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Taylor JK, Peek N, Greenstein AS, Sammut-Powell C, Martin GP, Ahmed FZ. Remotely monitored physical activity from older people with cardiac devices associates with physical functioning. BMC Geriatr 2024; 24:526. [PMID: 38886679 PMCID: PMC11184810 DOI: 10.1186/s12877-024-05083-1] [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: 02/27/2024] [Accepted: 05/16/2024] [Indexed: 06/20/2024] Open
Abstract
INTRODUCTION Accelerometer-derived physical activity (PA) from cardiac devices are available via remote monitoring platforms yet rarely reviewed in clinical practice. We aimed to investigate the association between PA and clinical measures of frailty and physical functioning. METHODS The PATTErn study (A study of Physical Activity paTTerns and major health Events in older people with implantable cardiac devices) enrolled participants aged 60 + undergoing remote cardiac monitoring. Frailty was measured using the Fried criteria and gait speed (m/s), and physical functioning by NYHA class and SF-36 physical functioning score. Activity was reported as mean time active/day across 30-days prior to enrolment (30-day PA). Multivariable regression methods were utilised to estimate associations between PA and frailty/functioning (OR = odds ratio, β = beta coefficient, CI = confidence intervals). RESULTS Data were available for 140 participants (median age 73, 70.7% male). Median 30-day PA across the analysis cohort was 134.9 min/day (IQR 60.8-195.9). PA was not significantly associated with Fried frailty status on multivariate analysis, however was associated with gait speed (β = 0.04, 95% CI 0.01-0.07, p = 0.01) and measures of physical functioning (NYHA class: OR 0.73, 95% CI 0.57-0.92, p = 0.01, SF-36 physical functioning: β = 4.60, 95% CI 1.38-7.83, p = 0.005). CONCLUSIONS PA from cardiac devices was associated with physical functioning and gait speed. This highlights the importance of reviewing remote monitoring PA data to identify patients who could benefit from existing interventions. Further research should investigate how to embed this into clinical pathways.
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Affiliation(s)
- J K Taylor
- Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, University of Manchester, Oxford Road, Manchester, M13 9P, UK.
- Department of Cardiology, Manchester University Hospitals NHS Foundation Trust, Oxford Rd, Manchester, UK.
| | - N Peek
- Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, University of Manchester, Oxford Road, Manchester, M13 9P, UK
- THIS Institute (The Healthcare Improvement Studies Institute), University of Cambridge, Cambridge, UK
| | - A S Greenstein
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - C Sammut-Powell
- Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, University of Manchester, Oxford Road, Manchester, M13 9P, UK
| | - G P Martin
- Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, University of Manchester, Oxford Road, Manchester, M13 9P, UK
| | - F Z Ahmed
- Department of Cardiology, Manchester University Hospitals NHS Foundation Trust, Oxford Rd, Manchester, UK
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
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Souza GS, Furtado BKA, Almeida EB, Callegari B, Pinheiro MDCN. Enhancing public health in developing nations through smartphone-based motor assessment. Front Digit Health 2024; 6:1345562. [PMID: 38835672 PMCID: PMC11148357 DOI: 10.3389/fdgth.2024.1345562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 05/10/2024] [Indexed: 06/06/2024] Open
Abstract
Several protocols for motor assessment have been validated for use on smartphones and could be employed by public healthcare systems to monitor motor functional losses in populations, particularly those with lower income levels. In addition to being cost-effective and widely distributed across populations of varying income levels, the use of smartphones in motor assessment offers a range of advantages that could be leveraged by governments, especially in developing and poorer countries. Some topics related to potential interventions should be considered by healthcare managers before initiating the implementation of such a digital intervention.
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Affiliation(s)
- Givago Silva Souza
- Núcleo de Medicina Tropical, Universidade Federal do Pará, Belém, Brazil
- Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil
| | | | | | - Bianca Callegari
- Núcleo de Medicina Tropical, Universidade Federal do Pará, Belém, Brazil
- Instituto de Ciências da Saúde, Universidade Federal do Pará, Belém, Brazil
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Zhang W, Wang J, Xie F, Wang X, Dong S, Luo N, Li F, Li Y. Development and validation of machine learning models to predict frailty risk for elderly. J Adv Nurs 2024. [PMID: 38605460 DOI: 10.1111/jan.16192] [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: 01/24/2022] [Revised: 03/16/2024] [Accepted: 03/28/2024] [Indexed: 04/13/2024]
Abstract
AIMS Early identification and intervention of the frailty of the elderly will help lighten the burden of social medical care and improve the quality of life of the elderly. Therefore, we used machine learning (ML) algorithm to develop models to predict frailty risk in the elderly. DESIGN A prospective cohort study. METHODS We collected data on 6997 elderly people from Chinese Longitudinal Healthy Longevity Study wave 6-7 surveys (2011-2012, 2014). After the baseline survey in 1998 (wave 1), the project conducted follow-up surveys (wave 2-8) in 2000-2018. The osteoporotic fractures index was used to assess frailty. Four ML algorithms (random forest [RF], support vector machine, XGBoost and logistic regression [LR]) were used to develop models to identify the risk factors of frailty and predict the risk of frailty. Different ML models were used for the prediction of frailty risk in the elderly and frailty risk was trained on a cohort of 4385 elderly people with frailty (split into a training cohort [75%] and internal validation cohort [25%]). The best-performing model for each study outcome was tested in an external validation cohort of 6997 elderly people with frailty pooled from the surveys (wave 6-7). Model performance was assessed by receiver operating curve and F2-score. RESULTS Among the four ML models, the F2-score values were similar (0.91 vs. 0.91 vs. 0.88 vs. 0.90), and the area under the curve (AUC) values of RF model was the highest (0.75), followed by LR model (0.74). In the final two models, the AUC values of RF and LR model were similar (0.77 vs. 0.76) and their accuracy was identical (87.4% vs. 87.4%). CONCLUSION Our study developed a preliminary prediction model based on two different ML approaches to help predict frailty risk in the elderly. IMPACT The presented models from this study can be used to inform healthcare providers to predict the frailty probability among older adults and maybe help guide the development of effective frailty risk management interventions. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE Detecting frailty at an early stage and implementing timely targeted interventions may help to improve the allocation of health care resources and to reduce frailty-related burden. Identifying risk factors for frailty could be beneficial to provide tailored and personalized care intervention for older adults to more accurately prevent or improve their frail conditions so as to improve their quality of life. REPORTING METHOD The study has adhered to STROBE guidelines. PATIENT OR PUBLIC CONTRIBUTION No patient or public contribution.
