Your health professional at the Centre may suggest that they make a referral for you, if there are problems affecting your diabetes management or your overall health and management. Alternatively you can ask your family doctor or nurse to refer you. If you are uncertain about whether it would be helpful to see us, you are most welcome to phone us directly to discuss this. Phone 3640 860 ext 89113.
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.
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.

For example, adjusting to having diabetes; difficulty in making the life changes necessary to stay well; difficulty managing anger, conflict and other emotions related to your health; depression, sadness and grief; anxiety, worries, panic and phobias related to your health; eating difficulties; and difficulty with coping with the complications of diabetes.
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.

Owing to time restrictions, longer term follow-up of participants was not feasible within the current study, although it is hoped that a two year follow-up of the present study’s participants is possible. The significant group difference seen at three months, dropping slightly at six months, but reaching significance again at nine months, could be an indication of sustained change. Another limitation of the study design was that secondary outcome assessors were not blinded to treatment allocation, which could have introduced bias in follow-up data collection of secondary variables.
With technology advancing rapidly, there is a call for mHealth to move towards more complex technology. However, this study has shown that text messaging—available on any mobile phone—although simple, is still potentially effective for improving glycaemic control. Equally, this study had very few technical difficulties, which probably contributed to the high satisfaction with the intervention. The individual tailoring of the intervention, and ability for participants to choose varying components and dosages, means that questions remain around the ideal duration for implementation as well as the components most important for effectiveness. Further research is needed to understand the components of this intervention that are most effective and the ideal intervention dosage to further refine this intervention and inform the development of future interventions. With participants highly satisfied with the intervention and largely happy with their intervention dosage, but great variance in the modules, durations, and dosages, SMS4BG may need to remain individually tailored in this way, resulting in a more complex intervention for delivery until further investigation on this can be made.
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).
We saw no significant interaction between the treatment group and any of the prespecified subgroups: type 1 versus type 2 diabetes (P=0.82), non-Māori/non-Pacific versus Māori/Pacific ethnicity (P=0.60), high urban versus high rural/remote region (P=0.38). Adjusted mean differences on change in HbA1c from baseline to nine months for patients with type 1 and type 2 diabetes were −5.75 mmol/mol (95% confidence interval −10.08 to −1.43, P=0.009) and −3.64 mmol/mol (−7.72 to 0.44, P=0.08), respectively. Adjusted mean differences for non-Māori/non-Pacific and Māori/Pacific people were −4.97 mmol/mol (−8.51 to −1.43, P=0.006) and −3.21 mmol/mol (−9.11 to 2.70, P=0.28), respectively. Adjusted mean differences for participants living in high urban and high rural/remote areas were −4.54 mmol/mol (−8.40 to −0.68, P=0.02) and −3.94 mmol/mol (−9.00 to 1.12, P=0.13), respectively (table 3).
Conclusion A tailored, text message based, self management support programme resulted in modest improvements in glycaemic control in adults with poorly controlled diabetes. Although the clinical significance of these results is unclear, the findings support further investigation into the use of SMS4BG and other text message based support for this patient population.
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.
Today’s first post is titled “Why ‘Stop Diabetes’?” can be found at 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.”

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).
In contrast with the extensive app problems presented in the literature, over half of the responders with an app reported no problems [5,11-13,15]. This discrepancy may be due to false self-report or responders may have tried multiple apps before finding the one they like. Our study is unable to add significantly to literature about insulin dose calculation problems [15], as only 7 responders reported using their app for insulin calculation. However it is notable that this feature is desired by users and reinforces the importance of having a regulated environment to ensure safety.
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).
In this large sample of people with diabetes attending a secondary care clinic in NZ, 19.6% (37/189) of patients reported using diabetes apps to support their self-management. Diabetes app users were younger and more often had T1DM. The most used app feature in current app users was a blood glucose diary (87%, 32/37). The most desirable feature of a future app was an insulin dose calculation function in app users (46%) and a blood glucose diary in non-app users (64.4%). A Scottish survey has reported similar results and observed that people with T1DM were more likely to desire insulin calculators in an app [23].
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).
Clinical psychologists have studied psychology at University, usually for at least seven years. They have specialised in learning about how the feelings, actions, beliefs, experiences and culture of people affect the way they live. They have learned how to listen to and understand people’s emotional and psychological problems and how to help people make changes in their lives.

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