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 American Diabetes Association launched its first official blog today to help put a face on a disease that kills more people each year than breast cancer and AIDS combined.  The blog, called Diabetes Stops Here: Living with Diabetes; Inspired to Stop It, aims to document the Stop Diabetes® movement by reaching and engaging the 23.6 million Americans living with diabetes as well as the 57 million who are at risk for developing type 2 diabetes. 


Height and weight were recorded for 660 patients at their required first post-diagnostic clinic (on average 15 weeks from diagnosis) from 1994 onwards. Annual mean BMI SDS of newly diagnosed type 1 diabetes did not alter (average non-significant change smaller than ±0.02 SDS/year) over the period for the entire population, or for any gender, age, or ethnicity sub-group. There was no association between BMI SDS and age at diagnosis.

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.
Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: support from Waitemata District Health Board for the development of SMS4BG, and support from the Health Research Council of New Zealand in partnership with the Waitemata District Health Board and Auckland District Health Board, and the New Zealand Ministry of Health for the randomised controlled trial; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.
96.2% (181/188) of responders reported owning a mobile phone and 84.0% identified this device as a mobile phone (158/188), (Android 52.6% [80/152], iPhone 44.1% [67/152], Windows 3.3% [5/152]). Of the mobile phone owners 23.4% (37/158) reported using a diabetes app. Over half of app users (54%, 20/37) used the app daily, 22% (8/37) used it for a few days per week, and 14% (5/37) used the app less than weekly; 4 responders never used the app.
The survey was completed by 189 of the 539 patients (35.0% response rate, 158/491 from participants with email addresses, 31/48 from telephone contact). Table 1 shows the characteristics of responders. Responders (N=189) were older, with a mean age of 50.0 years (SD 15.7) than non-responders (N=350), who had a mean age of 45.9 years (SD 16.1; P=.004) and had lower HbA1c of 62.2 mmol/mol (SD 14.0) (7.8, SD 1.1%) than non-responders (N=325) with mean of 68.9 mmol/mol (SD 18.2; 8.5, SD 2.3%; P<.001). There were no significant differences in the rate and type of anti-hypertensive, lipid lowering, and anti-hyperglycemic medications used between responders and non-responders (P=.28, −.32, and −.17, respectively). Clinical variables by type of diabetes are shown in Table 2. As expected, responders with T1DM were more likely to be on Insulin than those with T2DM (P<.001) whereas responders with T2DM were more likely to be on anti-hypertensive (P<.001) and lipid lowering medication (P<.001).
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SMS4BG was delivered in the English language (with the exception of some Māori, Samoan, and Tongan words). With high rates of diabetes in ethnic minority groups, delivery of this type of intervention in languages native to these groups could provide greater benefit. It is likely that some people were not referred to the study, or were unable to take part, due to the criteria that they must be able to read English. SMS health programmes have been translated into other languages such as Te Reo;44 thus, further research needs to look at whether such translations would be of benefit in SMS4BG.
The look of the Dario appealed straight away to me. Small and compact. Easy for me to carry with my phone which goes everywhere with me. Love the fantastic app on my phone. Clear, informative and easy to use. Love it! I can look back at previous readings to see any patterns. Sara and Assaf have been brilliant at helping out with any issues I have come across, which I thank them hugely for. The Dario Lounge is a great community for all users, who all share advice.
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96.2% (181/188) of responders reported owning a mobile phone and 84.0% identified this device as a mobile phone (158/188), (Android 52.6% [80/152], iPhone 44.1% [67/152], Windows 3.3% [5/152]). Of the mobile phone owners 23.4% (37/158) reported using a diabetes app. Over half of app users (54%, 20/37) used the app daily, 22% (8/37) used it for a few days per week, and 14% (5/37) used the app less than weekly; 4 responders never used the app.
Similar to a national American mHealth survey, a large proportion of patients are not using health apps [26]. However, there was a higher rate (20%) of diabetes app use in this patient group compared to the 4% found in a survey of diabetes app use in the USA in 2015 [14] and 7% in Scotland in 2016 [23]. Our findings are consistent with previous surveys showing people using apps are more likely to be younger [26]. It has been suggested that people who are more in need of diabetes care are less likely to use apps [27]; however, we found no significant difference in HbA1c between app users and non-app users. The most favored feature being the blood glucose diary is not surprising given it is the most common feature included in the apps available [5,14]. However some responders are also using health apps that are not specific to diabetes, such as apps for dietary advice.
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.
This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
The main effect of the intervention on secondary outcomes are presented in table 4. No significant differences were observed between the two groups for self efficacy (SEDM). A significant improvement in foot care behaviour was seen in the intervention group compared with the control group (adjusted mean difference 0.85 (95% confidence interval 0.40 to 1.29), P<0.001) but no significant group differences were observed for diet (general or specific), exercise, blood glucose testing, and smoking behaviours (SDSCA). No significant group differences were observed for diabetes distress (DDS2).
The 1177 people with diabetes attending clinics at Capital and Coast District Health Board (CCDHB), Wellington, New Zealand over a 12-month period (10th September 2014 to 10th September 2015) were the sample population. Out of the total patients, 521 patients with an email address in the hospital management system were invited to participate via email. To include a representation of people without a recorded email address in the sample (n=656), every 5th person was telephoned (up to twice) and invited to provide an email address. Of the 131 patients telephoned, 54 (41.2%) were reached, of whom 49 (91%) agreed to participate. Patients without phone numbers or unable to provide an email address were excluded. This generated a sample population of 570 people.
The main treatment effect on the primary outcome is presented in table 2. The reduction in HbA1c from baseline to nine month follow-up was significantly greater in the intervention group than in the control group (mean −8.85 mmol/mol (standard deviation 14.84) v −3.96 mmol/mol (17.02), adjusted mean difference −4.23 (95% confidence interval −7.30 to −1.15), P=0.007). The adjusted mean difference on change in HbA1c at three and six months were −4.76 (−8.10 to −1.43), P=0.005) and −2.36 (−5.75 to 1.04), P=0.17), respectively (table 2).
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]).
This study contributes to the evidence around the use of SMS to support diabetes management.131415 The improvements in HbA1c seen in this study are similar to those reported in meta-analyses of SMS interventions in diabetes not limited to those with poor control.141641 Unlike previous studies that typically focus on a particular population defined by diabetes type, age, or treatment, the current study provided an intervention for all adults with either type 1 or type 2 diabetes under any treatment regimen, enhancing potential reach and generalisability. The only limit on the population was the requirement that participants had to have poor diabetes control. This criterion was particularly important given associated costs and debilitating complications of poorly controlled diabetes. Although few trials so far have examined the effectiveness of mHealth interventions in this population,42 this study provides evidence to support the use of this modality to provide diabetes education and support to individuals with poor control.
Phoenix Health Centre carries out pre employment medical assessments for several large employers in Whakatane. These give a base line recording of an employee’s health status at the time they were employed. It is then possible to monitor the employee’s health in relation to the hazards they may be exposed to in the workplace. If required we also undertake monitored urine sampling for ESR drug testing.
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