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Amirthanayagam A, Zecca M, Barber S, Singh B, Moss EL. Impact of minimally invasive surgery on surgeon health (ISSUE) study: protocol of a single-arm observational study conducted in the live surgery setting. BMJ Open 2023; 13:e066765. [PMID: 36882245 PMCID: PMC10008445 DOI: 10.1136/bmjopen-2022-066765] [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] [Indexed: 03/09/2023] Open
Abstract
INTRODUCTION The rapid evolution of minimally invasive surgery has had a positive impact on patient outcomes; however, it is reported to be associated with work-related musculoskeletal symptoms (WMS) in surgeons. Currently there is no objective measure to monitor the physical and psychological impact of performing a live surgical procedure on the surgeon. METHODS AND ANALYSIS A single-arm observational study with the aim of developing a validated assessment tool to quantify the impact of surgery (open/laparoscopic/robotic-assisted) on the surgeon. Development and validation cohorts of major surgical cases of varying levels of complexity performed by consultant gynaecological and colorectal surgeons will be recruited. Recruited surgeons wear three Xsens DOT monitors (muscle activity) and an Actiheart monitor (heart rate). Salivary cortisol levels will be taken and questionnaires (WMS and State-Trait Anxiety Inventory) completed by the participants preoperatively and postoperatively. All the measures will be incorporated to produce a single score that will be called the 'S-IMPACT' score. ETHICS AND DISSEMINATION Ethical approval for this study has been granted by the East Midlands Leicester Central Research Ethics Committee REC ref 21/EM/0174. Results will be disseminated to the academic community through conference presentations and peer-reviewed journal publications. The S-IMPACT score developed within this study will be taken forward for use in definitive multicentre prospective randomised control trials.
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Affiliation(s)
| | - Massimiliano Zecca
- Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, UK
| | - Shaun Barber
- Leicester Clinical Trials Unit, University of Leicester, Leicester, Leicestershire, UK
- NIHR Research Design Service East Midlands, Leicester, UK
| | - Baljit Singh
- Department of Surgery, University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Esther L Moss
- Leicester Cancer Research Centre, University of Leicester, Leicester, UK
- Department of Gynaecological Oncology, University Hospitals of Leicester NHS Trust, Leicester, UK
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Yau JC, Girault B, Feng T, Mundnich K, Nadarajan A, Booth BM, Ferrara E, Lerman K, Hsieh E, Narayanan S. TILES-2019: A longitudinal physiologic and behavioral data set of medical residents in an intensive care unit. Sci Data 2022; 9:536. [PMID: 36050329 PMCID: PMC9436730 DOI: 10.1038/s41597-022-01636-4] [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: 04/12/2022] [Accepted: 08/16/2022] [Indexed: 11/09/2022] Open
Abstract
The TILES-2019 data set consists of behavioral and physiological data gathered from 57 medical residents (i.e., trainees) working in an intensive care unit (ICU) in the United States. The data set allows for the exploration of longitudinal changes in well-being, teamwork, and job performance in a demanding environment, as residents worked in the ICU for three weeks. Residents wore a Fitbit, a Bluetooth-based proximity sensor, and an audio-feature recorder. They completed daily surveys and interviews at the beginning and end of their rotation. In addition, we collected data from environmental sensors (i.e., Internet-of-Things Bluetooth data hubs) and obtained hospital records (e.g., patient census) and residents’ job evaluations. This data set may be may be of interest to researchers interested in workplace stress, group dynamics, social support, the physical and psychological effects of witnessing patient deaths, predicting survey data from sensors, and privacy-aware and privacy-preserving machine learning. Notably, a small subset of the data was collected during the first wave of the COVID-19 pandemic. Measurement(s) | Stress • Burnout • Affect • Depression • Sleep • Physical Activity Measurement • Alcohol Use History • Frequency Any Tobacco Use • Personality • Social Support • Intragroup Conflict • Challenge and Hindrance Stressors • Demographics • Context and Atypical Events • Daily Stressors • Most Stressful Event • Work Context • Job Performance • Job Satisfaction • Stressors at Work • Charting at Home • Coworker Trust • Social Networks at Work • Socialization Outside of Work • Use of Wellness Resources • Heart Rate • Step Count • Acoustic Features • Team Interactions • Proximity to Key Objects • Cell Phone Use • Hospital Contextual Data • Coping with Stress • Productivity at Work • Pride at Work • Teamwork • Support System | Technology Type(s) | Perceived Stress Scale - 14 Questionnaire • Survey • Patient Health Questionnaire - 9 Item • Pittsburgh Sleep Quality Index • FitBit • International Physical Activity Questionnaire (August 2002) Short Last 7 Days Self-Administered Format • Unihertz Atom Phone • Minew E8- TILES Interaction Sensors • Minew E8- Eddystone Beach • Rescuetime • Evaluations • Patient Census • Interview | Sample Characteristic - Organism | Homo sapiens | Sample Characteristic - Location | Los Angeles County and University of Southern California Medical Center |
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Affiliation(s)
- Joanna C Yau
- Signal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.
| | - Benjamin Girault
- Signal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA
| | - Tiantian Feng
- Signal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA
| | - Karel Mundnich
- Signal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA
| | - Amrutha Nadarajan
- Signal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA
| | - Brandon M Booth
- Signal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA
| | - Emilio Ferrara
- Information Sciences Institute (USC), Marina del Rey, CA, USA
| | - Kristina Lerman
- Information Sciences Institute (USC), Marina del Rey, CA, USA
| | - Eric Hsieh
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Shrikanth Narayanan
- Signal Analysis and Interpretation Lab, University of Southern California, Los Angeles, CA, USA.,Information Sciences Institute (USC), Marina del Rey, CA, USA
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Basu A, Kelada M, Anto A. Better preparing for ward rounds: UK medical students' perspectives. MEDICAL TEACHER 2022; 44:1062-1063. [PMID: 34613855 DOI: 10.1080/0142159x.2021.1986208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Affiliation(s)
- Arunima Basu
- School of Medicine, Imperial College London, London, UK
| | - Monica Kelada
- School of Medicine, Imperial College London, London, UK
| | - Ailin Anto
- School of Medicine, Imperial College London, London, UK
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