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Tzikas A, Koulierakis G, Athanasakis K, Merakou K. Nudging Interventions on Stair Use: A Scoping Review. JOURNAL OF PREVENTION (2022) 2024; 45:685-722. [PMID: 38877174 DOI: 10.1007/s10935-024-00790-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/01/2024] [Indexed: 06/16/2024]
Abstract
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.
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Affiliation(s)
- Alexandros Tzikas
- Department of Public Health Policy, School of Public Health, University of West Attica, Athens, Greece.
| | - George Koulierakis
- Laboratory of Epidemiology, Health Determinants and Well-Being, Division of Epidemiology, Prevention and Quality of Life, Department of Public Health Policy, School of Public Health, University of West Attica, Athens, Greece
| | - Konstantinos Athanasakis
- Laboratory for Health Technology Assessment, Division of Health Systems and Policy, Department of Public Health Policy, School of Public Health, University of West Attica, Athens, Greece
| | - Kyriakoula Merakou
- Laboratory of Epidemiology, Health Determinants and Well-Being, Division of Epidemiology, Prevention and Quality of Life, Department of Public Health Policy, School of Public Health, University of West Attica, Athens, Greece
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Koranteng E, Lewis ACF, Abel GA. Compassionate Machines: The Ethics of "Artificial Empathy" in Cancer Care. JAMA Oncol 2024; 10:857-858. [PMID: 38696207 DOI: 10.1001/jamaoncol.2024.0824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/19/2024]
Abstract
This Viewpoint discusses the ethics of artificial intelligence–generated compassion in cancer care and outlines 4 main points of concern.
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Affiliation(s)
- Erica Koranteng
- Division of Population Sciences, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Anna C F Lewis
- Harvard Medical School, Boston, Massachusetts
- Division of Genetics, Brigham and Women's Hospital, Boston, Massachusetts
| | - Gregory A Abel
- Division of Population Sciences, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
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3
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Nelson AJ, Pagidipati NJ, Bosworth HB. Improving medication adherence in cardiovascular disease. Nat Rev Cardiol 2024; 21:417-429. [PMID: 38172243 DOI: 10.1038/s41569-023-00972-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/27/2023] [Indexed: 01/05/2024]
Abstract
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.
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Affiliation(s)
- Adam J Nelson
- Victorian Heart Institute, Melbourne, Victoria, Australia
- Duke Clinical Research Institute, Duke University, Durham, NC, USA
| | | | - Hayden B Bosworth
- Duke Clinical Research Institute, Duke University, Durham, NC, USA.
- Population Health Sciences, Duke University, Durham, NC, USA.
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4
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Rizvi RF, Schoephoerster JA, Desphande SS, Usher M, Oien AE, Peters MM, Loth MS, Bahr MW, Ventz S, Koopmeiners JS, Melton GB. Decreasing Opioid Addiction and Diversion Using Behavioral Economics Applied Through a Digital Engagement Solution: Protocol for a Randomized Controlled Trial. JMIR Res Protoc 2024; 13:e52882. [PMID: 38457203 PMCID: PMC10960208 DOI: 10.2196/52882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 01/22/2024] [Accepted: 01/24/2024] [Indexed: 03/09/2024] Open
Abstract
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.
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Affiliation(s)
- Rubina Fatima Rizvi
- Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, United States
- Center for Learning Health System Sciences, University of Minnesota, Minneapolis, MN, United States
- University of Minnesota Medical School, Minneapolis, MN, United States
| | | | - Sagar Satish Desphande
- University of Minnesota Medical School, Minneapolis, MN, United States
- Division of Plastic and Reconstructive Surgery, Department of Surgery, University of Minnesota, Minneapolis, MN, United States
| | - Michael Usher
- University of Minnesota Medical School, Minneapolis, MN, United States
- M Health Fairview Systems, Minneapolis, MN, United States
| | - Andy Elaine Oien
- Center for Learning Health System Sciences, University of Minnesota, Minneapolis, MN, United States
| | - Maya Marie Peters
- Center for Learning Health System Sciences, University of Minnesota, Minneapolis, MN, United States
| | - Matthew Scott Loth
- Center for Learning Health System Sciences, University of Minnesota, Minneapolis, MN, United States
| | | | - Steffen Ventz
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States
| | - Joseph Stephen Koopmeiners
- Center for Learning Health System Sciences, University of Minnesota, Minneapolis, MN, United States
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States
| | - Genevieve B Melton
- Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, United States
- Center for Learning Health System Sciences, University of Minnesota, Minneapolis, MN, United States
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States
- Division of Colon and Rectal Surgery, Department of Surgery, University of Minnesota, Minneapolis, MN, United States
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Martindale APL, Llewellyn CD, de Visser RO, Ng B, Ngai V, Kale AU, di Ruffano LF, Golub RM, Collins GS, Moher D, McCradden MD, Oakden-Rayner L, Rivera SC, Calvert M, Kelly CJ, Lee CS, Yau C, Chan AW, Keane PA, Beam AL, Denniston AK, Liu X. Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines. Nat Commun 2024; 15:1619. [PMID: 38388497 PMCID: PMC10883966 DOI: 10.1038/s41467-024-45355-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 01/22/2024] [Indexed: 02/24/2024] Open
Abstract
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.
