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Tiago V. Barreira, John P. Bennett, and Minsoo Kang

Purpose:

To obtain validity evidence for the measurement of step counts by spring-levered and piezoelectric pedometers during dance.

Methods:

Thirty-five adults in a college dance class participated in this study. Participants completed trials of 3- and 5-min of different styles of dance wearing Walk4life MVP and Omron HJ-303 pedometers, while their steps were visually counted. Pearson correlation, paired t-test, mean absolute percent error (MAPE), and mean bias were calculated between actual step and pedometer step counts for the 3- and 5-min dances separately.

Results:

For the Walk4life trials the correlations were .92 and .77 for the 3- and 5-min dances. No significant differences were shown by t-test for the 3- (P = .16) and 5-min dances (P = .60). However, MAPE was high, 17.7 ± 17.7% and 19.4 ± 18.3% for the 2 dance durations, respectively. For the Omron, the correlations were .44 and .58 for the 3- and 5-min dances, respectively. No significant differences were shown by t-test for the 3-min (P = .38) and for the 5-min (P = .88) dances. However, MAPE was high, 19.3 ± 16.4% and 26.6 ± 15.2% for the 2 dance durations, respectively.

Conclusions:

This study demonstrated that pedometers can be used to estimate the number of steps taken by a group of college students while dancing, however caution is necessary with individual values.

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Kyle R. Leister, Jessica Garay, and Tiago V. Barreira

Purpose: To determine accuracy of activPAL Technologies’ CREA algorithm to assess bedtime, wake time, and sleep time. Methods: As part of a larger study, 104 participants recorded nightly sleep logs (LOGs) and wore the activPAL accelerometer at the thigh and ActiGraph accelerometer at the hip for 24 hr/day, for seven consecutive days. For sleep LOGs, participants recorded nightly bed and daily wake times. Previously validated ActiGraph, proprietary activPAL, and the Winkler sleep algorithm were used to compute sleep variables. Eighty-seven participants provided 2+ days of valid data. Pearson correlations, paired samples t tests, and equivalency tests were used to examine relationships and differences between methods (activPAL vs. ActiGraph, activPAL vs. LOG, and activPAL vs. Winkler algorithm). Results: For screened data, moderately high to high correlations but significant mean differences were found between activPAL versus ActiGraph for bedtime (t 86 = −6.80, p ≤ .01, r = .84), wake time (t 86 = 4.80, p ≤ .01, r = .93), and sleep time (t 86 = 7.99, p ≤ .01, r = .88). activPAL versus LOG comparisons also yielded significant mean differences and moderately high to high correlations for bedtime (t 86 = −4.68, p ≤ .01, r = .82), wake time (t 86 = 8.14, p ≤ .01, r = .93), and sleep time (t 86 = 8.60, p ≤ .01, r = .72). Equivalency testing revealed that equivalency could not be claimed between activPAL versus LOG or activPAL versus ActiGraph comparisons, though the activPAL and Winkler algorithm were equivalent. Conclusion: The activPAL algorithm overestimated sleep time by detecting earlier bedtimes and later wake times. Because of the significant differences between algorithms, bedtime, wake time, and sleep time are not interchangeable between methods.

Open access

John M. Schuna Jr., Tiago V. Barreira, Daniel S. Hsia, William D. Johnson, and Catrine Tudor-Locke

Background:

Energy expenditure (EE) estimates for a broad age range of youth performing a variety of activities are needed.

Methods:

106 participants (6–18 years) completed 6 free-living activities (seated rest, movie watching, coloring, stair climbing, basketball dribbling, jumping jacks) and up to 9 treadmill walking bouts (13.4 to 120.7 m/min; 13.4 m/min increments). Breath-by-breath oxygen uptake (VO2) was measured using the COSMED K4b2 and EE was quantified as youth metabolic equivalents (METy1:VO2/measured resting VO2, METy2:VO2/estimated resting VO2). Age trends were evaluated with ANOVA.

Results:

Seated movie watching produced the lowest mean METy1 (6- to 9-year-olds: 0.94 ± 0.13) and METy2 values (13- to 15-year-olds: 1.10 ± 0.19), and jumping jacks produced the highest mean METy1 (13- to 15-year-olds: 6.89 ± 1.47) and METy2 values (16- to 18-year-olds: 8.61 ± 2.03). Significant age-related variability in METy1 and METy2 were noted for 8 and 2 of the 15 evaluated activities, respectively.

Conclusions:

Descriptive EE data presented herein will augment the Youth Compendium of Physical Activities.

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Catrine Tudor-Locke, Tiago V. Barreira, Robert M. Brouillette, Heather C. Foil, and Jeffrey N. Keller

Background:

The relationship between clinically assessed and free-living walking is unclear. Cadence (steps/min) can be measured accurately under both conditions using modern technologies, thus providing a common measurement metric. Therefore, the purpose of this study was to compare clinical and free-living cadence in older adults.

