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Abraham A, Cule M, Thanaj M, Basty N, Hashemloo MA, Sorokin EP, Whitcher B, Burgess S, Bell JD, Sattar N, Thomas EL, Yaghootkar H. Genetic Evidence for Distinct Biological Mechanisms That Link Adiposity to Type 2 Diabetes: Toward Precision Medicine. Diabetes 2024; 73:1012-1025. [PMID: 38530928 PMCID: PMC11109787 DOI: 10.2337/db23-1005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 03/22/2024] [Indexed: 03/28/2024]
Abstract
We aimed to unravel the mechanisms connecting adiposity to type 2 diabetes. We used MR-Clust to cluster independent genetic variants associated with body fat percentage (388 variants) and BMI (540 variants) based on their impact on type 2 diabetes. We identified five clusters of adiposity-increasing alleles associated with higher type 2 diabetes risk (unfavorable adiposity) and three clusters associated with lower risk (favorable adiposity). We then characterized each cluster based on various biomarkers, metabolites, and MRI-based measures of fat distribution and muscle quality. Analyzing the metabolic signatures of these clusters revealed two primary mechanisms connecting higher adiposity to reduced type 2 diabetes risk. The first involves higher adiposity in subcutaneous tissues (abdomen and thigh), lower liver fat, improved insulin sensitivity, and decreased risk of cardiometabolic diseases and diabetes complications. The second mechanism is characterized by increased body size and enhanced muscle quality, with no impact on cardiometabolic outcomes. Furthermore, our findings unveil diverse mechanisms linking higher adiposity to higher disease risk, such as cholesterol pathways or inflammation. These results reinforce the existence of adiposity-related mechanisms that may act as protective factors against type 2 diabetes and its complications, especially when accompanied by reduced ectopic liver fat. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Angela Abraham
- Joseph Banks Laboratories, College of Health and Science, University of Lincoln, Lincoln, U.K
| | | | - Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - M. Amin Hashemloo
- Department of Life Sciences, Brunel University London, Uxbridge, U.K
| | | | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
- MRI Unit, Department of Radiology, The Royal Marsden National Health Service Foundation Trust, London, U.K
| | - Stephen Burgess
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, U.K
| | - Jimmy D. Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, U.K
| | - E. Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, U.K
| | - Hanieh Yaghootkar
- Joseph Banks Laboratories, College of Health and Science, University of Lincoln, Lincoln, U.K
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Figueiredo JC, Bhowmick NA, Karlstaedt A. Metabolic basis of cardiac dysfunction in cancer patients. Curr Opin Cardiol 2024; 39:138-147. [PMID: 38386340 PMCID: PMC11185275 DOI: 10.1097/hco.0000000000001118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
PURPOSE OF REVIEW The relationship between metabolism and cardiovascular diseases is complex and bidirectional. Cardiac cells must adapt metabolic pathways to meet biosynthetic demands and energy requirements to maintain contractile function. During cancer, this homeostasis is challenged by the increased metabolic demands of proliferating cancer cells. RECENT FINDINGS Tumors have a systemic metabolic impact that extends beyond the tumor microenvironment. Lipid metabolism is critical to cancer cell proliferation, metabolic adaptation, and increased cardiovascular risk. Metabolites serve as signals which provide insights for diagnosis and prognosis in cardio-oncology patients. SUMMARY Metabolic processes demonstrate a complex relationship between cancer cell states and cardiovascular remodeling with potential for therapeutic interventions.
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Affiliation(s)
- Jane C. Figueiredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Neil Adri Bhowmick
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA
- Division of Hematology and Oncology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Anja Karlstaedt
- Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
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Abraham A, Yaghootkar H. Identifying obesity subtypes: A review of studies utilising clinical biomarkers and genetic data. Diabet Med 2023; 40:e15226. [PMID: 37704218 DOI: 10.1111/dme.15226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 09/08/2023] [Accepted: 09/12/2023] [Indexed: 09/15/2023]
Abstract
Obesity is a complex and multifactorial condition that poses significant health risks. Recent advancements in our understanding of obesity have highlighted the heterogeneity within this disorder. Identifying distinct subtypes of obesity is crucial for personalised treatment and intervention strategies. This review paper aims to examine studies that have utilised clinical biomarkers and genetic data to identify clusters or subtypes of obesity. The findings of these studies may provide valuable insights into the underlying mechanisms and potential targeted approaches for managing obesity-related health issues such as type 2 diabetes.
