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Alameraw TA, Asemahagn MA, Gashu KD, Walle AD, Kelkay JM, Mitiku AB, Dube GN, Guadie HA. Intention to use telemonitoring for chronic illness management and its associated factors among nurses and physicians at public hospitals in Bahir Dar, northwest Ethiopia: using a modified UTAUT2 model. FRONTIERS IN HEALTH SERVICES 2025; 5:1460077. [PMID: 40115328 PMCID: PMC11923627 DOI: 10.3389/frhs.2025.1460077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2024] [Accepted: 01/14/2025] [Indexed: 03/23/2025]
Abstract
Background Patients with chronic illnesses need to take care of themselves and seek ongoing medical attention. By using technology, telemonitoring can minimize hospitalization and care costs, while increasing professional productivity, providing constant medical attention and enhancing patient self-care management. Despite all these advantages, nothing is known regarding the intentions of Ethiopian professionals and nurses to adopt telemonitoring technologies. Therefore, the purpose of this study is to evaluate the telemonitoring intention of Ethiopian professionals and nurses, as well as the factors related to it. Methods A total of 781 randomly chosen nurses and physicians who worked at public hospitals in Bahir Dar City, northwest Ethiopia, participated in a cross-sectional survey. To give everyone an opportunity, the sample size was distributed equitably among the hospitals and the profession according to staffing numbers. The sample was obtained using a simple random sampling technique. Data were gathered by skilled data collectors utilizing a self-administered questionnaire. For additional cleaning and descriptive statistical analysis, the data were imported into EpiData version 4.6 and exported to Statistical Package for Social Science version 25. Analysis of Moment Structure version 23 structural equation modeling was used to ascertain the degree of the association between the variables. Result The response rate was 732/781 (93.7%), with 55.7% (408/732) of the participants being men and two-thirds (67.6%, 495/732) being nurses. About 55.9% [95% confidence interval (CI): 52.3-59.6] of respondents intended to use telemonitoring. The desire to employ telemonitoring is positively impacted by performance expectancy (β = 0.375, 95% CI: 0.258-0.494), effort expectancy (β = 0.158, 95% CI: 0.058-0.252), facilitating condition (β = 0.255, 95% CI: 0.144-0.368), and habit (β = 0.147, 95% CI: 0.059-0.233). Age and gender positively affected the link between effort expectancy and intention to employ telemonitoring. It was discovered that being young and male has a beneficial relationship impact. Age positively moderated the association between the intention to use telemonitoring and the facilitating conditions, and adults were strongly linked with the relationship. Conclusion In Bahir Dar City public hospitals, over half of the doctors and nurses have the intention to use telemonitoring. Predictive indicators of intention to utilize telemonitoring that were statistically significant were performance expectancy, effort expectancy, facilitating condition, and habit.
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Affiliation(s)
- Temesgen Ayenew Alameraw
- Department of Health Informatics, School of Public Health, College of Medicine and Health Sciences, Arba Minch University, Arba Minch, Ethiopia
| | - Mulusew Andualem Asemahagn
- Department of Health System Management and Health Economics, School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia
| | - Kassahun Dessie Gashu
- Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Agmasie Damtew Walle
- Department of Health Informatics, College of Health Sciences, Mettu University, Mettu, Ethiopia
| | - Jenberu Mekurianew Kelkay
- Department of Health Informatics, School of Public Health, College of Medicine and Health Sciences, Dilla University, Dilla, Ethiopia
| | - Abebaw Belew Mitiku
- Department of Health Informatics, Arba Minch College of Health Sciences, Arba Minch, Ethiopia
| | - Geleta Nenko Dube
- Department of Health Informatics, College of Health Sciences, Mettu University, Mettu, Ethiopia
| | - Habtamu Alganeh Guadie
- Department of Health System Management and Health Economics, School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia
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Viderman D, Seri E, Aubakirova M, Abdildin Y, Badenes R, Bilotta F. Reply to Farahani, P.; Wahid, L. Comment on "Viderman et al. Remote Monitoring of Chronic Critically Ill Patients after Hospital Discharge: A Systematic Review. J. Clin. Med. 2022, 11, 1010". J Clin Med 2023; 12:6797. [PMID: 37959262 PMCID: PMC10648006 DOI: 10.3390/jcm12216797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/15/2023] Open
Abstract
Thank you very much for taking the time to read this systematic review and for sharing your thoughts [...].