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Affiliation(s)
- Wei Zhang
- First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Junchao Wang
- China-Japan Union Hospital of Jilin University, Changchun, China
| | - Fang Xie
- Zhejiang University School of Medicine, Hangzhou, China
| | - Xinghui Wang
- School of Nursing, Jilin University, Changchun, China
| | - Shanshan Dong
- Hepatopancreatobiliary Surgery Department, General External Center, First Hospital of Jilin University, Changchun, China
| | - Nan Luo
- The Second Hospital of Jilin University, Changchun, China
| | - Feng Li
- School of Nursing, Jilin University, Changchun, China
| | - Yuewei Li
- School of Nursing, Jilin University, Changchun, China
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Farrahi V, Clare P. Artificial Intelligence and Machine Learning-Powerful Yet Underutilized Tools and Algorithms in Physical Activity and Sedentary Behavior Research. J Phys Act Health 2024; 21:320-322. [PMID: 38335946 DOI: 10.1123/jpah.2024-0021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 01/17/2024] [Indexed: 02/12/2024]
Affiliation(s)
- Vahid Farrahi
- Institute for Sport and Sport Science, TU Dortmund University, Dortmund, Germany
- Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
| | - Philip Clare
- Prevention Research Collaboration, School of Public Health, The University of Sydney, Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
- National Drug and Alcohol Research Centre, UNSW Sydney, Sydney, NSW, Australia
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Mishra RK, Bara RO, Zulbaran-Rojas A, Park C, Fernando ME, Ross J, Lepow B, Najafi B. The Application of Digital Frailty Screening to Triage Nonhealing and Complex Wounds. J Diabetes Sci Technol 2024; 18:389-396. [PMID: 35856398 PMCID: PMC10973858 DOI: 10.1177/19322968221111194] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
OBJECTIVE We investigated the association between the complexity of diabetic foot ulcers (DFUs) and frailty. RESEARCH DESIGN AND METHODS Individuals (n = 38) with Grade 2 Wagner DFU were classified into 3 groups based on the Society for Vascular Surgery risk-stratification for major limb amputation as Stage 1 at very low risk (n = 19), Stage 2 at low risk (n = 9), and Stage 3 to 4 at moderate-to-high risk (n = 10) of major limb amputation. Frailty status was objectively assessed using a validated digital frailty meter (FM). The FM works by quantifying weakness, slowness, rigidity, and exhaustion over a 20-second repetitive elbow flexion-extension exercise using a wrist-worn sensor. FM generates a frailty index (FI) ranging from 0 to 1; higher values indicate progressively greater severity of frailty. Skin perfusion pressure (SPP), albumin, and tissue oxygenation level (SatO2) were also measured. One-way analysis of variance (ANOVA) was used to identify group effect for wound complexity. Pearson's correlation coefficient was used to assess the associations with frailty and clinical endpoints. RESULTS Frailty index was higher in Stage 3 and 4 as compared to Stage 1 (d = 1.4, P < .01) and Stage 2 (d = 1.2, P < .01). Among assessed frailty phenotypes, exhaustion was correlated with SPP (r = -0.63, P < .01) and albumin (r = -0.5, P < .01). CONCLUSION Digital biomarkers of frailty may predict complexity of DFU and thus triage individuals who can be treated more simply in their primary clinic versus higher risk patients who require prompt referral to multidisciplinary, more complex care.
