The Need to Develop Standard Measures of Patient Adherence for Big Data: Viewpoint

Botulinum Toxin Type A (BoNT-A) Use for Post-Stroke Spasticity: A Multicenter Study Using Natural Language Processing and Machine Learning

We conducted a multicenter and retrospective study to describe the use of botulinum toxin type A (BoNT-A) to treat post-stroke spasticity (PSS). Data were extracted from free-text in electronic health records (EHRs) in five Spanish hospitals. We included adults diagnosed with PSS between January 2015 and December 2019, stratified into BoNT-A-treated and untreated groups. We used EHRead® technology, which incorporates natural language processing and machine learning, as well as SNOMED CT terminology. We analyzed demographic data, stroke characteristics, BoNT-A use patterns, and other treatments. We reviewed the EHRs of 1,233,929 patients and identified 2190 people with PSS with a median age of 69 years; in total, 52.1% were men, 70.7% had cardiovascular risk factors, and 63.2% had suffered an ischemic stroke. Among the PSS patients, 25.5% received BoNT-A at least once. The median time from stroke to spasticity onset was 205 days, and the time from stroke to the first BoNT-A injection was 364 days. The primary goal of BoNT-A treatment was pain control. Among the study cohort, rehabilitation was the most common non-pharmacological treatment (95.5%). Only 3.3% had recorded monitoring scales. In conclusion, a quarter of patients with PSS received BoNT-A mainly for pain relief, typically one year after the stroke. Early treatment, disease monitoring, and better data documentation in EHRs are crucial to improve PSS patients' care.

Healthcare professionals' perspectives towards the digitalisation of paediatric growth hormone therapies: expert panels in Italy and Korea

Introduction

To analyse the perspectives of healthcare professionals (HCPs) regarding the acceptance of digital health solutions for growth hormone (GH) deficiency care. This study identified factors impacting HCPs' intent to use and recommend digital solutions supporting recombinant-human growth hormone (r-hGH) therapy in Italy and Korea with a use case of connected drug delivery system (Aluetta® with Smartdot™) integrated in a platform for GH treatment support (the Growzen™ digital health ecosystem).

Methods

Participatory workshops were conducted in Rome, Italy, and Seoul, Korea, to collect the perspectives of 22 HCPs on various predefined topics. HCPs were divided into two teams, each moderated by a facilitator. The workshops progressed in five phases: introduction of the project and experts, capturing views on the current context of digitalisation, perceived usefulness and ease of use of Aluetta® with Smartdot™, exploration of the perception of health technology evolution, and combined team recommendations. Data shared by HCPs on technology acceptance were independently analysed using thematic analysis, and relevant findings were shared and validated with experts.

Results

HCPs from both Italy and Korea perceived Aluetta® with Smartdot™ and the Growzen™ based digital health ecosystem as user-friendly, intuitive, and easy-to-use solutions. These solutions can result in increased adherence, a cost-effective healthcare system, and medication self-management. Although technology adoption and readiness may vary across countries, it was agreed that using digital solutions tailored to the needs of users may help in data-driven clinical decisions and strengthen HCP-patient relationships.

Conclusion

HCPs' perspectives on the digitalisation in paediatric GH therapies suggested that digital solutions enable automatic, real-time injection data transmission to support adherence monitoring and evidence-based therapy, strengthen HCP-patient relationships, and empower patients throughout the GH treatment process.

Evaluation of adherence to antipsychotics: A real-world data study using four different dosing assumptions

AIMS

This study aimed to assess the frequency of dosing inconsistencies in prescription data and the effect of four dosing assumption strategies on adherence estimates for antipsychotic treatment.

METHODS

A retrospective cohort, which linked prescription and dispensing data of adult patients with ≥1 antipsychotic prescription between 2015-2016 and followed up until 2019, in Catalonia (Spain). Four strategies were proposed for selecting the recommended dosing in overlapping prescription periods for the same patient and antipsychotic drug: (i) the minimum dosing prescribed; (ii) the dose corresponding to the latest prescription issued; (iii) the highest dosing prescribed; and (iv) all doses included in the overlapped period. For each strategy, one treatment episode per patient was selected, and the Continuous Medication Availability measure was used to assess adherence. Descriptive statistics were used to describe results by strategy.