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Affiliation(s)
| | - Carrie D Llewellyn
- Department of Primary Care and Public Health, Brighton and Sussex Medical School, Brighton, UK
| | - Richard O de Visser
- Department of Primary Care and Public Health, Brighton and Sussex Medical School, Brighton, UK
| | - Benjamin Ng
- Birmingham and Midland Eye Centre, Sandwell and West Birmingham NHS Trust, Birmingham, UK
- Christ Church, University of Oxford, Oxford, UK
| | - Victoria Ngai
- University College London Medical School, London, UK
| | - Aditya U Kale
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK
| | | | - Robert M Golub
- Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Gary S Collins
- Centre for Statistics in Medicine//UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - David Moher
- Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottowa, Canada
| | - Melissa D McCradden
- Department of Bioethics, The Hospital for Sick Children, Toronto, Canada
- Genetics & Genome Biology Research Program, Peter Gilgan Centre for Research & Learning, Toronto, Canada
- Division of Clinical and Public Health, Dalla Lana School of Public Health, Toronto, Canada
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia
| | - Samantha Cruz Rivera
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- Centre for Patient Reported Outcomes Research (CPROR), Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Melanie Calvert
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- Centre for Patient Reported Outcomes Research (CPROR), Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- NIHR Applied Research Collaboration (ARC) West Midlands, University of Birmingham, Birmingham, UK
- NIHR Blood and Transplant Research Unit (BTRU) in Precision Transplant and Cellular Therapeutics, University of Birmingham, Birmingham, UK
| | | | | | - Christopher Yau
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Health Data Research UK, London, UK
| | - An-Wen Chan
- Department of Medicine, Women's College Hospital. University of Toronto, Toronto, Canada
| | - Pearse A Keane
- NIHR Biomedical Research Centre at Moorfields, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Andrew L Beam
- Department of Epidemiology, Harvard. T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Alastair K Denniston
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- NIHR Biomedical Research Centre at Moorfields, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Xiaoxuan Liu
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK.
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK.
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Akamine A, Nagasaki Y, Tomizawa A, Otori K. Risk Factors for Non-Adherence to Pharmacist or Non-Pharmacist Explanations on Preoperative Medication Discontinuation: A Retrospective Japanese Study. Patient Prefer Adherence 2024; 18:255-266. [PMID: 38313826 PMCID: PMC10838503 DOI: 10.2147/ppa.s445474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/20/2024] [Indexed: 02/06/2024] Open
Abstract
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.
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Affiliation(s)
| | - Yuya Nagasaki
- Department of Pharmacy, Kitasato University Hospital, Kanagawa, Japan
| | - Atsushi Tomizawa
- Department of Pharmacy, Kitasato University Hospital, Kanagawa, Japan
| | - Katsuya Otori
- Department of Pharmacy, Kitasato University Hospital, Kanagawa, Japan
- Research and Education Center for Clinical Pharmacy, Division of Clinical Pharmacy, Laboratory of Pharmacy Practice and Science 1, Kitasato University School of Pharmacy, Tokyo, Japan
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Sumner J, Bundele A, Lim HW, Phan P, Motani M, Mukhopadhyay A. Developing an Artificial Intelligence-Driven Nudge Intervention to Improve Medication Adherence: A Human-Centred Design Approach. J Med Syst 2023; 48:3. [PMID: 38063940 PMCID: PMC10709244 DOI: 10.1007/s10916-023-02024-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023]
Abstract
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.
