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Khanum F, Khan AR, Khan A, Aafreen A, Khan AA, Ahmad A, Akhtar SMF, Farooq O, Shaphe MA, Alshehri MM, Shahi FI, Alqahtani AS, Albakri A, Obaidat SM. Predicting mechanical neck pain intensity in computer professionals using machine learning: identification and correlation of key features. Front Public Health 2024; 12:1307592. [PMID: 38577273 PMCID: PMC10993996 DOI: 10.3389/fpubh.2024.1307592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 02/29/2024] [Indexed: 04/06/2024] Open
Abstract
Introduction Mechanical neck pain has become prevalent among computer professionals possibly because of prolonged computer use. This study aimed to investigate the relationship between neck pain intensity, anthropometric metrics, cervical range of motion, and related disabilities using advanced machine learning techniques. Method This study involved 75 computer professionals, comprising 27 men and 48 women, aged between 25 and 44 years, all of whom reported neck pain following extended computer sessions. The study utilized various tools, including the visual analog scale (VAS) for pain measurement, anthropometric tools for body metrics, a Universal Goniometer for cervical ROM, and the Neck Disability Index (NDI). For data analysis, the study employed SPSS (v16.0) for basic statistics and a suite of machine-learning algorithms to discern feature importance. The capability of the kNN algorithm is evaluated using its confusion matrix. Results The "NDI Score (%)" consistently emerged as the most significant feature across various algorithms, while metrics like age and computer usage hours varied in their rankings. Anthropometric results, such as BMI and body circumference, did not maintain consistent ranks across algorithms. The confusion matrix notably demonstrated its classification process for different VAS scores (mild, moderate, and severe). The findings indicated that 56% of the pain intensity, as measured by the VAS, could be accurately predicted by the dataset. Discussion Machine learning clarifies the system dynamics of neck pain among computer professionals and highlights the need for different algorithms to gain a comprehensive understanding. Such insights pave the way for creating tailored ergonomic solutions and health campaigns for this population.
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Affiliation(s)
- Fatima Khanum
- Department of Physiotherapy, Integral University, Lucknow, India
| | | | - Ashfaque Khan
- Department of Physiotherapy, Integral University, Lucknow, India
| | - Aafreen Aafreen
- Department of Physiotherapy, Integral University, Lucknow, India
| | | | - Ausaf Ahmad
- Department of Community Medicine, IIMS&R, Integral University, Lucknow, India
| | | | - Omar Farooq
- Department of Electronics Engineering, Aligarh Muslim University, Aligarh, India
| | - Mohammad Abu Shaphe
- Department of Physical Therapy, College of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Mohammed M. Alshehri
- Department of Physical Therapy, College of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Fazal Imam Shahi
- Deanship of E-Learning & Information Technology, Jazan University, Jazan, Saudi Arabia
| | - Abdulfattah S. Alqahtani
- Department of Health Rehabilitation Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Ashwag Albakri
- Department of Computer Science, College of Computer Science & Information Technology, Jazan University, Jazan, Saudi Arabia
| | - Sakher M. Obaidat
- Department of Physical Therapy and Occupational Therapy, Faculty of Applied Medical Sciences, The Hashemite University, Zaraq, Jordan
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Seok H, Lee S, Jun D. The effect of deep neck flexor endurance on craniovertebral angle changes and neck and shoulder muscle activities during a computer task. INTERNATIONAL JOURNAL OF OCCUPATIONAL SAFETY AND ERGONOMICS 2024; 30:100-107. [PMID: 37667557 DOI: 10.1080/10803548.2023.2254151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
The deep neck flexors are important for maintaining neck posture by stabilizing and supporting it through low-intensity contraction, ensuring static endurance. The subjects performed computer tasks after having their deep neck flexor endurance measured using a pressure biofeedback unit. The craniovertebral angle (CVA) and the muscle activities were measured while participants were completing computer tasks for 10 min. The deep neck flexor endurance was significantly negatively correlated with CVA changes at 0-2 min. The deep neck flexor endurance was significantly negatively correlated with sternocleidomastoid muscle and upper trapezius activity at 0-2 min. The deep neck flexor endurance negatively predicted CVA changes at 0-2 min and explained 28.8% of the variance in the CVA changes. The lower the deep neck flexor endurance, the earlier the simultaneous changes occurred in the CVA and the neck and shoulder muscle activities during computer tasks.
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Affiliation(s)
- Him Seok
- Department of Physical Therapy, Kyungsung University, Republic of Korea
| | - Sangyeol Lee
- Department of Physical Therapy, Kyungsung University, Republic of Korea
| | - Deokhoon Jun
- Department of Rehabilitation science, Daegu University, Republic of Korea
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Jaskólski A, Lucka E, Lucki M, Lisiński P. Evaluating the Accuracy of Upper Limb Movement in the Sagittal Plane among Computer Users during the COVID-19 Pandemic. Healthcare (Basel) 2024; 12:384. [PMID: 38338269 PMCID: PMC10855468 DOI: 10.3390/healthcare12030384] [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: 01/18/2024] [Revised: 01/29/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
(1) Background: The most common musculoskeletal pathology among healthcare professionals is neck and/or shoulder pain. The aim of this study was to determine the dominant upper limb functionality concerning the ability to replicate a given movement pattern among employees reporting neck or upper limb pain while using a computer during the COVID-19 pandemic. (2) Methods: The study was conducted from March to April 2021 on a group of 45 medical employees who used a computer workstation for 4 to 6 h of their working time. In the design of this study, three study groups were created: a group of patients with pain syndrome of segment C5/C7 of the spine, a group of patients with shoulder pain syndrome, and a control group of healthy volunteers. (3) Results: The examined groups significantly differed in the correctness of performing the given movement (p = 0.001) and the minimum value of inclination during the exercise session (p = 0.026), as well as the maximum lowering (p = 0.03) in relation to the control group. (4) Conclusions: The VECTIS device can be used to assess the accuracy of reflecting the prescribed movement of the upper limb in rehabilitation programs for patients with cervical spine pain syndrome and shoulder pain syndrome.
