Department of Public and Occupational Health, Amsterdam UMC, location VUmc, van der Boechorststraat 7, 1081 BT Amsterdam, The Netherlands.
Scand J Work Environ Health. 2020 Jan 1;46(1):32-42. doi: 10.5271/sjweh.3827. Epub 2019 Apr 23.
Objective Emerging evidence suggests that excessive sitting has negative health effects. However, this evidence largely relies on research using self-reported sitting time, which is known to be biased. To correct this bias, we aimed at developing a calibration model estimating "true" sitting from self-reported sitting. Methods Occupational sitting time was estimated by self-reports (the International Physical Activity Questionnaire) and objective measurements (thigh-worn accelerometer) among 99 Swedish office workers at a governmental agency, at baseline and 3 and 12 months afterwards. Following compositional data analysis procedures, both sitting estimates were transformed into isometric log-ratios (ILR). This effectively addresses that times spent in various activities are inherently dependent and can be presented as values of only 0-100%. Linear regression was used to develop a simple calibration model estimating objectively measured "true" sitting ILR (dependent variable) from self-reported sitting ILR (independent variable). Additional self-reported variables were then added to construct a full calibration model. Performance of the models was assessed by root-mean-square (RMS) differences between estimated and objectively measured values. Models developed on baseline data were validated using the follow-up datasets. Results Uncalibrated self-reported sitting ILR showed an RMS error of 0.767. Simple and full calibration models (incorporating body mass index, office type, and gender) reduced this error to 0.422 (55%) and 0.398 (52%), respectively. In the validations, model performance decreased to 57%/62% (simple models) and 57%/62% (full models) for the two follow-up data sets, respectively. Conclusions Calibration adjusting for errors in self-reported sitting led to substantially more correct estimates of "true" sitting than uncalibrated self-reports. Validation indicated that model performance would change somewhat in new datasets and that full models perform no better than simple models, but calibration remained effective.
目的 新出现的证据表明，久坐对健康有负面影响。然而，这些证据主要依赖于使用自我报告的久坐时间进行的研究，而自我报告的久坐时间是有偏差的。为了纠正这种偏差，我们旨在开发一种校准模型，从自我报告的久坐时间中估计“真实”的久坐时间。
方法 在基线以及 3 个月和 12 个月后，我们在一家政府机构的 99 名瑞典上班族中，使用国际体力活动问卷（International Physical Activity Questionnaire）对职业久坐时间进行了自我报告和客观测量（大腿佩戴的加速度计）。在进行了成分数据分析程序后，我们将这两种久坐时间的估计值都转换为等距对数比（isometric log-ratios，ILR）。这有效地解决了各种活动中所花费的时间本质上是相互依赖的，并只能表示为 0-100%的值。我们使用线性回归来开发一个简单的校准模型，从自我报告的 ILR（因变量）估计客观测量的“真实”的 ILR（自变量）。然后添加了其他自我报告的变量来构建完整的校准模型。通过估计值和客观测量值之间的均方根（root-mean-square，RMS）差异来评估模型的性能。使用随访数据集对基于基线数据开发的模型进行验证。
结果 未经校准的自我报告的 ILR 显示 RMS 误差为 0.767。简单和完整的校准模型（纳入体重指数、办公室类型和性别）将此误差分别降低至 0.422（55%）和 0.398（52%）。在验证中，对于两个随访数据集，模型的性能分别下降到 57%/62%（简单模型）和 57%/62%（完整模型）。
结论 对自我报告的久坐时间进行校准调整可大大提高对“真实”久坐时间的正确估计。验证表明，模型在新数据集上的性能会有所变化，并且完整模型的性能并不优于简单模型，但校准仍然有效。
