{"id": "bigideas-012-week05-2020-05-07-cv", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "012", "window_label": "week 5 (2020-05-07 to 2020-05-13)", "metric": "cv", "ground_truth": {"cv_pct": 14.1025, "sd_mgdl": 17.64, "mean_mgdl": 125.06, "n_readings": 1233}, "question": "What is the glycemic variability (CV%) for subject 012 in the Big IDEAs dataset over week 5 (2020-05-07 to 2020-05-13)?", "answer": "Window: week 5 (2020-05-07 to 2020-05-13) (2020-05-07 00:02 to 2020-05-11 09:31, 1,233 EGV readings spanning 105.5 hours). Coefficient of variation (CV%) was 14.10%, computed as standard deviation / mean x 100. SD = 17.6 mg/dL, mean = 125.1 mg/dL.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-003-day-2020-02-24-gmi", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "003", "window_label": "day 2020-02-24", "metric": "gmi", "ground_truth": {"gmi_pct": 5.8719, "mean_mgdl": 107.1, "formula": "GMI = 3.31 + 0.02392 * mean_mgdl", "n_readings": 288}, "question": "What is the Glucose Management Indicator (GMI) for subject 003 in the Big IDEAs dataset over day 2020-02-24?", "answer": "Window: day 2020-02-24 (2020-02-24 00:01 to 2020-02-24 23:56, 288 EGV readings spanning 23.9 hours). GMI was 5.87%, computed as 3.31 + 0.02392 x mean_mgdl, where mean_mgdl = 107.1.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet. GMI formula: Bergenstal et al. (2018), \"Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring,\" Diabetes Care, 41(11): 2275-2280.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-001-day-2020-02-19-mean", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "001", "window_label": "day 2020-02-19", "metric": "mean", "ground_truth": {"mean_mgdl": 120.31, "sd_mgdl": 12.8, "n_readings": 284}, "question": "What is the average glucose for subject 001 in the Big IDEAs dataset over day 2020-02-19?", "answer": "Window: day 2020-02-19 (2020-02-19 00:03 to 2020-02-19 23:38, 284 EGV readings spanning 23.6 hours). Average glucose was 120.3 mg/dL (standard deviation 12.8 mg/dL).\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-016-day-2020-07-19-day_vs_night", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "016", "window_label": "day 2020-07-19", "metric": "day_vs_night", "ground_truth": {"day_mean_mgdl": 101.7, "night_mean_mgdl": 106.66, "day_minus_night_mgdl": -4.96, "n_day": 192, "n_night": 96}, "question": "What is the daytime vs nighttime average BG for subject 016 in the Big IDEAs dataset over day 2020-07-19?", "answer": "Window: day 2020-07-19 (2020-07-19 00:03 to 2020-07-19 23:58, 288 EGV readings spanning 23.9 hours). Daytime average BG (06:00-21:59) was 101.7 mg/dL (n=192 readings). Nighttime average BG (22:00-05:59) was 106.7 mg/dL (n=96 readings). Daytime minus nighttime = -5.0 mg/dL.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-006-day-2020-03-04-mean", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "006", "window_label": "day 2020-03-04", "metric": "mean", "ground_truth": {"mean_mgdl": 122.91, "sd_mgdl": 27.77, "n_readings": 288}, "question": "What is the average glucose for subject 006 in the Big IDEAs dataset over day 2020-03-04?", "answer": "Window: day 2020-03-04 (2020-03-04 00:03 to 2020-03-04 23:58, 288 EGV readings spanning 23.9 hours). Average glucose was 122.9 mg/dL (standard deviation 27.8 mg/dL).\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-005-week04-2020-03-01-cv", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "005", "window_label": "week 4 (2020-03-01 to 2020-03-07)", "metric": "cv", "ground_truth": {"cv_pct": 13.6041, "sd_mgdl": 13.96, "mean_mgdl": 102.63, "n_readings": 1856}, "question": "What is the glycemic variability (CV%) for subject 005 in the Big IDEAs dataset over week 4 (2020-03-01 to 2020-03-07)?", "answer": "Window: week 4 (2020-03-01 to 2020-03-07) (2020-03-01 00:00 to 2020-03-07 11:46, 1,856 EGV readings spanning 155.8 hours). Coefficient of variation (CV%) was 13.60%, computed as standard deviation / mean x 100. SD = 14.0 mg/dL, mean = 102.6 mg/dL.