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Mercurio G, Gottardelli B, Lenkowicz J, Patarnello S, Bellavia S, Scala I, Rizzo P, de Belvis AG, Del Signore AB, Maviglia R, Bocci MG, Olivi A, Franceschi F, Urbani A, Calabresi P, Valentini V, Antonelli M, Frisullo G. A novel risk score predicting 30-day hospital re-admission of patients with acute stroke by machine learning model. Eur J Neurol 2024; 31:e16153. [PMID: 38015472 PMCID: PMC11235732 DOI: 10.1111/ene.16153] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 09/29/2023] [Accepted: 10/31/2023] [Indexed: 11/29/2023]
Abstract
BACKGROUND The 30-day hospital re-admission rate is a quality measure of hospital care to monitor the efficiency of the healthcare system. The hospital re-admission of acute stroke (AS) patients is often associated with higher mortality rates, greater levels of disability and increased healthcare costs. The aim of our study was to identify predictors of unplanned 30-day hospital re-admissions after discharge of AS patients and define an early re-admission risk score (RRS). METHODS This observational, retrospective study was performed on AS patients who were discharged between 2014 and 2019. Early re-admission predictors were identified by machine learning models. The performances of these models were assessed by receiver operating characteristic curve analysis. RESULTS Of 7599 patients with AS, 3699 patients met the inclusion criteria, and 304 patients (8.22%) were re-admitted within 30 days from discharge. After identifying the predictors of early re-admission by logistic regression analysis, RRS was obtained and consisted of seven variables: hemoglobin level, atrial fibrillation, brain hemorrhage, discharge home, chronic obstructive pulmonary disease, one and more than one hospitalization in the previous year. The cohort of patients was then stratified into three risk categories: low (RRS = 0-1), medium (RRS = 2-3) and high (RRS >3) with re-admission rates of 5%, 8% and 14%, respectively. CONCLUSIONS The identification of risk factors for early re-admission after AS and the elaboration of a score to stratify at discharge time the risk of re-admission can provide a tool for clinicians to plan a personalized follow-up and contain healthcare costs.
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Affiliation(s)
- Giovanna Mercurio
- Department of Emergency Science, Anesthesiology and Intensive CareFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - Benedetta Gottardelli
- Department of Diagnostic Imaging, Oncological Radiotherapy and HematologyUniversità Cattolica del Sacro CuoreRomeItaly
| | - Jacopo Lenkowicz
- Gemelli Generator RWD, Fondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - Stefano Patarnello
- Gemelli Generator RWD, Fondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - Simone Bellavia
- Department of Aging, Neurological, Orthopedic and Head and Neck SciencesFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
- Catholic University of Sacred HeartRomeItaly
| | - Irene Scala
- Department of Aging, Neurological, Orthopedic and Head and Neck SciencesFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
- Catholic University of Sacred HeartRomeItaly
| | - Pierandrea Rizzo
- Department of Aging, Neurological, Orthopedic and Head and Neck SciencesFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
- Catholic University of Sacred HeartRomeItaly
| | - Antonio Giulio de Belvis
- Department of Life Sciences and Public Health, Section of HygieneUniversità Cattolica del Sacro CuoreRomeItaly
- Clinical Pathways and Outcome Evaluation UnitFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - Anna Benedetta Del Signore
- Department of Emergency Science, Anesthesiology and Intensive CareFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
- Global Medical Department‐Primary Care Unit, Angelini PharmaRomeItaly
| | - Riccardo Maviglia
- Department of Emergency Science, Anesthesiology and Intensive CareFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - Maria Grazia Bocci
- Department of Emergency Science, Anesthesiology and Intensive CareFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - Alessandro Olivi
