International Investment Report Malaysia Company Essay

International Investment Report Country: Malaysia Company: Tesla 1. Introduction Country Basics Malaysia is a federation of thirteen states and three federal territories in Southeast Asia. The capital of Malaysia is Kuala Lumpur which has a population of almost seven million. It consists of two regions separated by the South China Sea and is bordered by Brunei, Indonesia and Thailand. The government is Constitutional Monarchy, where head of state is king and head of government is prime-minister. The economic system is mixed where private freedom combines with centralized economic planning and government regulation. Malaysia is a member of Asia Pacific Economic Co-operation(APEC), the Association of Southeast Asian Nations(ASEAN), and the Trans-Partnership(TPP). Company Basics Tesla motors was founded in 2003 by a group of engineers in Silicon Valley. The company was founded on the guiding mission statement; to accelerate the world’s transition to sustainable energy. 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Analysis Of The Book Bas N ¡ Ball By James Naismith

Bas ·ket ·ball(ˈbaskÉ™tËÅ'bà ´l)- a game played between two teams of five players in which goals are scored by throwing a ball through a netted hoop fixed above each end of the court. If you were to ask the average person what basketball is that is the answer you ll probably get, but it is much more than that. Bas ·ket ·ball- a game that has evolved drastically invented by James Naismith played by millions of people around the world. Ah! That s like it. Basketball has changed a lot over the past years and still continues to grow, but many wonder â€Å"How did this all start?† The start of basketball began in the early 1890s. Brian Faurschou acknowledges that Well before James Naismith invented the game of basketball in 1891, the peoples of Mesoamerica had a very similar game where the point was to get a ball through a hoop. Ancient Mexican people such as the Aztecs and the Mayans had very similar games to basketball, but instead of a ball they used their conquered foes skulls. He also implies that The first basketball type game may have been played by the early Olmec people of Ancient Mexico as early as 500 years ago (Faurschou). The game of basketball was invented by Dr. James Naismith. Naismith, a P.E. teacher from Canada, invented the game in 1891. At the time James Naismith as working at the YMCA Training School in Massachusetts (Who Invented). Wyckoff implies that James was great at teaching summer sports, but when winter came, there was a problem. The men at the college were

Enhancing Logistical Process of E-Commerce

Question: Discuss about the Enhancing Logistical Process of E-Commerce. Answer: Introduction: I am studying a course on Engineering Management which a practice that combines the technical skills of engineering with the business and management skills so as to prepare us on solving business problems that are complex in nature. A practical understanding of the business situation is needed to be a good engineering manager in addition to the technical knowledge is required. This includes practical understanding of engineering management processes used in companies(Belanger, Pupulin, Coope, P.Geoph, Lowe, 2004). As a professional, I can assume from any of the available roles such as project manager, systems manager, technical consultant, corporate strategy planner, strategic consulting manager, and material logistics professional. Logistics being one professional area of development for management engineers attracts me. With an increase in the ecommerce organizations in India, it is seen that logistics decisions play a critical role in ecommerce space. I would develop myself as a professional engineering manager who can provide supply chain consulting to ecommerce organizations. Thus, I have chosen a practical project on ecommerce logistics which aims at understanding how logistics operations can be established and streamlined to achieve strategic performance advantages. This would help me understand how Indian ecommerce organizations work and manage their logistics and develop myself. This knowledge would make a pathway for me to reach out to organizations into ecommerce space after I graduate as they would be able to see my potential as an Engineering manager from the research I would be conducting(Lloyd, 2000). Professionalism in Engineering demands specialized technical knowledge on subject which I have with my college learning which would help in assessing operational alternatives. As a professional engineering manager, I would also need to have certain skill sets including ability to solve challenging issues, ability to do modeling for visualizing solutions, and ability to apply theories learnt or abstract concepts learnt to practical engineering applications. Logistics is a complex problem to solve when it is about developing in-house capabilities for an organization and thus, this project would help me gain a practical problem solving skills. Also, as I am taking the case of a company that has already developed its logistics that I would have to visualize to create a model to present during my research, the project would also help in understanding the concepts of business modeling. Further, many of the concepts and theories that I have learnt during my classroom sessions would be put to use in practice through this project. I have learnt about plant layouts, material handling, supply chain networks, and systems engineering that would be very useful as I would be able to apply them to the case study and learn the professional trait of applying theories to practice(Szpytko J.Lis, 2005). Besides these traits, a professional engineering manager also needs to have commitment to standards, ethical responsibilities, ability to handle uncertainty, communication skills, ability to appreciate management concepts, and ability to work with, lead or manage teams. As I am focused on one organization and would conduct research on the same, this would give me an opportunity to work with teams and develop these professional traits. Research Background In-sourcing of logistics services has become less use practice used by organizations all over the world because of costs and complexities involved. There are only a third of multinational organizations that have in sourced operations including logistics. Outsourcing on the other side has many advantages such as lower costs, more flexibility, access to more capabilities not present in-house, facilities consolidation, faster entry into new market and so on. Organizational Background The chosen organization is Flipkart which is into ecommerce space and had started with outsourcing model to slowly move to adoption of the in-sourced logistics. The company was founded in 2007 by IIT; Delhi