In many African countries, including South Africa, much attention has been centred on the management of HIV/AIDS and tuberculosis epidemics. However, there is growing awareness in South Africa that life-style related non-communicable conditions, such as diabetes and obesity, represent an important health priority (Pirie, 2005:42). The general objective of this study was to evaluate the treatment of diabetic patients in clinics on primary healthcare level and to determine what contributions can be made in the prevention of diabetic complications. The research method consisted out of the selection of the study population, data collection (questionnaire) and the data analysis. There was no structural way of deciding which patients would be selected to be interviewed. As the patients arrived for their appointments the interviewer was informed. No patient was forced to participate in this study and after they agreed to the interview, they signed a consent form that releases the University of any liability that may occur and to give their permission for the interview. The questionnaire was compiled which covered all the aspects of diabetes. This included diagnostic data, life-style, well-being, compliance and monitoring. The researcher completed the questionnaires whilst interviewing the patients. The data obtained from the questionnaires were statistically analysed by using the Statistical Analysis System, SAS 9.1. Effect size, which was given by the Phi coefficient, was used as a descriptive statistic. In this particular study population, the majority of patients were classified as type 2 diabetics. This can be viewed in table 4.8 where 62.14% of the total study population was classified as group B, which means that these patients use oral glucose lowering drugs to control their disease. A further 33.98% of the population was classified as group C diabetics, which means that these patients need oral glucose lowering drugs as well as exogenous insulin to maintain a healthy life. The latter group obviously consists of patients whose diabetic status was not under control in the past, thus the need for the insulin. This clearly shows that these patients have not been informed about how they can manage the disease by dietary modification and lifestyle interventions. Lifestyle, socio-economic and education played a major role in the development of this disease in these patients. The weight status of the study population was determined and can be viewed in table 4.15. Only 20.39% of them were of normal weight with a body mass index (BMI) ranging between 18.5 - 24.9 kg/m2. 39.81% of them were overweight with their BMI ranging between 25 - 29.9 kg/m2 and the remaining 39.81% of the study population were classified as obese with their BMI's above 30 kg/m2. The majority (an estimated 80%) of the study population were above optimal weight. This may cause the development of chronic complications, such as retinopathy, neuropathy and nephropathy. The socio-economic status of the study population was relatively poor because of unemployment. Although 90.07% of them said they had no difficulty to follow their diet (table 4.56) almost half of the patients said they had some difficulty to get the correct food for their specific needs (table 4.53). The first may be because they are still eating they way they used to with no modifications and the latter may be because of their financial status. Not being able to find work has a major effect on their lives. They cannot afford to buy foods suitable for their needs. As previously stated, patient education is fundamental in the managing and controlling diabetes. When these patients were asked whether they know what diabetes is, and what the complications of the disease might hold, most of them answered that it means they have 'sugar', and cannot eat sugary foods any more. This clearly indicates that they did not have a complete knowledge of their disease. After having explained to them in uncomplicated terms what the disease implicates, many of them said it had not been not explained to them previously and that they now understood it better. It was concluded that the majority of the studied population were under a false impression of what diabetes implied. This is partly due to the lack of time the clinic staffs have to spend with each patient, educating them about the disease. One aspect that was most obvious during this study was the fact that an estimated 20% of all patients studied had their own blood glucose monitor (table 4.80). This is somewhat concerning because to have optimal control over one's blood glucose levels, one needs to has a blood glucose monitor for regular monitoring. An estimated 70% of the studied population measures their blood glucose only once a month when they attend the clinic for their monthly visit (table 4.81). This is not nearly enough to ensure optimal control. The average blood glucose levels were calculated and described in section 4.7. Even with the minimal measurement, about 50% of these patients' blood glucose levels were fairly under control with an average of 6-9mmol/L (table 4.88). But the other estimated 50% of the population were not controlled with averages of either below 5mmol/L or above 9mmol/L. This is concerning because the possibility that these uncontrolled cases may develop chronic complications, might be unavoidable unless they start taking control of their lives. And for this to happen, these patients need all the possible education from qualified health care providers and the support of their families. Certain recommendations and restrictions were formulated and discussed. / Thesis (M.Pharm. (Pharmacy Practice))--North-West University, Potchefstroom Campus, 2008.

