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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.

Habitstrength for taking medication is associated with medication adherence. However,habit strength is typically measured via self-reports, which have limitations.Objective sensors may provide advantages to self-reports. Toevaluate whether habit-strength metrics derived from objective sensor data(MEMS® Caps; AARDEX Group) are associated with self-reported habit strength andadherence (objective and self-reported) and whether objective and self-reportedhabit strength are independently associated with adherence. Patients(N = 79) on oral medications for type 2 diabetes completed self-reports of habitstrength and medication adherence and used MEMS® Caps to take their prescribedmedication for one month. MEMS® Caps data were used to create five objective metricsof habit strength (e.g., individual-level variance in pill timing) and quantifymedication adherence (% days correct dosing). Consistencyin behavior from week to week (versus across each day) had the greatestassociation with self-reported habit strength (r(78) = 0.29, p = 0.01),self-reported adherence (r(78) = 0.32, p = 0.005), and objective adherence (r(78) = 0.61, p < 0.001). Objective and self-reported habit strength wereindependently associated with adherence. Weeklypill-timing consistency may be more useful than daily pill-timing consistencyfor predicting adherence and understanding patients' medication-taking habits.Self-reports and objective metrics of habit strength may be measuring differentconstructs, warranting further research., Author(s): L. Alison Phillips [sup.1] , Antoine Pironet [sup.2] , Bernard Vrijens [sup.2] Author Affiliations: (1) grid.34421.30, 0000 0004 1936 7312, Department of Psychology, Iowa State University, , Ames, IA, [...]

Background: This study aims to create a comprehensive framework for the development and implementation of digital medication adherence technologies (DMATech), focusing on critical stages where engagement of medication users (MU) is considered meaningful, i.e. adds significant value, as agreed upon by participating stakeholders. Methods: Through a literature review and expert consensus, a framework was outlined covering key DMATech development and implementation phases and steps. An in-person workshop with MU representatives and adherence experts, using the Nominal Group Technique, further refined these stages for MU engagement. Results: The DMATech framework included three phases: 'Innovation,' 'Research and Development,' and 'Launch and Implementation,' each encompassing multiple steps. The workshop, attended by five MU representatives and nine adherence experts, identified critical stages for MU input including context analysis, ideation, proof of concept, prototype creation, DMATech's iteration, critical evaluation, healthcare implementation, real-world assessment, and improvement. Nevertheless, there was a divergence of consensus regarding the importance of MUs engagement in regulatory, financial, and marketing aspects. Conclusions: This study provides a holistic framework for DMATech development and implementation and underscores the necessity of MU engagement at various stages. Modes of MU engagement cannot be generalized; a case-by-case evaluation of engagement strategies is essential. [ABSTRACT FROM AUTHOR]

Aims: This research aims to provide an overview of the consequences of undiagnosed nonadherence (noninitiation, suboptimal implementation, nonpersistence) in randomized clinical trials (RCTs). Methods: This research was conducted by combining a literature review and qualitative semistructured interviews with key opinion leaders. Based on this groundwork, the consequences of undiagnosed nonadherence in RCTs were summarized and reported in a figure. This study focused on phases II, III and post‐marketing in ambulatory settings across a variety of therapeutic areas and indications. Results: Various consequences of nonadherence in RCTs were investigated. In phase II, drug efficacy may be underestimated, variability in the outcomes may be high and a distorted picture of side effects could be reported, resulting in an uncertain impression of the investigational product's profile and complicating decision‐making. The sponsor may need to increase the sample size of the upcoming phase III study to improve its power, representing additional costs, or even terminate the study. In phase III, similar phenomena may be observed, making demonstration of efficacy to the regulatory bodies more difficult. Lastly, after commercialization, a distortion in pharmacometrics may occur: the drug may underperform, prescriptions may be refilled less often than expected or extra expenses may be incurred by the payers. This can result in post‐marketing dose reduction, new competitors coming into the market and, eventually, product withdrawal. Conclusions: This research highlighted the many potential adverse consequences of undiagnosed nonadherence in RCTs, including additional costs. Collecting accurate data appeared to be crucial for decision‐making throughout the drug development process. [ABSTRACT FROM AUTHOR]

