US Army physical demands study: Prevalence and frequency of performing physically demanding tasks in deployed and non-deployed settings

Journal of Science and Medicine in Sport(2017)

引用 15|浏览5
暂无评分
摘要
Objectives: To compare percentages of on-duty time spent performing physically demanding soldier tasks in non-deployed and deployed settings, and secondarily examine the number of physically demanding tasks performed among five Army combat arms occupational specialties. Design: Job task analysis. Methods: Soldiers (n = 1295; over 99% serving on active duty) across five Army jobs completed one of three questionnaires developed using reviews of job and task related documents, input from subject matter experts, observation of task performance, and conduct of focus groups. Soldiers reported estimates of the total on-duty time spent performing physically demanding tasks in both deployed and non-deployed settings. One-way analyses of variance and Duncan post-hoc tests were used to compare percentage time differences by job. Two-tailed t-tests were used to evaluate differences by setting. Frequency analyses were used to present supplementary findings. Results: Soldiers reported performing physically demanding job-specific tasks 17.7% of the time while non-deployed and 19.6% of the time while deployed. There were significant differences in time spent on job-specific tasks across settings (p < 0.05) for three of five occupational specialties. When categories of physically demanding tasks were grouped, all soldiers reported spending more time on physically demanding tasks when deployed (p < 0.001). Twenty-five percent reported performing less than half the physically demanding tasks represented on the questionnaire in the last two years. Conclusion: Soldiers spent more time performing physically demanding tasks while deployed compared to non-deployed but spent similar amounts of time performing job-specific tasks. Published by Elsevier Ltd on behalf of Sports Medicine Australia.
更多
查看译文
关键词
Army personnel,Job task analysis,Questionnaire,Training
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要