优先考虑数字健康研究中最重要的事情

作者：NursingResearch护理研究前沿

分享智慧共同成长

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Digital health technologies provide diverse opportunities to change the way patients manage their health and interact with healthcare professionals and systems. For instance, mHealth solutions, such as smartphone apps (herein apps) linked to sensors or wearables with triggers for out-of-range events, offer patients the opportunity for accurate and real-time self-monitoring and form the basis for precise self-management.1

Yet the rapid growth in the health app ecosystem is based on limited evidence of the effectiveness of such interventions in the cardiovascular disease (CVD) population, and few reports of successful implementation into clinical practice. In this editorial, we explore the tensions between emerging mHealth solutions and CVD patient needs.

Involvement of patients

Involving patients in the development of CVD mHealth solutions that they will ultimately use is the best strategy to ensure that users’ needs remain central, and that the benefits are clear and outweigh the perceived or actual burden of use. Despite the ethical reasons to involve patients in the development and assessment of mHealth interventions, many recent clinical studies have not done so. In fact, excluding patients from the development and testing of new interventions is a long-standing research convention. For instance, evaluation of many new medications often does not involve patients in any way outside of being a passive recipient of dosing regimens. Evidence for efficacy and benefit is sought and then effective medications are embedded into clinical guidelines and practice. So why is there such a push to implement patient co-design approaches with digital health interventions?

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A paradigm shift is occurring. During the last decade, the value of patient involvement in clinical research has been clearly demonstrated.2 Patient factors, including poor medication adherence, have been demonstrated repetitively to influence medication effectiveness in real-life conditions, with hypertension as an enduring example. Medication taking requires patient engagement.3 Efficacious medications may not be seen by patients to have sufficient benefit to outweigh the burden of daily dosing and side-effects. Yet if patients had been involved early, challenges like medication adherence could have been identified sooner and addressed more effectively prior to large-scale treatment roll-out.

Furthermore, engagement with novel technological solutions, such as health apps, does not occur in isolation. Patient-level opportunities and challenges parallel those of healthcare professionals. Health staff are compelled to expand their knowledge and skills, understand their own and patients’ digital healthcare needs, and share critical insights on integration into routine clinical practice.4 Researchers investigating health apps recognize this and are increasingly building in patient–clinician communication features to facilitate engagement. Acknowledging simple approaches such as these make patients active (rather than passive) partners in their own healthcare management.5

Whose healthcare need is it anyway?

If digital health represents the convergence between healthcare needs and emerging digital technologies, with mHealth applications intended for patients, for healthcare professionals, or both, why are we not seeing them being more readily sought out and frequently used? Common challenges facing the mHealth field include low app engagement and poor sustained usage over time, often demonstrated by low retention rates across many clinical studies.6

One reason for low engagement might be a mismatch between perceptions of patient’s most important healthcare needs by digital health researchers, and how patients rank these needs amongst the numerous others in their lives. A randomized controlled trial7 explored this mismatch in people experiencing atrial fibrillation (AF), a problem currently affecting 33.5 million people.8 The study explored factors that support sustained use of a mHealth app [with additional heart rate and rhythm monitoring capabilities using the AliveCorVR KardiaMobile electrocardiogram (ECG) monitor] over 6 months. While researchers reported frequent use of the app and AliveCorVR monitor was correctly associated with AF trigger events, optimal use duration, frequency of appropriate engagement, and engagement levels for clinical benefit could not be determined, and neither could important self-predictors that influence favourable app usage.

In contrast, when an app and handheld ECG monitor were used to provide ECG feedback to patients experiencing palpitations much higher study participant retention rates (>80%) enabled a more complete understanding.9 The results demonstrated that instant analysis of the ECG with a direct response to patients experiencing palpitations significantly decreased symptoms of anxiety, and depression, and increased health-related quality of life. As a result, the investigators determined that direct feedback of the underlying heart rhythm can alter such outcomes in real-time. Greater involvement of patients in such study designs may improve recruitment and retention, and the acceptability of the intervention over time.

