Does self monitoring of blood blucose as opposed to urinalysis provide additional benefit in patients newly diagnosed with type 2 diabetes receiving structured education? The DESMOND SMBG randomised controlled trial protocol

Background
The benefit of self-monitoring of blood glucose (SMBG) in people with type 2 diabetes on diet or oral agents other than sulphonylureas remains uncertain. Trials of interventions incorporating education about self-monitoring of blood glucose have reported mixed results. A recent systematic review concluded that SMBG was not cost-effective. However, what was unclear was whether a cheaper method of self-monitoring (such as urine glucose monitoring) could produce comparable benefit and patient acceptability for less cost.

Methods/Design
The DESMOND SMBG trial is comparing two monitoring strategies (blood glucose monitoring and urine testing) over 18 months when incorporated into a comprehensive self-management structured education programme. It is a multi-site cluster randomised controlled trial, conducted across 8 sites (7 primary care trusts) in England, UK involving individuals with newly diagnosed Type 2 diabetes.

The trial has 80% power to demonstrate equivalence in mean HbA1c (the primary end-point) at 18 months of within ± 0.5% assuming 20% drop out and 20% non-consent. Secondary end-points include blood pressure, lipids, body weight and psychosocial measures as well as a qualitative sub-study.

Practices were randomised to one of two arms: participants attend a DESMOND programme incorporating a module on self-monitoring of either urine or blood glucose. The programme is delivered by accredited educators who received specific training about equipoise. Biomedical data are collected and psychosocial scales completed at baseline, and 6, 12, and 18 months post programme. Qualitative research with participants and educators will explore views and experiences of the trial and preferences for methods of monitoring.

Discussion
The DESMOND SMBG trial is designed to provide evidence to inform the debate about the value of self-monitoring of blood glucose in people with newly diagnosed type 2 diabetes. Strengths include a setting in primary care, a cluster design, a health economic analysis, a comparison of different methods of monitoring while controlling for other components of training within the context of a quality assured structured education programme and a qualitative sub-study.

Feedback reporting of survey data to healthcare aides

Background

This project occurred during the course of the Translating Research in Elder Care (TREC) program of research. TREC is a multilevel and longitudinal research program being conducted in the three Canadian Prairie Provinces of Alberta, Saskatchewan, and Manitoba. The main purpose of TREC is to increase understanding about the role of organizational context in influencing knowledge use in residential long-term care settings. The purpose of this study was to evaluate healthcare aides’ (HCAs) perceptions of a one-page poster designed to feed back aggregated data (including demographic information and perceptions about influences on best practice) from the TREC survey they had recently completed.

Methods
A convenience sample of 7 of the 15 nursing homes participating in the TREC research program in Alberta were invited to participate. Specific facility-level summary data were provided to each facility in the form of a one-page poster report. Two weeks following delivery of the report, a convenience sample of HCAs was surveyed using one-to-one structured interviews.

Results
One hundred twenty-three HCAs responded to the evaluation survey. Overall, HCAs’ opinions about presentation of the feedback report and the understandability, usability, and usefulness of the content were positive. For each report, analysis of data and production and inspection of the report took up to one hour. Information sessions to introduce and explain the reports averaged 18 minutes. Two feedback reports (minimum) were supplied to each facility at a cost of CAN$2.39 per report, for printing and laminating.

Conclusions
This study highlights not only the feasibility of producing understandable, usable, and useful feedback reports of survey data but also the value and importance of providing feedback to survey respondents. More broadly, the findings suggest that modest strategies may have a positive and desirable effect in participating sites.

Implementing scanned medical record systems in Australia : a structured case study on envisioned changes to elective admissions process in a Victorian hospital

In recent years, influenced by the pervasive power of technology, standards and mandates, Australian hospitals have begun exploring digital forms of keeping this record. The main rationale is the ease of accessing different data sources at the same time by varied staff members. The initial step in this transition was implementation of scanned medical record systems, which converts the paper based records to digitised form, which required process flow redesign and changes to existing modes of work. For maximising the benefits of scanning implementation and to better prepare for the changes, Austin Hospital in the State of Victoria commissioned this research focused on elective admissions area. This structured case study redesigned existing processes that constituted the flow of external patient forms and recommended a set of best practices at the same time highlighting the significance of user participation in maximising the potential benefits anticipated. In the absence of published academic studies focused on Victorian hospitals, this study has become a conduit for other departments in the hospital as well as other hospitals in the incursion.