Methods:

15 community-dwelling older adults (7 men, 8 women; 61–81 years) completed GAITRite-determined normal and dual-task walks and wore objective monitors for 1 week. Descriptive data included gait speed (cm/sec), steps/day, as well as cadence. Nonparametric tests evaluated differences between normal and dual-task walks and between accelerometer- and pedometer-determined steps/day. Free-living time detected above clinically determined cadence was calculated.

Results:

Participants crossed the GAITRite at 125.56 ± 15.51 cm/sec (men) and 107.93 ± 9.41 steps/min (women) during their normal walk and at 112.59 ± 17.90 cm/sec and 103.10 ± 1.30 steps/min during their dual-task walk (differences between walks P < .05). Overall, they averaged 7159 ± 2480 (accelerometer) and 7813 ± 2919 steps/day (pedometer; difference NS). On average, < 10 min/day was spent above clinically determined cadences.

Conclusions:

High-functioning, community-dwelling older adults are capable of walking at relatively high cadences (ie, > 100 steps/min). However, the same behavior appears to be uncommon in daily life, even for a minute.

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Minsoo Kang, David R. Bassett, Tiago V. Barreira, Catrine Tudor-Locke, and Barbara E. Ainsworth

Background:

The seasonal and monthly variability of pedometer-determined physical activity and its effects on accurate measurement have not been examined. The purpose of the study was to reduce measurement error in step-count data by controlling a) the length of the measurement period and b) the season or month of the year in which sampling was conducted.

Methods:

Twenty-three middle-aged adults were instructed to wear a Yamax SW-200 pedometer over 365 consecutive days. The step-count measurement periods of various lengths (eg, 2, 3, 4, 5, 6, 7 days, etc.) were randomly selected 10 times for each season and month. To determine accurate estimates of yearly step-count measurement, mean absolute percentage error (MAPE) and bias were calculated. The year-round average was considered as a criterion measure. A smaller MAPE and bias represent a better estimate.

Results:

Differences in MAPE and bias among seasons were trivial; however, they varied among different months. The months in which seasonal changes occur presented the highest MAPE and bias.

Conclusions:

Targeting the data collection during certain months (eg, May) may reduce pedometer measurement error and provide more accurate estimates of year-round averages.

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Tiago V. Barreira, Robert M. Brouillette, Heather C. Foil, Jeffrey N. Keller, and Catrine Tudor-Locke

The purpose of this study was to compare the steps/d derived from the ActiGraph GT3X+ using the manufacturer’s default filter (DF) and low-frequency-extension filter (LFX) with those from the NL-1000 pedometer in an older adult sample. Fifteen older adults (61–82 yr) wore a GT3X+ (24 hr/day) and an NL-1000 (waking hours) for 7 d. Day was the unit of analysis (n = 86 valid days) comparing (a) GT3X+ DF and NL-1000 steps/d and (b) GT3X+ LFX and NL-1000 steps/d. DF was highly correlated with NL-1000 (r = .80), but there was a significant mean difference (–769 steps/d). LFX and NL-1000 were highly correlated (r = .90), but there also was a significant mean difference (8,140 steps/d). Percent difference and absolute percent difference between DF and NL-1000 were –7.4% and 16.0%, respectively, and for LFX and NL-1000 both were 121.9%. Regardless of filter used, GT3X+ did not provide comparable pedometer estimates of steps/d in this older adult sample.

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Stephen D. Herrmann, Tiago V. Barreira, Minsoo Kang, and Barbara E. Ainsworth

Background:

There is little consensus on how many hours of accelerometer wear time is needed to reflect a usual day. This study identifies the bias in daily physical activity (PA) estimates caused by accelerometer wear time.

Methods:

124 adults (age = 41 ± 11 years; BMI = 27 ± 7 kg·m-2) contributed approximately 1,200 days accelerometer wear time. Five 40 day samples were randomly selected with 10, 11, 12, 13, and 14 h·d-1 of wear time. Four semisimulation data sets (10, 11, 12, 13 h·d-1) were created from the reference 14 h·d-1 data set to assess Absolute Percent Error (APE). Repeated-measures ANOVAs compared min·d-1 between 10, 11, 12, 13 h·d-1 and the reference 14 h·d-1 for inactivity (<100 cts·min-1), light (100−1951 cts·min-1), moderate (1952−5724 cts·min-1), and vigorous (≥5725 cts·min-1) PA.

