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Grant S, Tonkin E, Craddock I, Blom A, Holmes M, Judge A, Masullo A, Perello Nieto M, Song H, Whitehouse M, Flach P, Gooberman-Hill R. Toward Enhanced Clinical Decision Support for Patients Undergoing a Hip or Knee Replacement: Focus Group and Interview Study With Surgeons. JMIR Perioper Med 2023; 6:e36172. [PMID: 37093626 PMCID: PMC10167586 DOI: 10.2196/36172] [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: 01/07/2022] [Revised: 11/14/2022] [Accepted: 02/16/2023] [Indexed: 04/25/2023] Open
Abstract
BACKGROUND The current assessment of recovery after total hip or knee replacement is largely based on the measurement of health outcomes through self-report and clinical observations at follow-up appointments in clinical settings. Home activity-based monitoring may improve assessment of recovery by enabling the collection of more holistic information on a continuous basis. OBJECTIVE This study aimed to introduce orthopedic surgeons to time-series analyses of patient activity data generated from a platform of sensors deployed in the homes of patients who have undergone primary total hip or knee replacement and understand the potential role of these data in postoperative clinical decision-making. METHODS Orthopedic surgeons and registrars were recruited through a combination of convenience and snowball sampling. Inclusion criteria were a minimum required experience in total joint replacement surgery specific to the hip or knee or familiarity with postoperative recovery assessment. Exclusion criteria included a lack of specific experience in the field. Of the 9 approached participants, 6 (67%) orthopedic surgeons and 3 (33%) registrars took part in either 1 of 3 focus groups or 1 of 2 interviews. Data were collected using an action-based approach in which stimulus materials (mock data visualizations) provided imaginative and creative interactions with the data. The data were analyzed using a thematic analysis approach. RESULTS Each data visualization was presented sequentially followed by a discussion of key illustrative commentary from participants, ending with a summary of key themes emerging across the focus group and interview data set. CONCLUSIONS The limitations of the evidence are as follows. The data presented are from 1 English hospital. However, all data reflect the views of surgeons following standard national approaches and training. Although convenience sampling was used, participants' background, skills, and experience were considered heterogeneous. Passively collected home monitoring data offered a real opportunity to more objectively characterize patients' recovery from surgery. However, orthopedic surgeons highlighted the considerable difficulty in navigating large amounts of complex data within short medical consultations with patients. Orthopedic surgeons thought that a proposed dashboard presenting information and decision support alerts would fit best with existing clinical workflows. From this, the following guidelines for system design were developed: minimize the risk of misinterpreting data, express a level of confidence in the data, support clinicians in developing relevant skills as time-series data are often unfamiliar, and consider the impact of patient engagement with data in the future. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.1136/bmjopen-2018-021862.
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Affiliation(s)
- Sabrina Grant
- Musculoskeletal Research Unit, University of Bristol, Southmead Hospital, Bristol Medical School, Bristol, United Kingdom
| | - Emma Tonkin
- Digital Health, Faculty of Engineering, Bristol, United Kingdom
| | - Ian Craddock
- Digital Health, Faculty of Engineering, Bristol, United Kingdom
| | - Ashley Blom
- Faculty of Medicine, Dentistry and Health, University of Sheffield, Sheffield, United Kingdom
| | - Michael Holmes
- Digital Health, Faculty of Engineering, Bristol, United Kingdom
| | - Andrew Judge
- Musculoskeletal Research Unit, University of Bristol, Southmead Hospital, Bristol Medical School, Bristol, United Kingdom
| | | | | | - Hao Song
- Digital Health, Faculty of Engineering, Bristol, United Kingdom
| | - Michael Whitehouse
- Musculoskeletal Research Unit, University of Bristol, Southmead Hospital, Bristol Medical School, Bristol, United Kingdom
| | - Peter Flach
- Intelligent Systems Laboratory, Department of Computer Science, University of Bristol, Bristol, United Kingdom
| | - Rachael Gooberman-Hill
- Musculoskeletal Research Unit, University of Bristol, Southmead Hospital, Bristol Medical School, Bristol, United Kingdom
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Tonkin EL, Holmes M, Song H, Twomey N, Diethe T, Kull M, Perello Nieto M, Camplani M, Hannuna S, Fafoutis X, Zhu N, Woznowski PR, Tourte GJL, Santos-Rodríguez R, Flach PA, Craddock I. A multi-sensor dataset with annotated activities of daily living recorded in a residential setting. Sci Data 2023; 10:162. [PMID: 36959280 PMCID: PMC10036321 DOI: 10.1038/s41597-023-02017-1] [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: 09/02/2021] [Accepted: 02/13/2023] [Indexed: 03/25/2023] Open
Abstract
SPHERE is a large multidisciplinary project to research and develop a sensor network to facilitate home healthcare by activity monitoring, specifically towards activities of daily living. It aims to use the latest technologies in low powered sensors, internet of things, machine learning and automated decision making to provide benefits to patients and clinicians. This dataset comprises data collected from a SPHERE sensor network deployment during a set of experiments conducted in the 'SPHERE House' in Bristol, UK, during 2016, including video tracking, accelerometer and environmental sensor data obtained by volunteers undertaking both scripted and non-scripted activities of daily living in a domestic residence. Trained annotators provided ground-truth labels annotating posture, ambulation, activity and location. This dataset is a valuable resource both within and outside the machine learning community, particularly in developing and evaluating algorithms for identifying activities of daily living from multi-modal sensor data in real-world environments. A subset of this dataset was released as a machine learning competition in association with the European Conference on Machine Learning (ECML-PKDD 2016).