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Affiliation(s)
- Angela Abraham
- Joseph Banks Laboratories, College of Health and Science, University of Lincoln, Lincoln, Lincolnshire, UK
| | - Hanieh Yaghootkar
- Joseph Banks Laboratories, College of Health and Science, University of Lincoln, Lincoln, Lincolnshire, UK
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Flanagan EW, Spann R, Berry SE, Berthoud HR, Broyles S, Foster GD, Krakoff J, Loos RJF, Lowe MR, Ostendorf DM, Powell-Wiley TM, Redman LM, Rosenbaum M, Schauer PR, Seeley RJ, Swinburn BA, Hall K, Ravussin E. New insights in the mechanisms of weight-loss maintenance: Summary from a Pennington symposium. Obesity (Silver Spring) 2023; 31:2895-2908. [PMID: 37845825 PMCID: PMC10915908 DOI: 10.1002/oby.23905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/18/2023] [Accepted: 08/04/2023] [Indexed: 10/18/2023]
Abstract
Obesity is a chronic disease that affects more than 650 million adults worldwide. Obesity not only is a significant health concern on its own, but predisposes to cardiometabolic comorbidities, including coronary heart disease, dyslipidemia, hypertension, type 2 diabetes, and some cancers. Lifestyle interventions effectively promote weight loss of 5% to 10%, and pharmacological and surgical interventions even more, with some novel approved drugs inducing up to an average of 25% weight loss. Yet, maintaining weight loss over the long-term remains extremely challenging, and subsequent weight gain is typical. The mechanisms underlying weight regain remain to be fully elucidated. The purpose of this Pennington Biomedical Scientific Symposium was to review and highlight the complex interplay between the physiological, behavioral, and environmental systems controlling energy intake and expenditure. Each of these contributions were further discussed in the context of weight-loss maintenance, and systems-level viewpoints were highlighted to interpret gaps in current approaches. The invited speakers built upon the science of obesity and weight loss to collectively propose future research directions that will aid in revealing the complicated mechanisms involved in the weight-reduced state.
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Affiliation(s)
| | - Redin Spann
- Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA
| | - Sarah E. Berry
- Department of Nutritional Sciences, King’s College London, London, UK
| | | | | | - Gary D. Foster
- WW International, New York, New York, USA
- Center for Weight and Eating Disorders, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jonathan Krakoff
- Obesity and Diabetes Clinical Research Section, Phoenix Epidemiology & Clinical Research Branch, NIDDK-Phoenix, Phoenix, Arizona, USA
| | - Ruth J. F. Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Danielle M. Ostendorf
- Department of Medicine, Anschutz Health and Wellness Center, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- Division of Endocrinology, Metabolism, and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Tiffany M. Powell-Wiley
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
- Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland, USA
| | - Leanne M. Redman
- Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA
| | - Michael Rosenbaum
- Division of Molecular Genetics and Irving Center for Clinical and Translational Research, Columbia University Irving Medical Center, New York, New York, USA
| | | | - Randy J. Seeley
- Department of Surgery, University of Michigan, Ann Arbor, Michigan, USA
| | - Boyd A. Swinburn
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Kevin Hall
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, Maryland, USA
| | - Eric Ravussin
- Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA
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Johansson Å, Andreassen OA, Brunak S, Franks PW, Hedman H, Loos RJ, Meder B, Melén E, Wheelock CE, Jacobsson B. Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification. J Intern Med 2023; 294:378-396. [PMID: 37093654 PMCID: PMC10523928 DOI: 10.1111/joim.13640] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease-both with regards to symptoms and underlying causal mechanisms-and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases-including psychiatric disorders and allergies-available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.
