Development of a Digital Patient Assistant for the Management of Cyclic Vomiting Syndrome: Patient-Centric Design Study.

BACKGROUND: Cyclic vomiting syndrome (CVS) is an enigmatic and debilitating disorder of gut-brain interaction that is characterized by recurrent episodes of severe vomiting and nausea. It significantly impairs patients' quality of life and can lead to frequent medical visits and substantial health care costs. The diagnosis for CVS is often protracted and complex, primarily due to its exclusionary diagnosis nature and the lack of specific biomarkers. This typically leads to a considerable delay in accurate diagnosis, contributing to increased patient morbidity. Additionally, the absence of approved therapies for CVS worsens patient hardship and reflects the urgent need for innovative, patient-centric solutions to improve CVS management. OBJECTIVE: We aim to develop a digital patient assistant (DPA) for patients with CVS to address their unique needs, and iteratively enhance the technical features and user experience on the initial DPA versions. METHODS: The development of the DPA for CVS used a design thinking approach, prioritizing user needs. A literature review and Patient Advisory Board shaped the initial prototype, focusing on diagnostic support and symptom tracking. Iterative development, informed by the design thinking approach and feedback from patients with CVS and caregivers through interviews and smartphone testing, led to significant enhancements in user interaction and artificial intelligence integration. The final DPA's effectiveness was validated using the System Usability Scale and feedback questions, ensuring it met the specific needs of the CVS community. RESULTS: The DPA developed for CVS integrates an introductory bot, daily and weekly check-in bots, and a knowledge hub, all accessible via a patient dashboard. This multicomponent solution effectively addresses key unmet needs in CVS management: efficient symptom and impacts tracking, access to comprehensive disease information, and a digital health platform for disease management. Significant improvements, based on user feedback, include the implementation of artificial intelligence features like intent recognition and data syncing, enhancing the bot interaction and reducing the burden on patients. The inclusion of the knowledge hub provides educational resources, contributing to better disease understanding and management. The DPA achieved a System Usability Scale score of 80 out of 100, indicating high ease of use and relevance. Patient feedback highlighted the DPA's potential in disease management and suggested further applications, such as integration into health care provider recommendations for patients with suspected or confirmed CVS. This positive response underscores the DPA's role in enhancing patient engagement and disease management through a patient-centered digital solution. CONCLUSIONS: The development of this DPA for patients with CVS, via an iterative design thinking approach, offers a patient-centric solution for disease management. The DPA development framework may also serve to guide future patient digital support and research scenarios.

Ebbing Strength, Fading Power: Unveiling the Impact of Persistent Fatigue on Muscle Performance in COVID-19 Survivors.

The total number of confirmed cases of COVID-19 caused by SARS-CoV-2 virus infection is over 621 million. Post-COVID-19 syndrome, also known as long COVID or long-haul COVID, refers to a persistent condition where individuals experience symptoms and health issues after the acute phase of COVID-19. The aim of this study was to assess the strength and fatigue of skeletal muscles in people recovered from COVID-19. A total of 94 individuals took part in this cross-sectional study, with 45 participants (referred to as the Post-COVID Cohort, PCC) and 49 healthy age-matched volunteers (Healthy Control Cohort, HCC). This research article uses the direct dynamometry method to provide a detailed analysis of post-COVID survivors' strength and power characteristics. The Biodex System 4 Pro was utilized to evaluate muscle strength characteristics during the fatigue test. The fatigue work in extensors and flexors was significantly higher in the PCC. The PCC also showed significantly less power in both extensors and flexors compared to the HCC. In conclusion, this study provides compelling evidence of the impact of post-COVID-19 fatigue on muscle performance, highlighting the importance of considering these effects in the rehabilitation and care of individuals recovering from the virus. PCC achieved lower muscle strength values than HCC.

ASTHMAXcel PRO patient satisfaction and usability field testing.