RESULTS

Of the 277 324 prescriptions included, 76% overlapped with other prescriptions (40% with different recommended dosing instructions). The number and characteristics of patients and treatment episodes (18 292, 18 303, 18 339 and 18 536, respectively per strategy) were similar across strategies. Mean adherence was similar between strategies, ranging from 57 to 60%. However, the proportion of patients with adherence ≥90% was lower when selecting all doses (28%) compared with the other strategies (35%).

CONCLUSION

Despite the high prevalence of overlapping prescriptions, the strategies proposed did not show a major effect on the adherence estimates for antipsychotic treatment. Taking into consideration the particularities of antipsychotic prescription practices, selecting the highest dose in the overlapped period seemed to provide a more accurate adherence estimate.

From non-adherence to adherence: Can innovative solutions resolve a longstanding problem?

Non-adherence to medication is a longstanding problem, profoundly affecting patient outcomes and sustainability of healthcare systems. Historically, non-adherence has been observed since the time of Hippocrates, however, when potent drugs became available in the midst of the 20th century, it became a pivotal concern. Despite numerous studies and interventions designed, medication adherence rates remain suboptimal, currently reaching about 50 % still, as described in WHO report two decades ago. What is worse, many healthcare professionals feel neither responsible nor able to change it. Enhancing adherence requires in-depth understanding of the concept, as many intuitive approaches fail to work. For example, contrary to expectations, patient education alone proves insufficient when addressing this issue. Both behavioural models and recent experience reflecting low acceptance of COVID-19 vaccinations strongly suggest that patients' decisions are driven by emotions, which often results in intentional non-adherence. Several technical innovations, such as smart inhalers and electronic pill dispensers, offer potential solutions. However, their effectiveness varies, and standardized certification procedures are lacking. Altogether, technical solutions do not eliminate the problem entirely. To move forward, social and health system innovation is equally needed. Multiple stakeholders could benefit from improved adherence, therefore their greater involvement is advisable to create an adherence-supporting environment. In conclusion, despite available evidence-based interventions, non-adherence remains a complex challenge. Technical and social innovations, combined with a shift in policy priorities, could lead to improved medication adherence and better patient outcomes. The global tide of non-communicable chronic conditions, and aging of societies urges us to take this problem seriously.

Strategies for Improving Nurses' Performance Regarding Cardiovascular Patient's Adherence to Treatment Regimen: Content Analysis

Background

Patient adherence to the treatment regimen is an important goal of treatment; finding appropriate strategies to improve treatment adherence is a nursing challenge. This study aimed to explore strategies to improve nurses' performance to enhance cardiovascular patients' adherence to treatment regimens.

Materials and Methods

This is a qualitative content analysis study that was performed in an educational hospital of the Urmia University of Medical Science. Data were collected through semi-structured individual and group interviews. Sixteen individual interviews were conducted with nurses, patients, and physicians. A focus group was conducted with the presence of six nurses and nursing managers. Data were analyzed by conventional content analysis using MAXQDA 2020.

Results

After analyzing the interviews, five strategies were explored; these strategies include: follow-up of the patient after discharge (systemic and organizational follow-up, educational follow-up, counseling follow-up, and motivational follow-up), sending reminders for necessary cases to the patients (risk reminders, care reminders); improving patient education methods (modern and up-to-date education, conventional and routine education), improving support services (home care services, social support, Psychological support), and optimizing the structure and processes of the hospital (making structural changes, making process changes).

Conclusions

Much needs to be done to improve patient's adherence to treatment. Nurses are at the forefront of this work. In this study, we introduced ways to improve nurses' performance in order to increase patient adherence.

