Vittoria Ardito, Georgi Golubev, Oriana Ciani, Rosanna Tarricone
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However, a persistent challenge of digital health technologies, including mHealth, is that they are characterized by early dropouts in clinical practice and struggle to be used outside experimental settings or on larger scales.</p><p><strong>Objective: </strong>This study aimed to explore barriers and enablers to the uptake of mHealth solutions used by patients with cancer undergoing treatment, using a theory-guided implementation science model, that is, the Consolidated Framework for Implementation Research (CFIR).</p><p><strong>Methods: </strong>A scoping literature review was conducted using PubMed (MEDLINE), Web of Science, and ScienceDirect databases in March 2022. We selected studies that analyzed the development, evaluation, and implementation of mHealth solutions for patients with cancer that were used in addition to the standard of care. Only empirical designs (eg, randomized controlled trials, observational studies, and qualitative studies) were considered. First, information on the study characteristics, patient population, app functionalities, and study outcomes was extracted. Then, the CFIR model was used as a practical tool to guide data collection and interpretation of evidence on mHealth uptake.</p><p><strong>Results: </strong>Overall, 91 papers were included in the data synthesis. The selected records were mostly randomized controlled trials (26/91, 29%) and single-arm, noncomparative studies (52/91, 57%). Most of the apps (42/73, 58%) were designed for both patients and clinicians and could be used to support any type of cancer (29/73, 40%) and a range of oncological treatments. Following the CFIR scheme (intervention, outer setting, inner setting, individuals, process), multistakeholder co-design, codevelopment, and testing of mHealth interventions were identified as key enablers for later uptake. A variety of external drivers emerged, although the most relevant outer incentive fostering mHealth use was addressing patient needs. Among organizational factors likely to influence technology uptake, interoperability was the most prominent, whereas other providers' dimensions such as managerial attitudes or organizational culture were not systematically discussed. Technology-related impediments that could hamper the use of mHealth at the individual level were considered least often.</p><p><strong>Conclusions: </strong>The hype surrounding mHealth in cancer care is hindered by several factors that can affect its use in real world and nonexperimental settings. Compared with the growing evidence on mHealth efficacy, knowledge to inform the uptake of mHealth solutions in clinical cancer care is still scarce. Although some of our findings are supported by previous implementation research, our analysis elaborates on the distinguishing features of mHealth apps and provides an integrated perspective on the factors that should be accounted for implementation efforts. 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However, a persistent challenge of digital health technologies, including mHealth, is that they are characterized by early dropouts in clinical practice and struggle to be used outside experimental settings or on larger scales.</p><p><strong>Objective: </strong>This study aimed to explore barriers and enablers to the uptake of mHealth solutions used by patients with cancer undergoing treatment, using a theory-guided implementation science model, that is, the Consolidated Framework for Implementation Research (CFIR).</p><p><strong>Methods: </strong>A scoping literature review was conducted using PubMed (MEDLINE), Web of Science, and ScienceDirect databases in March 2022. We selected studies that analyzed the development, evaluation, and implementation of mHealth solutions for patients with cancer that were used in addition to the standard of care. Only empirical designs (eg, randomized controlled trials, observational studies, and qualitative studies) were considered. First, information on the study characteristics, patient population, app functionalities, and study outcomes was extracted. Then, the CFIR model was used as a practical tool to guide data collection and interpretation of evidence on mHealth uptake.</p><p><strong>Results: </strong>Overall, 91 papers were included in the data synthesis. The selected records were mostly randomized controlled trials (26/91, 29%) and single-arm, noncomparative studies (52/91, 57%). Most of the apps (42/73, 58%) were designed for both patients and clinicians and could be used to support any type of cancer (29/73, 40%) and a range of oncological treatments. Following the CFIR scheme (intervention, outer setting, inner setting, individuals, process), multistakeholder co-design, codevelopment, and testing of mHealth interventions were identified as key enablers for later uptake. A variety of external drivers emerged, although the most relevant outer incentive fostering mHealth use was addressing patient needs. 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引用次数: 4
