Interventions to improve medication adherence in inflammatory bowel disease: A systematic review

Natural approaches for the management of ulcerative colitis: evidence of preclinical and clinical investigations

Ulcerative colitis (UC) is a recurring autoimmune disorder characterized by persistent inflammation in the mucosal lining of the lower part of the large intestine. Conventional treatment options such as salicylates, corticosteroids, and immunosuppressants often come with severe side effects, limited bioavailability, and the development of drug resistance, which hampers their therapeutic effectiveness. Therefore, it is imperative to explore natural strategies as safe and alternative treatments for UC. Currently, around 40% of UC patients find relief through natural constituents, which can help reduce toxic side effects and maintain clinical remission. This review aims to provide a summary of both preclinical and clinical evidence supporting the efficacy of various natural substances in the prophylaxis of UC. These natural options include plant extracts, essential oils, nutraceuticals, and phytochemicals. Furthermore, we will delve into the potential mechanisms that underlie the protective and curative actions of these novel herbal agents. In summary, this review will explore the effectiveness of natural remedies for UC, shedding light on their preclinical and clinical findings and the mechanisms behind their therapeutic actions. These alternatives offer hope for improved treatment outcomes and reduced side effects for individuals suffering from this challenging autoimmune condition.

Engagement of medication users in the development and implementation of digital medication adherence technologies: a multi-stakeholder study

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.

Applying Machine Learning Models Derived From Administrative Claims Data to Predict Medication Nonadherence in Patients Self-Administering Biologic Medications for Inflammatory Bowel Disease

Background

Adherence to self-administered biologic therapies is important to induce remission and prevent adverse clinical outcomes in Inflammatory bowel disease (IBD). This study aimed to use administrative claims data and machine learning methods to predict nonadherence in an academic medical center test population.

Methods

A model-training dataset of beneficiaries with IBD and the first unique dispense of a self-administered biologic between June 30, 2016 and June 30, 2019 was extracted from the Commercial Claims and Encounters and Medicare Supplemental Administrative Claims Database. Known correlates of medication nonadherence were identified in the dataset. Nonadherence to biologic therapies was defined as a proportion of days covered ratio <80% at 1 year. A similar dataset was obtained from a tertiary academic medical center's electronic medical record data for use in model testing. A total of 48 machine learning models were trained and assessed utilizing the area under the receiver operating characteristic curve as the primary measure of predictive validity.

Results

The training dataset included 6998 beneficiaries (n = 2680 nonadherent, 38.3%) while the testing dataset included 285 patients (n = 134 nonadherent, 47.0%). When applied to test data, the highest performing models had an area under the receiver operating characteristic curve of 0.55, indicating poor predictive performance. The majority of models trained had low sensitivity and high specificity.

Conclusions

Administrative claims-trained models were unable to predict biologic medication nonadherence in patients with IBD. Future research may benefit from datasets with enriched demographic and clinical data in training predictive models.

The impact of non-pharmacological interventions on adherence to medication and persistence in dyslipidaemia and hypertension: a systematic review

INTRODUCTION

Suboptimal medication adherence is common among patients with cardiovascular diseases. We sought evidence on non-pharmacological interventions used to support adherence for patients with hypertension and/or dyslipidemia.

METHODS

We searched MEDLINE, EMBASE, MEDLINE In-Process, ClinicalTrials.gov, EUCTR, and conference proceedings from July 2011 to July 2021 to identify trials evaluating effects of health education, phone reminders, or digital interventions on medication adherence or persistence of adult patients with hypertension and/or dyslipidemia. Risk of bias was assessed using the Cochrane Risk of Bias Assessment Tool v2.

RESULTS

Of 64 studies, 62 used health education approaches (e.g. educational interviews, motivational meetings, advice from physicians, and mobile health content), 16 phone reminders (e.g. text reminders, electronic pill-box linked reminders, bi-directional text messaging), and 10 digital applications as interventions (e.g., various self-management applications). All studies assessed medication adherence; only two persistence. Overall, 30 studies (83%) assessing health education approaches alone and 25 (78%) combined with other strategies, 12 (75%) phone reminders and eight studies (80%) digital applications combined with other strategies reported improved medication adherence. Two studies assessing health education approaches reported improved persistence.

CONCLUSIONS

Our findings indicate non-pharmacological interventions may positively impact adherence. Therefore, 'beyond the pill' approaches could play a role in preventing cardiovascular diseases.