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Affiliation(s)
- Ram Kinker Mishra
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Rasha O. Bara
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Alejandro Zulbaran-Rojas
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
- Division of Vascular Surgery and Endovascular Therapy, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Catherine Park
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
- VA HSR&D Center for Innovations in Quality, Effectiveness, and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, USA
- Big Data Scientist Training Enhancement Program, VA Office of Research and Development, Washington, DC, USA
| | - Malindu E. Fernando
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
- Ulcer and Wound Healing Consortium (UHEAL), Queensland Research Centre for Peripheral Vascular Disease, College of Medicine and Dentistry, James Cook University, Townsville, Queensland, Australia
- Faculty of Health and Medicine, School of Health Sciences, University of Newcastle, Newcastle, New South Wales, Australia
| | - Jeffrey Ross
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
- Division of Vascular Surgery and Endovascular Therapy, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Brian Lepow
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
- Division of Vascular Surgery and Endovascular Therapy, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
- School of Health Professions, Baylor College of Medicine, Houston, TX, USA
| | - Bijan Najafi
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
- Division of Vascular Surgery and Endovascular Therapy, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
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Cay G, Sada YH, Dehghan Rouzi M, Uddin Atique MM, Rodriguez N, Azarian M, Finco MG, Yellapragada S, Najafi B. Harnessing physical activity monitoring and digital biomarkers of frailty from pendant based wearables to predict chemotherapy resilience in veterans with cancer. Sci Rep 2024; 14:2612. [PMID: 38297103 PMCID: PMC10831115 DOI: 10.1038/s41598-024-53025-z] [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: 11/28/2023] [Accepted: 01/26/2024] [Indexed: 02/02/2024] Open
Abstract
This study evaluated the use of pendant-based wearables for monitoring digital biomarkers of frailty in predicting chemotherapy resilience among 27 veteran cancer patients (average age: 64.6 ± 13.4 years), undergoing bi-weekly chemotherapy. Immediately following their first day of chemotherapy cycle, participants wore a water-resistant pendant sensor for 14 days. This device tracked frailty markers like cadence (slowness), daily steps (inactivity), postural transitions (weakness), and metrics such as longest walk duration and energy expenditure (exhaustion). Participants were divided into resilient and non-resilient groups based on adverse events within 6 months post-chemotherapy, including dose reduction, treatment discontinuation, unplanned hospitalization, or death. A Chemotherapy-Resilience-Index (CRI) ranging from 0 to 1, where higher values indicate poorer resilience, was developed using regression analysis. It combined physical activity data with baseline Eastern Cooperative Oncology Group (ECOG) assessments. The protocol showed a 97% feasibility rate, with sensor metrics effectively differentiating between groups as early as day 6 post-therapy. The CRI, calculated using data up to day 6 and baseline ECOG, significantly distinguished resilient (CRI = 0.2 ± 0.27) from non-resilient (CRI = 0.7 ± 0.26) groups (p < 0.001, Cohen's d = 1.67). This confirms the potential of remote monitoring systems in tracking post-chemotherapy functional capacity changes and aiding early non-resilience detection, subject to validation in larger studies.
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Affiliation(s)
- Gozde Cay
- Digital Health and Access Center (DiHAC), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Yvonne H Sada
- Michael E. DeBakey Department of Veterans Affairs Medical Center, Houston, TX, 77030, USA
| | - Mohammad Dehghan Rouzi
- Digital Health and Access Center (DiHAC), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Md Moin Uddin Atique
- Digital Health and Access Center (DiHAC), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Naima Rodriguez
- Digital Health and Access Center (DiHAC), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Mehrnaz Azarian
- Digital Health and Access Center (DiHAC), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - M G Finco
- Digital Health and Access Center (DiHAC), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Sarvari Yellapragada
- Michael E. DeBakey Department of Veterans Affairs Medical Center, Houston, TX, 77030, USA
| | - Bijan Najafi
- Digital Health and Access Center (DiHAC), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA.
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Velazquez-Diaz D, Arco JE, Ortiz A, Pérez-Cabezas V, Lucena-Anton D, Moral-Munoz JA, Galán-Mercant A. Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review. J Med Internet Res 2023; 25:e47346. [PMID: 37862082 PMCID: PMC10625070 DOI: 10.2196/47346] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 05/09/2023] [Accepted: 07/27/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND Frailty syndrome (FS) is one of the most common noncommunicable diseases, which is associated with lower physical and mental capacities in older adults. FS diagnosis is mostly focused on biological variables; however, it is likely that this diagnosis could fail owing to the high biological variability in this syndrome. Therefore, artificial intelligence (AI) could be a potential strategy to identify and diagnose this complex and multifactorial geriatric syndrome. OBJECTIVE The objective of this scoping review was to analyze the existing scientific evidence on the use of AI for the identification and diagnosis of FS in older adults, as well as to identify which model provides enhanced accuracy, sensitivity, specificity, and area under the curve (AUC). METHODS A search was conducted using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines on various databases: PubMed, Web of Science, Scopus, and Google Scholar. The search strategy followed Population/Problem, Intervention, Comparison, and Outcome (PICO) criteria with the population being older adults; intervention being AI; comparison being compared or not to other diagnostic methods; and outcome being FS with reported sensitivity, specificity, accuracy, or AUC values. The results were synthesized through information extraction and are presented in tables. RESULTS We identified 26 studies that met the inclusion criteria, 6 of which had a data set over 2000 and 3 with data sets below 100. Machine learning was the most widely used type of AI, employed in 18 studies. Moreover, of the 26 included studies, 9 used clinical data, with clinical histories being the most frequently used data type in this category. The remaining 17 studies used nonclinical data, most frequently involving activity monitoring using an inertial sensor in clinical and nonclinical contexts. Regarding the performance of each AI model, 10 studies achieved a value of precision, sensitivity, specificity, or AUC ≥90. CONCLUSIONS The findings of this scoping review clarify the overall status of recent studies using AI to identify and diagnose FS. Moreover, the findings show that the combined use of AI using clinical data along with nonclinical information such as the kinematics of inertial sensors that monitor activities in a nonclinical context could be an appropriate tool for the identification and diagnosis of FS. Nevertheless, some possible limitations of the evidence included in the review could be small sample sizes, heterogeneity of study designs, and lack of standardization in the AI models and diagnostic criteria used across studies. Future research is needed to validate AI systems with diverse data sources for diagnosing FS. AI should be used as a decision support tool for identifying FS, with data quality and privacy addressed, and the tool should be regularly monitored for performance after being integrated in clinical practice.