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Affiliation(s)
- Åsa Johansson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala university, Sweden
| | - Ole A. Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopment Research, University of Oslo, Oslo, Norway
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark
- Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, DK-2200 Copenhagen, Denmark
| | - Paul W. Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Sweden
- Novo Nordisk Foundation, Denmark
| | - Harald Hedman
- Department of Medical Biosciences, Umeå University, Umeå, Sweden
| | - Ruth J.F. Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin Meder
- Precision Digital Health, Cardiogenetics Center Heidelberg, Department of Cardiology, University Of Heidelberg, Germany
| | - Erik Melén
- Department of Clinical Sciences and Education, Södersjukhuset, Karolinska Institutet, Stockholm
- Sachś Children and Youth Hospital, Södersjukhuset, Stockholm, Sweden
| | - Craig E Wheelock
- Unit of Integrative Metabolomics, Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
- Department of Respiratory Medicine and Allergy, Karolinska University Hospital, 171 76 Stockholm, Sweden
| | - Bo Jacobsson
- Department of Obstetrics and Gynecology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Obstetrics and Gynaecology, Sahlgrenska University Hospital, Göteborg, Sweden
- Department of Genetics and Bioinformatics, Domain of Health Data and Digitalisation, Institute of Public Health, Oslo, Norway
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Abstract
In this thematic issue on phenotyping the obesities, prominent international experts offer an insightful and comprehensive collection of articles covering the current knowledge in the field. In order to actually capture all the polyhedral determinants of the diverse types of obesity, the granularity of the phenotypic information acquired must be expanded in the context of a personalized approach. Whilst the use of precision medicine has been successfully implemented in areas like cancer and other diseases, health care providers are more reluctant to embrace detailed phenotyping to guide diagnosis, treatment and prevention in obesity. Given its multiple complex layers, phenotyping necessarily needs to go beyond the multi-omics approach and incorporate all the diverse spheres that conform the reality of people living with obesity. Potential barriers, difficulties, roadblocks and opportunities together with their interaction in a syndemic context are analyzed. Plausible lacunae are also highlighted in addition to pointing to the need of redefining new conceptual frameworks. Therefore, this extraordinary collection of state-ofthe-art reviews provides useful information to both experienced clinicians and trainees as well as academics to steer clinical practice and research in the management of people living with obesity irrespective of practice setting or career stage.
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Affiliation(s)
- Piero Portincasa
- Department of Precision and Regenerative Medicine and Ionian Area (DiMePre-J), Clinica Medica "A. Murri", University of Bari Medical School, 70124, Bari, Italy
| | - Gema Frühbeck
- Department of Endocrinology and Nutrition, Clínica Universidad de Navarra, 31008, Pamplona, Spain.
- Metabolic Research Laboratory, Clínica Universidad de Navarra, 31008, Pamplona, Spain.
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad Y Nutrición (CIBEROBN), 31008, Pamplona, Spain.
- Navarra Institute for Health Research, IdiSNA, 31008, Pamplona, Spain.
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Junaid SB, Imam AA, Balogun AO, De Silva LC, Surakat YA, Kumar G, Abdulkarim M, Shuaibu AN, Garba A, Sahalu Y, Mohammed A, Mohammed TY, Abdulkadir BA, Abba AA, Kakumi NAI, Mahamad S. Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey. Healthcare (Basel) 2022; 10:healthcare10101940. [PMID: 36292387 PMCID: PMC9601636 DOI: 10.3390/healthcare10101940] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/26/2022] [Accepted: 09/28/2022] [Indexed: 11/16/2022] Open
Abstract
In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.
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Affiliation(s)
| | - Abdullahi Abubakar Imam
- School of Digital Science, Universiti Brunei Darussalam, Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei
- Correspondence: (A.A.I.); or (A.O.B.)
| | - Abdullateef Oluwagbemiga Balogun
- Department of Computer Science, University of Ilorin, Ilorin 1515, Nigeria
- Department of Computer and Information Science, Universiti Teknologi PETRONAS, Sri Iskandar 32610, Malaysia
- Correspondence: (A.A.I.); or (A.O.B.)