Aerobic exercise interventions involving individuals with type 1 diabetes have had little positive effect on blood glucose control as reflected by hemoglobin A1c. The few existing interventions involving resistance exercise, either alone or combined with aerobic exercise, while small in sample size, have had better outcomes. The purpose of this research program was to examine the changes in blood glucose levels during activity and for 24 hours post-exercise (as measured by continuous glucose monitoring) when resistance exercise is performed, either on its own or combined with aerobic exercise, as compared to aerobic exercise alone or no exercise. Twelve physically active individuals with type 1 diabetes performed 5 separate exercise sessions in random order separated by at least five days: 1) no exercise/control; 2) aerobic exercise (45 minutes of treadmill running at 60% VO2peak); 3) resistance exercise (45 minutes of weight lifting – 3 sets of 8 repetitions of 7 different exercises); 4) aerobic then resistance exercise (2 and 3 combined with the aerobic exercise first); 5) resistance then aerobic exercise (2 and 3 combined with the resistance exercise first). We found that resistance exercise was associated with a lower risk of hypoglycemia during exercise, less carbohydrate intake during exercise, less post-exercise hyperglycemia and more frequent (but less severe) nocturnal hypoglycemia than aerobic exercise. When aerobic and resistance exercise were combined, performing resistance exercise prior to aerobic exercise (rather than the reverse) resulted in attenuated declines in blood glucose during aerobic exercise, accompanied by a lower need for carbohydrate supplementation during exercise and a trend towards milder post-exercise nocturnal hypoglycemia.

In many African countries, including South Africa, much attention has been centred on the management of HIV/AIDS and tuberculosis epidemics. However, there is growing awareness in South Africa that life-style related non-communicable conditions, such as diabetes and obesity, represent an important health priority (Pirie, 2005:42). The general objective of this study was to evaluate the treatment of diabetic patients in clinics on primary healthcare level and to determine what contributions can be made in the prevention of diabetic complications. The research method consisted out of the selection of the study population, data collection (questionnaire) and the data analysis. There was no structural way of deciding which patients would be selected to be interviewed. As the patients arrived for their appointments the interviewer was informed. No patient was forced to participate in this study and after they agreed to the interview, they signed a consent form that releases the University of any liability that may occur and to give their permission for the interview. The questionnaire was compiled which covered all the aspects of diabetes. This included diagnostic data, life-style, well-being, compliance and monitoring. The researcher completed the questionnaires whilst interviewing the patients. The data obtained from the questionnaires were statistically analysed by using the Statistical Analysis System, SAS 9.1. Effect size, which was given by the Phi coefficient, was used as a descriptive statistic. In this particular study population, the majority of patients were classified as type 2 diabetics. This can be viewed in table 4.8 where 62.14% of the total study population was classified as group B, which means that these patients use oral glucose lowering drugs to control their disease. A further 33.98% of the population was classified as group C diabetics, which means that these patients need oral glucose lowering drugs as well as exogenous insulin to maintain a healthy life. The latter group obviously consists of patients whose diabetic status was not under control in the past, thus the need for the insulin. This clearly shows that these patients have not been informed about how they can manage the disease by dietary modification and lifestyle interventions. Lifestyle, socio-economic and education played a major role in the development of this disease in these patients. The weight status of the study population was determined and can be viewed in table 4.15. Only 20.39% of them were of normal weight with a body mass index (BMI) ranging between 18.5 - 24.9 kg/m2. 39.81% of them were overweight with their BMI ranging between 25 - 29.9 kg/m2 and the remaining 39.81% of the study population were classified as obese with their BMI's above 30 kg/m2. The majority (an estimated 80%) of the study population were above optimal weight. This may cause the development of chronic complications, such as retinopathy, neuropathy and nephropathy. The socio-economic status of the study population was relatively poor because of unemployment. Although 90.07% of them said they had no difficulty to follow their diet (table 4.56) almost half of the patients said they had some difficulty to get the correct food for their specific needs (table 4.53). The first may be because they are still eating they way they used to with no modifications and the latter may be because of their financial status. Not being able to find work has a major effect on their lives. They cannot afford to buy foods suitable for their needs. As previously stated, patient education is fundamental in the managing and controlling diabetes. When these patients were asked whether they know what diabetes is, and what the complications of the disease might hold, most of them answered that it means they have 'sugar', and cannot eat sugary foods any more. This clearly indicates that they did not have a complete knowledge of their disease. After having explained to them in uncomplicated terms what the disease implicates, many of them said it had not been not explained to them previously and that they now understood it better. It was concluded that the majority of the studied population were under a false impression of what diabetes implied. This is partly due to the lack of time the clinic staffs have to spend with each patient, educating them about the disease. One aspect that was most obvious during this study was the fact that an estimated 20% of all patients studied had their own blood glucose monitor (table 4.80). This is somewhat concerning because to have optimal control over one's blood glucose levels, one needs to has a blood glucose monitor for regular monitoring. An estimated 70% of the studied population measures their blood glucose only once a month when they attend the clinic for their monthly visit (table 4.81). This is not nearly enough to ensure optimal control. The average blood glucose levels were calculated and described in section 4.7. Even with the minimal measurement, about 50% of these patients' blood glucose levels were fairly under control with an average of 6-9mmol/L (table 4.88). But the other estimated 50% of the population were not controlled with averages of either below 5mmol/L or above 9mmol/L. This is concerning because the possibility that these uncontrolled cases may develop chronic complications, might be unavoidable unless they start taking control of their lives. And for this to happen, these patients need all the possible education from qualified health care providers and the support of their families. Certain recommendations and restrictions were formulated and discussed. / Thesis (M.Pharm. (Pharmacy Practice))--North-West University, Potchefstroom Campus, 2008.

