-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.
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).
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.
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…)
Pre-diabetes and type 2 diabetes are at epidemic proportions in New Zealand with the Auckland region over represented in certain populations. This programme works with those who have the highest rates of pre-diabetes and type 2 diabetes in Auckland creating that awareness and preventing diabetes where possible that is needed on a more intimate level within the community.
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].
Patients were involved in all stages of the study, including the initial conceptualisation and formative work leading to the development of SMS4BG (for more information, see the development paper28). Patient feedback informed the intervention modality, purpose, and structure, and patients reviewed intervention content before it was finalised. Patient feedback on the acceptability of SMS4BG through the pilot study28 led to improvements to the intervention including additional modules, the option for feedback graphs to be posted, additional tailoring variables, and a longer duration of intervention. Patient feedback also informed the design of this trial—specifically its duration, the inclusion criteria, and recruitment methods. Additionally, patients contributed to workshops of key stakeholders held to discuss interpretation, dissemination of the findings, and potential implementation. We have thanked all participants for their involvement and they will be given access to all published results when these are made publicly available.
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).

Contributors: RW obtained funding for this trial. All coauthors had input into the study protocol. RD, RW, RMu, and MS contributed to the development of the intervention content. RD managed the day-to-day running of the trial and delivery of the intervention. RD and RW collected the data. YJ and RD did the data analyses. All coauthors were involved in the interpretation of the results. RD wrote the article with input from all coauthors. All authors, external and internal, had full access to all of the data (including statistical reports and tables) in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis. All authors approved the final version of this manuscript. RD is guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

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Phoenix Health Centre carries out pre employment medical assessments for several large employers in Whakatane. These give a base line recording of an employee’s health status at the time they were employed. It is then possible to monitor the employee’s health in relation to the hazards they may be exposed to in the workplace. If required we also undertake monitored urine sampling for ESR drug testing.
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