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年轻、中年和老年人的驾驶状态、出行方式和加速度计评估的身体活动:90810 名英国生物库参与者的前瞻性研究。

Driving status, travel modes and accelerometer-assessed physical activity in younger, middle-aged and older adults: a prospective study of 90 810 UK Biobank participants.

机构信息

MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, UK.

UKCRC Centre for Diet and Activity Research (CEDAR), University of Cambridge, School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, UK.

出版信息

Int J Epidemiol. 2019 Aug 1;48(4):1175-1186. doi: 10.1093/ije/dyz065.

Abstract

BACKGROUND

Associations between driving and physical-activity (PA) intensities are unclear, particularly among older adults. We estimated prospective associations of travel modes with total PA, sedentary time (ST), light-intensity PA (LPA), and moderate-to-vigorous intensity PA (MVPA) among adults aged 39-70 years.

METHODS

We studied 90 810 UK Biobank participants (56.1 ± 7.8 years). Driving status, specific travel modes (non-work travel; commuting to/from work) and covariates were assessed by questionnaire (2006-10). PA was assessed over 7 days by wrist-worn accelerometers (2013-15). We estimated associations using overall and age-stratified multivariable linear-regression models.

RESULTS

Drivers accumulated 1.4% more total PA (95% confidence interval: 0.9, 1.9), 11.2 min/day less ST (-12.9, -9.5), 12.2 min/day more LPA (11.0, 13.3) and 0.9 min/day less MVPA (-1.6, -0.2) than non-drivers. Compared with car/motor-vehicle users, cyclists and walkers had the most optimal activity profiles followed by mixed-mode users (e.g. for non-work travel, cyclists: 10.7% more total PA, 9.0, 12.4; 20.5 min/day less ST, -26.0, -15.0; 14.5 min/day more MVPA, 12.0, 17.2; walkers: 4.2% more total PA, 3.5, 5.0; 7.5 min/day less ST -10.2, -4.9; 10.1 min/day more MVPA, 8.9, 11.3; mixed-mode users: 2.3% more total PA, 1.9, 2.7; 3.4 min/day less ST -4.8, -2.1; 4.9 min/day more MVPA, 4.3, 5.5). Some associations varied by age (p interaction < 0.05), but these differences appeared small.

CONCLUSIONS

Assessing specific travel modes rather than driving status alone may better capture variations in activity. Walking, cycling and, to a lesser degree, mixed-mode use are associated with more optimal activity profiles in adults of all ages.

摘要

背景

驾驶与身体活动(PA)强度之间的关系尚不清楚,尤其是在老年人中。我们估计了 39-70 岁成年人中不同出行模式与总 PA、久坐时间(ST)、低强度 PA(LPA)和中高强度 PA(MVPA)的前瞻性关联。

方法

我们研究了 90810 名英国生物库参与者(39-70 岁,平均年龄 56.1±7.8 岁)。通过问卷(2006-10 年)评估驾驶状态、特定出行模式(非工作出行;上下班通勤)和协变量。通过佩戴在手腕上的加速度计在 7 天内评估 PA(2013-15 年)。我们使用整体和按年龄分层的多变量线性回归模型来估计关联。

结果

与非驾驶员相比,驾驶员累计多完成 1.4%的总 PA(95%置信区间:0.9,1.9),减少 11.2 分钟/天的 ST(-12.9,-9.5),多完成 12.2 分钟/天的 LPA(11.0,13.3)和少完成 0.9 分钟/天的 MVPA(-1.6,-0.2)。与汽车/机动车使用者相比,骑自行车和步行者的活动水平最理想,其次是混合模式使用者(例如,对于非工作出行,骑自行车者:总 PA 多 10.7%,12.0,17.2;ST 减少 9.0 分钟/天,-26.0,-15.0;MVPA 多 14.5 分钟/天,12.0,17.2;步行者:总 PA 多 4.2%,3.5,5.0;ST 减少 7.5 分钟/天,-10.2,-4.9;MVPA 多 10.1 分钟/天,8.9,11.3;混合模式使用者:总 PA 多 2.3%,1.9,2.7;ST 减少 3.4 分钟/天,-4.8,-2.1;MVPA 多 4.9 分钟/天,4.3,5.5)。一些关联因年龄而异(p 交互<0.05),但这些差异似乎很小。

结论

评估特定出行模式而非仅评估驾驶状态可能更好地捕捉活动变化。在各个年龄段的成年人中,步行、骑自行车和在一定程度上混合模式使用与更理想的活动水平相关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3466/6693808/ad32a4c3f3ff/dyz065f1.jpg

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