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-005-day-2020-03-05-tbr", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "005", "window_label": "day 2020-03-05", "metric": "tbr", "ground_truth": {"tbr_pct": 0.0, "n_readings": 288}, "question": "What is the time below range (<70 mg/dL) for subject 005 in the Big IDEAs dataset over day 2020-03-05?", "answer": "Window: day 2020-03-05 (2020-03-05 00:01 to 2020-03-05 23:56, 288 EGV readings spanning 23.9 hours). Time below range (<70 mg/dL) was 0.0% of CGM readings.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-003-week02-2020-02-23-cv", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "003", "window_label": "week 2 (2020-02-23 to 2020-02-29)", "metric": "cv", "ground_truth": {"cv_pct": 16.1709, "sd_mgdl": 17.33, "mean_mgdl": 107.14, "n_readings": 2004}, "question": "What is the glycemic variability (CV%) for subject 003 in the Big IDEAs dataset over week 2 (2020-02-23 to 2020-02-29)?", "answer": "Window: week 2 (2020-02-23 to 2020-02-29) (2020-02-23 00:01 to 2020-02-29 23:56, 2,004 EGV readings spanning 167.9 hours). Coefficient of variation (CV%) was 16.17%, computed as standard deviation / mean x 100. SD = 17.3 mg/dL, mean = 107.1 mg/dL.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-016-day-2020-07-18-gmi", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "016", "window_label": "day 2020-07-18", "metric": "gmi", "ground_truth": {"gmi_pct": 5.8286, "mean_mgdl": 105.29, "formula": "GMI = 3.31 + 0.02392 * mean_mgdl", "n_readings": 284}, "question": "What is the Glucose Management Indicator (GMI) for subject 016 in the Big IDEAs dataset over day 2020-07-18?", "answer": "Window: day 2020-07-18 (2020-07-18 00:03 to 2020-07-18 23:58, 284 EGV readings spanning 23.9 hours). GMI was 5.83%, computed as 3.31 + 0.02392 x mean_mgdl, where mean_mgdl = 105.3.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet. GMI formula: Bergenstal et al. (2018), \"Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring,\" Diabetes Care, 41(11): 2275-2280.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-003-full-tbr", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "003", "window_label": "the full subject period", "metric": "tbr", "ground_truth": {"tbr_pct": 0.6516, "n_readings": 2302}, "question": "What is the time below range (<70 mg/dL) for subject 003 in the Big IDEAs dataset over the full subject period?", "answer": "Window: the full subject period (2020-02-22 10:51 to 2020-03-01 11:36, 2,302 EGV readings spanning 192.7 hours). Time below range (<70 mg/dL) was 0.7% of CGM readings.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-014-week02-2020-06-06-tbr", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "014", "window_label": "week 2 (2020-06-06 to 2020-06-12)", "metric": "tbr", "ground_truth": {"tbr_pct": 0.0, "n_readings": 2016}, "question": "What is the time below range (<70 mg/dL) for subject 014 in the Big IDEAs dataset over week 2 (2020-06-06 to 2020-06-12)?", "answer": "Window: week 2 (2020-06-06 to 2020-06-12) (2020-06-06 00:03 to 2020-06-12 23:57, 2,016 EGV readings spanning 167.9 hours). Time below range (<70 mg/dL) was 0.0% of CGM readings.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-016-day-2020-07-19-gmi", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "016", "window_label": "day 2020-07-19", "metric": "gmi", "ground_truth": {"gmi_pct": 5.7821, "mean_mgdl": 103.35, "formula": "GMI = 3.31 + 0.02392 * mean_mgdl", "n_readings": 288}, "question": "What is the Glucose Management Indicator (GMI) for subject 016 in the Big IDEAs dataset over day 2020-07-19?", "answer": "Window: day 2020-07-19 (2020-07-19 00:03 to 2020-07-19 23:58, 288 EGV readings spanning 23.9 hours). GMI was 5.78%, computed as 3.31 + 0.02392 x mean_mgdl, where mean_mgdl = 103.4.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet. GMI formula: Bergenstal et al. (2018), \"Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring,\" Diabetes Care, 41(11): 2275-2280.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-011-day-2020-04-11-gmi", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "011", "window_label": "day 2020-04-11", "metric": "gmi", "ground_truth": {"gmi_pct": 6.076, "mean_mgdl": 115.64, "formula": "GMI = 3.31 + 0.02392 * mean_mgdl", "n_readings": 288}, "question": "What is the Glucose Management Indicator (GMI) for subject 011 in the Big IDEAs dataset over day 2020-04-11?", "answer": "Window: day 2020-04-11 (2020-04-11 00:01 to 2020-04-11 23:55, 288 EGV readings spanning 23.9 hours). GMI was 6.08%, computed as 3.31 + 0.02392 x mean_mgdl, where mean_mgdl = 115.6.