Usability Evaluation of a Web-Based Remote Patient Monitoring Application: An Ongoing Study.

This study aims to discover problems and user experiences in a new released version of Sleepiz web application using heuristic evaluation and eye-tracking retrospective think-aloud performed by domain experts and end users. The web application is designed to support healthcare professionals in decision-making and monitoring of elderly people diagnosed with chronic respiratory diseases. Identification of usability problems and user experiences might contribute to improve the platform and will be reported to the developers.

Evaluating the Usability of a Telehealth System for People with COPD: A Cross Sectional Study.

Pulmonary Disease (COPD) exacerbations. However, the effect of telehealth for COPD remains uncertain, which may be due to a lack of attention to usability during the development of telehealth solutions. The aim was to evaluate the usability of a telehealth system for COPD using the Danish Telehealth Usability Questionnaire. A total of 96 people with COPD, who were already using a telehealth system consisting of weekly measurements of physiological parameters and symptom-related questionnaires, were included. The D-TUQ was used to assess the usability of the telehealth system. The overall experience with the usability of the telehealth system was mainly positive, but there was room for improvement.

Discordance Between Mealtimes Reported by Trial Participants with Type 2 Diabetes and Healthcare Professionals.

Healthy lifestyle behaviors are essential in the treatment of type 2 diabetes, and meal registration is therefore important. Manual meal registration is cumbersome and could be automated using continuous glucose monitoring (CGM). If such an algorithm is based on patient-reported meals, potential errors might be induced. Thus, the aim was to investigate potential errors in patient-reported mealtimes and the effect on automatic meal detection. Two healthcare professionals (HCPs) reported the mealtimes of the 18 included patients based on the patients' CGM data to assess the agreement between HCP- and patient-reported mealtimes. A developed meal detection algorithm based on detecting the post-prandial glucose response using cross-correlation was used to assess the impact of errors in patient-reported meals. The results showed poor disagreement between HCP- and patient-reported meals and that the meal detection algorithm had a moderately better performance on the HCP-reported meals. Therefore, the possibility of errors in patient-reported mealtimes should be considered in the development of meal detection algorithms. However, more research is needed to confirm the results of this study.