Systematic Review of Outcomes for Assessment of Medication Adherence Enhancing Interventions: An ISPOR Special Interest Group Report

OBJECTIVES

The lack of universal guidance on outcome measures for evaluating medication adherence enhancing interventions (MAEIs) poses a challenge for assessing their effectiveness. This literature review aimed to provide a systematic overview of outcome measures currently used for the value assessment of MAEIs.

METHODS

We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and searched MEDLINE, PsycINFO, Scopus, CINAHL, and Academic Search Complete for randomized and nonrandomized clinical trials, prospective cohort studies, model-based economic evaluations, and value frameworks published in English between January 2010 and September 2020. Two independent reviewers screened all titles and abstracts, followed by a full-text review. Due to the large number of relevant studies, data extraction was limited to articles published between January 2018 and September 2020. We collected data on the general characteristics of the study, the type of intervention, and the outcomes measured.

RESULTS

We screened 14 685 records and identified 308 articles for data extraction. Behavioral interventions were the most common (n = 143), followed by educational interventions (n = 110) and mixed-method interventions (n = 73). Outcomes were clustered into 7 categories with medication adherence (n = 286) being the most frequently measured, followed by clinical outcomes (n = 155), health-related quality of life (n = 57), resource use (n = 43), patient satisfaction (n = 31), economic outcomes (n = 18), and other outcomes (n = 76).

CONCLUSIONS

Various outcomes measures have been used to evaluate MAEIs, with only a small number of studies exploring economic and patient-reported outcomes. Future research is warranted to develop a consensus-based set of criteria for assessing MAEIs to facilitate the comparison of interventions and enable informed decision making.

Use of Behavior Change Techniques and Quality of Commercially Available Inflammatory Bowel Disease Apps

BACKGROUND

Inclusion of evidence-based behavior change techniques (e.g., self-monitoring) in mobile health apps has the potential to promote adherence to inflammatory bowel disease treatment. While inflammatory bowel disease management apps exist, the extent to which they incorporate behavior change techniques remains unknown.

AIMS

The present study systematically evaluated the content and quality of free, commercially available inflammatory bowel disease management apps.

METHODS

Apps were identified using a systematic search of the Apple App and Google Play stores. Apps were evaluated using Abraham and Michie's taxonomy of 26 behavior change techniques. A literature search was conducted to identify behavior change techniques specific and relevant for people with inflammatory bowel disease. App quality was assessed using the Mobile App Rating Scale with scores ranging from 1 (Inadequate) to 5 (Excellent).

RESULTS

A total of 51 inflammatory bowel disease management apps were evaluated. Apps included 0-16 behavior change techniques (Mean = 4.55) and 0-10 inflammatory bowel disease management behavior change techniques (Mean = 3.43). App quality ranged from 2.03 to 4.62 (Mean = 3.39) out of 5.00. Two apps, My IBD Care: Crohn's & Colitis and MyGiHealth GI Symptom Tracker, included the highest number of overall and inflammatory bowel disease management behavior change techniques along with high-quality scores. Bezzy IBD was the only app with a high number of overall and inflammatory bowel disease management behavior change techniques with a primary focus on social support/change.

CONCLUSION

Most inflammatory bowel disease management apps reviewed included evidence-based inflammatory bowel disease management behavior change techniques.

Reimbursed Medication Adherence Enhancing Interventions in European Countries: Results of the EUREcA Study

Introduction: Current literature lacks detailed understanding of the reimbursement framework of medication adherence enhancing interventions (MAEIs). As part of the ENABLE COST Action, the EUREcA (“EUropen REimbursement strategies for interventions targeting medication Adherence”) study aimed to provide an in-depth overview of reimbursed MAEIs currently available in European countries at national and regional levels and to pave the way for further MAEIs to be implemented in the future.

Methods: A web-based, cross-sectional survey was performed across 38 European countries and Israel. The survey questionnaire was developed as a result of an iterative process of discussion informed by a desk review. The survey was performed among invited ENABLE collaborators from June to July 2021. Besides descriptive analysis, association between country income and health care expenditure, and the availability of reimbursed MAEIs were also assessed.

Results: The survey identified 13 reimbursed MAEIs in nine countries: multi-dose drug dispensing (n = 5), medication review (n = 4), smart device (n = 2), mobile application (n = 1), and patient education (n = 1). The median GDP per capita of countries having ≥1 reimbursed MAEI was significantly higher compared to countries having no reimbursed adherence intervention (33,888 EUR vs 16,620 EUR, respectively; p = 0.05).

Conclusions: Our findings highlight that to date only a small number of MAEIs have been reimbursed in European countries. Comprehensive health technology assessment recommendations and multi-stakeholder collaboration could help removing barriers related to the implementation and reimbursement of MAEIs.