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Affiliation(s)
| | | | - Hao Song
- University of Bristol, Bristol, UK
| | - Niall Twomey
- University of Bristol, Bristol, UK
- Amazon, Bellevue, USA
| | - Tom Diethe
- University of Bristol, Bristol, UK
- Amazon, Bellevue, USA
| | | | | | | | | | | | - Ni Zhu
- China Mobile International, Beijing, China
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Rasouli Dezfouli E, Delen D, Zhao H, Davazdahemami B. A Machine Learning Framework for Assessing the Risk of Venous Thromboembolism in Patients Undergoing Hip or Knee Replacement. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2022; 6:423-441. [PMID: 36744082 PMCID: PMC9892391 DOI: 10.1007/s41666-022-00121-2] [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: 01/27/2022] [Revised: 10/11/2022] [Accepted: 10/17/2022] [Indexed: 02/07/2023]
Abstract
Venous thromboembolism (VTE) is a well-recognized complication that is prevalent in patients undergoing major orthopedic surgery (e.g., total hip arthroplasty and total knee arthroplasty). For years, to identify patients at high risk of developing VTE, physicians have relied on traditional risk scoring systems, which are too simplistic to capture the risk level accurately. In this paper, we propose a data-driven machine learning framework to identify such high-risk patients before they undergo a major hip or knee surgery. Using electronic health records of more than 392,000 patients who undergone a major orthopedic surgery, and following a guided feature selection using the genetic algorithm, we trained a fully connected deep neural network model to predict high-risk patients for developing VTE. We identified several risk factors for VTE that were not previously recognized. The best FCDNN model trained using the selected features yielded an area under the ROC curve (AUC) of 0.873, which was remarkably higher than the best AUC obtained by including only risk factors previously known in the medical literature. Our findings suggest several interesting and important insights. The traditional risk scoring tables that are being widely used by physicians to identify high-risk patients are not considering a comprehensive set of risk factors, nor are they as powerful as cutting-edge machine learning methods in distinguishing low- from high-risk patients.
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Affiliation(s)
| | - Dursun Delen
- Oklahoma State University, Stillwater, OK USA
- Istinye University, Istanbul, Turkey
| | - Huimin Zhao
- University of Wisconsin-Milwaukee, Milwaukee, WI USA
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Babaei N, Hannani N, Dabanloo NJ, Bahadori S. A Systematic Review of the Use of Commercial Wearable Activity Trackers for Monitoring Recovery in Individuals Undergoing Total Hip Replacement Surgery. CYBORG AND BIONIC SYSTEMS (WASHINGTON, D.C.) 2022; 2022:9794641. [PMID: 36751476 PMCID: PMC9636847 DOI: 10.34133/2022/9794641] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 10/06/2022] [Indexed: 11/05/2022]
Abstract
The innovation of wearable devices is advancing rapidly. Activity monitors can be used to improve the total hip replacement (THR) patients' recovery process and reduce costs. This systematic review assessed the body-worn accelerometers used in studies to enhance the rehabilitation process and monitor THR patients. Electronic databases such as Cochrane Database of Systematic Reviews library, CINAHL CompleteVR, Science Citation Index, and MedlineVR from January 2000 to January 2022 were searched. Due to inclusion criteria, fourteen eligible studies that utilised commercial wearable technology to monitor physical activity both before and after THR were identified. Their evidence quality was assessed with RoB 2.0 and ROBINS-I. This study demonstrates that wearable device technology might be feasible to predict, monitor, and detect physical activity following THR. They could be used as a motivational tool to increase patients' mobility and enhance the recovery process. Also, wearable activity monitors could provide a better insight into the individual's activity level in contrast to subjective self-reported questionnaires. However, they have some limitations, and further evidence is needed to establish this technology as the primary device in THR rehabilitation.