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet. GMI formula: Bergenstal et al. (2018), \"Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring,\" Diabetes Care, 41(11): 2275-2280.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-002-week01-2020-02-21-tar", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "002", "window_label": "week 1 (2020-02-21 to 2020-02-27)", "metric": "tar", "ground_truth": {"tar_pct": 1.4577, "n_readings": 1715}, "question": "What is the time above range (>180 mg/dL) for subject 002 in the Big IDEAs dataset over week 1 (2020-02-21 to 2020-02-27)?", "answer": "Window: week 1 (2020-02-21 to 2020-02-27) (2020-02-21 11:08 to 2020-02-27 23:58, 1,715 EGV readings spanning 156.8 hours). Time above range (>180 mg/dL) was 1.5% of CGM readings.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-012-full-tbr", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "012", "window_label": "the full subject period", "metric": "tbr", "ground_truth": {"tbr_pct": 0.0, "n_readings": 2169}, "question": "What is the time below range (<70 mg/dL) for subject 012 in the Big IDEAs dataset over the full subject period?", "answer": "Window: the full subject period (2020-05-03 17:52 to 2020-05-11 09:31, 2,169 EGV readings spanning 183.7 hours). Time below range (<70 mg/dL) was 0.0% of CGM readings.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-008-week06-2020-03-19-tir", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "008", "window_label": "week 6 (2020-03-19 to 2020-03-25)", "metric": "tir", "ground_truth": {"tir_pct": 100.0, "n_readings": 1263, "span_hours": 107.58}, "question": "What is the time in range (70-180 mg/dL) for subject 008 in the Big IDEAs dataset over week 6 (2020-03-19 to 2020-03-25)?", "answer": "Window: week 6 (2020-03-19 to 2020-03-25) (2020-03-19 00:04 to 2020-03-23 11:39, 1,263 EGV readings spanning 107.6 hours). Time in range (70-180 mg/dL) was 100.0% of CGM readings.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-001-day-2020-02-21-tbr", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "001", "window_label": "day 2020-02-21", "metric": "tbr", "ground_truth": {"tbr_pct": 0.0, "n_readings": 288}, "question": "What is the time below range (<70 mg/dL) for subject 001 in the Big IDEAs dataset over day 2020-02-21?", "answer": "Window: day 2020-02-21 (2020-02-21 00:03 to 2020-02-21 23:58, 288 EGV readings spanning 23.9 hours). Time below range (<70 mg/dL) was 0.0% of CGM readings.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-001-day-2020-02-20-cv", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "001", "window_label": "day 2020-02-20", "metric": "cv", "ground_truth": {"cv_pct": 13.9293, "sd_mgdl": 15.33, "mean_mgdl": 110.04, "n_readings": 255}, "question": "What is the glycemic variability (CV%) for subject 001 in the Big IDEAs dataset over day 2020-02-20?", "answer": "Window: day 2020-02-20 (2020-02-20 02:48 to 2020-02-20 23:58, 255 EGV readings spanning 21.2 hours). Coefficient of variation (CV%) was 13.93%, computed as standard deviation / mean x 100. SD = 15.3 mg/dL, mean = 110.0 mg/dL.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-002-week02-2020-02-22-dawn", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "002", "window_label": "week 2 (2020-02-22 to 2020-02-28)", "metric": "dawn", "ground_truth": {"dawn_mean_mgdl": 150.88, "midnight_mean_mgdl": 131.94, "dawn_delta_mgdl": 18.93, "n_dawn": 144, "n_midnight": 216}, "question": "What is the dawn phenomenon delta (avg 5-7am minus avg 0-3am) for subject 002 in the Big IDEAs dataset over week 2 (2020-02-22 to 2020-02-28)?", "answer": "Window: week 2 (2020-02-22 to 2020-02-28) (2020-02-22 00:03 to 2020-02-28 23:58, 1,848 EGV readings spanning 167.9 hours). Average BG 05:00-06:59 was 150.9 mg/dL (n=144 readings). Average BG 00:00-02:59 was 131.9 mg/dL (n=216 readings). Dawn delta = 18.9 mg/dL (positive = dawn rise).