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.
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.
The incidence of type 1 diabetes mellitus has been increasing worldwide [1], [2], [3], and it appears to have been particularly pronounced among children <5 years of age (yr) [3], [4], [5]. This increase has been suggested to be associated with the ‘accelerator hypothesis’ [6]. Although this hypothesis is not universally accepted [7], it predicts that higher BMI is associated with younger age at type 1 diabetes diagnosis [8], which has been demonstrated in some studies [9], [10], [11].
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…)
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.
From 1994 onwards, anthropometric data were recorded at each clinic visit, and for the purposes of this study we used data from the first post-diagnosis clinic that usually occurred 3–4 months afterwards. Standard deviation scores (SDS) were calculated based on the British 1990 Growth Reference Data [17] to obtain height SDS, weight SDS, and body mass index (BMI) SDS.
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.
In a perfect world, the answer to the question “should someone with diabetes take steroids?” would be a simple “no”. Of course, not only do we not live in a perfect world, there are also few simple answers for diabetics. Steroids can play havoc with blood sugar levels, but they can also be the best choice in treating some very serious conditions. So, perhaps the better answer would be “maybe” with the added caveat of making sure you are aware of the consequences and prepared to be proactive in managing them.   (more…)
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.
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.

Type 2 Diabetes is one of the major consequences of the obesity epidemic and according to Diabetes New Zealand is New Zealand’s fastest-growing health crisis. In terms of diabetes diagnosis, Type 2 currently accounts for around 90% of all cases. Also of concern to health professionals is that there are large numbers of people with silent, undiagnosed Type 2 Diabetes which may be damaging their bodies. An estimated 258,000 New Zealanders are estimated to have some form of diabetes, with than number doubling over the past decade.
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.
Blood glucose tracking is the most common feature of diabetes apps [5,14], with other features including record of medications, dietary advice, and tracking, such as carbohydrate content calculation, and weight management support [5,11,12,14-16]. Additionally some apps recommend insulin dosing based on users inputs of glucose levels and estimated meal carbohydrate. Meta-analysis of 22 trials including 1657 patients in which use of mobile phone apps supporting diabetes management was compared to usual care or other Web-based supports showed that app use led to a mean reduction in HbA1c of 6mmol/mol that is 0.5% [9]. This compares favorably with the glucose lowering of lifestyle change, namely diet [17] and oral diabetes medication [18].
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.
!function(n,t){function r(e,n){return Object.prototype.hasOwnProperty.call(e,n)}function i(e){return void 0===e}if(n){var o={},s=n.TraceKit,a=[].slice,l="?";o.noConflict=function(){return n.TraceKit=s,o},o.wrap=function(e){function n(){try{return e.apply(this,arguments)}catch(e){throw o.report(e),e}}return n},o.report=function(){function e(e){l(),h.push(e)}function t(e){for(var n=h.length-1;n>=0;--n)h[n]===e&&h.splice(n,1)}function i(e,n){var t=null;if(!n||o.collectWindowErrors){for(var i in h)if(r(h,i))try{h[i].apply(null,[e].concat(a.call(arguments,2)))}catch(e){t=e}if(t)throw t}}function s(e,n,t,r,s){var a=null;if(w)o.computeStackTrace.augmentStackTraceWithInitialElement(w,n,t,e),u();else if(s)a=o.computeStackTrace(s),i(a,!0);else{var l={url:n,line:t,column:r};l.func=o.computeStackTrace.guessFunctionName(l.url,l.line),l.context=o.computeStackTrace.gatherContext(l.url,l.line),a={mode:"onerror",message:e,stack:[l]},i(a,!0)}return!!f&&f.apply(this,arguments)}function l(){!0!