiello EM, Deshpande S, Ozaslan B, Wolkowicz KL, Dassau E, Pinsker JE, Doyle FJ. Review of Automated Insulin Delivery Systems for Individuals with Type 1 Diabetes: Tailored Solutions for Subpopulations. Curr Opin Biomed Eng. 2021 Sep;19:100312. doi: 10.1016/j.cobme.2021.100312. Epub 2021 Jun 18.'}, {'pmid': '24622717', 'type': 'BACKGROUND', 'citation': 'Bell KJ, Barclay AW, Petocz P, Colagiuri S, Brand-Miller JC. Efficacy of carbohydrate counting in type 1 diabetes: a systematic review and meta-analysis. Lancet Diabetes Endocrinol. 2014 Feb;2(2):133-40. doi: 10.1016/S2213-8587(13)70144-X. Epub 2013 Oct 25.'}, {'pmid': '31980116', 'type': 'BACKGROUND', 'citation': 'Boughton CK, Hovorka R. Automated Insulin Delivery in Adults. Endocrinol Metab Clin North Am. 2020 Mar;49(1):167-178. doi: 10.1016/j.ecl.2019.10.007. Epub 2019 Dec 16.'}, {'pmid': '23146371', 'type': 'BACKGROUND', 'citation': 'Brazeau AS, Mircescu H, Desjardins K, Leroux C, Strychar I, Ekoe JM, Rabasa-Lhoret R. Carbohydrate counting accuracy and blood glucose variability in adults with type 1 diabetes. Diabetes Res Clin Pract. 2013 Jan;99(1):19-23. doi: 10.1016/j.diabres.2012.10.024. Epub 2012 Nov 10.'}, {'pmid': '29759100', 'type': 'BACKGROUND', 'citation': 'Bryant EJ, Thivel D, Chaput JP, Drapeau V, Blundell JE, King NA. Development and validation of the Child Three-Factor Eating Questionnaire (CTFEQr17). Public Health Nutr. 2018 Oct;21(14):2558-2567. doi: 10.1017/S1368980018001210. Epub 2018 May 15.'}, {'pmid': '35436364', 'type': 'BACKGROUND', 'citation': 'Builes-Montano CE, Ortiz-Cano NA, Ramirez-Rincon A, Rojas-Henao NA. Efficacy and safety of carbohydrate counting versus other forms of dietary advice in patients with type 1 diabetes mellitus: a systematic review and meta-analysis of randomised clinical trials. J Hum Nutr Diet. 2022 Dec;35(6):1030-1042. doi: 10.1111/jhn.13017. Epub 2022 May 11.'}, {'pmid': '19399021', 'type': 'BACKGROUND', 'citation': 'Cappelleri JC, Bushmakin AG, Gerber RA, Leidy NK, Sexton CC, Lowe MR, Karlsson J. Psychometric analysis of the Three-Factor Eating Questionnaire-R21: results from a large diverse sample of obese and non-obese participants. Int J Obes (Lond). 2009 Jun;33(6):611-20. doi: 10.1038/ijo.2009.74. Epub 2009 Apr 28.'}, {'pmid': '29752345', 'type': 'BACKGROUND', 'citation': 'Castle JR, El Youssef J, Wilson LM, Reddy R, Resalat N, Branigan D, Ramsey K, Leitschuh J, Rajhbeharrysingh U, Senf B, Sugerman SM, Gabo V, Jacobs PG. Randomized Outpatient Trial of Single- and Dual-Hormone Closed-Loop Systems That Adapt to Exercise Using Wearable Sensors. Diabetes Care. 2018 Jul;41(7):1471-1477. doi: 10.2337/dc18-0228. Epub 2018 May 11.'}, {'pmid': '26004091', 'type': 'BACKGROUND', 'citation': 'Dong D, Jackson T, Wang Y, Chen H. Spontaneous regional brain activity links restrained eating to later weight gain among young women. Biol Psychol. 2015 Jul;109:176-83. doi: 10.1016/j.biopsycho.2015.05.003. Epub 2015 May 21.'}, {'pmid': '34503600', 'type': 'BACKGROUND', 'citation': 'Frappier I, Jacob R, Panahi S, Larose D, Bryant EJ, Chaput JP, Thivel D, Drapeau V. Translation and validation of the Child Three-Factor Eating Questionnaire (CTFEQr17) in French-speaking Canadian children and adolescents. Public Health Nutr. 2022 Mar;25(3):543-553. doi: 10.1017/S136898002100392X. Epub 2021 Sep 10.'}, {'pmid': '32569476', 'type': 'BACKGROUND', 'citation': 'Fuchs J, Hovorka R. Closed-loop control in insulin pumps for type-1 diabetes mellitus: safety and efficacy. Expert Rev Med Devices. 2020 Jul;17(7):707-720. doi: 10.1080/17434440.2020.1784724. Epub 2020 Jul 3.'}, {'type': 'BACKGROUND', 'citation': 'Gagnon C, Aimé A, Bélanger C. French Validation of the Diabetes Eating Problem Survey-Revised (DEPS-R). Can J Diabetes. 2013;37(1):60. doi:10.1016/j.jcjd.2013.03.009'}, {'pmid': '18223612', 'type': 'BACKGROUND', 'citation': 'Hays NP, Roberts SB. Aspects of eating behaviors "disinhibition" and "restraint" are related to weight gain and BMI in women. Obesity (Silver Spring). 