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Despite half a century of dedicated studies, medication adherence remains far from perfect, with many patients not taking their medications as prescribed. The magnitude of this problem is rising, jeopardizing the effectiveness of evidence-based therapies. An important reason for this is the unprecedented demographic change at the beginning of the 21st century. Aging leads to multimorbidity and complex therapeutic regimens that create a fertile ground for nonadherence. As this scenario is a global problem, it needs a worldwide answer. Could this answer be provided, given the new opportunities created by the digitization of health care? Daily, health-related information is being collected in electronic health records, pharmacy dispensing databases, health insurance systems, and national health system records. These big data repositories offer a unique chance to study adherence both retrospectively and prospectively at the population level, as well as its related factors. In order to make full use of this opportunity, there is a need to develop standardized measures of adherence, which can be applied globally to big data and will inform scientific research, clinical practice, and public health. These standardized measures may also enable a better understanding of the relationship between adherence and clinical outcomes, and allow for fair benchmarking of the effectiveness and cost-effectiveness of adherence-targeting interventions. Unfortunately, despite this obvious need, such standards are still lacking. Therefore, the aim of this paper is to call for a consensus on global standards for measuring adherence with big data. More specifically, sound standards of formatting and analyzing big data are needed in order to assess, uniformly present, and compare patterns of medication adherence across studies. Wide use of these standards may improve adherence and make health care systems more effective and sustainable.

Summary: Objective. Healthcare professionals play a crucial role for promoting medication adherence in older adults. This research aimed to assess changes in professionals' opinions about medication adherence after attending a course, collecting suggestions for future educational programs. Method. A one-week course on medication adherence in older adults was held involving 32 healthcare professionals and students from Italy, Portugal and Poland as part of the Erasmus+ Skills4Adherence Project. Prior to and at the end of the course, participants provided three keyword answers through a Google Form. Responses were collectively discussed and commented on. Results. At the end of the course a general tendency to put more attention on patient's beliefs and engagement was revealed. The caregivers' role was also underlined. As to suggestions for education, three keywords were considered not enough to characterize adherence issues. Conversely, professionals considered collective discussions and roleplaying to be effective for increasing awareness on this theme. Discussion and conclusion. Several changes in healthcare professionals' opinions regarding determinants of medicationadherence were revealed after this dedicated course. Overall, multidisciplinary and practical training programs should be proposed for increasing healthcare professionals' awareness of factors impacting on medication adherence in older adults., Competing Interests: The authors of this article have no conflict of interests to disclose., (Copyright© by GIMLE.)

The digital era, that we are living nowadays, is transforming health, health care models and services, and the role of society in this new reality. We currently have a large amount of stored health data, including clinical, biometric, and scientific research data. Nonetheless, its potential is not being fully exploited. It is essential to foster the sharing and reuse of this data not only in research but also towards the development of health technologies in order to improve health care efficiency, as well as products, services or digital health apps, to promote preventive and individualized medicine and to empower citizens in health literacy and self-management. In this sense, the FAIR concept has emerged, which implies that health data is findable, accessible, shared and reusable, facilitating interoperability between systems, ensuring the protection of personal and sensitive data. In this paper we review the FAIR concept, 'FAIRification' process, FAIR data versus open access data, ethical issues and the general data protection regulation, and digital health and citizen science.

Background: Mobile technology may help to better understand the adherence to treatment. MASK-rhinitis (Mobile Airways Sentinel NetworK for allergic rhinitis) is a patient-centred ICT system. A mobile phone app (the Allergy Diary) central to MASK is available in 22 countries., Objectives: To assess the adherence to treatment in allergic rhinitis patients using the Allergy Diary App., Methods: An observational cross-sectional study was carried out on all users who filled in the Allergy Diary from 1 January 2016 to 1 August 2017. Secondary adherence was assessed by using the modified Medication Possession Ratio (MPR) and the Proportion of days covered (PDC) approach., Results: A total of 12 143 users were registered. A total of 6 949 users reported at least one VAS data recording. Among them, 1 887 users reported ≥7 VAS data. About 1 195 subjects were included in the analysis of adherence. One hundred and thirty-six (11.28%) users were adherent (MPR ≥70% and PDC ≤1.25), 51 (4.23%) were partly adherent (MPR ≥70% and PDC = 1.50) and 176 (14.60%) were switchers. On the other hand, 832 (69.05%) users were non-adherent to medications (MPR <70%). Of those, the largest group was non-adherent to medications and the time interval was increased in 442 (36.68%) users., Conclusion and Clinical Relevance: Adherence to treatment is low. The relative efficacy of continuous vs on-demand treatment for allergic rhinitis symptoms is still a matter of debate. This study shows an approach for measuring retrospective adherence based on a mobile app. This also represents a novel approach for analysing medication-taking behaviour in a real-world setting., (© 2019 John Wiley & Sons Ltd.)