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Affiliation(s)
- Jennifer Sumner
- Medical Affairs - Research Innovation & Enterprise, Alexandra Hospital, National University Health System, Singapore, Singapore.
| | - Anjali Bundele
- Medical Affairs - Research Innovation & Enterprise, Alexandra Hospital, National University Health System, Singapore, Singapore
| | - Hui Wen Lim
- Medical Affairs - Research Innovation & Enterprise, Alexandra Hospital, National University Health System, Singapore, Singapore
| | - Phillip Phan
- Johns Hopkins Carey Business School and the Department of Medicine, Baltimore, USA
| | - Mehul Motani
- Department of Electrical & Computer Engineering, National University of Singapore, Singapore, Singapore
| | - Amartya Mukhopadhyay
- Medical Affairs - Research Innovation & Enterprise, Alexandra Hospital, National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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Kazemian A, Hoseinzadeh M, Banihashem Rad SA, Jouya A, Tahani B. Nudging oral habits; application of behavioral economics in oral health promotion: a critical review. Front Public Health 2023; 11:1243246. [PMID: 38145077 PMCID: PMC10739307 DOI: 10.3389/fpubh.2023.1243246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 11/24/2023] [Indexed: 12/26/2023] Open
Abstract
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.
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Affiliation(s)
- Ali Kazemian
- Department of Community Oral Health, School of Dentistry, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Melika Hoseinzadeh
- Dental Research Center, Mashhad Dental School, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Seyed Ahmad Banihashem Rad
- Department of Restorative, Preventive and Pediatric Dentistry, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Anahid Jouya
- Department of Community Oral Health, School of Dentistry, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Bahareh Tahani
- Department of Oral Public Health, Dental Research Center, School of Dentistry, Dental Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
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Anderson JL, Knowlton KU, May HT, Le VT, Lappe’ DL, Cripps ST, Schwab LH, Winslow T, Bair TL, Muhlestein JB. Impact of Active vs Passive Statin Selection for Primary Prevention: The CorCal Vanguard Trial. JACC. ADVANCES 2023; 2:100676. [PMID: 38938499 PMCID: PMC11198348 DOI: 10.1016/j.jacadv.2023.100676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 08/09/2023] [Accepted: 08/30/2023] [Indexed: 06/29/2024]
Abstract
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.
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Affiliation(s)
- Jeffrey L. Anderson
- Intermountain Medical Center Heart and Vascular Clinical Program, Murray, Utah, USA
- The University of Utah School of Medicine, Department of Internal Medicine, Salt Lake City, Utah, USA
| | - Kirk U. Knowlton
- Intermountain Medical Center Heart and Vascular Clinical Program, Murray, Utah, USA
- The University of Utah School of Medicine, Department of Internal Medicine, Salt Lake City, Utah, USA
| | - Heidi T. May
- Intermountain Medical Center Heart and Vascular Clinical Program, Murray, Utah, USA
| | - Viet T. Le
- Intermountain Medical Center Heart and Vascular Clinical Program, Murray, Utah, USA
- The Rocky Mountain University of Health Professions Master of PA Studies, Provo, Utah, USA
| | - Donald L. Lappe’
- Intermountain Medical Center Heart and Vascular Clinical Program, Murray, Utah, USA
- The University of Utah School of Medicine, Department of Internal Medicine, Salt Lake City, Utah, USA
| | - Shanelle T. Cripps
- Intermountain Medical Center Heart and Vascular Clinical Program, Murray, Utah, USA
| | - Lesley H. Schwab
- Intermountain Medical Center Heart and Vascular Clinical Program, Murray, Utah, USA
| | - Tyler Winslow
- Intermountain Medical Center Heart and Vascular Clinical Program, Murray, Utah, USA
| | - Tami L. Bair
- Intermountain Medical Center Heart and Vascular Clinical Program, Murray, Utah, USA
| | - Joseph B. Muhlestein
- Intermountain Medical Center Heart and Vascular Clinical Program, Murray, Utah, USA
- The University of Utah School of Medicine, Department of Internal Medicine, Salt Lake City, Utah, USA
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Raparelli V, Wright CX, Corica B, Sharma G, Lindley K, Brackett A, Pilote L, Wood MJ, Dreyer RP. Interventions Targeted to Address Social Determinants of Health in Ischemic Heart Disease: A Sex- and Gender-Oriented Scoping Review. Can J Cardiol 2022; 38:1881-1892. [PMID: 35809812 DOI: 10.1016/j.cjca.2022.06.025] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/21/2022] [Accepted: 06/28/2022] [Indexed: 12/14/2022] Open
Abstract
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.
