Behavioral Nudges as Patient Decision Support for Medication Adherence: The ENCOURAGE Randomized Controlled Trial

Nudging Interventions on Stair Use: A Scoping Review

Stair use is a physical activity that can be incorporated into the daily lifestyle of a majority of the population, resulting in several health benefits. Nudges are increasingly used in public health interventions to encourage healthy behaviours such as physical activity in a cost-effective manner. This scoping review aimed to investigate the effect and the characteristics of nudges used on interventions to promote stair use. We reviewed the relevant literature published in PubMed, Cochrane Library, Mendeley and Google Scholar, from January 2009 to May 2022. Eligibility criteria included original studies of any type of design, written in English, targeting healthy adults, reporting nudging interventions, using elevator or escalator as comparators and defining a baseline for comparisons. Initially, 118 publications were identified, and after applying exclusion criteria, 27 articles were included in the analysis. Results showed that most of the nudging interventions had significant positive effect on stair use in several settings. The evidence from this review suggests that incorporating nudges into public health interventions can effectively promote physical activity through increased stair usage. Emphasizing prevention measures in public health interventions may contribute to better health outcomes.

Compassionate Machines: The Ethics of "Artificial Empathy" in Cancer Care

This Viewpoint discusses the ethics of artificial intelligence–generated compassion in cancer care and outlines 4 main points of concern.

Improving medication adherence in cardiovascular disease

Non-adherence to medication is a global health problem with far-reaching individual-level and population-level consequences but remains unappreciated and under-addressed in the clinical setting. With increasing comorbidity and polypharmacy as well as an ageing population, cardiovascular disease and medication non-adherence are likely to become increasingly prevalent. Multiple methods for detecting non-adherence exist but are imperfect, and, despite emerging technology, a gold standard remains elusive. Non-adherence to medication is dynamic and often has multiple causes, particularly in the context of cardiovascular disease, which tends to require lifelong medication to control symptoms and risk factors in order to prevent disease progression. In this Review, we identify the causes of medication non-adherence and summarize interventions that have been proven in randomized clinical trials to be effective in improving adherence. Practical solutions and areas for future research are also proposed.

Decreasing Opioid Addiction and Diversion Using Behavioral Economics Applied Through a Digital Engagement Solution: Protocol for a Randomized Controlled Trial

BACKGROUND

Despite strong and growing interest in ending the ongoing opioid health crisis, there has been limited success in reducing the prevalence of opioid addiction and the number of deaths associated with opioid overdoses. Further, 1 explanation for this is that existing interventions target those who are opiate-dependent but do not prevent opioid-naïve patients from becoming addicted.

OBJECTIVE

Leveraging behavioral economics at the patient level could help patients successfully use, discontinue, and dispose of their opioid medications in an acute pain setting. The primary goal of this project is to evaluate the effect of the 3 versions of the Opioid Management for You (OPY) tool on measures of opioid use relative to the standard of care by leveraging a pragmatic randomized controlled trial (RCT).

METHODS

A team of researchers from the Center for Learning Health System Sciences (CLHSS) at the University of Minnesota partnered with M Health Fairview to design, build, and test the 3 versions of the OPY tool: social influence, precommitment, and testimonial version. The tool is being built using the Epic Care Companion (Epic Inc) platform and interacts with the patient through their existing MyChart (Epic Systems Corporation) personal health record account, and Epic patient portal, accessed through a phone app or the MyChart website. We have demonstrated feasibility with pilot data of the social influence version of the OPY app by targeting our pilot to a specific cohort of patients undergoing upper-extremity procedures. This study will use a group sequential RCT design to test the impact of this important health system initiative. Patients who meet OPY inclusion criteria will be stratified into low, intermediate, and high risk of opiate use based on their type of surgery.

RESULTS

This study is being funded and supported by the CLHSS Rapid Prospective Evaluation and Digital Technology Innovation Programs, and M Health Fairview. Support and coordination provided by CLHSS include the structure of engagement, survey development, data collection, statistical analysis, and dissemination. The project was initially started in August 2022. The pilot was launched in February 2023 and is still running, with the data last counted in August 2023. The actual RCT is planned to start by early 2024.

CONCLUSIONS

Through this RCT, we will test our hypothesis that patient opioid use and diverted prescription opioid availability can both be improved by information delivery applied through a behavioral economics lens via sending nudges directly to the opioid users through their personal health record.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06124079; https://clinicaltrials.gov/study/NCT06124079.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/52882.

Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines

The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77-94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.

Risk Factors for Non-Adherence to Pharmacist or Non-Pharmacist Explanations on Preoperative Medication Discontinuation: A Retrospective Japanese Study

Purpose

The risk factors for non-adherence to pharmacist or non-pharmacist explanations of preoperative medication discontinuation are unknown. The primary outcome of this study was to determine whether the final explainer's occupation was a risk factor for non-adherence. The secondary outcomes were to determine the risk factors for non-adherence after limiting the departments or adjusting for age.

