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 use of apps to record blood glucose was the most favored function in apps used by people with diabetes, with interest in insulin dose calculating function. HPs do not feel confident in recommending insulin dose calculators. There is an urgent need for an app assessment process to give confidence in the quality and safety of diabetes management apps to people with diabetes (potential app users) and HPs (potential app prescribers).
This study showed that a tailored and automated SMS self management support programme has potential for improving glycaemic control in adults with poorly controlled diabetes. Although the clinical significance of these results is unclear, and the full duration of these effects is yet to be determined, exploration of SMS4BG to supplement current practice is warranted.

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.
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.
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.”
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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