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Affiliation(s)
- Daniel Velazquez-Diaz
- ExPhy Research Group, Department of Physical Education, Faculty of Education Sciences, University of Cadiz, Cádiz, Spain
- Advent Health Research Institute, Neuroscience Institute, Orlando, FL, United States
| | - Juan E Arco
- Department of Communications Engineering, University of Malaga, Málaga, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence, Granada, Spain
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Andres Ortiz
- Department of Communications Engineering, University of Malaga, Málaga, Spain
- Andalusian Research Institute in Data Science and Computational Intelligence, Granada, Spain
| | - Verónica Pérez-Cabezas
- MOVE-IT Research Group, Department of Nursing and Physiotherapy, Faculty of Health Sciences, University of Cádiz, Cádiz, Spain
- Biomedical Research and Innovation Institute of Cádiz, Cádiz, Spain
| | - David Lucena-Anton
- Biomedical Research and Innovation Institute of Cádiz, Cádiz, Spain
- Department of Nursing and Physiotherapy, Faculty of Nursing and Physiotherapy, University of Cadiz, Cádiz, Spain
| | - Jose A Moral-Munoz
- Biomedical Research and Innovation Institute of Cádiz, Cádiz, Spain
- Department of Nursing and Physiotherapy, Faculty of Nursing and Physiotherapy, University of Cadiz, Cádiz, Spain
| | - Alejandro Galán-Mercant
- MOVE-IT Research Group, Department of Nursing and Physiotherapy, Faculty of Health Sciences, University of Cádiz, Cádiz, Spain
- Biomedical Research and Innovation Institute of Cádiz, Cádiz, Spain
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Fan S, Ye J, Xu Q, Peng R, Hu B, Pei Z, Yang Z, Xu F. Digital health technology combining wearable gait sensors and machine learning improve the accuracy in prediction of frailty. Front Public Health 2023; 11:1169083. [PMID: 37546315 PMCID: PMC10402732 DOI: 10.3389/fpubh.2023.1169083] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 06/30/2023] [Indexed: 08/08/2023] Open
Abstract
Background Frailty is a dynamic and complex geriatric condition characterized by multi-domain declines in physiological, gait and cognitive function. This study examined whether digital health technology can facilitate frailty identification and improve the efficiency of diagnosis by optimizing analytical and machine learning approaches using select factors from comprehensive geriatric assessment and gait characteristics. Methods As part of an ongoing study on observational study of Aging, we prospectively recruited 214 individuals living independently in the community of Southern China. Clinical information and fragility were assessed using comprehensive geriatric assessment (CGA). Digital tool box consisted of wearable sensor-enabled 6-min walk test (6MWT) and five machine learning algorithms allowing feature selections and frailty classifications. Results It was found that a model combining CGA and gait parameters was successful in predicting frailty. The combination of these features in a machine learning model performed better than using either CGA or gait parameters alone, with an area under the curve of 0.93. The performance of the machine learning models improved by 4.3-11.4% after further feature selection using a smaller subset of 16 variables. SHapley Additive exPlanation (SHAP) dependence plot analysis revealed that the most important features for predicting frailty were large-step walking speed, average step size, age, total step walking distance, and Mini Mental State Examination score. Conclusion This study provides evidence that digital health technology can be used for predicting frailty and identifying the key gait parameters in targeted health assessments.
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Affiliation(s)
- Shaoyi Fan
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jieshun Ye
- School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China
| | - Qing Xu
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Runxin Peng
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Bin Hu
- Division of Translational Neuroscience, Department of Clinical Neurosciences, Hotchkiss Brain Institute, Alberta Children’s Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Zhong Pei
- Department of Neurology, First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Zhimin Yang
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Fuping Xu
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
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Lorenz EC, Hickson LJ, Khairallah P, Najafi B, Kennedy CC. Cellular Senescence and Frailty in Transplantation. CURRENT TRANSPLANTATION REPORTS 2023; 10:51-59. [PMID: 37576589 PMCID: PMC10414789 DOI: 10.1007/s40472-023-00393-6] [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] [Accepted: 02/16/2023] [Indexed: 03/28/2023]
Abstract
Purpose of review To summarizes the literature on cellular senescence and frailty in solid-organ transplantation and highlight the emerging role of senotherapeutics as a treatment for cellular senescence. Recent findings Solid-organ transplant patients are aging. Many factors contribute to aging acceleration in this population, including cellular senescence. Senescent cells accumulate in tissues and secrete proinflammatory and profibrotic proteins which result in tissue damage. Cellular senescence contributes to age-related diseases and frailty. Our understanding of the role cellular senescence plays in transplant-specific complications such as allograft immunogenicity and infections is expanding. Promising treatments, including senolytics, senomorphics, cell-based regenerative therapies, and behavioral interventions, may reduce cellular senescence abundance and frailty in patients with solid-organ transplants. Summary Cellular senescence and frailty contribute to adverse outcomes in solid-organ transplantation. Continued pursuit of understanding the role cellular senescence plays in transplantation may lead to improved senotherapeutic approaches and better graft and patient outcomes.
