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This study shows the potential of SMS4BG to provide a low cost, scalable solution for increasing the reach of diabetes self management support. It showed that a text messaging programme can increase a patient’s feelings of support without the need for personal contact from a healthcare professional. Half of the intervention group reported sharing the messages with others. Traditional education for diabetes self management is delivered to individual patients, but there is benefit of support from other people being involved.45 This is particularly pertinent to ethnic populations such as Māori groups, in whom family have an important role in supporting diabetes self management.46
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).

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.”


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.
The World Health Organisation (WHO) has mandated November 14 as World Diabetes Day, an international event to raise awareness about diabetes. Close to 350 million people in the world have diabetes and WHO reports that a person dies from this disease every 6 seconds – that’s 5 million deaths. Currently 1 in 11 adults have diabetes worldwide and this is predicted to increase to 1 adult in 10 (652 million) by 2040. Sobering statistics indeed.
We thank the participants who took part in this study as well as the staff at the primary care practices and diabetes clinics across New Zealand who referred their patients to the study; the National Institute for Health Innovation’s IT team for their work on the text message delivery system, and all those involved in the study design and set up; Coral Skipper, Louise Elia, Erana Poulsen, and Hamish Johnstone (Māori Advisory Group members); Aumea Herman (Pacific adviser); Joanna Naylor and Michelle Garrett (content development advisers); Richard Edlin (health economist); Mahalah Ensor (assistance with recruitment); Hannah Bartley, Rachel Sullivan, Anne Duncan, and Gillian Lockhart (research assistants); Michelle Jenkins and John Faatui (data management support); and Karen Carter and Angela Wadham (project management support).
The SMS4BG (self management support for blood glucose) intervention was developed to address the need for innovative solutions to support self management in adults with poorly controlled diabetes.28 The individually tailored intervention provides information and support designed to motivate a person to engage in the behaviours required to manage their diabetes effectively for long term health improvement. The development of SMS4BG followed the mHealth Development and Evaluation Framework29 (including extensive formative work and end user engagement to ensure that it met the needs of the population it was designed to reach) is evidence based and theoretically grounded. A previous pilot study found SMS4BG to be acceptable and perceived it as useful.28 This study aimed to determine the effectiveness of the mHealth diabetes self management support programme—SMS4BG in adults with poorly controlled type 1 or type 2 diabetes, in addition to their usual diabetes care.
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].

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 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.
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.
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