Large clinical trials have shown that intensive management of glycaemia and other vascular risk factors can prevent or delay the development of microvascular and macrovascular complications in both type 1 diabetes (T1DM) and type 2 diabetes (T2DM) [1–4]. Achieving optimal glycaemic control in people with T1DM [1, 2] and advanced T2DM  often requires intensive insulin therapy, which involves either use of insulin pumps or multiple daily insulin injections (MDI) [1, 2, 5]. Such therapies divide insulin doses into basal, prandial and corrective elements, which can be adjusted independently to achieve optimal results. Despite the benefits of intensive diabetes management, many people with T1DM and insulin-treated T2DM do not follow and/or adjust their insulin regimens as needed [6–9]. A recent survey of 331 people with T1DM showed that 64% of the participants assessed their prandial insulin need inappropriately .
A significant obstacle to intensive insulin management is fear of hypoglycaemia [11–14], which has detrimental effects on people’s willingness to effectively manage their diabetes, particularly in terms of appropriate insulin dosing (e.g., under-dosing of insulin to avoid hypoglycaemia). This can lead to poor metabolic control and subsequent poor health outcomes . Other key contributors to treatment non-adherence are lack of self-efficacy and difficulties associated with insulin dose determination. Calculation of an insulin dose is a complex process that must take numerous factors into account, such as the current preprandial glucose level, grams of carbohydrate (CHO) to be ingested, insulin sensitivity, insulin-to-CHO ratio, and active insulin on board. Further, poor glycaemic control has been correlated with poor numeracy skills . This may lead to an inability to count carbohydrates, errors in interpreting blood glucose (bG) results to determine correction doses and inaccuracies in calculating insulin doses based on insulin-to-CHO ratios.
Although many insulin pumps now feature automated bolus advisors, which automatically calculate bolus insulin dosages to cover carbohydrate (CHO) intake and address out-of-range bG levels based on individualised insulin parameter estimates, people using MDI therapy must perform these dosage calculations manually. However, because manual calculation of insulin boluses is both complex and time consuming, people may rely on empirical estimates, which can result in persistent hypoglycaemia and/or hyperglycaemia [16, 17]. In addition, manual bolus calculation does not take into account the effect of the active insulin that remains from the initial bolus (insulin-on-board), which creates a high potential for errors, particularly when determining a correction bolus.
Studies have demonstrated that use of automated bolus calculators helps insulin pump users more accurately meet prandial insulin dosage requirements, improve postprandial glycaemic excursions, and achieve optimal glycaemic control with an increased time within target range [18, 19]. It is, indeed, possible that such automated calculations may be a major source of the perceived benefits of pump therapy for many patients. For example, a study by Garg and colleagues  showed that use of a bolus advisor containing an early algorithm based on sliding scales for insulin dosing in people using MDI resulted in improved HbA1c levels. Results from a recent survey  suggest that use of an automated bolus advisor may reduce fear of hypoglycaemia, increase confidence in bolus calculation, improve ability to control bG levels and achieve glycaemic goals, create a sense of increased flexibility in lifestyle, and improve overall well being. In a small pilot study, Schmidt and colleagues  found that automated bolus advisor use, in conjunction with training in CHO counting and MDI therapy, improves treatment satisfaction. However, to date, no large randomised trials have been conducted to determine whether use of an automated bolus advisor can improve glycaemic control and promote greater adherence to therapy in people treated with MDI therapy.
We hypothesised that, in addition to reducing HbA1c, use of an automated bolus advisor in people treated with MDI therapy can increase the time bG stays within the target range, reduce the magnitude of post-prandial excursions, reduce the frequency/severity of hypoglycaemia, and improve psychosocial outcomes, including treatment satisfaction, social functioning and factors important to quality of life. We designed a study to test this hypothesis, using a new automated bolus advisor system that integrates bolus calculation into a bG meter.