[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-421533-105":59,"doc-detail-421533-en":124},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":117,"head_meta":119,"extra_data":121,"updated_unix":123},105,"en","exploring-device-wear-criteria-to-characterize-glucose-patterns-in-prefrail-older-adults-without-diabetes","EXPLORING DEVICE WEAR CRITERIA TO - CHARACTERIZE GLUCOSE PATTERNS IN PREFRAIL OLDER ADULTS WITHOUT DIABETES","","Time-of-day and missingness effects were analyzed to improve estimates of mean ambulatory glucose in prefrail older adults without diabetes. Weekends showed higher mean glucose than weekdays, and the lowest levels occurred around 3 AM while the highest occurred around 3 PM. Baseline data from 37 START participants were used, with CGM worn up to 14 days. Missing data days had substantially lower mean glucose than complete-data days (p\u003C0.001).",{"@graph":69,"@context":116},[70,84,99],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/exploring-device-wear-criteria-to-characterize-glucose-patterns-in-prefrail-older-adults-without-diabetes/421533/",{"url":83,"name":65,"@type":85,"author":86,"headline":65,"publisher":89,"fileFormat":92,"inLanguage":63,"description":67,"dateModified":93,"datePublished":93,"encodingFormat":92,"isAccessibleForFree":94,"interactionStatistic":95},"DigitalDocument",{"name":87,"@type":88},"Maya Linwood","Person",{"url":74,"name":90,"@type":91},"DocShare","Organization","application/pdf","2026-09-28",true,{"@type":96,"interactionType":97,"userInteractionCount":4},"InteractionCounter",{"@type":98},"ViewAction",{"@type":100,"mainEntity":101},"FAQPage",[102,108,112],{"name":103,"@type":104,"acceptedAnswer":105},"Why study glucose patterns in prefrail older adults without diabetes?","Question",{"text":106,"@type":107},"Frail at-risk individuals may experience low blood sugar episodes that can accelerate frailty onset. The study examines how to accurately estimate ambulatory glucose levels using CGM in this population.","Answer",{"name":109,"@type":104,"acceptedAnswer":110},"What did the analysis show about time-of-day glucose levels?",{"text":111,"@type":107},"Mean glucose was lowest around 3 AM and highest around 3 PM, based on observed time-of-day patterns.",{"name":113,"@type":104,"acceptedAnswer":114},"How did missing CGM data affect glucose estimates?",{"text":115,"@type":107},"Days with missing data had lower mean glucose than days with no missing data, with a significant difference (p\u003C0.001). The findings support capturing both fasting (early morning) and postprandial (afternoon) effects and measuring at least one weekend day.","https://schema.org",{"og:url":83,"og:type":118,"og:title":65,"og:site_name":90,"og:description":67},"article",{"robots":120,"canonical":83},"index,follow",{"doc_id":122,"site_id":62},421533,1790625082,{"code":4,"msg":5,"data":125},{"doc_id":122,"user_id":126,"nickname":87,"user_avatar":127,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":128,"file_id":129,"file_url":130,"file_type":131,"file_size":132,"view_count":4,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":133,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":134,"faqs":135,"seo_title":136,"seo_description":67,"update_tm":123,"read_time":81},962084928432,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Innovation in Aging, 2025, Vol. 9, No. S2 193  \nTime-of-day patterns showed that mean glucose levels were lowest around 3 AM (94.5 mg/dL, SD = 27.2) and highest around  \n3 PM (113.4 mg/dL, SD= 16.0). Mean glucose levels were higher by 3 mg/dL on weekends compared to weekdays (p = 0.07) . Findings suggest that addressing incomplete data collection would improve estimates of mean ambulatory glucose levels by capturing both fasting (early morning) and postprandial (afternoon) effects on glucose levels. Findings also suggest that at least one weekend day should be measured for valid ambulatory glucose assessment.  \nAbstract citation ID: igaf122.644  \nEXPLORING DEVICE WEAR CRITERIA TO  \nCHARACTERIZE GLUCOSE PATTERNS IN PREFRAIL  \nOLDER ADULTS WITHOUT DIABETES  \nAmal Wanigatunga1, Xiaowen Chen1, Sydney Schultz1, Elizabeth Selvin1, Karen Bandeen-Roche2, Eleanor Simonsick3, and Jennifer Schrack1, 1. Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States, 2. Johns Hopkins Center on Aging and Health, Baltimore, Maryland, United States, 3. National Institute on Aging, Baltimore, Maryland, United States  \nFrail at-risk individuals may experience episodes of low blood sugar that may accelerate frailty onset. Continuous glucose monitoring (CGM) provides detailed interstitial glucose information throughout the day but device wear criteria (e.g., 24-hour wear; weekend behavior differences) to accurately estimate ambulatory glucose levels in prefrail older adults living without diabetes are not well defined. Baseline data from 37 participants enrolled in the Sedentary To Active Rising to Thrive (START) trial were examined (Dec 2023–Nov 2024). Each participant wore a CGM sensor for up to 14 days. Linear regression was used to estimate differences in mean ambulatory glucose level between: 1) wear days with and without complete data collection and 2) weekdaysand weekends. Among 37 START participants, 32(86%) provided CGM data (mean age:76 years, 94%women) . Mean wear  \nperiod was 14-days (range: 9–15) and mean CGM glucose was  \n106 mg/dL (range:95–113 mg/dL) . Glucose levels were lower on days with missing data (97.67 mg/dL; n = 64 days) compared todays with no missing (107.64 mg/dL; n = 393 days) (p \u003C 0.001) .","cbCaibQmL0zKukLK","https://ap.wps.com/l/cbCaibQmL0zKukLK","pdf",228728,"English","# Background\n## Study Design and Participants\n# Results\n## Time-of-day Patterns\n## Weekend Versus Weekday Differences\n## Impact of Missing Data\n# Implications","[{\"question\":\"Why study glucose patterns in prefrail older adults without diabetes?\",\"answer\":\"Frail at-risk individuals may experience low blood sugar episodes that can accelerate frailty onset. The study examines how to accurately estimate ambulatory glucose levels using CGM in this population.\"},{\"question\":\"What did the analysis show about time-of-day glucose levels?\",\"answer\":\"Mean glucose was lowest around 3 AM and highest around 3 PM, based on observed time-of-day patterns.\"},{\"question\":\"How did missing CGM data affect glucose estimates?\",\"answer\":\"Days with missing data had lower mean glucose than days with no missing data, with a significant difference (p\\u003c0.001). The findings support capturing both fasting (early morning) and postprandial (afternoon) effects and measuring at least one weekend day.\"}]","EXPLORING DEVICE WEAR CRITERIA TO - CHARACTERIZE GLUCOSE PATTERNS IN PREFRAIL OLDER ADULTS WITHOUT DIABETES | PDF"]