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Affiliation(s)
| | - LaTonya J. Hickson
- Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, Florida
| | | | - Bijan Najafi
- Division of Vascular Surgery and Endovascular Therapy, Baylor College of Medicine, Houston, Texas
| | - Cassie C. Kennedy
- Division of Pulmonary, Critical Care, and Sleep Medicine, Mayo Clinic, Rochester, Minnesota
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Mishra RK, Hamad A, Ibrahim R, Mathew M, Talal T, Al-Ali F, Park C, Davuluri V, Fernando ME, Najafi B. Objective assessment of mobility among adults with diabetes and end-stage renal disease using walking aid: A cross-sectional cohort study. Clin Biomech (Bristol, Avon) 2023; 107:106014. [PMID: 37290375 DOI: 10.1016/j.clinbiomech.2023.106014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 05/17/2023] [Accepted: 05/22/2023] [Indexed: 06/10/2023]
Abstract
BACKGROUND This cross-sectional study aimed to compare physical activity levels, plantar sensation, and fear of falling between individuals with diabetes undergoing hemodialysis, with or without walking aids. METHODS Sixty-four participants were recruited, with 37 not using walking aids (age = 65.8 ± 0.7 years, 46% female) and 27 using walking aids (age = 69.2 ± 1.2 years, 63% female). Physical activity was measured using validated pendant sensors over two consecutive days. Concern for falling and plantar numbness were assessed using the Falls Efficacy Scale-International and vibration perception threshold test, respectively. FINDINGS Participants using walking aids exhibited a greater fear of falling (84% vs. 38%, p < 0.01) and fewer walking bouts (p < 0.01, d = 0.67) and stand-to-walk transitions (p < 0.01, d = 0.72) compared to those not using walking aids. The number of walking bouts was negatively correlated with concern for falling scores (ρ = -0.35, p = 0.034) and vibration perception threshold (R = -0.411, p = 0.012) among individuals not using walking aids. However, these correlations were not significant among those using the walking aid. There was no significant group difference in active behavior (walking + standing %) and sedentary behavior (sitting + lying %). INTERPRETATION Those undergoing hemodialysis often lead sedentary lives, with mobility affected by fear of falling and plantar numbness. Using walking aids can help, but it doesn't guarantee more walking. A combined psychosocial and physical therapy approach is key for managing fall concerns and improving mobility.
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Affiliation(s)
- Ram Kinker Mishra
- Interdisciplinary Consortium on Advanced Motion Performance, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Abdullah Hamad
- Department of Nephrology, Hamad General Hospital, Doha, Qatar
| | - Rania Ibrahim
- Department of Nephrology, Hamad General Hospital, Doha, Qatar
| | - Mincy Mathew
- Department of Nephrology, Hamad General Hospital, Doha, Qatar
| | - Talal Talal
- Diabetic Foot and Wound Clinic, Hamad Medical Co, Doha, Qatar
| | - Fadwa Al-Ali
- Department of Nephrology, Hamad General Hospital, Doha, Qatar
| | - Catherine Park
- Interdisciplinary Consortium on Advanced Motion Performance, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Vyshnavi Davuluri
- Interdisciplinary Consortium on Advanced Motion Performance, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Malindu E Fernando
- Interdisciplinary Consortium on Advanced Motion Performance, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA; Ulcer and wound Healing consortium, Queensland Research Centre for Peripheral Vascular Disease, College of Medicine and Dentistry, James Cook University, Townsville, Queensland, Australia; Faculty of Health and Medicine, School of Health Sciences, University of Newcastle, Newcastle, New South Wales, Australia
| | - Bijan Najafi
- Interdisciplinary Consortium on Advanced Motion Performance, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA.
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11
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Freytag J, Mishra RK, Street RL, Catic A, Dindo L, Kiefer L, Najafi B, Naik AD. Using Wearable Sensors to Measure Goal Achievement in Older Veterans with Dementia. SENSORS (BASEL, SWITZERLAND) 2022; 22:9923. [PMID: 36560290 PMCID: PMC9782012 DOI: 10.3390/s22249923] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/01/2022] [Accepted: 12/06/2022] [Indexed: 06/17/2023]
Abstract
Aligning treatment with patients' self-determined goals and health priorities is challenging in dementia care. Wearable-based remote health monitoring may facilitate determining the active participation of individuals with dementia towards achieving the determined goals. The present study aimed to demonstrate the feasibility of using wearables to assess healthcare goals set by older adults with cognitive impairment. We present four specific cases that assess (1) the feasibility of using wearables to monitor healthcare goals, (2) differences in function after goal-setting visits, and (3) goal achievement. Older veterans (n = 17) with cognitive impairment completed self-report assessments of mobility, then had an audio-recorded encounter with a geriatrician and wore a pendant sensor for 48 h. Follow-up was conducted at 4-6 months. Data obtained by wearables augments self-reported data and assessed function over time. Four patient cases illustrate the utility of combining sensors, self-report, notes from electronic health records, and visit transcripts at baseline and follow-up to assess goal achievement. Using data from multiple sources, we showed that the use of wearable devices could support clinical communication, mainly when patients, clinicians, and caregivers work to align care with the patient's priorities.
