Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.
Department of Biostatistics & Informatics, Colorado School of Public Health, University of Colorado, Aurora.
J Gerontol A Biol Sci Med Sci. 2021 Jul 13;76(8):1486-1494. doi: 10.1093/gerona/glaa250.
Objective measures of physical activity (PA) derived from wrist-worn accelerometers are compared with traditional risk factors in terms of mortality prediction performance in the UK Biobank.
A subset of participants in the UK Biobank study wore a tri-axial wrist-worn accelerometer in a free-living environment for up to 7 days. A total of 82 304 individuals over the age of 50 (439 707 person-years of follow-up, 1959 deaths) had both accelerometry data that met specified quality criteria and complete data on a set of traditional mortality risk factors. Predictive performance was assessed using cross-validated Concordance (C) for Cox regression models. Forward selection was used to obtain a set of best predictors of mortality.
In univariate Cox regression, age was the best predictor of all-cause mortality (C = 0.681) followed by 12 PA predictors, led by minutes of moderate-to-vigorous PA (C = 0.661) and total acceleration (C = 0.661). Overall, 16 of the top 20 predictors were objective PA measures (C = 0.578-0.661). Using a threshold of 0.001 improvement in Concordance, the Concordance for the best model that did not include PA measures was 0.735 (9 covariates) compared with 0.748 (12 covariates) for the best model with PA variables (p-value < .001).
Objective measures of PA derived from accelerometry outperform traditional predictors of all-cause mortality in the UK Biobank except age and substantially improve the prediction performance of mortality models based on traditional risk factors. Results confirm and complement previous findings in the National Health and Nutrition Examination Survey (NHANES).
在英国生物库中，与传统危险因素相比，腕戴加速度计得出的身体活动（PA）客观指标在预测死亡率方面表现如何。
英国生物库研究的一部分参与者在自由生活环境中佩戴三轴腕戴加速度计，时间长达 7 天。共有 82304 名年龄在 50 岁以上（439707人年随访，1959 人死亡）的个体同时具有符合特定质量标准的加速度计数据和一套完整的传统死亡率危险因素数据。使用 Cox 回归模型的交叉验证一致性（C）评估预测性能。使用前向选择获得一组最佳死亡率预测指标。
在单变量 Cox 回归中，年龄是所有原因死亡率的最佳预测指标（C=0.681），其次是 12 项 PA 预测指标，以中等至剧烈 PA 分钟数（C=0.661）和总加速度（C=0.661）为主。总体而言，前 20 个预测指标中有 16 个是客观 PA 测量指标（C=0.578-0.661）。使用一致性提高 0.001 的阈值，不包括 PA 测量指标的最佳模型的一致性为 0.735（9 个协变量），而包含 PA 变量的最佳模型的一致性为 0.748（12 个协变量）（p 值<0.001）。
与传统的全因死亡率预测指标相比，加速度计得出的 PA 客观指标在英国生物库中表现出色，除年龄外，还大大提高了基于传统危险因素的死亡率模型的预测性能。结果证实并补充了国家健康和营养检查调查（NHANES）的先前发现。
