Feasibility of a live-stream dance class for people with chronic stroke.

IF 4.6 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Sarah Gregman, Wade W Michaelchuk, Lauren C Belfiore, Kara K Patterson
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引用次数: 0

Abstract

Background: Adapted dance is a promising intervention to improve physical and psychosocial well-being post-stroke. At-home participation with a live-streamed dance program could address barriers to in-person attendance (e.g., limited community ambulation, and transportation). However, psychosocial benefits may be diminished because of reduced social interactions. The primary study objective was to assess the feasibility and safety of a live-streamed dance program for chronic stroke. Secondary objectives were to characterize participants who chose live-stream vs in-person options and quantify pre-post-changes in balance, gait and social connection.

Methods: People with chronic stroke chose to attend a live-streamed adapted dance program in-person or at home twice a week for 4 weeks. Feasibility criteria were tracked, and participants were characterized with self-report (Center for Epidemiologic Studies-Depression Scale; CES-D) and performance-based measures (e.g., Montreal Cognitive Assessment and Chedoke McMaster Assessment) at baseline. Pre-post-secondary measures included gait speed, Mini-Balance Evaluation Systems Test, Activities of Balance Confidence Scale, and Inclusion of Community in Self scale. Unpaired median/mean differences in baseline clinical presentation were used to compare groups. Paired median/mean differences were used to examine change in secondary outcomes.

Results: Interest and enrollment rates for both groups combined were 87% and 38%, respectively. Of those enrolled, 8/13 chose in-person and 5/13 chose live stream. In-person and live-stream attendance rates were 83% and 89%, and retention rates were 80% and 75%, respectively. There were no major adverse events related to the classes. There were 3 unrelated minor events of fatigue and 1 minor event of muscle soreness as expected with exercise. At baseline, the in-person group had greater depressive symptoms (CES-D score, median [IQR] difference: 11.5 [-21.5, -5]), and faster mean gait speed (-25.8 cm/s [-50.98, 0.006]) than the live-stream group. There were no pre-post-changes in secondary outcome measures.

Conclusions: A live-streamed dance intervention featuring in-class and at-home participation is safe and feasible for people with chronic stroke. These results will inform a future randomized controlled trial of a live-stream dance program with a longer duration while considering how factors such as gait function and mood may relate to the choice between in-person and at-home attendance.

为慢性中风患者开设在线舞蹈课的可行性。
背景:适应性舞蹈是一种很有希望的干预措施,可以改善中风后的身体和心理健康。在家参与直播舞蹈节目可以解决亲自出席的障碍(例如,有限的社区活动和交通)。然而,由于社会互动减少,心理社会益处可能会减少。本研究的主要目的是评估一种直播舞蹈节目治疗慢性中风的可行性和安全性。次要目标是描述选择直播与面对面选择的参与者的特征,并量化平衡、步态和社会联系的前后变化。方法:慢性脑卒中患者选择参加现场直播改编舞蹈节目或在家,每周两次,为期4周。追踪可行性标准,并以自我报告(流行病学研究中心抑郁量表;CES-D)和基于绩效的测量(如蒙特利尔认知评估和切多克麦克马斯特评估)为基线对参与者进行特征描述。中学前后测量包括步态速度、迷你平衡评估系统测试、平衡活动信心量表和自我社区纳入量表。基线临床表现的未配对中位数/平均值差异用于组间比较。使用配对中位数/平均值差异来检查次要结局的变化。结果:两组患者的兴趣和入组率分别为87%和38%。在被录取的人中,8/13选择了面对面,5/13选择了直播。现场和直播的出勤率分别为83%和89%,留存率分别为80%和75%。没有与班级相关的主要不良事件。有3个不相关的轻微疲劳事件和1个轻微肌肉酸痛事件与预期的运动一致。在基线时,面对面组的抑郁症状更严重(CES-D评分,中位[IQR]差值:11.5[-21.5,-5]),平均步速(-25.8 cm/s[-50.98, 0.006])比直播组更快。次要结果测量没有前后变化。结论:对慢性脑卒中患者进行课堂和家庭参与的直播舞蹈干预是安全可行的。这些结果将为未来更长时间的实时舞蹈节目的随机对照试验提供信息,同时考虑步态功能和情绪等因素如何影响面对面和在家出勤的选择。
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来源期刊
BioMedical Engineering OnLine
BioMedical Engineering OnLine 工程技术-工程:生物医学
CiteScore
6.70
自引率
2.60%
发文量
79
审稿时长
1 months
期刊介绍: BioMedical Engineering OnLine is an open access, peer-reviewed journal that is dedicated to publishing research in all areas of biomedical engineering. BioMedical Engineering OnLine is aimed at readers and authors throughout the world, with an interest in using tools of the physical and data sciences and techniques in engineering to understand and solve problems in the biological and medical sciences. Topical areas include, but are not limited to: Bioinformatics- Bioinstrumentation- Biomechanics- Biomedical Devices & Instrumentation- Biomedical Signal Processing- Healthcare Information Systems- Human Dynamics- Neural Engineering- Rehabilitation Engineering- Biomaterials- Biomedical Imaging & Image Processing- BioMEMS and On-Chip Devices- Bio-Micro/Nano Technologies- Biomolecular Engineering- Biosensors- Cardiovascular Systems Engineering- Cellular Engineering- Clinical Engineering- Computational Biology- Drug Delivery Technologies- Modeling Methodologies- Nanomaterials and Nanotechnology in Biomedicine- Respiratory Systems Engineering- Robotics in Medicine- Systems and Synthetic Biology- Systems Biology- Telemedicine/Smartphone Applications in Medicine- Therapeutic Systems, Devices and Technologies- Tissue Engineering
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