The survey was completed by 189 of the 539 patients (35.0% response rate, 158/491 from participants with email addresses, 31/48 from telephone contact). Table 1 shows the characteristics of responders. Responders (N=189) were older, with a mean age of 50.0 years (SD 15.7) than non-responders (N=350), who had a mean age of 45.9 years (SD 16.1; P=.004) and had lower HbA1c of 62.2 mmol/mol (SD 14.0) (7.8, SD 1.1%) than non-responders (N=325) with mean of 68.9 mmol/mol (SD 18.2; 8.5, SD 2.3%; P<.001). There were no significant differences in the rate and type of anti-hypertensive, lipid lowering, and anti-hyperglycemic medications used between responders and non-responders (P=.28, −.32, and −.17, respectively). Clinical variables by type of diabetes are shown in Table 2. As expected, responders with T1DM were more likely to be on Insulin than those with T2DM (P<.001) whereas responders with T2DM were more likely to be on anti-hypertensive (P<.001) and lipid lowering medication (P<.001).

Most people know that diabetes involves the inability to control glucose, or blood sugar, by not producing enough insulin or not managing it correctly. This leads to elevated levels of glucose in the body, which can result in very serious complications, such as heart attack, stroke, kidney disease, nerve damage, hardening of the arteries, foot and leg amputation and blindness. (more…)
The biggest study limitation was the difficulty with recruitment, which resulted in a sample size smaller than initially planned. One reason for the low recruitment was the required time needed by clinicians to identify and refer patients to the study, which was not always available. Furthermore, many referred patients who did not meet the HbA1c inclusion criteria were still referred because clinicians had thought these individuals would benefit from the programme. This limitation highlights the difference between research and implementation where strict criteria can be relaxed. Alternative methods of recruitment could be explored, such as through laboratory test facilities to ensure access to the intervention regardless of clinician availability.
Today’s first post is titled “Why ‘Stop Diabetes’?” can be found at www.diabetesstopshere.org. This initial post seeks to explain why the Stop Diabetes movement was created and its goal for engaging the public.  “The goal of the Stop Diabetes movement is to grow to epic proportions, to be bigger than the disease itself,” the blog explains. “In short, it’s the answer to why the Association does the work that it does.”
The annual incidence of type 1 diabetes in children <15 yr in the Auckland population in 1990–2009 was 16.4/100,000 (95% CI 15.3–17.5). Considering the underlying 36% population growth over the 1990–2009 period, there was still a progressive increase in the incidence of new cases (p<0.0001; Figure 1A). By Poisson regression the type 1 diabetes incidence in children <15 yr in 2009 was 22.5 per 100,000 (95% CI 17.5–28.4), in comparison to 10.9 per 100,000 in 1990 (95% CI 7.0–16.1) (Figure 1A). Overall incidence among males and females across the 20-year period was similar (p = 0.49). The increase in incidence was greatest among children 10–14 yr (average increase of +0.81/year; p<0.0001) and lowest among children 0–4 yr (+0.32/year; p = 0.02); incidences by 2009 were 27.0 (95% CI 18.1–38.8) for children 10–14 yr, 25.4 (95% CI 16.5–37.3; +0.66/year; p = 0.0002) for children 5–9 yr, and 14.9 per 100,000 (95% CI 8.4–24.5) for those aged 0–4 yr (Figure 1B).
Only children aged <15 yr were included. Type 1 diabetes was diagnosed based on clinical features. All patients had elevated blood glucose at presentation: either a random measurement of ≥11.1 mmol/l and presence of classical symptoms, or fasting blood glucose ≥7.1 mmol/l. In addition, all patients met at least one of the following criteria: a) diabetic ketoacidosis; b) presence of at least two type 1 diabetes antibodies (to glutamic acid decarboxylase, islet antigen 2, islet cell, or insulin autoantibodies); or c) ongoing requirement for insulin therapy. Clinical and demographic data were prospectively recorded on all patients at each outpatient visit.
This study contributes to the evidence around the use of SMS to support diabetes management.131415 The improvements in HbA1c seen in this study are similar to those reported in meta-analyses of SMS interventions in diabetes not limited to those with poor control.141641 Unlike previous studies that typically focus on a particular population defined by diabetes type, age, or treatment, the current study provided an intervention for all adults with either type 1 or type 2 diabetes under any treatment regimen, enhancing potential reach and generalisability. The only limit on the population was the requirement that participants had to have poor diabetes control. This criterion was particularly important given associated costs and debilitating complications of poorly controlled diabetes. Although few trials so far have examined the effectiveness of mHealth interventions in this population,42 this study provides evidence to support the use of this modality to provide diabetes education and support to individuals with poor control.

The main treatment effect on the primary outcome is presented in table 2. The reduction in HbA1c from baseline to nine month follow-up was significantly greater in the intervention group than in the control group (mean −8.85 mmol/mol (standard deviation 14.84) v −3.96 mmol/mol (17.02), adjusted mean difference −4.23 (95% confidence interval −7.30 to −1.15), P=0.007). The adjusted mean difference on change in HbA1c at three and six months were −4.76 (−8.10 to −1.43), P=0.005) and −2.36 (−5.75 to 1.04), P=0.17), respectively (table 2).


