Most people know that diabetes involves the inability to control glucose, or blood sugar, by not producing enough insulin or not managing it correctly. This leads to elevated levels of glucose in the body, which can result in very serious complications, such as heart attack, stroke, kidney disease, nerve damage, hardening of the arteries, foot and leg amputation and blindness. (more…)
It’s heart-wrenching to watch all that people go through as natural disasters play out on our television screens. Tucked away, along with sympathy for those in the midst of a hurricane, earthquake, flood or other catastrophic events, is the very understandable thought, “I’m so glad that’s not happening to me!”. The truth is, however, that we are all susceptible to major life-changing events, and they can happen with very little notice. Those with a chronic medical condition, like diabetes, are especially vulnerable and should take seriously the advice to be prepared.    (more…)
A large patient sample size was obtained by contacting all patients seen in the last 12 months with an email address. The risk of overrepresentation by more technology-literate responders through recruitment via email was minimized by also recruiting via telephone and by providing paper surveys at the HPs’ conference. The demographic and clinical data of responders and non-responders were compared, and most variables showed no difference. Responders were actually older than non-responders and had better glycemic control. This study focused on the beliefs and opinions of people with diabetes (potential app users) and HPs (potential app prescribers) rather than simply describing apps for diabetes . It is one of the first papers to describe app use in people with diabetes in New Zealand.
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.
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.

Data were imported into SPSS version 24 (IBM). Incomplete responses were included in the analysis. In the patient survey, independent sample t tests were conducted to compare mean clinical variables (age, BP, C:HDL, LDL, HbA1c) by type of diabetes, method of recruitment, and whether the responder used a diabetes mobile phone app. Adjustment was made for unequal variances. Normal distribution was assumed for all variables, apart from urinary microalbumin to creatinine for which a Wilcoxin test was used. No statistically significant differences in these variables or in mobile phone app use were found between patients with recorded email addresses and patients phoned for their email address. Therefore, all 189 responses were combined for further analysis. Chi-square tests were used to compare medications and survey responses by type of diabetes. Statistical significance was determined by exact 2-sided P values less than .05. In the HP survey, mean values on the usefulness and confidence Likert scales were calculated to compare app features.
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]).
Almost two-thirds of HPs responding had recommended a diabetes app to patients. Dieticians were more likely to recommend an app than others. Blood glucose and carbohydrate diaries were considered the most useful feature and HPs were most confident to recommend blood glucose diaries. HPs are the least confident recommending insulin dose calculation functions. Over one-third of HPs desire guidance with app recommendations.

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 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 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).
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.
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Before my type1 insulin dependent diagnosis, I had a pancreas that worked, going out for dinner was ...really exciting. I didn’t even know what type one autoimmune disease was. Id pick whatever I wanted from the menu. Didn’t think of my blood sugars at all! Sitting at the table and I would drink my drink without a thought of what it will be doing when the drink rushes into my blood stream. I wouldn’t be calculating in my head if carbs totals and portion sizes are going to bring me into hyper or hypoglycaemia . I wouldn’t be hoping that the exercise id just done before going to the restaurant will change my blood glucose reading....Now....my pancreas hasn’t worked for 11years and while everyone’s chatting away at the table I’m half there in mind and half of me is not living in the moment of enjoying myself because I’m caught up in the complete intensity of trying to deal with my type one condition. Very overwhelming and my mind plays a 🤹‍♂️ juggling game where One ball is exercise, one ball is long and Quick acting insulin and one ball is carbs/food portion. Also, my will power either is good or it’s shocking. The others get their big portions while I’m still at bg testing stage and haven’t injected for the meal yet!! Everyone is trying each others food next to me and across the table. I have invisible blinkers on my eyes so I’m not aware of food sharing that’s going on. Once my food arrived it’s then that I can calculate how many units of my insulin that I inject depending on how many carbohydrates in the meal , making sure I inject in a different area to my lunchtime injection. Finally I begin to eat and the other people are almost finished their meal!!! I am a type one hero in more ways than one. See More

Strengths of the intervention were that it was theoretically based, the information reinforced messages from standard care, and it was system initiated, personally tailored, and used simple technology. These strengths result in high relevance to diverse individuals, increasing the intervention’s reach and acceptability. Unlike SMS4BG, previous diabetes SMS programmes have largely focused on specific groups—for example, limiting their generalisability. Furthermore, the SMS4BG intervention was tailored and personalised to the individual. Although this specificity results in a more complex intervention in relation to its delivery, it appears to be a worthwhile endeavour with high satisfaction and the majority of participants happy with their message dosage.
Only children aged <15 yr were included. Type 1 diabetes was diagnosed based on clinical features. All patients had elevated blood glucose at presentation: either a random measurement of ≥11.1 mmol/l and presence of classical symptoms, or fasting blood glucose ≥7.1 mmol/l. In addition, all patients met at least one of the following criteria: a) diabetic ketoacidosis; b) presence of at least two type 1 diabetes antibodies (to glutamic acid decarboxylase, islet antigen 2, islet cell, or insulin autoantibodies); or c) ongoing requirement for insulin therapy. Clinical and demographic data were prospectively recorded on all patients at each outpatient visit.
Only children aged <15 yr were included. Type 1 diabetes was diagnosed based on clinical features. All patients had elevated blood glucose at presentation: either a random measurement of ≥11.1 mmol/l and presence of classical symptoms, or fasting blood glucose ≥7.1 mmol/l. In addition, all patients met at least one of the following criteria: a) diabetic ketoacidosis; b) presence of at least two type 1 diabetes antibodies (to glutamic acid decarboxylase, islet antigen 2, islet cell, or insulin autoantibodies); or c) ongoing requirement for insulin therapy. Clinical and demographic data were prospectively recorded on all patients at each outpatient visit.

In this large sample of people with diabetes attending a secondary care clinic in NZ, 19.6% (37/189) of patients reported using diabetes apps to support their self-management. Diabetes app users were younger and more often had T1DM. The most used app feature in current app users was a blood glucose diary (87%, 32/37). The most desirable feature of a future app was an insulin dose calculation function in app users (46%) and a blood glucose diary in non-app users (64.4%). A Scottish survey has reported similar results and observed that people with T1DM were more likely to desire insulin calculators in an app [23].


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.
I act as a care giver for my grandparents who both suffer from type 2 diabetes. This article is right on point with having to make changes to one’s diet to help control blood glucose and overall health such as heart disease as well as staying active and exercising. The two naturally go hand in hand, but many diabetics like my grandparents have foot complications with swelling and neuropathy, requiring proper fitting footwear that is hard to find if you don’t know where to look. I found this guide on shoes for diabetics that helps explain what they are and their importance, especially for diabetics. Hopefully others find it as helpful as I did when caring for those diagnosed with diabetes.
In this large sample of people with diabetes attending a secondary care clinic in NZ, 19.6% (37/189) of patients reported using diabetes apps to support their self-management. Diabetes app users were younger and more often had T1DM. The most used app feature in current app users was a blood glucose diary (87%, 32/37). The most desirable feature of a future app was an insulin dose calculation function in app users (46%) and a blood glucose diary in non-app users (64.4%). A Scottish survey has reported similar results and observed that people with T1DM were more likely to desire insulin calculators in an app [23].
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