Diss. (sammanfattning) Stockholm : Karolinska institutet, 2005. / Härtill 4 uppsatser.

Diss. (sammanfattning) Stockholm : Karol. inst., 2003. / Härtill 4 uppsatser.

Diss. (sammanfattning) Stockholm : Karol. inst., 2003. / Härtill 5 uppsatser.

Diss. (sammanfattning) Stockholm : Karol. inst., 2005. / Härtill 5 uppsatser.

Thesis (doctoral)--Lund University, 1995. / Added t.p. with thesis statement inserted.

Στην παρούσα διπλωματική παρουσιάζεται η εξομοίωση της μεθόδου για την μη επεμβατική μέτρηση της γλυκόζης στο αίμα. Η καρδιά της μεθόδου είναι ο μικροεπεξεργαστής της Texas Instruments Msp430f169, τον οποίο και προγραμματίσαμε σε γλώσσα C. Η φυσική αρχή λειτουργίας που στηριχθήκαμε είναι ο νόμος απορρόφησης του φωτός του Beer - Lambert. Το αναλογικό σήμα το μετατρέψαμε σε ψηφιακό με τη χρήση του ADC12 μετατροπέα, το οποίο είναι περιφερειακό στοιχείο του μικροεπεξεργαστή. / This thesis presents the simulation method for non-invasive measurement of blood glucose. The heart of this method is its microprocessor, Texas Instruments Msp430f169, which was programmed in language C. The physical principle of operation supported is the law of light absorption of the Beer - Lambert. The analog signal is converted to digital, using the ADC12 converter, which is a regional component of the microprocessor.

Aim The aim of this study is to form a descriptive picture of Swedish national elite (SNLE) swimmers blood glucose (BG) levels in relation to their nutritional intake.  - Do SNLE swimmers have BG level in a normal range of ≥3.9 mmol/L when measured during a six-day period? - Is there a relationship between the SNLE swimmers blood glucose levels and how often or when they eat, and how they perceive their workout effort? Method Four test subjects, three females and one male, were recruited based on their competitive level, National Championship qualifying cut, geographical location, in Stockholm, and amount of swimming workouts completed weekly, seven on average. The test subjects wore a Continuous Glucose Monitor for a week and in addition a brief meal journal and perceived effort (ranking) of each workout was recorded. The data gathered was analyzed based on three main variables; time spent LOW (blood glucose level below 3.9mmol/L), amount of meals, and ranking. Results This study found that three out of four test subjects had occurrences of a LOW during the week. The test subjects spent on average 1.75 ±1.26 days with a LOW, the average time spent with a LOW per day was 37.3 ±29.7 minutes, the average amount during the whole week was 224 ±177 minutes, and the percent of the whole week spent with a LOW was 2.59 ±0.02%. In addition to this a correlation was found between (total) time spent LOW and (total) amount of meals with an R-value of 0.99, an R2 of 0.979, and P-value of 0.044. Conclusions The results showed that three test subjects spent time with a LOW and indicates similar results should be found in the general population of SNLE swimmers, however, this is definitely in need of further research. The results also indicated that there is a relationship between the amount of meals ingested and the BG level over the full six-day period but not on a daily basis. A regression analysis between (total) time spent LOW and (total) number of meals showed correlations with statistical significance, however, there was too small of a data sample (N=4) to draw conclusions based from this. This study forms a descriptive picture of the situation, which could serve as a platform for further research in this field, and give a first glance at the possible potential use of CGM systems within the sports nutrition field.