Multidisciplinary Development and Initial Validation of a Clinical Knowledge Base on Chronic Respiratory Diseases for mHealth Decision Support Systems.

Most mobile health (mHealth) decision support systems currently available for chronic obstructive respiratory diseases (CORDs) are not supported by clinical evidence or lack clinical validation. The development of the knowledge base that will feed the clinical decision support system is a crucial step that involves the collection and systematization of clinical knowledge from relevant scientific sources and its representation in a human-understandable and computer-interpretable way. This work describes the development and initial validation of a clinical knowledge base that can be integrated into mHealth decision support systems developed for patients with CORDs. A multidisciplinary team of health care professionals with clinical experience in respiratory diseases, together with data science and IT professionals, defined a new framework that can be used in other evidence-based systems. The knowledge base development began with a thorough review of the relevant scientific sources (eg, disease guidelines) to identify the recommendations to be implemented in the decision support system based on a consensus process. Recommendations were selected according to predefined inclusion criteria: (1) applicable to individuals with CORDs or to prevent CORDs, (2) directed toward patient self-management, (3) targeting adults, and (4) within the scope of the knowledge domains and subdomains defined. Then, the selected recommendations were prioritized according to (1) a harmonized level of evidence (reconciled from different sources); (2) the scope of the source document (international was preferred); (3) the entity that issued the source document; (4) the operability of the recommendation; and (5) health care professionals' perceptions of the relevance, potential impact, and reach of the recommendation. A total of 358 recommendations were selected. Next, the variables required to trigger those recommendations were defined (n=116) and operationalized into logical rules using Boolean logical operators (n=405). Finally, the knowledge base was implemented in an intelligent individualized coaching component and pretested with an asthma use case. Initial validation of the knowledge base was conducted internally using data from a population-based observational study of individuals with or without asthma or rhinitis. External validation of the appropriateness of the recommendations with the highest priority level was conducted independently by 4 physicians. In addition, a strategy for knowledge base updates, including an easy-to-use rules editor, was defined. Using this process, based on consensus and iterative improvement, we developed and conducted preliminary validation of a clinical knowledge base for CORDs that translates disease guidelines into personalized patient recommendations. The knowledge base can be used as part of mHealth decision support systems. This process could be replicated in other clinical areas.

A Hybrid Model to Classify Patients with Chronic Obstructive Respiratory Diseases.

Over the last decades, an increase in the ageing population and age-related diseases has been observed, with the increase in healthcare costs. As so, new solutions to provide more efficient and affordable support to this group of patients are needed. Such solutions should never discard the user and instead should focus on promoting more healthy lifestyles and provide tools for patients' active participation in the treatment and management of their diseases. In this concern, the Personal Health Empowerment (PHE) project presented in this paper aims to empower patients to monitor and improve their health, using personal data and technology assisted coaching. The work described in this paper focuses on defining an approach for user modelling on patients with chronic obstructive respiratory diseases using a hybrid modelling approach to identify different groups of users. A classification model with 90.4% prediction accuracy was generated combining agglomerative hierarchical clustering and decision tree classification techniques. Furthermore, this model identified 5 clusters which describe characteristics of 5 different types of users according to 7 generated rules. With the modelling approach defined in this study, a personalized coaching solution will be built considering patients with different necessities and capabilities and adapting the support provided, enabling the recognition of early signs of exacerbations and objective self-monitoring and treatment of the disease. The novel factor of this approach resides in the possibility to integrate personalized coaching technologies adapted to each kind of user within a smartphone-based application resulting in a reliable and affordable alternative for patients to manage their disease.

Defining an Architecture for a Coaching Module to Support Self-Monitoring of Chronic Obstructive Respiratory Diseases.

Over time there has been an increase of the number of people affected by chronic obstructive respiratory diseases, burdening healthcare providers. Following the growth of information technologies, it has been sought the development of innovative solutions that help monitoring and treating patients. In this work is proposed the architecture for a coaching module to be integrated in the system developed by the PHE project. With the goal to improve patients' health condition by providing innovative and intelligent measuring and monitoring tools for preventive healthcare and allow affordable solutions with increased patient involvement. With this work, we define the architecture for a module that can generate recommendations adapted to each patient. By doing so, we believe to be possible to motivate the adoption of behaviors that benefit the health condition of the patient and decrease the risk of complications associated to the disease.

Development of weCope, a mobile app for illness self-management in schizophrenia

Abstract Objective To describe the development of weCope, a Portuguese mobile app for people with schizophrenia. Methods The development of weCope followed 4 stages: I-102 people with psychotic disorders completed an online questionnaire; II-a multidisciplinary focus group was conducted among five mental health professionals; III-we developed the app; IV-9 participants used weCope during 8 weeks to assess its efficiency. Results weCope targets coping with voices, problem solving, goals setting and stress management, and results indicated that: weCope improved symptoms, sense of recovery and personal and social functioning; 59% of the participants were willing to download an application for illness self-management; professionals revealed some concerns about mHealth but enhanced the high importance to develop these apps; usability testing revealed that 89% considered weCope useful for illness self-management. However, the more years with mental illness, the less importance is given to a mobile application for this purpose. Discussion weCope was developed through a comprehensive development process and may contribute to a subjective perception of the patient's better well-being and health condition.

A novel approach for classifying protruding ears is easy to calculate and implement in daily clinical practice.

AIM: Protruding ears are a prevalent deformity, with a reported incidence of 5% in the paediatric population, but it lacks a simple digital classification. The aim of this study was to find a parameter that would objectively allow the photographic classification of protrusion, by comparing frontal facial images of patients with protruding ears with aged-matched controls. METHODS: This prospective cohort study compared the frontal facial images of 105 patients with protruding ears with 112 aged-matched controls without protruding ears. A rectangle was drawn on the image for each ear, encompassing its full visible anatomy. The width of each rectangle was divided by its height to create an index. The mean value of both ears was defined as the Frontal Aesthetics Translation Index for Measurement of Amplitude of the Ears (FATIMAE). RESULTS: The calculated values for group with protruding ears were significantly higher than for the controls. No gender differences were found. However, the FATIMAE values decreased with age, establishing different classification criteria for separate age groups. CONCLUSION: The FATIMAE is easy to calculate and implement in daily clinical practice and establishes a practical approach for classification of protrusion, as well as for referral criteria for a specialised surgical consultation.