2008 Jan;16(1):52-8. doi: 10.1038/oby.2007.12.'}, {'pmid': '24022608', 'type': 'BACKGROUND', 'citation': 'Hanlan ME, Griffith J, Patel N, Jaser SS. Eating Disorders and Disordered Eating in Type 1 Diabetes: Prevalence, Screening, and Treatment Options. Curr Diab Rep. 2013 Sep 12:10.1007/s11892-013-0418-4. doi: 10.1007/s11892-013-0418-4. Online ahead of print.'}, {'pmid': '28626906', 'type': 'BACKGROUND', 'citation': 'Kahkoska AR, Mayer-Davis EJ, Hood KK, Maahs DM, Burger KS. Behavioural implications of traditional treatment and closed-loop automated insulin delivery systems in Type 1 diabetes: applying a cognitive restraint theory framework. Diabet Med. 2017 Nov;34(11):1500-1507. doi: 10.1111/dme.13407. Epub 2017 Sep 11.'}, {'pmid': '29744106', 'type': 'BACKGROUND', 'citation': 'Keane S, Clarke M, Murphy M, McGrath D, Smith D, Farrelly N, MacHale S. Disordered eating behaviour in young adults with type 1 diabetes mellitus. J Eat Disord. 2018 May 2;6:9. doi: 10.1186/s40337-018-0194-2. eCollection 2018.'}, {'pmid': '35049354', 'type': 'BACKGROUND', 'citation': 'Kaur RJ, Deshpande S, Pinsker JE, Gilliam WP, McCrady-Spitzer S, Zaniletti I, Desjardins D, Church MM, Doyle Iii FJ, Kremers WK, Dassau E, Kudva YC. Outpatient Randomized Crossover Automated Insulin Delivery Versus Conventional Therapy with Induced Stress Challenges. Diabetes Technol Ther. 2022 May;24(5):338-349. doi: 10.1089/dia.2021.0436. Epub 2022 Apr 25.'}, {'pmid': '30575114', 'type': 'BACKGROUND', 'citation': 'Lawton J, Blackburn M, Rankin D, Allen J, Campbell F, Leelarathna L, Tauschmann M, Thabit H, Wilinska ME, Hovorka R; APCam11 Consortium. The impact of using a closed-loop system on food choices and eating practices among people with Type 1 diabetes: a qualitative study involving adults, teenagers and parents. Diabet Med. 2019 Jun;36(6):753-760. doi: 10.1111/dme.13887. Epub 2019 Jan 29.'}, {'pmid': '36180717', 'type': 'BACKGROUND', 'citation': 'Maiano C, Aime A, Almenara CA, Gagnon C, Barrada JR. Psychometric properties of the Teruel Orthorexia Scale (TOS) among a French-Canadian adult sample. Eat Weight Disord. 2022 Dec;27(8):3457-3467. doi: 10.1007/s40519-022-01482-8. Epub 2022 Sep 30.'}, {'pmid': '23550556', 'type': 'BACKGROUND', 'citation': 'Markowitz JT, Alleyn CA, Phillips R, Muir A, Young-Hyman D, Laffel LM. Disordered eating behaviors in youth with type 1 diabetes: prospective pilot assessment following initiation of insulin pump therapy. Diabetes Technol Ther. 2013 May;15(5):428-33. doi: 10.1089/dia.2013.0008. Epub 2013 Apr 3.'}, {'pmid': '28303543', 'type': 'BACKGROUND', 'citation': 'Migueles JH, Cadenas-Sanchez C, Ekelund U, Delisle Nystrom C, Mora-Gonzalez J, Lof M, Labayen I, Ruiz JR, Ortega FB. Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations. Sports Med. 2017 Sep;47(9):1821-1845. doi: 10.1007/s40279-017-0716-0.'}, {'pmid': '24615054', 'type': 'BACKGROUND', 'citation': 'Martyn-Nemeth P, Quinn L, Hacker E, Park H, Kujath AS. Diabetes distress may adversely affect the eating styles of women with type 1 diabetes. Acta Diabetol. 2014 Aug;51(4):683-6. doi: 10.1007/s00592-014-0575-1. Epub 2014 Mar 11.'}, {'pmid': '36409539', 'type': 'BACKGROUND', 'citation': 'Moyen A, Rappaport AI, Fleurent-Gregoire C, Tessier AJ, Brazeau AS, Chevalier S. Relative Validation of an Artificial Intelligence-Enhanced, Image-Assisted Mobile App for Dietary Assessment in Adults: Randomized Crossover Study. J Med Internet Res. 2022 Nov 21;24(11):e40449. doi: 10.2196/40449.'}, {'pmid': '30862656', 'type': 'BACKGROUND', 'citation': 'Nip ASY, Reboussin BA, Dabelea D, Bellatorre A, Mayer-Davis EJ, Kahkoska AR, Lawrence JM, Peterson CM, Dolan L, Pihoker C; SEARCH for Diabetes in Youth Study Group. Disordered Eating Behaviors in Youth and Young Adults With Type 1 or Type 2 Diabetes Receiving Insulin Therapy: The SEARCH for Diabetes in Youth Study. Diabetes Care. 2019 May;42(5):859-866. doi: 10.2337/dc18-2420. Epub 2019 Mar 12.'