{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T10:28:33Z","timestamp":1794306513977,"version":"4.9.0"},"reference-count":35,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T00:00:00Z","timestamp":1789862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T00:00:00Z","timestamp":1789862400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Diabetes Sci Technol"],"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Over-the-counter continuous glucose monitors (OTC CGMs) are increasingly used by individuals without diabetes for lifestyle modification, weight management, athletics, and metabolic screening, and have potential as an affordable tool for glucose data collection in research. However, accuracy and agreement data in their intended populations are limited. No independent study has yet evaluated OTC CGM accuracy or agreement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method:<\/jats:title>\n                    <jats:p>We evaluated OTC CGMs worn by participants (n\u2009=\u200939) without diabetes across sessions combining rest or exercise with placebo or sugar-sweetened drink consumption that mimic scenarios in which CGM values may be used to make health-related decisions. We assessed agreement with capillary-meter glucose, interparticipant differences in CGM performance, and delay of CGM recordings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      Overall mean absolute relative difference (MARD) was 12.9%, mean bias \u22124.8 mg\/dL, and within-15%\/15-mg\/dL agreement 66.5%. Agreement was significantly worse under sugar than placebo consumption (MARD 15.3% vs 10.6%,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      \u2009&lt;\u2009.01). Exercise had no consistent effect. Disagreement increased with faster rates of change and, less consistently, higher glucose. A uniform 5-minute delay improved MARD to 11.2% (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      \u2009&lt;\u2009.001), and cross-correlation estimated a 6.2-minute population lag. Interparticipant agreement differed significantly, unattributable to measured biological variables. Participant\/device identity explained direction of error more than magnitude. High-MARD participants did not have consistent high MARD across conditions. 13.8% of CGM-change-indicator directional arrows were classified as moderate to extreme risk.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>Over-the-counter continuous glucose monitors seemingly agreed with overall capillary glucose trends but were less reliable for exact-value agreement. They may aid lifestyle monitoring but warrant caution for diagnostic thresholds, real-time change-indicator use, and research.<\/jats:p>\n                    <jats:p>\n                      We analyze data from a clinical trial: ClinicalTrials.gov,\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/clinicaltrials.gov\/study\/NCT07255183?id=NCT07255183\">https:\/\/clinicaltrials.gov\/study\/NCT07255183?id=NCT07255183<\/jats:ext-link>\n                      , NCT07255183.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical Impact:<\/jats:title>\n                    <jats:p>Over-the-counter continuous glucose monitors are reaching patients through primary care recommendations and direct consumer purchase, often before any structured education on interpretation. This first independent evaluation in adults without diabetes shows that OTC CGMs agree reasonably well with capillary trends but are less reliable for exact values, particularly after carbohydrate intake, at higher glucose levels, and during rapid change. Clinicians can reasonably guide patients toward using OTC CGMs to observe postprandial trends and behavioral patterns that fingerstick logs cannot practically capture, while cautioning against threshold-based decisions such as hypoglycemia detection or self-screening for diabetes, which warrant confirmatory testing.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/19322968261482150","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T06:34:12Z","timestamp":1789972452000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Agreement of Over-the-Counter Continuous Glucose Monitors With Capillary-Meter Glucose in Young Adults Without Diabetes"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5175-000X","authenticated-orcid":false,"given":"Navneet","family":"Prakash","sequence":"first","affiliation":[{"name":"Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea H.","family":"Barrows","sequence":"additional","affiliation":[{"name":"Exercise Science Program, University of South Florida, Tampa, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew","family":"Stults-Kolehmainen","sequence":"additional","affiliation":[{"name":"Division of Digestion, Yale-New Haven Hospital, CT, USA"},{"name":"Department of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew J.","family":"Loza","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcus W.","family":"Kilpatrick","sequence":"additional","affiliation":[{"name":"Exercise Science Program, University of South Florida, Tampa, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8655-7525","authenticated-orcid":false,"given":"Garrett I.","family":"Ash","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA"},{"name":"Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA"},{"name":"Pain, Research, Informatics, Medical Comorbidities and Education Center, Veterans Affairs Connecticut Healthcare System, West Haven, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,9,20]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.2337\/dc19-1459"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1210\/jc.2019-00240"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2020.00578"},{"key":"e_1_3_5_5_2","unstructured":"Riddell MC Skroce K Turner LV Zignoli A Zisser HC Hinkley JM. 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