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.
We all have our favorite holiday activities. It might be watching fireworks on the 4th of July, heading to the beach for Labor Day, as summer winds down, or finding the perfect pumpkin to carve for Halloween. For many of us, it’s the non-stop activities that seem to begin with the Macy’s Day Parade, early Thanksgiving morning, and continue through the last bowl game on New Year’s Day. But, no matter what holiday or activity tops your list, you can bet that it involves not only extreme amounts of food and drink but the kind designed to send blood sugar levels through the roof. (more…)
Eligible participants were randomised to either an intervention or control group in a 1:1 ratio. Randomisation was stratified by health district category (high urban or high rural/remote), diabetes type (1 or 2), and ethnicity (Māori and Pacific, or non-Māori/non-Pacific). The randomisation sequence was generated by computer programme using variable block sizes of two or four, and overseen by the study statistician. Following participant consent and completion of the baseline interview, the research assistant then randomised the participant to intervention or control, using the REDCap randomisation module. The REDCap randomisation module ensured that treatment allocation was concealed until the point of randomisation. Due to the nature of the intervention, participants were aware of their treatment allocation. Research assistants conducting the phone interviews were also aware of the treatment allocation. However, the objective primary outcome was measured by blinded assessors throughout the study period.
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 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.
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.

Among the intervention participants, 169 (92%) completed questions at follow-up about satisfaction and acceptability of the intervention (table 5). Participants reported high levels of satisfaction with SMS4BG, and all but two participants thought that text messaging was a good way to deliver this type of support. Ten participants reported technical issues while receiving the intervention, most commonly issues replying to the messages (n=4), issues accessing graphs (n=2), and mobile reception issues (n=2).
This study showed that a tailored and automated SMS self management support programme has potential for improving glycaemic control in adults with poorly controlled diabetes. Although the clinical significance of these results is unclear, and the full duration of these effects is yet to be determined, exploration of SMS4BG to supplement current practice is warranted.
“There have been so many touching moments in the movement to Stop Diabetes since we launched last year,” commented Larry Hausner, CEO, American Diabetes Association. “People have shared courageous stories of facing their diabetes head on, while others have shared their heart-breaking experiences of losing a loved one because of diabetes. The blog is a new way to raise our collective voices and tell people why we need to Stop Diabetes once and for all.”  
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.
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).
In conclusion, there has been a steady increase in type 1 diabetes incidence in children <15 yr in Auckland over a 20-year span. However, in contrast to observations elsewhere, age at diagnosis in Auckland has increased over the study period. Our data do not support the ‘accelerator hypothesis’, and factors other than simply increasing adiposity are likely to be at play.
In conclusion, there has been a steady increase in type 1 diabetes incidence in children <15 yr in Auckland over a 20-year span. However, in contrast to observations elsewhere, age at diagnosis in Auckland has increased over the study period. Our data do not support the ‘accelerator hypothesis’, and factors other than simply increasing adiposity are likely to be at play.
The Endocrinology Service at Starship Children's Health provides specialist care for all children diagnosed with type 1 diabetes in the Auckland region (New Zealand). Its Paediatric Diabetes Service provides centralised medical care for all diabetic children up to 15 yr who reside in the Auckland region, drawing from the regional population of approximately 1.5 million [12]. All children or adolescents diagnosed with type 1 diabetes who attended the Paediatric Service between 1 January 1990 and 31 December 2009 were eligible for this study. Subjects were captured from a comprehensive database (Starbase) that gathers data on all children with type 1 diabetes in the Auckland region. This information was cross-referenced with hospital admission data and subsequent clinical follow up, leading to a case ascertainment >95% for children with type 1 diabetes [13].

Participants were referred to the study by healthcare professionals at their primary and secondary care centres across New Zealand. Additionally, participants could self refer to the study. Eligible participants were English speaking adults aged 16 years and over with poorly controlled type 1 or 2 diabetes (defined as glycated haemoglobin (HbA1c) concentration ≥65 mmol/mol or 8% in the preceding nine months). The initial protocol required HbA1c concentration above the cutoff level within the past three months, but after feedback from patients and clinicians, this period was extended to nine months to ensure a greater reach across those people not having regular tests. Participants required access to a mobile phone and needed to be available for the nine month study duration.


Of mobile phone owners, those using diabetes apps were more likely to have T1DM (30/96) than T2DM (n=7/61); (P=.006). App users were younger with a mean age of 39.0 years (SD 11.1) compared to non-app users having a mean of 52.5 years (SD 15.6), (P<.001). There were no other significant differences in clinical variables between app and non-app users.
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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.
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.