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Affiliation(s)
- Valeria Raparelli
- Department of Translational Medicine, University of Ferrara, Ferrara, Italy; University Centre for Studies on Gender Medicine, University of Ferrara, Ferrara, Italy; Faculty of Nursing, University of Alberta, Edmonton, Alberta, Canada
| | - Catherine X Wright
- Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Bernadette Corica
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy
| | - Garima Sharma
- Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kathryn Lindley
- Cardiovascular Division, Washington University School of Medicine, St Louis, Missouri, USA
| | - Alexandria Brackett
- Harvey Cushing/John Hay Whitney Medical Library, Yale University, New Haven, Connecticut, USA
| | - Louise Pilote
- Centre for Outcomes Research and Evaluation, McGill University Health Centre Research Institute, Montréal, Québec, Canada; Divisions of Clinical Epidemiology and General Internal Medicine, McGill University Health Centre Research Institute, Montréal, Québec, Canada
| | - Malissa J Wood
- Massachusetts General Hospital Corrigan Minehan Heart Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Rachel P Dreyer
- Center for Outcomes Research and Evaluation, New Haven, Connecticut, USA; Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA; Department of Biostatistics (Health Informatics), Yale School of Public Health, New Haven, Connecticut, USA.
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11
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Daniore P, Nittas V, von Wyl V. Enrollment and retention of participants in remote digital health studies: a scoping review and framework proposal (Preprint). J Med Internet Res 2022; 24:e39910. [PMID: 36083626 PMCID: PMC9508669 DOI: 10.2196/39910] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 07/12/2022] [Accepted: 07/31/2022] [Indexed: 11/13/2022] Open
Abstract
Background Objective Methods Results Conclusions
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Affiliation(s)
- Paola Daniore
- Institute for Implementation Science in Healthcare, University of Zurich, Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
| | - Vasileios Nittas
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Viktor von Wyl
- Institute for Implementation Science in Healthcare, University of Zurich, Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
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12
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Laroia G, Horne BD, Esplin S, Ramaswamy VK. A unified health algorithm that teaches itself to improve health outcomes for every individual: How far into the future is it? Digit Health 2022; 8:20552076221074126. [PMID: 35083061 PMCID: PMC8785270 DOI: 10.1177/20552076221074126] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 11/22/2021] [Accepted: 12/27/2021] [Indexed: 11/23/2022] Open
Abstract
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.
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Affiliation(s)
| | - Benjamin D Horne
- Cardiovascular and Genetic Epidemiology, Intermountain Medical Center Heart Institute, USA.,Division of Cardiovascular Medicine, Department of Medicine, Stanford University, USA
| | - Sean Esplin
- Department of Obstetrics and Gynecology, Intermountain Healthcare Maternal-Fetal Medicine, and University of Utah Health, USA
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13
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Chen Y, Harris S, Rogers Y, Ahmad T, Asselbergs FW. OUP accepted manuscript. Eur Heart J 2022; 43:1296-1306. [PMID: 35139182 PMCID: PMC8971005 DOI: 10.1093/eurheartj/ehac030] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 01/11/2022] [Accepted: 01/17/2022] [Indexed: 12/15/2022] Open
Abstract
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.
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Affiliation(s)
- Yang Chen
- Institute of Health Informatics, University College London,
222 Euston Road, London NW1 2DA, UK
- Clinical Research Informatics Unit, University College London Hospitals NHS
Healthcare Trust, London, UK
- Barts Heart Centre, St Bartholomew’s Hospital, London,
UK
| | - Steve Harris
- Institute of Health Informatics, University College London,
222 Euston Road, London NW1 2DA, UK
| | - Yvonne Rogers
- UCL Interaction Centre, University College London, London,
UK
| | - Tariq Ahmad
- Section of Cardiovascular Medicine, School of Medicine, Yale
University, New Haven, CT, USA
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