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).
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]).
Lack of insulin results in ketoacidosis. Ketones are acids that develop in the blood and appear in the urine. Ketones could poison the body and this is a warning sign that the diabetes is out of control. Symptoms of diabetes involve nausea, shortness of breath, vomiting, fruity flavor in breath, dry mouth, and high glucose levels. Complications associated with diabetes are retinopathy, neuropathy, nephropathy, heart disease and gangrene. Hypoglycemia or low blood sugar is yet another problem associated with diabetes mellitus. Symptoms include hunger, tremor, seizure, sweating, dizziness, jerks, tingling sensation and pale skin color. Improper management of diabetes causes low blood sugar, which in turn causes hypoglycemic coma. It is a life threatening condition.
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.
One of the most important aspects of diabetes management is to maintain a healthy body weight. Being overweight not only increases your risk of heart disease, stroke and some cancers, it also makes your diabetes harder to manage. Small changes in your diet such as reducing your portion sizes and swapping to low-fat dairy products can help you to achieve a healthy body weight and manage your diabetes.
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.
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.
Diabetes mellitus (DM) requires tight control of blood glucose to minimize complications and mortality [1,2]. However, many people with DM have suboptimal glycemic control [3,4]. Use of mobile phone apps in diabetes management has been shown to modestly improve glycemic control [5-10]. Despite this promise, health apps remain largely unregulated, and diabetes apps have not always had safety approval [11] or incorporated evidence-based guidelines [12,13].
ED is a failure to obtain/maintain penile erection sufficient for intercourse is more prevalent in men with diabetes and increases with age.  It is important to distinguish erectile failure from premature ejaculation, decreased libido and other problems as these have different causes and treatment. ED in diabetes is largely due to failure of vascular smooth muscle relaxation secondary to endothelial dysfunction and/or autonomic neuropathy.

‘I was very pleased to contact your service. I was feeling overwhelmed with my current situation however knew that I needed to get a diabetes test done. While I was waiting for my turn to be tested Susan welcomed me, helped my overwhelming feelings calm down, she was very approachable and understanding. Sandy followed through by assisting me with assurance that things were going to be okay and was very understanding. She encouraged that I seek more medical advice for my blood pressure results. She phoned my manager and found me a local GP that I could visit right away. I was very appreciative of these ladies and all the help, care and advice they gave me. Thank you so much!’
The survey was piloted with the first 30 patients with an email addresses (chronological order of clinic visits). Responses were reviewed after response rate reached 50%. As 4 questions were unanswered by some participants, a “none of the above” option was added. The invitations were sent out to the remaining 540 participants. A further 31 participants were excluded (4 email address errors, 13 gestational diabetes, 10 deceased, 4 did not have diabetes) resulting in a final total of 539 participants. This survey remained open for 3 weeks, with reminders sent to non-responders at one week and two weeks.
New Zealand has a population of approximately 4.4 million people, the majority being of European descent. Auckland, the largest city in New Zealand, is the most ethnically diverse, with approximately 11% of people identifying themselves as indigenous Maori, 14% as Pacific, and 19% as Asian [12]. By international standards, the incidence of type 1 diabetes in young New Zealanders was assessed as moderate at 17.9 per 100,000 [13]. However, this figure was obtained from a 2-year snapshot, and did not provide information on possible time trends on type 1 diabetes incidence. In addition, previous studies on type 1 diabetes incidence in New Zealand are out of date or refer to a specific geographical region [14], [15], [16].
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.
New Zealand has a population of approximately 4.4 million people, the majority being of European descent. Auckland, the largest city in New Zealand, is the most ethnically diverse, with approximately 11% of people identifying themselves as indigenous Maori, 14% as Pacific, and 19% as Asian [12]. By international standards, the incidence of type 1 diabetes in young New Zealanders was assessed as moderate at 17.9 per 100,000 [13]. However, this figure was obtained from a 2-year snapshot, and did not provide information on possible time trends on type 1 diabetes incidence. In addition, previous studies on type 1 diabetes incidence in New Zealand are out of date or refer to a specific geographical region [14], [15], [16].
Interventions The intervention group received a tailored package of text messages for up to nine months in addition to usual care. Text messages provided information, support, motivation, and reminders related to diabetes self management and lifestyle behaviours. The control group received usual care. Messages were delivered by a specifically designed automated content management system.
In relation to perceptions and beliefs about diabetes, a significant reduction in illness identity (how much patients experience diabetes related symptoms) on the BIPQ was observed in favour of the intervention (adjusted mean difference −0.54 (95% confidence interval −1.04 to −0.03), P=0.04). However, we saw no significant group differences for perceptions of consequences, timeline, control, concern, emotions, and illness comprehensibility. A significant improvement in health status on the EQ-5D VAS was observed in favour of the intervention (4.38 (0.44 to 8.33), P=0.03) but no significant differences were observed between groups for the quality of life index score. Finally, the measure of perceived support for diabetes management showed a significant improvement between the groups in how supported the participants felt in relation to their diabetes management overall (0.26 (0.03 to 0.50), P=0.03) but no significant group differences on appraisal, emotional, and informational support.
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.
Funding: The development of SMS4BG was funded by Waitemata District Health Board. The randomised controlled trial was funded by the Health Research Council of New Zealand in partnership with the Waitemata District Health Board and Auckland District Health Board (through the Research Partnerships for New Zealand Health Delivery initiative), and the New Zealand Ministry of Health. The funders were not involved in any way in the preparation of the manuscript or analysis of the study results. No payment has been received for writing this publication.
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