\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-005-day-2020-03-03-dawn", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "005", "window_label": "day 2020-03-03", "metric": "dawn", "ground_truth": {"dawn_mean_mgdl": 114.17, "midnight_mean_mgdl": 111.64, "dawn_delta_mgdl": 2.53, "n_dawn": 24, "n_midnight": 36}, "question": "What is the dawn phenomenon delta (avg 5-7am minus avg 0-3am) for subject 005 in the Big IDEAs dataset over day 2020-03-03?", "answer": "Window: day 2020-03-03 (2020-03-03 00:04 to 2020-03-03 23:49, 286 EGV readings spanning 23.7 hours). Average BG 05:00-06:59 was 114.2 mg/dL (n=24 readings). Average BG 00:00-02:59 was 111.6 mg/dL (n=36 readings). Dawn delta = 2.5 mg/dL (positive = dawn rise).\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-005-week01-2020-02-27-gmi", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "005", "window_label": "week 1 (2020-02-27 to 2020-03-04)", "metric": "gmi", "ground_truth": {"gmi_pct": 5.8571, "mean_mgdl": 106.48, "formula": "GMI = 3.31 + 0.02392 * mean_mgdl", "n_readings": 1840}, "question": "What is the Glucose Management Indicator (GMI) for subject 005 in the Big IDEAs dataset over week 1 (2020-02-27 to 2020-03-04)?", "answer": "Window: week 1 (2020-02-27 to 2020-03-04) (2020-02-27 13:30 to 2020-03-04 23:56, 1,840 EGV readings spanning 154.4 hours). GMI was 5.86%, computed as 3.31 + 0.02392 x mean_mgdl, where mean_mgdl = 106.5.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet. GMI formula: Bergenstal et al. (2018), \"Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring,\" Diabetes Care, 41(11): 2275-2280.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-010-week01-2020-03-22-tar", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "010", "window_label": "week 1 (2020-03-22 to 2020-03-28)", "metric": "tar", "ground_truth": {"tar_pct": 3.1126, "n_readings": 1767}, "question": "What is the time above range (>180 mg/dL) for subject 010 in the Big IDEAs dataset over week 1 (2020-03-22 to 2020-03-28)?", "answer": "Window: week 1 (2020-03-22 to 2020-03-28) (2020-03-22 11:24 to 2020-03-28 21:09, 1,767 EGV readings spanning 153.7 hours). Time above range (>180 mg/dL) was 3.1% of CGM readings.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-012-day-2020-05-06-tir", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "012", "window_label": "day 2020-05-06", "metric": "tir", "ground_truth": {"tir_pct": 91.3194, "n_readings": 288, "span_hours": 23.92}, "question": "What is the time in range (70-180 mg/dL) for subject 012 in the Big IDEAs dataset over day 2020-05-06?", "answer": "Window: day 2020-05-06 (2020-05-06 00:02 to 2020-05-06 23:57, 288 EGV readings spanning 23.9 hours). Time in range (70-180 mg/dL) was 91.3% of CGM readings.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-001-day-2020-02-19-day_vs_night", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "001", "window_label": "day 2020-02-19", "metric": "day_vs_night", "ground_truth": {"day_mean_mgdl": 120.84, "night_mean_mgdl": 119.2, "day_minus_night_mgdl": 1.64, "n_day": 192, "n_night": 92}, "question": "What is the daytime vs nighttime average BG for subject 001 in the Big IDEAs dataset over day 2020-02-19?", "answer": "Window: day 2020-02-19 (2020-02-19 00:03 to 2020-02-19 23:38, 284 EGV readings spanning 23.6 hours). Daytime average BG (06:00-21:59) was 120.8 mg/dL (n=192 readings). Nighttime average BG (22:00-05:59) was 119.2 mg/dL (n=92 readings). Daytime minus nighttime = 1.6 mg/dL.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-011-day-2020-04-15-cv", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "011", "window_label": "day 2020-04-15", "metric": "cv", "ground_truth": {"cv_pct": 18.4397, "sd_mgdl": 23.64, "mean_mgdl": 128.2, "n_readings": 288}, "question": "What is the glycemic variability (CV%) for subject 011 in the Big IDEAs dataset over day 2020-04-15?", "answer": "Window: day 2020-04-15 (2020-04-15 00:00 to 2020-04-15 23:55, 288 EGV readings spanning 23.9 hours). Coefficient of variation (CV%) was 18.44%, computed as standard deviation / mean x 100. SD = 23.6 mg/dL, mean = 128.2 mg/dL.