Alumnis and is headquartered in Bangalore. It started as a book store but now sells an array of products through ecommerce including electronics, white goods, consumer goods, lifestyle products, stationeries and more(NIGAM, NAYAK, VYAS, 2015). Aims Objectives Aim of this research is to discover ways organizations can use in-sourced logistics to build on strategic advantages and achieve better performance in the ecommerce industry. Specific objectives of the research would be: Understanding the impact of outsourcing and in-sourcing of logistics on organizational supply chains Understand how Indian ecommerce operate their logistics Explore the impact of in-sourced logistics on ecommerce organizations performances Literature Review When an organization procures components required for its production processes from the company itself, the process of in-sourcing. In-sourcing can be done within the home ground, process called onshore in sourcing or through a foreign subsidiary, which is called offshore in sourcing. A major challenge with in sourcing is that the flow of products or its components happens across transnational borders making it difficult to make interpretations of the trade statistics. Outsourcing can help company reduce these complexities while at the same time, reduce investments and get more returns. There are other benefits of outsourcing that further add value to the arrangement such as access to different supplier capabilities, improved performance, and more. This is the reason why many international organizations have higher focus on the outsourcing of their operations. However, in 2005, Gottfredson, Puryear, and Phillips discovered that around 50% of these organizations adopting outsourcing model actually fell short of expectations. There were only 10% of these companies that were able to save costs and only 6% were satisfied with the overall performance of outsourced operations(Akili, 2011). Watson, Zinkhan, and Pitt had a theory devised a framework for understanding performance implications of outsourcing. The framework identifies two types of costs that are incurred in the outsourcing model - coordination costs and suboptimality costs. Networks can increase both increase or decrease coordination costs based on the level of autonomy(Miguel, 2005). Early adopters of outsourcing usually gain high returns while late adopters gain more of institutional legitimacy. The relationship between outsourcing and performance is affected in the long run and if there is too much of outsourcing, the performance can suffer. With more and more operations outsourced, management of multiple suppliers becomes difficult adding to coordination costs. Further, the cost of offshore outsourcing is much more than domestic outsourcing. Rottman and Lacity conducted a study on US companies and found that offshore outsourcing projects cost them 50% of whole contract prices while domestic outsourcing contributed up to 10%. Offshore outsourcing models can majorly be affected by currency exchange rate fluctuations adding to costs of operations(Li, Fan, Abid, Haartman, 2014). The ecommerce organization outsourcing its logistics operations risk losing their differentiation as they cannot differentiate their services against competition. They also lose on the bargaining power with suppliers as more and more organizations start to outsource their logistics to limited suppliers. Another challenge with outsourcing of logistics is that interfaces that connect the firm with customers reduce causing a lack of learning and thus, doing innovation becomes difficult(Kotabe, Mol, Murray, 2007). In the long term, outsourcing has a negative impact on performance and thus, it can be said that lesser the outsourcing, better is the performance of the organization. While in in-sourcing, there is more control over functions and thus, there are no hidden costs that can affect the operations in longer run. Instead, the company with in-sourced logistics can actually achieve economies of scale over time and thus, gain a competitive advantage(ASL, 2014). The choice of the logistics sourcing from outsourcing and in-sourcing can actually be affected by various factors other than their direct and indirect company advantages. These factors can be differences between countries, industries, and transactions. Walker has identified two levels of uncertainties in contracts including volume uncertainty which can increase costs when volumes are inappropriately estimated making in-sourcing of logistics a better approach while technological uncertainties raise questions on future technological developments making outsourcing a better option(Daniel Nordigardeh Chicksand, 2015). Research Methodology This research would use a secondary case analysis methodology to identify ways organizations can use in-sourced logistics practices to improve upon its strategic capabilities and achieve better performance for their ecommerce business. The research would collect textual data of a selected organization with in-sourced logistics and would analyze their business and supply chain models. For collecting secondary data on Flipkart supply chain and logistics practices, the researcher would make use of journal articles, books, Flipkart website, its case studies, news sources, and other authentic data sources about company operations. The case study would explore the logistics operations of the organization, would make an attempt to understand their decision of moving to in-source logistics and explore the performance benefits achieved by the organization after adopting in-house logistics. References Akili, E. A. (2011). Impact of the design of Logistics Outsourcing strategy on the firms logistic performance. University of Adger. ASL. (2014). Outsourcing versus Insourcing. ASL. Belanger, R., Pupulin, R., Coope, H., P.Geoph, Lowe, G. (2004). Concept of Professionalism. APEGGA. Daniel Nordigardeh, J. R., Chicksand, D. (2015). Sourcing Uncertainties: The Case of Outsourcing and Global Sourcing. Modern Management Science Engineering . Kotabe, M., Mol, M. J., Murray, J. Y. (2007). Outsourcing, performance, and the role of e-commerce: A dynamic perspective. University of Warwick . Li, Y., Fan, R., Abid, M., Haartman, R. v. (2014). The coordination of E-commerce and Logistics: A case study of Amazon.com. Hogskolan Igavle. Lloyd, B. E. (2000). Engineering Professionalism. Journal of The Institution of Engineers. Miguel, P. T. (2005). The Impact Of Logistics And Manufacturing Outsourcing On Shareholder Value . Graduate School of Vanderbilt University . NIGAM, P., NAYAK, K. M., VYAS, P. H. (2015). E- Commerce Challenges: A Case Study of Flipkart.com Versus Amazon. in. Indian Journal of Applied Research, 332-339. Szpytko, J., J.Lis. (2005). Professionalism in engineering practice. Mickiewicza: AGH University of Science and Technology.