A novel method for continuous measurements of clinical practice guideline adherence

Introduction

Clinical practice guidelines (hereafter 'guidelines') are crucial in providing evidence-based recommendations for physicians and multidisciplinary teams to make informed decisions regarding diagnostics and treatment in various diseases, including cancer. While guideline implementation has been shown to reduce (unwanted) variability and improve outcome of care, monitoring of adherence to guidelines remains challenging. Real-world data collected from cancer registries can provide a continuous source for monitoring adherence levels. In this work, we describe a novel structured approach to guideline evaluation using real-world data that enables continuous monitoring. This method was applied to endometrial cancer patients in the Netherlands and implemented through a prototype web-based dashboard that enables interactive usage and supports various analyses.

Method

The guideline under study was parsed into clinical decision trees (CDTs) and an information standard was drawn up. A dataset from the Netherlands Cancer Registry (NCR) was used and data items from both instruments were mapped. By comparing guideline recommendations with real-world data an adherence classification was determined. The developed prototype can be used to identify and prioritize potential topics for guideline updates.

Results

CDTs revealed 68 data items for recording in an information standard. Thirty-two data items from the NCR were mapped onto information standard data items. Four CDTs could sufficiently be populated with NCR data.

Conclusion

The developed methodology can evaluate a guideline to identify potential improvements in recommendations and the success of the implementation strategy. In addition, it is able to identify patient and disease characteristics that influence decision-making in clinical practice. The method supports a cyclical process of developing, implementing and evaluating guidelines and can be scaled to other diseases and settings. It contributes to a learning healthcare cycle that integrates real-world data with external knowledge.

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

BACKGROUND

Increasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data-rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery.

METHODS

We conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications.

RESULTS

Our study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision-making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality.

CONCLUSION

Advanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.

Spanish translation of the ABC taxonomy for medication adherence

BACKGROUND

The Ascertaining Barriers to Compliance (ABC) taxonomy was developed aiming at systematizing definitions and operationalizations of medication adherence. Its translation is crucial to improve the generalizability, application and comparison of study findings.

OBJECTIVE

To provide a consensus translation of the ABC taxonomy from English to Spanish.

METHODS

A two-phased approach was used, according to the Preferred Methods for the Translation of the ABC Taxonomy for Medication Adherence. Two literature reviews were conducted: to identify Spanish synonyms and definitions of the ABC taxonomy, and to identify a panel of Spanish-speaking experts in medication adherence. A Delphi survey was designed based on the synonyms and definitions found. The experts previously identified were invited to participate in the Delphi. A consensus of ≥85% was established for the first round. A moderate consensus (50-75%), a consensus (75-95%) or a strong consensus (>95%) were considered to be necessary in the second round.

RESULTS

Forty potential synonyms of the ABC taxonomy terms were identified from a total of 270 papers. The response rate during the first Delphi round was 32% (63/197) and in the second round 86% (54/63). A strong consensus was reached for the term "inicio del tratamiento" (96%) and consensus for the term "implementación" (83%). A moderate consensus was obtained for "adherencia a la medicación" (70%), "interrupción del tratamiento" (52%), "manejo de la adherencia" (54%) and "disciplinas relacionadas con la adherencia" (74%). No consensus was reached for the term persistence. Five out of the seven definitions reached a consensus in the first round, and two definitions a moderate consensus after the second round.

CONCLUSION

The adoption of the Spanish taxonomy will increase transparency, comparability and transferability of results in the field of medication adherence. This may facilitate benchmarking of adherence strategies between Spanish-speaking researchers and practitioners, and other language speakers.

Exploring the Multidimensional Relationship Between Medication Beliefs and Adherence to Medications Among Older Adults Living With Multimorbidity Using Polynomial Regression: An Observational Cohort Study

BACKGROUND

People living with multimorbidity may hold complex beliefs about medicines, potentially influencing adherence. Polynomial regression offers a novel approach to examining the multidimensional relationship between medication beliefs and adherence, overcoming limitations associated with difference scores.

PURPOSE

To explore the multidimensional relationship between medication beliefs and adherence among people living with multimorbidity.

