UK Biobank is a large prospective cohort study containing accelerometer-based physical activity data with strong validity collected from 100,000 participants approximately 5 years after baseline. In contrast, the main cohort has multiple self-reported physical behaviours from > 500,000 participants with longer follow-up time, offering several epidemiological advantages. However, questionnaire methods typically suffer from greater measurement error, and at present there is no tested method for combining these diverse self-reported data to more comprehensively assess the overall dose of physical activity. This study aimed to use the accelerometry sub-cohort to calibrate the self-reported behavioural variables to produce a harmonised estimate of physical activity energy expenditure, and subsequently examine its reliability, validity, and associations with disease outcomes. We calibrated 14 self-reported behavioural variables from the UK Biobank main cohort using the wrist accelerometry sub-cohort (n = 93,425), and used published equations to estimate physical activity energy expenditure (PAEESR). For comparison, we estimated physical activity based on the scoring criteria of the International Physical Activity Questionnaire, and by summing variables for occupational and leisure-time physical activity with no calibration. Test-retest reliability was assessed using data from the UK Biobank repeat assessment (n = 18,905) collected a mean of 4.3 years after baseline. Validity was assessed in an independent validation study (n = 98) with estimates based on doubly labelled water (PAEEDLW). In the main UK Biobank cohort (n = 374,352), Cox regression was used to estimate associations between PAEESR and fatal and non-fatal outcomes including all-cause, cardiovascular diseases, respiratory diseases, and cancers. PAEESR explained 27% variance in gold-standard PAEEDLW estimates, with no mean bias. However, error was strongly correlated with PAEEDLW (r = −.98; p < 0.001), and PAEESR had narrower range than the criterion. Test-retest reliability (Λ = .67) and relative validity (Spearman = .52) of PAEESR outperformed two common approaches for processing self-report data with no calibration. Predictive validity was demonstrated by associations with morbidity and mortality, e.g. 14% (95%CI: 11–17%) lower mortality for individuals meeting lower physical activity guidelines. The PAEESR variable has good reliability and validity for ranking individuals, with no mean bias but correlated error at individual-level. PAEESR outperformed uncalibrated estimates and showed stronger inverse associations with disease outcomes.

中文翻译：

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使用网络协调根据大规模人口研究中的自我报告行为估算身体活动：英国生物银行的发现以及与疾病结果的关联。

UK Biobank 是一项大型前瞻性队列研究，包含从基线后大约 5 年后从 100,000 名参与者收集的基于加速度计的身体活动数据，该数据具有很强的有效性。相比之下，主要队列有超过 500,000 名参与者的多种自我报告的身体行为，且随访时间较长，具有多种流行病学优势。然而，问卷法通常存在较大的测量误差，目前还没有经过测试的方法可以结合这些不同的自我报告数据来更全面地评估体力活动的总体剂量。本研究旨在使用加速度测量子队列来校准自我报告的行为变量，以生成体力活动能量消耗的统一估计，并随后检查其可靠性、有效性以及与疾病结果的关联。我们使用手腕加速度测量子队列 (n = 93,425) 校准了英国生物银行主要队列中的 14 个自我报告的行为变量，并使用已发布的方程来估计体力活动能量消耗 (PAEESR)。为了进行比较，我们根据国际身体活动问卷的评分标准，并通过对未校准的职业和休闲时间身体活动的变量进行求和来估计身体活动。使用英国生物银行重复评估（n = 18,905）收集的基线后平均 4.3 年的数据来评估重测可靠性。有效性在一项独立验证研究 (n = 98) 中进行评估，估计值基于双标签水 (PAEEDLW)。在英国生物银行的主要队列（n = 374,352）中，Cox 回归用于估计 PAEESR 与致命和非致命结果（包括全因、心血管疾病、呼吸系统疾病和癌症）之间的关联。PAEESR 解释了黄金标准 PAEEDLW 估计值中 27% 的方差，没有平均偏差。然而，误差与 PAEEDLW 密切相关（r = −.98；p < 0.001），并且 PAEESR 的范围比标准更窄。PAEESR 的重测可靠性 (Λ = .67) 和相对有效性 (Spearman = .52) 优于两种无需校准的处理自我报告数据的常见方法。预测有效性通过与发病率和死亡率的关联来证明，例如，满足较低体力活动指南的个体死亡率降低 14%（95%CI：11-17%）。PAEESR变量对个体进行排名具有良好的信度和效度，在个体层面上不存在均值偏差，但存在相关误差。PAEESR 的表现优于未校​​准的估计值，并显示出与疾病结果更强的负相关性。