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-005-full-dawn", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "005", "window_label": "the full subject period", "metric": "dawn", "ground_truth": {"dawn_mean_mgdl": 110.81, "midnight_mean_mgdl": 113.31, "dawn_delta_mgdl": -2.5, "n_dawn": 216, "n_midnight": 311}, "question": "What is the dawn phenomenon delta (avg 5-7am minus avg 0-3am) for subject 005 in the Big IDEAs dataset over the full subject period?", "answer": "Window: the full subject period (2020-02-27 13:30 to 2020-03-07 11:46, 2,558 EGV readings spanning 214.3 hours). Average BG 05:00-06:59 was 110.8 mg/dL (n=216 readings). Average BG 00:00-02:59 was 113.3 mg/dL (n=311 readings). Dawn delta = -2.5 mg/dL (positive = dawn rise).\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-015-week02-2020-07-20-tar", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "015", "window_label": "week 2 (2020-07-20 to 2020-07-26)", "metric": "tar", "ground_truth": {"tar_pct": 0.0, "n_readings": 1468}, "question": "What is the time above range (>180 mg/dL) for subject 015 in the Big IDEAs dataset over week 2 (2020-07-20 to 2020-07-26)?", "answer": "Window: week 2 (2020-07-20 to 2020-07-26) (2020-07-20 00:03 to 2020-07-26 23:58, 1,468 EGV readings spanning 167.9 hours). Time above range (>180 mg/dL) was 0.0% of CGM readings.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-014-day-2020-06-07-tar", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "014", "window_label": "day 2020-06-07", "metric": "tar", "ground_truth": {"tar_pct": 0.0, "n_readings": 288}, "question": "What is the time above range (>180 mg/dL) for subject 014 in the Big IDEAs dataset over day 2020-06-07?", "answer": "Window: day 2020-06-07 (2020-06-07 00:03 to 2020-06-07 23:58, 288 EGV readings spanning 23.9 hours). Time above range (>180 mg/dL) was 0.0% of CGM readings.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-015-day-2020-07-22-gmi", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "015", "window_label": "day 2020-07-22", "metric": "gmi", "ground_truth": {"gmi_pct": 5.9412, "mean_mgdl": 110.0, "formula": "GMI = 3.31 + 0.02392 * mean_mgdl", "n_readings": 288}, "question": "What is the Glucose Management Indicator (GMI) for subject 015 in the Big IDEAs dataset over day 2020-07-22?", "answer": "Window: day 2020-07-22 (2020-07-22 00:03 to 2020-07-22 23:58, 288 EGV readings spanning 23.9 hours). GMI was 5.94%, computed as 3.31 + 0.02392 x mean_mgdl, where mean_mgdl = 110.0.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet. GMI formula: Bergenstal et al. (2018), \"Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring,\" Diabetes Care, 41(11): 2275-2280.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-008-week05-2020-03-18-mean", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "008", "window_label": "week 5 (2020-03-18 to 2020-03-24)", "metric": "mean", "ground_truth": {"mean_mgdl": 116.35, "sd_mgdl": 14.78, "n_readings": 1551}, "question": "What is the average glucose for subject 008 in the Big IDEAs dataset over week 5 (2020-03-18 to 2020-03-24)?", "answer": "Window: week 5 (2020-03-18 to 2020-03-24) (2020-03-18 00:04 to 2020-03-23 11:39, 1,551 EGV readings spanning 131.6 hours). Average glucose was 116.3 mg/dL (standard deviation 14.8 mg/dL).\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-005-day-2020-03-04-mean", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "005", "window_label": "day 2020-03-04", "metric": "mean", "ground_truth": {"mean_mgdl": 109.51, "sd_mgdl": 13.64, "n_readings": 275}, "question": "What is the average glucose for subject 005 in the Big IDEAs dataset over day 2020-03-04?", "answer": "Window: day 2020-03-04 (2020-03-04 01:06 to 2020-03-04 23:56, 275 EGV readings spanning 22.8 hours). Average glucose was 109.5 mg/dL (standard deviation 13.6 mg/dL).\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-009-day-2020-03-26-dawn", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "009", "window_label": "day 2020-03-26", "metric": "dawn", "ground_truth": {"dawn_mean_mgdl": 127.08, "midnight_mean_mgdl": 131.5, "dawn_delta_mgdl": -4.42, "n_dawn": 24, "n_midnight": 36}, "question": "What is the dawn phenomenon delta (avg 5-7am minus avg 0-3am) for subject 009 in the Big IDEAs dataset over day 2020-03-26?", "answer": "Window: day 2020-03-26 (2020-03-26 00:01 to 2020-03-26 23:56, 288 EGV readings spanning 23.9 hours). Average BG 05:00-06:59 was 127.1 mg/dL (n=24 readings). Average BG 00:00-02:59 was 131.5 mg/dL (n=36 readings). Dawn delta = -4.4 mg/dL (positive = dawn rise).