Technology Has Changed the Live of Teen Agers Essay Example

Technology Has Changed the Live of Teen Agers Essay DOI: 10. 1111/j. 1464-5491. 2006. 01868. x Glycaemic control Review Article 23 0742-3071Publishing, alcohol Diabetic Medicine and2006 consumption D. Ismail et al. DME UK Oxford, article Blackwell Publishing Ltd Social consumption of alcohol in adolescents with Type 1 diabetes is associated with increased glucose lability, but not hypoglycaemia D. Ismail, R. Gebert, P. J. Vuillermin, L. Fraser*, C. M. McDonnell, S. M. Donath†  and F. J. Cameron Abstract Department of Endocrinology and Diabetes, Royal Children’s Hospital, Melbourne, *Wimmera Base Hospital*, Horsham and † Clinical Epidemiology and Biostatistics Unit, Royal Children’s Hospital, Melbourne, Australia Accepted 10 June 2005 Aims To determine the effects of social consumption of alcohol by diabetic adolescents on glycaemic control. Methods Fourteen (five male) patients aged 16 years were recruited from the diabetes clinic at the Royal Children’s Hospital. The continuous glucose monitoring system (CGMS) was attached at a weekend when alcohol consumption was planned for one night only. For each patient, the 12-h period from 18. 00 h to 06. 00 h for the night with alcohol consumption (study period) was compared with the same period with non-alcohol consumption (control period) either 24 h before or after the alcohol study night. Thus, each subject was his /her own control. Glycaemic outcomes calculated from continuous glucose monitoring included mean blood glucose (MBG), percentage of time spent at low glucose levels (CGMS 4. 0 mmol/l), normal glucose levels (CGMS 4. 0–10. 0 mmol/ l) and high glucose levels ( 10. mmol/ l) and continuous overall net glycaemic action (CONGA). Results The mean number of standard alcohol drinks consumed during the study period was 9. 0 for males and 6. 3 for females. There was no difference in percentage of time at high and normal glucose levels in the study and control periods. During the control period, there was a higher percentage of time with low glucose levels compared with the study period (P 0. 05). There was an increas ed level of glycaemic variation during the study time when compared with the control period. We will write a custom essay sample on Technology Has Changed the Live of Teen Agers specifically for you for only $16.38 $13.9/page Order now We will write a custom essay sample on Technology Has Changed the Live of Teen Agers specifically for you FOR ONLY $16.38 $13.9/page Hire Writer We will write a custom essay sample on Technology Has Changed the Live of Teen Agers specifically for you FOR ONLY $16.38 $13.9/page Hire Writer Conclusions In an uncontrolled, social context, moderately heavy alcohol consumption by adolescents with Type 1 diabetes appears to be associated with increased glycaemic variation, but not with low glucose levels. Diabet. Med. 23, 830–833 (2006) Keywords adolescence, alcohol, glycaemic control Abbreviations CGMS, continuous glucose monitoring system; CONGA, continuous overall net glycaemic action; MBG, mean blood glucose; RCH, Royal Children’s Hospital Introduction Adolescents with Type 1 diabetes frequently engage in risk-taking activities [1]. Amongst these activities is the social Correspondence to: Dr Fergus Cameron, Deputy Director, Department of Endocrinology and Diabetes, Royal Children’s Hospital, Flemington Road, Parkville, Victoria 3052, Australia. E-mail: fergus. [emailprotected] org. au consumption of alcohol, frequently as underage drinkers [2]. Whilst the effects of alcohol consumption upon glycaemia have been well described in a controlled setting [3– 6], little is known about the impact on glucose levels of alcohol consumption by adolescents within an ambulant, social context. The purpose of this project was to utilize continuous glucose monitoring to study the impact of social alcohol consumption on glycaemic control in a group of alcohol-using adolescents.  © 2006 The Authors. 830 Journal compilation  © 2006 Diabetes UK. Diabetic Medicine, 23, 830–833 Review article 831 Patients and methods This study was approved by the Human Ethics Research Committee of the Royal Children’s Hospital (RCH). That approval was contingent upon the fact that the investigators should not be seen to encourage underage drinking in adolescents. Consequently, we only approached adolescents who we knew were drinking socially and, despite our previous counselling, elected to continue to drink alcohol on a semi-regular basis. We recruited 22 adolescents with Type 1 diabetes from the RCH diabetes clinic. The adolescents were considered eligible only if 16 years old and parental/patient consent was obtained. HbA 1c (Bayer DCA 2000 immunoagglutination method, Calabria, Barcelona, Spain) was measured, and diabetes duration and insulin doses were recorded. The MiniMed continuous glucose monitoring system (CGMS) was attached to the study patients over a weekend period. Patients were required to have an alcohol-free period for at least 24 continuous hours during the weekend trace period. A diary was kept of activities during the trace period (insulin injections, meal, snacks, dancing, alcohol consumption, sport). There was no change in insulin doses between study and control periods. In the evening when alcohol was consumed, patients were asked to recall how many and what type of drinks were consumed and how inebriated they became. Patients recall of alcohol consumption was converted to ‘standard drinks’ (one standard drink contains the equivalent of 12. ml 100% alcohol) using The Australian Alcohol Guidelines [7]. CGMS data was recorded between 18. 00 and 06. 00 h on the evening when alcohol was consumed (the study period) and between 18. 00 and 06. 