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Affiliation(s)
- Jennifer Freytag
- Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX 77030, USA
| | - Ram Kinker Mishra
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USA
- BioSensics, Boston, MA 02458, USA
| | - Richard L. Street
- Department of Communications, Texas A&M University, College Station, TX 77843, USA
| | - Angela Catic
- Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX 77030, USA
- Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Lilian Dindo
- Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX 77030, USA
- Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Lea Kiefer
- Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX 77030, USA
- Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Bijan Najafi
- Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX 77030, USA
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Aanand D. Naik
- Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX 77030, USA
- Department of Management, Policy and Community Health, School of Public Health, University of Texas Health Science Center, Houston, TX 77030, USA
- UTHealth Consortium on Aging, University of Texas Health Science Center, Houston, TX 77030, USA
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12
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Mohammadi-Ghazi R, Nguyen H, Mishra RK, Enriquez A, Najafi B, Stephen CD, Gupta AS, Schmahmann JD, Vaziri A. Objective Assessment of Upper-Extremity Motor Functions in Spinocerebellar Ataxia Using Wearable Sensors. SENSORS (BASEL, SWITZERLAND) 2022; 22:7993. [PMID: 36298343 PMCID: PMC9609238 DOI: 10.3390/s22207993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/14/2022] [Accepted: 10/17/2022] [Indexed: 06/16/2023]
Abstract
The study presents a novel approach to objectively assessing the upper-extremity motor symptoms in spinocerebellar ataxia (SCA) using data collected via a wearable sensor worn on the patient's wrist during upper-extremity tasks associated with the Assessment and Rating of Ataxia (SARA). First, we developed an algorithm for detecting/extracting the cycles of the finger-to-nose test (FNT). We extracted multiple features from the detected cycles and identified features and parameters correlated with the SARA scores. Additionally, we developed models to predict the severity of symptoms based on the FNT. The proposed technique was validated on a dataset comprising the seventeen (n = 17) participants' assessments. The cycle detection technique showed an accuracy of 97.6% in a Bland-Altman analysis and a 94% accuracy (F1-score of 0.93) in predicting the severity of the FNT. Furthermore, the dependency of the upper-extremity tests was investigated through statistical analysis, and the results confirm dependency and potential redundancies in the upper-extremity SARA assessments. Our findings pave the way to enhance the utility of objective measures of SCA assessments. The proposed wearable-based platform has the potential to eliminate subjectivity and inter-rater variabilities in assessing ataxia.
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Affiliation(s)
| | - Hung Nguyen
- BioSensics LLC, 57 Chapel St, Newton, MA 02458, USA
| | | | - Ana Enriquez
- BioSensics LLC, 57 Chapel St, Newton, MA 02458, USA
| | - Bijan Najafi
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Christopher D. Stephen
- Ataxia Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, 100 Cambridge St, Boston, MA 02115, USA
| | - Anoopum S. Gupta
- Ataxia Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, 100 Cambridge St, Boston, MA 02115, USA
| | - Jeremy D. Schmahmann
- Ataxia Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, 100 Cambridge St, Boston, MA 02115, USA
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13
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Park C, Atique MMU, Mishra R, Najafi B. Association between Fall History and Gait, Balance, Physical Activity, Depression, Fear of Falling, and Motor Capacity: A 6-Month Follow-Up Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:10785. [PMID: 36078500 PMCID: PMC9517805 DOI: 10.3390/ijerph191710785] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/26/2022] [Accepted: 08/26/2022] [Indexed: 06/10/2023]
Abstract
Maintaining function in older adults is key to the quality of life and longevity. This study examined the potential impact of falls on accelerating further deterioration over time in gait, balance, physical activity, depression, fear of falling, and motor capacity in older adults. 163 ambulatory older adults (age = 76.5 ± 7.7 years) participated and were followed for 6 months. They were classified into fallers or non-fallers based on a history of falling within the past year. At baseline and 6 months, all participants were objectively assessed for gait, balance, and physical activity using wearable sensors. Additional assessments included psychosocial concerns (depression and fear of falling) and motor capacity (Timed Up and Go test). The fallers showed lower gait performance, less physical activity, lower depression level, higher fear of falling, and less motor capacity than non-fallers at baseline and 6-month follow-up. Results also revealed acceleration in physical activity and motor capacity decline compared to non-fallers at a 6-month follow-up. Our findings suggest that falls would accelerate deterioration in both physical activity and motor performance and highlight the need for effective therapy to reduce the consequences of falls in older adults.