A data mining framework for electricity consumption analysis from meter data

This paper presents a novel data mining framework for the exploration and extraction of actionable knowledge from data generated by electricity meters. Although a rich source of information for energy consumption analysis, electricity meters produce a voluminous, fast-paced, transient stream of data that conventional approaches are unable to address entirely. In order to overcome these issues, it is important for a data mining framework to incorporate functionality for interim summarization and incremental analysis using intelligent techniques. The proposed Incremental Summarization and Pattern Characterization (ISPC) framework demonstrates this capability. Stream data is structured in a data warehouse based on key dimensions enabling rapid interim summarization. Independently, the IPCL algorithm incrementally characterizes patterns in stream data and correlates these across time. Eventually, characterized patterns are consolidated with interim summarization to facilitate an overall analysis and prediction of energy consumption trends. Results of experiments conducted using the actual data from electricity meters confirm applicability of the ISPC framework.

Turning Gigabytes into Gigs: “Songification” and Live Music Data

Complex data is challenging to understand when it is represented as written communication even when it is structured in a table. How- ever, choosing to represent data in creative ways can aid our under- standing of complex ideas and patterns. In this regard, the creative industries have a great deal to offer data-intensive scholarly disci- plines. Music, for example, is not often used to interpret data, yet the rhythmic nature of music lends itself to the representation and anal- ysis of temporal data.Taking the music industry as a case study, this paper explores how data about historical live music gigs can be analysed, extend- ed and re-presented to create new insights. Using a unique process called ‘songification’ we demonstrate how enhanced auditory data design can provide a medium for aural intuition. The case study also illustrates the benefits of an expanded and inclusive view of research; in which computation and communication, method and media, in combination enable us to explore the larger question of how we can employ technologies to produce, represent, analyse, deliver and exchange knowledge.

Self-monitoring of blood glucose versus self-monitoring of urine glucose in adults with newly diagnosed Type 2 diabetes receiving structured education: a cluster randomized controlled trial.

AIMS: To compare the effectiveness and acceptability of self-monitoring of blood glucose with self-monitoring of urine glucose in adults with newly diagnosed Type 2 diabetes. METHODS: We conducted a multi-site cluster randomized controlled trial with practice-level randomization. Participants attended a structured group education programme, which included a module on self-monitoring using blood glucose or urine glucose monitoring. HbA1c and other biomedical measures as well as psychosocial data were collected at 6, 12 and 18 months. A total of 292 participants with Type 2 diabetes were recruited from 75 practices. RESULTS: HbA1c levels were significantly lower at 18 months than at baseline in both the blood monitoring group [mean (se) -12 (2) mmol/mol; -1.1 (0.2) %] and the urine monitoring group [mean (se) -13 (2) mmol/mol; -1.2 (0.2)%], with no difference between groups [mean difference adjusted for cluster effect and baseline value = -1 mmol/mol (95% CI -3, 2); -0.1% (95% CI -0.3, 0.2)]. Similar improvements were observed for the other biomedical outcomes, with no differences between groups. Both groups showed improvements in total treatment satisfaction, generic well-being, and diabetes-specific well-being, and had a less threatening view of diabetes, with no differences between groups at 18 months. Approximately one in five participants in the urine monitoring arm switched to blood monitoring, while those in the blood monitoring arm rarely switched (18 vs 1% at 18 months; P < 0.001). CONCLUSIONS: Participants with newly diagnosed Type 2 diabetes who attended structured education showed similar improvements in HbA1c levels at 18 months, regardless of whether they were assigned to blood or urine self-monitoring.

Structured type 1 diabetes education delivered in routine care in Australia reduces diabetes-related emergencies and severe diabetes-related distress: the OzDAFNE program

AIMS: To evaluate structured type 1 diabetes education delivered in routine practice throughout Australia.

METHODS: Participants attended a five-day training program in insulin dose adjustment and carbohydrate counting between April 2007 and February 2012. Using an uncontrolled before-and-after study design, we investigated: HbA1c (% and mmol/mol); severe hypoglycaemia; diabetes ketoacidosis (DKA) requiring hospitalisation, and diabetes-related distress (Problem Areas in Diabetes scale; PAID), weight (kg); body mass index. Data were collected pre-training and 6-18 months post-training. Change in outcome scores were examined overall as well as between groups stratified by baseline HbA1c quartiles. Data are mean±SD or % (n).

RESULTS: 506 participants had data eligible for analysis. From baseline to follow-up, significant reductions were observed in the proportion of participants reporting at least one severe hypoglycaemic event (24.7% (n=123) vs 12.1% (n=59), p<0.001); and severe diabetes-related distress (29.3% (n=145) vs 12.6% (n=60), p<0.001). DKA requiring hospitalisation in the past year reduced from 4.1% (n=20) to 1.2% (n=6). For those with above target baseline HbA1c there was a small, statistically significant improvement (n=418, 8.4±1.1% (69±12mmol/mol) to 8.2±1.1% (66±12mmol/mol). HbA1c improvement was clinically significant among those in the highest baseline quartile (n=122, 9.7±1.1% (82±11mmol/mol) to 9.0±1.2% (75±13mmol/mol), p<0.001).