00 h on the evening when no alcohol was consumed (the control period). CGMS data were only analysed if there had been regular calibrations with intermittent capillary blood glucose readings at a maximum of 8-h intervals. Each CGMS trace was qualitatively and quantitatively analysed using mean glucose values, per cent time in glycaemic ranges and ontinuous overlapping net glycaemic action (CONGA) [8]. CONGA values were calculated to assess glycaemic variation over 1-, 2- and 4-h intervals. Low glucose values were defined as CGMS values 4 mmol/ l, normal glucose values when CGMS values were 4– 10 mmo/ l and high glucose values when CGMS values were 10 mmol/ l. Each patient acted as their own control with study periods and control periods being compared. Inter-individual values were grouped for comparison. Differences between study and control periods were analysed using paired t-tests. Analyses were done in Stata [9]. ales and nine females. The mean age was 18. 5 years (range: 17. 4 – 19. 5). The mean duration of diabetes was 9. 4 years (range: 3 – 16. 3). Six of our subjects took four insulin injections per day and eight took two injections daily . The mean insulin dose was 1. 1 units /kg/day (range: 0. 7 –1. 8), and the mean HbA1c was 9. 6% (range: 8. 2 – 10. 8). Activities during the study period Thirteen subjects had dinner before drinking and only one subject did not consume any food before going out. Three subjects ‘danced a lot’ and six subjects went dancing but did not dance a lot. Ten subjects had something to eat after drinking. Alcohol consumption during the study period The mean number of alcohol drinks consumed on the study night was 9. 0 (range 3–16) for males and 6. 3 (range 3–14) for females. All the females consumed pre-mixed sweetened alcohol drinks (5% alcohol), with only one consuming beer and one consuming wine. Four of the males consumed mixed spirits, one mixed spirits and beer and one beer only. Forty per cent of the males had more than seven standard drinks during the study and 67% of the females had more than five drinks. In total, 80% of the subjects had pre-mixed sweetened alcohol drinks at some point during the study period. Forty-three per cent of the subjects reported that they became inebriated and 14. 3% consumed alcohol to the point where they became physically sick. None of the subjects lost consciousness or took recreational drugs during the study period. Comparative CGMS data between study and control periods Results Patients There was no significant difference between the overall mean glucose levels of patients when comparing study and control periods (Table 1; P = 0. 43). Similarly, there were no significant differences in the amount of time spent with either normal or high glucose values between study and control periods (Table 1). A larger proportion of time was spent with low glucose values during the control period when compared with the study period (1. 9 vs. 16. 8%, P = 0. 03). A significantly larger degree of glycaemic variation was seen in the CONGA values in the study period when compared with the control period (Table 1). The difference in CONGA values were consistent and independent of whether glycaemic variation was assessed over 1-, 2- or 4-h intervals. Of the 22 subjects recruited, eight were excluded because their CGMS traces did not have sufficiently frequent calibration points with intermittent capillary measures of blood glucose. Of the 14 subjects remaining, we were able to obtain study period data on 14 patients and matched control period data on only 12 patients. The study period occurred on the night prior to the control period in nine subjects. There were five Discussion It has long been recognized that a prohibitionist approach is usually ineffective when counselling adolescents who engage in risk-taking behaviours [10]. Many centres today, ourselves included, have instead adopted a harm minimization approach in dealing with such behaviours. An important component  © 2006 The Authors. Journal compilation  © 2006 Diabetes UK. Diabetic Medicine, 23, 830–833 832 Glycaemic control and alcohol consumption †¢ D. Ismail et al. Outcome measure Mean difference between Study period Control period study period and mean value mean value control period (95%CI) P-value 10. 6 16. 8 58. 6 24. 6 2. 1 3. 2 3. 7 1. 2 (? 2. 1, 4. 4) ? 14. 9 (? 28. 1, ? 1. 8) ? 0. 8 (? 27. 3, 25. 8) 15. 7 (? 4. 5, 35. 8) 0. 6 (0. 2, 1. 0) 1. 1 (0. , 1. 9) 1. 8 (0. 4, 3. 1) 0. 43 0. 03 0. 95 0. 12 0. 006 0. 01 0. 01 Table 1 CGMS outcomes, study and control periods Blood glucose levels (mmol/l) 11. 8 Per cent time low glucose 1. 9 Per cent time high glucose 57. 8 Per cent time normal glucose 40. 3 CONGA1* 2. 7 CONGA2* 4. 3 CONGA4* 5. 5 *CONGA calculated at 1-, 2- and 4-h intervals. CONGAn is the standard deviation of differ ent glucose measures n hours apart for the duration of the CGMS trace. of counselling using a harm minimization approach is that the information provided be credible and reflective of ‘real’ or ‘lived’ circumstances. Continuous glucose monitoring provides a technique whereby the glycaemic consequences of various behaviours can be documented in an ambulant or non-artificial setting. Adolescents with Type 1 diabetes frequently consume alcohol in a social context [11]. Alcohol is known to inhibit the gluconeogenic pathway, to inhibit lipolysis, impair glucose counter-regulation and blunt hypoglycaemia awareness [3,4]. Previous studies in young adults with Type 1 diabetes have shown that moderate consumption of alcohol in the evenings without concomitant food intake may cause hypoglycaemia the following morning [5]. Consumption of alcohol after a meal, however, has shown no similar adverse effects on glucose [6]. It is reasonable to assume, therefore, that alcohol consumption may be a significant risk factor for hypoglycaemia in adolescents with Type 1 diabetes [5]. Studies of the glycaemic effects of alcohol consumption in an ambulant adolescent/young adult population can be difficult. This is because such