Results:

APE ranged from 5.6%−41.6% (10 h·d-1 = 28.2%−41.6%; 11 h·d-1 = 20.3%−36.0%; 12 h·d-1 = 13.5%−14.3%; 13 h·d-1 = 5.6%−7.8%). Min·d-1 differences were observed for inactivity, light, and moderate PA between 10, 11, 12, and 13 h·d-1 and the reference (P < .05).

Conclusions:

This suggests a minimum accelerometer wear time of 13 h·d-1 is needed to provide a valid measure of daily PA when 14 h·d-1 is used as a reference.

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Tiago V. Barreira, Catrine Tudor-Locke, Catherine M. Champagne, Stephanie T. Broyles, William D. Johnson, and Peter T. Katzmarzyk

Background:

The purpose of this study was to compare steps/day detected by the YAMAX SW-200 pedometer versus the Actigraph GT3X accelerometer in free-living adults.

Methods:

Daily YAMAX and GT3X steps were collected from a sample of 23 overweight and obese participants (78% female; age = 52.6 ± 8.4 yr.; BMI = 31.0 ± 3.7 m·kg-2). Because a pedometer is more likely to be used in a community-based intervention program, it was used as the standard for comparison. Percent difference (PD) and absolute percent difference (APD) were calculated to examine between-instrument agreement. In addition, days were categorized based on PD: a) under-counting (> −10 PD), b) acceptable counting (−10 to 10 PD), and c) over-counting (> 10 PD).

Results:

The YAMAX and GT3X detected 8,025 ± 3,967 and 7131 ± 3066 steps/day, respectively, and the outputs were highly correlated (r = .87). Average PD was −3.1% ± 30.7% and average APD was 23.9% ± 19.4%. Relative to the YAMAX, 53% of the days detected by the GT3X were classified as under-counting, 25% acceptable counting, and 23% over-counting.

Conclusion:

Although the output of these 2 instruments is highly correlated, caution is advised when directly comparing or using their output interchangeably.

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Gerson Luis de Moraes Ferrari, Victor Matsudo, Tiago V. Barreira, Catrine Tudor-Locke, Peter T. Katzmarzyk, and Mauro Fisberg

Background:

Few studies have used ecological models to study multiple levels of association with objectively measured moderate-to-vigorous physical activity (MVPA) in young children from middle-income countries. The purpose of this study was to examine potential correlates of objectively measured MVPA in Brazilian children.

Methods:

The sample consisted of 328 children. An Actigraph GT3X+ accelerometer was used to monitor MVPA over 7 days. Body mass index and body fat percentage were measured using a bioelectrical impedance scale. Questionnaires completed by the children, their parents, and school personnel queried individual, family and home, and school-level environmental correlates.

Results:

Children averaged 59.3 min/d in MVPA (44.5% met MVPA guidelines), and 51.8% were overweight/obese. For boys and girls combined, significant correlates (P < .05) of MVPA were waist circumference (β = –.007), travel mode to school (β = .140), maternal employment status (β = –.119) and TV in bedroom (β –.107). In boys, significant correlates of MVPA were waist circumference (β = –.011), travel mode to school (β = .133), and maternal employment status (β = –.195). In girls, the only significant correlate of MVPA was travel mode to school (β = .143).

Conclusions:

Several factors were identified as correlates of MVPA in Brazilian children; however, only travel mode to school was common for both boys and girls.

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Gerson Luis de Moraes Ferrari, Timoteo Leandro Araujo, Luis Oliveira, Victor Matsudo, Emily Mire, Tiago V. Barreira, Catrine Tudor-Locke, and Peter T. Katzmarzyk

Background:

Studies have found an association between television (TV) viewing and physical activity levels. The purpose of this study was to examine the association between TV viewing and physical activity in 10-year-old Brazilian children.

Methods:

The sample consisted of 485 children. Self-reported TV viewing on weekdays and weekends was assessed by questionnaire. An Actigraph GT3X+ accelerometer was used to monitor the range of physical activity intensities (including moderate-to-vigorous physical activity; MVPA), sedentary behavior (SB) and steps/day over 7 days.

Results:

Daily MVPA was highest among children viewing TV <1 hour/day (69 min) compared with children viewing 1 to 2 hours/day (61 min), 3 to 4 hours/day (55 min) and ≥ 5 hours/day (59 min) on weekdays (P = .0015). Differences in MVPA were not observed across TV categories on weekends. The prevalence of reaching 60 min/day of MVPA and 12,000 steps/day on weekdays was significantly greater in children viewing ≤ 2 hours/day (51.7% and 23.5%, respectively) compared with those viewing > 2 hours/day (38.6%, P = .0058; and 15.1%, P = .0291, respectively). There was no difference in SB across TV viewing categories.

Conclusion:

Time spent in MVPA and the frequency of meeting MVPA guidelines were significantly higher among children viewing ≤ 2 hours/day of TV on weekdays compared with those viewing more.