Telemonitoring and wearable devices are transforming healthcare by enabling continuous patient monitoring and personalized interventions. However, their integration remains fragmented due to technical, ethical, and regulatory barriers. This systematic review, following PRISMA 2020 guidelines, synthesizes insights from 165 studies (2020-2024) to assess key advancements and persistent challenges. AI-driven diagnostics show potential but require clinical validation, while energy-efficient communication protocols lack standardization. Data security improvements, such as hybrid encryption, enhance protection but introduce computational overhead. Regulatory misalignment and clinician skepticism hinder interoperability and large-scale adoption. Despite usability advancements, disparities in patient accessibility and clinician engagement highlight the need for human-centered design. To convert promises into practice, we outline a blueprint that pairs harmonized regulatory pathways with explainable, edge-deployed AI, leverages low-latency data exchange, and promotes cross-sector interoperability convergence. Realizing this agenda through longitudinal, multicenter trials and inclusive, community-anchored implementation will unlock equitable and scalable telemonitoring ecosystems.

Air quality modelling that relates meteorological, car traffic, and pollution data is a fundamental problem, approached in several different ways in the recent literature. In particular, a set of such data sampled at a specific location and during a specific period of time can be seen as a multivariate time series, and modelling the values of the pollutant concentrations can be seen as a multivariate temporal regression problem. In this paper, we propose a new method for symbolic multivariate temporal regression, and we apply it to several data sets that contain real air quality data from the city of Wrocław (Poland). Our experiments show that our approach is superior to classical, especially symbolic, ones, both in statistical performances and the interpretability of the results.