Some of the most vocal diabetes stories come from blogs and other social media platforms which create a broad online community of people who have diabetes or whose loved ones are living with the disease.  “By means of this blog,” noted Hausner, “we hope to add our voice to this dialogue and further engage with those who may be well aware of the effects diabetes can have on their lives.”
Strengths of the current study included its sample size, diverse population, very low loss to follow-up, pragmatic design, absence of protocol violations, and objectively measured primary outcome. Although the initial sample size target was not reached, the final sample of 366 participants is larger than previous randomised controlled trials in this area. This study contributes valuable evidence to the literature on the use of text messages in diabetes particularly for individuals with poor control. Considering poorer outcomes are experienced by ethnic minority groups, a strength of this study was its high proportion of participants representing these groups.
Owing to individual tailoring, participants in the intervention group received varying numbers of messages. Half the participants (92/183) received messages for three months, an additional 18% (33/183) chose to continue the messages for six months, and the remaining 32% (58/183) chose to continue the messages to the maximum nine months. Only three participants chose to stop their messages early. A total number of 76 523 messages were sent by the system to participants (median number of messages per participant 242 (interquartile range 122-511; range 14-2050)), and 16 251 messages of blood glucose results were sent into the system by participants receiving the reminders (68 (1-169; 0-917)).
Diabetes has become so common in the U.S. that there may be a danger of losing sight of just how serious a disease it is. In the diabetic community, there has long been a saying that diabetes won’t kill you, but its complications will. The list of complications is long and includes, heart disease, nerve damage, kidney failure, foot and leg amputation, blindness, Alzheimer’s and a host of others. And, while the saying about diabetes not killing you may be catchy, the truth, according to the American Diabetes Association, is that, “Diabetes remains the 7th leading cause of death in the United States in 2015, with 79,535 death certificates listing it as the underlying cause of death, and a total of 252,806 death certificates listing diabetes as an underlying or contributing cause of death.” (more…)

Additional data on all patients were collected from the hospital management system, including age, and the most recent values within the previous 12 months from date of survey for blood pressure (BP), glycated hemoglobin (HbA1c), urinary microalbumin to Creatinine ratio (ACR), low density lipoprotein cholesterol (LDL), and total cholesterol to HDL ratio (C:HDL). Prescription of lipid lowering drugs, anti-hypertensive drugs, insulin, or other hypoglycemic medication were also extracted from the medication list from the last visit within the sample period. Type of diabetes was self-reported in the survey (type 1 [T1DM], type 2 [T2DM], other or unknown) and in four participants who had selected ‘other’ or ‘unknown’ diabetes type was determined by examination of the clinical records. For categorization of participants by app use, 4 responders who did not indicate if they had a mobile phone or not were included in the non-app group.

Eligible participants were randomised to either an intervention or control group in a 1:1 ratio. Randomisation was stratified by health district category (high urban or high rural/remote), diabetes type (1 or 2), and ethnicity (Māori and Pacific, or non-Māori/non-Pacific). The randomisation sequence was generated by computer programme using variable block sizes of two or four, and overseen by the study statistician. Following participant consent and completion of the baseline interview, the research assistant then randomised the participant to intervention or control, using the REDCap randomisation module. The REDCap randomisation module ensured that treatment allocation was concealed until the point of randomisation. Due to the nature of the intervention, participants were aware of their treatment allocation. Research assistants conducting the phone interviews were also aware of the treatment allocation. However, the objective primary outcome was measured by blinded assessors throughout the study period.
Participants were referred to the study by healthcare professionals at their primary and secondary care centres across New Zealand. Additionally, participants could self refer to the study. Eligible participants were English speaking adults aged 16 years and over with poorly controlled type 1 or 2 diabetes (defined as glycated haemoglobin (HbA1c) concentration ≥65 mmol/mol or 8% in the preceding nine months). The initial protocol required HbA1c concentration above the cutoff level within the past three months, but after feedback from patients and clinicians, this period was extended to nine months to ensure a greater reach across those people not having regular tests. Participants required access to a mobile phone and needed to be available for the nine month study duration.
The biggest study limitation was the difficulty with recruitment, which resulted in a sample size smaller than initially planned. One reason for the low recruitment was the required time needed by clinicians to identify and refer patients to the study, which was not always available. Furthermore, many referred patients who did not meet the HbA1c inclusion criteria were still referred because clinicians had thought these individuals would benefit from the programme. This limitation highlights the difference between research and implementation where strict criteria can be relaxed. Alternative methods of recruitment could be explored, such as through laboratory test facilities to ensure access to the intervention regardless of clinician availability.
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.

-Keep your blood pressure under control. The same lifestyle changes that control blood glucose levels (dietary modifications and exercise) may also help you keep your blood pressure at safe levels. The American Diabetes Association recommends that people with diabetes keep their blood pressure below 140/80, but check with your health care professional about what target is best for you.


As published in the protocol, a sample size of 500 participants (250 per arm) was estimated to provide 90% power at the 5% significance level to detect a clinically meaningful group difference of 0.5% (5.5 mmol/mol) in HbA1c at nine months, assuming a standard deviation of 1.7% (18.6 mmol/mol). Despite extensive efforts, recruitment for the study was slower than expected, and with the limited overall study period available, a post hoc power calculation was conducted in September 2016. A revised sample size of 366 participants (183 per arm) was targeted, which would provide 80% power to detect the same effect size under the same assumptions.
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