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Introduction Patients are compelled to become more involved in shared decision making with healthcare professionals in the self-management of chronic disease and general adherence to treatment. Therefore, it is valuable to be able to identify patients with low functional health literacy so they can be given special instructions about the management of chronic disease and medications. However, time spent by both patients and clinicians is a concern when introducing a screening instrument in the clinical setting, which raises the need for short instruments for assessing health literacy that can be used by patients without the involvement of healthcare personnel. This paper describes the development of a short version of the full-length Danish TOFHLA (DS-TOFHLA) that is easily applicable in the clinical context and where the use does not require a trained interviewer.Materials and Methods Data were collected as a part of a large-scale telehomecare project (TeleCare North), which was a randomized controlled trial that included 1225 patients with chronic obstructive pulmonary disease.The DS-TOFHLA was developed solely using an algorithm-based selection of variables and multivariable linear regression models. A multivariable regression model was developed using an exhaustive search strategy.Results The exhaustive search showed that the number of items in the full-length TOFHLA could be reduced from 17 numeracy items and 50 reading comprehension items to 20 reading comprehension items while maintaining a correlation of r = 0.90. A generic model-based approach was developed, which is suitable for development of short versions of the TOFHLA in other languages, including the original American version.Conclusions This study demonstrated how a generic model-based approach could be applied in the development of a short version of the TOFHLA, thereby reducing the 67 items to 20 items in the short version. Furthermore, this study showed that the inclusion of numeracy items was not necessary. The development of the DS-TOFHLA presents an opportunity to reliably identify patients with inadequate functional health literacy in approximately 5 minutes without involvement of healthcare personnel. The approach may be used in the development of short versions of any scaling questionnaire.

This work sought to explore the potential of using standalone continuous glucose monitor (CGM) data for the prediction of hypoglycemia utilizing a large cohort of type 1 diabetes patients during free-living. We trained and tested an algorithm for the prediction of hypoglycemia within 40 minutes on 3.7 million CGM measurements from 225 patients using ensemble learning. The algorithm was also validated using 11.5 million synthetic CGM data. The results yielded a receiver operating characteristic area under the curve (ROC AUC) of 0.988 and a precision-recall area under the curve (PR AUC) of 0.767. In an event-based analysis for predicting hypoglycemic events, the algorithm had a sensitivity of 90%, a lead-time of 17.5 minutes and a false-positive rate of 38%. In conclusion, this work demonstrates the potential of using ensemble learning to predict hypoglycemia, using only CGM data. This could help alarm patients of a future hypoglycemic event so countermeasures can be initiated.

Formålet med denne eksempelsamling er at fremme kendskabet til de mange gode forskningsresultater på sundhedsområdet, som skabes på grundlag af danske registerdata - både sundhedsdata og registerdata på andre områder. Ved at fremme kendskabet til forskningsresultater kan vi styrke forståelsen for vigtigheden af at indsamle og bruge sundhedsdata under forsvarlige rammer til forskning af væsentlig samfundsmæssig betydning. Danmark har indsamlet sundhedsdata i mange år. Disse data kan og skal anvendes til forskning, da det giver helt unikke muligheder for at få ny viden, der er svær at opnå gennem anden forskning, fx kliniske forsøg. Viden ikke bare til gavn for sundhedsvæsenet men for mange områder af samfundet, der alle har indƇydelse på danskernes sundhed.Samlingen skal vise værdien af samfundsrelevant registerforskning og inspirere tilbrugen af danske registre.

Supplemental material, sj-pdf-1-dst-10.1177_19322968211015206 for Classification of Gastroparesis from Glycemic Variability in Type 1 Diabetes: A Proof-of-Concept Study by Simon Lebech Cichosz and Ole Hejlesen in Journal of Diabetes Science and Technology

Supplemental material, Supplement for Precise Prediction of Total Body Lean and Fat Mass From Anthropometric and Demographic Data: Development and Validation of Neural Network Models by Simon Lebech Cichosz, Nicklas Højgaard Rasmussen, Peter Vestergaard and Ole Hejlesen in Journal of Diabetes Science and Technology

In this study, we investigated which predictors from people with type 1 diabetes at initiation of intensive treatment that increase the risk of not achieving glycemic target. Data from a clinical trial with type 1 diabetes people (n=460) were used in a logistic regression model to analyze the effect of the predictors on achievement of glycemic target. Results indicate that age, smoking, glycated hemoglobin, 1,5-anhydroglucitol and fluctuation from continuous glucose monitoring are predictors of achievement of glycemic target, which can be used in an algorithm to predict people who fail to achieve glycemic target.