Abstract
Over the last years, accessibility has been gaining more recognition, hence there is a market demand for professionals skilled in accessibility. Therefore, there is a trend towards incorporating accessibility in computer science curricula. Many approaches were presented in order to teach accessibility in the academy, however many failed in the department of motivation and engagement. Moreover, gamification is a strong contender when it comes to engaging, motivating and improving the students' performance using game design elements in non-game context, and it has not been much explored to teach accessibility in the academy. Consequently, this paper proposes to teach accessibility in the academy through a gamification-based approach. The paper starts with a presentation of the proposed approach and finally concludes the paper along with future research direction.

Abstract
Crowdsourcing has received considerable attention over the last fifteen years and has been the subject of several experiments that demonstrate its large potential for use in real-world situations. With the rapid growth of and access to crowd work environments, there is a need for new ways to ensure more equitable access for all people. Task design is one of the core aspects of the crowdsourcing process and its optimization is a priority for many requesters that want to have their tasks solved in short times and with high levels of accuracy. Aligned with this goal, a cognitive personalization framework can make it feasible to assess the information processing preferences of crowd workers in order to provide a useful user interface (UI) adaptation. In an effort to address this issue, this study recruited a total of 64 crowd workers to take cognitive style tests and perform prototypical tasks. The results indicate that it is possible to apply short tests and then obtain some useful indicators for better matching tasks to workers with implications for improving the general outcomes and acceptance rates in crowdsourcing.

Abstract
Today digital labor increasingly advocates for the inclusion of people who are excluded from society in someway. The proliferation of crowdsourcing as a new form of digital labor consisting mainly of microtasks that are characterized by a low level of complexity and short time periods in terms of accomplishment has allowed a wide spectrum of people to access the digital job market. However, there is a long-recognized mismatch between the expectations of employers and the capabilities of workers in microwork crowdsourcing marketplaces. Cognitive personalization has the potential to tailor microtasks to crowd workers, thus ensuring increased accessibility by providing the necessary coverage for individuals with disabilities and special needs. In this paper an architecture for a crowdsourcing system intended to support cognitive personalization in the design of microtasks is introduced. The architecture includes an ontology built for the representation of knowledge on the basis of the concepts of microtasks, cognitive abilities, and types of adaptation in order to personalize the interface to the crowd worker. The envisioned system contains a backend and a frontend that serve as an intermediary layer between the crowdsourcing platform and the workers. Finally, some results obtained to evaluate the proposed system are presented.

Abstract
In recent years, digital technologies have been used to support discussions about the city and also to involve citizens in participatory public processes. However, despite the widespread use of social media platforms, old issues related to engagement and participation still persist in digital initiatives. The main goal of this study is to carry out an empirical evaluation of a collective intelligence model that combines crowdsourcing and social storytelling to support discussions about the city from a bottom-up perspective. Within a design science research approach we designed a participatory action study that was carried out through a workshop with students and professionals from different areas, such as architecture, urban design and information technology. As a result, we were able to assess whether the collective intelligence model was acceptable to the participants by investigating whether the behavioral assumptions were valid and thus outlining some contributions to the field of urban informatics.

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.