Patients and Methods

We retrospectively examined the data (including patient age, sex, prescription medications, comorbidities, presence of roommate, and number of days between receiving explanation and surgery) of 1132 patients on medications that could affect surgery at a Japanese university hospital between April 1, 2017, and March 31, 2020. The primary endpoint was whether the occupation of the last person explaining medication discontinuation to the patient was an independent risk factor for non-adherence (age ≥65 years vs <65 years). Secondary endpoints included subgroup analyses in urological, gastrointestinal, and otolaryngological areas, as well as a sensitivity analysis (age as a continuous variable) to confirm the validity of the primary endpoint results. A multivariate binary logistic regression identified independent non-adherence risk factors.

Results

The main analysis showed that discontinuing two or more medications was a risk factor for non-adherence (adjusted odds ratio (AOR): 1.67; 95% confidence interval (CI): 1.13-2.47; p = 0.01). However, in analyses coordinated by department (urological, gastrointestinal, and otolaryngological), ≥65 (versus <65) years of age was determined as a risk factor for increased nonadherence (AOR: 2.27, 95% CI: 1.11-4.63; p=0.024). Age-adjusted analysis (continuous variables) showed similar results to the primary endpoint (AOR: 1.68, 95% CI: 1.14-2.49, p = 0.009).

Conclusion

Two or more medications, and not the final explainer's occupation, were associated with pre-surgery medication non-adherence. To prevent non-adherence, pharmacists and non-pharmacists should educate patients about preoperative medication discontinuation. These findings could help identify high-risk non-adherence patients.

Developing an Artificial Intelligence-Driven Nudge Intervention to Improve Medication Adherence: A Human-Centred Design Approach

To improve medication adherence, we co-developed a digital, artificial intelligence (AI)-driven nudge intervention with stakeholders (patients, providers, and technologists). We used a human-centred design approach to incorporate user needs in creating an AI-driven nudge tool. We report the findings of the first stage of a multi-phase project: understanding user needs and ideating solutions. We interviewed healthcare providers (n = 10) and patients (n = 10). Providers also rated example nudge interventions in a survey. Stakeholders felt the intervention could address existing deficits in medication adherence tracking and were optimistic about the solution. Participants identified flexibility of the intervention, including mode of delivery, intervention intensity, and the ability to stratify to user ability and needs, as critical success factors. Reminder nudges and provision of healthcare worker contact were rated highly by all. Conversely, patients perceived incentive-based nudges poorly. Finally, participants suggested that user burden could be minimised by leveraging existing software (rather than creating a new App) and simplifying or automating the data entry requirements where feasible. Stakeholder interviews generated in-depth data on the perspectives and requirements for the proposed solution. The participatory approach will enable us to incorporate user needs into the design and improve the utility of the intervention. Our findings show that an AI-driven nudge tool is an acceptable and appropriate solution, assuming it is flexible to user requirements.

Nudging oral habits; application of behavioral economics in oral health promotion: a critical review

Background

Oral health disorders significantly contribute to the global incidence of chronic diseases. Nudge interventions have demonstrated effectiveness in enhancing people's decision-making and self-management capacities in a cost-efficient manner. As a result, these interventions could be valuable tools for fostering improved oral care habits. This critical review explores potential behavioral nudges applicable to promoting oral health.

Methods

A thorough electronic literature search was conducted on Scopus, Embase, and PubMed databases for papers published post-2008. The search focused on empirical evidence concerning the direct and indirect application of Nudge theory in oral health enhancement. In addition, the investigation included the nudge intervention's role in managing common non-communicable disease risk factors (tobacco, alcohol, and sugar) and their use in other health sectors.

Results and conclusion

There is a dearth of studies on behavioral economics, particularly those involving reward and reminder techniques. However, various successful nudge interventions have been identified in other sectors that aim to improve health decisions. These include strategies encouraging healthier nutritional choices, tobacco and alcohol cessation, medication compliance, routine physical activity, and regular health check-ups. Such interventions can also have direct or indirect positive impacts on oral health. Implementing these interventions within an oral care framework could promote oral health due to similar underlying cognitive mechanisms. However, different types of nudge interventions have varying degrees of effectiveness. Furthermore, factors such as the method of delivery and the characteristics of the targeted population significantly influence the outcome of the intervention. Hence, it is imperative to conduct extensive studies in diverse socioeconomic settings to fully understand the potentials, limitations, and impacts of nudge interventions in promoting oral health.

Impact of Active vs Passive Statin Selection for Primary Prevention: The CorCal Vanguard Trial

Background

Statins can improve outcomes in high-risk primary prevention populations. However, application in clinical practice has lagged.

Objectives

The objective of this study was to compare an active vs a passive strategy (ie, usual care) to statin prescription for primary prevention of atherosclerotic cardiovascular disease (ASCVD).

Methods

A total of 3,770 patients ≥50 years of age without a history of ASCVD or statin use were invited to enroll in CorCal, with 601 consenting to participate. These patients were randomized 1:1 to statin initiation guided by the pooled cohort equation or by coronary artery calcium scoring (CACS). Outcomes (2.8-year follow-up) compared patients managed actively vs passively (randomly invited but declined or did not respond).