Objective: To assess tofacitinib and self‐injectable tumor necrosis factor inhibitor (TNFi) adherence using the Medication Event Monitoring System (MEMS) and characterize association with adherence in patients with rheumatoid arthritis (RA). Methods: Eligible patients were enrolled from the Forward Databank within 6 months of initiating tofacitinib or injectable TNFi or from participating clinics where these were first prescribed. MEMS caps and patient diaries were used to compile dosing over 9 months. Demographics and disease characteristics were collected every 6 months, and the Beliefs about Medicines Questionnaire only at baseline. Adherence along with its components, initiation, implementation, and persistence, were calculated. Results: Of the 112 consented to participate, 82 (73%) remained in the final analysis with recruitment from clinics 47 (57%) and Forward 35 (43%). Sixty‐two (76%) initiated tofacitinib with 87% taking it quaque die and twenty (24%) TNFi. At 9 months, 77% of tofacitinib were persistent versus 70% for TNFi (P = 0.65), and implementation was similar (0.84 vs. 0.82; P = 0.57). In multivariable models, increased baseline patient global assessment was consistently associated with discontinuation (hazard ratio 1.31 [1.07‐1.61]). There was increased adherence to methotrexate (MTX) when taking tofacitinib that led to higher combined adherence for tofacitinib than TNFi (0.81 vs. 0.69; P = 0.03), but no significant differences remained in multivariable models. In sensitivity analysis, consistent morning intake for tofacitinib and evening intake for MTX was associated with improved adherence. Conclusion: We found no statistical differences in adherence between patients with RA initiating tofacitinib and self‐injectable TNFi, although 15% to 30% were nonadherent. Concomitant MTX, patient global assessment, and a consistent time of day intake were associated with adherence. [ABSTRACT FROM AUTHOR]

Aims: Measuring adherence to medication is complex due to the diversity of contexts in which medications are prescribed, dispensed and used. The Timelines‐Events‐Objectives‐Sources (TEOS) framework outlined a process to operationalize adherence. We aimed to develop practical recommendations for quantification of medication adherence using self‐report (SR), electronic monitoring (EM) and electronic healthcare databases (EHD) consistent with the TEOS framework for adherence operationalization. Methods: An adherence methodology working group of the International Society for Medication Adherence (ESPACOMP) analysed implications of the process of medication adherence for all data sources and discussed considerations specific to SR, EM and EHD regarding the information available on the prescribing, dispensing, recommended and actual use timelines, the four events relevant for distinguishing the adherence phases, the study objectives commonly addressed with each type of data, and the potential sources of measurement error and quality criteria applicable. Results: Four key implications for medication adherence measurement are common to all data sources: adherence is a comparison between two series of events (recommended and actual use); it refers to one or more specific medication(s); it applies to regular repeated events coinciding with known recommended dosing; and it requires separate measurement of the three adherence phases for a complete picture of patients' adherence. We propose recommendations deriving from these statements, and aspects to be considered in study design when measuring adherence with SR, EM and EHD using the TEOS framework. Conclusion: The quality of medication adherence estimates is the result of several design choices that may optimize the data available. [ABSTRACT FROM AUTHOR]

Introduction: Successful population-level antiretroviral therapy (ART) adherence will be necessary to realize both the clinical and prevention benefits of antiretroviral scale-up and, ultimately, the end of AIDS. Although many people living with HIV are adhering well, others struggle and most are likely to experience challenges in adherence that may threaten virologic suppression at some point during lifelong therapy. Despite the importance of ART adherence, supportive interventions have generally not been implemented at scale. The objective of this review is to summarize the recommendations of clinical, research, and public health experts for scalable ART adherence interventions in resource-limited settings. Methods: In July 2015, the Bill and Melinda Gates Foundation convened a meeting to discuss the most promising ART adherence interventions for use at scale in resource-limited settings. This article summarizes that discussion with recent updates. It is not a systematic review, but rather provides practical considerations for programme implementation based on evidence from individual studies, systematic reviews, meta-analyses, and the World Health Organization Consolidated Guidelines for HIV, which include evidence from randomized controlled trials in low- and middle-income countries. Interventions are categorized broadly as education and counselling; information and communication technology-enhanced solutions; healthcare delivery restructuring; and economic incentives and social protection interventions. Each category is discussed, including descriptions of interventions, current evidence for effectiveness, and what appears promising for the near future. Approaches to intervention implementation and impact assessment are then described. Results and discussion: The evidence base is promising for currently available, effective, and scalable ART adherence interventions for resource-limited settings. Numerous interventions build on existing health care infrastructure and leverage available resources. Those most widely studied and implemented to date involve peer counselling, adherence clubs, and short message service (SMS). Many additional interventions could have an important impact on ART adherence with further development, including standardized counselling through multi-media technology, electronic dose monitoring, decentralized and differentiated models of care, and livelihood interventions. Optimal targeting and tailoring of interventions will require improved adherence measurement. Conclusions: The opportunity exists today to address and resolve many of the challenges to effective ART adherence, so that they do not limit the potential of ART to help bring about the end of AIDS. Keywords: HIV; antiretroviral therapy adherence; interventions; resource-limited settings, Introduction Antiretroviral therapy (ART) has transformed HIV infection from a terminal disease into a manageable chronic illness [1]. ART can also reduce viral load (i.e. HIV RNA levels) and the [...]