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Affiliation(s)
- Catherine Park
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USA
- VA’s Health Services Research and Development Service (HSR&D), Center for Innovations in Quality, Effectiveness, and Safety, Michael E. DeBakey VA Medical Center, Houston, TX 77030, USA
- Big Data Scientist Training Enhancement Program, VA Office of Research and Development, Washington, DC 20420, USA
| | - Md Moin Uddin Atique
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ramkinker Mishra
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Bijan Najafi
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USA
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14
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Schütz N, Knobel SEJ, Botros A, Single M, Pais B, Santschi V, Gatica-Perez D, Buluschek P, Urwyler P, Gerber SM, Müri RM, Mosimann UP, Saner H, Nef T. A systems approach towards remote health-monitoring in older adults: Introducing a zero-interaction digital exhaust. NPJ Digit Med 2022; 5:116. [PMID: 35974156 PMCID: PMC9381599 DOI: 10.1038/s41746-022-00657-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 07/13/2022] [Indexed: 11/09/2022] Open
Abstract
Using connected sensing devices to remotely monitor health is a promising way to help transition healthcare from a rather reactive to a more precision medicine oriented proactive approach, which could be particularly relevant in the face of rapid population ageing and the challenges it poses to healthcare systems. Sensor derived digital measures of health, such as digital biomarkers or digital clinical outcome assessments, may be used to monitor health status or the risk of adverse events like falls. Current research around such digital measures has largely focused on exploring the use of few individual measures obtained through mobile devices. However, especially for long-term applications in older adults, this choice of technology may not be ideal and could further add to the digital divide. Moreover, large-scale systems biology approaches, like genomics, have already proven beneficial in precision medicine, making it plausible that the same could also hold for remote-health monitoring. In this context, we introduce and describe a zero-interaction digital exhaust: a set of 1268 digital measures that cover large parts of a person’s activity, behavior and physiology. Making this approach more inclusive of older adults, we base this set entirely on contactless, zero-interaction sensing technologies. Applying the resulting digital exhaust to real-world data, we then demonstrate the possibility to create multiple ageing relevant digital clinical outcome assessments. Paired with modern machine learning, we find these assessments to be surprisingly powerful and often on-par with mobile approaches. Lastly, we highlight the possibility to discover novel digital biomarkers based on this large-scale approach.
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Affiliation(s)
- Narayan Schütz
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
| | - Samuel E J Knobel
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Angela Botros
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Michael Single
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Bruno Pais
- LaSource School of Nursing Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Lausanne, Switzerland
| | - Valérie Santschi
- LaSource School of Nursing Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Lausanne, Switzerland
| | - Daniel Gatica-Perez
- Idiap Research Institute, Martigny, Switzerland.,School of Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Prabitha Urwyler
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Stephan M Gerber
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - René M Müri
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.,Department of Neurology, Inselspital, Bern, Switzerland
| | - Urs P Mosimann
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Hugo Saner
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.,Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Tobias Nef
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.,Department of Neurology, Inselspital, Bern, Switzerland
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15
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Tang A, Woldemariam S, Roger J, Sirota M. Translational Bioinformatics to Enable Precision Medicine for All: Elevating Equity across Molecular, Clinical, and Digital Realms. Yearb Med Inform 2022; 31:106-115. [PMID: 36463867 PMCID: PMC9719766 DOI: 10.1055/s-0042-1742513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022] Open
Abstract
OBJECTIVES Over the past few years, challenges from the pandemic have led to an explosion of data sharing and algorithmic development efforts in the areas of molecular measurements, clinical data, and digital health. We aim to characterize and describe recent advanced computational approaches in translational bioinformatics across these domains in the context of issues or progress related to equity and inclusion. METHODS We conducted a literature assessment of the trends and approaches in translational bioinformatics in the past few years. RESULTS We present a review of recent computational approaches across molecular, clinical, and digital realms. We discuss applications of phenotyping, disease subtype characterization, predictive modeling, biomarker discovery, and treatment selection. We consider these methods and applications through the lens of equity and inclusion in biomedicine. CONCLUSION Equity and inclusion should be incorporated at every step of translational bioinformatics projects, including project design, data collection, model creation, and clinical implementation. These considerations, coupled with the exciting breakthroughs in big data and machine learning, are pivotal to reach the goals of precision medicine for all.
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Affiliation(s)
- Alice Tang
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
- Graduate Program in Bioengineering, UCSF, San Francisco, CA, USA
- School of Medicine, UCSF, San Francisco, CA, USA
| | - Sarah Woldemariam
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
- School of Medicine, UCSF, San Francisco, CA, USA
| | - Jacquelyn Roger
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
- Graduate Program in Biological and Medical Informatics, UCSF, San Francisco, CA, USA
| | - Marina Sirota
- Bakar Computational Health Sciences Institute, UCSF, San Francisco, CA, USA
- Department of Pediatrics, UCSF, San Francisco, CA, USA
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16
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India's Opportunities and Challenges in Establishing a Twin Registry: An Unexplored Human Resource for the World's Second-Most Populous Nation. Twin Res Hum Genet 2022; 25:156-164. [PMID: 35786423 DOI: 10.1017/thg.2022.24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Nature and nurture have always been a prerogative of evolutionary biologists. The environment's role in shaping an organism's phenotype has always intrigued us. Since the inception of humankind, twinning has existed with an unsettled parley on the contribution of nature (i.e. genetics) versus nurture (i.e. environment), which can influence the phenotypes. The study of twins measures the genetic contribution and that of the environmental influence for a particular trait, acting as a catalyst, fine-tuning the phenotypic trajectories. This is further evident because a number of human diseases show a spectrum of clinical manifestations with the same underlying molecular aberration. As of now, there is no definite way to conclude just from the genomic data the severity of a disease or even to predict who will get affected. This greatly justifies initiating a twin registry for a country as diverse and populated as India. There is an unmet need to set up a nationwide database to carefully curate the information on twins, serving as a valuable biorepository to study their overall susceptibility to disease. Establishing a twin registry is of paramount importance to harness the wealth of human information related to the biomedical, anthropological, cultural, social and economic significance.
