Master of Science / Department of Animal Sciences and Industry / Teresa L. Slough / Teresa L. Slough / There is increasing interest in the use of point-of-care glucometers to monitor glucose concentrations in horses with metabolic disorders. The first study reported herein compared equine glucose concentrations obtained by a handheld glucometer using whole blood or plasma, a YSI 2300 bench top glucose analyzer using whole blood or plasma, and a SEVEN continuous glucose monitoring (CGM) device that measured glucose in interstitial fluid to readings obtained by a standard laboratory glucose analyzer utilizing plasma. In addition, glucose concentrations obtained by the CGM were compared to those obtained by the handheld glucometer using whole blood or plasma. Post-prandial increases and decreases in glucose concentrations were detected utilizing all glucometers tested. When glucose measurements obtained with the CGM in interstitial fluid were compared to glucose measured using the handheld glucometer in plasma or whole blood, glucose measurements from plasma had better reproducibility. Although the CGM could be a useful instrument for collecting nearly continuous data for the researcher and clinician, there are technical difficulties related to the CGM that must first be overcome. The second study was designed to compare the effects of consuming a twice-daily meal of sweet feed (SF) to ad libitum access to a molasses-based block (BL) supplement on patterns of interstitial glucose concentrations in horses. A novelty effect of the BL was observed, in which horses consumed increased quantities in the first 12 h. Treatments had no effect on intake of forage in this study. The range and means of glucose values were similar between treatments, and significant glucose responses to treatments had lag times that were indirectly similar, even though molasses intake was greater for horses on BL. Variability between horses was noted in quantity of BL consumed as well as timing and magnitude of glucose responses. Based on the results of this experiment, there does not appear to be a clear advantage to either treatment, SF or BL, in attenuating post-prandial glucose increases or in minimizing glucose fluctuations in the horse.

abstract: Most studies that explored the health benefits of interrupting sitting time focused on using different modalities (i.e., comparing walking vs standing breaks)33,36,59. However, experimental studies that directly compare patterns of interrupting sitting time through standing only are needed to advance the field. This study aimed to (i) determine if there is a difference in glucose response between continuous sitting (CS) and two intermittent standing regimes (high frequency, low duration breaks (HFLD) and low frequency, high duration breaks (LFHD)) and (ii) to determine if there is a difference in glucose response between the two strategies (HFLD vs. LFHD). Ten sedentary employees (mean±SD age 46.8±10.6 years; 70% female) with impaired fasting glucose (mean glucose= 109.0±9.8 mg/dL) participated. Eligible participants were invited to three 7.5 hour laboratory visits where they were randomized to perform each study conditions: (i) CS, (ii) HFLD and (iii) LFHD. Standardized meals (breakfast and lunch) were given with each meal providing 33% of the participant’s total daily caloric needs following a typical American diet (50-60% carbohydrates, 25-30% fat, and 10-20% protein). Participants wore an activPAL device to measure compliance with the sit-stand condition and a continuous glucose monitor to measure post-prandial glucose response. Post-prandial mean glucose, incremental area under the curve and mean amplitude glycemic excursion between conditions were evaluated using linear mixed models. Participants demonstrated high compliance with the study condition. The results indicated that the mean glucose of the HFLD condition were significantly lower (p< .01) than the CS condition with mean difference of -7.70 (-11.98, -3.42) mg/dL·3.5h and -5.76 (-9.50, -2.03) mg/dL·7h for lunch and total time, respectively. Furthermore, the mean post-prandial glucose during lunch and total time were significantly lower in the HFLD condition compared to the LFHD condition with mean difference of -9.94 (-14.13, -5.74) mg/dL·3.5h and -6.23 (-9.93, -2.52) mg/dL·7h, respectively. No differences were found between the CS and LFHD conditions. This study provides evidence favoring the use of frequent interruptions in sitting time to improve glycemic control of prediabetic individuals. In contrast, less frequent, although longer bouts of standing resulted in similar post-prandial glucose profile to that of the continuous sitting condition despite total standing time being equal to the LFHD condition. / Dissertation/Thesis / Doctoral Dissertation Physical Activity, Nutrition and Wellness 2019

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Automatically detecting events for people with diabetes mellitus using continuousglucose monitors is an important step in allowing insulin pumps to automaticallycorrect the blood glucose levels and for a more hands-off approach to thedisease. The automatic detection of events could also aid physicians whenassisting their patients when referring to their continuous glucose monitordata. A range of different deep learning algorithms has been applied forpredictions of different events for continuous glucose monitor data, such asthe onset for hyperglycemia, hypoglycemia or mealtime events. This thesisfocused on constructing sequences labelled from an unbalanced and assumedmisslabelled dataset to classify them as such using four different deep learningnetworks using convoluted neural networks and recurrent neural networks.Manual correction of the dataset allowed for only clear events starting witha high positive gradient to be labelled as positive. The classification wasperformed on exact timepoints and in time windows to allow the classificationto to be done around the beginning of an event instead of the exact timepoint.The results from using the unbalanced and assumed misslabelled datasetshowed the networks performing similarly, with high Recall and Precisionbelow 0.5, thus not found to be of use in a for automatic event detection.Further testing by using another dataset or further configurations is neededto clarify the capabilities of automatically detecting events. DDAnalytics willnot use any of the developed networks in any of their products. / Automatisk detection av event för personer med diabetes från deras kontinuerligaglukosmätare är ett viktigt steg för att låta insulinpumpar automatiskt korrigeraglukosnivåer och möjliggöra en mindre självreglering av personens diabetes.Denna automatiska detektion skulle även kunna hjälpa läkare vid samtalmed patienter och deras data från kontinuerliga glukosmätarna. En mängd avolika djupinlärningsalgoritmer har använts för förutsägelser av olika event förkontinuerlig glukosmätardata, som början av hyperglykemier, hypoglykemiereller måltider. Detta examensarbete fokuserar på skapandet av sekvenserfrån ett obalanserat och antaget inte helt korrekt markerade event i dataset,för att kunna klassificera dessa event med fyra olika djupinlärningsnätverk.Dessa nätverk bygger på konvolution och rekursiva neurala nätverk. Manuellkorrektion av datasetet möjliggjorde så att endast tydliga event som börjar meden kraftig positiv ökning av gradienten var markerade som positiva event.Klassificeringen genomfördes på både exakta tidssteg och i tidsfönster såatt början av event kunde detekteras snarare än bara det exakta tidssteget.Resultaten genom användandet av detta tidigare nämnda dataset visade liknanderesultat för samtliga nätverk, med hög Återkallelse och Precision under 0.5.Dessa resultat ledde till att nätverken inte kan antas kunna utföra automatiskevent detektion, och skulle behöva ytterligare testning på ett annat dataset medmer korrekta markerade event eller ytterligare konfigureringar på nätverken föratt verifiera dessas möjligheter att automatiskt klassificera event i kontinuerligglukosdata. DDanalytics kommer inte använda något av dessa framtagnanätverk i några av deras produkter.