{"doi":"10.2337/dc22-2297","title":"A Randomized Crossover Trial to Compare Automated Insulin Delivery (the Artificial Pancreas) With Carbohydrate Counting or Simplified Qualitative Meal-Size Estimation in Type 1 Diabetes","abstract":"OBJECTIVE: Qualitative meal-size estimation has been proposed instead of quantitative carbohydrate (CHO) counting with automated insulin delivery. We aimed to assess the noninferiority of qualitative meal-size estimation strategy. RESEARCH DESIGN AND METHODS: We conducted a two-center, randomized, crossover, noninferiority trial to compare 3 weeks of automated insulin delivery with 1) CHO counting and 2) qualitative meal-size estimation in adults with type 1 diabetes. Qualitative meal-size estimation categories were low, medium, high, or very high CHO and were defined as <30 g, 30-60 g, 60-90 g, and >90 g CHO, respectively. Prandial insulin boluses were calculated as the individualized insulin to CHO ratios multiplied by 15, 35, 65, and 95, respectively. Closed-loop algorithms were otherwise identical in the two arms. The primary outcome was time in range 3.9-10.0 mmol/L, with a predefined noninferiority margin of 4%. RESULTS: A total of 30 participants completed the study (n = 20 women; age 44 (SD 17) years; A1C 7.4% [0.7%]). The mean time in the 3.9-10.0 mmol/L range was 74.1% (10.0%) with CHO counting and 70.5% (11.2%) with qualitative meal-size estimation; mean difference was -3.6% (8.3%; noninferiority P = 0.78). Frequencies of times at <3.9 mmol/L and <3.0 mmol/L were low (<1.6% and <0.2%) in both arms. Automated basal insulin delivery was higher in the qualitative meal-size estimation arm (34.6 vs. 32.6 units/day; P = 0.003). CONCLUSIONS: Though the qualitative meal-size estimation method achieved a high time in range and low time in hypoglycemia, noninferiority was not confirmed.","journal":"Diabetes Care","year":2023,"id":331054,"datarank":1.9445337274313284,"base_score":3.4657359027997265,"endowment":3.4657359027997265,"self_citation_contribution":0.519860385419959,"citation_network_contribution":1.4246733420113693,"self_endowment_contribution":0.519860385419959,"citer_contribution":1.4246733420113693,"corpus_percentile":null,"corpus_rank":null,"citation_count":31,"citer_count":28,"citers_with_citation_signal":22,"citers_with_endowment":22,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9412,"is_data_producer":true,"deposit_databanks":{"figshare":["10.2337/figshare.22595938"]},"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":452479,"name":"Laurent Legault","orcid":"0000-0003-1767-1997","position":1,"is_corresponding":false},{"id":1055467,"name":"Marie Raffray","orcid":"0000-0001-5596-4200","position":2,"is_corresponding":false},{"id":1055860,"name":"Nikita Gouchie‐Provencher","orcid":null,"position":3,"is_corresponding":false},{"id":898241,"name":"Adnan Jafar","orcid":"0000-0001-5579-2496","position":4,"is_corresponding":false},{"id":1055468,"name":"Marie‐Françoise Devaux","orcid":"0000-0003-4687-3432","position":5,"is_corresponding":false},{"id":1055861,"name":"Milad Ghanbari","orcid":null,"position":6,"is_corresponding":false},{"id":452481,"name":"Rémi Rabasa‐Lhoret","orcid":"0000-0003-4706-5170","position":7,"is_corresponding":false},{"id":898244,"name":"Ahmad Haidar","orcid":"0000-0002-6700-0385","position":0,"is_corresponding":true}],"reference_count":32,"raw_metadata":null,"created_at":"2026-07-19T01:09:14.691473Z","pmid":"37134305","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}