Nonadherence to inhaled medication leads to poor asthma control and increased healthcare utilisation. Many studies exploring adherence determinants have been conducted, but summaries of the evidence are scarce. We performed a systematic review of observational research on determinants of asthma inhaler adherence among adults.We searched for articles in English reporting quantitative observational studies on inhaler adherence correlates among adults in developed countries, published in EMBASE, Medline, PsychInfo and PsychArticles in 1990–2014. Two coders independently assessed eligibility and extracted data, and assessed study quality. Results were summarised qualitatively into social and economic, and healthcare-, therapy-, condition- and patient-related factors.The 51 studies included mainly examined patient-related factors and found consistent links between adherence and stronger inhaler-necessity beliefs, and possibly older age. There was limited evidence on the relevance of other determinants, partly due to study heterogeneity regarding the types of determinants examined. Methodological quality varied considerably and studies performed generally poorly on their definitions of variables and measures, risk of bias, sample size and data analysis.A broader adoption of common methodological standards and health behaviour theories is needed before cumulative science on the determinants of adherence to asthma inhalers among adults can develop further.

Adequate medication adherence is key for optimal benefit of pharmacological treatments. A wealth of research has been conducted to understand and identify opportunities to intervene to improve medication adherence, but variations in adherence definitions within prior research have led to ambiguity in study findings. The lack of a standard taxonomy hinders the development of cumulative science in adherence research. This article reviews the newly established Ascertaining Barriers to Compliance (ABC) taxonomy for medication adherence with a particular focus on its relevance and applicability within the context of asthma and chronic obstructive pulmonary disease management. Building on traditional definitions and concepts within medication adherence, the ABC taxonomy considers the temporal sequence of steps a patient must undertake to be defined as "adherent to treatment": (A) initiation, (B) implementation, and (C) persistence. We explain the clinical and research relevance of differentiating between these phases, point to differences in its applicability in observational and experimental research, review strengths and limitations of available measures, and highlight recent findings on specific determinants of these behaviors. Finally, we provide recommendations for research and practice with a view to supporting and sign posting opportunities to improve future respiratory medication adherence and associated research.

Outcomes for patients with chronic respiratory diseases remain poor despite the development of novel therapies. In part, this reflects the fact that adherence to therapy is low and clinicians lack accurate methods to assess this issue. Digital technologies hold promise to overcome these barriers to care. For example, algorithmic analysis of large amounts of information collected on health status and treatment use, along with other disease relevant information such as environmental data, can be used to help guide personalised interventions that may have a positive health impact, such as establishing habitual and correct inhaler use. Novel approaches to data analysis also offer the possibility of statistical algorithms that are better able to predict exacerbations, thereby creating opportunities for preventive interventions that may adapt therapy as disease activity changes. To realise these possibilities, digital approaches to disease management should be supported by strong evidence, have a solid infrastructure, be designed collaboratively as clinically effective and cost-effective systems, and reflect the needs of patients and healthcare providers. Regulatory standards for digital interventions and strategies to handle the large amounts of data generated are also needed. This review highlights the opportunities provided by digital technologies for managing patients with respiratory diseases.

Background: Applied health science research commonly measures concepts via multiple-item tools (scales), such as self-reported questionnaires or observation checklists. They are usually validated in more detail in separate psychometric studies or very cursorily in substantive studies. However, methodologists advise that, as validity is a property of the inferences based on measurement in a context, psychometric analyses should be performed in substantive studies as well. Until recently, performing comprehensive psychometrics required expert knowledge of different, often proprietary, software. The increasing availability of statistical techniques in the R environment now makes it possible to integrate such analyses in applied research. Methods: In this tutorial, I introduce a 6-step protocol which allows detailed diagnosis of core psychometric properties (e.g. structural validity, internal consistency) for scales with binary and ordinal response options aiming to measure differences in degree or quantity, the most common in applied research. The protocol includes investigations of (1) item distributions and summary statistics, item properties via (2) non-parametric and (3) parametric item response theory, (4) scale structure using factor analysis, (5) reliability via classical test theory, and (6) calculation and description of global scores. I illustrate the procedure on a measure of self-reported disability, the 24-item Sickness Impact Profile Roland Scale (RM-SIP), administered in a survey of 222 chronic pain sufferers. An R Markdown script is provided that generates reproducible reports. Results: In this sample, 15 of 24 RM-SIP items formed a unidimensional ordinal scale with good homogeneity (H = 0.43) and reliability (α = .86[.84–.89]; ω = .87[.85–.88]). The two versions were highly correlated (r = .96), and regression models predicting RM-SIP disability produced comparable results. Conclusions: The example analysis illustrates how psychometric properties may be assessed in substantive studies and identify avenues for measure improvement. Applied researchers can adapt this script to perform and communicate these analyses as part of questionnaire validation and substantive studies.

Despite the popularity of the Persuasion Knowledge Model, and its persistent relevance given the omnipresence of sponsored content (e.g. brand placement in TV programmes and video games, or paid product reviews in blogs), the way scholars measure persuasion knowledge varies widely. This study aims to develop valid and reliable scales for standardized measurement of consumers' persuasion knowledge of sponsored content. In three phases, we developed the Persuasion Knowledge Scales of Sponsored Content (PKS-SC) that measures nine components: (1) recognition of sponsored content, (2) understanding of selling and persuasive intent, (3) recognition of the commercial source of sponsored content, (4) understanding of persuasive tactics, (5) understanding of the economic model, (6) self-reflective awareness of the effectiveness of sponsored content, (7) skepticism toward sponsored content, (8) appropriateness of sponsored content, and (9) liking of sponsored content. All scales have good to appropriate validity and reliability. Recommendations for future research are discussed.

Adherence to medications is an important indicator of the quality of medication management and impacts on health outcomes and cost-effectiveness of healthcare delivery. Electronic healthcare data (EHD) are increasingly used to estimate adherence in research and clinical practice, yet standardization and transparency of data processing are still a concern. Comprehensive and flexible open-source algorithms can facilitate the development of high-quality, consistent, and reproducible evidence in this field. Some EHD-based clinical decision support systems (CDSS) include visualization of medication histories, but this is rarely integrated in adherence analyses and not easily accessible for data exploration or implementation in new clinical settings. We introduce AdhereR, a package for the widely used open-source statistical environment R, designed to support researchers in computing EHD-based adherence estimates and in visualizing individual medication histories and adherence patterns. AdhereR implements a set of functions that are consistent with current adherence guidelines, definitions and operationalizations. We illustrate the use of AdhereR with an example dataset of 2-year records of 100 patients and describe the various analysis choices possible and how they can be adapted to different health conditions and types of medications. The package is freely available for use and its implementation facilitates the integration of medication history visualizations in open-source CDSS platforms.