Background: ASTHMAXcel PRO, an enhanced version of the ASTHMAXcel mobile application, has been developed to deliver comprehensive, guideline-based asthma education while also facilitating the collection of patient-reported outcomes (PROs) and enhancing user experience.Objective: To perform field testing and conduct formative and summative evaluation of the ASTHMAXcel PRO application to assess its impact on patient satisfaction, usability, and usage.Methods: Twenty-eight adult patients completed a baseline visit during which ASTHMAXcel PRO was introduced, health literacy was assessed, and demographic data were collected. They were instructed to use the app for 4 weeks. The Questionnaire for User Interface Satisfaction (QUIS) and the Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire were administered at baseline and 4 weeks to assess user satisfaction and technology acceptance, respectively. Semi-structured interviews were conducted to gather feedback regarding the application from patients.Results: The baseline total scores were high for both UTAUT and QUIS (mean (SD): 64.2 (10.1), 6.8 (2.2) respectively) indicating that user satisfaction and acceptance began at high levels. UTAUT total score, as well as all domain scores, improved significantly from baseline to 4 weeks (p < 0.02). QUIS total score along with several domain scores (screen, system capabilities, usability) also increased from baseline to 4-weeks (p = 0.03, 0.01, 0.03, 0.01, respectively). These improvements remained significant when adjusting for age, gender, education, and health literacy. Patients reported that the application was helpful, informative, and easy to understand and use.Conclusion: The significant increases in satisfaction and technology adoption observed among ASTHMAXcel PRO users demonstrate that the application is viable and has the potential to improve upon usability challenges faced by existing mobile health applications.

Effect of COVID-19 on Musculoskeletal Performance in Gait and the Timed-Up and Go Test.

INTRODUCTION: The total number of confirmed cases of COVID-19 caused by the SARS-CoV-2 virus infection is over 621 million in the world. In approximately 63% of cases, the patient still experiences persistent symptoms 30 days after the onset of symptoms or hospitalisation, and 45.9% of patients have experienced or will experience symptoms for at least three months. Despite the prevalence of chronic symptoms and pathological changes that may affect gait and functional mobility in people with a history of COVID-19, there are few publications investigating the impact of these abnormalities. This study aims to determine the long-term effects of COVID-19 on gait and the Timed-Up and Go Task. MATERIAL AND METHODS: A total of 30 individuals took part in the experiment. The subjects in the study group were infected with the COVID-19 virus and required hospital treatment. Prior to the study, the subjects had no chronic diseases or other conditions affecting the musculoskeletal system. The non-infected by COVID-19 group was a healthy population with no history of COVID-19 disease. The study used the inertial system wireless motion analysis system based on 15 inertial sensors (inertial measurement units, IMUs). IMU sensors were placed on the following body segments: head, sternum, middle and lower spine, shoulder, arm, forearm, hand, shank, for the left and right limb. Movement task reports generated from the recording were created using myoRESEARCH 3.10. The subjects in the study group were asked to perform a movement task test-the Timed-Up and Go Test (TUG): sit-to-stand, walk (3 m) without change in direction, walk termination, and stand-to-sit. RESULTS: It took 46% longer for those infected by COVID-19 (participants) to complete the entire movement task compared to those in the not-infected by COVID-19 group. Sit-to-Stand Time [s] was greater in the infected by COVID-19 group and was 2.1 ± 0.7. Mean Walking Speed [m/s] was lower than in the not-infected by COVID-19 group and was 0.26 ± 0.07. Walking cadence [steps/min] was lower and was 21.2 ± 1.2. Infected by COVID-19 participants achieved a smaller anterior pelvic tilt angle (p < 0.001) and a smaller hip flexion angle (p = 0.025), with an increase in knee (p < 0.001) and ankle (p < 0.001) flexion angles. CONCLUSIONS: Individuals in the infected by COVID-19 group present changes in the ranges of motion and the time to complete the TUG task, despite the fact that at least eight weeks passed after hospital discharge.

Trustworthiness judgments without the halo effect: A data-driven computational modeling approach.

Trustworthy-looking faces are also perceived as more attractive, but are there other meaningful cues that contribute to perceived trustworthiness? Using data-driven models, we identify these cues after removing attractiveness cues. In Experiment 1, we show that both judgments of trustworthiness and attractiveness of faces manipulated by a model of perceived trustworthiness change in the same direction. To control for the effect of attractiveness, we build two new models of perceived trustworthiness: a subtraction model, which forces the perceived attractiveness and trustworthiness to be negatively correlated (Experiment 2), and an orthogonal model, which reduces their correlation (Experiment 3). In both experiments, faces manipulated to appear more trustworthy were indeed perceived to be more trustworthy, but not more attractive. Importantly, in both experiments, these faces were also perceived as more approachable and with more positive expressions, as indicated by both judgments and machine learning algorithms. The current studies show that the visual cues used for trustworthiness and attractiveness judgments can be separated, and that apparent approachability and facial emotion are driving trustworthiness judgments and possibly general valence evaluation.