- Department of Aging, Neurological, Orthopedic and Head and Neck SciencesFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
- Catholic University of Sacred HeartRomeItaly
| | - Francesco Franceschi
- Department of Emergency Science, Anesthesiology and Intensive CareFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
- Catholic University of Sacred HeartRomeItaly
| | - Andrea Urbani
- Catholic University of Sacred HeartRomeItaly
- Department of Laboratory and Infectious SciencesFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
| | - Paolo Calabresi
- Department of Aging, Neurological, Orthopedic and Head and Neck SciencesFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
- Catholic University of Sacred HeartRomeItaly
| | - Vincenzo Valentini
- Department of Diagnostic Imaging, Oncological Radiotherapy and HematologyUniversità Cattolica del Sacro CuoreRomeItaly
- Catholic University of Sacred HeartRomeItaly
| | - Massimo Antonelli
- Department of Emergency Science, Anesthesiology and Intensive CareFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
- Catholic University of Sacred HeartRomeItaly
| | - Giovanni Frisullo
- Department of Aging, Neurological, Orthopedic and Head and Neck SciencesFondazione Policlinico Universitario A. Gemelli IRCCSRomeItaly
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Schönenberger N, Meyer-Massetti C. Risk factors for medication-related short-term readmissions in adults - a scoping review. BMC Health Serv Res 2023; 23:1037. [PMID: 37770912 PMCID: PMC10536731 DOI: 10.1186/s12913-023-10028-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 09/12/2023] [Indexed: 09/30/2023] Open
Abstract
BACKGROUND Hospital readmissions due to medication-related problems occur frequently, burdening patients and caregivers emotionally and straining health care systems economically. In times of limited health care resources, interventions to mitigate the risk of medication-related readmissions should be prioritized to patients most likely to benefit. Focusing on general internal medicine patients, this scoping review aims to identify risk factors associated with drug-related 30-day hospital readmissions. METHODS We began by searching the Medline, Embase, and CINAHL databases from their inception dates to May 17, 2022 for studies reporting risk factors for 30-day drug-related readmissions. We included all peer-reviewed studies, while excluding literature reviews, conference abstracts, proceeding papers, editorials, and expert opinions. We also conducted backward citation searches of the included articles. Within the final sample, we analyzed the types and frequencies of risk factors mentioned. RESULTS After deduplication of the initial search results, 1159 titles and abstracts were screened for full-text adjudication. We read 101 full articles, of which we included 37. Thirteen more were collected via backward citation searches, resulting in a final sample of 50 articles. We identified five risk factor categories: (1) patient characteristics, (2) medication groups, (3) medication therapy problems, (4) adverse drug reactions, and (5) readmission diagnoses. The most commonly mentioned risk factors were polypharmacy, prescribing problems-especially underprescribing and suboptimal drug selection-and adherence issues. Medication groups associated with the highest risk of 30-day readmissions (mostly following adverse drug reactions) were antithrombotic agents, insulin, opioid analgesics, and diuretics. Preventable medication-related readmissions most often reflected prescribing problems and/or adherence issues. CONCLUSIONS This study's findings will help care teams prioritize patients for interventions to reduce medication-related hospital readmissions, which should increase patient safety. Further research is needed to analyze surrogate social parameters for the most common drug-related factors and their predictive value regarding medication-related readmissions.
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Affiliation(s)
- N Schönenberger
- Clinical Pharmacology and Toxicology, Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland.
