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 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).
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 1177 people with diabetes attending clinics at Capital and Coast District Health Board (CCDHB), Wellington, New Zealand over a 12-month period (10th September 2014 to 10th September 2015) were the sample population. Out of the total patients, 521 patients with an email address in the hospital management system were invited to participate via email. To include a representation of people without a recorded email address in the sample (n=656), every 5th person was telephoned (up to twice) and invited to provide an email address. Of the 131 patients telephoned, 54 (41.2%) were reached, of whom 49 (91%) agreed to participate. Patients without phone numbers or unable to provide an email address were excluded. This generated a sample population of 570 people.
Type 2 Diabetes is one of the major consequences of the obesity epidemic and according to Diabetes New Zealand is New Zealand’s fastest-growing health crisis. In terms of diabetes diagnosis, Type 2 currently accounts for around 90% of all cases. Also of concern to health professionals is that there are large numbers of people with silent, undiagnosed Type 2 Diabetes which may be damaging their bodies. An estimated 258,000 New Zealanders are estimated to have some form of diabetes, with than number doubling over the past decade.
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.
Statistical analyses were performed by SAS version 9.4 (SAS Institute). All statistical tests were two sided at a 5% significance level. Analyses were performed on the principle of intention to treat, including all randomised participants who provided at least one valid measure on the primary outcome after randomisation. Demographics and baseline characteristics of all participants were first summarised by treatment group with descriptive statistics. No formal statistical tests were conducted at baseline, because any baseline imbalance observed between two groups could have occurred by chance with randomisation.

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.


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.

A total of 793 individuals were referred to the study and assessed for eligibility between June 2015 and November 2016. Of these, 366 were randomised to the intervention and control groups (n=183 each; fig 1). The final nine month follow-up assessments were completed in August 2017, with loss to follow-up (that is, no follow-up data on any outcome) low in both groups (overall 7/366=2%). A total of 12 participants (six per group) were excluded from the primary outcome analysis because of no follow-up HbA1c results after randomisation. Baseline characteristics of participants are presented in table 1, and no adverse events were recorded from the study or protocol deviations.
Constipation Cancer Athletic Injuries Mental Health Urgent Care Injuries Pregnancy Injuries Depression Aches Asthma Eating Disorders Fevers Acne Colds Skin Lesions Stds Alcoholism Chest Pain Sore Throats Astigmatism Altitude Sickness Hivaids Diabetes Blood Pressure Chronic Pain Infections Strains Obesity Accidents Endometriosis Moles Abscesses More Less
×