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Yao Z, Zou W, Zhang X, Nie P, Lv H, Wang W, Zhao X, Yang Y, Yang L. Integrating mid-infrared spectroscopy, machine learning, and graphical bias correction for fatty acid prediction in water buffalo milk. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024. [PMID: 38501395 DOI: 10.1002/jsfa.13471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 02/25/2024] [Accepted: 03/19/2024] [Indexed: 03/20/2024]
Abstract
BACKGROUND Buffalo milk, constituting 15% of global production, has higher fatty acids content than Holstein milk. Fourier-transform mid-infrared (FT-MIR) spectroscopy is widely used for dairy analysis, but its application to buffalo milk, with larger fat globules, remains understudied. The ultimate goal of this study is to develop machine learning models based on FT-MIR spectroscopy for predicting fatty acids in buffalo milk and to assess the accuracy of commercial milk analyzers. This research provides a convenient, fast, and environmentally friendly method for detecting the fatty acid composition in buffalo milk. RESULTS We employed six machine learning algorithms to establish a detection model for 34 fatty acids in buffalo milk. The predictive models demonstrated robust capabilities for high-content fatty acids [C14:0, C15:0, C16:0, C17:0, C18:0, C18:1, saturated fatty acid (SFA), monounsaturated fatty acid (MUFA)], with errors within a 15% range. Traditional FT6000 detection methods exhibited limitations in measuring SFAs and polyunsaturated fatty acids (PUFA). Implementing a mean difference correction of 0.21 for MUFAs and applying regression equations (SFA × 1.0639 + 0.0705; PUFA × 0.5472 + 0.0047) significantly improved measurement accuracy. CONCLUSION This study successfully developed a predictive model for fatty acids in Mediterranean buffalo milk based on FT-MIR spectroscopy. Additionally, a correction was applied to the existing measurement device, FT6000, enabling more accurate measurements of fatty acids in buffalo milk. The findings have practical implications for the food industry, offering a faster and more reliable approach to assess and monitor fatty acid composition in buffalo milk, potentially influencing product development and quality control processes. © 2024 Society of Chemical Industry.
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Affiliation(s)
- Zhiqiu Yao
- International Joint Research Center for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Wenna Zou
- International Joint Research Center for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Xinxin Zhang
- International Joint Research Center for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Pei Nie
- International Joint Research Center for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Hunan Agricultural University, Changsha, China
| | - Haimiao Lv
- International Joint Research Center for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Wei Wang
- International Joint Research Center for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Xuhong Zhao
- International Joint Research Center for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Ying Yang
- International Joint Research Center for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Liguo Yang
- International Joint Research Center for Animal Genetics, Breeding and Reproduction (IJRCAGBR), Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
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McCrossan P, Shields MD, McElnay JC. Medication Adherence in Children with Asthma. Patient Prefer Adherence 2024; 18:555-564. [PMID: 38476591 PMCID: PMC10929205 DOI: 10.2147/ppa.s445534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 02/06/2024] [Indexed: 03/14/2024] Open
Abstract
Asthma is the most common chronic disease in childhood. If untreated, asthma can lead to debilitating daily symptoms which affect quality of life, but more importantly can lead to fatal asthma attacks which unfortunately still occur globally. The most effective treatment strategy for controlling asthma is for the patient to follow a personalised asthma action plan (PAAP) which will invariably include regular use of an inhaled corticosteroid. To examine medication adherence in children with asthma, we collated recent evidence from systematic reviews in this area to address the following 5 key questions; What is adherence? Is there evidence that children are not adhering to preventer medication? Why is adherence poor and what are the barriers to adherence? Does good adherence improve outcomes in asthma? And lastly, how can treatment adherence be improved?
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Affiliation(s)
- Paddy McCrossan
- Paediatric Respiratory Medicine, Royal Belfast Hospital for Sick Children, Belfast, Northern Ireland
| | - Michael D Shields
- Wellcome-Wolfson Institute for Experimental Medicine, Queen’s University Belfast, Belfast, Northern Ireland
| | - James C McElnay
- School of Pharmacy, Queen’s University Belfast, Belfast, Northern Ireland
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Xu Y, Zheng X, Li Y, Ye X, Cheng H, Wang H, Lyu J. Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review. J Evid Based Med 2023; 16:342-375. [PMID: 37718729 DOI: 10.1111/jebm.12548] [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] [Received: 07/04/2023] [Accepted: 08/30/2023] [Indexed: 09/19/2023]
Abstract
BACKGROUND Increasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data-rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery. METHODS We conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications. RESULTS Our study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision-making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality. CONCLUSION Advanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.
