This study shows that the incidence of type 1 diabetes in the Auckland region has increased steadily over the last two decades. However, unlike other studies [3], [4], [5], the rate of increase in incidence has been particularly marked in older children (10–14 yr), which was approximately 2.5-fold greater than that in children 0–4 yr. Interestingly, the incidence of type 1 diabetes in children 0–4 and 10–14 in Auckland are very similar to those reported in Australia, our closest geographical and ethnic neighbours [19], both of which had very high case ascertainment levels (close to 100%).
Diabetes is a metabolic disorder, which is accompanied by high blood glucose levels. It is a result of improper functioning of the pancreas, which secretes the insulin hormone. Lack of insulin, result in ketoacidosis. Makhana or Fox nut is a sweet and sour seed, which is also known as Euryale ferox. These seeds contain starch and ten percent of protein. There is no supporting literature for its positive association with diabetes. Therapeutic effects of fox nut involve its strengthening of kidney. It also helps to relieve the dampness, associated with leucorrhoea. It also regulates hypertension or high blood pressure. It is also beneficial for individuals with impotence and arthritis. Fox nuts are effective for individuals with high risk of premature ageing. It is also known as gorgon nut, is also helpful.
The look of the Dario appealed straight away to me. Small and compact. Easy for me to carry with my phone which goes everywhere with me. Love the fantastic app on my phone. Clear, informative and easy to use. Love it! I can look back at previous readings to see any patterns. Sara and Assaf have been brilliant at helping out with any issues I have come across, which I thank them hugely for. The Dario Lounge is a great community for all users, who all share advice.
Clinical psychologists have studied psychology at University, usually for at least seven years. They have specialised in learning about how the feelings, actions, beliefs, experiences and culture of people affect the way they live. They have learned how to listen to and understand people’s emotional and psychological problems and how to help people make changes in their lives.
There are over 30 million people in the U.S. who have diabetes, even if nearly a quarter of them have not been diagnosed. 13 million individuals in the U.S. have been diagnosed with urinary incontinence, and it is believed that the percentage of undiagnosed incontinence is likely to be significant. Diabetes is a disease, while incontinence is a symptom related to lifestyle choices, physical issues or an underlying medical condition. Urinary incontinence is often linked to diabetes because diabetes is one of the more common medical conditions that contribute to incontinence. (more…)
One of the most important aspects of diabetes management is to maintain a healthy body weight. Being overweight not only increases your risk of heart disease, stroke and some cancers, it also makes your diabetes harder to manage. Small changes in your diet such as reducing your portion sizes and swapping to low-fat dairy products can help you to achieve a healthy body weight and manage your diabetes.

Understanding how food affects blood sugar level and constantly monitoring it is a way of life for those with diabetes. This largely involves the balance between the amount of insulin currently in the body at any given time and how the foods we eat change that it. At center stage for this daily drama are carbohydrates. Knowing the difference between how the various types of carbohydrates are processed by the body is key to maintaining blood sugar levels. (more…)
“There have been so many touching moments in the movement to Stop Diabetes since we launched last year,” commented Larry Hausner, CEO, American Diabetes Association. “People have shared courageous stories of facing their diabetes head on, while others have shared their heart-breaking experiences of losing a loved one because of diabetes. The blog is a new way to raise our collective voices and tell people why we need to Stop Diabetes once and for all.”   

New Zealand Europeans had a significantly higher incidence rate than Non-Europeans, which is consistent with other studies [21], [22]. There was a marked decrease in the proportion of Europeans in Auckland over the study period, so that the increase in type 1 diabetes incidence was not due to a shift in ethnic distribution. Furthermore, the incidence has been increasing in both Europeans and non-Europeans. A number of studies have shown that immigrant groups display higher rates of type 1 diabetes than in their countries of origin, particularly those that move into societies with a westernised lifestyle [23], [24]. For example, although type 1 diabetes in Polynesia is extremely rare, an abrupt increase in incidence occurs in Pacific Island peoples who migrate to New Zealand [25]. Our study provides evidence that the factors leading to an increase in incidence are operating across all ethnicities. Indeed, the incidence of type 1 diabetes has been remarkably similar over time for the indigenous Maori and the largely newly immigrant Pacific Island and Other ethnic groups.