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Affiliation(s)
- Dmitriy Viderman
- Department of Surgery, Section of Anesthesiology, Intensive Care, and Pain Medicine, Nazarbayev University School of Medicine, Astana 010000, Kazakhstan;
| | - Elena Seri
- Department of Anesthesiology, Critical Care and Pain Medicine, Policlinico Umberto I, “Sapienza” University of Rome, 00185 Rome, Italy; (E.S.); (F.B.)
| | - Mina Aubakirova
- Department of Biomedical Sciences, Nazarbayev University School of Medicine, Astana 010000, Kazakhstan;
| | - Yerkin Abdildin
- Department of Mechanical and Aerospace Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, Kazakhstan;
| | - Rafael Badenes
- Department of Anesthesiology and Surgical-Trauma Intensive Care, Hospital Clínic Universitari, University of Valencia, 46010 Valencia, Spain
| | - Federico Bilotta
- Department of Anesthesiology, Critical Care and Pain Medicine, Policlinico Umberto I, “Sapienza” University of Rome, 00185 Rome, Italy; (E.S.); (F.B.)
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Farahani P, Wahid L. Comment on Viderman et al. Remote Monitoring of Chronic Critically Ill Patients after Hospital Discharge: A Systematic Review. J. Clin. Med. 2022, 11, 1010. J Clin Med 2023; 12:6433. [PMID: 37892570 PMCID: PMC10606988 DOI: 10.3390/jcm12206433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 10/03/2023] [Accepted: 10/07/2023] [Indexed: 10/29/2023] Open
Abstract
Viderman et al. undertook this systematic review with the intent of providing evidence on the potential application of remote monitoring in chronic critically ill patients post-hospital discharge [...].
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Affiliation(s)
- Parisa Farahani
- Division of General Internal Medicine, Department of Medicine, Duke University Medical Center, Durham, NC 27710, USA;
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Alvarado E, Grágeda N, Luzanto A, Mahu R, Wuth J, Mendoza L, Stern RM, Yoma NB. Automatic Detection of Dyspnea in Real Human-Robot Interaction Scenarios. SENSORS (BASEL, SWITZERLAND) 2023; 23:7590. [PMID: 37688044 PMCID: PMC10490721 DOI: 10.3390/s23177590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/20/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023]
Abstract
A respiratory distress estimation technique for telephony previously proposed by the authors is adapted and evaluated in real static and dynamic HRI scenarios. The system is evaluated with a telephone dataset re-recorded using the robotic platform designed and implemented for this study. In addition, the original telephone training data are modified using an environmental model that incorporates natural robot-generated and external noise sources and reverberant effects using room impulse responses (RIRs). The results indicate that the average accuracy and AUC are just 0.4% less than those obtained with matched training/testing conditions with simulated data. Quite surprisingly, there is not much difference in accuracy and AUC between static and dynamic HRI conditions. Moreover, the beamforming methods delay-and-sum and MVDR lead to average improvement in accuracy and AUC equal to 8% and 2%, respectively, when applied to training and testing data. Regarding the complementarity of time-dependent and time-independent features, the combination of both types of classifiers provides the best joint accuracy and AUC score.
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Affiliation(s)
- Eduardo Alvarado
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile; (E.A.); (N.G.); (A.L.); (R.M.); (J.W.)
| | - Nicolás Grágeda
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile; (E.A.); (N.G.); (A.L.); (R.M.); (J.W.)
| | - Alejandro Luzanto
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile; (E.A.); (N.G.); (A.L.); (R.M.); (J.W.)
| | - Rodrigo Mahu
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile; (E.A.); (N.G.); (A.L.); (R.M.); (J.W.)
| | - Jorge Wuth
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile; (E.A.); (N.G.); (A.L.); (R.M.); (J.W.)
| | - Laura Mendoza
- Hospital Clínico Universidad de Chile, Santiago 8380420, Chile;
- Clínica Alemana, Santiago 7630000, Chile
| | - Richard M. Stern
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA;
| | - Néstor Becerra Yoma
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile; (E.A.); (N.G.); (A.L.); (R.M.); (J.W.)
