Abstract
User experience can be affected by the amount and intensity of information presented. Four scenarios were developed to assess the insertion of information elements (chronometer and hint system) and tested with 37 users to find out if they affected the user's sense of presence and symptoms of cybersickness. In order to instruct users and using virtual reality using the Unity 3D game engine, we created a virtual world where the user has the role of exploring the environment and looking for mushrooms, and can consult a description about it. For tests with users, the IPQp and SSQ questionnaires were applied. The results indicate that it is possible to create a virtual world with the addition of informational components without significantly disturbing the user experience.

Abstract

Abstract
Background: Physical exercise is a first-line treatment for peripheral arterial disease (PAD) and intermittent claudication (IC) reducing pain and increasing the distances walked. Home-based exercise therapy (HBET) has the advantage of reaching a higher number of patients and increasing adherence to physical exercise as it is performed in the patient's residential area and does not have the time, cost, and access restrictions of supervised exercise therapy (SET) implemented in a clinical setting. Even so, rates of adherence to physical exercise are relatively low, and therefore, m-health tools are promising in increasing motivation to behavior change and adherence to physical exercise. A built-in virtual assistant is a patient-focused tool available in a mobile interface, providing a variety of functions including health education, motivation, and implementation of behavior change techniques. Methods: This is a single-center, prospective, three-arm, single-blind, randomized, controlled, superior clinical trial with stratified and blocked random allocation. Three hundred participants with PAD and IC will be recruited from an Angiology and Vascular Surgery Department, Centro Hospitalar Universitario Porto (CHUPorto), Porto, Portugal. All patients will receive the same medical care recommended by current guidelines. Participants in all three groups will receive a personalized prescription for an HBET program and a behavioral change and motivational intervention. Participants in experimental groups 1 and 2 will receive a smartphone with the WalkingPad app to monitor exercise sessions. Experimental group 2 WalkingPad app will have a built-in virtual assistant that will promote behavioral change and provide motivational support. Participants allocated to the active control group will not receive the m-health tool, but a practice diary to encourage monitoring. The program will last for 6 months with three evaluation moments (baseline, 3, and 6 months). The primary outcome will be the change in distances walked (maximal and pain-free) from baseline to 3 and 6 months. Secondary outcomes will be changes in quality of life, patients' perception of resistance, and walking speed. Discussion: This study will allow measuring the effectiveness of an m-health tool in increasing motivation for behavior change and adherence to an HBET program in patients with PAD. The superiority of experimental group 2 in the primary and secondary outcomes will indicate that the virtual assistant is effective for motivating behavioral change and encouraging the practice and adherence to physical exercise. The use of m-health tools and virtual health assistants can potentially fill a gap in the access and quality of health services and information, reducing the burden on the health system and promoting self-management and self-care in chronic illness.

Abstract
Objective: Supervised exercise therapy is recommended as first line in the management of intermittent claudication. Its use is often limited by accessibility, compliance and cost. Home-based exercise therapy (HBET) programs emerged as an alternative solution, but have shown inferior results. The use of structured monitoring with the use of external wearable activity monitors (WAM) has been shown to improve outcomes. Mobile applications (apps) can make use of built-in accelerometers of modern smartphones and become an alternative solution for monitoring patients during HBET, potentially providing wider accessibility. This review aims to assess current use of smartphone technology (ie, mobile apps) for monitoring or tracking patients' activity in exercise therapy for peripheral arterial disease (PAD). Methods: The PubMed database was searched from January 2011 to September 2021. Eligible articles had to include a population of patients with PAD, conduct a mobile-health exercise intervention and use smartphone technology for monitoring or tracking patients' activity. Randomized controlled trials, prospective studies, and study protocols were included. Results: A total of seven artic les met the selection criteria. These articles described six different studies and five different mobile apps. Three were fitness apps (FitBit, Nike+ FuelBand, and Garmin Connect) that synchronized with commercially available WAMs to provide users with feedback. Two were PAD-specific apps (TrackPAD and Movn) developed specifically to assess patients' activity during exercise therapy. PAD-specific apps also incorporated coaching and educational elements such as weekly goal setting, claudication reminders, messaging, gamification, training advice, and PAD education. Conclusions: Current HBET programs use smartphone apps mainly via commercially available fitness apps that synchronize with WAM devices to register and access data. PAD-specific apps are scarce, but show promising features that can be used to monitor, train, coach, and educate patients during HBET programs. Larger studies combining these elements into HBET programs should provide future direction.

Abstract
The study of data quality in crowdsourcing campaigns is currently a prominent research topic, given the diverse range of participants involved. A potential solution to enhancing data quality processes in crowdsourcing is cognitive personalization, which involves appropriately adapting or assigning tasks based on a crowd worker's cognitive profile. There are two common methods for assessing a crowd worker's cognitive profile: administering online cognitive tests, and inferring behavior from task fingerprinting based on user interaction log events. This article presents the findings of a study that investigated the complementarity of both approaches in a microtask scenario, focusing on personalizing task design. The study involved 134 unique crowd workers recruited from a crowdsourcing marketplace. The main objective was to examine how the administration of cognitive ability tests can be used to allocate crowd workers to microtasks with varying levels of difficulty, including the development of a deep learning model. Another goal was to investigate if task fingerprinting can be used to allocate crowd workers to different microtasks in a personalized manner. The results indicated that both objectives were accomplished, validating the usage of cognitive tests and task fingerprinting as effective mechanisms for microtask personalization, including the development of a deep learning model with 95% accuracy in predicting the accuracy of the microtasks. While we achieved an accuracy of 95%, it is important to note that the small dataset size may have limited the model's performance.

Abstract
Peripheral arterial disease (PAD) causes blockage of the arteries, altering the blood flow to the lower limbs. This blockage can cause the individual with PAD to feel severe pain in the lower limbs. The main contribution of this research is the discovery of a solution that allows the automatic detection of the onset of claudication based on data analysis from patients' smartphones. For the data-collection procedure, 40 patients were asked to walk with a smartphone on a thirty-meter path, back and forth, for six minutes. Each patient conducted the test twice on two different days. Several machine learning models were compared to detect the onset of claudication on two different datasets. The results suggest that we can identify the onset of claudication using inertial sensors with a best case accuracy of 92.25% for the Extreme Gradient Boosting model.