Developing an Instrument to Measure Keyboarding Style

开发测量键盘风格的仪器

• 批准号: 6892324
• 负责人: NANCY A BAKER
• 金额: $ 8.09万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-05-01 至 2007-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6892324
• 关键词: bioengineering /biomedical engineering; biomechanics; carpal tunnel syndrome; clinical research; computer human interaction; diagnosis design /evaluation; ergonomics; hand; human subject; injury prevention; measurement; muscle disorder diagnosis; musculoskeletal disorder; nonbiomedical equipment; occupational hazard; patient oriented research; psychometrics; repetitive motion injury; statistics /biometry; technology /technique development; video recording system

DESCRIPTION: Dr. Nancy A. Baker, ScD, the candidate for this Special Emphasis Research Career Award, is an Assistant Professor in the School of Health and Rehabilitation Sciences (SHRS) at the University of Pittsburgh. Her immediate career goals are to develop her skills as an independent researcher and improve her expertise in biomechanical theory, test development, epidemiology, and statistical analysis through mentored research and coursework. She plans to apply for an R01 award with the data acquired during this study. Her long term career goal is to develop a research program which will evaluate clinical interventions for preventing and remediating musculoskeletal disorders of the upper extremity (MSD-UE) in computer users. This proposed three-year study will be divided into two phases. Phase I will be devoted to developing and refining an instrument, the Pre-PeCKS, into a valid and reliable data gathering observational tool. /n Phase 1I the Pre-PeCKS will be used to develop a predictive model that can discriminate between those with and without MSD-UE. The model developed will be used to select the construction and weighting of the subscales of the final instrument, the PECKS. The specific aim of Phase I is to evaluate the inter- and intra- rater reliability and the concurrent criterion-related validity of the Pre-PeCKS; the specific aim of Phase II is to use information gathered using the Pre-PeCKS to develop a diagnostic instrument that can identify those with and without MSD-UE. In Phase I, 50 subjects will be digitally recorded while typing while at the same time the kinematics of their hands will be recorded using a VICON TM motion analysis system and a keyboard force plate. The video of the typing performance will be rated by independent raters using the Pre-PeCKS and reliability statistics will be calculated. Concurrent validity will be obtained by comparing the results of the VICON TM with the results of the Pre-PECKS. In Phase 11, 20 typists with MSD-UE and 20 typists without MSD-UE will be rated using the Pre-PeCKS. Variables from the Pre-PeCKS along with other variables of interest will be used to build models that can discriminate between those with and without MSD-UE. The best model will be used to assign weights to Pre-PeCKS parameters and to develop how subscores will be combined to best identify those with MSD-UE. This information will be integrated into the final tool, the PECKS. The PECKS can then be used to identify an individual who have keyboarding...styles that have been associated with MSD-UE.

描述：Nancy A. Baker 博士，科学博士，本次特别重点研究职业奖的候选人，是匹兹堡大学健康与康复科学学院 (SHRS) 的助理教授。她的近期职业目标是发展她作为独立研究员的技能，并通过指导研究和课程提高她在生物力学理论、测试开发、流行病学和统计分析方面的专业知识。她计划利用本研究中获得的数据申请 R01 奖。她的长期职业目标是开发一项研究计划，评估预防和治疗计算机用户上肢肌肉骨骼疾病 (MSD-UE) 的临床干预措施。 这项为期三年的研究将分为两个阶段。第一阶段将致力于开发和完善 Pre-PeCKS 仪器，使其成为有效且可靠的数据收集观测工具。 /n 第 1I 阶段，Pre-PeCKS 将用于开发一个预测模型，该模型可以区分具有和不具有 MSD-UE 的患者。开发的模型将用于选择最终仪器 PECKS 的子量表的结构和权重。第一阶段的具体目标是评估 Pre-PeCKS 的评估者间和评估者内可靠性以及并发标准相关的有效性；第二阶段的具体目标是利用 Pre-PeCKS 收集的信息来开发一种诊断仪器，可以识别患有和不患有 MSD-UE 的人。在第一阶段，将对 50 名受试者打字时进行数字记录，同时使用 VICON TM 运动分析系统和键盘测力台记录他们手部的运动学。打字表现视频将由独立评估者使用 Pre-PeCKS 进行评级，并计算可靠性统计数据。通过将 VICON TM 的结果与 Pre-PECKS 的结果进行比较来获得同时有效性。在第 11 阶段，将使用 Pre-PeCKS 对 20 名拥有 MSD-UE 的打字员和 20 名没有 MSD-UE 的打字员进行评级。 Pre-PeCKS 中的变量以及其他感兴趣的变量将用于构建可以区分具有和不具有 MSD-UE 的模型。最佳模型将用于为 Pre-PeCKS 参数分配权重，并开发如何组合子分数以最好地识别那些具有 MSD-UE 的人。这些信息将被集成到最终的工具 PECKS 中。然后，PECKS 可用于识别具有与 MSD-UE 相关的键盘敲击风格的个人。