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Coutu FA, Iorio OC, Ross BA. Remote patient monitoring strategies and wearable technology in chronic obstructive pulmonary disease. Front Med (Lausanne) 2023; 10:1236598. [PMID: 37663662 PMCID: PMC10470466 DOI: 10.3389/fmed.2023.1236598] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/03/2023] [Indexed: 09/05/2023] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is highly prevalent and is associated with a heavy burden on patients and health systems alike. Exacerbations of COPD (ECOPDs) are a leading cause of acute hospitalization among all adult chronic diseases. There is currently a paradigm shift in the way that ECOPDs are conceptualized. For the first time, objective physiological parameters are being used to define/classify what an ECOPD is (including heart rate, respiratory rate, and oxygen saturation criteria) and therefore a mechanism to monitor and measure their changes, particularly in an outpatient ambulatory setting, are now of great value. In addition to pre-existing challenges on traditional 'in-person' health models such as geography and seasonal (ex. winter) impacts on the ability to deliver in-person visit-based care, the COVID-19 pandemic imposed additional stressors including lockdowns, social distancing, and the closure of pulmonary function labs. These health system stressors, combined with the new conceptualization of ECOPDs, rapid advances in sophistication of hardware and software, and a general openness by stakeholders to embrace this technology, have all influenced the propulsion of remote patient monitoring (RPM) and wearable technology in the modern care of COPD. The present article reviews the use of RPM and wearable technology in COPD. Context on the influences, factors and forces which have helped shape this health system innovation is provided. A focused summary of the literature of RPM in COPD is presented. Finally, the practical and ethical principles which must guide the transition of RPM in COPD into real-world clinical use are reviewed.
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Affiliation(s)
- Felix-Antoine Coutu
- Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Department of Medicine, McGill University, Montreal, QC, Canada
| | - Olivia C. Iorio
- Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Bryan A. Ross
- Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Department of Medicine, McGill University, Montreal, QC, Canada
- Division of Respiratory Medicine, Department of Medicine, McGill University Health Centre, Montreal, QC, Canada
- Montreal Chest Institute, McGill University Health Centre, Montreal, QC, Canada
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Lubkowski P, Krygier J, Sondej T, Dobrowolski AP, Apiecionek L, Znaniecki W, Oskwarek P. Decision Support System Proposal for Medical Evacuations in Military Operations. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115144. [PMID: 37299871 DOI: 10.3390/s23115144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/20/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023]
Abstract
The area of military operations is a big challenge for medical support. A particularly important factor that allows medical services to react quickly in the case of mass casualties is the ability to rapidly evacuation of wounded soldiers from a battlefield. To meet this requirement, an effective medical evacuation system is essential. The paper presented the architecture of the electronically supported decision support system for medical evacuation during military operations. The system can also be used by other services such as police or fire service. The system meets the requirements for tactical combat casualty care procedures and is composed of following elements: measurement subsystem, data transmission subsystem and analysis and inference subsystem. The system, based on the continuous monitoring of selected soldiers' vital signs and biomedical signals, automatically proposes a medical segregation of wounded soldiers (medical triage). The information on the triage was visualized using the Headquarters Management System for medical personnel (first responders, medical officers, medical evacuation groups) and for commanders, if required. All elements of the architecture were described in the paper.