METHODS

Secondary analysis was conducted using observational data from a cohort of older adults living with ≥2 chronic conditions, recruited from 15 family practices in Ireland in 2010 (n = 812) and followed up in 2012 (n = 515). Medication beliefs were measured with the Beliefs about Medicines Questionnaire-Specific. Adherence was assessed with the medication possession ratio using prescription data from the national primary care reimbursement service. Polynomial regression was used to explore the best-fitting multidimensional models for the relationship between (i) beliefs and adherence at baseline, and (ii) beliefs at baseline and adherence at follow-up.

RESULTS

Confirmatory polynomial regression rejected the difference-score model, and exploratory polynomial regression indicated quadratic models for both analyses. Reciprocal effects were present in both analyses (slope [Analysis 1]: β = 0.08, p = .007; slope [Analysis 2]: β = 0.07, p = .044), indicating that adherence was higher when necessity beliefs were high and concern beliefs were low. Nonreciprocal effects were also present in both analyses (slope [Analysis 1]: β = 0.05, p = .006; slope [Analysis 2]: β = 0.04, p = .043), indicating that adherence was higher when both necessity and concern beliefs were high.

CONCLUSIONS

Among people living with multimorbidity, there is evidence that the relationship between medication beliefs and adherence is multidimensional. Attempts to support adherence should consider the combined role of necessity and concern beliefs.

Medication Adherence and Healthcare Costs in Chronically Ill Patients Using German Claims Data

BACKGROUND AND OBJECTIVE

Despite the importance of medication adherence for chronically ill patients and the vast literature on its relationship to costs, this field suffers from methodological limitations. These are caused, amongst others, by the lack of generalizability of data sources, varying definitions of adherence, costs, and model specification. We aim to address this with different modeling approaches and to contribute evidence on the research question.

METHODS

We extracted large cohorts of nine chronic diseases (n = 6747-402,898) from German claims data of stationary health insurances between 2012 and 2015 (t0-t3). Defined as the proportion of days covered by medication, we examined the relationship of adherence using several multiple regression models at baseline year t0 with annual total healthcare costs and four sub-categories. Models with concurrent, and differently time-lagged measurements of adherence and costs were compared. Exploratively, we applied non-linear models.

RESULTS

Overall, we found a positive association between the proportion of days covered by medication and total costs, a weak association with outpatient costs, positive with pharmacy costs, and frequently negative with inpatient costs. There were major differences by disease and its severity but little between years, provided adherence and costs were not measured concurrently. The fit of linear models was mainly not inferior to that of non-linear models.

CONCLUSIONS

The estimated effect on total costs differed from most other studies, which highlights concerns about generalizability, although effect estimates in sub-categories were as expected. Comparison of time lags indicates the importance of avoiding concurrent measurement. A non-linear relationship should be considered. These methodological approaches are valuable in future research on adherence and its consequences.

Adherence to endocrine therapy in early breast cancer in relation to Cytochrome P450 2D6 genotype: a comparison between pharmacy dispensation data and medical records

PURPOSE

Suboptimal adherence to adjuvant endocrine treatment (AET) is an important clinical concern. A correlation between CYP2D6 activity and tamoxifen discontinuation has been described. The main aim of this study was to investigate the consistency between pharmacy dispensation data and medical records on adherence to AET.

METHODS

Adherence was calculated for patients with at least 4.5 years of follow up and was defined as Medical Possession Rate ≥ 80%. Subgroup analyses were performed based on menopausal status, recurrence risk and CYP2D6 activity.

RESULTS

In 86% of the 1235 included patients the consistency between the two sources of information was within 80-125%. Poor consistency, < 80%, was most frequent in the premenopausal/ high-risk group and CYP2D6 Poor Metabolizers (PMs). Among 899 patients with at least 4.5 years follow up, 72% were adherent to tamoxifen based on pharmacy dispensation data, compared with 77% as reported by medical records. When including patients who switched to aromatase inhibitors after tamoxifen, adherence increased to 82% and 88%, respectively. Adherence did not differ by menopausal status or risk for recurrence. CYP2D6 PMs had poorer adherence (54%) to tamoxifen compared to patients with the highest CYP2D6 activity (83%).