摘要
背景:移动医疗(mHealth)解决方案已被证明在广泛的患者结果中是有效的,并且随着时间的推移而激增。然而,包括移动医疗在内的数字医疗技术面临的一个持续挑战是,它们的特点是在临床实践中早期退出,难以在实验环境之外或更大规模地使用。目的:本研究旨在利用理论指导的实施科学模型,即实施研究综合框架(CFIR),探索正在接受治疗的癌症患者采用移动医疗解决方案的障碍和推动因素。方法:于2022年3月使用PubMed (MEDLINE)、Web of Science和ScienceDirect数据库进行范围文献综述。我们选择了一些研究,这些研究分析了针对癌症患者的移动医疗解决方案的开发、评估和实施,这些解决方案是在标准护理之外使用的。只考虑经验性设计(如随机对照试验、观察性研究和定性研究)。首先,提取有关研究特征、患者群体、应用程序功能和研究结果的信息。然后,CFIR模型被用作指导数据收集和解释移动医疗吸收证据的实用工具。结果:共纳入91篇论文。所选记录多为随机对照试验(26/91,29%)和单臂非比较研究(52/91,57%)。大多数应用程序(42/ 73,58%)是为患者和临床医生设计的,可用于支持任何类型的癌症(29/ 73,40%)和一系列肿瘤治疗。根据CFIR方案(干预、外部环境、内部环境、个人、流程),确定了多利益相关者共同设计、共同开发和测试移动健康干预措施是后期采用的关键推动因素。出现了各种外部驱动因素,尽管促进移动医疗使用的最相关外部激励因素是满足患者需求。在可能影响技术吸收的组织因素中,互操作性是最突出的,而其他供应商的维度,如管理态度或组织文化,没有系统地讨论。可能妨碍个人使用移动医疗的技术相关障碍被考虑得最少。结论:围绕移动医疗在癌症治疗中的宣传受到几个因素的阻碍,这些因素可能影响其在现实世界和非实验环境中的使用。与越来越多的关于移动医疗疗效的证据相比,在临床癌症治疗中采用移动医疗解决方案的知识仍然很少。虽然我们的一些发现得到了之前实施研究的支持,但我们的分析详细阐述了移动健康应用程序的显著特征,并提供了一个综合的视角,说明了应该考虑实施工作的因素。今后的综合应将这些方面与在成功的执行倡议中观察到的战略联系起来。
Evaluating Barriers and Facilitators to the Uptake of mHealth Apps in Cancer Care Using the Consolidated Framework for Implementation Research: Scoping Literature Review.
Background: Mobile health (mHealth) solutions have proven to be effective in a wide range of patient outcomes and have proliferated over time. However, a persistent challenge of digital health technologies, including mHealth, is that they are characterized by early dropouts in clinical practice and struggle to be used outside experimental settings or on larger scales.
Objective: This study aimed to explore barriers and enablers to the uptake of mHealth solutions used by patients with cancer undergoing treatment, using a theory-guided implementation science model, that is, the Consolidated Framework for Implementation Research (CFIR).
Methods: A scoping literature review was conducted using PubMed (MEDLINE), Web of Science, and ScienceDirect databases in March 2022. We selected studies that analyzed the development, evaluation, and implementation of mHealth solutions for patients with cancer that were used in addition to the standard of care. Only empirical designs (eg, randomized controlled trials, observational studies, and qualitative studies) were considered. First, information on the study characteristics, patient population, app functionalities, and study outcomes was extracted. Then, the CFIR model was used as a practical tool to guide data collection and interpretation of evidence on mHealth uptake.
Results: Overall, 91 papers were included in the data synthesis. The selected records were mostly randomized controlled trials (26/91, 29%) and single-arm, noncomparative studies (52/91, 57%). Most of the apps (42/73, 58%) were designed for both patients and clinicians and could be used to support any type of cancer (29/73, 40%) and a range of oncological treatments. Following the CFIR scheme (intervention, outer setting, inner setting, individuals, process), multistakeholder co-design, codevelopment, and testing of mHealth interventions were identified as key enablers for later uptake. A variety of external drivers emerged, although the most relevant outer incentive fostering mHealth use was addressing patient needs. Among organizational factors likely to influence technology uptake, interoperability was the most prominent, whereas other providers' dimensions such as managerial attitudes or organizational culture were not systematically discussed. Technology-related impediments that could hamper the use of mHealth at the individual level were considered least often.
Conclusions: The hype surrounding mHealth in cancer care is hindered by several factors that can affect its use in real world and nonexperimental settings. Compared with the growing evidence on mHealth efficacy, knowledge to inform the uptake of mHealth solutions in clinical cancer care is still scarce. Although some of our findings are supported by previous implementation research, our analysis elaborates on the distinguishing features of mHealth apps and provides an integrated perspective on the factors that should be accounted for implementation efforts. Future syntheses should liaise these dimensions with strategies observed in successful implementation initiatives.