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Affiliation(s)
- Nasibeh Babaei
- Department of Biomedical Engineering, Science And Research Branch, Islamic Azad University, Tehran, Iran
| | - Negin Hannani
- Department of Biomedical Engineering, Science And Research Branch, Islamic Azad University, Tehran, Iran
| | - Nader Jafarnia Dabanloo
- Department of Biomedical Engineering, Science And Research Branch, Islamic Azad University, Tehran, Iran
| | - Shayan Bahadori
- Faculty of Health and Social Science, Bournemouth University, Bournemouth, UK
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Mulita F, Verras GI, Anagnostopoulos CN, Kotis K. A Smarter Health through the Internet of Surgical Things. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22124577. [PMID: 35746359 PMCID: PMC9231158 DOI: 10.3390/s22124577] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/10/2022] [Accepted: 06/14/2022] [Indexed: 05/14/2023]
Abstract
(1) Background: In the last few years, technological developments in the surgical field have been rapid and are continuously evolving. One of the most revolutionizing breakthroughs was the introduction of the IoT concept within surgical practice. Our systematic review aims to summarize the most important studies evaluating the IoT concept within surgical practice, focusing on Telesurgery and surgical Telementoring. (2) Methods: We conducted a systematic review of the current literature, focusing on the Internet of Surgical Things in Telesurgery and Telementoring. Forty-eight (48) studies were included in this review. As secondary research questions, we also included brief overviews of the use of IoT in image-guided surgery, and patient Telemonitoring, by systematically analyzing fourteen (14) and nineteen (19) studies, respectively. (3) Results: Data from 219 patients and 757 healthcare professionals were quantitively analyzed. Study designs were primarily observational or based on model development. Palpable advantages from the IoT incorporation mainly include less surgical hours, accessibility to high quality treatment, and safer and more effective surgical education. Despite the described technological advances, and proposed benefits of the systems presented, there are still identifiable gaps in the literature that need to be further explored in a systematic manner. (4) Conclusions: The use of the IoT concept within the surgery domain is a widely incorporated but less investigated concept. Advantages have become palpable over the past decade, yet further research is warranted.
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Affiliation(s)
- Francesk Mulita
- Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece;
- Department of Surgery, General University Hospital of Patras, 26504 Rio, Greece;
- Correspondence: (F.M.); (K.K.); Tel.: +30-6974822712 (K.K.)
| | | | | | - Konstantinos Kotis
- Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece;
- Correspondence: (F.M.); (K.K.); Tel.: +30-6974822712 (K.K.)
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Kennedy MR, Huxtable R, Birchley G, Ives J, Craddock I. "A Question of Trust" and "a Leap of Faith"-Study Participants' Perspectives on Consent, Privacy, and Trust in Smart Home Research: Qualitative Study. JMIR Mhealth Uhealth 2021; 9:e25227. [PMID: 34842551 PMCID: PMC8665399 DOI: 10.2196/25227] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 01/15/2021] [Accepted: 08/01/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Ubiquitous, smart technology has the potential to assist humans in numerous ways, including with health and social care. COVID-19 has notably hastened the move to remotely delivering many health services. A variety of stakeholders are involved in the process of developing technology. Where stakeholders are research participants, this poses practical and ethical challenges, particularly if the research is conducted in people's homes. Researchers must observe prima facie ethical obligations linked to participants' interests in having their autonomy and privacy respected. OBJECTIVE This study aims to explore the ethical considerations around consent, privacy, anonymization, and data sharing with participants involved in SPHERE (Sensor Platform for Healthcare in a Residential Environment), a project for developing smart technology for monitoring health behaviors at home. Participants' unique insights from being part of this unusual experiment offer valuable perspectives on how to properly approach informed consent for similar smart home research in the future. METHODS Semistructured qualitative interviews were conducted with 7 households (16 individual participants) recruited from SPHERE. Purposive sampling was used to invite participants from a range of household types and ages. Interviews were conducted in participants' homes or on-site at the University of Bristol. Interviews were digitally recorded, transcribed verbatim, and analyzed using an inductive thematic approach. RESULTS Four themes were identified-motivation for participating; transparency, understanding, and consent; privacy, anonymity, and data use; and trust in research. Motivations to participate in SPHERE stemmed from an altruistic desire to support research directed toward the public good. Participants were satisfied with the consent process despite reporting some difficulties-recalling and understanding the information received, the timing and amount of information provision, and sometimes finding the information to be abstract. Participants were satisfied that privacy was assured and judged that the goals of the research compensated for threats to privacy. Participants trusted SPHERE. The factors that were relevant to developing and maintaining this trust were the trustworthiness of the research team, the provision of necessary information, participants' control over their participation, and positive prior experiences of research involvement. CONCLUSIONS This study offers valuable insights into the perspectives of participants in smart home research on important ethical considerations around consent and privacy. The findings may have practical implications for future research regarding the types of information researchers should convey, the extent to which anonymity can be assured, and the long-term duty of care owed to the participants who place trust in researchers not only on the basis of this information but also because of their institutional affiliation. This study highlights important ethical implications. Although autonomy matters, trust appears to matter the most. Therefore, researchers should be alert to the need to foster and maintain trust, particularly as failing to do so might have deleterious effects on future research.
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Affiliation(s)
- Mari-Rose Kennedy
- Centre for Ethics in Medicine, University of Bristol, Bristol, United Kingdom
| | - Richard Huxtable
- Centre for Ethics in Medicine, University of Bristol, Bristol, United Kingdom
| | - Giles Birchley
- Centre for Ethics in Medicine, University of Bristol, Bristol, United Kingdom
| | - Jonathan Ives
- Centre for Ethics in Medicine, University of Bristol, Bristol, United Kingdom
| | - Ian Craddock
- Department of Electrical & Electronic Engineering, University of Bristol, Bristol, United Kingdom
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