Diabetes mellitus (DM) requires tight control of blood glucose to minimize complications and mortality [1,2]. However, many people with DM have suboptimal glycemic control [3,4]. Use of mobile phone apps in diabetes management has been shown to modestly improve glycemic control [5-10]. Despite this promise, health apps remain largely unregulated, and diabetes apps have not always had safety approval [11] or incorporated evidence-based guidelines [12,13].
New Zealand celebrates Diabetes Action Month – and the results of last year’s risk factor assessment highlight the importance of getting involved: Last year, more than 3,500 people undertook an assessment of their risk factors during the month, with 68% learning they potentially have a greater propensity for type 2 diabetes. The core purpose of the first Diabetes Action Month was to alert New Zealand that everyone is at risk of diabetes. Activities in November included a national roadshow that visited 33 locations in 14 towns and cities, and the launch of an online version of the risk awareness tool, so everyone could assess their risk
Nearly half of American adults have diabetes or prediabetes; more than 30 million adults and children have diabetes; and every 21 seconds, another individual is diagnosed with diabetes in the U.S. Founded in 1940, the American Diabetes Association (ADA) is the nation’s leading voluntary health organization whose mission is to prevent and cure diabetes, and to improve the lives of all people affected by diabetes. The ADA drives discovery by funding research to treat, manage and prevent all types of diabetes, as well as to search for cures; raises voice to the urgency of the diabetes epidemic; and works to safeguard policies and programs that protect people with diabetes. In addition, the ADA supports people living with diabetes, those at risk of developing diabetes, and the health care professionals who serve them through information and programs that can improve health outcomes and quality of life. For more information, please call the ADA at 1-800-DIABETES (1-800-342-2383) or visit diabetes.org. Information from both of these sources is available in English and Spanish. Find us on Facebook (American Diabetes Association), Twitter (@AmDiabetesAssn) and Instagram (@AmDiabetesAssn)
This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
In Type 1 Diabetes, symptoms are often sudden and can be life-threatening; therefore it is usually diagnosed quite quickly. In Type 2 Diabetes, many people have no symptoms at all, while other signs can go unnoticed, being seen as part of ‘getting older’. Therefore, by the time symptoms are noticed, complications of diabetes may already be present.

The 60.2% of HPs in our survey who had recommended a diabetes app is significantly higher than previously documented amongst physicians across a range of specialties [28], although it is similar to HPs’ recommendation for any type of health app [19]. We did not observe any effect of HPs’ age on app recommendation, although it is previously well established that younger HPs are more likely to adopt mHealth for diabetes [28].


Overall, all five potential app features were considered useful, with more than 60% of responders selecting that these features were useful, very useful, or extremely useful on the scale of scale 1 (not at all useful) to 5 (extremely useful). Equally, the mean usefulness score was higher than 3 for all 5 features. Blood glucose and carbohydrate intake diaries were rated as being the most useful app feature (Figure 1), with the highest mean score of 3.64 (SD 0.948) for usefulness (Table 7).

Having a healthy lifestyle includes daily physical activity which can prevent or delay Type 2 Diabetes. There are plenty of organised activities you can take part in such as Walk to Work, but you can also do your own thing and get moving with family and friends in any way you like. It’s most important to remember that activity is for life, not just one day. Regular physical activity could include walking, riding a bike, dancing or swimming.


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-Keep your cholesterol levels in normal range. The liver makes cholesterol and it is also found in the foods we eat such as eggs, meats and dairy products. High cholesterol levels can clog your arteries and put you at risk of developing heart disease and stroke. If you have high cholesterol, you can help lower it by losing weight, exercising and eating a healthful diet.
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…)
The HPs’ survey was completed by 115 out of 286 HPs (40.2% response rate, 78 online, 37 paper). Table 6 shows the characteristics of responders. Almost all HPs (96.5%, 111/115) owned a mobile phone and of the 113 who answered, 60.2% (68/113) had recommended an app for diabetes management to a patient. Dieticians were most likely to have recommended an app (83%, 10/12), followed by nurses (66%, 42/64), (P=.006). There was no relationship between app recommendation and the number of years of treating diabetes (P=.48) or the responder’s age (P=.49).

The reasons underpinning the considerable increase in incidence over the study period are unclear. This may reflect an actual change in the type 1 diabetes incidence in patients <15 yr. Alternatively, it may reflect an earlier age of onset without change in incidence over all ages, so that greater numbers of people are being diagnosed with type 1 diabetes in adolescence rather than in young adulthood. This would be consistent with the ‘accelerator hypothesis’, which suggests that an increasing rate of obesity is a primary driver for an earlier age of diabetes onset [6]. Studies have shown an association between higher BMI and younger age at diagnosis [9], [10], [11], indicating greater adiposity in childhood may hasten the onset of diabetes mellitus. The ‘accelerator hypothesis’ predicts an early onset rather than increased risk [11], and a Swedish study examining type 1 diabetes incidence on a nation-wide cohort 0–34 yr showed a shift in age of onset towards younger ages, rather than an increase in incidence per se across the whole population [20]. Although we cannot rule out a similar phenomenon in Auckland, we did not observe an increase in BMI SDS among children recently diagnosed with type 1 diabetes, or an association between BMI SDS and age at diagnosis. In fact, we observed an actual increase in age at diagnosis which is inconsistent with the ‘accelerator hypothesis’. Thus, our data suggest a true increase in the incidence of type 1 diabetes in the Auckland region, and not changes driven by increasing adiposity.


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.
New Zealand has a population of approximately 4.4 million people, the majority being of European descent. Auckland, the largest city in New Zealand, is the most ethnically diverse, with approximately 11% of people identifying themselves as indigenous Maori, 14% as Pacific, and 19% as Asian [12]. By international standards, the incidence of type 1 diabetes in young New Zealanders was assessed as moderate at 17.9 per 100,000 [13]. However, this figure was obtained from a 2-year snapshot, and did not provide information on possible time trends on type 1 diabetes incidence. In addition, previous studies on type 1 diabetes incidence in New Zealand are out of date or refer to a specific geographical region [14], [15], [16].
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