}, {'pmid': '34326234', 'type': 'BACKGROUND', 'citation': 'Perkins BA, Sherr JL, Mathieu C. Type 1 diabetes glycemic management: Insulin therapy, glucose monitoring, and automation. Science. 2021 Jul 30;373(6554):522-527. doi: 10.1126/science.abg4502.'}, {'pmid': '36598841', 'type': 'BACKGROUND', 'citation': 'Petrovski G, Campbell J, Pasha M, Day E, Hussain K, Khalifa A, van den Heuvel T. Simplified Meal Announcement Versus Precise Carbohydrate Counting in Adolescents With Type 1 Diabetes Using the MiniMed 780G Advanced Hybrid Closed Loop System: A Randomized Controlled Trial Comparing Glucose Control. Diabetes Care. 2023 Mar 1;46(3):544-550. doi: 10.2337/dc22-1692.'}, {'pmid': '35853530', 'type': 'BACKGROUND', 'citation': 'Polonsky WH, Hood KK, Levy CJ, MacLeish SA, Hirsch IB, Brown SA, Bode BW, Carlson AL, Shah VN, Weinstock RS, Bhargava A, Jones TC, Aleppo G, Mehta SN, Laffel LM, Forlenza GP, Sherr JL, Huyett LM, Vienneau TE, Ly TT; Omnipod 5 Research Group. How introduction of automated insulin delivery systems may influence psychosocial outcomes in adults with type 1 diabetes: Findings from the first investigation with the Omnipod(R) 5 System. Diabetes Res Clin Pract. 2022 Aug;190:109998. doi: 10.1016/j.diabres.2022.109998. Epub 2022 Jul 16.'}, {'pmid': '33826771', 'type': 'BACKGROUND', 'citation': 'Priesterroth L, Grammes J, Clauter M, Kubiak T. Diabetes technologies in people with type 1 diabetes mellitus and disordered eating: A systematic review on continuous subcutaneous insulin infusion, continuous glucose monitoring and automated insulin delivery. Diabet Med. 2021 Jul;38(7):e14581. doi: 10.1111/dme.14581. Epub 2021 May 5.'}]}, 'descriptionModule': {'briefSummary': 'Type 1 diabetes is an autoimmune health condition that requires daily injections of insulin. Insulin allows the body to use energy from carbohydrates in food. Disordered eating behaviours, like restricting food intake to lose body weight, are more common in women and people with type 1 diabetes, compared to those without because they must practice carbohydrate counting. Carbohydrate counting means identifying, measuring, and planning carbohydrate intake to match insulin dosage. New technologies, such as automated insulin delivery (AID) systems adjust insulin delivery in a blood sugar responsive manner. AID is rapidly replacing conventional insulin delivery like injections or non-automated insulin pumps since it reduces management burden and improves blood sugar levels. It is not known if AID reduces food management and disordered eating behaviours. This study aims to: 1. investigate the relationship between AID and eating behaviours according to gender for youth (12 to 17 years), and adults (18 years and older). 2. Determine the limit of carbohydrate counting inaccuracy to maintain stable blood sugar levels according to insulin delivery method (AID, injections, or pumps). It is hypothesized that those who use AID will have lower disordered eating behaviours and will maintain stable blood sugar levels while allowing for higher carbohydrate counting inaccuracy. This will be a cross-sectional cohort study of people with type 1 diabetes who are 12 years of age or over. Participants will be recruited through the BETTER registry and social medias across Canada. This research is needed to improve nutrition guidelines for type 1 diabetes in the context of new technologies like AID. Evidence from this study may reduce food management burden, lower the risk of disordered eating behaviours, and prevent eating disorders and medical complications.', 'detailedDescription': "Introduction: Type 1 diabetes occurs when the pancreas cannot produce insulin and a person must be given insulin exogenously. Managing this health condition involves strict diet planning including carbohydrate counting (i.e. identifying and counting carbohydrates to match insulin dose). In addition, to