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Affiliation(s)
- Piotr Lubkowski
- Faculty of Electronics, Military University of Technology, Gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland
| | - Jaroslaw Krygier
- Faculty of Electronics, Military University of Technology, Gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland
| | - Tadeusz Sondej
- Faculty of Electronics, Military University of Technology, Gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland
| | - Andrzej P Dobrowolski
- Faculty of Electronics, Military University of Technology, Gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland
| | - Lukasz Apiecionek
- Institute of Computer Science, Kazimierz Wielki University, Jana Karola Chodkiewicza 30, 85-064 Bydgoszcz, Poland
- Teldat Sp. z o.o. sp.k, Cicha 19, 85-650 Bydgoszcz, Poland
| | | | - Pawel Oskwarek
- Military Institute of Medicine-National Research Institute, Szaserów 128, 04-141 Warsaw, Poland
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Alvarado E, Grágeda N, Luzanto A, Mahu R, Wuth J, Mendoza L, Yoma NB. Dyspnea Severity Assessment Based on Vocalization Behavior with Deep Learning on the Telephone. SENSORS (BASEL, SWITZERLAND) 2023; 23:2441. [PMID: 36904646 PMCID: PMC10007248 DOI: 10.3390/s23052441] [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: 12/13/2022] [Revised: 02/08/2023] [Accepted: 02/17/2023] [Indexed: 06/18/2023]
Abstract
In this paper, a system to assess dyspnea with the mMRC scale, on the phone, via deep learning, is proposed. The method is based on modeling the spontaneous behavior of subjects while pronouncing controlled phonetization. These vocalizations were designed, or chosen, to deal with the stationary noise suppression of cellular handsets, to provoke different rates of exhaled air, and to stimulate different levels of fluency. Time-independent and time-dependent engineered features were proposed and selected, and a k-fold scheme with double validation was adopted to select the models with the greatest potential for generalization. Moreover, score fusion methods were also investigated to optimize the complementarity of the controlled phonetizations and features that were engineered and selected. The results reported here were obtained from 104 participants, where 34 corresponded to healthy individuals and 70 were patients with respiratory conditions. The subjects' vocalizations were recorded with a telephone call (i.e., with an IVR server). The system provided an accuracy of 59% (i.e., estimating the correct mMRC), a root mean square error equal to 0.98, false positive rate of 6%, false negative rate of 11%, and an area under the ROC curve equal to 0.97. Finally, a prototype was developed and implemented, with an ASR-based automatic segmentation scheme, to estimate dyspnea on line.
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Affiliation(s)
- Eduardo Alvarado
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile
| | - Nicolás Grágeda
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile
| | - Alejandro Luzanto
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile
| | - Rodrigo Mahu
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile
| | - Jorge Wuth
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile
| | - Laura Mendoza
- Clinical Hospital, University of Chile, Santiago 8380420, Chile
| | - Néstor Becerra Yoma
- Speech Processing and Transmission Laboratory, Electrical Engineering Department, University of Chile, Santiago 8370451, Chile
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Optical Chemical Sensor Based on Fast-Protein Liquid Chromatography for Regular Peritoneal Protein Loss Assessment in End-Stage Renal Disease Patients on Continuous Ambulatory Peritoneal Dialysis. CHEMOSENSORS 2022. [DOI: 10.3390/chemosensors10060232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Point-of-care testing (POCT) devices are becoming increasingly popular in the medical community as an alternative to conventional laboratory testing, especially for home treatments or other forms of outpatient care. Multiple-use chemical sensors with minimal requirements for disposables are among the most practical and cost-effective POC diagnostic instruments, especially in managing chronic conditions. An affordable, simple, and easy-to-use optical sensor based on fast protein liquid chromatography with direct UV absorption detection was developed for the rapid determination of the total protein concentration in effluent peritoneal dialysate and for the assessment of protein losses in end-stage renal disease (ESRD) patients on constant ambulatory peritoneal dialysis (CAPD). The sensor employs non-disposable PD-10 desalting columns for the separation of molecules with different molecular weights and a deep UV LED (maximum at 285 nm) as a light source for optical detection. The analytic procedure is relatively simple, takes 10–15 min, and potentially can be performed by patients themselves or nursing staff without laboratory training. Preliminary clinical trials on a group of 23 patients on CAPD revealed a good concordance between the protein concentrations in dialysate samples measured with the sensor and an automated biochemical analyzer; the mean relative error was about 10%, which is comparable with routine clinical laboratory methods.
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