CONCLUSIONS

There was a good consistency between medical records and pharmacy dispensing data on the use of AET. Adherence to AET was adequate, especially when including switch to aromatase inhibitors. Surprisingly, CYP2D6 PMs had low adherence to tamoxifen, despite a likely reduced risk of side effects according to previous data.

Half a Century of Fragmented Research on Deviations from Advised Therapies: Is This a Good Time to Call for Multidisciplinary Medication Adherence Research Centres of Excellence?

Medication adherence is a key precondition of the effectiveness of evidence-based therapies. However, in real-life settings, non-adherence to medication is still very common. This leads to profound health and economic consequences at both individual and public health levels. The problem of non-adherence has been extensively studied in the last 50 years. Unfortunately, with more than 130,000 scientific papers published on that subject so far, we are still far from finding an ultimate solution. This is, at least partly, due to fragmented and poor-quality research that has been conducted in this field sometimes. To overcome this deadlock, there is a need to stimulate the adoption of best practices in medication adherence-related research in a systematic way. Therefore, herein we propose the establishment of dedicated medication adherence research Centres of Excellence (CoEs). These Centres could not only conduct research but could also create a profound societal impact, directly serving the needs of patients, healthcare providers, systems and economies. Additionally, they could play a role as local advocates for good practices and education. In this paper, we propose some practical steps that might be taken in order to establish such CoEs. We describe two success stories, i.e., Dutch and Polish Medication Adherence Research CoEs. The COST Action "European Network to Advance Best practices & technoLogy on medication adherencE" (ENABLE) aims to develop a detailed definition of the Medication Adherence Research CoE in the form of a list of minimal requirements regarding their objectives, structure and activities. We hope that it will help to create a critical mass and catalyse the setup of regional and national Medication Adherence Research CoEs in the near future. This, in turn, may not only increase the quality of the research but also raise the awareness of non-adherence and promote the adoption of the best medication adherence-enhancing interventions.

Validation Parameters of Patient-Generated Data for Digitally Recorded Allergic Rhinitis Symptom and Medication Scores in the @IT.2020 Project: Exploratory Study

Background

Mobile health technologies enable allergists to monitor disease trends by collecting daily patient-reported outcomes of allergic rhinitis. To this end, patients with allergies are usually required to enter their symptoms and medication repetitively over long time periods, which may present a risk to data completeness and quality in the case of insufficient effort reporting. Completeness of patient’s recording is easily measured. In contrast, the intrinsic quality and accuracy of the data entered by the patients are more elusive.

Objective

The aim of this study was to explore the association of adherence to digital symptom recording with a predefined set of parameters of the patient-generated symptom and medication scores and to identify parameters that may serve as proxy measure of the quality and reliability of the information recorded by the patient.

Methods

The @IT.2020 project investigates the diagnostic synergy of mobile health and molecular allergology in patients with seasonal allergic rhinitis. In its pilot phase, 101 children with seasonal allergic rhinitis were recruited in Rome and instructed to record their symptoms, medication intake, and general conditions daily via a mobile app (AllergyMonitor) during the relevant pollen season. We measured adherence to daily recording as the percentage of days with data recording in the observation period. We examined the patient’s trajectories of 3 disease indices (Rhinoconjunctivitis Total Symptom Score [RTSS], Combined Symptom and Medication Score [CSMS], and Visual Analogue Scale [VAS]) as putative proxies of data quality with the following 4 parameters: (1) intravariation index, (2) percentage of zero values, (3) coefficient of variation, and (4) percentage of changes in trend. Lastly, we examined the relationship between adherence to recording and each of the 4 proxy measures.