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Teh SK, Rawtaer I, Tan HP. Predictive Accuracy of Digital Biomarker Technologies for Detection of Mild Cognitive Impairment and Pre-Frailty Amongst Older Adults: A Systematic Review and Meta-Analysis. IEEE J Biomed Health Inform 2022; 26:3638-3648. [PMID: 35737623 DOI: 10.1109/jbhi.2022.3185798] [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: 11/07/2022]
Abstract
Digital biomarker technologies coupled with predictive models are increasingly applied for early detection of age-related potentially reversible conditions including mild cognitive impairment (MCI) and pre-frailty (PF). We aimed to determine the predictive accuracy of digital biomarker technologies to detect MCI and PF with systematic review and meta-analysis. A computer-assisted search on major academic research databases including IEEE-Xplore was conducted. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines were adopted reporting in this study. Summary receiver operating characteristic curve based on random-effect bivariate model was used to evaluate overall sensitivity and specificity for detection of the respective age-related conditions. A total of 43 studies were selected for final systematic review and meta-analysis. 26 studies reported on detection of MCI with sensitivity and specificity of 0.48-1.00 and 0.55-1.00, respectively. On the other hand, there were 17 studies that reported on the detection of PF with reported sensitivity of 0.53-1.00 and specificity of 0.61-1.00. Meta-analysis further revealed pooled sensitivities of 0.84 (95% CI: 0.79-0.88) and 0.82 (95% CI: 0.74-0.88) for in-home detection of MCI and PF, respectively, while pooled specificities were 0.85 (95% CI: 0.80-0.89) and 0.82 (95% CI: 0.75-0.88), respectively. Besides MCI, and PF, in this work during systematic review, we also found one study which reported a sensitivity of 0.93 and a specificity of 0.57 for detection of cognitive frailty (CF). The meta-analytic result, for the first time, quantifies the predictive efficacy of digital biomarker technologies for detection of MCI and PF. Additionally, we found the number of studies for detection of CF to be notably lower, indicating possible research gaps to explore predictive models on digital biomarker technology for detection of CF.
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Dimitriadis I, Mavroudopoulos I, Kyrama S, Toliopoulos T, Gounaris A, Vakali A, Billis A, Bamidis P. Scalable real-time health data sensing and analysis enabling collaborative care delivery. SOCIAL NETWORK ANALYSIS AND MINING 2022. [DOI: 10.1007/s13278-022-00891-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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19
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Mishra RK, Thrasher AT. Effect of concurrent transcranial direct current stimulation on instrumented timed up and go task performance in people with Parkinson's disease: A double-blind and cross-over study. J Clin Neurosci 2022; 100:184-191. [PMID: 35487026 DOI: 10.1016/j.jocn.2022.04.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 03/25/2022] [Accepted: 04/22/2022] [Indexed: 11/28/2022]
Abstract
Transcranial direct current stimulation (tDCS) delivered to the dorsolateral prefrontal cortex (DLPFC) can improve mobility among people with Parkinson's disease (PD). Previous studies suggest that delivering tDCS during task performance might be beneficial. However, only a few studies explored the effect of combining tDCS with task. We investigated the effect of stimulating the DLPFC using anodal tDCS while performing a timed up and go (TUG) test and its sustained effects. In this sham-controlled, cross-over, and double-blind study, twenty participants with PD (age = 67.8 ± 8.3 years and 6 females) completed two sessions (anodal or sham tDCS), conducted in the randomized and counterbalanced manner, with at least a 1-week gap. Stimulation involved transferring 2 mA current through the DLPFC for 30 min. Single-trial of TUG test was performed under single- and dual-task conditions before, during, immediately after, 15 and 30 min after stimulation ceased. We estimated durations of completing different components of TUG. Phoneme verbal fluency task was given as the cognitive distractor during the dual-tasking. An improvement was observed in cognitive performance due to the tDCS condition (d = 0.7, p < 0.01) over time. However, we found no effect of tDCS condition on iTUG related outcomes under single- or dual-task conditions. In conclusion, DLPFC stimulation combined with task improved cognitive performance only, and the improvement was sustained after tDCS ceased. Future studies may investigate stimulating multiple brain regions to improve motor and cognitive performance.
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Affiliation(s)
- Ram Kinker Mishra
- Center for Neuromotor and Biomechanics Research, Department of Health and Human Performance, University of Houston, Houston, TX, USA.
| | - Adam Timothy Thrasher
- Center for Neuromotor and Biomechanics Research, Department of Health and Human Performance, University of Houston, Houston, TX, USA.
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