Burden versus benefit

Patient-users are best positioned to determine the relative benefit vs. burden of health apps. Patients begin making decisions on digital health engagement well before being offered the opportunity in research studies. From the patient’s perspective, these assessments might include (i) the clinical importance of their healthcare needs, (ii) the impact on their daily life associated with managing the condition, or (iii) the accessibility of healthcare services.

These perspectives were examined in a pilot study of a mHealth insulation titration intervention for patients with type 2 diabetes (vs. standard care weekly telephone calls or clinic consultations).10 The clinical importance of the healthcare need and requirement for regular monitoring and management was recognized by both patients and healthcare professionals. The study found while the time to titrate was similar in both groups, mHealth participants had significantly fewer events due to more successful contact and communication with staff. While the clinical benefit was the same, the daily life burden was reduced for those participants who used the app because in standard care patients struggled to access healthcare professionals due to their fixed clinic hours and overall limited availability.

The feasibility of mHealth was also explored in young (<45 years old) African American women at increased risk of CVD.11 Compared to usual care, mHealth participants completed four face-to-face sessions on CVD risk reduction education, followed by a 6-month smartphone monitoring and coaching intervention targeting heart-healthy behaviour modifications. Additionally, participants had regular tracking of activity and blood pressure, automated coaching messages, and individual messages from a nurse practitioner. The app was reported to be easy to use, and over 60% of participants observed that their family’s nutrition improved, a true clinical benefit. However, challenges were encountered by 40% of participants, including the phone being too hot and uncomfortable to wear and the daily burden of answering questions, or taking and sending blood pressure readings. The results provide comprehensive information on how mHealth solutions match with patients’ needs, both in terms of clinical benefit and a reduced burden of healthcare management to daily life. Indeed, the mHealth studies discussed in this editorial merely highlight the otherwise hidden patient needs and healthcare service delivery gaps.

Conclusion

Most digital health interventions, including apps, are complex, and ultimately their effectiveness will be determined by the level of active patient engagement and the optimal balance of people and environmental factors. Answering such challenges may be achieved by involving patients in digital health research from the start, prioritizing their health and digital health needs, and continually reviewing and adjusting such interventions to ensure the clinical benefit outweighs the engagement burden.

全文翻译（仅供参考）

数字健康技术为改变患者管理健康以及与医疗保健专业人员和系统互动的方式提供了多种机会Miniswap。例如，mHealth 解决方案，例如与传感器或可穿戴设备连接的智能手机应用程序（此处为应用程序），可触发超出范围的事件，为患者提供准确和实时自我监测的机会，并构成精确自我监测的基础。管理。1

然而，健康应用生态系统的快速增长是基于此类干预措施在心血管疾病 (CVD) 人群中的有效性的有限证据，以及很少有成功实施到临床实践的报告Miniswap。在这篇社论中，我们探讨了新兴 mHealth 解决方案与 CVD 患者需求之间的紧张关系。

患者的参与

让患者参与他们最终将使用的 CVD mHealth 解决方案的开发，是确保用户需求始终处于中心地位的最佳策略，并且好处是明确的，并且超过了感知或实际使用的负担Miniswap。尽管出于道德原因让患者参与 mHealth 干预措施的开发和评估，但最近的许多临床研究并未这样做。事实上，将患者排除在新干预措施的开发和测试之外是一项长期存在的研究惯例。例如，对许多新药物的评估通常不涉及患者，除了作为给药方案的被动接受者之外。寻求有效性和益处的证据，然后将有效的药物嵌入临床指南和实践中。

范式转变正在发生Miniswap。在过去十年中，患者参与临床研究的价值已得到明确证明。2患者因素，包括服药依从性差，已被反复证明会影响现实生活条件下的药物有效性，高血压就是一个经久不衰的例子。服药需要患者参与。3患者可能认为有效药物的益处不足以超过每日给药和副作用的负担。然而，如果患者及早参与，则可以在大规模治疗推出之前更早发现并更有效地解决药物依从性等挑战。

此外，与健康应用程序等新型技术解决方案的互动并非孤立存在Miniswap。患者层面的机遇和挑战与医疗保健专业人员类似。卫生人员被迫扩展他们的知识和技能，了解他们自己和患者的数字医疗保健需求，并分享有关融入常规临床实践的重要见解。4调查健康应用程序的研究人员认识到这一点，并越来越多地构建医患沟通功能以促进参与。承认这些简单的方法使患者成为他们自己的医疗保健管理中的主动（而不是被动）合作伙伴。5