behaviours are uncontrolled, often spontaneous and usually in the context of other social activities (parties, dancing, etc. ). In order to ensure that we only reported accurate CGMS data during these activities, capillary blood glucose calibration was considered vital and those patients who failed in this regard were excluded from analysis. Just over 60% of the patients recruited were able to successfully wear and calibrate a CGMS unit during these activities. Given that patients who experience hypoglycaemic symptoms are more likely to perform capillary self measures of blood glucose, we feel that it is unlikely that those patients excluded from the analysis had a greater frequency of hypoglycaemia than those patients reported. We were unable to record our subjects’ alcohol consumption in a contemporaneous fashion and hence were reliant upon their recall. It is possible that their remembered patterns of consumption were not entirely accurate. This potential inaccuracy should not be seen as a weakness of this study, as we only set out to determine patterns of glycaemia in adolescents engaging in spontaneous and uncontrolled alcohol consumption. We neither specified the type nor the amount of alcohol to be consumed (our ethical approval was contingent on this not occurring). The data as to amount of alcohol consumed have been included for descriptive purposes only. The results of this study show that alcohol consumption by adolescents in a social context is associated with a greater degree of glycaemic variation and less time spent with low glucose values than evenings where no alcohol is consumed. Whilst the second of these findings appears counter-intuitive, there may be several possible explanations. Firstly, the vast majority of our study group ate a meal prior to going out and ate upon their return before going to bed. These are practices that we have instilled as harm minimization strategies to avoid alcohol-induced hypoglycaemia in our clinic. Secondly, most of the alcohol consumed was as pre-mixed spirit and sweetened, carbonated beverages. Finally, alcohol consumption was only associated with vigorous exercise (dancing) in a minority of our study group. All of these factors could have combined to negate the hypoglycaemic effects of alcohol. In a previous study of glycaemia during alcohol consumption in adult men [5], hypoglycaemia occurred most often 10–12 h after wine consumption when the evening before ended at 23. 0 h. We analysed our data to see if a similar phenomenon occurred in this study and found that the per cent of time spent with CGMS readings 4 mmol/l between 06. 00 and 12. 00 h on the morning after the study period (i. e. the morning after the drinking night) was only 1. 1%. Notwithstanding the fact that our cohort frequently consumed alcohol later than 23. 00 h, the facto rs that impacted upon glycaemic control during the study night appear to have carried over to the ‘morning after’. The findings in this study highlight the importance of ambulant testing. It is important to note that the findings of the group studied here may not be seen in adolescents who drink non-sweetened alcoholic drinks or in those adolescents with better underlying metabolic control. Whilst alcohol consumption in isolation may reasonably be thought to cause hypoglycaemia, alcohol consumption by adolescents in the context of meals, sweetened mixers and little activity did not result in more hypoglycaemia than an alcohol-free evening. Whether the increase in glycaemic variation seen on an evening  © 2006 The Authors. Journal compilation  © 2006 Diabetes UK. Diabetic Medicine, 23, 830–833 Review article 833 of alcohol consumption has negative clinical outcomes remains an area for further investigation. Competing interests CMM was a Novo Nordisk research fellow. FJC received fees for speaking at conferences and funds for research from Novo Nordisk. References 1 Cameron F, Werther G. Adolescents with diabetes mellitus. In: Menon, RK, Sperling, MA, eds. Pediatric Diabetes. Boston: Kluwer Academic Publishers, 2003: 319–335. 2 Frey MA, Guthrie B, Lovelandcherry C, Park PS, Foster CM. Risky behaviours and risk in adolescents with IDDM. J Adol Health 1997; 20: 38–45. 3 Avogaro A, Beltramello P, Gnudi L, Maran A, Valerio A, Miola M et al. Alcohol intake impairs glucose counterregulation during acute insulin-induced hypoglycaemia in IDDM patients. Diabetes 1993; 42: 1626–1634. 4 Kerr D, Macdonald IA, Heller SR, Tattersal RB. Alcohol causes hypoglycaemic unawareness in healthy volunteers and patients with type 1 diabetes. Diabetologia 1990; 33: 216–221. 5 Turner BC, Jenkins E, Kerr D, Sherwin RS, Cavan DA. The effect of evening alcohol consumption on next morning glucose control in type 1 diabetes. Diabetes Care 2001; 24: 1888–1893. 6 Koivisto VA, Tulokas S, Toivonen M, Haapa E, Pelkonen R. Alcohol with a meal has no adverse effects on postprandial glucose homeostasis in diabetic patients. Diabetes Care 1993; 16: 1612–1614. 7 National Health and Medical Research Council. Australian Alcohol Guidelines: Health Risks and Benefits. DS9. Available from: http://www7. health. gov. au/nhmrc/publications/synopses/ds9syn. htm. 8 McDonnell CM, Donath SM, Vidmar SI, Werther GA, Cameron FJ. A novel approach to continuous glucose analysis utilising glycaemic variation. Diab Tech Therap 2005; 7: 253–263. 9 StataCorp. Stata statistical software. Release 8. 0. College Station, TX: Stata Corporation, 2003. 10 Kyngas H, Hentinen M, Barlow JH. Adolescents perceptions of physicians, nurses, parents and friends: help or hindrance in compliance with diabetes self-care? J Adv Nurs 1998; 27: 760–769. 11 Patterson JM, Garwick AW. Coping with chronic illness. In: Werther, GA, Court, JM, eds. Diabetes and the Adolescent. Melbourne: Miranova Publishers 1998, 3–34.  © 2006 The Authors. Journal compilation  © 2006 Diabetes UK. Diabetic Medicine, 23, 830–833 Technology Has Changed the Live of Teen Agers Essay Example Technology Has Changed the Live of Teen Agers Essay DOI: 10. 1111/j. 1464-5491. 2006. 01868. x Glycaemic control Review Article 23 0742-3071Publishing, alcohol Diabetic Medicine and2006 consumption D. Ismail et al. DME UK Oxford, article Blackwell Publishing Ltd Social consumption of alcohol in adolescents with Type 1 diabetes is associated with increased glucose lability, but not hypoglycaemia D. Ismail, R. Gebert, P. J. Vuillermin, L. Fraser*, C. M. McDonnell, S. M. Donath†  and F. J. Cameron Abstract Department of Endocrinology and Diabetes, Royal Children’s Hospital, Melbourne, *Wimmera Base Hospital*, Horsham and † Clinical Epidemiology and Biostatistics Unit, Royal Children’s Hospital, Melbourne, Australia Accepted 10 June 2005 Aims To determine the effects of social consumption of alcohol by diabetic adolescents on glycaemic control. Methods Fourteen (five male) patients aged 16 years were recruited from the diabetes clinic at the Royal Children’s Hospital. The continuous glucose monitoring system (CGMS) was attached at a weekend when alcohol consumption was planned for one night only. For each patient, the 12-h period from 18. 00 h to 06. 00 h for the night with alcohol consumption (study period) was compared with the same period with non-alcohol consumption (control period) either 24 h before or after the alcohol study night. Thus, each subject was his /her own control. Glycaemic outcomes calculated from continuous glucose monitoring included mean blood glucose (MBG), percentage of time spent at low glucose levels (CGMS 4. 0 mmol/l), normal glucose levels (CGMS 4. 0–10. 0 mmol/ l) and high glucose levels ( 10. mmol/ l) and continuous overall net glycaemic action (CONGA). Results The mean number of standard alcohol drinks consumed during the study period was 9. 0 for males and 6. 3 for females. There was no difference in percentage of time at high and normal glucose levels in the study and control periods. During the control period, there was a higher percentage of time with low glucose levels compared with the study period (P 0. 05). There was an increas ed level of glycaemic variation during the study time when compared with the control period. We will write a custom essay sample on Technology Has Changed the Live of Teen Agers specifically for you for only $16.38 $13.9/page Order now We will write a custom essay sample on Technology Has Changed the Live of Teen Agers specifically for you FOR ONLY $16.38 $13.9/page Hire Writer We will write a custom essay sample on Technology Has Changed the Live of Teen Agers specifically for you FOR ONLY $16.38 $13.9/page Hire Writer Conclusions In an uncontrolled, social context, moderately heavy alcohol consumption by adolescents with Type 1 diabetes appears to be associated with increased glycaemic variation, but not with low glucose levels. Diabet. Med. 23, 830–833 (2006) Keywords adolescence, alcohol, glycaemic control Abbreviations CGMS, continuous glucose monitoring system; CONGA, continuous overall net glycaemic action; MBG, mean blood glucose; RCH, Royal Children’s Hospital Introduction Adolescents with Type 1 diabetes frequently engage in risk-taking activities [1]. Amongst these activities is the social Correspondence to: Dr Fergus Cameron, Deputy Director, Department of Endocrinology and Diabetes, Royal Children’s Hospital, Flemington Road, Parkville, Victoria 3052, Australia. E-mail: fergus. [emailprotected] org. au consumption of alcohol, frequently as underage drinkers [2]. Whilst the effects of alcohol consumption upon glycaemia have been well described in a controlled setting [3– 6], little is known about the impact on glucose levels of alcohol consumption by adolescents within an ambulant, social context. The purpose of this project was to utilize continuous glucose monitoring to study the impact of social alcohol consumption on glycaemic control in a group of alcohol-using adolescents.  © 2006 The Authors. 830 Journal compilation  © 2006 Diabetes UK. Diabetic Medicine, 23, 830–833 Review article 831 Patients and methods This study was approved by the Human Ethics Research Committee of the Royal Children’s Hospital (RCH). That approval was contingent upon the fact that the investigators should not be seen to encourage underage drinking in adolescents. Consequently, we only approached adolescents who we knew were drinking socially and, despite our previous counselling, elected to continue to drink alcohol on a semi-regular basis. We recruited 22 adolescents with Type 1 diabetes from the RCH diabetes clinic. The adolescents were considered eligible only if 16 years old and parental/patient consent was obtained. HbA 1c (Bayer DCA 2000 immunoagglutination method, Calabria, Barcelona, Spain) was measured, and diabetes duration and insulin doses were recorded. The MiniMed continuous glucose monitoring system (CGMS) was attached to the study patients over a weekend period. Patients were required to have an alcohol-free period for at least 24 continuous hours during the weekend trace period. A diary was kept of activities during the trace period (insulin injections, meal, snacks, dancing, alcohol consumption, sport). There was no change in insulin doses between study and control periods. In the evening when alcohol was consumed, patients were asked to recall how many and what type of drinks were consumed and how inebriated they became. Patients recall of alcohol consumption was converted to ‘standard drinks’ (one standard drink contains the equivalent of 12. ml 100% alcohol) using The Australian Alcohol Guidelines [7]. CGMS data was recorded between 18. 00 and 06. 00 h on the evening when alcohol was consumed (the study period) and between 18. 00 and 06. 