| | | | | | - Ganesh Kumar
- Department of Computer and Information Science, Universiti Teknologi PETRONAS, Sri Iskandar 32610, Malaysia
| | - Muhammad Abdulkarim
- Department of Computer Science, Ahmadu Bello University, Zaria 810211, Nigeria
| | - Aliyu Nuhu Shuaibu
- Department of Electrical Engineering, University of Jos, Bauchi Road, Jos 930105, Nigeria
| | - Aliyu Garba
- Department of Computer Science, Ahmadu Bello University, Zaria 810211, Nigeria
| | - Yusra Sahalu
- SEHA Abu Dhabi Health Services Co., Abu Dhabi 109090, United Arab Emirates
| | - Abdullahi Mohammed
- Department of Computer Science, Ahmadu Bello University, Zaria 810211, Nigeria
| | | | | | | | - Nana Aliyu Iliyasu Kakumi
- Patient Care Department, General Ward, Saudi German Hospital Cairo, Taha Hussein Rd, Huckstep, El Nozha, Cairo Governorate 4473303, Egypt
| | - Saipunidzam Mahamad
- Department of Computer and Information Science, Universiti Teknologi PETRONAS, Sri Iskandar 32610, Malaysia
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Goetzinger C, Alleaume C, Schritz A, Vrijens B, Préau M, Fagherazzi G, Huiart L. Analysing breast cancer survivors’ acceptance profiles for using an electronic pillbox connected to a smartphone application using Seintinelles, a French community-based research tool. Front Pharmacol 2022; 13:889695. [PMID: 36238564 PMCID: PMC9551449 DOI: 10.3389/fphar.2022.889695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 09/05/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction: Up to 50% of breast cancer (BC) survivors discontinue their adjuvant endocrine therapy (AET) before the recommended 5 years, raising the issue of medication non-adherence. eHealth technologies have the potential to support patients to enhance their medication adherence and may offer an effective way to complement the healthcare. In order for eHealth technologies to be successfully implemented into the healthcare system, end-users need to be willing and accepting to use these eHealth technologies. Aim: This study aims to evaluate the current usability of eHealth technologiesin and to identify differences in BC SURVIVORS BC survivors accepting a medication adherence enhancing eHealth technology to support their AET to BC survivors that do not accept such a medication adherence enhancing eHealth technology. Methods: This study was conducted in 2020 including volunteering BC survivors belonging to the Seintinelles Association. Eligible participants were women, diagnosed with BC within the last 10 years, and been exposed to, an AET. Univariable and multivariable logistic regression analyses were performed to investigate medication adherence enhancing eHealth technology acceptance profiles among BC survivors. The dependent variable was defined as acceptance of an electronic pillbox connected to a smartphone application (hereafter: medication adherence enhancing eHealth technology). Results: Overall, 23% of the participants already use a connected device or health application on a regular basis. The mean age of the participants was 52.7 (SD 10.4) years. In total, 67% of 1268 BC survivors who participated in the survey declared that they would accept a medication adherence enhancing eHealth technology to improve their AET. BC survivors accepting a medication adherence enhancing eHealth technology for their AET, are younger (OR = 0.97, 95% CI [0.95; 0.98]), do take medication for other diseases (OR = 0.31, 95% CI [0.13; 0.68]), already use a medication adherence enhancing eHealth technology or technique (OR = 1.74, 95% CI [1.06; 2.94]) and are willing to possess or currently possess one or more connected devices or health applications (OR = 2.89, 95% CI [2.01; 4.19]). Conclusion: Understanding acceptance profiles of BC survivors is fundamental for conceiving an effective eHealth technology enhancing AET among BC survivors. Hence, such profiling will foster the development of personalized medication adherence enhancing eHealth technology.
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Affiliation(s)
- Catherine Goetzinger
- Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
- University of Luxembourg, Faculty of Science, Technology and Medicine, Esch-sur-Alzette, Luxembourg
- *Correspondence: Catherine Goetzinger,
| | | | - Anna Schritz
- Competence Center for Methodology and Statistics, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Bernard Vrijens
- AARDEX Group & Department of Public Health, Liège University, Liège, Belgium
| | - Marie Préau
- Institut de Psychologie, Université Lumière Lyon 2, Lyon, France
| | - Guy Fagherazzi
- Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
- University of Luxembourg, Faculty of Science, Technology and Medicine, Esch-sur-Alzette, Luxembourg
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