Individual and contextual factors associated with follow-up use of diabetes self-management education programmes: a multisite prospective analysis.

AIMS: Although a considerable body of research supports the efficacy of diabetes self-management education (DSME), these programmes are often challenged by high attrition rates. Little is known about factors influencing follow-up use of DSME services, thus the aim of this study was to identify these factors. METHODS: In this multisite prospective analysis, adults with Type 2 diabetes (n = 268) who attended one of two diabetes management centres (DMCs) were followed over a 1-year period from their initial visit. The influence of individual and contextual factors on the number of contacts with DMC providers was examined. Data were analysed within the context of the Health Behavioral Model of Health Services Utilization. RESULTS: In a multivariable negative binomial regression model, the number of contacts over 1 year was greater for those who were female, non-smokers, unemployed, self-referred to the DMC, lived closer to the DMC, had a lower body mass index, or had a longer known duration of diabetes. Follow-up use of services differed significantly between the two sites. Provider contacts were greater at the centre that offered flexible hours of services and a variety of optional educational modules. CONCLUSIONS: Healthcare professionals need to encourage ongoing use of DSME, particularly for individuals prone to lower follow-up use of these services. Providing services that are accessible, convenient, and can easily fit into patients' schedules may increase follow-up use. Further exploration into how operations and delivery of these services influence utilization patterns is strongly recommended.

Transmembrane topology prediction methods: a re-assessment and improvement by a consensus method using a dataset of experimentally-characterized transmembrane topologies.

We selected 10 transmembrane (TM) prediction methods (KKD, TMpred, TopPred II, DAS, TMAP, MEMSAT 2, SOSUI, PRED-TMR2, TMHMM 2.0 and HMMTOP 2.0) and re-assessed its prediction performance using a reliable dataset with 122 entries of experimentally-characterized TM topologies. Then, we improved prediction performance by a consensus prediction method. Prediction performance during re-assessment and consensus prediction were based on four attributes: (i) the number of transmembrane segments (TMSs), (ii) the number of TMSs plus TMS-position, (iii) N-tail location and (iv) TM topology. We noted that hidden Markov model-based methods dominate over other methods by individual prediction performance for all four attributes. In addition, all top-performing methods generally were model-based. Among prokaryotic sequences, HMMTOP 2.0 solely topped among other methods with prediction accuracies ranging from 64% to 86% across all attributes. However, among eukaryotic sequences, prediction performance for all the attributes was relatively poor compared with prokaryotic ones. On the other hand, our results showed that our proposed consensus prediction method significantly improved prediction performance by, at least, an additional nine percentage points particularly among prokaryotic sequences for the number of TMS (84%), number of TMS and position (80%), and TM topology attributes (74%). Although our consensus prediction method improved also the prediction performance among eukaryotic sequences, the obtained accuracies for all attributes were relatively lower than that obtained by prokaryotic counterparts particularly for TM topology.