Dixon William G, van der Veer Sabine N, Ali Syed Mustafa, Laidlaw Lynn, Dobson Richard Jb, Sudlow Cathie, Chico Tim, MacArthur Jacqueline Al, Doherty Aiden
Centre for Epidemiology Versus Arthritis, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.
Centre for Health Informatics, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.
J Med Internet Res. 2023 Feb 7;25:e42449. doi: 10.2196/preprints.42449.
The use of data from smartphones and wearable devices has huge potential for population health research given high device ownership, the range of novel health-relevant data types available from consumer devices, and the frequency and duration over which data are, or could be, collected. Yet the uptake and success of large-scale mobile health research in the last decade has not matched the hyped opportunity. We make the argument that digital person-generated health data is required and necessary to answer many top priority research questions through illustrative examples taken from the James Lind Alliance Priority Setting Partnership. We then summarise the findings from two UK initiatives that considered the challenges and possible solutions for what needs to be done, and in what way, to realise the future opportunities of digital person-generated health data for clinically important population health research. Examples of important areas to be addressed to advance the field include digital inequality and addressing possible selection bias, easy access for researchers to the appropriate data collection tools including how best to harmonise data items, analysis methodology for time series data, methods for patient and public involvement and engagement to optimise recruitment, retention and public trust, and providing greater control of their data to research participants. There is also a major opportunity through the linkage of digital persongenerated health data to routinely-collected data to support novel population health research, bringing together clinician-reported and patient-reported measures. We recognise that well conducted studies need a wide range of diverse challenges to be skilfully addressed in unison: for example, epidemiology, data science and biostatistics, psychometrics, behavioural and social science, software engineering, user interface design, information governance, data management and patient and public involvement and engagement. Consequently, progress would be accelerated by the establishment of a new interdisciplinary community where all relevant and necessary skills are brought together to allow excellence throughout the lifecycle of a research study. This will require a partnership of diverse people, of methods and of technology. Get this right and the synergy has the potential to transform many millions of people's lives for the better.
鉴于智能手机和可穿戴设备的高拥有率、消费设备可提供的一系列与健康相关的新型数据类型，以及数据收集的频率和时长（或可收集的数据时长），利用这些设备的数据在人群健康研究方面具有巨大潜力。然而，过去十年大规模移动健康研究的应用和成效并未达到预期的高度。我们通过詹姆斯·林德联盟优先事项设定合作组织的实例表明，数字个人生成的健康数据对于回答许多首要研究问题是必要且不可或缺的。接着，我们总结了两项英国倡议的研究结果，这两项倡议探讨了为实现数字个人生成的健康数据在具有临床重要性的人群健康研究中的未来机遇，需要做什么以及如何做所面临的挑战和可能的解决方案。推进该领域需要解决的重要领域包括数字不平等问题以及应对可能的选择偏差、让研究人员能够轻松获取合适的数据收集工具，包括如何最好地统一数据项、时间序列数据的分析方法、患者和公众参与及互动以优化招募、留存和公众信任的方法，以及赋予研究参与者对其数据更大的控制权。通过将数字个人生成的健康数据与常规收集的数据相联系，以支持新型人群健康研究，将临床医生报告的措施和患者报告的措施结合起来，也存在重大机遇。我们认识到，开展良好的研究需要巧妙地同时应对广泛多样的挑战：例如，流行病学、数据科学和生物统计学、心理测量学、行为和社会科学、软件工程、用户界面设计、信息治理、数据管理以及患者和公众参与及互动。因此，建立一个新的跨学科社区将加速进展，在这个社区中，所有相关且必要的技能汇聚一堂，以确保研究在整个生命周期内都能达到卓越水平。这将需要不同人员之间、方法之间以及技术之间的合作。如果做好这一点，这种协同效应有可能使数百万人的生活变得更美好。
