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.

This cross-sectional observational study used two surveys (see Multimedia Appendices 1 and 2), one for people with diabetes attending a secondary care diabetes outpatient clinic and the second for HPs (who treat people with diabetes) attending a national diabetes conference. Both surveys were multi-choice format, collected, and managed using REDCap electronic data capture tools. REDCap (Research Electronic Data Capture) is a secure, Web-based app designed to support data capture for research studies [24]. The survey questions were derived from criteria in the Mobile app rating scale [25] to address attitudes and practices of both the people with diabetes and HPs. The list of apps was compiled by searching Apple and Android App stores and included the first consecutive ten diabetes apps. We eliminated any apps not specific to diabetes by reviewing app store descriptions. We reviewed the main features from these apps to develop the list of app features. The patient survey asked responders to select any useful app features from a list. Responders could select more than one useful app feature. The HP survey listed app features and used a scale to assess usefulness of app features (from 1 [not at all useful] to 5 [extremely useful]) and their confidence in recommending apps (from 1 [not at all confident] to 5 [extremely confident]).
A total of 884 new patients aged <15 yr were diagnosed with type 1 diabetes over the 20-year period covered by this study. There was an increase in the mean age at diagnosis from 7.6 yr in 1990/1 to 8.9 yr in 2008/9 (0.07/yr, r2 = 0.31, p = 0.009). This was observed in both males (0.07/yr, r2 = 0.22, p = 0.04) and females (0.06/yr, r2 = 0.13, p = 0.12).
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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.
Statistical analyses were performed by SAS version 9.4 (SAS Institute). All statistical tests were two sided at a 5% significance level. Analyses were performed on the principle of intention to treat, including all randomised participants who provided at least one valid measure on the primary outcome after randomisation. Demographics and baseline characteristics of all participants were first summarised by treatment group with descriptive statistics. No formal statistical tests were conducted at baseline, because any baseline imbalance observed between two groups could have occurred by chance with randomisation.
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.
To obtain data on HPs’ knowledge and recommendation of apps to people with diabetes, a second survey was conducted of the HPs attending the annual scientific meeting of the New Zealand Society for the Study of Diabetes (NZSSD) in May 2016. Immediately prior to the meeting all registered attendees (n=286) were invited to participate in the online survey via email. The data from the patient survey was presented at the conference in a 15-min oral presentation and attendees were encouraged to complete the survey. Paper copies of the survey were also available at the meeting. This survey remained open for 2 weeks, with a reminder sent at 1 week.
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.
Results The reduction in HbA1c at nine months was significantly greater in the intervention group (mean −8.85 mmol/mol (standard deviation 14.84)) than in the control group (−3.96 mmol/mol (17.02); adjusted mean difference −4.23 (95% confidence interval −7.30 to −1.15), P=0.007). Of 21 secondary outcomes, only four showed statistically significant improvements in favour of the intervention group at nine months. Significant improvements were seen for foot care behaviour (adjusted mean difference 0.85 (95% confidence interval 0.40 to 1.29), P<0.001), overall diabetes support (0.26 (0.03 to 0.50), P=0.03), health status on the EQ-5D visual analogue scale (4.38 (0.44 to 8.33), P=0.03), and perceptions of illness identity (−0.54 (−1.04 to −0.03), P=0.04). High levels of satisfaction with SMS4BG were found, with 161 (95%) of 169 participants reporting it to be useful, and 164 (97%) willing to recommend the programme to other people with diabetes.
The control group also experienced a decrease in HbA1c from baseline to the nine month follow-up, and experienced improvements in secondary outcomes, which could indicate trial effects. Previous research has shown that recruitment to a clinical trial alone can result in improvements in HbA1c,43 but it is not expected that these improvements would be sustainable past the initial few months without intervention.
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