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Are we missing the sitting? Agreement between accelerometer non-wear time validation methods used with older adults’ data

机译:我们错过座位了吗?老年人数据使用的加速度计非佩戴时间验证方法之间的协议

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摘要

We used Bland Altman plots to compare agreement between a self-report diary and five different non-wear time algorithms [an algorithm that uses ≥60 min of consecutive zeroes (Troiano) and four variations of an algorithm that uses ≥90 min of consecutive zeroes to define a non-wear period] for estimating community-dwelling older adults’ (n = 106) sedentary behaviour and wear time (min/day) as measured by accelerometry. We found that the Troiano algorithm may overestimate sedentary behaviour and wear time by ≥30 min/day. Algorithms that use ≥90 min of continuous zeroes more closely approximate participants’ sedentary behaviour and wear time. Across the self-report diary vs. ≥90 min algorithm comparisons, mean differences ranged between −4.4 to 8.1 min/day for estimates of sedentary behaviour and between −10.8 to 1.0 min/day for estimates of wear time; all 95% confidence intervals for mean differences crossed zero. We also found that 95% limits of agreement were wide for all comparisons, highlighting the large variation in estimates of sedentary behaviour and wear time. Given the importance of reducing sedentary behaviour and encouraging physical activity for older adult health, we conclude that it is critical to establish accurate approaches for measurement.
机译:我们使用布兰德·奥特曼(Bland Altman)图来比较自我报告日记和五种不同的非佩戴时间算法之间的一致性[一种算法使用连续时间≥60分钟的零(Troiano),以及四种使用连续时间≥90分钟的零的算法[定义一个非佩戴期],以估计通过加速度计测得的社区居民老年人(n = 106)的久坐行为和佩戴时间(min / day)。我们发现,Troiano算法可能高估久坐行为和穿戴时间至少30分钟/天。使用≥90分钟的连续零点的算法会更接近地估计参与者的久坐行为和佩戴时间。在自我报告日记与90分钟以上算法的比较中,久坐行为的平均差异介于-4.4至8.1分钟/天之间,磨损时间的平均差异介于-10.8至1.0分钟/天之间;均值差异的所有95%置信区间都为零。我们还发现95%的一致性限制适用于所有比较,突显了久坐行为和穿戴时间估算值的巨大差异。考虑到减少久坐行为和鼓励体育锻炼对老年人健康的重要性,我们得出结论,建立精确的测量方法至关重要。

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