Smart Devices for Older Adults Managing Chronic Disease: A Scoping Review.
Smart Devices for Older Adults Managing Chronic Disease: A Scoping Review.
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DOI：
10.2196/mhealth.7141
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发表时间：
2017-05-23
影响因子：
5
通讯作者：
Lee J
中科院分区：
文献类型：
--
作者：
Kim BY;Lee J
具有先进功能（例如传感器、计算能力、交互性）的智能手机和平板电脑的出现改变了移动医疗干预支持老年人慢性病管理的方式。婴儿潮一代已经开始广泛采用智能设备，并表示希望将技术融入他们的慢性病护理中。尽管智能设备在研究中被积极使用，但人们对基于智能设备的干预措施的程度、特征和范围知之甚少。我们进行了范围界定审查，以（1）了解基于智能设备的研究活动的性质、程度和范围，（2）确定当前研究的局限性和知识差距，以及（3）建议未来的研究方向。我们使用 Arksey 和 O’Malley 框架进行范围界定审查。我们使用与移动健康、慢性病和老年人相关的搜索词从 MEDLINE、Embase、CINAHL 和 Web of Science 数据库中识别出相关研究。选定的研究使用智能设备，以老年人为样本，并于 2010 年或之后发表。排除标准是完全依赖短信（短消息服务，SMS）或交互式语音应答、电子版调查问卷的验证、术后监测和可用性评估。我们回顾了参考文献。我们绘制了定量数据并使用主题综合分析了定性研究。为了整理和总结数据，我们使用了慢性护理模型。共有 51 篇文章符合资格标准。 2014 年研究活动急剧增加（17/51，33%），实验前设计占主导地位（16/50，32%）。最常研究的是糖尿病（16/46，35%）和心力衰竭治疗（9/46，20%）。我们确定了慢性病内部和之间的生物特征收集和患者报告的结果测量的多样性和异质性。通过研究，我们发现了 8 种自我管理支持策略和 4 种不同的沟通渠道来支持决策过程。特别是，自我监控（38/40，95％）、自动反馈（15/40，38％）和患者教育（13/40，38％）通常被用作自我管理支持策略。在实施决策支持策略的 23 项研究中，有 10 项研究 (43%) 将临床决策权委托给患者。对患者结果的影响与使用手机的研究一致。心力衰竭和哮喘患者的生活质量得到改善。定性分析得出了 2 个促进老年人采用技术的主题和 3 个障碍主题。当前研究的局限性包括缺乏老年学重点、实验前设计占主导地位、研究范围狭窄、对参与者的支持不足以及临床结果证据不足。对未来研究的建议包括为基于智能设备的项目生成证据、使用患者生成的数据进行先进的数据挖掘技术、验证患者决策支持系统以及通过创新技术扩展移动医疗实践。
The emergence of smartphones and tablets featuring vastly advancing functionalities (eg, sensors, computing power, interactivity) has transformed the way mHealth interventions support chronic disease management for older adults. Baby boomers have begun to widely adopt smart devices and have expressed their desire to incorporate technologies into their chronic care. Although smart devices are actively used in research, little is known about the extent, characteristics, and range of smart device-based interventions. We conducted a scoping review to (1) understand the nature, extent, and range of smart device-based research activities, (2) identify the limitations of the current research and knowledge gap, and (3) recommend future research directions. We used the Arksey and O’Malley framework to conduct a scoping review. We identified relevant studies from MEDLINE, Embase, CINAHL, and Web of Science databases using search terms related to mobile health, chronic disease, and older adults. Selected studies used smart devices, sampled older adults, and were published in 2010 or after. The exclusion criteria were sole reliance on text messaging (short message service, SMS) or interactive voice response, validation of an electronic version of a questionnaire, postoperative monitoring, and evaluation of usability. We reviewed references. We charted quantitative data and analyzed qualitative studies using thematic synthesis. To collate and summarize the data, we used the chronic care model. A total of 51 articles met the eligibility criteria. Research activity increased steeply in 2014 (17/51, 33%) and preexperimental design predominated (16/50, 32%). Diabetes (16/46, 35%) and heart failure management (9/46, 20%) were most frequently studied. We identified diversity and heterogeneity in the collection of biometrics and patient-reported outcome measures within and between chronic diseases. Across studies, we found 8 self-management supporting strategies and 4 distinct communication channels for supporting the decision-making process. In particular, self-monitoring (38/40, 95%), automated feedback (15/40, 38%), and patient education (13/40, 38%) were commonly used as self-management support strategies. Of the 23 studies that implemented decision support strategies, clinical decision making was delegated to patients in 10 studies (43%). The impact on patient outcomes was consistent with studies that used cellular phones. Patients with heart failure and asthma reported improved quality of life. Qualitative analysis yielded 2 themes of facilitating technology adoption for older adults and 3 themes of barriers. Limitations of current research included a lack of gerontological focus, dominance of preexperimental design, narrow research scope, inadequate support for participants, and insufficient evidence for clinical outcome. Recommendations for future research include generating evidence for smart device-based programs, using patient-generated data for advanced data mining techniques, validating patient decision support systems, and expanding mHealth practice through innovative technologies.