\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-011-week07-2020-04-12-day_vs_night", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "011", "window_label": "week 7 (2020-04-12 to 2020-04-18)", "metric": "day_vs_night", "ground_truth": {"day_mean_mgdl": 118.44, "night_mean_mgdl": 132.75, "day_minus_night_mgdl": -14.3, "n_day": 785, "n_night": 456}, "question": "What is the daytime vs nighttime average BG for subject 011 in the Big IDEAs dataset over week 7 (2020-04-12 to 2020-04-18)?", "answer": "Window: week 7 (2020-04-12 to 2020-04-18) (2020-04-12 00:00 to 2020-04-16 07:20, 1,241 EGV readings spanning 103.3 hours). Daytime average BG (06:00-21:59) was 118.4 mg/dL (n=785 readings). Nighttime average BG (22:00-05:59) was 132.7 mg/dL (n=456 readings). Daytime minus nighttime = -14.3 mg/dL.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-006-day-2020-03-05-tbr", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "006", "window_label": "day 2020-03-05", "metric": "tbr", "ground_truth": {"tbr_pct": 0.0, "n_readings": 288}, "question": "What is the time below range (<70 mg/dL) for subject 006 in the Big IDEAs dataset over day 2020-03-05?", "answer": "Window: day 2020-03-05 (2020-03-05 00:03 to 2020-03-05 23:58, 288 EGV readings spanning 23.9 hours). Time below range (<70 mg/dL) was 0.0% of CGM readings.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-001-day-2020-02-14-cv", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "001", "window_label": "day 2020-02-14", "metric": "cv", "ground_truth": {"cv_pct": 15.5338, "sd_mgdl": 15.53, "mean_mgdl": 99.98, "n_readings": 288}, "question": "What is the glycemic variability (CV%) for subject 001 in the Big IDEAs dataset over day 2020-02-14?", "answer": "Window: day 2020-02-14 (2020-02-14 00:03 to 2020-02-14 23:58, 288 EGV readings spanning 23.9 hours). Coefficient of variation (CV%) was 15.53%, computed as standard deviation / mean x 100. SD = 15.5 mg/dL, mean = 100.0 mg/dL.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-016-week01-2020-07-16-tar", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "016", "window_label": "week 1 (2020-07-16 to 2020-07-22)", "metric": "tar", "ground_truth": {"tar_pct": 0.0, "n_readings": 1865}, "question": "What is the time above range (>180 mg/dL) for subject 016 in the Big IDEAs dataset over week 1 (2020-07-16 to 2020-07-22)?", "answer": "Window: week 1 (2020-07-16 to 2020-07-22) (2020-07-16 10:43 to 2020-07-22 23:58, 1,865 EGV readings spanning 157.2 hours). Time above range (>180 mg/dL) was 0.0% of CGM readings.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-004-day-2020-02-28-day_vs_night", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "004", "window_label": "day 2020-02-28", "metric": "day_vs_night", "ground_truth": {"day_mean_mgdl": 110.7, "night_mean_mgdl": 110.44, "day_minus_night_mgdl": 0.27, "n_day": 192, "n_night": 96}, "question": "What is the daytime vs nighttime average BG for subject 004 in the Big IDEAs dataset over day 2020-02-28?", "answer": "Window: day 2020-02-28 (2020-02-28 00:01 to 2020-02-28 23:56, 288 EGV readings spanning 23.9 hours). Daytime average BG (06:00-21:59) was 110.7 mg/dL (n=192 readings). Nighttime average BG (22:00-05:59) was 110.4 mg/dL (n=96 readings). Daytime minus nighttime = 0.3 mg/dL.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-015-day-2020-07-21-cv", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "015", "window_label": "day 2020-07-21", "metric": "cv", "ground_truth": {"cv_pct": 12.868, "sd_mgdl": 14.36, "mean_mgdl": 111.57, "n_readings": 255}, "question": "What is the glycemic variability (CV%) for subject 015 in the Big IDEAs dataset over day 2020-07-21?", "answer": "Window: day 2020-07-21 (2020-07-21 00:03 to 2020-07-21 23:58, 255 EGV readings spanning 23.9 hours). Coefficient of variation (CV%) was 12.87%, computed as standard deviation / mean x 100. SD = 14.4 mg/dL, mean = 111.6 mg/dL.