Tudor-Locke Catrine, Bassett David R, Swartz Ann M, Strath Scott J, Parr Brian B, Reis Jared P, Dubose Katrina D, Ainsworth Barbara E
Department of Exercise and Wellness, Arizona Statte University, Mesa, AZ 85212, USA.
Ann Behav Med. 2004 Dec;28(3):158-62. doi: 10.1207/s15324796abm2803_3.
Long-term pedometer monitoring has not been attempted.
The purpose of this project was to collect 365 days of continuous self-monitored pedometer data to explore the natural variability of physical activity.
Twenty-three participants (7 men, 16 women; M age = 38 +- 9.9 years; M body mass index = 27.7 +- 6.2 kg/m2) were recruited by word of mouth at two southern U.S. universities. Participants were asked to wear pedometers at their waist during waking hours and record steps per day and daily behaviors (e.g., sport/exercise, work or not) on a simple calendar. In total, participants wore pedometers and recorded 8,197 person-days of data (of a possible 8,395 person-days, or 98%) for a mean of 10,090 +- 3,389 steps/day. Missing values were estimated using the Missing Values Analysis EM function in SPSS, Version 11.0.1.
A mean of 10,082 +- 3,319 steps/day was computed. Using the corrected data, differences in steps/day were significant for season (summer > winter, F = 7.57, p = .001), day of the week (weekday > weekend, F = 3.97, p = .011), type of day (workday vs. nonworkday, F = 9.467, p = .008), and participation in sport/exercise (day with sport/exercise > day without sport/exercise, F = 102.5, p < .0001).
These data suggest that surveillance should be conducted in the spring/fall or that an appropriate correction factor should be considered if the intent is to capture values resembling the year-round average.
尚未尝试进行长期计步器监测。
本项目的目的是收集365天连续自我监测的计步器数据,以探索身体活动的自然变异性。
通过美国南部两所大学的口碑招募了23名参与者(7名男性,16名女性;平均年龄 = 38 ± 9.9岁;平均体重指数 = 27.7 ± 6.2 kg/m²)。要求参与者在清醒时间将计步器佩戴在腰部,并在简易日历上记录每日步数和日常行为(例如,运动/锻炼、是否工作)。总共,参与者佩戴计步器并记录了8197人日的数据(可能的8395人日中的,即98%),平均每日步数为10090 ± 3389步。使用SPSS 11.0.1版中的缺失值分析EM函数估计缺失值。
计算得出平均每日步数为10082 ± 3319步。使用校正后的数据,每日步数在季节(夏季 > 冬季,F = 7.57,p = 0.001)、一周中的日期(工作日 > 周末,F = 3.97,p = 0.011)、日期类型(工作日与非工作日,F = 9.467,p = 0.008)以及是否参与运动/锻炼(有运动/锻炼的日子 > 没有运动/锻炼的日子,F = 102.5,p < 0.0001)方面存在显著差异。
这些数据表明,如果旨在获取接近全年平均值的值,应在春季/秋季进行监测,或者应考虑使用适当的校正因子。