Further investigation of the psychometric properties of the Insulin Treatment Appraisal Scale among insulin-using and non-insulin-using adults with type 2 diabetes : results from Diabetes MILES-Australia

Negative attitudes towards insulin are commonly reported by people with type 2 diabetes mellitus (T2DM) and can act as a barrier to timely insulin initiation. The Insulin Treatment Appraisal Scale (ITAS) is a widely used 20-item measure of attitudes towards insulin. While designed for completion by both insulin using and non-insulin using adults with T2DM, its psychometric properties have not been investigated separately for these groups. Furthermore, the total score is routinely reported in preference to the published two-factor structure (negative/positive appraisals). Further psychometric validation of the ITAS is required to examine its properties.

Subjective wellbeing among adults with diabetes : results from Diabetes MILES—Australia

This study examines the subjective wellbeing of Australian adults with diabetes who completed the Diabetes MILES—Australia survey, investigating by diabetes type and treatment, and by comparing with the subjective wellbeing of the general Australian adult population. In addition, the extent to which depression and socio-demographic factors account for subjective wellbeing is investigated. People with type 1 or type 2 diabetes have significantly lower subjective wellbeing compared to the general population, even after controlling for covariates (demographic and socio-economic status, diabetes duration, body mass index, number of diabetes-related complications, and depression). Furthermore, adults with type 2 diabetes using insulin to manage their condition report the lowest levels of subjective wellbeing, and are also most likely to report dissatisfaction with their current health. These findings suggest that living with diabetes, and in particular, living with type 2 diabetes and using insulin, strongly challenges the maintenance of subjective wellbeing.

Mining allocating patterns in one-sum weighted items

An Association Rule (AR) is a common knowledge model in data mining that describes an implicative cooccurring relationship between two disjoint sets of binary-valued transaction database attributes (items), expressed in the form of an "antecedent⇒ consequent" rule. A variant of the AR is the Weighted Association Rule (WAR). With regard to a marketing context, this paper introduces a new knowledge model in data mining -ALlocating Pattern (ALP). An ALP is a special form of WAR, where each rule item is associated with a weighting score between 0 and 1, and the sum of all rule item scores is 1. It can not only indicate the implicative co-occurring relationship between two (disjoint) sets of items in a weighted setting, but also inform the "allocating" relationship among rule items. ALPs can be demonstrated to be applicable in marketing and possibly a surprising variety of other areas. We further propose an Apriori based algorithm to extract hidden and interesting ALPs from a "one-sum" weighted transaction database. The experimental results show the effectiveness of the proposed algorithm. © 2008 IEEE.