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-008-week06-2020-03-19-tbr", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "008", "window_label": "week 6 (2020-03-19 to 2020-03-25)", "metric": "tbr", "ground_truth": {"tbr_pct": 0.0, "n_readings": 1263}, "question": "What is the time below range (<70 mg/dL) for subject 008 in the Big IDEAs dataset over week 6 (2020-03-19 to 2020-03-25)?", "answer": "Window: week 6 (2020-03-19 to 2020-03-25) (2020-03-19 00:04 to 2020-03-23 11:39, 1,263 EGV readings spanning 107.6 hours). Time below range (<70 mg/dL) was 0.0% of CGM readings.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-007-day-2020-03-20-tir", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "007", "window_label": "day 2020-03-20", "metric": "tir", "ground_truth": {"tir_pct": 94.4444, "n_readings": 288, "span_hours": 23.92}, "question": "What is the time in range (70-180 mg/dL) for subject 007 in the Big IDEAs dataset over day 2020-03-20?", "answer": "Window: day 2020-03-20 (2020-03-20 00:02 to 2020-03-20 23:57, 288 EGV readings spanning 23.9 hours). Time in range (70-180 mg/dL) was 94.4% of CGM readings.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-004-full-day_vs_night", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "004", "window_label": "the full subject period", "metric": "day_vs_night", "ground_truth": {"day_mean_mgdl": 110.36, "night_mean_mgdl": 117.64, "day_minus_night_mgdl": -7.28, "n_day": 1484, "n_night": 680}, "question": "What is the daytime vs nighttime average BG for subject 004 in the Big IDEAs dataset over the full subject period?", "answer": "Window: the full subject period (2020-02-27 10:51 to 2020-03-06 06:51, 2,164 EGV readings spanning 188.0 hours). Daytime average BG (06:00-21:59) was 110.4 mg/dL (n=1,484 readings). Nighttime average BG (22:00-05:59) was 117.6 mg/dL (n=680 readings). Daytime minus nighttime = -7.3 mg/dL.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-005-day-2020-03-03-tir", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "005", "window_label": "day 2020-03-03", "metric": "tir", "ground_truth": {"tir_pct": 100.0, "n_readings": 286, "span_hours": 23.75}, "question": "What is the time in range (70-180 mg/dL) for subject 005 in the Big IDEAs dataset over day 2020-03-03?", "answer": "Window: day 2020-03-03 (2020-03-03 00:04 to 2020-03-03 23:49, 286 EGV readings spanning 23.7 hours). Time in range (70-180 mg/dL) was 100.0% of CGM readings.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-016-week02-2020-07-17-mean", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "016", "window_label": "week 2 (2020-07-17 to 2020-07-23)", "metric": "mean", "ground_truth": {"mean_mgdl": 104.82, "sd_mgdl": 16.83, "n_readings": 1993}, "question": "What is the average glucose for subject 016 in the Big IDEAs dataset over week 2 (2020-07-17 to 2020-07-23)?", "answer": "Window: week 2 (2020-07-17 to 2020-07-23) (2020-07-17 00:03 to 2020-07-23 23:58, 1,993 EGV readings spanning 167.9 hours). Average glucose was 104.8 mg/dL (standard deviation 16.8 mg/dL).\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-007-day-2020-03-19-tbr", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "007", "window_label": "day 2020-03-19", "metric": "tbr", "ground_truth": {"tbr_pct": 4.5139, "n_readings": 288}, "question": "What is the time below range (<70 mg/dL) for subject 007 in the Big IDEAs dataset over day 2020-03-19?", "answer": "Window: day 2020-03-19 (2020-03-19 00:02 to 2020-03-19 23:57, 288 EGV readings spanning 23.9 hours). Time below range (<70 mg/dL) was 4.5% of CGM readings.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-002-week02-2020-02-22-cv", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "002", "window_label": "week 2 (2020-02-22 to 2020-02-28)", "metric": "cv", "ground_truth": {"cv_pct": 15.7907, "sd_mgdl": 20.42, "mean_mgdl": 129.29, "n_readings": 1848}, "question": "What is the glycemic variability (CV%) for subject 002 in the Big IDEAs dataset over week 2 (2020-02-22 to 2020-02-28)?", "answer": "Window: week 2 (2020-02-22 to 2020-02-28) (2020-02-22 00:03 to 2020-02-28 23:58, 1,848 EGV readings spanning 167.9 hours). Coefficient of variation (CV%) was 15.79%, computed as standard deviation / mean x 100. SD = 20.4 mg/dL, mean = 129.3 mg/dL.