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Affiliation(s)
- Yixian Xu
- Department of Anesthesiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xinkai Zheng
- Department of Dermatology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yuanjie Li
- Planning & Discipline Construction Office, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Xinmiao Ye
- Department of Anesthesiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Hongtao Cheng
- School of Nursing, Jinan University, Guangzhou, China
| | - Hao Wang
- Department of Anesthesiology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jun Lyu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangzhou, China
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Establishment and Validation of Fourier Transform Infrared Spectroscopy (FT–MIR) Methodology for the Detection of Linoleic Acid in Buffalo Milk. Foods 2023; 12:foods12061199. [PMID: 36981127 PMCID: PMC10048274 DOI: 10.3390/foods12061199] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 02/28/2023] [Accepted: 03/10/2023] [Indexed: 03/14/2023] Open
Abstract
Buffalo milk is a dairy product that is considered to have a higher nutritional value compared to cow’s milk. Linoleic acid (LA) is an essential fatty acid that is important for human health. This study aimed to investigate and validate the use of Fourier transform mid-infrared spectroscopy (FT-MIR) for the quantification of the linoleic acid in buffalo milk. Three machine learning models were used to predict linoleic acid content, and random forest was employed to select the most important subset of spectra for improved model performance. The validity of the FT-MIR methods was evaluated in accordance with ICH Q2 (R1) guidelines using the accuracy profile method, and the precision, the accuracy, and the limit of quantification were determined. The results showed that Fourier transform infrared spectroscopy is a suitable technique for the analysis of linoleic acid, with a lower limit of quantification of 0.15 mg/mL milk. Our results showed that FT-MIR spectroscopy is a viable method for LA concentration analysis.
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Bosnic-Anticevich S, Bakerly ND, Chrystyn H, Hew M, van der Palen J. Advancing Digital Solutions to Overcome Longstanding Barriers in Asthma and COPD Management. Patient Prefer Adherence 2023; 17:259-272. [PMID: 36741814 PMCID: PMC9891071 DOI: 10.2147/ppa.s385857] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 12/09/2022] [Indexed: 01/30/2023] Open
Abstract
Maintenance therapy delivered via inhaler is central to asthma and chronic obstructive pulmonary disease (COPD) management. Poor adherence to inhaled medication and errors in inhalation technique have long represented major barriers to the optimal management of these chronic conditions. Technological innovations may provide a means of overcoming these barriers. This narrative review examines ongoing advances in digital technologies relevant to asthma and COPD with the potential to inform clinical decision-making and improve patient care. Digital inhaler devices linked to mobile apps can help bring about changes in patients' behaviors and attitudes towards disease management, particularly when they build in elements of interactivity and gamification. They can also support ongoing technique education, empowering patients and helping providers maximize the value of consultations and develop effective action plans informed by insights into the patient's inhaler use patterns and their respiratory health. When combined with innovative techniques such as machine learning, digital devices have the potential to predict exacerbations and prompt pre-emptive intervention. Finally, digital devices may support an advanced precision medicine approach to respiratory disease management and help support shared decision-making. Further work is needed to increase uptake of digital devices and integrate their use into care pathways before their full potential in personalized asthma and COPD management can be realized.
