Medication adherence plays an important role in improving health outcomes related to diabetes and comorbidity. The potential factors influencing medication adherence and how they contribute to health behaviors have not been synthesized to date. This review synthesized qualitative studies that identified factors influencing medication adherence among adults living with diabetes and comorbidity.
Twenty-eight findings were extracted and synthesized into four themes: perceived support, lack of knowledge, medication issues, and the importance of routine. The findings highlight the factors that support medication adherence and areas that can be targeted to support and promote medication adherence. The findings also support the potential role of healthcare providers in supporting people living with diabetes and comorbidity to adhere to and maintain medication regimes. Several factors were identified that are amenable to intervention within the clinical practice setting and have the potential to enhance medication adherence and improve health outcomes for people living with diabetes and comorbidities. The development of acceptable and effective interventions could have a positive effect on medication adherence and health outcomes.

BACKGROUND: This is the first scoping review to focus broadly on the topics of machine learning and medication adherence.
OBJECTIVE: This review aims to categorize, summarize, and analyze literature focused on using machine learning for actions related to medication adherence.
METHODS: PubMed, Scopus, ACM Digital Library, IEEE, and Web of Science were searched to find works that meet the inclusion criteria. After full-text review, 43 works were included in the final analysis. Information of interest was systematically charted before inclusion in the final draft. Studies were placed into natural categories for additional analysis dependent upon the combination of actions related to medication adherence. The protocol for this scoping review was created using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines.
RESULTS: Publications focused on predicting medication adherence have uncovered 20 strong predictors that were significant in two or more studies. A total of 13 studies that predicted medication adherence used either self-reported questionnaires or pharmacy claims data to determine medication adherence status. In addition, 13 studies that predicted medication adherence did so using either logistic regression, artificial neural networks, random forest, or support vector machines. Of the 15 studies that predicted medication adherence, 6 reported predictor accuracy, the lowest of which was 77.6%. Of 13 monitoring systems, 12 determined medication administration using medication container sensors or sensors in consumer electronics, like smartwatches or smartphones. A total of 11 monitoring systems used logistic regression, artificial neural networks, support vector machines, or random forest algorithms to determine medication administration. The 4 systems that monitored inhaler administration reported a classification accuracy of 93.75% or higher. The 2 systems that monitored medication status in patients with Parkinson disease reported a classification accuracy of 78% or higher. A total of 3 studies monitored medication administration using only smartwatch sensors and reported a classification accuracy of 78.6% or higher. Two systems that provided context-aware medication reminders helped patients to achieve an adherence level of 92% or higher. Two conversational artificial intelligence reminder systems significantly improved adherence rates when compared against traditional reminder systems.
CONCLUSIONS: Creation of systems that accurately predict medication adherence across multiple data sets may be possible due to predictors remaining strong across multiple studies. Higher quality measures of adherence should be adopted when possible so that prediction algorithms are based on accurate information. Currently, medication adherence can be predicted with a good level of accuracy, potentially allowing for the development of interventions aimed at preventing nonadherence. Monitoring systems that track inhaler use currently classify inhaler-related actions with an excellent level of accuracy, allowing for tracking of adherence and potentially proper inhaler technique. Systems that monitor medication states in patients with Parkinson disease can currently achieve a good level of classification accuracy and have the potential to inform medication therapy changes in the future. Medication administration monitoring systems that only use motion sensors in smartwatches can currently achieve a good level of classification accuracy but only when differentiating between a small number of possible activities. Context-aware reminder systems can help patients achieve high levels of medication adherence but are also intrusive, which may not be acceptable to users. Conversational artificial intelligence reminder systems can significantly improve adherence.

