We summarised the primary and secondary outcomes using descriptive statistics at each scheduled visit. A random effects mixed model was used to evaluate the effect of intervention on HbA1c at three, six, and nine months’ follow-up, adjusting for baseline HbA1c and stratification factors and accounting for repeated measures over time. Model adjusted mean differences in HbA1c between the two groups were estimated at each visit, by including an interaction term between treatment and month. Missing data on the primary outcome were taken into account in modelling based on the missing at random assumption. Both 95% confidence intervals and P values were reported. Treatment effects sizes were also compared between important subgroups considered in stratification, including diabetes type (1 and 2), ethnicity (Māori/Pacific and non-Māori/non-Pacific), and region (urban and rural). For other secondary outcomes measured at nine months, we used generalised linear regression models with same covariate adjustment using a link function appropriate to the distribution of outcomes. Model adjusted estimates on the treatment difference between the two groups at nine months were reported, together with 95% confidence intervals and P values. No imputation was considered on secondary outcomes.
It is well documented that any reduction in HbA1c is likely to be associated with a decrease in the risk of diabetic complications.38 Reductions in HbA1c are much more clinically important at higher levels, given that the association between vascular complications and HbA1c is non-linear and that similar reductions at lower HbA1c levels have much less effect.383940 In a less ethnically diverse population of people with type 2 diabetes who had levels of HbA1c higher than 6.5% (53 mmol/mol), a decrease of 1% (11 mmol/mol) has been found to result in reduced microvascular complications by 37%, myocardial infarction by 14%, and risk of death by 21%.38 A total of 75% of participants in the intervention group experienced a decrease in HbA1c at nine months, with a mean reduction in HbA1c of 8.9 mmol/mol (0.8%) from baseline, and a significant group difference of 4.2 mmol/mol (0.4%) in favour of the intervention. Therefore, the results in this study have potential to be clinically relevant in reducing the risk of vascular complications and death, although further investigation is needed.

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.

Of the 189 responders (35.0% response rate) to the patient survey, 19.6% (37/189) had used a diabetes app. App users were younger and in comparison to other forms of diabetes mellitus, users prominently had type 1 DM. The most favored feature of the app users was a glucose diary (87%, 32/37), and an insulin calculator was the most desirable function for a future app (46%, 17/37). In non-app users, the most desirable feature for a future app was a glucose diary (64.4%, 98/152). Of the 115 responders (40.2% response rate) to the HPs survey, 60.1% (68/113) had recommended a diabetes app. Diaries for blood glucose levels and carbohydrate counting were considered the most useful app features and the features HPs felt most confident to recommend. HPs were least confident in recommending insulin calculation apps.
New Zealand Europeans had a significantly higher incidence rate than Non-Europeans, which is consistent with other studies [21], [22]. There was a marked decrease in the proportion of Europeans in Auckland over the study period, so that the increase in type 1 diabetes incidence was not due to a shift in ethnic distribution. Furthermore, the incidence has been increasing in both Europeans and non-Europeans. A number of studies have shown that immigrant groups display higher rates of type 1 diabetes than in their countries of origin, particularly those that move into societies with a westernised lifestyle [23], [24]. For example, although type 1 diabetes in Polynesia is extremely rare, an abrupt increase in incidence occurs in Pacific Island peoples who migrate to New Zealand [25]. Our study provides evidence that the factors leading to an increase in incidence are operating across all ethnicities. Indeed, the incidence of type 1 diabetes has been remarkably similar over time for the indigenous Maori and the largely newly immigrant Pacific Island and Other ethnic groups.
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].
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.
The incidence of type 1 diabetes was higher in New Zealand Europeans than other ethnic groups throughout the study period (Figure 2, p<0.0001). There was little difference in incidence among non-European ethnic groups. The annual incidences (per 100,000) by 2009 were: Europeans 32.5 (95% CI 23.8–43.3), Non-Europeans 14.4 (95% CI 9.2–21.4), Maori 13.9 (95% CI 5.2–29.7), Pacific Islanders 15.4 (95% CI 7.3–28.5), and Other 13.5 (95% CI 5.8–26.8). The rate of increase in incidence over the study period was very similar across all ethnicities, as illustrated by the slopes in Figure 2. However, while the average increase in incidence was higher for Europeans than Non-Europeans in children of all age groups (Table 1), the increase was proportionally lower in Europeans (2-fold) than Non-Europeans (3-fold) due to a lower baseline incidence in the latter group (Figure 2). Nonetheless, in both ethnic groups type 1 diabetes incidence in children 10–14 yr increased at a higher rate than in the youngest 0–4 yr group, with a >2-fold difference observed among both Europeans and Non-Europeans (Table 1). Age at diagnosis across the study period was similar in both ethnic groups (p = 0.47).
Some of the most vocal diabetes stories come from blogs and other social media platforms which create a broad online community of people who have diabetes or whose loved ones are living with the disease.  “By means of this blog,” noted Hausner, “we hope to add our voice to this dialogue and further engage with those who may be well aware of the effects diabetes can have on their lives.”
Funding: The development of SMS4BG was funded by Waitemata District Health Board. The randomised controlled trial was funded by the Health Research Council of New Zealand in partnership with the Waitemata District Health Board and Auckland District Health Board (through the Research Partnerships for New Zealand Health Delivery initiative), and the New Zealand Ministry of Health. The funders were not involved in any way in the preparation of the manuscript or analysis of the study results. No payment has been received for writing this publication.
In the U.S., there are nearly 26 million people living with diabetes, and more seniors have diabetes than any other age group. Currently, one in four Americans (10.9 million, or 26.9 percent) over the age of 60 is living with diabetes. With age comes an increased risk for specific complications that require diligence and care to properly mitigate them.
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.
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.