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Affiliation(s)
- Sinthia Bosnic-Anticevich
- Woolcock Institute of Medical Research, University of Sydney, Sydney, NSW, Australia
- Correspondence: Sinthia Bosnic-Anticevich, Woolcock Institute of Medical Research, 431 Glebe Point Road, Glebe, 2037, NSW, Australia, Tel +61 414 015 614, Email
| | - Nawar Diar Bakerly
- Manchester Metropolitan University, Manchester, United Kingdom, Salford Royal NHS Foundation Trust, Manchester, UK
| | | | - Mark Hew
- Allergy, Asthma, and Clinical Immunology, Alfred Health, Melbourne, VIC, Australia
| | - Job van der Palen
- Medical School Twente, Medisch Spectrum Twente, Enschede, the Netherlands, and Section Cognition, Data and Education, University of Twente, Enschede, the Netherlands
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Bousquet J, Sousa-Pinto B, Anto J, Amaral R, Brussino L, Canonica G, Cruz A, Gemicioglu B, Haahtela T, Kupczyk M, Kvedariene V, Larenas-Linnemann D, Louis R, Pham-Thi N, Puggioni F, Regateiro F, Romantowski J, Sastre J, Scichilone N, Taborda-Barata L, Ventura M, Agache I, Bedbrook A, Bergmann K, Bosnic-Anticevich S, Bonini M, Boulet LP, Brusselle G, Buhl R, Cecchi L, Charpin D, Chaves-Loureiro C, Czarlewski W, de Blay F, Devillier P, Joos G, Jutel M, Klimek L, Kuna P, Laune D, Pech J, Makela M, Morais-Almeida M, Nadif R, Niedoszytko M, Ohta K, Papadopoulos N, Papi A, Yeverino D, Roche N, Sá-Sousa A, Samolinski B, Shamji M, Sheikh A, Suppli Ulrik C, Usmani O, Valiulis A, Vandenplas O, Yorgancioglu A, Zuberbier T, Fonseca J. Identification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air® mHealth app. Pulmonology 2022:S2531-0437(22)00252-5. [DOI: 10.1016/j.pulmoe.2022.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 11/23/2022] Open
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Tsang KCH, Pinnock H, Wilson AM, Salvi D, Shah SA. Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol. BMJ Open 2022; 12:e064166. [PMID: 36192103 PMCID: PMC9535155 DOI: 10.1136/bmjopen-2022-064166] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
INTRODUCTION Supported self-management empowering people with asthma to detect early deterioration and take timely action reduces the risk of asthma attacks. Smartphones and smart monitoring devices coupled with machine learning could enhance self-management by predicting asthma attacks and providing tailored feedback.We aim to develop and assess the feasibility of an asthma attack predictor system based on data collected from a range of smart devices. METHODS AND ANALYSIS A two-phase, 7-month observational study to collect data about asthma status using three smart monitoring devices, and daily symptom questionnaires. We will recruit up to 100 people via social media and from a severe asthma clinic, who are at risk of attacks and who use a pressurised metered dose relief inhaler (that fits the smart inhaler device).Following a preliminary month of daily symptom questionnaires, 30 participants able to comply with regular monitoring will complete 6 months of using smart devices (smart peak flow meter, smart inhaler and smartwatch) and daily questionnaires to monitor asthma status. The feasibility of this monitoring will be measured by the percentage of task completion. The occurrence of asthma attacks (definition: American Thoracic Society/European Respiratory Society Task Force 2009) will be detected by self-reported use (or increased use) of oral corticosteroids. Monitoring data will be analysed to identify predictors of asthma attacks. At the end of the monitoring, we will assess users' perspectives on acceptability and utility of the system with an exit questionnaire. ETHICS AND DISSEMINATION Ethics approval was provided by the East of England - Cambridge Central Research Ethics Committee. IRAS project ID: 285 505 with governance approval from ACCORD (Academic and Clinical Central Office for Research and Development), project number: AC20145. The study sponsor is ACCORD, the University of Edinburgh.Results will be reported through peer-reviewed publications, abstracts and conference posters. Public dissemination will be centred around blogs and social media from the Asthma UK network and shared with study participants.
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Affiliation(s)
- Kevin Cheuk Him Tsang
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Hilary Pinnock
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Andrew M Wilson
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Norwich Medical School, University of East Anglia, Norwich, UK
- Norwich University Hospital Foundation Trust, Colney Lane, Norwich, UK
| | - Dario Salvi
- Internet of Things and People Research Centre, Malmo University, Malmo, Sweden
| | - Syed Ahmar Shah
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
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Tsang KCH, Pinnock H, Wilson AM, Shah SA. Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review. J Asthma Allergy 2022; 15:855-873. [PMID: 35791395 PMCID: PMC9250768 DOI: 10.2147/jaa.s285742] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 06/16/2022] [Indexed: 12/21/2022] Open
Abstract
Background Asthma is a variable long-term condition. Currently, there is no cure for asthma and the focus is, therefore, on long-term management. Mobile health (mHealth) is promising for chronic disease management but to be able to realize its potential, it needs to go beyond simply monitoring. mHealth therefore needs to leverage machine learning to provide tailored feedback with personalized algorithms. There is a need to understand the extent of machine learning that has been leveraged in the context of mHealth for asthma management. This review aims to fill this gap. Methods We searched PubMed for peer-reviewed studies that applied machine learning to data derived from mHealth for asthma management in the last five years. We selected studies that included some human data other than routinely collected in primary care and used at least one machine learning algorithm. Results Out of 90 studies, we identified 22 relevant studies that were then further reviewed. Broadly, existing research efforts can be categorized into three types: 1) technology development, 2) attack prediction, 3) patient clustering. Using data from a variety of devices (smartphones, smartwatches, peak flow meters, electronic noses, smart inhalers, and pulse oximeters), most applications used supervised learning algorithms (logistic regression, decision trees, and related algorithms) while a few used unsupervised learning algorithms. The vast majority used traditional machine learning techniques, but a few studies investigated the use of deep learning algorithms. Discussion In the past five years, many studies have successfully applied machine learning to asthma mHealth data. However, most have been developed on small datasets with internal validation at best. Small sample sizes and lack of external validation limit the generalizability of these studies. Future research should collect data that are more representative of the wider asthma population and focus on validating the derived algorithms and technologies in a real-world setting.
