There were 884 new cases of type 1 diabetes, and age at diagnosis rose from 7.6 yr in 1990/1 to 8.9 yr in 2008/9 (r2 = 0.31, p = 0.009). There was a progressive increase in type 1 diabetes incidence among children <15 yr (p<0.0001), reaching 22.5 per 100,000 in 2009. However, the rise in incidence did not occur evenly among age groups, being 2.5-fold higher in older children (10–14 yr) than in the youngest group (0–4 yr). The incidence of new cases of type 1 diabetes was highest in New Zealand Europeans throughout the study period in all age groups (p<0.0001), but the rate of increase was similar in New Zealand Europeans and Non-Europeans. Type 1 diabetes incidence and average annual increase were similar in both sexes. There was no change in BMI SDS shortly after diagnosis, and no association between BMI SDS and age at diagnosis.
The annual incidence of type 1 diabetes in children <15 yr in the Auckland population in 1990–2009 was 16.4/100,000 (95% CI 15.3–17.5). Considering the underlying 36% population growth over the 1990–2009 period, there was still a progressive increase in the incidence of new cases (p<0.0001; Figure 1A). By Poisson regression the type 1 diabetes incidence in children <15 yr in 2009 was 22.5 per 100,000 (95% CI 17.5–28.4), in comparison to 10.9 per 100,000 in 1990 (95% CI 7.0–16.1) (Figure 1A). Overall incidence among males and females across the 20-year period was similar (p = 0.49). The increase in incidence was greatest among children 10–14 yr (average increase of +0.81/year; p<0.0001) and lowest among children 0–4 yr (+0.32/year; p = 0.02); incidences by 2009 were 27.0 (95% CI 18.1–38.8) for children 10–14 yr, 25.4 (95% CI 16.5–37.3; +0.66/year; p = 0.0002) for children 5–9 yr, and 14.9 per 100,000 (95% CI 8.4–24.5) for those aged 0–4 yr (Figure 1B).
The average reduction of 4.2 mmol/mol (0.4%) in HbA1c seen in this study did not reach the level chosen to signify clinical significance in the initial power calculation (5.5 mmol/mol (0.5%) reduction in HbA1c). Therefore, this study is unable to conclude that the effects of the SMS4BG intervention are clinically significant. Although further investigation is needed, we believe the results have the potential to still be clinically relevant in practice, particularly among individuals with high levels of HbA1c, such as the participants with poorly controlled diabetes in this study. The unadjusted group difference on change in HbA1c from baseline was −5.89, −3.05 and −5.24 mmol/mol at three, six, and nine months, respectively. The main analysis, with adjustment for baseline value and stratification factors, showed a smaller treatment effect, although both results were significant at three and nine months. Similar results were found across major subgroups of interest despite the fact that these analyses were not specifically powered. These consistent findings led us to believe that the intervention shows promising effects on treating people with poorly controlled diabetes and warrants further investigation.

Data were imported into SPSS version 24 (IBM). Incomplete responses were included in the analysis. In the patient survey, independent sample t tests were conducted to compare mean clinical variables (age, BP, C:HDL, LDL, HbA1c) by type of diabetes, method of recruitment, and whether the responder used a diabetes mobile phone app. Adjustment was made for unequal variances. Normal distribution was assumed for all variables, apart from urinary microalbumin to creatinine for which a Wilcoxin test was used. No statistically significant differences in these variables or in mobile phone app use were found between patients with recorded email addresses and patients phoned for their email address. Therefore, all 189 responses were combined for further analysis. Chi-square tests were used to compare medications and survey responses by type of diabetes. Statistical significance was determined by exact 2-sided P values less than .05. In the HP survey, mean values on the usefulness and confidence Likert scales were calculated to compare app features.


Data were imported into SPSS version 24 (IBM). Incomplete responses were included in the analysis. In the patient survey, independent sample t tests were conducted to compare mean clinical variables (age, BP, C:HDL, LDL, HbA1c) by type of diabetes, method of recruitment, and whether the responder used a diabetes mobile phone app. Adjustment was made for unequal variances. Normal distribution was assumed for all variables, apart from urinary microalbumin to creatinine for which a Wilcoxin test was used. No statistically significant differences in these variables or in mobile phone app use were found between patients with recorded email addresses and patients phoned for their email address. Therefore, all 189 responses were combined for further analysis. Chi-square tests were used to compare medications and survey responses by type of diabetes. Statistical significance was determined by exact 2-sided P values less than .05. In the HP survey, mean values on the usefulness and confidence Likert scales were calculated to compare app features.
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%).

There was a steady increase in the annual number of newly diagnosed cases of type 1 diabetes in children <15 yr (r2 = 0.80; p<0.0001) of 2.0 additional cases per year, from 23 in 1990/1 to 60 cases per year in 2008/9. There was no appreciable difference in the rate of increase between males and females (p = 0.08), but the rise in number of new type 1 diabetes cases did not occur evenly among age groups (p = 0.0001). The yearly increase among older children (10–14 yr) was 3-fold greater than in the youngest (0–4 yr) group (0–4 yr = +0.4/yr; 5–9 yr = +0.8/yr; 10–14 yr = +1.2/yr). Over the 20-year period, new cases were moderately more frequent in winter and less frequent in spring (29.4% and 22.0%, respectively; test of equal proportions across all four seasons: p = 0.02).
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).
The majority of responders were not using diabetes apps (80.4%, 152/189), although 60.5% (89/147) reported they would be interested in trying one. Of the 118 people who answered the question, the reasons for not using an app was not knowing they existed (66.9%, 79/118), feeling confident without one (16.9%, 20/118), discontinued use after having used an app previously 16.9% (20/118).
Wednesday Walks are a joint venture between Korowhai Aroha Health Centre and Diabetes NZ Rotorua Branch. Join Mary every Wednesday morning for some gentle exercise in good company. The idea is to have fun and encourage each other to exercise. Our Wednesday Walks set out from the Waka on the Lakefront at 9am sharp. The walk lasts for up to an hour. You can go at your own pace and there is no minimum level of fitness required. Wear a hat and bring walking shoes, water & extra carbohydrate foods if you are prone to low blood sugar levels. Bring your partner, friend, kids or mokopuna.
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