Results

Adherence to recording ranged from 20% (11/56) to 100% (56/56), with 64.4% (65/101) and 35.6% (36/101) of the patients’ values above (highly adherent patients) or below (low adherent patients) the threshold of 80%, respectively. The percentage of zero values, the coefficient of variation, and the intravariation index did not significantly change with the adherence to recording. By contrast, the proportion of changes in trend was significantly higher among highly adherent patients, independently from the analyzed score (RTSS, CSMS, and VAS).

Conclusions

The percentage of changes in the trend of RTSS, CSMS, and VAS is a valuable candidate to validate the quality and accuracy of the data recorded by patients with allergic rhinitis during the pollen season. The performance of this parameter must be further investigated in real-life conditions before it can be recommended for routine use in apps and electronic diaries devoted to the management of patients with allergic rhinitis.

Prior Cardiovascular Treatments-A Key Characteristic in Determining Medication Adherence After an Acute Myocardial Infarction

Objective: To investigate long-term adherence to guideline-recommended cardioprotective medications following hospitalization for an acute myocardial infarction (AMI), and identify characteristics associated with adherence.

Methods: An Australian population-based cohort study was used to identify participants who had their first AMI between 2006 and 2014 and were alive after 12 months. Linked routinely collected hospital, and prescription medication claims data was used to study adherence over time. Predictors and rates of adherence to both lipid-lowering medication and renin-angiotensin system blockade at 12 months post-AMI was assessed.

Results: 14,200 people (mean age 69.9 years, 38.7% female) were included in our analysis. At 12 months post-AMI, 29.5% (95% CI: 28.8–30.3%) of people were adherent to both classes of medication. Individuals receiving treatment with both lipid-lowering medication and renin-angiotensin system blockade during the 6 months prior to their AMI were over 9 times more likely to be adherent to both medications at 12 months post-AMI (66.2% 95% CI: 64.8–67.5%) compared to those with no prior medication use (treatment naïve) (7.1%, 95% CI: 6.4–7.9%). Prior cardiovascular treatment was the strongest predictor of long-term adherence even after adjusting for age, sex, education and income.

Conclusions: Despite efforts to improve long-term medication adherence in patients who have experienced an acute coronary event, considerable gaps remain. Of particular concern are people who are commencing guideline-recommended cardioprotective medication at the time of their AMI. The relationship between prior cardiovascular treatments and post AMI adherence offers insight into the support needs for the patient. Health care intervention strategies, strengthened by enabling policies, are needed to provide support to patients through the initial months following their AMI.

Integrating Pharmacy and Registry Data Strengthens Clinical Assessments of Patient Adherence

Background: Accurate clinical assessment of patient adherence using reliable and valid measures is essential in establishing the presence of adherence issues and support practices for pharmacists.

Objective: This investigation aims to conduct a novel assessment of patient adherence to asthma controller therapy by combining 1) patient-specific dosage data found in pharmacy dispensing data with 2) centrally collected administrative claims records, to determine the added value of using both sources of data.

Methods: A total of 381 clinically uncontrolled asthma patients, from 95 community pharmacies across three Australian States were recruited and provided consent for the retrieval of their claims records and pharmacy dispensing data. Patients were stratified as multiple or single pharmacy users and adherence scores were calculated via the proportion of days covered (PDC) method using 1) patient claims records, 2) patient pharmacy dispensing data, and 3) combined claims records and pharmacy dispensing data. Cohort and subgroup adherence estimates were then compared.

Results: Low levels of adherence were evident amongst the cohort irrespective of the data source used. PDC estimates based on claims records alone or combined claims records and pharmacy dispensing data were significantly higher than estimates based on pharmacy dispensing data for the total cohort (56%, 52%, 42% respectively, p &lt; 0.001) and more noticeably for multiple pharmacy users (67%, 64%, 35% respectively, p &lt; 0.001). PDC estimates based on combined claims records and pharmacy dispensing data were significantly lower than estimates based on claims records alone, indicating that perhaps standard daily dose is not a robust proxy for prescribed dosage to inhaled respiratory devices in adherence approximations. Poorer adherence was found amongst single pharmacy users than multiple pharmacy users when combined claims records and pharmacy dispensing data (46% compared to 64% respectively, p &lt; 0.001) or claims records alone (51% compared to 67% respectively, p &lt; 0.001) were compared.

