PURPOSE: The study's purpose was to characterize accelerometer-derived estimates of physical inactivity collected during five consecutive weeks in middle-age women. METHODS: Data were obtained from 63 participants (95.5%) enrolled in the Evaluation of Physical Activity Measures in Middle-Age Women Study. Inactive time (min · d(-1)) was estimated as the sum of activity counts <100, and inactive-to-active transitions were defined as an interruption in which a period of inactivity was immediately followed by a minute or more above 100 counts. A repeated-measures ANOVA using PROC MIXED (SAS/STAT software, v. 9.2) was used to describe hourly, daily, and weekly variation in estimates of physical inactivity. RESULTS: Participants were 52.7 ± 5.5 yr, 85.7% non-Hispanic white, and 63.5% postmenopausal, with a body mass index of 26.7 ± 5.1 kg · m(-2). Inactive time gradually increased as the day continued, particularly on weekend days. When compared with weekdays, average inactive time was lower on Saturday and Sunday (all P < 0.01 except for Saturday vs Monday, P < 0.10); Saturdays were not significantly different from Sundays. Breaks in inactive time were significantly lower on Sunday when compared with weekdays and Saturday (all P < 0.05), and fewer breaks were noted on Saturday when compared with Wednesday and Friday (both P < 0.01). After adjustment for total wear time or inactive time, most day-to-day differences were attenuated. Week-by-week differences in physical inactivity estimates were also not statistically significant. CONCLUSIONS: The results of this study suggest that inactive time increases as the day continues and that daily physical inactivity estimates are more stable after 1) adjustment for wear time or 2) when averaged over the week. Researchers should carefully consider the intended application of physical inactivity estimates before data collection and processing, analysis, and final data reporting.
PURPOSE: The study's purpose was to characterize accelerometer-derived estimates of physical inactivity collected during five consecutive weeks in middle-age women. METHODS: Data were obtained from 63 participants (95.5%) enrolled in the Evaluation of Physical Activity Measures in Middle-Age Women Study. Inactive time (min · d(-1)) was estimated as the sum of activity counts <100, and inactive-to-active transitions were defined as an interruption in which a period of inactivity was immediately followed by a minute or more above 100 counts. A repeated-measures ANOVA using PROC MIXED (SAS/STAT software, v. 9.2) was used to describe hourly, daily, and weekly variation in estimates of physical inactivity. RESULTS:Participants were 52.7 ± 5.5 yr, 85.7% non-Hispanic white, and 63.5% postmenopausal, with a body mass index of 26.7 ± 5.1 kg · m(-2). Inactive time gradually increased as the day continued, particularly on weekend days. When compared with weekdays, average inactive time was lower on Saturday and Sunday (all P < 0.01 except for Saturday vs Monday, P < 0.10); Saturdays were not significantly different from Sundays. Breaks in inactive time were significantly lower on Sunday when compared with weekdays and Saturday (all P < 0.05), and fewer breaks were noted on Saturday when compared with Wednesday and Friday (both P < 0.01). After adjustment for total wear time or inactive time, most day-to-day differences were attenuated. Week-by-week differences in physical inactivity estimates were also not statistically significant. CONCLUSIONS: The results of this study suggest that inactive time increases as the day continues and that daily physical inactivity estimates are more stable after 1) adjustment for wear time or 2) when averaged over the week. Researchers should carefully consider the intended application of physical inactivity estimates before data collection and processing, analysis, and final data reporting.
Authors: Jennifer A Schrack; Vadim Zipunnikov; Jeff Goldsmith; Jiawei Bai; Eleanor M Simonsick; Ciprian Crainiceanu; Luigi Ferrucci Journal: J Gerontol A Biol Sci Med Sci Date: 2013-12-14 Impact factor: 6.053
Authors: Jeremy A Steeves; Eric J Shiroma; Scott A Conger; Dane Van Domelen; Tamara B Harris Journal: Disabil Health J Date: 2019-03-03 Impact factor: 2.554
Authors: Kerem Shuval; Harold W Kohl; Ira Bernstein; Dunlei Cheng; Kelley Pettee Gabriel; Carolyn E Barlow; Liu Yinghui; Loretta DiPietro Journal: Br J Sports Med Date: 2013-10-21 Impact factor: 13.800
Authors: Robert G McMurray; Diane C Berry; Todd A Schwartz; Emily G Hall; Madeline N Neal; Siying Li; Diana Lam Journal: BMC Public Health Date: 2016-02-06 Impact factor: 3.295