Among animals defined as \"pests\", cockroaches and rodents (mouse and rat) represent the most common cause of airway allergic sensitization and bronchial asthma worldwide. Their frequency of sensitization has been widely assessed in US and other countries but poorly in Western Europe. This narrative review aims to provide a synthesis of data resulting in MEDLINE concerning allergic sensitization/asthma to pests as well as their related environmental/social risk factors, specifically in the European area.
We performed a literature research in MEDLINE for clinical trials, randomized controlled trials, systematic reviews and meta-analyses.
We selected studies to the following key words: allergic sensitization, allergic rhinitis, bronchial asthma, cockroach, hypersensitivity, integrated pest management, material hardship, medication compliance, mouse, pest, poverty, rat, rodents.
Current evidence indicates that residence in poor and urban areas, exposure to outdoor/indoor pollutants and tobacco smoke, poverty, material hardship, poor-quality housing, differences in health care quality, medication compliance, health care access contribute to increased pest-related allergic sensitization and asthma morbidity.
Further research should be done on many aspects of pest allergy such as a better characterization of allergens and epidemiological aspects. Relevant social actions should be carried out against poverty, healthcare disparities, psycho-social stress, poor compliance to therapy, with economic contributions to improve private and public living environments. Allergic sensitization to pests and pest-allergic respiratory diseases like asthma are \"paradoxical\" conditions, as they typically affect the poorest communities but can only be corrected by high-cost (diagnostic and preventive) interventions. We hope that progress can be made in this direction in the future.

To review the mobile apps in the Spanish market to improve adherence to medications and evaluate their characteristics and quality to identify high-quality applications.
A review was carried out following a stepwise procedure similar to a systematic review of the scientific literature. Apple Apps Store and Google Play Store mobile application digital distribution platforms. Applications aimed at supporting self-management of treatment, which generate reminders, in Spanish, updated in the last 2 years and free. We evaluate the applications according to a set of characteristics considered desirable and the quality with the Mobile App Rating Scale tool.
Out of 708 applications, 3 applications were selected. The Medisafe and Mytherapy applications had 89% and 78% of the desirable characteristics, respectively. Sergio Licea\'s application only had 56%. The highest global quality score was obtained by the MyTherapy application (3.79/5, IQR: 3-4), followed by Medisafe (3.72/5, (IQR: 3-4) and, finally, Sergio Licea (2.87/5, IQR: 2-4). The quality assessment coincides with the user assessment. There are many available applications, however, most did not meet the selection criteria.
A systematic stepwise process was able to identify the quality application to be tested in a future study that will provide evidence on the use of a multicomponent intervention to improve medication adherence.

Unintentional medication nonadherence is common and has been associated with poor health outcomes and increased health care costs. Earlier research demonstrated a relationship between habit strength and medication adherence. Previous research also examined a habit\'s direct effect on adherence and how habit interacts with more conscious factors to influence or overrule them. However, the relationship between habit and adherence and the role of habit-based mobile health (mHealth) interventions remain unclear.
This review aimed to systematically evaluate the most recent evidence for habit strength, medication adherence, and habit-based mHealth interventions across chronic medical conditions.
A keyword search with combinations of the terms habit, habit strength, habit index, medication adherence, and medication compliance was conducted on the PubMed database. After duplicates were removed, two authors conducted independent abstract and full-text screening. The guidelines for the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) were followed when reporting evidence across the included and reviewed studies.
Of the 687 records examined, 11 met the predefined inclusion criteria and were finalized for data extraction, grading, and synthesis. Most included studies (6/11, 55%) were cross-sectional and used a theoretical model (8/11, 73%). The majority of studies measured habit strength using the self-report habit index and self-report behavioral automaticity index (9/11, 82%). Habit strength was positively correlated with medication adherence in most studies (10/11, 91%). Habit mediated the effects of self-efficacy on medication adherence (1/11, 9%), and social norms moderated the effects of habit strength on medication adherence (1/11, 9%). Habit strength also moderated the effects of poor mental health symptoms and medication adherence (1/11, 9%). None of the included studies reported on using or proposing a habit-based mHealth behavioral intervention to promote medication adherence.
Habit strength was strongly correlated with medication adherence, and stronger habit was associated with higher medication adherence rates, regardless of the theoretical model and/or guiding framework. Habit-based interventions should be used to increase medication adherence, and these interventions could leverage widely available mobile technology tools such as mobile apps or text messaging, and existing routines.

