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
Application execution requests in cloud architecture and fog paradigm are generally heterogeneous in terms of contexts at the device and application level. The scheduling of requests in these architectures is an optimization problem with multiple constraints. Despite numerous efforts, task scheduling in these architectures and paradigms still presents some enticing challenges that make us question how tasks are routed between different physical devices, fog, and cloud nodes. The fog is defined as an extension of the cloud, which provides processing, storage, and network services near the edge network, and due to the density and heterogeneity of devices, the scheduling is very complex, and, in the literature, we still find few studies. Trying to bring innovative contributions in these areas, in this paper, we propose a solution to the context-aware task-scheduling problem for fog paradigm. In our proposal, different context parameters are normalized through Min-Max normalization, requisition priorities are defined through the application of the Multiple Linear Regression (MLR) technique and scheduling is performed using Multi-Objective Non-Linear Programming Optimization (MONLIP) technique. The results obtained from simulations in the iFogSim toolkit, show that our proposal performs better compared to the non-context-aware proposals.

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Reducing office buildings’ energy consumption can contribute significantly towards carbon reduction commitments since it represents 10% of total energy consumption. Major components are lighting (40% of consumption), electrical equipment (35%), and heating and central cooling systems (25\%). Occupants’ behaviours impact these energy consumption components, with solid evidence on the role of individual behaviours. In this work, we propose a methodology that uses virtual choreographies to identify and prioritize behaviour-change interventions towards office users based on the potential impact on energy consumption. The data shows that some behaviours with significant consumption have little potential for behavioural change impact, while other behaviours hold substantial potential for lowering energy consumption via behavioural change.

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