00 h on the evening when no alcohol was consumed (the control period). CGMS data were only analysed if there had been regular calibrations with intermittent capillary blood glucose readings at a maximum of 8-h intervals. Each CGMS trace was qualitatively and quantitatively analysed using mean glucose values, per cent time in glycaemic ranges and ontinuous overlapping net glycaemic action (CONGA) [8]. CONGA values were calculated to assess glycaemic variation over 1-, 2- and 4-h intervals. Low glucose values were defined as CGMS values 4 mmol/ l, normal glucose values when CGMS values were 4– 10 mmo/ l and high glucose values when CGMS values were 10 mmol/ l. Each patient acted as their own control with study periods and control periods being compared. Inter-individual values were grouped for comparison. Differences between study and control periods were analysed using paired t-tests. Analyses were done in Stata [9]. ales and nine females. The mean age was 18. 5 years (range: 17. 4 – 19. 5). The mean duration of diabetes was 9. 4 years (range: 3 – 16. 3). Six of our subjects took four insulin injections per day and eight took two injections daily . The mean insulin dose was 1. 1 units /kg/day (range: 0. 7 –1. 8), and the mean HbA1c was 9. 6% (range: 8. 2 – 10. 8). Activities during the study period Thirteen subjects had dinner before drinking and only one subject did not consume any food before going out. Three subjects ‘danced a lot’ and six subjects went dancing but did not dance a lot. Ten subjects had something to eat after drinking. Alcohol consumption during the study period The mean number of alcohol drinks consumed on the study night was 9. 0 (range 3–16) for males and 6. 3 (range 3–14) for females. All the females consumed pre-mixed sweetened alcohol drinks (5% alcohol), with only one consuming beer and one consuming wine. Four of the males consumed mixed spirits, one mixed spirits and beer and one beer only. Forty per cent of the males had more than seven standard drinks during the study and 67% of the females had more than five drinks. In total, 80% of the subjects had pre-mixed sweetened alcohol drinks at some point during the study period. Forty-three per cent of the subjects reported that they became inebriated and 14. 3% consumed alcohol to the point where they became physically sick. None of the subjects lost consciousness or took recreational drugs during the study period. Comparative CGMS data between study and control periods Results Patients There was no significant difference between the overall mean glucose levels of patients when comparing study and control periods (Table 1; P = 0. 43). Similarly, there were no significant differences in the amount of time spent with either normal or high glucose values between study and control periods (Table 1). A larger proportion of time was spent with low glucose values during the control period when compared with the study period (1. 9 vs. 16. 8%, P = 0. 03). A significantly larger degree of glycaemic variation was seen in the CONGA values in the study period when compared with the control period (Table 1). The difference in CONGA values were consistent and independent of whether glycaemic variation was assessed over 1-, 2- or 4-h intervals. Of the 22 subjects recruited, eight were excluded because their CGMS traces did not have sufficiently frequent calibration points with intermittent capillary measures of blood glucose. Of the 14 subjects remaining, we were able to obtain study period data on 14 patients and matched control period data on only 12 patients. The study period occurred on the night prior to the control period in nine subjects. There were five Discussion It has long been recognized that a prohibitionist approach is usually ineffective when counselling adolescents who engage in risk-taking behaviours [10]. Many centres today, ourselves included, have instead adopted a harm minimization approach in dealing with such behaviours. An important component  © 2006 The Authors. Journal compilation  © 2006 Diabetes UK. Diabetic Medicine, 23, 830–833 832 Glycaemic control and alcohol consumption †¢ D. Ismail et al. Outcome measure Mean difference between Study period Control period study period and mean value mean value control period (95%CI) P-value 10. 6 16. 8 58. 6 24. 6 2. 1 3. 2 3. 7 1. 2 (? 2. 1, 4. 4) ? 14. 9 (? 28. 1, ? 1. 8) ? 0. 8 (? 27. 3, 25. 8) 15. 7 (? 4. 5, 35. 8) 0. 6 (0. 2, 1. 0) 1. 1 (0. , 1. 9) 1. 8 (0. 4, 3. 1) 0. 43 0. 03 0. 95 0. 12 0. 006 0. 01 0. 01 Table 1 CGMS outcomes, study and control periods Blood glucose levels (mmol/l) 11. 8 Per cent time low glucose 1. 9 Per cent time high glucose 57. 8 Per cent time normal glucose 40. 3 CONGA1* 2. 7 CONGA2* 4. 3 CONGA4* 5. 5 *CONGA calculated at 1-, 2- and 4-h intervals. CONGAn is the standard deviation of differ ent glucose measures n hours apart for the duration of the CGMS trace. of counselling using a harm minimization approach is that the information provided be credible and reflective of ‘real’ or ‘lived’ circumstances. Continuous glucose monitoring provides a technique whereby the glycaemic consequences of various behaviours can be documented in an ambulant or non-artificial setting. Adolescents with Type 1 diabetes frequently consume alcohol in a social context [11]. Alcohol is known to inhibit the gluconeogenic pathway, to inhibit lipolysis, impair glucose counter-regulation and blunt hypoglycaemia awareness [3,4]. Previous studies in young adults with Type 1 diabetes have shown that moderate consumption of alcohol in the evenings without concomitant food intake may cause hypoglycaemia the following morning [5]. Consumption of alcohol after a meal, however, has shown no similar adverse effects on glucose [6]. It is reasonable to assume, therefore, that alcohol consumption may be a significant risk factor for hypoglycaemia in adolescents with Type 1 diabetes [5]. Studies of the glycaemic effects of alcohol consumption in an ambulant adolescent/young adult population can be difficult. This is because such