90 research outputs found
Participatory heuristic evaluation of the second iteration of the eWALL interface application
Cognitive walkthrough - an element in system development and evaluation:experiences from the eWALL telehealth system
AbstractEpidemiological changes in the population lead to an increasing number of elderly people with a chronic disease. Telehealth is proposed as one of the solutions for the growing challenges of the health care system caused by these changes. The telehealth system eWALL seeks to promote the independent living of people with chronic obstructive pulmonary disease, mild cognitive impairment or age related impairments. The eWALL system is in a developmental stage in which partners from 14 different European countries are included. A three phase cognitive walkthrough-approach was performed on the eWALL system in order to evaluate the usability of the system. First the cognitive walkthrough performed by experts, second, rating of the identified usability problems identified by other medical partners, and third, discussion on a plenary telecommunication call among medical partners and technical partners. (n=119) usability problems were identified distributed among the 14 functionalities of the telehealth system. The majority of the usability problems were discovered in the functionalities: ‘TV’ (n=21), ‘Calendar’ (n=20), and ‘Environmental box’ (n=18). The least usability problems were identified in the functionalities: ‘My sleep’ (n=1), ‘Photo frame’ (n=2), and ‘My Everyday Life’ (n=3).The results of the cognitive walkthrough served as a concrete, structured and constructive collaborative tool between the medical partners and the technical partners involved in the eWALL project
Assessing the use of telemedicine among people with diabetes:A Danish translation and cross-cultural adaptation of the Telemedicine Usability Questionnaire
Using Distance Communication for the User-Centered Development of a Smartphone-Based Serious Game for Children With Type 1 Diabetes:Participatory Design Approach
BACKGROUND: The complications of type 1 diabetes (T1D) can be delayed or prevented in children with T1D who receive proper self-management education. Smartphone-based serious games are increasingly being used as an effective tool for teaching self-management. When developing a serious game, it is important that the development process be user-centered. Traditionally, different face-to-face methods have been used when children participate in the development process. However, face-to-face data collection is not always feasible. In such situations, distance communication can be used when developing a serious game. OBJECTIVE: The objective of this study is to develop a user-centered smartphone-based serious game that teaches self-management focused on carbohydrate intake in children aged 8-14 years with T1D using distance communication in both the development and evaluation of the game. METHODS: The development and evaluation of a smartphone-based serious game prototype was inspired by the Lean principles, and a user-centered approach was applied. The development process included 1 expert interview and design workshops with children with T1D. On the basis of the interview and design workshop results, a serious game prototype was developed using Microsoft PowerPoint. The evaluation of the serious game prototype included an interview with a dietitian and a playtest with children with T1D. All data were collected using distance communication. RESULTS: A user-centered smartphone-based serious game prototype was developed and evaluated. The expert interview with the dietitian formed the basis for the learning outcomes in the game. Four children and their parents contributed to the preferences, needs, requirements, and ideas for selected parts of the game design. The dietitian evaluated the prototype positively and validated its content and accuracy. The serious game prototype was well-received by the children and their parents during the playtest. The serious game prototype was perceived as a useful and engaging way to learn. However, the difficulty level was not appropriate, and the information was too basic for participants who had been diagnosed over a year ago. The use of digital communication platforms did not cause any problems. CONCLUSIONS: The smartphone-based serious game prototype has the potential to be a useful and attractive tool for teaching disease self-management. The use of distance communication proved to be a useful approach in the development of a serious game
Effectiveness of Telemedicine Solutions for the Management of Patients With Diabetes:Protocol for a Systematic Review and Meta-Analysis
Quantification of insulin adherence in adults with insulin-treated type 2 diabetes:A systematic review
Aims: This systematic review aims to identify current methods used for the assessment of insulin adherence in adults with insulin-treated type 2 diabetes. The primary goal is to offer recommendations for clinical practice to improve quantification of adherence. Methods: The review was conducted in accordance with PRISMA 2020 and registered at PROSPERO (CRD42022334134). PubMed, Embase, CINAHL, and PsycINFO were searched on 15 November 2022 and included three blocks: Type 2 diabetes, insulin, and adherence. We considered primary full-text studies describing an assessment method and a threshold for assessment of insulin adherence in adults with insulin-treated type 2 diabetes. Results: A final sample of 50 studies were included. Identified methods fell into four categories: self-report, pharmacy claims, inulin count, and data from an insulin pen device. Commonly reported methods included: The Morisky Medication Adherence Scale, the (adjusted) Medication Possession Ratio, and the Proportions of Days Covered. A threshold of <80% was used to define non-adherence in nearly half of the studies. Yet, several thresholds were reported. Conclusions: Most available methods for assessing insulin adherence in adults with insulin-treated type 2 diabetes are severely limited in providing in-depth insights into timing, dosing size, injection patterns, and adherence behavior. However, recognizing diverse types of non-adherence is crucial, as they denote unique behavioral entities requiring targeted intervention. Employing insulin injection data (e.g., from a smart insulin pen cap) to underlie an assessment method is a potential new approach to objectively assess insulin timing and dosing adherence in adults with insulin-treated type 2 diabetes.</p
A Study on Optimization and Evaluation of the Visualization of Complex Algorithm Results in Remote Monitoring of COPD
BACKGROUND: Artificial intelligence (AI) can potentially increase the quality of telemonitoring in chronic obstructive pulmonary disease (COPD). However, the output from AI is often difficult for clinicians to understand due to the complexity. This challenge may be accommodated by visualizing the AI results, however it hasn't been studied how this could be done specifically, i.e., considering which visual elements to include.AIM: To investigate how complex results from a predictive algorithm for patients with COPD can be translated into easily understandable data for the clinicians.METHODS: Semi-structured interviews were conducted to explore clinicians' needs when visualizing the results of a predictive algorithm. This formed a basis for creating a prototype of an updated user interface. The user interface was evaluated using usability tests through the "Think aloud" method.RESULTS: The clinicians pointed out the need for visualization of exacerbation alerts and the development in patients' data. Furthermore, they wanted the system to provide more information about what caused exacerbation alerts. Elements such as color and icons were described as particularly useful. The usability of the prototype was primarily assessed as easily understandable and advantageous in connection to the functions of the predictive algorithm.CONCLUSION: Predictive algorithm use in telemonitoring of COPD can be optimized by clearly visualizing the algorithm's alerts, clarifying the reasons for algorithm output, and by providing a clear overview of the development in the patient's data. This can contribute to clarity when the clinicians should act and why they should act on alerts from predictive algorithms.</p
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