| | - C Meyer-Massetti
- Clinical Pharmacology and Toxicology, Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Institute of Primary Healthcare (BIHAM), University of Bern, Bern, Switzerland
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Laura T, Melvin C, Yoong DY. Depressive symptoms and malnutrition are associated with other geriatric syndromes and increase risk for 30-Day readmission in hospitalized older adults: a prospective cohort study. BMC Geriatr 2022; 22:634. [PMID: 35918652 PMCID: PMC9344637 DOI: 10.1186/s12877-022-03343-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 07/27/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Readmission in older adults is typically complex with multiple contributing factors. We aim to examine how two prevalent and potentially modifiable geriatric conditions - depressive symptoms and malnutrition - relate to other geriatric syndromes and 30-day readmission in hospitalized older adults. METHODS Consecutive admissions of patients ≥ 65 years to a general medical department were recruited over 16 months. Patients were screened for depression, malnutrition, delirium, cognitive impairment, and frailty at admission. Medical records were reviewed for poor oral intake and functional decline during hospitalization. Unplanned readmission within 30-days of discharge was tracked through the hospital's electronic health records and follow-up telephone interviews. We use directed acyclic graphs (DAGs) to depict the relationship of depressive symptoms and malnutrition with geriatric syndromes that constitute covariates of interest and 30-day readmission outcome. Multiple logistic regression was performed for the independent associations of depressive symptoms and malnutrition with 30-day readmission, adjusting for variables based on DAG-identified minimal adjustment set. RESULTS We recruited 1619 consecutive admissions, with mean age 76.4 (7.9) years and 51.3% females. 30-day readmission occurred in 331 (22.0%) of 1,507 patients with follow-up data. Depressive symptoms, malnutrition, higher comorbidity burden, hospitalization in the one-year preceding index admission, frailty, delirium, as well as functional decline and poor oral intake during the index admission, were more commonly observed among patients who were readmitted within 30 days of discharge (P < 0.05). Patients with active depressive symptoms were significantly more likely to be frail (OR = 1.62, 95% CI 1.22-2.16), had poor oral intake (OR = 1.35, 95% CI 1.02-1.79) and functional decline during admission (OR = 1.58, 95% CI 1.11-2.23). Malnutrition at admission was significantly associated with frailty (OR = 1.53, 95% CI 1.07-2.19), delirium (OR = 2.33, 95% CI 1.60-3.39) cognitive impairment (OR = 1.88, 95% CI 1.39-2.54) and poor oral intake during hospitalization (OR = 2.70, 95% CI 2.01-3.64). In minimal adjustment set identified by DAG, depressive symptoms (OR = 1.38, 95% CI 1.02-1.86) remained significantly associated with 30-day readmission. The association of malnutrition with 30-day readmission was no longer statistically significant after adjusting for age, ethnicity and depressive symptoms in the minimal adjustment set (OR = 1.40, 95% CI 0.99-1.98). CONCLUSION The observed causal associations support screening and targeted interventions for depressive symptoms and malnutrition during admission and in the post-acute period.
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Affiliation(s)
- Tay Laura
- Department of General Medicine, Sengkang General Hospital, 110 Sengkang East Way, 544886, Singapore, Singapore. .,Geriatric Education and Research Institute, Singapore, Singapore.
| | - Chua Melvin
- Department of General Medicine, Sengkang General Hospital, 110 Sengkang East Way, 544886, Singapore, Singapore
| | - Ding Yew Yoong
- Geriatric Education and Research Institute, Singapore, Singapore.,Department of Geriatric Medicine, Tan Tock Seng Hospital, Singapore, Singapore
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Armitage MN, Srivastava V, Allison BK, Williams MV, Brandt-Sarif M, Lee G. A prospective cohort study of two predictor models for 30-day emergency readmission in older patients. Int J Clin Pract 2021; 75:e14478. [PMID: 34107148 DOI: 10.1111/ijcp.14478] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 06/06/2021] [Indexed: 11/27/2022] Open
Abstract
AIM To undertake a prospective study of the accuracy of two models (LACE and BOOST) in predicting unplanned hospital readmission in older patients (>75 years). METHODS Data were collected from a single centre prospectively on 110 patients over 75 years old admitted to the acute medical unit. Follow-up was conducted at 30 days. The primary outcome was the c-statistic for both models. RESULTS The readmission rate was 32.7% and median age 82 years, and both BOOST and LACE scores were significantly higher in those readmitted compared with those who were not. C-statistics were calculated for both tools with BOOST score 0.667 (95% CI 0.559-0.775, P = .005) and LACE index 0.685 (95% CI 0.579-0.792, P = .002). CONCLUSION In this prospective study, both the BOOST and LACE scores were found to be significant yet poor, predictive models of hospital readmission. Recent hospitalisation (within the previous 6 months) was found to be the most significant contributing factor.
