Training in flexible, intensive insulin management to enable dietary freedom in people with type 1 diabetes : dose adjustment for normal eating (DAFNE) randomised controlled trial

Objectives: To evaluate whether a course teaching flexible intensive insulin treatment combining dietary freedom and insulin adjustment can improve both glycaemic control and quality of life in type 1 diabetes.

Design: Randomised design with participants either attending training immediately (immediate DAFNE) or acting as waiting list controls and attending “delayed DAFNE” training 6 months later.
Setting: Secondary care diabetes clinics in three English health districts.

Participants: 169 adults with type 1 diabetes and moderate or poor glycaemic control.

Main outcome measures: Glycated haemoglobin (HbA1c), severe hypoglycaemia, impact of diabetes on quality of life (ADDQoL).

Results: At 6 months, HbA1c was significantly better in immediate DAFNE patients (mean 8.4%) than in delayed DAFNE patients (9.4%) (t=6.1, P<0.0001). The impact of diabetes on dietary freedom was significantly improved in immediate DAFNE patients compared with delayed DAFNE patients (t=−5.4, P<0.0001), as was the impact of diabetes on overall quality of life (t=2.9, P<0.01). General wellbeing and treatment satisfaction were also significantly improved, but severe hypoglycaemia, weight, and lipids remained unchanged. Improvements in “present quality of life” did not reach significance at 6 months but were significant by 1 year.

Conclusion: Skills training promoting dietary freedom improved quality of life and glycaemic control in people with type 1 diabetes without worsening severe hypoglycaemia or cardiovascular risk. This approach has the potential to enable more people to adopt intensive insulin treatment and is worthy of further investigation.

Predicting outcomes in the Dose Adjustment For Normal Eating (DAFNE) trial

Background: In the UK, DAFNE training in flexible intensive insulin therapy significantly reduced the negative impact of diabetes on quality of life (QoL) and improved blood glucose (BG) control without significantly increasing severe hypoglycaemia or body mass index (BMI). Analyses were conducted to predict who would benefit most from the generally highly successful DAFNE training and who might experience undesirable effects (e.g. weight gain).

Methods: Multiple regression was used to predict change in outcomes (6-months post-DAFNE) using baseline data: demographic, biomedical, ADDQoL (measure of the impact of diabetes on QoL), extended DTSQ (Diabetes Treatment Satisfaction Questionnaire), and other psychological measures including diabetes-specific well-being and locus of control.

Findings: Greatest improvement in ADDQoL scores was achieved by those reporting less dietary freedom and less treatment satisfaction at baseline (R2=0.21). BG improvement was predicted by higher baseline BG, lower perceived frequency of hypoglycaemia, greater expectations of DAFNE, and greater BMI (R2=0.30). Increase in BMI was predicted by less dietary freedom, DAFNE training centre, and less ‘satisfaction with insulin’ at baseline (R2=0.23).

Conclusions/Discussion: DAFNE has important benefits to offer. Lifting dietary restrictions had substantial benefits for QoL. BG improvement was predicted by baseline BG but also by expectations (perhaps reflecting greater optimism or determination). Prediction of weight gain was more complex. The influence of training centre will have involved implicit messages conveyed by Educators before and during DAFNE. While DAFNE was successful overall, outcomes are likely to be maximised for individuals if their expectations and personal goals are considered by DAFNE Educators.

Detecting K-complexes for sleep stage identification using nonsmooth optimisation

The process of sleep stage identification is a labour-intensive task that involves the specialized interpretation of the polysomnographic signals captured from a patient’s overnight sleep session. Automating this task has proven to be challenging for data mining algorithms because of noise, complexity and the extreme size of data. In this paper we apply nonsmooth optimization to extract key features that lead to better accuracy. We develop a specific procedure for identifying K-complexes, a special type of brain wave crucial for distinguishing sleep stages. The procedure contains two steps. We first extract “easily classified” K-complexes, and then apply nonsmooth optimization methods to extract features from the remaining data and refine the results from the first step. Numerical experiments show that this procedure is efficient for detecting K-complexes. It is also found that most classification methods perform significantly better on the extracted features.

K-Complex Detection Using a Hybrid-Synergic Machine Learning Method

Sleep stage identification is the first step in modern sleep disorder diagnostics process. K-complex is an indicator for the sleep stage 2. However, due to the ambiguity of the translation of the medical standards into a computer-based procedure, reliability of automated K-complex detection from the EEG wave is still far from expectation. More specifically, there are some significant barriers to the research of automatic K-complex detection. First, there is no adequate description of K-complex that makes it difficult to develop automatic detection algorithm. Second, human experts only provided the label for whether a whole EEG segment contains K-complex or not, rather than individual labels for each subsegment. These barriers render most pattern recognition algorithms inapplicable in detecting K-complex. In this paper, we attempt to address these two challenges, by designing a new feature extraction method that can transform visual features of the EEG wave with any length into mathematical representation and proposing a hybrid-synergic machine learning method to build a K-complex classifier. The tenfold cross-validation results indicate that both the accuracy and the precision of this proposed model are at least as good as a human expert in K-complex detection.