There are significant disparities in medication adherence among underserved minority groups such as Latinos. Adherence to medication is a primary determinant of treatment success. Little is known about medication adherence among Latino children. This integrated review aims to describe what is known about medication adherence among Latino children and explore barriers and facilitators to medication adherence.
This review was guided by Whittemore and Knafl\'s method of integrative review and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement.
Of the 20 articles reviewed about medication adherence among Latino children, the analysis of these articles revealed four major themes: (1) low adherence, (2) low adherence associations, (3) child outcomes, and (4) effective interventions.
Health practitioners should consider medication adherence associations and interventions when collaborating with the family caregiver to improve child outcomes.

BACKGROUND: Improving real-world medication adherence to injectable antihyperglycemics in type 2 diabetes mellitus (T2DM) is a clinical challenge. Quantification of the level of adherence required to achieve a minimal clinically important difference (MCID) in glycemic control would assist in meeting this goal. The study objective was to review the literature regarding the relationships of medication adherence and persistence with health outcomes in adult T2DM patients using injectable antihyperglycemics.
METHODS: Systematic searches were conducted using electronic databases to identify publications over the last decade. Publications were screened against established eligibility criteria. Study data were extracted, evaluated, and used to identify strengths, limitations, and gaps in current evidence.
RESULTS: Eligibility criteria were met by 38 studies, and this report analyzed 34 studies related to glycemic control (n = 25), healthcare resource use (n = 9), and healthcare costs (n = 14). Eight of these studies examined adherence to glucagon-like peptide-1 receptor agonists (GLP-1 RA), including 1 study regarding adherence to GLP-1 RA or to insulin, and 1 study investigating a GLP-1 RA/insulin combination; the remaining studies involved insulin. Studies used a broad range of measures to classify adherence and persistence, and most measures were unable to reliably evaluate the complexities of patient behavior over time. Better adherence to injectable antihyperglycemic medications was generally found to be associated with improved glycemic control, although no studies attempted to identify a MCID. Although higher diabetes-related pharmacy and total healthcare costs were reported for adherent or persistent patients, these patients tended to have lower diabetes-related and all-cause medical costs.
CONCLUSIONS: Results of this review confirmed the effectiveness of injectable antihyperglycemic medications for glycemic control, suggesting that there are clinical and financial consequences to nonadherence. Although attempts were made to quantify the effects of nonadherence, the interpretation of study results was limited by the lack of a MCID and inadequate study design.
BACKGROUND: Novo Nordisk, Inc., Plainsboro Township, NJ, USA. Plain language summary available for this article.