high food management burden, frequent bodyweight monitoring, and weight gain related to insulin usage, makes people living with type 1 diabetes more susceptible to disordered eating behaviours (like intentional food restriction), compared to those without. Automated insulin delivery systems (AID), which automatically adjusts insulin as a response to continuously measured blood glucose levels has shown to improve quality of life, and improve glycemic levels, however it's impact on eating behaviours and diet is unknown. There is also a need to determine the carbohydrate counting inaccuracy threshold to maintain glycemic stability depending on the type of delivery system used (AID vs. Not).\n\nObjectives:\n\n1. Determine the relationship between AID and eating behaviours\n2. Determine the carbohydrate counting inaccuracy threshold to maintain glycemic stability and understand whether AID use modifies this relationship.\n\nMethods: This is an observational cross-section analysis of people with type 1 diabetes.\n\nEligibility criteria included: those living with type 1 diabetes for more than 1 year, using at least 2 insulin injections per day or using an insulin pump, who were living in Canada, 12 years of age or older, and using their current insulin delivery system for 3 months or more.\n\nParticipants are excluded if they were pregnant/currently breastfeeding and did not speak English or French.\n\nDemographic, and diabetes-related information (including AID use), are determined through an initial questionnaire, which takes about 15 minutes to complete. Disordered eating behaviours were determined through validated questionnaires. The Three Factor Eating Questionnaire (TFEQR-21) identified behaviours such as cognitive restraint (score ranged from 6 to 24), emotional eating (score ranged from 6 to 24), and uncontrolled eating (score ranged from 9 to 36). The Diabetes Eating Problem Survey Revised (DEPS-R) identified DEBs specific to diabetes (score ranged from 0 to 80 with \\> 20 representing those at risk of DEB). The Teruel Orthorexia Scale (TOS) identified orthorexia nervosa behaviours defined as an obsession with healthy eating which may lead to emotional impairments (score ranged from 0 to 24).\n\nDietary intake information will be collected through a 4 day picture food journal (3 weekdays and 1 weekend) application called Keenoa and analyzed by a Registered Dietitian.\n\nGlycemic outcomes such as glucose time in range (TIR), which measures the amount of time glucose levels are between 3.9-10.0mmol/L, and Coefficient Variation, were reported through a 14 day CGM report (Clarity, Dexcom, Medtronic).\n\nPhysical activity will be measured through an Actigraph GT3X for 8 days.\n\nDescriptive analysis will be completed at enrollment, to determine the mean (SD) socio-demographic information, eating behaviour scores and dietary intake (macro/micronutrient profile) by insulin delivery system (AID and injections/insulin pumps).\n\nMultivariate linear regression will be used to determine the relationship between AID compared to injections/insulin pumps and disordered eating behaviour scores.\n\nSecondly, carbohydrate counting inaccuracy will be determined by comparing participant reported carbohydrate counts measured in mean (SD) grams per meal to RD measured carbohydrate counts by analyzing 4-day dietary reports, as generated by Keenoa.\n\nCarbohydrate counting inaccuracy threshold will be determined through multivariate linear regression by exploring the relationship between percent carbohydrate counting inaccuracy and % of glucose Time in Range and Coefficient Variation. Type of insulin delivery will be used as an effect modifier to determine how this relationship is modified by AID and injections/insulin pumps."