FW-HTF-P: Future of Work for Strength and Movement Training Professionals

这项计划拨款将侧重于力量和运动训练专业人员(SMTP)的未来工作挑战，这些专业人员包括物理治疗师、职业治疗师和私人教练。这些工人构成了一个对伤害预防和恢复至关重要的领域，主要是女性，92.4万人的增长速度远远快于平均水平(每年15%-29%)。不幸的是，这一领域目前被工作压力、行政负担和精力枯竭所突显。SMTP面临着越来越多的远程护理趋势(因COVID大流行而加速)，基于结果的报销在过去十年中一直在发展。这一领域未来的工作将需要多功能和负担得起的工具，以收集客观和明确的业绩衡量标准。随着可穿戴传感器在人体力量、运动和稳定性监控方面的进步，与机器学习相结合，有机会推动客户/患者的责任，更好地洞察进展，并丰富越来越受欢迎的远程培训课程。这将为这一领域的未来工作者提供更大的工作灵活性，改善结果，并为培训师/治疗师和客户/患者之间的人际联系创造更多空间，从而获得满意的结果。该项目将利用最近开发的一项技术，该技术可以跟踪一个人在力量训练中的表现。它测量力量、运动范围和速度、休息时间、重复和成套动作的数量、施加在锻炼器材上的力量和力量。算法使用这些数据来提供关于一个人表现的详细报告，并提供可访问的可视化。这笔规划赠款将帮助制定一项研究议程，其中包括所有必要的融合学科，以改变这一领域工作的未来，并为该行业提供急需的工具，通过增加培训成功来促进更高的工作满意度和减轻工作压力。此外，它将使这一领域的未来工人更容易获得，并能够更灵活地获得康复，这将意味着受伤后更快地重返工作岗位，并在更广泛的范围内改善健康，减轻一般人口的疾病负担。这个项目将汇集几个学科，包括工程师、社会科学家、力量和运动训练科学家以及经济学家。研究人员团队的结构是为了实现多个趋同目标。这笔规划赠款将寻求通过定性研究，包括一系列工作人员和利益攸关方面谈以及审查相关的医疗保健政策，建立一个清晰的工作环境。它还将专注于开发最小可行的原型，以从适当的身体部分或力量训练附件收集数据，适当解释传感器数据的算法，以及初始网络应用程序。最后，它将能够开发和评价一个带有SMTP的培训模块。在评估规划拨款的结果时，将考虑三个研究推动力--未来的工作、未来的工人和未来的技术。在适当的情况下，将使用定量方法来判断技术绩效等情况，并将使用访谈和标准化调查等定性评估来评估社会和感知驱动因素。该项目由人类-技术前沿交叉部门工作未来计划资助，旨在通过推进与人类工人和谐运作的智能工作技术的设计，促进对工作环境中相互依赖的人类-技术伙伴关系的更深层次的基本了解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This planning grant will focus on the future of work challenges for strength and movement training professionals(SMTPs) that include physical therapists, occupational therapists, and personal trainers. These workers make up a field that is essential to injury prevention and recovery, predominantly female, and 924,000 strong growing much faster than average (15-29% per year). Unfortunately, this field is currently underscored by job strain, administrative burden, and burn out. SMTPs are facing an increasing remote care trend (accelerated by the COVID pandemic), and outcome-based reimbursement has been evolving over the last decade. The future of work in this area will require versatile and affordable tools to collect objective and clear performance metrics. With advances in human strength, movement, and stability monitoring with wearable sensors, combined with machine learning there is an opportunity to drive client/patient accountability, give better insights into progress, and enrich increasing popular remote training sessions. This will give future workers in this area greater work flexibility, improve outcomes, and create more room for the human connection both trainer/therapist and client/patient draw satisfaction from. The project will leverage a recently developed technology that can track a person’s performance during strength training. It measures strength, range and speed of motion, rest time, number of reps and sets, force applied to the exercise equipment, and power. Algorithms use these data to provide detailed reports on a person’s performance with accessible visualization. This planning grant will help develop a research agenda thatincludes all the necessary convergent disciplines to transform the future of work in this area and provide the industry with a much-needed tool that promotes greater job satisfaction and reduced job strain through increased training successes. In addition, it will enable future workers in this area to be more accessible and enable more flexible access to rehabilitation, which will mean faster return to work after injury and more broadly improve wellness and reduce disease burden in the general population. This project will bring together several disciplines, including engineers, social scientists, strength and movement training scientists, and economists. The investigator team is structured to achieve multiple convergent goals. This planning grant will seek to establish a clear landscape of the work context through qualitative research including a series of worker and stakeholder interviews and review of relevant healthcare policies. It will also focus on the development of a minimum viable prototype to collect data from the appropriate body segment or strength training accessory, algorithms to appropriately interpret the sensor data, and an initial web application. Finally, it will enable the development and evaluation of a training module with SMTPs. Each of the three research thrusts – future work, future workers, and future technology – will be considered when evaluating the outcomes of the planning grant. Quantitative methods will be used where appropriate to judge things like technology performance and qualitative assessments such as interviews and standardized surveys will be used to assess the social and perception driven elements. This project has been funded by the Future of Work at the Human-Technology Frontier cross-directorate program to promote deeper basic understanding of the interdependent human-technology partnership in work contexts by advancing design of intelligent work technologies that operate in harmony with human workers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.