PDF(Pubmed)
Abstract：
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.
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.
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.
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.
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.
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.
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.
摘要：
■机械性颈部疼痛在计算机专业人员中变得普遍，可能是因为长时间使用计算机。本研究旨在探讨颈部疼痛强度之间的关系,人体测量指标，颈椎活动范围,以及使用先进机器学习技术的相关残疾。
■这项研究涉及75名计算机专业人员，由27名男性和48名女性组成，年龄在25至44岁之间，所有这些人都报告了延长计算机会话后的颈部疼痛。这项研究利用了各种工具，包括疼痛测量的视觉模拟量表(VAS)，人体测量工具，用于身体测量，宫颈ROM的通用测角仪，颈部残疾指数（NDI）。对于数据分析，该研究使用SPSS（v16.0）进行基本统计，并使用一套机器学习算法来识别特征重要性。使用其混淆矩阵来评估kNN算法的能力。
■“NDI分数（％）”始终成为各种算法中最重要的特征，而年龄和计算机使用时间等指标的排名各不相同。人体测量结果,比如BMI和体围，没有在算法之间保持一致的排名。混淆矩阵特别展示了其对不同VAS评分的分类过程(轻度，中度,和严重）。研究结果表明，56%的疼痛强度，由VAS测量，可以通过数据集准确预测。
■机器学习阐明了计算机专业人员中颈部疼痛的系统动力学，并强调了需要不同的算法才能获得全面了解。这些见解为为该人群创建量身定制的人体工程学解决方案和健康运动铺平了道路。
■这项研究涉及75名计算机专业人员，由27名男性和48名女性组成，年龄在25至44岁之间，所有这些人都报告了延长计算机会话后的颈部疼痛。这项研究利用了各种工具，包括疼痛测量的视觉模拟量表(VAS)，人体测量工具，用于身体测量，宫颈ROM的通用测角仪，颈部残疾指数（NDI）。对于数据分析，该研究使用SPSS（v16.0）进行基本统计，并使用一套机器学习算法来识别特征重要性。使用其混淆矩阵来评估kNN算法的能力。
■“NDI分数（％）”始终成为各种算法中最重要的特征，而年龄和计算机使用时间等指标的排名各不相同。人体测量结果,比如BMI和体围，没有在算法之间保持一致的排名。混淆矩阵特别展示了其对不同VAS评分的分类过程(轻度，中度,和严重）。研究结果表明，56%的疼痛强度，由VAS测量，可以通过数据集准确预测。
■机器学习阐明了计算机专业人员中颈部疼痛的系统动力学，并强调了需要不同的算法才能获得全面了解。这些见解为为该人群创建量身定制的人体工程学解决方案和健康运动铺平了道路。
