AbstractNovel sources of population data, especially administrative and medical records, as well as the digital footprints generated through interactions with online services, present a considerable opportunity for advancing health research and policymaking. An illustrative example is shopping history records that can illuminate aspects of population health by scrutinizing extensive sets of everyday choices made in the real world.

摘要人口数据的新来源，特别是行政和医疗记录，以及通过与在线服务互动产生的数字足迹，为推进健康研究和决策提供了相当大的机会。一个说明性的例子是购物历史记录，它可以通过仔细检查现实世界中做出的大量日常选择来阐明人口健康的各个方面。

However, like any dataset, these sources possess specific limitations, including sampling biases, validity issues, and measurement errors. To enhance the applicability and potential of shopping data in health research, we advocate for the integration of individual-level shopping data with external datasets containing rich repositories of longitudinal population cohort studies.

然而，像任何数据集一样，这些来源具有特定的局限性，包括抽样偏差，有效性问题和测量误差。为了提高购物数据在健康研究中的适用性和潜力，我们主张将个人层面的购物数据与包含丰富的纵向人群队列研究存储库的外部数据集相结合。

This strategic approach holds the promise of devising innovative methodologies to address inherent data limitations and biases. By meticulously documenting biases, establishing validated associations, and discerning patterns within these amalgamated records, researchers can extrapolate their findings to encompass population-wide datasets derived from national supermarket chain.

。通过仔细记录偏见，建立经过验证的关联，并在这些合并记录中辨别模式，研究人员可以推断他们的发现，以涵盖来自全国超市连锁店的全人群数据集。

The validation and linkage of population health data with real-world choices pertaining to food, beverages, and over-the-counter medications, such as pain relief, present a significant opportunity to comprehend the impact of these choices and behavioural patterns associated with them on public health..

人口健康数据与食品、饮料和非处方药（如止痛药）相关的现实世界选择的验证和联系，为理解这些选择及其相关行为模式对公共健康的影响提供了重要机会。。

Every day, we leave behind ‘digital footprints’; a trail of data created through our on-line and in-person activities, collected through technology. These ‘digital footprints’ are a reflection of our choices, activities, preferences and habits. The advent of digital technology has transformed comprehension of health and related behaviours, driven by the meticulous collection of these digital footprints across various data repositories1,2,3,4,5.

每天，我们都会留下“数字足迹”；通过我们的在线和面对面的活动创建的数据线索，通过技术收集。这些“数字足迹”反映了我们的选择、活动、偏好和习惯。数字技术的出现改变了对健康和相关行为的理解，这是由于在各种数据存储库中仔细收集了这些数字足迹1,2,3,4,5。

This has led to a wealth of research that utilises digital footprints data, for example wearables to track population health indicators6,7,8 and assess the success of interventions9; consumer app data analysed to derive prevalence and course of health symptoms in different domains10,11 and consumer behavioural data, such as shopping and banking records, being examined to derive predictors of life outcomes including health and well-being12,13,14,15.Shopping data are collected when we engage in supermarket transactions and use loyalty cards (known as club cards in USA and other countries) to get future benefits, such as discounts.

这导致了大量利用数字足迹数据的研究，例如可穿戴设备来跟踪人口健康指标6,7,8并评估干预措施的成功9；分析消费者应用程序数据以得出不同领域的健康症状的患病率和病程10,11以及消费者行为数据，如购物和银行记录，以得出包括健康和幸福感在内的生活结果的预测因子12,13,14,15。当我们参与超市交易并使用忠诚卡（在美国和其他国家称为俱乐部卡）获得折扣等未来利益时，会收集购物数据。

In this process, supermarkets record our purchases to better understand their consumers to maximise profits. While collected for commercial purposes, shopping data can be uniquely beneficial to public health research because it can provide a comprehensive portrait of our consumption habits and preferences.Conventional approaches to scrutinizing the interplay between lifestyle choices, health behaviours and outcomes rely heavily on self-report questionnaires, wherein individuals are asked to recollect and/or recount their daily choices and behaviours (e.g., in the areas of diet, tobacco and alcohol consumption16,17).

在这个过程中，超市会记录我们的购买情况，以更好地了解消费者，从而实现利润最大化。虽然购物数据是为了商业目的而收集的，但它对公共卫生研究具有独特的益处，因为它可以全面描述我们的消费习惯和偏好。检查生活方式选择，健康行为和结果之间相互作用的传统方法在很大程度上依赖于自我报告问卷，其中要求个人回忆和/或叙述他们的日常选择和行为（例如，在饮食，烟草和酒精消费领域16,17）。

Such a methodological approach contains potential biases, as responses are frequently colo.

这种方法论方法包含潜在的偏见，因为回应通常是colo。

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Download referencesAcknowledgementsSkatova is supported by a UKRI Future Leaders Fellowship (MR/T043520/1).Author informationAuthors and AffiliationsDigital Footprints Lab & Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Population Health Science, Bristol Medical School, University of Bristol, Bristol, UKAnya SkatovaAuthorsAnya SkatovaView author publicationsYou can also search for this author in.

下载参考文献致谢斯卡托娃（Download ReferencesAcknowlementsskatova）得到了英国皇家理工学院未来领导者奖学金（MR/T043520/1）的支持。作者信息作者和附属机构布里斯托尔大学数字足迹实验室和医学研究委员会综合流行病学部门，人口健康科学，布里斯托尔医学院，布里斯托尔大学，布里斯托尔，英国布里斯托尔，布里斯托尔斯卡托瓦Authorsanya SkatovaView作者出版物您也可以在中搜索这位作者。

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Reprints and permissionsAbout this articleCite this articleSkatova, A. Overcoming biases of individual level shopping history data in health research.

转载和许可本文引用本文Skatova，A。克服健康研究中个人层面购物历史数据的偏见。

npj Digit. Med. 7, 264 (2024). https://doi.org/10.1038/s41746-024-01231-4Download citationReceived: 15 December 2023Accepted: 19 August 2024Published: 30 September 2024DOI: https://doi.org/10.1038/s41746-024-01231-4Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

npj数字。医学杂志7264（2024）。https://doi.org/10.1038/s41746-024-01231-4Download引文收到日期：2023年12月15日接受日期：2024年8月19日发布日期：2024年9月30日OI：https://doi.org/10.1038/s41746-024-01231-4Share本文与您共享以下链接的任何人都可以阅读此内容：获取可共享链接对不起，本文目前没有可共享的链接。复制到剪贴板。

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