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-008-day-2020-03-17-tar", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "008", "window_label": "day 2020-03-17", "metric": "tar", "ground_truth": {"tar_pct": 0.0, "n_readings": 275}, "question": "What is the time above range (>180 mg/dL) for subject 008 in the Big IDEAs dataset over day 2020-03-17?", "answer": "Window: day 2020-03-17 (2020-03-17 00:04 to 2020-03-17 23:59, 275 EGV readings spanning 23.9 hours). Time above range (>180 mg/dL) was 0.0% of CGM readings.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-002-week03-2020-02-23-day_vs_night", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "002", "window_label": "week 3 (2020-02-23 to 2020-02-29)", "metric": "day_vs_night", "ground_truth": {"day_mean_mgdl": 128.34, "night_mean_mgdl": 136.85, "day_minus_night_mgdl": -8.51, "n_day": 1116, "n_night": 560}, "question": "What is the daytime vs nighttime average BG for subject 002 in the Big IDEAs dataset over week 3 (2020-02-23 to 2020-02-29)?", "answer": "Window: week 3 (2020-02-23 to 2020-02-29) (2020-02-23 00:03 to 2020-02-29 09:38, 1,676 EGV readings spanning 153.6 hours). Daytime average BG (06:00-21:59) was 128.3 mg/dL (n=1,116 readings). Nighttime average BG (22:00-05:59) was 136.8 mg/dL (n=560 readings). Daytime minus nighttime = -8.5 mg/dL.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-007-week03-2020-03-16-dawn", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "007", "window_label": "week 3 (2020-03-16 to 2020-03-22)", "metric": "dawn", "ground_truth": {"dawn_mean_mgdl": 91.37, "midnight_mean_mgdl": 93.27, "dawn_delta_mgdl": -1.91, "n_dawn": 144, "n_midnight": 252}, "question": "What is the dawn phenomenon delta (avg 5-7am minus avg 0-3am) for subject 007 in the Big IDEAs dataset over week 3 (2020-03-16 to 2020-03-22)?", "answer": "Window: week 3 (2020-03-16 to 2020-03-22) (2020-03-16 00:02 to 2020-03-22 11:06, 1,783 EGV readings spanning 155.1 hours). Average BG 05:00-06:59 was 91.4 mg/dL (n=144 readings). Average BG 00:00-02:59 was 93.3 mg/dL (n=252 readings). Dawn delta = -1.9 mg/dL (positive = dawn rise).\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-007-day-2020-03-21-tir", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "007", "window_label": "day 2020-03-21", "metric": "tir", "ground_truth": {"tir_pct": 87.1528, "n_readings": 288, "span_hours": 23.92}, "question": "What is the time in range (70-180 mg/dL) for subject 007 in the Big IDEAs dataset over day 2020-03-21?", "answer": "Window: day 2020-03-21 (2020-03-21 00:02 to 2020-03-21 23:56, 288 EGV readings spanning 23.9 hours). Time in range (70-180 mg/dL) was 87.2% of CGM readings.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}
{"id": "bigideas-012-day-2020-05-06-gmi", "specialty": "diabetic-nutrition", "domain": "diabetes", "tier": "royal-jelly", "bucket": "bigideas-cgm-pattern", "source": "Big IDEAs Lab Glycemic Variability Database (PhysioNet)", "citation": "Data from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet.", "subject": "012", "window_label": "day 2020-05-06", "metric": "gmi", "ground_truth": {"gmi_pct": 6.237, "mean_mgdl": 122.37, "formula": "GMI = 3.31 + 0.02392 * mean_mgdl", "n_readings": 288}, "question": "What is the Glucose Management Indicator (GMI) for subject 012 in the Big IDEAs dataset over day 2020-05-06?", "answer": "Window: day 2020-05-06 (2020-05-06 00:02 to 2020-05-06 23:57, 288 EGV readings spanning 23.9 hours). GMI was 6.24%, computed as 3.31 + 0.02392 x mean_mgdl, where mean_mgdl = 122.4.\n\nData from the Big IDEAs Lab Glycemic Variability Database (PhysioNet). Cho et al., Big IDEAs Lab Glycemic Variability Database, PhysioNet. GMI formula: Bergenstal et al. (2018), \"Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring,\" Diabetes Care, 41(11): 2275-2280.\n\nThis describes computed metrics from one subject in a published research dataset. It is not advice. Your individual CGM metrics, and what they mean for you, are a conversation with your healthcare provider."}