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.
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 In addition, previous studies on type 1 diabetes incidence in New Zealand are out of date or refer to a specific geographical region [14], [15], [16].
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].

For example, adjusting to having diabetes; difficulty in making the life changes necessary to stay well; difficulty managing anger, conflict and other emotions related to your health; depression, sadness and grief; anxiety, worries, panic and phobias related to your health; eating difficulties; and difficulty with coping with the complications of diabetes.


This study found that a tailored, theoretically based, SMS based, diabetes self management support programme led to modest improvements in glycaemic control. The effects of intervention were also seen in four of 21 secondary outcomes, including foot care behaviour and ratings of diabetes support. The programme showed a high level of acceptability with the overwhelming majority of participants finding the intervention useful and willing to recommend it to others.
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.

We all have our favorite holiday activities. It might be watching fireworks on the 4th of July, heading to the beach for Labor Day, as summer winds down, or finding the perfect pumpkin to carve for Halloween. For many of us, it’s the non-stop activities that seem to begin with the Macy’s Day Parade, early Thanksgiving morning, and continue through the last bowl game on New Year’s Day. But, no matter what holiday or activity tops your list, you can bet that it involves not only extreme amounts of food and drink but the kind designed to send blood sugar levels through the roof. (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.

Your health professional at the Centre may suggest that they make a referral for you, if there are problems affecting your diabetes management or your overall health and management. Alternatively you can ask your family doctor or nurse to refer you. If you are uncertain about whether it would be helpful to see us, you are most welcome to phone us directly to discuss this. Phone 3640 860 ext 89113.

For example, adjusting to having diabetes; difficulty in making the life changes necessary to stay well; difficulty managing anger, conflict and other emotions related to your health; depression, sadness and grief; anxiety, worries, panic and phobias related to your health; eating difficulties; and difficulty with coping with the complications of diabetes.
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].
SMS4BG is an automated self management support programme delivered by SMS (short messaging service) to motivate and support people to engage in the behaviours needed for successful diabetes management. The programme was tailored by the needs and goals of the individual, and demographic factors. As well as core motivational and support messages (in Māori, Pacific, or non-Māori/Pacific cultural versions), participants could opt to receive additional modules including those for: insulin control, young adult support, smoking cessation, lifestyle behaviour (exercise, healthy eating, or stress/mood management), and foot care (further module details in supplementary table 1).
Eligible participants were randomised to either an intervention or control group in a 1:1 ratio. Randomisation was stratified by health district category (high urban or high rural/remote), diabetes type (1 or 2), and ethnicity (Māori and Pacific, or non-Māori/non-Pacific). The randomisation sequence was generated by computer programme using variable block sizes of two or four, and overseen by the study statistician. Following participant consent and completion of the baseline interview, the research assistant then randomised the participant to intervention or control, using the REDCap randomisation module. The REDCap randomisation module ensured that treatment allocation was concealed until the point of randomisation. Due to the nature of the intervention, participants were aware of their treatment allocation. Research assistants conducting the phone interviews were also aware of the treatment allocation. However, the objective primary outcome was measured by blinded assessors throughout the study period.
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%).
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].
Almost two-thirds of HPs responding had recommended a diabetes app to patients. Dieticians were more likely to recommend an app than others. Blood glucose and carbohydrate diaries were considered the most useful feature and HPs were most confident to recommend blood glucose diaries. HPs are the least confident recommending insulin dose calculation functions. Over one-third of HPs desire guidance with app recommendations.

To obtain data on HPs’ knowledge and recommendation of apps to people with diabetes, a second survey was conducted of the HPs attending the annual scientific meeting of the New Zealand Society for the Study of Diabetes (NZSSD) in May 2016. Immediately prior to the meeting all registered attendees (n=286) were invited to participate in the online survey via email. The data from the patient survey was presented at the conference in a 15-min oral presentation and attendees were encouraged to complete the survey. Paper copies of the survey were also available at the meeting. This survey remained open for 2 weeks, with a reminder sent at 1 week.