Some of the most vocal diabetes stories come from blogs and other social media platforms which create a broad online community of people who have diabetes or whose loved ones are living with the disease.  “By means of this blog,” noted Hausner, “we hope to add our voice to this dialogue and further engage with those who may be well aware of the effects diabetes can have on their lives.”
-Learn to eat well-balanced meals that include healthful food choices (vegetables, fruits, whole grains, etc.) and watch your portion sizes. Even foods that are good for you can add pounds to your waistline, if you consume too much of them. Losing those extra pounds will help you manage not only your diabetes, but also other health problems you may have.
The Endocrinology Service at Starship Children's Health provides specialist care for all children diagnosed with type 1 diabetes in the Auckland region (New Zealand). Its Paediatric Diabetes Service provides centralised medical care for all diabetic children up to 15 yr who reside in the Auckland region, drawing from the regional population of approximately 1.5 million [12]. All children or adolescents diagnosed with type 1 diabetes who attended the Paediatric Service between 1 January 1990 and 31 December 2009 were eligible for this study. Subjects were captured from a comprehensive database (Starbase) that gathers data on all children with type 1 diabetes in the Auckland region. This information was cross-referenced with hospital admission data and subsequent clinical follow up, leading to a case ascertainment >95% for children with type 1 diabetes [13].
Along with a long list of other complications, gum disease can result from diabetes that is not properly controlled. The two main forms of gum disease are gingivitis and periodontitis. With gingivitis, the gums become red and swollen and may easily bleed. If not treated, this milder form of gum disease can become full-blown periodontitis, which is where the gums pull away from the teeth and infection takes a firm hold, leading to bone, tissue and tooth loss.
The Endocrinology Service at Starship Children's Health provides specialist care for all children diagnosed with type 1 diabetes in the Auckland region (New Zealand). Its Paediatric Diabetes Service provides centralised medical care for all diabetic children up to 15 yr who reside in the Auckland region, drawing from the regional population of approximately 1.5 million [12]. All children or adolescents diagnosed with type 1 diabetes who attended the Paediatric Service between 1 January 1990 and 31 December 2009 were eligible for this study. Subjects were captured from a comprehensive database (Starbase) that gathers data on all children with type 1 diabetes in the Auckland region. This information was cross-referenced with hospital admission data and subsequent clinical follow up, leading to a case ascertainment >95% for children with type 1 diabetes [13].
To assess whether changes in incidence were more marked in certain age groups (as observed overseas [3], [4]), patients were also categorised into three bands according to age at diagnosis: 0–4 yr (children less than 5 yr), 5–9 yr (equal or greater than 5 yr but less than 10 yr), and 10–14 yr (equal or greater than 10 yr but less than 15 yr). These age bands also match national census classifications. The incidence of type 1 diabetes was assessed as the number of new diagnoses per 100,000 age-matched inhabitants on a given year, based on the 5-yearly national census data from Statistics New Zealand [12] and interpolated estimates of the population for the intervening years. Incidence was modelled using the Poisson distribution. Point estimates were calculated with exact Poisson confidence limits, and change in incidence over time were analysed using Poisson regression. Changes in patient numbers, age at diagnosis, and anthropometric data over time were assessed by linear regression. Poisson modelling was undertaken using StatsDirect v2.7.8 (StatsDirect Ltd, UK); other analyses were undertaken using JMP v. 5.1 (SAS Inc, USA).

Owing to time restrictions, longer term follow-up of participants was not feasible within the current study, although it is hoped that a two year follow-up of the present study’s participants is possible. The significant group difference seen at three months, dropping slightly at six months, but reaching significance again at nine months, could be an indication of sustained change. Another limitation of the study design was that secondary outcome assessors were not blinded to treatment allocation, which could have introduced bias in follow-up data collection of secondary variables.
In relation to perceptions and beliefs about diabetes, a significant reduction in illness identity (how much patients experience diabetes related symptoms) on the BIPQ was observed in favour of the intervention (adjusted mean difference −0.54 (95% confidence interval −1.04 to −0.03), P=0.04). However, we saw no significant group differences for perceptions of consequences, timeline, control, concern, emotions, and illness comprehensibility. A significant improvement in health status on the EQ-5D VAS was observed in favour of the intervention (4.38 (0.44 to 8.33), P=0.03) but no significant differences were observed between groups for the quality of life index score. Finally, the measure of perceived support for diabetes management showed a significant improvement between the groups in how supported the participants felt in relation to their diabetes management overall (0.26 (0.03 to 0.50), P=0.03) but no significant group differences on appraisal, emotional, and informational support.
It’s heart-wrenching to watch all that people go through as natural disasters play out on our television screens. Tucked away, along with sympathy for those in the midst of a hurricane, earthquake, flood or other catastrophic events, is the very understandable thought, “I’m so glad that’s not happening to me!”. The truth is, however, that we are all susceptible to major life-changing events, and they can happen with very little notice. Those with a chronic medical condition, like diabetes, are especially vulnerable and should take seriously the advice to be prepared.    (more…)