到底是谁的医疗保健需求Miniswap？

如果数字健康代表了医疗保健需求和新兴数字技术之间的融合，mHealth 应用程序旨在为患者、医疗保健专业人员或两者兼而有之，为什么我们没有看到它们更容易被寻找和频繁使用？移动医疗领域面临的共同挑战包括应用程序参与度低和长期持续使用不佳，这通常表现为许多临床研究的低保留率Miniswap。6

参与度低的一个原因可能是数字健康研究人员对患者最重要的医疗保健需求的看法与患者如何将这些需求与他们生活中的众多其他需求进行排名不匹配Miniswap。一项随机对照试验7探讨了心房颤动 (AF) 患者的这种不匹配情况，该问题目前影响 3350 万人。8该研究探讨了支持 mHealth 应用程序持续使用的因素 [使用 AliveCorVR KardiaMobile 心电图 (ECG) 监测器具有额外的心率和节律监测功能] 超过 6 个月。虽然研究人员报告说频繁使用该应用程序并且 AliveCorVR 监视器与 AF 触发事件正确相关，但无法确定最佳使用持续时间、适当参与的频率和临床获益的参与水平，也无法确定影响有利应用程序的重要自我预测因素用法。

相比之下，当使用应用程序和手持式心电图监测仪向心悸患者提供心电图反馈时，研究参与者的保留率（>80%）更高，从而能够更全面地了解Miniswap。9结果表明，对心悸患者的直接反应的即时心电图分析显着减少了焦虑和抑郁症状，并提高了与健康相关的生活质量。结果，研究人员确定，潜在心律的直接反馈可以实时改变这些结果。患者更多地参与此类研究设计可能会提高招募和保留率，以及随着时间的推移干预的可接受性。

负担与收益

患者用户最适合确定健康应用程序的相对收益与负担Miniswap。患者在获得研究机会之前就开始就数字健康参与做出决定。从患者的角度来看，这些评估可能包括（i）他们的医疗保健需求的临床重要性，（ii）与管理病情相关的对他们日常生活的影响，或（iii）医疗保健服务的可及性。

这些观点在一项针对 2 型糖尿病患者的 mHealth 绝缘滴定干预的试点研究中得到了检验（与每周标准护理电话或诊所咨询相比）Miniswap。10患者和医疗保健专业人员都认识到医疗保健需求和定期监测和管理要求的临床重要性。研究发现，虽然两组的滴定时间相似，但由于与工作人员的更成功的联系和沟通，mHealth 参与者的事件显着减少。虽然临床益处相同，但使用该应用程序的参与者的日常生活负担有所减轻，因为在标准护理中，由于门诊时间固定且总体可用性有限，患者难以获得医疗保健专业人员的帮助。

在 CVD 风险增加的年轻（<45 岁）非洲裔美国女性中也探讨了 mHealth 的可行性Miniswap。11与常规护理相比，mHealth 参与者完成了四次关于 CVD 风险降低教育的面对面会议，随后是为期 6 个月的智能手机监测和针对心脏健康行为改变的指导干预。此外，参与者定期跟踪活动和血压、自动指导信息以及来自执业护士的个人信息。据报道，该应用程序易于使用，超过 60% 的参与者观察到他们家人的营养得到改善，这是一个真正的临床益处。然而，40% 的参与者遇到了挑战，包括手机太热且佩戴不舒服，以及每天回答问题或获取和发送血压读数的负担。结果提供了有关移动医疗解决方案如何满足患者需求的全面信息，在临床益处和减轻日常生活中的医疗保健管理负担方面。事实上，本社论中讨论的移动医疗研究仅仅强调了隐藏的患者需求和医疗服务提供差距。

结论

大多数数字健康干预措施（包括应用程序）都很复杂，最终其有效性将取决于患者的积极参与程度以及人和环境因素的最佳平衡Miniswap。通过从一开始就让患者参与数字健康研究，优先考虑他们的健康和数字健康需求，并不断审查和调整此类干预措施，以确保临床收益超过参与负担，可以应对这些挑战。

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