Gooch Helen J, Jarvis Kathryn A, Stockley Rachel C
Stroke Research Team, School of Nursing and Midwifery, University of Central Lancashire, Preston, United Kingdom.
J Med Internet Res. 2024 Apr 24;26:e48725. doi: 10.2196/48725.
Digital health technologies (DHTs) are increasingly used in physical stroke rehabilitation to support individuals in successfully engaging with the frequent, intensive, and lengthy activities required to optimize recovery. Despite this, little is known about behavior change within these interventions.
This scoping review aimed to identify if and how behavior change approaches (ie, theories, models, frameworks, and techniques to influence behavior) are incorporated within physical stroke rehabilitation interventions that include a DHT.
Databases (Embase, MEDLINE, PsycINFO, CINAHL, Cochrane Library, and AMED) were searched using keywords relating to behavior change, DHT, physical rehabilitation, and stroke. The results were independently screened by 2 reviewers. Sources were included if they reported a completed primary research study in which a behavior change approach could be identified within a physical stroke rehabilitation intervention that included a DHT. Data, including the study design, DHT used, and behavior change approaches, were charted. Specific behavior change techniques were coded to the behavior change technique taxonomy version 1 (BCTTv1).
From a total of 1973 identified sources, 103 (5%) studies were included for data charting. The most common reason for exclusion at full-text screening was the absence of an explicit approach to behavior change (165/245, 67%). Almost half (45/103, 44%) of the included studies were described as pilot or feasibility studies. Virtual reality was the most frequently identified DHT type (58/103, 56%), and almost two-thirds (65/103, 63%) of studies focused on upper limb rehabilitation. Only a limited number of studies (18/103, 17%) included a theory, model, or framework for behavior change. The most frequently used BCTTv1 clusters were feedback and monitoring (88/103, 85%), reward and threat (56/103, 54%), goals and planning (33/103, 32%), and shaping knowledge (33/103, 32%). Relationships between feedback and monitoring and reward and threat were identified using a relationship map, with prominent use of both of these clusters in interventions that included virtual reality.
Despite an assumption that DHTs can promote engagement in rehabilitation, this scoping review demonstrates that very few studies of physical stroke rehabilitation that include a DHT overtly used any form of behavior change approach. From those studies that did consider behavior change, most did not report a robust underpinning theory. Future development and research need to explicitly articulate how including DHTs within an intervention may support the behavior change required for optimal engagement in physical rehabilitation following stroke, as well as establish their effectiveness. This understanding is likely to support the realization of the transformative potential of DHTs in stroke rehabilitation.
数字健康技术（DHTs）越来越多地应用于中风后的物理康复中，以帮助患者成功参与那些为优化康复效果而开展的频繁、密集且耗时的活动。尽管如此，对于这些干预措施中的行为改变情况，我们知之甚少。
本范围综述旨在确定行为改变方法（即影响行为的理论、模型、框架和技术）是否以及如何被纳入包含DHT的中风物理康复干预措施中。
使用与行为改变、DHT、物理康复和中风相关的关键词搜索数据库（Embase、MEDLINE、PsycINFO、CINAHL、Cochrane图书馆和AMED）。结果由两名评审员独立筛选。如果文献报道了一项完整的初步研究，且在包含DHT的中风物理康复干预措施中能够识别出行为改变方法，则将其纳入。记录数据，包括研究设计、使用的DHT和行为改变方法。特定的行为改变技术被编码到行为改变技术分类法第1版（BCTTv1）中。
在总共1973篇已识别的文献中，有103篇（5%）研究被纳入数据记录。全文筛选时最常见的排除原因是缺乏明确的行为改变方法（165/245，67%）。几乎一半（45/103，44%）的纳入研究被描述为试点或可行性研究。虚拟现实是最常被识别的DHT类型（58/103，56%），并且几乎三分之二（65/103，63%）的研究聚焦于上肢康复。只有少数研究（18/103，17%）纳入了行为改变的理论、模型或框架。最常使用的BCTTv1类别是反馈与监测（88/103，85%）、奖励与威胁（56/103，54%）、目标与计划（33/103，32%）以及塑造知识（33/103，32%）。通过关系图确定了反馈与监测以及奖励与威胁之间的关系，在包含虚拟现实的干预措施中，这两个类别都有显著应用。
尽管人们认为DHTs可以促进康复参与，但本范围综述表明，在包含DHT的中风物理康复研究中，很少有研究公开使用任何形式的行为改变方法。在那些确实考虑行为改变的研究中，大多数没有报告坚实的基础理论。未来的发展和研究需要明确阐述在干预措施中纳入DHTs如何支持中风后物理康复中最佳参与所需的行为改变，并确定其有效性。这种理解可能有助于实现DHTs在中风康复中的变革潜力。