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Affiliation(s)
| | | | | | | | | | - Geraldine Lee
- Florence Nightingale Faculty of Nursing, Midwifery & Palliative Care, King's College London, London, UK
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Man vs. Machine: Comparing Physician vs. Electronic Health Record-Based Model Predictions for 30-Day Hospital Readmissions. J Gen Intern Med 2021; 36:2555-2562. [PMID: 33443694 PMCID: PMC8390613 DOI: 10.1007/s11606-020-06355-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 11/19/2020] [Indexed: 10/22/2022]
Abstract
BACKGROUND Electronic health record (EHR)-based readmission risk prediction models can be automated in real-time but have modest discrimination and may be missing important readmission risk factors. Clinician predictions of readmissions may incorporate information unavailable in the EHR, but the comparative usefulness is unknown. We sought to compare clinicians versus a validated EHR-based prediction model in predicting 30-day hospital readmissions. METHODS We conducted a prospective survey of internal medicine clinicians in an urban safety-net hospital. Clinicians prospectively predicted patients' 30-day readmission risk on 5-point Likert scales, subsequently dichotomized into low- vs. high-risk. We compared human with machine predictions using discrimination, net reclassification, and diagnostic test characteristics. Observed readmissions were ascertained from a regional hospitalization database. We also developed and assessed a "human-plus-machine" logistic regression model incorporating both human and machine predictions. RESULTS We included 1183 hospitalizations from 106 clinicians, with a readmission rate of 20.8%. Both clinicians and the EHR model had similar discrimination (C-statistic 0.66 vs. 0.66, p = 0.91). Clinicians had higher specificity (79.0% vs. 48.9%, p < 0.001) but lower sensitivity (43.9 vs. 75.2%, p < 0.001) than EHR model predictions. Compared with machine, human was better at reclassifying non-readmissions (non-event NRI + 30.1%) but worse at reclassifying readmissions (event NRI - 31.3%). A human-plus-machine approach best optimized discrimination (C-statistic 0.70, 95% CI 0.67-0.74), sensitivity (65.5%), and specificity (66.7%). CONCLUSION Clinicians had similar discrimination but higher specificity and lower sensitivity than EHR model predictions. Human-plus-machine was better than either alone. Readmission risk prediction strategies should incorporate clinician assessments to optimize the accuracy of readmission predictions.
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Schultz MA, Walden RL, Cato K, Coviak CP, Cruz C, D'Agostino F, Douthit BJ, Forbes T, Gao G, Lee MA, Lekan D, Wieben A, Jeffery AD. Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review. Comput Inform Nurs 2021; 39:654-667. [PMID: 34747890 PMCID: PMC8578863 DOI: 10.1097/cin.0000000000000705] [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] [Indexed: 11/26/2022]
Abstract
Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploration.
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Affiliation(s)
- Mary Anne Schultz
- Author Affiliations: California State University (Dr Schultz); Annette and Irwin Eskind Family Biomedical Library, Vanderbilt University (Ms Walden); Department of Emergency Medicine, Columbia University School of Nursing (Dr Cato); Grand Valley State University (Dr Coviak); Global Health Technology & Informatics, Chevron, San Ramon, CA (Mr Cruz); Saint Camillus International University of Health Sciences, Rome, Italy (Dr D'Agostino); Duke University School of Nursing (Mr Douthit); East Carolina University College of Nursing (Dr Forbes); St Catherine University Department of Nursing (Dr Gao); Texas Woman's University College of Nursing (Dr Lee); Assistant Professor, University of North Carolina at Greensboro School of Nursing (Dr Lekan); University of Wisconsin School of Nursing (Ms Wieben); and Vanderbilt University School of Nursing, and Tennessee Valley Healthcare System, US Department of Veterans Affairs (Dr Jeffery)