Conclusion: Access to routine collected data increases clinical acuity over patient adherence to asthma controller medications and is a valuable resource for health care professionals. A policy of secure accessibility of such data at the patient-pharmacist or patient-GP interface may allow real-time intervention and assist in decision making across numerous therapeutic areas.

Does providing everyone with free-of-charge organised exercise opportunities work in public health?

BACKGROUND AND PURPOSE

Population-level initiatives of free-of-charge organised exercise have been implemented to encourage residents to take up regular physical activity. However, there exists a paucity of evidence on the ability of these interventions to attract and engage residents, especially targeted subgroups. Seeking to contribute to this evidence base, we evaluated a proportionate universal programme providing free exercise sessions, Leeds Let's Get Active.

METHODS

Descriptive statistics were used to summarise the programme data and participants. Time to event, count and logistic regression models examined how different population subgroups engaged with the programme in terms of number of entries, weekly participation rates and drop-off patterns.

RESULTS

51,874 adult residents registered to the programme and provided baseline data (2013-2016). A small proportion (1.6%) attended the free sessions on a weekly basis. Higher participation rates were estimated for the groups of males, retired and non-inactive participants. A neighbourhood-level deprivation status was found to have no marginal effect on the level and frequency of participation, but to be negatively associated with participation drop-off (HR 0.93, 95% CI 0.89-0.97, p = 0.001).

CONCLUSIONS

Providing everyone with free-of-charge organised exercise opportunities in public leisure centres located in deprived areas can attract large volumes of residents, but may not sufficiently encourage adults, especially inactive residents and those living in disadvantaged neighbourhoods, to take up regular exercise.

Integrated Digital Health Solutions in the Management of Growth Disorders in Pediatric Patients Receiving Growth Hormone Therapy: A Retrospective Analysis

Digital health has seen rapid advancements over the last few years in helping patients and their healthcare professionals better manage treatment for a variety of illnesses, including growth hormone (GH) therapy for growth disorders in children and adolescents. For children and adolescents requiring such therapy, as well as for their parents, the treatment is longitudinal and often involves daily injections plus close progress monitoring; a sometimes daunting task when young children are involved. Here, we describe our experience in offering devices and digital health tools to support GH therapy across some 40 countries. We also discuss how this ecosystem of care has evolved over the years based on learnings and advances in technology. Finally, we offer a glimpse of future planned enhancements and directions for digital health to play a bigger role in better managing conditions treated with GH therapy, as well as model development for adherence prediction. The continued aim of these technologies is to improve clinical decision making and support for GH-treated patients, leading to better outcomes.

Artificial Intelligence Solutions to Increase Medication Adherence in Patients With Non-communicable Diseases

Artificial intelligence (AI) tools are increasingly being used within healthcare for various purposes, including helping patients to adhere to drug regimens. The aim of this narrative review was to describe: (1) studies on AI tools that can be used to measure and increase medication adherence in patients with non-communicable diseases (NCDs); (2) the benefits of using AI for these purposes; (3) challenges of the use of AI in healthcare; and (4) priorities for future research. We discuss the current AI technologies, including mobile phone applications, reminder systems, tools for patient empowerment, instruments that can be used in integrated care, and machine learning. The use of AI may be key to understanding the complex interplay of factors that underly medication non-adherence in NCD patients. AI-assisted interventions aiming to improve communication between patients and physicians, monitor drug consumption, empower patients, and ultimately, increase adherence levels may lead to better clinical outcomes and increase the quality of life of NCD patients. However, the use of AI in healthcare is challenged by numerous factors; the characteristics of users can impact the effectiveness of an AI tool, which may lead to further inequalities in healthcare, and there may be concerns that it could depersonalize medicine. The success and widespread use of AI technologies will depend on data storage capacity, processing power, and other infrastructure capacities within healthcare systems. Research is needed to evaluate the effectiveness of AI solutions in different patient groups and establish the barriers to widespread adoption, especially in light of the COVID-19 pandemic, which has led to a rapid increase in the use and development of digital health technologies.

