背景:下腰痛(LBP)是全球因残疾而损失数年的主要原因,给医疗系统带来巨大的经济负担。数字治疗护理(DTC)计划提供可扩展、普遍可及,以及LBP多学科治疗的低成本方法。此外,新颖的决策支持干预措施,如个性化反馈消息,推送通知,和数据驱动的活动建议通过指导用户完成计划,同时旨在提高整体参与度和可持续的行为改变,从而放大DTC。
目的:本系统综述旨在综合有关DTC应用对LBP患者的影响的最新科学文献,并概述附加决策支持干预措施的实施。包括它们对用户保留率和流失率的影响。
方法:我们搜索了书目数据库,包括MEDLINE,科克伦图书馆,WebofScience,和物理治疗证据数据库,从2016年3月1日至2020年10月15日,根据PRISMA(系统审查和荟萃分析的首选报告项目)指南,并根据先前发表的相关系统审查进行了本审查。除了随机对照试验(RCTs),我们还纳入了至少一项回顾性比较研究的证据水平的研究设计.这使得能够考虑现实世界的用户生成的数据,并提供有关在现实环境中DTC应用程序的采用和有效性的信息。对于偏差风险的评估,我们在随机对照试验和非随机试验的干预工具的非随机研究中使用了偏差风险2工具和偏差风险,分别。纳入的研究是关于主要和次要结局指标的叙述性综合,DTC部件,应用决策支持干预措施,用户保留,和流失率。
结果:我们检索到1388条引文,其中12项研究纳入本综述。在12项研究中,6项(50%)为随机对照试验,6项(50%)为非随机试验。在所有纳入的研究中,与基线值相比,DTC干预组的疼痛水平降低,功能增强.组间比较显示,67%(4/6)的随机对照试验显示疼痛和功能水平显著改善。研究人群大多是同质的,以女性为主,正常至中等体重的年轻至中年参与者。方法学质量评估揭示了中度到高度的偏差风险,尤其是在非随机试验中。
结论:本系统评价显示DTC对LBP患者的益处。还有证据表明,决策支持干预措施有利于应用程序的整体参与,并提高参与者自我管理恢复过程的能力。最后,在未来对数字健康干预试验的系统评价中,包括对现实世界用户生成数据的回顾性评估研究,可以揭示对益处的新见解,挑战,以及DTC程序的实际采用。
BACKGROUND: Low back pain (LBP) is the leading cause of worldwide years lost because of disability, with a tremendous economic burden for health care systems. Digital therapeutic care (DTC) programs provide a scalable, universally accessible, and low-cost approach to the multidisciplinary treatment of LBP. Moreover, novel decision support interventions such as personalized feedback messages, push notifications, and data-driven activity recommendations amplify DTC by guiding the user through the program while aiming to increase overall engagement and sustainable behavior change.
OBJECTIVE: This systematic
review aims to synthesize recent scientific literature on the impact of DTC apps for people with LBP and outline the implementation of add-on decision support interventions, including their effect on user retention and attrition rates.
METHODS: We searched bibliographic databases, including MEDLINE, Cochrane Library, Web of Science, and the Physiotherapy Evidence Database, from March 1, 2016, to October 15, 2020, in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and conducted this
review based on related previously published systematic reviews. Besides randomized controlled trials (RCTs), we also included study designs with the evidence level of at least a retrospective comparative study. This enables the consideration of real-world user-generated data and provides information regarding the adoption and effectiveness of DTC apps in a real-life setting. For the appraisal of the risk of bias, we used the Risk of Bias 2 Tool and the Risk of Bias in Non-Randomized Studies of Interventions Tool for the RCTs and nonrandomized trials, respectively. The included studies were narratively synthesized regarding primary and secondary outcome measures, DTC components, applied decision support interventions, user retention, and attrition rates.
RESULTS: We retrieved 1388 citations, of which 12 studies are included in this
review. Of the 12 studies, 6 (50%) were RCTs and 6 (50%) were nonrandomized trials. In all included studies, lower pain levels and increased functionality compared with baseline values were observed in the DTC intervention group. A between-group comparison revealed significant improvements in pain and functionality levels in 67% (4/6) of the RCTs. The study population was mostly homogeneous, with predominantly female, young to middle-aged participants of normal to moderate weight. The methodological quality assessment revealed moderate to high risks of biases, especially in the nonrandomized trials.
CONCLUSIONS: This systematic
review demonstrates the benefits of DTC for people with LBP. There is also evidence that decision support interventions benefit overall engagement with the app and increase participants\' ability to self-manage their recovery process. Finally, including retrospective evaluation studies of real-world user-generated data in future systematic reviews of digital health intervention trials can reveal new insights into the benefits, challenges, and real-life adoption of DTC programs.