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Catrine Tudor-Locke, David R. Bassett, Michael F. Shipe and James J. McClain

Background:

The purpose of this review is to update the methodological aspects of pedometry to encourage the consistent use of pedometers for assessment, to decrease sources of error, and to facilitate comparison and interpretation of results.

Methods:

The specific measurement topics addressed include: instrument choice, metric choice, validity, reliability, data collection and retrieval, time worn, day-to-day variability, monitoring time frame, reactivity, and data treatment.

Results:

A wide variety of valid and reliable instruments are commercially available and we can expect continued evolutions in value-added features as supporting technology improves. Data collection and retrieval has been achieved through various methods, including face-to-face contact, fax, e-mail, website, and conventional mail, and sometimes a combination of these. Day-to-day variation is not random, as would be expected from inconsistent pedometer performance, but rather exposes true behavior instability that can be explained by other factors and described using a coefficient of variation. Data reduction should be conducted cautiously and only after a full discovery (and disclosure) of its impact on aggregated group statistics and their relationship with other parameters.

Conclusions:

We have no doubt that research with pedometers will continue to yield new and important insights in the coming years.

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Kate Giles and Alison L. Marshall

Background:

One- to two-week test–retest reliability and construct validity (against pedometer step counts) of the CHAMPS physical activity questionnaire were evaluated in older Australian adults.

Methods:

Participants (n = 100, age >65 years) were invited to complete CHAMPS by mail. Spearman correlation coefficients are reported for physical activity constructs time (min/wk) and sessions per week for walking, moderate-, and vigorous-intensity activity and total physical activity. Correct classification of participants as meeting physical activity recommendations was assessed using percent agreement and kappa statistics.

Results:

Seventy-three participants completed CHAMPS at T1; 54 provided repeat data (T2). Sixty percent of the participants provided complete data. Good to excellent test– retest reliability was observed for all the physical activity constructs (r s = .70 to .89 for sessions/wk and r s = .65 to .75 for min/wk). Agreement between proportions classified as meeting recommendations at T1 and T2 was good (79%; kappa = 0.55). Fair to low validity coefficients were observed between steps and T1 CHAMPS walking and total activity sessions/wk (r s = .57 and r s = .52), and min/wk (r s = .40 and r s = .21).

Conclusions:

Mailed self-complete CHAMPS data provided reliable and valid estimates of physical activity in older Australian adults. Observed measurement coefficients were comparable to those reported in previous evaluations of CHAMPS. Further work is required to identify strategies to prevent data loss.

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Mark B. Andersen, Penny McCullagh and Gabriel J. Wilson

Many of the measurements used in sport psychology research are arbitrary metrics, and researchers often cannot make the jump from scores on paper-and-pencil tests to what those scores actually mean in terms of real-world behaviors. Effect sizes for behavioral data are often interpretable, but the meaning of a small, medium, or large effect for an arbitrary metric is elusive. We reviewed all the issues in the 2005 volumes of the Journal of Sport and Exercise Psychology, The Sport Psychologist, and the Journal of Applied Sport Psychology to determine whether the arbitrary metrics used in sport psychology research were interpreted, or calibrated, against real-world variables. Of the 54 studies that used quantitative methods, 25 reported only paper-and-pencil arbitrary metrics with no connections to behavior or other real-world variables. Also, 44 of the 54 studies reported effect sizes, but only 7 studies, using both arbitrary and behavioral metrics, had calculated effect indicators and interpreted them in terms of real-world meaning.

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Patricia A. Sharpe, Sara Wilcox, Laura J. Rooney, Donna Strong, Rosie Hopkins-Campbell, Jean Butel, Barbara Ainsworth and Deborah Parra-Medina

Background:

Objective measurement of physical activity with accelerometers is a challenging task in community-based intervention research. Challenges include distribution of and orientation to monitors, nonwear, incorrect placement, and loss of equipment. Data collection among participants from disadvantaged populations may be further hindered by factors such as transportation challenges, competing responsibilities, and cultural considerations.

Methods:

Research staff distributed accelerometers and provided an orientation that was tailored to the population group. General adherence strategies such as follow-up calls, daily diaries, verbal and written instructions, and incentives were accompanied by population-specific strategies such as assisting with transportation, reducing obstacles to wearing the accelerometer, tailoring the message to the participant population, and creating a nonjudgmental environment.

Results:

Sixty women asked to wear the Actigraph GT1M returned the accelerometer, and 57 of them provided sufficient data for analysis (at least 10 hours a day for a minimum of 4 days) resulting in 95% adherence to the protocol. Participants wore the accelerometers for an average of 5.98 days and 13.15 hours per day.

Conclusions:

The high accelerometer monitoring adherence among this group of economically disadvantaged women demonstrates that collection of high-quality, objective physical activity data from disadvantaged populations in field-based research is possible.

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Ryan S. Falck, Glenn J. Landry, Keith Brazendale and Teresa Liu-Ambrose

Evidence suggests sleep and physical activity (PA) are associated with each other and dementia risk. Thus, identifying reliable methods to quantify sleep and PA concurrently in older adults is important. The MotionWatch 8© (MW8) wrist-worn actigraph provides reliable estimates of sleep quality via 14 days of measurement; however, the number of days needed to monitor PA by MW8 for reliable estimates is unknown. Thus, we investigated the number of days of MW8 wear required to assess PA in older adults. Ninety-five adults aged > 55 years wore MW8 for ≥ 14 days. Spearman-Brown analyses indicated the number of monitoring days needed for an ICC = 0.95 was 6–7 days for sedentary activity, 9–10 days for light activity, and 7–8 days for moderate-to-vigorous PA. These results indicate 14 days of MW8 monitoring provides reliable estimates for both sleep and PA. Thus, MW8 is ideal for future investigations requiring concurrent measures of both sleep quality and PA.