Results

Patient demographics were well matched. Statin recommendation was common among enrolled patients (41.7%). During follow-up, 25.3% of active patients were taking a statin vs 9.8% managed passively (P < 0.0001). Active patients had more lipid panels (median 2.0 vs 1.0), lower low-density lipoprotein cholesterol (109 vs 117 mg/dL) (both P < 0.0001), and a low rate of major adverse cardiovascular events during follow-up (0.6% vs 1.0%, P = 0.47). Statistical comparisons included t-tests, chi-squared tests, nonparametric tests, and time-to-event tests as appropriate.

Conclusions

An active approach to statin selection for primary ASCVD prevention identified a large treatment opportunity and led to over twice as many patients on statins compared to passive (usual care) management. A large CorCal Outcomes Trial is underway to more definitively assess the impact on outcomes of active management of statins for primary prevention.

Interventions Targeted to Address Social Determinants of Health in Ischemic Heart Disease: A Sex- and Gender-Oriented Scoping Review

The burden of ischemic heart disease (IHD) is a major health problem worldwide. The detrimental effect of gendered (ie, unevenly distributed between female and male) socioeconomic determinants of health (SDOH) on outcomes has been demonstrated, more so in female individuals. Therefore, addressing SDOH is a priority for the care implementation of patients with IHD. We conducted a scoping review to identify the types of SDOH-tailored interventions tested in randomised controlled trials (RCTs) among IHD patients, and whether the reporting of findings was sex-unbiased. We identified 8 SDOH domains: education, physical environment, health care system, economic stability, social support, sexual orientation, culture/language, and systemic racism. A total of 28 RCTs (2 ongoing) were evaluated. Since the 1990s, 26 RCTs have been conducted, mainly in the Middle East and Asia, and addressed only education, physical environment, health care system, and social support. The 77% of studies focused on patient-education interventions, and around 80% on SDOH-based interventions achieved positive effects on a variety of primary outcome(s). Among the limitations of the conducted RCTs, the most relevant were an overall low participation of female and racial/ethnical minority participants, a lack of sex-stratified analyses, and a missing opportunity of tailoring some SDOH interventions relevant for health. The SDOH-tailored interventions tested so far in RCTs, enrolling predominantly male patients and mainly targeting education and health literacy, were effective in improving outcomes among patients with IHD. Future studies should focus on a wider range of SDOH with an adequate representation of female and minority patients who would most benefit from such interventions.

Enrollment and retention of participants in remote digital health studies: a scoping review and framework proposal (Preprint)

Background

Objective

Methods

Results

Conclusions

A unified health algorithm that teaches itself to improve health outcomes for every individual: How far into the future is it?

The single biggest factor driving health outcomes is patient behavior. The CHR Model (County Health Rankings Model) weights socioeconomic factors, lifestyle behaviors, and physical environment factors collectively at 80% in driving impact on health outcomes, to the 20% weight for access to and quality of clinical care. Commercial determinants of health affect everyone today and unhealthy choices worsen pre-existing economic, social, and racial inequities. Yet there is a disproportionate focus on therapeutic intervention to the exclusion of shaping patient behaviors to improve healthcare. If the recent pandemic taught us a critically important lesson, it is the imperative to look beyond clinical care. According to the Centers for Disease Control and Prevention (CDC), long-standing systemic health and social inequities put various groups of people at higher risk of getting sick and dying from COVID-19, including many racial and ethnic minority groups. The virus was simply more efficient in detecting such vulnerabilities than the guardians of these physiologies. These insights from the pandemic come at the heel of a confluence of three major accelerants that may radically reshape our approaches to hot-spotting vulnerabilities and managing them before they manifest in a derangement or disease. They are the recent strides in behavioral economics and behavior science; advances in remote monitoring and personal health technologies; and developments in artificial intelligence and data sciences. These accelerants allow us to imagine a previously impossible vision—we can now build and maintain a unified health algorithm for every individual that can dynamically track the two interdependent streams of risk, clinical and behavioral.

OUP accepted manuscript

The increasing volume and richness of healthcare data collected during routine clinical practice have not yet translated into significant numbers of actionable insights that have systematically improved patient outcomes. An evidence-practice gap continues to exist in healthcare. We contest that this gap can be reduced by assessing the use of nudge theory as part of clinical decision support systems (CDSS). Deploying nudges to modify clinician behaviour and improve adherence to guideline-directed therapy represents an underused tool in bridging the evidence-practice gap. In conjunction with electronic health records (EHRs) and newer devices including artificial intelligence algorithms that are increasingly integrated within learning health systems, nudges such as CDSS alerts should be iteratively tested for all stakeholders involved in health decision-making: clinicians, researchers, and patients alike. Not only could they improve the implementation of known evidence, but the true value of nudging could lie in areas where traditional randomized controlled trials are lacking, and where clinical equipoise and variation dominate. The opportunity to test CDSS nudge alerts and their ability to standardize behaviour in the face of uncertainty may generate novel insights and improve patient outcomes in areas of clinical practice currently without a robust evidence base.