==d&&(f=n.onerror,n.onerror=s,d=!0)}function u(){var e=w,n=p;p=null,w=null,m=null,i.apply(null,[e,!1].concat(n))}function c(e){if(w){if(m===e)return;u()}var t=o.computeStackTrace(e);throw w=t,m=e,p=a.call(arguments,1),n.setTimeout(function(){m===e&&u()},t.incomplete?2e3:0),e}var f,d,h=[],p=null,m=null,w=null;return c.subscribe=e,c.unsubscribe=t,c}(),o.computeStackTrace=function(){function e(e){if(!o.remoteFetching)return"";try{var t=function(){try{return new n.XMLHttpRequest}catch(e){return new n.ActiveXObject("Microsoft.XMLHTTP")}},r=t();return r.open("GET",e,!1),r.send(""),r.responseText}catch(e){return""}}function t(t){if("string"!=typeof t)return[];if(!r(j,t)){var i="",o="";try{o=n.document.domain}catch(e){}var s=/(.*)\:\/\/([^:\/]+)([:\d]*)\/{0,1}([\s\S]*)/.exec(t);s&&s[2]===o&&(i=e(t)),j[t]=i?i.split("\n"):[]}return j[t]}function s(e,n){var r,o=/function ([^(]*)\(([^)]*)\)/,s=/['"]?([0-9A-Za-z$_]+)['"]?\s*[:=]\s*(function|eval|new Function)/,a="",u=10,c=t(e);if(!c.length)return l;for(var f=0;f0?s:null}function u(e){return e.replace(/[\-\[\]{}()*+?.,\\\^$|#]/g,"\\$&")}function c(e){return u(e).replace("<","(?:<|<)").replace(">","(?:>|>)").replace("&","(?:&|&)").replace('"','(?:"|")').replace(/\s+/g,"\\s+")}function f(e,n){for(var r,i,o=0,s=n.length;or&&(i=s.exec(o[r]))?i.index:null}function h(e){if(!i(n&&n.document)){for(var t,r,o,s,a=[n.location.href],l=n.document.getElementsByTagName("script"),d=""+e,h=/^function(?:\s+([\w$]+))?\s*\(([\w\s,]*)\)\s*\{\s*(\S[\s\S]*\S)\s*\}\s*$/,p=/^function on([\w$]+)\s*\(event\)\s*\{\s*(\S[\s\S]*\S)\s*\}\s*$/,m=0;m]+)>|([^\)]+))\((.*)\))? in (.*):\s*$/i,o=n.split("\n"),l=[],u=0;u=0&&(g.line=v+x.substring(0,j).split("\n").length)}}}else if(o=d.exec(i[y])){var _=n.location.href.replace(/#.*$/,""),T=new RegExp(c(i[y+1])),E=f(T,[_]);g={url:_,func:"",args:[],line:E?E.line:o[1],column:null}}if(g){g.func||(g.func=s(g.url,g.line));var k=a(g.url,g.line),A=k?k[Math.floor(k.length/2)]:null;k&&A.replace(/^\s*/,"")===i[y+1].replace(/^\s*/,"")?g.context=k:g.context=[i[y+1]],h.push(g)}}return h.length?{mode:"multiline",name:e.name,message:i[0],stack:h}:null}function y(e,n,t,r){var i={url:n,line:t};if(i.url&&i.line){e.incomplete=!1,i.func||(i.func=s(i.url,i.line)),i.context||(i.context=a(i.url,i.line));var o=/ '([^']+)' /.exec(r);if(o&&(i.column=d(o[1],i.url,i.line)),e.stack.length>0&&e.stack[0].url===i.url){if(e.stack[0].line===i.line)return!1;if(!e.stack[0].line&&e.stack[0].func===i.func)return e.stack[0].line=i.line,e.stack[0].context=i.context,!1}return e.stack.unshift(i),e.partial=!0,!0}return e.incomplete=!0,!1}function g(e,n){for(var t,r,i,a=/function\s+([_$a-zA-Z\xA0-\uFFFF][_$a-zA-Z0-9\xA0-\uFFFF]*)?\s*\(/i,u=[],c={},f=!1,p=g.caller;p&&!f;p=p.caller)if(p!==v&&p!==o.report){if(r={url:null,func:l,args:[],line:null,column:null},p.name?r.func=p.name:(t=a.exec(p.toString()))&&(r.func=t[1]),"undefined"==typeof r.func)try{r.func=t.input.substring(0,t.input.indexOf("{"))}catch(e){}if(i=h(p)){r.url=i.url,r.line=i.line,r.func===l&&(r.func=s(r.url,r.line));var m=/ '([^']+)' /.exec(e.message||e.description);m&&(r.column=d(m[1],i.url,i.line))}c[""+p]?f=!0:c[""+p]=!0,u.push(r)}n&&u.splice(0,n);var w={mode:"callers",name:e.name,message:e.message,stack:u};return y(w,e.sourceURL||e.fileName,e.line||e.lineNumber,e.message||e.description),w}function v(e,n){var t=null;n=null==n?0:+n;try{if(t=m(e))return t}catch(e){if(x)throw e}try{if(t=p(e))return t}catch(e){if(x)throw e}try{if(t=w(e))return t}catch(e){if(x)throw e}try{if(t=g(e,n+1))return t}catch(e){if(x)throw e}return{mode:"failed"}}function b(e){e=1+(null==e?0:+e);try{throw new Error}catch(n){return v(n,e+1)}}var x=!1,j={};return v.augmentStackTraceWithInitialElement=y,v.guessFunctionName=s,v.gatherContext=a,v.ofCaller=b,v.getSource=t,v}(),o.extendToAsynchronousCallbacks=function(){var e=function(e){var t=n[e];n[e]=function(){var e=a.call(arguments),n=e[0];return"function"==typeof n&&(e[0]=o.wrap(n)),t.apply?t.apply(this,e):t(e[0],e[1])}};e("setTimeout"),e("setInterval")},o.remoteFetching||(o.remoteFetching=!0),o.collectWindowErrors||(o.collectWindowErrors=!0),(!o.linesOfContext||o.linesOfContext<1)&&(o.linesOfContext=11),void 0!==e&&e.exports&&n.module!==e?e.exports=o:"function"==typeof define&&define.amd?define("TraceKit",[],o):n.TraceKit=o}}("undefined"!=typeof window?window:global)},"./webpack-loaders/expose-loader/index.js?require!./shared/require-global.js":function(e,n,t){(function(n){e.exports=n.require=t("./shared/require-global.js")}).call(n,t("../../../lib/node_modules/webpack/buildin/global.js"))}});