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Kurusart Konharn, Wichai Eungpinichpong, Kluaymai Promdee, Paramaporn Sangpara, Settapong Nongharnpitak, Waradanai Malila and Jirachai Karawa

Background:

The suitability of smartphone applications (apps) currently used to track walking/running may differ depending on a person’s weight condition. This study aimed to examine the validity and reliability of apps for both normal-weight and overweight/obese young adults.

Methods:

Thirty normal-weight (aged 21.7 ± 1.0 years, BMI 21.3 ± 1.9 kg/m2) and 30 overweight/ obese young adults (aged 21.0 ± 1.4 years, BMI 28.6 ± 3.7 kg/m2) wore a smartphone and pedometer on their right hip while walking/running at 3 different intensities on treadmills. Apps was randomly assigned to each individual for measuring average velocity, step count, distance, and energy expenditure (EE), and these measurements were then analyzed.

Results:

The apps were not accurate in counting most of the measured variables and data fell significantly lower in the parameters than those measured with standard-reference instruments in both light and moderate intensity activity among the normal-weight group. Among the overweight and obese group, the apps were not accurate in detecting velocity, distance, or EE during either light or vigorous intensities. The percentages of mean difference were 30.1% to 48.9%.

Conclusion:

Apps may not have sufficient accuracy to monitor important physical parameters of human body movement. Apps need to be developed that can, in particular, respond differently based on a person’s weight status.

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Erin K. Howie, Joanne A. McVeigh and Leon M. Straker

Background:

There are several practical issues when considering the use of hip-worn or wrist-worn accelerometers. This study compared compliance and outcomes between hip- and wrist-worn accelerometers worn simultaneously by children during an active video games intervention.

Methods:

As part of a larger randomized crossover trial, participants (n = 73, age 10 to 12 years) wore 2 Actical accelerometers simultaneously during waking hours for 7 days, on the hip and wrist. Measurements were repeated at 4 timepoints: 1) at baseline, 2) during traditional video games condition, 3) during active video games condition, 4) during no video games condition. Compliance and intervention effects were compared between hip and wrist.

Results:

There were no statistically significant differences at any timepoint in percentage compliance between hip (77% to 87%) and wrist (79% to 89%). Wrist-measured counts (difference of 64.3 counts per minute, 95% CI 4.4–124.3) and moderate-to-vigorous physical activity (MVPA) (12 min/day, 95% CI 0.3–23.7) were higher during the no video games condition compared with the traditional video games condition. There were no differences in hip-measured counts per minute or MVPA between conditions or sedentary time for hip or wrist.

Conclusions:

There were no differences in compliance between hip- and wrist-worn accelerometers during an intervention trial, however, intervention findings differed between hip and wrist.

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Natalie Jayne Taylor, Scott E. Crouter, Rebecca J. Lawton, Mark T. Conner and Andy Prestwich

Background:

Precise measurement of physical activity (PA) is required to identify current levels and changes in PA within a population, and to gauge effectiveness of interventions.

Methods:

The Online Self-reported Walking and Exercise Questionnaire (OSWEQ) was developed for monitoring PA via the Web. Forty-nine participants (mean ± SD; age = 27 ± 11.9yrs) completed the OSWEQ and International PA Questionnaire (IPAQ) short form 3 times [T1/T2/T3 (separated by 7-days)] and wore an Actigraph-GT3X-accelerometer for 7-days between T2-T3. For each measure, estimates of average MET·min·day−1 and time spent in moderate PA (MPA), vigorous PA (VPA) and moderate and vigorous PA (MVPA) were obtained.

Results:

The OSWEQ and IPAQ demonstrated test-retest reliability for MPA, VPA, and MVPA minutes and average MET·min·day−1 between T1-T2 (OSWEQ range, r = .71–.77; IPAQ range, r = .59–.79; all, P < .01). The OSWEQ and IPAQ, compared with the GT3X, had lower estimates (mean error ± 95% PI) of MVPA MET·min·day−1 by 150.4 ± 477.6 and 247.5 ± 477.5, respectively.

Conclusions:

The OSWEQ demonstrates good test-retest reliability over 7-days and better group level estimates of MET·min·day−1 than the IPAQ, compared with the GT3X. These results suggest that the OSWEQ is a reliable and valid measure among young/working age adults and could be useful for monitoring PA trends over time.

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Monica A.F. Lounsbery, Thomas L. McKenzie, James R. Morrow Jr., Kathryn A. Holt and Ronald G. Budnar

Background:

Physical activity (PA) levels in schools vary widely, and there is interest in studying how student PA accrual relates to school policy and environmental conditions. School PA policy research, however, is in its infancy and generalizable measurement tools do not exist. We developed and assessed reliability of items on the School Physical Activity Policy Assessment (S-PAPA), an instrument designed to assess school PA policy related to physical education (PE), recess, and other opportunities.

Methods:

To develop items, we perused associated literature, examined existing instruments, and consulted school policy makers. For test-retest reliability assessment, 31 elementary school PE teachers completed the survey twice, 14 days apart.

Results:

S-PAPA uses open-ended, dichotomous, multichotomous, and checklist formatting and has 3 modules: 1) Physical Education (47 items), 2) Recess (27 items), and 3) Other Before, During, and After School Programs (15 items). Responses to more than 95% of items were highly related between Times 1 and 2. Generally, physical education and recess items had fair to substantial levels of agreement, and items about other school PA programs had fair to perfect agreement.

Conclusions:

Test-retest results suggest S-PAPA items are reliable and useful in assessing PA policies in elementary schools.

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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.