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Affiliation(s)
- Kevin C H Tsang
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Hilary Pinnock
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Andrew M Wilson
- Asthma UK Centre for Applied Research, and Norwich Medical School, University of East Anglia, Norwich, UK
| | - Syed Ahmar Shah
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
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Tay TR, van Boven JFM, Chan A, Hew M. Electronic Inhaler Monitoring for Chronic Airway Disease: Development and Application of a Multidimensional Efficacy Framework. THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY. IN PRACTICE 2022; 10:1189-1201.e1. [PMID: 34915225 DOI: 10.1016/j.jaip.2021.11.027] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 11/12/2021] [Accepted: 11/23/2021] [Indexed: 06/14/2023]
Abstract
Inhaled therapy is the cornerstone of chronic airway disease therapy, but poor adherence to controller inhalers worsens clinical outcomes and increases cost. Monitoring of controller use is needed to improve adherence, and monitoring of reliever use can predict impending exacerbations. Both can be accurately achieved by electronic inhaler monitoring (EIM). However, evidence for EIM use in clinical practice is limited and varied, and knowledge gaps remain across different outcomes and health settings. We aimed to develop a framework to assess EIM systematically across all aspects of efficacy, apply this framework to the current literature, and identify gaps in efficacy to inform future development in the field. We adapted an existing framework for diagnostic tests, consisting of six levels of efficacy with ascending clinical relevance: technical, diagnostic accuracy, diagnostic thinking, therapeutic, patient outcome, and societal efficacy. Tailoring this framework to EIM, we incorporated expert feedback and applied it to the EIM efficacy literature. We found that EIM has good diagnostic accuracy, diagnostic thinking, and therapeutic efficacies, but evidence is lacking for specific aspects of technical, patient outcome, and societal efficacies. Further development of EIM requires improved reliability, usability, and data security for patients, and optimal integration with electronic medical records and overall patient care. Defining appropriate target patient groups and pairing EIM data with effective interventions, in conjunction with reducing costs through technological innovation and economies of scale, will enhance patient and societal outcome efficacies.
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Affiliation(s)
- Tunn Ren Tay
- Department of Respiratory and Critical Care Medicine, Changi General Hospital, Singapore
| | - Job F M van Boven
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD, University of Groningen, Groningen, the Netherlands; Centre for Medicine Use and Safety, Monash Institute of Pharmaceutical Sciences, Monash University, Melbourne, Victoria, Australia; Medication Adherence Expertise Center of the Northern Netherlands, Groningen, the Netherlands
| | - Amy Chan
- School of Pharmacy, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Mark Hew
- Allergy, Asthma, and Clinical Immunology, Alfred Hospital, Melbourne, Victoria, Australia; School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
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Novel Methods of Measuring Adherence Patterns Reveal Adherence Phenotypes with Distinct Asthma Outcomes. Ann Am Thorac Soc 2021; 19:933-942. [PMID: 34936847 DOI: 10.1513/annalsats.202106-653oc] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
RATIONALE Poor adherence with asthma controller medication contributes to worse symptom control and increased exacerbation risk. Adherence is often expressed as the mean proportion of prescribed doses taken over a period of 6-12 months. New metrics may capture individual day-to-day variability patterns linked with distinct clinical outcomes. OBJECTIVES To test the hypotheses that novel time- and dose-based adherence variability metrics offer independent value to mean adherence in identifying distinct adherence patterns, that are associated with symptom control (Asthma Control Test [ACT] score) and exacerbation risk, using electronically-recorded medication data from a 6-month cluster randomized trial examining the effect of inhaler reminders on adherence. METHODS Adherence metrics were calculated from the first two months (months 0-2) of the study period. In addition to mean adherence (%prescribed puffs/day taken, PTmean), we examined novel metrics including: time adherence area-under-curve (T-AUC), reflecting cumulative gaps in adherence over time, entropy (H), reflecting disorder in the ways in which a patient changed their medication dose adherence from day to day, and standard deviation of the %prescribed puffs/day taken (PTSD). Dominant metrics identified from factor analysis were included in hierarchical clustering analysis. We compared the resultant clusters in terms of outcomes over months 2-6, and exacerbation risk over the entire study period. RESULTS Two factors explained >65% of the total variance in adherence, primarily driven by T-AUC and H. Two main patient clusters based on their adherence metrics were identified: Cluster 1 ("high time adherence", n=75) had better T-AUC, i.e. fewer gaps between medication-taking days, than Cluster 2 ("low time adherence", n=23). Though both clusters had similar symptom control at 2 months, Cluster 1 showed less subsequent decline in ACT over months 2-6 (median(IQR) change in ACT score: 1(-1, 4) vs -2(-3.75,0.75); p=0.012), and had better symptom control at 6 months (ACT score: 20(17, 23) vs 17 (15, 20); p=0.034). There were no significant differences between the clusters in terms of proportion of exacerbators or time to exacerbation. CONCLUSIONS Novel metrics showed that low time adherence was associated with greater risk of decline in asthma symptom control. Adherence patterns may exhibit 'memory' relevant to future clinical status, warranting validation in a larger dataset.
