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PICO recognition is an information extraction task for detecting parts of text describing Participant (P), Intervention (I), Comparator (C), and Outcome (O) (PICO elements) in clinical trial literature. Each PICO description is further decomposed into finer semantic units. For example, in the sentence ‘The study involved 242 adult men with back pain.’, the phrase ‘242 adult men with back pain’ describes the participant, but this coarse-grained description is further divided into finer semantic units. The term ‘242’ shows “sample size” of the participants, ‘adult’ shows “age”, ‘men’ shows “sex”, and ‘back pain’ show the participant “condition”. Recognizing these fine-grained PICO entities in health literature is a challenging named-entity recognition (NER) task but it can help to fully automate systematic reviews (SR). Previous approaches concentrated on coarse-grained PICO recognition but focus on the fine-grained recognition still lacks. We revisit the previously unfruitful neural approaches to improve recognition performance for the fine-grained entities. In this paper, we test the feasibility and quality of multitask learning (MTL) to improve fine-grained PICO recognition using a related auxiliary task and compare it with single-task learning (STL). As a consequence, our end-to-end neural approach improves the state-of-the-art (SOTA) F1 score from 0.45 to 0.54 for the “participant” entity and from 0.48 to 0.57 for the “outcome” entity without any handcrafted features. We inspect the models to identify where they fail and how some of these failures are linked to the current benchmark data.

Exercise or physical activity is considered as medicine, and both have several positive effects, which outweigh largely the risks. For older adults with mental health issues, physical activity and exercise might be even more important than for healthy people: exercise and physical activity can prevent or at least delay the onset of some mental disorders; they have therapeutic effects, either as the sole intervention or as an adjunct treatment for mental disorders. Patients are more likely to recover from a mental illness if they are regularly physically active. It is recommended for older adults to be physically active for at least 150 min per week at moderate intensity or for at least 75 min at vigorous intensity. Exercise adherence is quite low, and measures to improve exercise adherence should be implemented. Health-care providers should help older adults to find physical activities or exercise modalities that are perceived as pleasant. Caregivers of older adults with mental health issues should also be physically active, as this has a positive effect on the burden of carers of persons with mental health problems.

Health tourism represents a promising niche still insufficiently exploited in Europe and Switzerland. Hiking has been a popular tourist activity for years and staying healthy is an important motivation for hiking. However, physical and psychological limitations in potential hikers often represent an unsurmountable barrier to complete a particular path. This mismatch between trail and user results in a poor visitor experience, affecting negatively both the user and the touristic destination. This paper presents SanTour, a novel concept in health tourism centered on the needs of visitors by considering their physical capacities and limits, as well as their expectations. SanTour exploits two main knowledge bases: one centered on the user, including a health profile, and another centered on the hiking trails. In a pilot phase, the concept has been prototyped and tested on a limited scale, with support from a tourist office in Switzerland. We plan to further develop this application that will provide an innovative service to hikers by cross-referencing their physical abilities and the characteristics of the hiking trails.

Telerehabilitation in older adults is most needed in the patient environments, rather than in formal ambulatories or hospitals. Supporting such practices brings significant advantages to patients, their family, formal and informal caregivers, clinicians, and researchers. Several techniques and technologies have been developed aiming at facilitating and enhancing the effectiveness of telerehabilitation. This paper gives a quick overview of the state of the art, investigating video-based, wearable, robotic, distributed, and gamified telerehabilitation solutions. In particular, agent-based solutions are analyzed and discussed addressing strength, limitations, and future challenges. Elaborating on functional requirements expressed by professional physiotherapists and researchers, the need for extending multi-agent systems (MAS) peculiarities at the sensing level in wearable solutions establishes new research challenges.

Electronic Data Capture (EDC) software solutions are progressively being adopted for conducting clinical trials and studies, carried out by biomedical, pharmaceutical and health-care research teams. In this paper we present the MedRed Ontology, whose goal is to represent the metadata of these studies, using well-established standards, and reusing related vocabularies to describe essential aspects, such as validation rules, composability, or provenance. The paper describes the design principles behind the ontology and how it relates to existing models and formats used in the industry. We also reuse well-known vocabularies and W3C recommendations. Furthermore, we have validated the ontology with existing clinical studies in the context of the MedRed project, as well as a collection of metadata of well-known studies. Finally, we have made the ontology available publicly following best practices and vocabulary sharing guidelines.