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Validity of Pedometers to Measure Step Counts During Dance

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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Validity of a Novel Algorithm to Detect Bedtime, Wake Time, and Sleep Time in Adults

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.

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Comparison of Older Adults’ Steps per Day Using an NL-1000 Pedometer and Two GT3X+ Accelerometer Filters

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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Measurement Effects of Seasonal and Monthly Variability on Pedometer-Determined Data

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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How Many Hours Are Enough? Accelerometer Wear Time May Provide Bias in Daily Activity Estimates

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.

Open access

Youth Energy Expenditure During Common Free-Living Activities and Treadmill Walking

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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Preliminary Comparison of Clinical and Free-Living Measures of Stepping Cadence in Older Adults

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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Parents’ Beliefs About Physical Activity for Their Children With Visual Impairments

Luis Columna, Denzil A. Streete, Samuel R. Hodge, Suzanna Rocco Dillon, Beth Myers, Michael L. Norris, Tiago V. Barreira, and Kevin S. Heffernan

Despite having the desire to become physically active as a family, parents of children with visual impairments often lack the skills and resources needed to provide appropriate physical activities (PAs) for their children. The purpose of this study was to explore the intentions of parents of children with visual impairments toward including their children in PAs after participating in a PA program. In this descriptive qualitative study, the participants were 10 parents of children with visual impairments. A series of workshops were designed to provide parents with the skills and resources needed to promote PA for their family. Upon completion of the workshops, parents took part in one-on-one semistructured interviews that were subsequently transcribed and analyzed using a thematic line-by-line process. Two interdependent themes emerged from the data analyses: (a) eye-opening experiences and (b) transformed, more hopeful, and optimistic outlook. The results revealed that through the PA intervention, parents learned teaching strategies that were intended to increase their PA opportunities and garnered resources that allowed them to teach their children to participate in PA.

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Normative Peak 30-Min Cadence (Steps per Minute) Values for Older Adults: NHANES 2005–2006

Elroy J. Aguiar, John M. Schuna Jr., Tiago V. Barreira, Emily F. Mire, Stephanie T. Broyles, Peter T. Katzmarzyk, William D. Johnson, and Catrine Tudor-Locke

Walking cadence (steps per minute) is associated with the intensity of ambulatory behavior. This analysis provides normative values for peak 30-min cadence, an indicator of “natural best effort” during free-living behavior. A sample of 1,196 older adults (aged from 60 to 85+) with accelerometer data from the National Health and Nutrition Examination Survey 2005–2006 was used. Peak 30-min cadence was calculated for each individual. Quintile-defined values were computed, stratified by sex and age groups. Smoothed sex-specific centile curves across the age span were fitted using the LMS method. Peak 30-min cadence generally trended lower as age increased. The uppermost quintile value was >85 steps/min (men: 60–64 years), and the lowermost quintile value was <22 steps/min (women: 85+). The highest 95th centile value was 103 steps/min (men: 64–70 years), and the lowest 5th centile value was 15 steps/min (women: 85+). These normative values may be useful for evaluating older adults’ “natural best effort” during free-living ambulatory behavior.

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Moderate-to-Vigorous Physical Activity and Sedentary Behavior: Independent Associations With Body Composition Variables in Brazilian Children

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

This study aimed to analyze the independent associations of accelerometer-determined sedentary behavior, physical activity, and steps/day with body composition variables in Brazilian children. 485 children wore accelerometers for 7 days. Variables included time in sedentary behavior and different physical activity intensities (light, moderate, vigorous, or moderate-to-vigorous) and steps/day. Body fat percentage was measured using a bioelectrical impedance scale, and BMI was calculated. Children spent 55.7% of the awake portion of the day in sedentary behavior, 37.6% in light physical activity, 4.6% in moderate physical activity, and 1.9% in vigorous physical activity. Moderate-to-vigorous physical activity and steps/day were negatively associated with body composition (BMI and body fat percentage) variables, independent of sex and sedentary behavior. Beta values were higher for vigorous physical activity than moderate physical activity. Vigorous physical activity was negatively associated with BMI (β-.1425) and body fat percentage (β-.3082; p < .0001). In boys, there were significant negative associations between moderate, vigorous, and moderate-to-vigorous physical activity and steps/day with body composition, and in girls, there was only a negative association with vigorous physical activity, independent of sedentary behavior. Moderate-to-vigorous physical activity and steps/day (in boys), but especially vigorous physical activity (in boys and girls), are associated with body composition, independent of sedentary behavior. Sedentary behavior was not related with any of the body composition variables once adjusted for moderate-to-vigorous physical activity.