CONCLUSIONS: The proportion of participants reporting severe hypoglycaemia, DKA and severe diabetes-related distress was at least halved, and HbA1c reduced by 0.7% (7mmol/mol) among those with highest baseline levels. Structured type 1 diabetes education delivered in routine practice offers clinically important benefits for those with greatest clinical need.

Physical health comorbidities in women with personality disorder: data from the Geelong osteoporosis study

BACKGROUND: Associations between common psychiatric disorders, psychotic disorders and physical health comorbidities are frequently investigated. The complex relationship between personality disorders (PDs) and physical health is less understood, and findings to date are varied. This study aims to investigate associations between PDs with a number of prevalent physical health conditions. METHODS: This study examined data collected from women (n=765;≥25years) participating in a population-based study located in south-eastern Australia. Lifetime history of psychiatric disorders was assessed using the semi-structured clinical interviews (SCID-I/NP and SCID-II). The presence of physical health conditions (lifetime) were identified via a combination of self-report, medical records, medication use and clinical data. Socioeconomic status, and information regarding medication use, lifestyle behaviors, and sociodemographic information was collected via questionnaires. Logistic regression models were used to investigate associations. RESULTS: After adjustment for sociodemographic variables (age, socioeconomic status) and health-related factors (body mass index, physical activity, smoking, psychotropic medication use), PDs were consistently associated with a range of physical health conditions. Novel associations were observed between Cluster A PDs and gastro-oesophageal reflux disease (GORD); Cluster B PDs with syncope and seizures, as well as arthritis; and Cluster C PDs with GORD and recurrent headaches. CONCLUSIONS: PDs were associated with physical comorbidity. The current data contribute to a growing evidence base demonstrating associations between PDs and a number of physical health conditions independent of psychiatric comorbidity, sociodemographic and lifestyle factors. Longitudinal studies are now required to investigate causal pathways, as are studies determining pathological mechanisms.

Understanding and development of supply chain agility and flexibility: a structured literature review

This paper provides a review of the literature while contributing to academic understanding of the concepts of agility and flexibility within the supply chain. The research identified 83 peer-reviewed articles through a structured review technique, which is based on a three-stage refinement process. Data reduction procedures using codification, sentence strings and a review of keywords, title, abstract and conclusion were used in the search. The papers identified focused on organizational and supply chain agility and flexibility. The acknowledged gaps in understanding and development of agility and flexibility in supply chains were identified and categorized in terms of conceptual, contextual and methodological gaps. Subsequent to the gap analysis, this paper argues that effective relationship integration with key partners is a fundamental mechanism for mitigating the problem of control dissipation, which has hindered academic understanding with respect to development and application of agile and flexible capabilities in supply chains. The findings in this paper will help academics to gain a better understanding and to develop the concepts of supply chain agility and flexibility. In addition, the findings indicate that supply chain stakeholders need to address the issue of relationship integration when undertaking, or participating in agility and flexibility development programmes, so as to maximize supply chain performance. The paper concludes by highlighting implications for managers and researchers, and proposes a number of areas for future investigation.

Nonparametric discovery and analysis of learning patterns and autism subgroups from therapeutic data

The spectrum nature and heterogeneity within autism spectrum disorders (ASD) pose as a challenge for treatment. Personalisation of syllabus for children with ASD can improve the efficacy of learning by adjusting the number of opportunities and deciding the course of syllabus. We research the data-motivated approach in an attempt to disentangle this heterogeneity for personalisation of syllabus. With the help of technology and a structured syllabus, collecting data while a child with ASD masters the skills is made possible. The performance data collected are, however, growing and contain missing elements based on the pace and the course each child takes while navigating through the syllabus. Bayesian nonparametric methods are known for automatically discovering the number of latent components and their parameters when the model involves higher complexity. We propose a nonparametric Bayesian matrix factorisation model that discovers learning patterns and the way participants associate with them. Our model is built upon the linear Poisson gamma model (LPGM) with an Indian buffet process prior and extended to incorporate data with missing elements. In this paper, for the first time we have presented learning patterns deduced automatically from data mining and machine learning methods using intervention data recorded for over 500 children with ASD. We compare the results with non-negative matrix factorisation and K-means, which being parametric, not only require us to specify the number of learning patterns in advance, but also do not have a principle approach to deal with missing data. The F1 score observed over varying degree of similarity measure (Jaccard Index) suggests that LPGM yields the best outcome. By observing these patterns with additional knowledge regarding the syllabus it may be possible to observe the progress and dynamically modify the syllabus for improved learning.