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Chan AHY, Pleasants RA, Dhand R, Tilley SL, Schworer SA, Costello RW, Merchant R. Digital Inhalers for Asthma or Chronic Obstructive Pulmonary Disease: A Scientific Perspective. Pulm Ther 2021; 7:345-376. [PMID: 34379316 PMCID: PMC8589868 DOI: 10.1007/s41030-021-00167-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 06/21/2021] [Indexed: 11/25/2022] Open
Abstract
Impressive advances in inhalation therapy for patients with asthma and chronic obstructive pulmonary disease (COPD) have occurred in recent years. However, important gaps in care remain, particularly relating to poor adherence to inhaled therapies. Digital inhaler health platforms which incorporate digital inhalers to monitor time and date of dosing are an effective disease and medication management tool, promoting collaborative care between clinicians and patients, and providing more in-depth understanding of actual inhaler use. With advances in technology, nearly all inhalers can be digitalized with add-on or embedded sensors to record and transmit data quantitating inhaler actuations, and some have additional capabilities to evaluate inhaler technique. In addition to providing an objective and readily available measure of adherence, they allow patients to interact with the device directly or through their self-management smartphone application such as via alerts and recording of health status. Clinicians can access these data remotely and during patient encounters, to better inform them about disease status and medication adherence and inhaler technique. The ability for remote patient monitoring is accelerating interest in and the use of these devices in clinical practice and research settings. More than 20 clinical studies of digital inhalers in asthma or COPD collectively show improvement in medication adherence, exacerbation risk, and patient outcomes with digital inhalers. These studies support previous findings about patient inhaler use and behaviors, but with greater granularity, and reveal some new findings about patient medication-taking behaviors. Digital devices that record inspiratory flows with inhaler use can guide proper inhaler technique and may prove to be a clinically useful lung function measure. Adoption of digital inhalers into practice is still early, and additional research is needed to determine patient and clinician acceptability, the appropriate place of these devices in the therapeutic regimen, and their cost effectiveness. Video: Digital Inhalers for Asthma or Chronic Obstructive Pulmonary Disease: A Scientific Perspective (MP4 74535 kb)
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Affiliation(s)
- Amy H. Y. Chan
- Faculty of Medical and Health Sciences, University of Auckland, Auckland, 1023 New Zealand
| | - Roy A. Pleasants
- Division of Pulmonary Diseases and Critical Care Medicine, University of North Carolina Chapel Hill, Chapel Hill, NC USA
| | - Rajiv Dhand
- Division of Pulmonary and Critical Care Medicine, University of Tennessee Graduate School of Medicine, Knoxville, TN USA
| | - Stephen L. Tilley
- Division of Pulmonary Diseases and Critical Care Medicine, University of North Carolina Chapel Hill, Chapel Hill, NC USA
| | - Stephen A. Schworer
- Division of Rheumatology, Allergy, and Immunology, Department of Medicine, University of North Carolina Chapel Hill, Chapel Hill, NC USA
| | - Richard W. Costello
- Royal College of Surgeons Ireland, 123 St Stephen’s Green, Dublin 2, D02 YN77 Ireland
| | - Rajan Merchant
- Dignity Health Woodland Clinic, 632 W Gibson Rd, Woodland, CA USA
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