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'minimumAge': '12 Years', 'genderBased': True, 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'People living with type 1 diabetes', 'genderDescription': 'self-identified gender: boy/man, girl/woman, non-binary, other', 'healthyVolunteers': True, 'eligibilityCriteria': "Inclusion Criteria:\n\n* 12 years of age or older\n* Living in Canada\n* Living with type 1 diabetes for more than 1 year\n* Using at least 2 insulin injections per day or using an insulin pump\n* Using current insulin delivery system for 3 months or more\n\nExclusion Criteria:\n\n* Are pregnant or currently are breastfeeding\n* Don't speak French or English\n* Does not have a smart phone (to download applications)"}, 'identificationModule': {'nctId': 'NCT07348432', 'briefTitle': 'The diabEAT Study: Insulin dElivery Technologies And eaTing Behaviours in People With Type 1 Diabetes', 'organization': {'class': 'OTHER', 'fullName': 'McGill University'}, 'officialTitle': 'The diabEAT Study: Insulin dElivery Technologies And eaTing Behaviours in People With Type 1 Diabetes', 'orgStudyIdInfo': {'id': '23-07-045'}}, 'armsInterventionsModule': {'interventions': [{'name': 'Automated Insulin Delivery (AID) Systems', 'type': 'DEVICE', 'description': "AID automatically adjusts insulin delivery by using continuously measured blood glucose levels. AID use will be determined through the initial questionnaire through the following questions:\n\nDo you currently use the pump as an automated insulin delivery system (connected to a CGM with automated insulin adjustments)? Yes, a commercial AID with control IQ (Tandem) or SmartGuard (Medtronic) Yes, a non-commercial open-source do-it yourself AID (e.g., Loop) No, they use it as a manual (non-automated) pump or with a suspend on low functionality (e.g., Basal IQ) I prefer not to answer I don't know\n\nThe type of AID system (hybrid, advanced hybrid, etc.) will also be confirmed.\n\nThe exposure variable will be coded as a binary-categorical variable of AID use (yes or no) with no representing all other non-AID insulin pumps or injections."}, {'name': 'Carbohydrate Counting Inaccuracy Percentage', 'type': 'BEHAVIORAL', 'description': 'Carbohydrate counting inaccuracy: will be determined by subtracting the estimated carbohydrates (by participant) by the actual amount of carbohydrate (through diet analysis) divided by the actual amount of carbohydrate, multiplied by 100, to determined the percentage. Estimated carbohydrate counts will be entered at each meal and snack by the participant in a daily log provided to the participant. Carbohydrate amounts will be collected through a 4-day food diary through the phone application Keenoa (carb count from the app will be blinded to the participant), and reviewed by a research assistant with education in dietetics.'}]}, 'contactsLocationsModule': {'locations': [{'zip': 'H9X 3V9', 'city': 'Montreal', 'state': 'Quebec', 'status': 'RECRUITING', 'country': 'Canada', 'contacts': [{'name': 'Anne-Sophie Brazeau, PhD', 'role': 'CONTACT'}], 'facility': 'McGill University', 'geoPoint': {'lat': 45.50884, 'lon': -73.58781}}], 'centralContacts': [{'name': 'Courtney South, MSc', 'role': 'CONTACT', 'email': 'courtney.south@mail.mcgill.ca', 'phone': '705-527-4843'}, {'name': 'Anne-Sophie Brazeau, PhD', 'role': 'CONTACT', 'email': 'anne-sophie.brazeau@mcgill.ca', 'phone': '514-398-7848'}], 'overallOfficials': [{'name': 'Anne-Sophie Brazeau, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'McGill University'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'Deidentified data may be shared to other researchers upon request in order to ensure the study results are reproducible.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'McGill University', 'class': 'OTHER'}, 'collaborators': [{'name': 'Laval University', 'class': 'OTHER'}, {'name': 'Université de Montréal', 'class': 'OTHER'}, {'name': 'University of Windsor', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Associate Professor', 'investigatorFullName': 'Anne-Sophie Brazeau', 'investigatorAffiliation': 'McGill University'}}}}