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Chakurian D, Popejoy L. Utilizing the care coordination Atlas as a framework: An integrative review of transitional care models. INTERNATIONAL JOURNAL OF CARE COORDINATION 2021. [DOI: 10.1177/20534345211001615] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Introduction Care coordination reduces care fragmentation and costs while improving health care quality. Transitional care programs, guided by tested models are an important component of effective care coordination, and have been found to reduce adverse events and prevent hospital readmissions. Using the Care Coordination Atlas as a framework, this article reports an integrative review of two transitional care models including analysis of model components, implementation factors, and associated 30-day all-cause hospital readmission rates. Methods Integrative review methodology. PubMed and Scopus databases were searched from January 2015 to July 2020. Fourteen studies set in 18 skilled nursing facilities and 50 hospitals were selected for data extraction and analysis. Results The ReEngineered Discharge model had five components and the Better Outcomes by Optimizing Safe Transitions model had eight components in the nine Care Coordination Atlas domains. Communication dominated activities in both models while neither addressed accountability/responsibility. Implementation was influenced by leadership commitment to understanding complexity of the models, culture change, integration of models into workflows, and associated labor costs. Model implementation studies consistently reported improvements in facilities’ 30-day all-cause hospital readmission rates. Discussion The Care Coordination Atlas was a useful framework to guide analysis of transitional care models. Leadership commitment to and participation in model implementation is vital. The models do not focus beyond the immediate post-discharge period limiting the impact on chronic disease management. Frameworks such as the Care Coordination Atlas are useful to help guide development of care coordination activities and associations with readmission rates.
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Wan CS, Reijnierse EM, Maier AB. Risk Factors of Readmissions in Geriatric Rehabilitation Patients: RESORT. Arch Phys Med Rehabil 2021; 102:1524-1532. [PMID: 33607077 DOI: 10.1016/j.apmr.2021.01.082] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 01/16/2021] [Accepted: 01/19/2021] [Indexed: 01/10/2023]
Abstract
OBJECTIVE To evaluate the risk factors associated with 30- and 90-day hospital readmissions in geriatric rehabilitation inpatients. DESIGN Observational, prospective longitudinal inception cohort. SETTING Tertiary hospital in Victoria, Australia. PARTICIPANTS Geriatric rehabilitation inpatients of the REStORing Health of Acutely Unwell AdulTs (RESORT) cohort evalutated by a comprehensive geriatric assessment including potential readmission risk factors (ie, demographic, social support, lifestyle, functional performance, quality of life, morbidity, length of stay in an acute ward). Of 693 inpatients, 11 died during geriatric rehabilitation. The mean age of the remaining 682 inpatients was 82.2±7.8 years, and 56.7% were women. INTERVENTIONS Not applicable. MAIN OUTCOME MEASURES Thirty- and 90-day readmissions after discharge from geriatric inpatient rehabilitation. RESULTS The 30- and 90-day unplanned all-cause readmission rates were 11.6% and 25.2%, respectively. Risk factors for 30- and 90-day readmissions were as follows: did not receive tertiary education, lower quality of life, higher Charlson Comorbidity Index and Cumulative Illness Rating Scale (CIRS) scores, and a higher number of medications used in the univariable models. Formal care was associated with increased risk for 90-day readmissions. In multivariable models, CIRS score was a significant risk factor for 30-day readmissions, whereas high fear of falling and CIRS score were significant risk factors for 90-day readmissions. CONCLUSIONS High fear of falling and CIRS score were independent risk factors for readmission in geriatric rehabilitation inpatients. These variables should be included in hospital readmission risk prediction model developments for geriatric rehabilitation inpatients.
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Affiliation(s)
- Ching S Wan
- Department of Medicine and Aged Care, @AgeMelbourne, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia
| | - Esmee M Reijnierse
- Department of Medicine and Aged Care, @AgeMelbourne, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia; Department of Human Movement Sciences, @AgeAmsterdam, Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences, Vrije Universiteit, Amsterdam, The Netherlands
| | - Andrea B Maier
- Department of Medicine and Aged Care, @AgeMelbourne, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia; Department of Human Movement Sciences, @AgeAmsterdam, Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences, Vrije Universiteit, Amsterdam, The Netherlands; Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Centre for Healthy Longevity, @AgeSingapore, National University Health System, Singapore.
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