BACKGROUND: Medication adherence is critical to the effectiveness of psychopharmacologic therapy. Psychiatric disorders present special adherence considerations, notably an altered capacity for decision making and the increased street value of controlled substances. A wide range of interventions designed to improve adherence in mental health and substance use disorders have been studied; recently, many have incorporated information technology (eg, mobile phone apps, electronic pill dispensers, and telehealth). Many intervention components have been studied across different disorders. Furthermore, many interventions incorporate multiple components, making it difficult to evaluate the effect of individual components in isolation.
OBJECTIVE: The aim of this study was to conduct a systematic scoping review to develop a literature-driven, transdiagnostic taxonomic framework of technology-based medication adherence intervention and measurement components used in mental health and substance use disorders.
METHODS: This review was conducted based on a published protocol (PROSPERO: CRD42018067902) in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses systematic review guidelines. We searched 7 electronic databases: MEDLINE, EMBASE, PsycINFO, the Cochrane Central Register of Controlled Trials, Web of Science, Engineering Village, and ClinicalTrials.gov from January 2000 to September 2018. Overall, 2 reviewers independently conducted title and abstract screens, full-text screens, and data extraction. We included all studies that evaluate populations or individuals with a mental health or substance use disorder and contain at least 1 technology-delivered component (eg, website, mobile phone app, biosensor, or algorithm) designed to improve medication adherence or the measurement thereof. Given the wide variety of studied interventions, populations, and outcomes, we did not conduct a risk of bias assessment or quantitative meta-analysis. We developed a taxonomic framework for intervention classification and applied it to multicomponent interventions across mental health disorders.
RESULTS: The initial search identified 21,749 results; after screening, 127 included studies remained (Cohen kappa: 0.8, 95% CI 0.72-0.87). Major intervention component categories include reminders, support messages, social support engagement, care team contact capabilities, data feedback, psychoeducation, adherence-based psychotherapy, remote care delivery, secure medication storage, and contingency management. Adherence measurement components include self-reports, remote direct visualization, fully automated computer vision algorithms, biosensors, smart pill bottles, ingestible sensors, pill counts, and utilization measures. Intervention modalities include short messaging service, mobile phone apps, websites, and interactive voice response. We provide graphical representations of intervention component categories and an element-wise breakdown of multicomponent interventions.
CONCLUSIONS: Many technology-based medication adherence and monitoring interventions have been studied across psychiatric disease contexts. Interventions that are useful in one psychiatric disorder may be useful in other disorders, and further research is necessary to elucidate the specific effects of individual intervention components. Our framework is directly developed from the substance use disorder and mental health treatment literature and allows for transdiagnostic comparisons and an organized conceptual mapping of interventions.

The mental and physical health of individuals with a psychotic illness are typically poor. Access to psychosocial interventions is important but currently limited. Telephone-delivered interventions may assist. In the current systematic review, we aim to summarise and critically analyse evidence for telephone-delivered psychosocial interventions targeting key health priorities in adults with a psychotic disorder, including (i) relapse, (ii) adherence to psychiatric medication and/or (iii) modifiable cardiovascular disease risk behaviours.
Ten peer-reviewed and four grey literature databases were searched for English-language studies examining psychosocial telephone-delivered interventions targeting relapse, medication adherence and/or health behaviours in adults with a psychotic disorder. Study heterogeneity precluded meta-analyses.
Twenty trials [13 randomised controlled trials (RCTs)] were included, involving 2473 participants (relapse prevention = 867; medication adherence = 1273; and health behaviour = 333). Five of eight RCTs targeting relapse prevention and one of three targeting medication adherence reported at least 50% of outcomes in favour of the telephone-delivered intervention. The two health-behaviour RCTs found comparable levels of improvement across treatment conditions.
Although most interventions combined telephone and face-to-face delivery, there was evidence to support the benefit of entirely telephone-delivered interventions. Telephone interventions represent a potentially feasible and effective option for improving key health priorities among people with psychotic disorders. Further methodologically rigorous evaluations are warranted.

Background Medication non-adherence is a major issue after transplant that can lead to misdiagnosis, rejection, poor health affecting quality of life, graft loss or death. Several estimations of adherence and related factors have previously been described but conclusions leave doubt as to the most accurate assessment method. Aim of the review To identify the factors most relevant to medication non-adherence in kidney transplant in current clinical practice. Method This systematic review is registered in the PROSPERO data base and follows the Prisma checklist. Articles in English in three databases from January 2009 to December 2014 were analysed. A synthesis was made to target adherence assessment methods, their prevalence and significance. Results Thirty-seven studies were analysed rates of non-adherence fluctuating from 1.6 to 96%. Assessment methods varied from one study to another, although self-reports were mainly used. It appears that youth (≤50 years old), male, low social support, unemployment, low education, ≥3 months post graft, living donor, ≥6 comorbidities, ≥5 drugs/d, ≥2 intakes/d, negative beliefs, negative behavior, depression and anxiety were the factors significantly related to non-adherence. Conclusion As there are no established guidelines, consideration should be given to more than one approach to identify medication non-adherence although self-reports should remain the cornerstone of adherence assessment.