Persistence as a Robust Indicator of Medication Adherence-Related Quality and Performance

Medication adherence is a priority for health systems worldwide and is widely recognised as a key component of quality of care for disease management. Adherence-related indicators were rarely explicitly included in national health policy agendas. One barrier is the lack of standardised adherence terminology and of routine measures of adherence in clinical practice. This paper discusses the possibility of developing adherence-related performance indicators highlighting the value of measuring persistence as a robust indicator of quality of care. To standardise adherence and persistence-related terminology allowing for benchmarking of adherence strategies, the European Ascertaining Barriers for Compliance (ABC) project proposed a Taxonomy of Adherence in 2012 consisting of three components: initiation, implementation, discontinuation. Persistence, which immediately precedes discontinuation, is a key element of taxonomy, which could capture adherence chronology allowing the examination of patterns of medication-taking behaviour. Advances in eHealth and Information Communication Technology (ICT) could play a major role in providing necessary structures to develop persistence indicators. We propose measuring persistence as an informative and pragmatic measure of medication-taking behaviour. Our view is to develop quality and performance indicators of persistence, which requires investing in ICT solutions enabling healthcare providers to review complete information on patients' medication-taking patterns, as well as clinical and health outcomes.

Assessing Predictors of Tamoxifen Nonadherence in Patients with Early Breast Cancer

Adjuvant endocrine therapy (AET) is generally proposed to all patients with hormone receptor-positive breast cancer to reduce the risk of recurrence and death. Adherence to therapy is crucial. However, non-adherence to AET is common, with estimates of up to 50% of patients not successfully completing a five-year course of treatment, and it is significantly associated with lower survival rates and a higher risk of recurrence. Currently, no gold standard is available to assess adherence. Several studies, most of them retrospective in nature, have used both direct and indirect methods to monitor the adherence to therapy in breast cancer. The indirect method is more widely used, and it is based on pharmacy prescription refills and patient administered questionnaires. On the other hand, direct methods such as a measurement of the level of the drug or its metabolites in blood or urine are much more precise, but more expensive and not routinely implemented. In this review, we analyzed the results of the major studies focused on the adherence to tamoxifen in breast cancer patients. We identified several factors associated with poor adherence, such as the side effects of therapy, the lack of shared decision-making between the physician and patient, the context in which the discussion takes place, and whether the patients are enrolled in a clinical trial. Moreover, we discussed possible methods to improve adherence to adjuvant therapy in breast cancer.

New Male Users of Lipid-Lowering Drugs for Primary Prevention of Cardiovascular Disease: The Impact of Treatment Persistence on Morbimortality. A Longitudinal Study

The objective of this study was to analyse persistence to lipid-lowering drug use for primary prevention of cardiovascular disease (CVD) in a new users cohort, to explore all-cause and cardiovascular related morbidity, comorbidity and mortality in this group and, finally, to study the relationship between persistence and morbimortality. We selected subjects who started lipid-lowering treatment for primary prevention of CVD between 1 January 2010 and 31 December 2017 (N = 1424), and classified them as treatment-persistent or -nonpersistent. Bivariate analyses were performed to compare sociodemographic and clinical variables, morbimortality and time to event between groups. The association between morbidities was explored using comorbidity network analysis. The effect of persistence was analysed using logistic regression and Cox survival analyses. Only 38.7% of users were persistent with treatment. Persistent and nonpersistent users had similar sociodemographic and clinical profiles, although differed in age, smoking status, and glycemia. Comorbidity networks revealed that the number of co-occurring diagnoses was higher in nonpersistent than persistent users. Adjusted analyses indicated a protective effect of treatment persistence, especially against major adverse cardiovascular events (MACE), but this effect was not statistically significant. Observational studies are crucial to characterize real-world effectiveness.