A large patient sample size was obtained by contacting all patients seen in the last 12 months with an email address. The risk of overrepresentation by more technology-literate responders through recruitment via email was minimized by also recruiting via telephone and by providing paper surveys at the HPs’ conference. The demographic and clinical data of responders and non-responders were compared, and most variables showed no difference. Responders were actually older than non-responders and had better glycemic control. This study focused on the beliefs and opinions of people with diabetes (potential app users) and HPs (potential app prescribers) rather than simply describing apps for diabetes . It is one of the first papers to describe app use in people with diabetes in New Zealand.
The average reduction of 4.2 mmol/mol (0.4%) in HbA1c seen in this study did not reach the level chosen to signify clinical significance in the initial power calculation (5.5 mmol/mol (0.5%) reduction in HbA1c). Therefore, this study is unable to conclude that the effects of the SMS4BG intervention are clinically significant. Although further investigation is needed, we believe the results have the potential to still be clinically relevant in practice, particularly among individuals with high levels of HbA1c, such as the participants with poorly controlled diabetes in this study. The unadjusted group difference on change in HbA1c from baseline was −5.89, −3.05 and −5.24 mmol/mol at three, six, and nine months, respectively. The main analysis, with adjustment for baseline value and stratification factors, showed a smaller treatment effect, although both results were significant at three and nine months. Similar results were found across major subgroups of interest despite the fact that these analyses were not specifically powered. These consistent findings led us to believe that the intervention shows promising effects on treating people with poorly controlled diabetes and warrants further investigation.
This study shows app usage is relatively low among people with diabetes, while 60.2% of HPs have recommended an app to patients. There is, however, interest amongst people with diabetes and HPs to use diabetes apps, with strong interest in an insulin dose calculator. Apps with this feature have the potential to improve diabetes control. However, the critical problem of app safety remains a barrier to the prescription and use of insulin dose calculators. Further work is needed to ensure apps are safe and provided in a regulated environment. An app assessment process would provide HPs with confidence in the apps they recommend and would ultimately ensure app quality and safety for app users. At present, however, app users and HPs must remain cautious with diabetes apps, especially those in the insulin dose calculator category.
“There have been so many touching moments in the movement to Stop Diabetes since we launched last year,” commented Larry Hausner, CEO, American Diabetes Association. “People have shared courageous stories of facing their diabetes head on, while others have shared their heart-breaking experiences of losing a loved one because of diabetes. The blog is a new way to raise our collective voices and tell people why we need to Stop Diabetes once and for all.”  
Data sharing: The research team will consider reasonable requests for sharing of deidentified patient level data. Requests should be made to the corresponding author. Consent for data sharing was not obtained but the presented data are anonymised and risk of identification is low. The original protocol30 is available from the corresponding author on request.
Additional data on all patients were collected from the hospital management system, including age, and the most recent values within the previous 12 months from date of survey for blood pressure (BP), glycated hemoglobin (HbA1c), urinary microalbumin to Creatinine ratio (ACR), low density lipoprotein cholesterol (LDL), and total cholesterol to HDL ratio (C:HDL). Prescription of lipid lowering drugs, anti-hypertensive drugs, insulin, or other hypoglycemic medication were also extracted from the medication list from the last visit within the sample period. Type of diabetes was self-reported in the survey (type 1 [T1DM], type 2 [T2DM], other or unknown) and in four participants who had selected ‘other’ or ‘unknown’ diabetes type was determined by examination of the clinical records. For categorization of participants by app use, 4 responders who did not indicate if they had a mobile phone or not were included in the non-app group.

Main outcome measures Primary outcome measure was change in glycaemic control (HbA1c) from baseline to nine months. Secondary outcomes included change in HbA1c at three and six months, and self efficacy, diabetes self care behaviours, diabetes distress, perceptions and beliefs about diabetes, health related quality of life, perceived support for diabetes management, and intervention engagement and satisfaction at nine months. Regression models adjusted for baseline outcome, health district category, diabetes type, and ethnicity.

Main outcome measures Primary outcome measure was change in glycaemic control (HbA1c) from baseline to nine months. Secondary outcomes included change in HbA1c at three and six months, and self efficacy, diabetes self care behaviours, diabetes distress, perceptions and beliefs about diabetes, health related quality of life, perceived support for diabetes management, and intervention engagement and satisfaction at nine months. Regression models adjusted for baseline outcome, health district category, diabetes type, and ethnicity.
Owing to individual tailoring, participants in the intervention group received varying numbers of messages. Half the participants (92/183) received messages for three months, an additional 18% (33/183) chose to continue the messages for six months, and the remaining 32% (58/183) chose to continue the messages to the maximum nine months. Only three participants chose to stop their messages early. A total number of 76 523 messages were sent by the system to participants (median number of messages per participant 242 (interquartile range 122-511; range 14-2050)), and 16 251 messages of blood glucose results were sent into the system by participants receiving the reminders (68 (1-169; 0-917)).
The incidence of type 1 diabetes was higher in New Zealand Europeans than other ethnic groups throughout the study period (Figure 2, p<0.0001). There was little difference in incidence among non-European ethnic groups. The annual incidences (per 100,000) by 2009 were: Europeans 32.5 (95% CI 23.8–43.3), Non-Europeans 14.4 (95% CI 9.2–21.4), Maori 13.9 (95% CI 5.2–29.7), Pacific Islanders 15.4 (95% CI 7.3–28.5), and Other 13.5 (95% CI 5.8–26.8). The rate of increase in incidence over the study period was very similar across all ethnicities, as illustrated by the slopes in Figure 2. However, while the average increase in incidence was higher for Europeans than Non-Europeans in children of all age groups (Table 1), the increase was proportionally lower in Europeans (2-fold) than Non-Europeans (3-fold) due to a lower baseline incidence in the latter group (Figure 2). Nonetheless, in both ethnic groups type 1 diabetes incidence in children 10–14 yr increased at a higher rate than in the youngest 0–4 yr group, with a >2-fold difference observed among both Europeans and Non-Europeans (Table 1). Age at diagnosis across the study period was similar in both ethnic groups (p = 0.47).
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