behaviours are uncontrolled, often spontaneous and usually in the context of other social activities (parties, dancing, etc. ). In order to ensure that we only reported accurate CGMS data during these activities, capillary blood glucose calibration was considered vital and those patients who failed in this regard were excluded from analysis. Just over 60% of the patients recruited were able to successfully wear and calibrate a CGMS unit during these activities. Given that patients who experience hypoglycaemic symptoms are more likely to perform capillary self measures of blood glucose, we feel that it is unlikely that those patients excluded from the analysis had a greater frequency of hypoglycaemia than those patients reported. We were unable to record our subjects’ alcohol consumption in a contemporaneous fashion and hence were reliant upon their recall. It is possible that their remembered patterns of consumption were not entirely accurate. This potential inaccuracy should not be seen as a weakness of this study, as we only set out to determine patterns of glycaemia in adolescents engaging in spontaneous and uncontrolled alcohol consumption. We neither specified the type nor the amount of alcohol to be consumed (our ethical approval was contingent on this not occurring). The data as to amount of alcohol consumed have been included for descriptive purposes only. The results of this study show that alcohol consumption by adolescents in a social context is associated with a greater degree of glycaemic variation and less time spent with low glucose values than evenings where no alcohol is consumed. Whilst the second of these findings appears counter-intuitive, there may be several possible explanations. Firstly, the vast majority of our study group ate a meal prior to going out and ate upon their return before going to bed. These are practices that we have instilled as harm minimization strategies to avoid alcohol-induced hypoglycaemia in our clinic. Secondly, most of the alcohol consumed was as pre-mixed spirit and sweetened, carbonated beverages. Finally, alcohol consumption was only associated with vigorous exercise (dancing) in a minority of our study group. All of these factors could have combined to negate the hypoglycaemic effects of alcohol. In a previous study of glycaemia during alcohol consumption in adult men [5], hypoglycaemia occurred most often 10–12 h after wine consumption when the evening before ended at 23. 0 h. We analysed our data to see if a similar phenomenon occurred in this study and found that the per cent of time spent with CGMS readings 4 mmol/l between 06. 00 and 12. 00 h on the morning after the study period (i. e. the morning after the drinking night) was only 1. 1%. Notwithstanding the fact that our cohort frequently consumed alcohol later than 23. 00 h, the facto rs that impacted upon glycaemic control during the study night appear to have carried over to the ‘morning after’. The findings in this study highlight the importance of ambulant testing. It is important to note that the findings of the group studied here may not be seen in adolescents who drink non-sweetened alcoholic drinks or in those adolescents with better underlying metabolic control. Whilst alcohol consumption in isolation may reasonably be thought to cause hypoglycaemia, alcohol consumption by adolescents in the context of meals, sweetened mixers and little activity did not result in more hypoglycaemia than an alcohol-free evening. Whether the increase in glycaemic variation seen on an evening  © 2006 The Authors. Journal compilation  © 2006 Diabetes UK. Diabetic Medicine, 23, 830–833 Review article 833 of alcohol consumption has negative clinical outcomes remains an area for further investigation. Competing interests CMM was a Novo Nordisk research fellow. FJC received fees for speaking at conferences and funds for research from Novo Nordisk. References 1 Cameron F, Werther G. Adolescents with diabetes mellitus. In: Menon, RK, Sperling, MA, eds. Pediatric Diabetes. Boston: Kluwer Academic Publishers, 2003: 319–335. 2 Frey MA, Guthrie B, Lovelandcherry C, Park PS, Foster CM. Risky behaviours and risk in adolescents with IDDM. J Adol Health 1997; 20: 38–45. 3 Avogaro A, Beltramello P, Gnudi L, Maran A, Valerio A, Miola M et al. Alcohol intake impairs glucose counterregulation during acute insulin-induced hypoglycaemia in IDDM patients. Diabetes 1993; 42: 1626–1634. 4 Kerr D, Macdonald IA, Heller SR, Tattersal RB. Alcohol causes hypoglycaemic unawareness in healthy volunteers and patients with type 1 diabetes. Diabetologia 1990; 33: 216–221. 5 Turner BC, Jenkins E, Kerr D, Sherwin RS, Cavan DA. The effect of evening alcohol consumption on next morning glucose control in type 1 diabetes. Diabetes Care 2001; 24: 1888–1893. 6 Koivisto VA, Tulokas S, Toivonen M, Haapa E, Pelkonen R. Alcohol with a meal has no adverse effects on postprandial glucose homeostasis in diabetic patients. Diabetes Care 1993; 16: 1612–1614. 7 National Health and Medical Research Council. Australian Alcohol Guidelines: Health Risks and Benefits. DS9. Available from: http://www7. health. gov. au/nhmrc/publications/synopses/ds9syn. htm. 8 McDonnell CM, Donath SM, Vidmar SI, Werther GA, Cameron FJ. A novel approach to continuous glucose analysis utilising glycaemic variation. Diab Tech Therap 2005; 7: 253–263. 9 StataCorp. Stata statistical software. Release 8. 0. College Station, TX: Stata Corporation, 2003. 10 Kyngas H, Hentinen M, Barlow JH. Adolescents perceptions of physicians, nurses, parents and friends: help or hindrance in compliance with diabetes self-care? J Adv Nurs 1998; 27: 760–769. 11 Patterson JM, Garwick AW. Coping with chronic illness. In: Werther, GA, Court, JM, eds. Diabetes and the Adolescent. Melbourne: Miranova Publishers 1998, 3–34.  © 2006 The Authors. Journal compilation  © 2006 Diabetes UK. Diabetic Medicine, 23, 830–833