Like my customers, I use an insulin pump to control my diabetes because it allows me to be spontaneous and flexible in managing my diabetes while reducing hypoglycemia. Reaching target blood glucose levels is not always easy, but being a Pharmacist, as well as a Certified Diabetes Educator and Certified Pump Trainer, I know the importance of managing my blood glucose to reduce long-term complications. You are welcome to contact me about any aspect of insulin pump therapy or with any questions about specific insulin pump supplies.
Mobile phone ownership rates are increasing. Similar to trends seen in the United States and Canada, where mobile phone ownership is 72% and 67%, respectively [20], 70% of New Zealanders own a mobile phone, making diabetes apps potentially available to most people [21]. Limited research exists into the use of diabetes apps in New Zealand. However with increasing rates of both diabetes prevalence and mobile phone ownership, access to safe apps is essential for both HPs as potential app prescribers and patients as app users [21,22]. In Scotland, a survey of people with diabetes found high mobile phone ownership (67%) with over half reporting an interest in using apps for self-management of diabetes, but app usage in only 7% of responders [23]. The objectives of this study were (1) To establish whether people with diabetes use apps to assist with diabetes self-management and which features are useful or desirable, and (2) To establish whether HPs treating people with diabetes recommend diabetes apps, which features were thought to be useful, and which features were they confident to recommend.
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.
The flexibility of mobile phones and their adoption into everyday life mean that they are an ideal tool in supporting people with diabetes whose condition needs constant management. Mobile phones, which have been used effectively to support diabetes management,13141516 offer an ideal avenue for providing care at the patient’s desired intensity. Additionally, they can provide effective methods of support to patients in rural and remote locations where access to healthcare providers can be limited.1718 Although there is growing support for the use of mobile health (mHealth) in diabetes, there is increasing evidence of a digital divide, with lower use of some technologies in specific population groups.1920 These groups include people who have low health literacy,21 have low income,222324 and are members of ethnic minorities.2526 Contributing factors include low technology literacy, mismatch between individual needs and the available tools, lack of local information, cost, literacy and language barriers, and lack of cultural appropriateness.27 For mHealth tools to be used to manage poor diabetes control, they need to be designed to the needs and preferences of those people who need the greatest support by considering these factors.
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.
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…)
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.
The {Dario} device has been perfect, I love it. I love that it’s small and discreet enough. I can now test my sugars within 20 seconds, all from the bottom of my iPhone and no one around is none the wiser… I also love that it’s “all in one”. I’ve been using it now for around 4 – 5 months. The app is great at logging and motivation with its % scoring system.
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).
Another goal of this blog is to give you a behind-the-scenes look at what the Association does on a daily basis to fulfill its mission: To prevent and cure diabetes and improve the lives of all people living with diabetes.  Our staff’s dedication – combined the stories that provide them with inspiration through the day – is a critical part of the Stop Diabetes movement.
Blood glucose tracking is the most common feature of diabetes apps [5,14], with other features including record of medications, dietary advice, and tracking, such as carbohydrate content calculation, and weight management support [5,11,12,14-16]. Additionally some apps recommend insulin dosing based on users inputs of glucose levels and estimated meal carbohydrate. Meta-analysis of 22 trials including 1657 patients in which use of mobile phone apps supporting diabetes management was compared to usual care or other Web-based supports showed that app use led to a mean reduction in HbA1c of 6mmol/mol that is 0.5% [9]. This compares favorably with the glucose lowering of lifestyle change, namely diet [17] and oral diabetes medication [18].

ED is a failure to obtain/maintain penile erection sufficient for intercourse is more prevalent in men with diabetes and increases with age.  It is important to distinguish erectile failure from premature ejaculation, decreased libido and other problems as these have different causes and treatment. ED in diabetes is largely due to failure of vascular smooth muscle relaxation secondary to endothelial dysfunction and/or autonomic neuropathy.
×