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Affiliation(s)
| | - Ewa Lucka
- Department of Rehabilitation and Physiotherapy, University of Medical Sciences, 28 Czerwca 1956 Str., No 135/147, 60-545 Poznań, Poland; (A.J.); (M.L.); (P.L.)
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Milaković M, Koren H, Bradvica-Kelava K, Bubaš M, Nakić J, Jeličić P, Bucić L, Bekavac B, Čvrljak J, Capak M. Telework-related risk factors for musculoskeletal disorders. Front Public Health 2023; 11:1155745. [PMID: 37465175 PMCID: PMC10350503 DOI: 10.3389/fpubh.2023.1155745] [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: 01/31/2023] [Accepted: 06/12/2023] [Indexed: 07/20/2023] Open
Abstract
Telework has become considerably more common during the ongoing pandemic. Although working remotely may have numerous advantages, negative impacts on workers' health and safety should also be considered. Telework is a major contributor to the development or aggravation of work-related musculoskeletal disorders where unsuited workstation ergonomics, sedentary behavior, as well as psychosocial and organizational factors play a role. This paper aims to identify telework-related risks and their impact on musculoskeletal health as well as provide recommendations that may be useful in constructing future preventive measures. A comprehensive literature search regarding the topic has been performed. Teleworkers experience musculoskeletal pain and discomfort mostly in low back area, neck, shoulders, arms, and hands. Poor ergonomic solutions when it comes to workstation design resulting in prolonged sitting in non-neutral positions contribute to the development and aggravation of musculoskeletal disorders in teleworkers. Working with inadequately placed screens and laptops and sitting in maladjusted seats without usual functionalities and ergonomic support is associated with musculoskeletal pain and discomfort. Extended working hours with fewer rest periods to meet increased work demands, social isolation, and lack of support from work colleagues and superiors as well as blurred work-home boundaries and omnipresence of work are commonly stated psychosocial and organizational factors associated with musculoskeletal disorders. Environmental factors such as poor lighting and glare, inadequate room temperature, and ventilation or noise, are frequently overlooked remote workstation risk factors. For a certain part of workers, telework will remain a common way of work in the post-pandemic period. Therefore, it is essential to identify telework-related risk factors for musculoskeletal disorders and address them with timely preventive measures tailored to each remote workstation's risks and individual workers' needs.
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Affiliation(s)
- Marina Milaković
- Division for Occupational Health, Croatian Institute of Public Health, Zagreb, Croatia
| | - Helena Koren
- Division for Occupational Health, Croatian Institute of Public Health, Zagreb, Croatia
| | | | - Marija Bubaš
- Division for Occupational Health, Croatian Institute of Public Health, Zagreb, Croatia
| | - Josipa Nakić
- Faculty of Kinesiology, University of Zagreb, Zagreb, Croatia
| | - Pavle Jeličić
- Division for Environmental Health, Croatian Institute of Public Health, Zagreb, Croatia
| | - Lovro Bucić
- Division for Environmental Health, Croatian Institute of Public Health, Zagreb, Croatia
| | - Barbara Bekavac
- Division for Environmental Health, Croatian Institute of Public Health, Zagreb, Croatia
| | - Jelena Čvrljak
- Andrija Stampar Teaching Institute of Public Health, Zagreb, Croatia
| | - Magdalena Capak
- Department of History, Croatian Catholic University, Zagreb, Croatia
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Rujiret U, Keerin M, Julaporn P, Petcharatana B, Wattana J, Chutima J. Validity of "OfficeCheck": A self-musculoskeletal assessment tool for screening work-related musculoskeletal disorders in office workers. Work 2023; 76:1501-1508. [PMID: 37393474 DOI: 10.3233/wor-220491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/03/2023] Open
Abstract
BACKGROUND Early self-assessment for work-related musculoskeletal disorders (WMSDs) is crucial in preventing severe symptoms and long-term consequences. Accessible tools are necessary for proactive management. OBJECTIVE To validate the OfficeCheck web application as a screening tool to classify office workers as capable of self-management for specific symptoms of WMSDs or requiring professional consultation. METHODS This study was conducted to determine the criterion-related validity of OfficeCheck using physical therapy assessment as the reference standard. In total, 223 office workers who work with a computer more than two hours a day with or without symptoms of WMSDs participated in this study. All of them were classified by self-assessment on the OfficeCheck process flow (Kappa = 0.841) and physical therapy assessment, respectively. For statistical analysis, classification numbers were calculated for sensitivity, specificity, false positive rate (FPR), false negative rate (FNR), positive predictive value (PPV), and negative predictive value (NPV). RESULTS A total of 223 workers with a mean age of 38.9±9.0 years and mean body mass index (BMI) of 24.3±5.2 kg/m2 were illustrated. The most common areas of complaint were neck/upper back and lower back/hip. The results indicated that OfficeCheck had high sensitivity (95.1%), low specificity (42.0%), low PPV (38.0%), and high NPV (95.8%). The FPR was 58.0% and the FNR was 4.9%. CONCLUSION OfficeCheck was found to have high sensitivity to classify office workers as capable of self-management for specific symptoms of WMSDs or requiring professional consultation. The use of OfficeCheck is thus recommended for self-detection and management to stop the consequences of WMSDs.
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Affiliation(s)
| | - Mekhora Keerin
- Faculty of Physical Therapy, Mahidol University, Nakhon Pathom, Thailand
| | - Pooliam Julaporn
- Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, NakhonPathom, Thailand
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