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.
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With technology advancing rapidly, there is a call for mHealth to move towards more complex technology. However, this study has shown that text messaging—available on any mobile phone—although simple, is still potentially effective for improving glycaemic control. Equally, this study had very few technical difficulties, which probably contributed to the high satisfaction with the intervention. The individual tailoring of the intervention, and ability for participants to choose varying components and dosages, means that questions remain around the ideal duration for implementation as well as the components most important for effectiveness. Further research is needed to understand the components of this intervention that are most effective and the ideal intervention dosage to further refine this intervention and inform the development of future interventions. With participants highly satisfied with the intervention and largely happy with their intervention dosage, but great variance in the modules, durations, and dosages, SMS4BG may need to remain individually tailored in this way, resulting in a more complex intervention for delivery until further investigation on this can be made.
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.
From 1994 onwards, anthropometric data were recorded at each clinic visit, and for the purposes of this study we used data from the first post-diagnosis clinic that usually occurred 3–4 months afterwards. Standard deviation scores (SDS) were calculated based on the British 1990 Growth Reference Data [17] to obtain height SDS, weight SDS, and body mass index (BMI) SDS.
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).
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.
Participants could choose to receive blood glucose monitoring reminders to which they could reply by sending in their result by text message. They could then view their results graphically over time on a password protected website. If they were identified as not having access to the internet at baseline they were mailed their graphs once a month. All messages were delivered in English although the Māori version included keywords in Te Reo Māori and the Pacific version had keywords in either Samoan or Tongan dependent on ethnicity. Examples of SMS4BG messages can be seen in the box. Participants were able to select the timing of messages and reminders, and identify the names of their support people and motivations for incorporation into the messages. The duration of the programme was also tailored to individual preferences. At three and six months, participants received a message asking if they would like to continue the programme for an additional three months, and had the opportunity to reselect their modules receiving up to a maximum nine months of messages. Participants could stop their messages by texting the word “STOP” or put messages on hold by texting “HOLIDAY.”
Of mobile phone owners, those using diabetes apps were more likely to have T1DM (30/96) than T2DM (n=7/61); (P=.006). App users were younger with a mean age of 39.0 years (SD 11.1) compared to non-app users having a mean of 52.5 years (SD 15.6), (P<.001). There were no other significant differences in clinical variables between app and non-app users.
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].

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%).


Conclusion A tailored, text message based, self management support programme resulted in modest improvements in glycaemic control in adults with poorly controlled diabetes. Although the clinical significance of these results is unclear, the findings support further investigation into the use of SMS4BG and other text message based support for this patient population.
Increase your physical activity. Exercise is a very important tool to help lower your blood glucose. Prior to starting any exercise program, you will need to consult with your doctor. Make exercise routine with activities you enjoy. In addition to helping manage your blood glucose, exercise helps lower blood pressure and improves balance, flexibility and muscle strength. Exercise may even help to reduce anxiety and depression. Go out and play!
The features most frequently used by current app users were blood glucose diaries (87%, 32/37), followed by carbohydrate/meal diaries (38%, 14/37) with 22% (8/37) reporting insulin dose calculation devices to be useful (Table 3). Table 3 demonstrates the features app users found useful in their current apps. App users reported the most desired feature for future use in an app was an insulin dose calculator (46%, 17/37; Table 4). Table 5 shows that non-app users reported insulin dose calculators to be the third most desired feature (54.6%, n=83/152). Blood glucose diaries were the most desired app feature amongst non-app users (64.4%, 98/152; Table 5). Non app users with T1DM were more likely to desire an insulin dose calculation device, than non-app users with T2DM, P=.01).
A nine month, two arm, parallel, randomised controlled trial was conducted in adults with poorly controlled diabetes between June 2015 and August 2017. The study received ethical approval from the Health and Disability Ethics Committee (14/STH/162), and the protocol was published30 and registered with the Australian New Zealand Clinical Trials Registry (ACTRN12614001232628). Trial development and reporting was guided by the CONSORT31 and CONSORT EHEALTH32 statements.
There are three forms of the disease. People with Type 1 Diabetes typically make none of their own insulin and therefore require insulin injections for survival. People with Type 2 Diabetes, the form that comprises the majority of all cases, usually produce their own insulin, but not enough or they are unable to use it properly. Then there is Gestational Diabetes; globally, 1 in 7 births is affected by gestational diabetes. While maternal blood glucose levels usually return to normal after the baby is born, there is an increased risk of both mother and child developing Type 2 Diabetes later in life.

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.


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).
In a perfect world, the answer to the question “should someone with diabetes take steroids?” would be a simple “no”. Of course, not only do we not live in a perfect world, there are also few simple answers for diabetics. Steroids can play havoc with blood sugar levels, but they can also be the best choice in treating some very serious conditions. So, perhaps the better answer would be “maybe” with the added caveat of making sure you are aware of the consequences and prepared to be proactive in managing them.   (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.
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.
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