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Comparison of Fitbit One and ActivPAL3TM in Adults With Multiple Sclerosis in a Free-Living Environment

Golnoush Mehrabani, Douglas P. Gross, Saeideh Aminian, and Patricia J. Manns

Walking is the most common and preferred way for people with multiple sclerosis (MS) to be active. Consumer-grade wearable activity monitors may be used as a tool to assist people with MS to track their walking by counting the number of steps. The authors evaluated the validity of Fitbit One activity tracker in individuals with MS by comparing step counts measured over a 7-day period against ActivPAL3TM (AP). Twenty-five ambulatory adults with MS with an average age 51.7 (10.2) years and gait speed 0.98 (0.47) m/s, median Expanded Disability Status Scale 5.5 (2.5–6.5), and 15 years post-MS diagnosis wore Fitbit One (using both waist and ankle placement) and AP for 7 consecutive days. Validity of Fitbit One for measuring step counts against AP was assessed using intraclass correlation coefficients (ICCs), Bland–Altman plots, and t tests. Regardless of wearing location (waist or ankle), there was good agreement between steps recorded by Fitbit One and AP (ICC: .86 [.82, .90]). The ankle-worn Fitbit measured steps more accurately (ICC: .91 [.81, .95]) than the waist-worn Fitbit (ICC: .81 [.62, .85]) especially in individuals (n = 12) who walked slowly (gait speed = 0.74 m/s). Fitbit One as a user-friendly, inexpensive, consumer-grade activity tracker can accurately record steps in persons with MS in a free-living environment.

Open access

Calibration of the Online Youth Activity Profile Assessment for School-Based Applications

Gregory J. Welk, Pedro F. Saint-Maurice, Philip M. Dixon, Paul R. Hibbing, Yang Bai, Gabriella M. McLoughlin, and Michael Pereira da Silva

A balance between the feasibility and validity of measures is an important consideration for physical activity (PA) research—particularly in school-based research with youth. The present study extends previously tested calibration methods to develop and test new equations for an online version of the youth activity profile (YAP) tool, a self-report tool designed for school applications. Data were collected across different regions and seasons to develop more robust, generalizable equations. The study involved a total of 717 youth from 33 schools (374 elementary [ages 9–11 years], 224 middle [ages 11–14 years], and 119 high school [ages 14–18 years]) in two different states in the United States. Participants wore a Sensewear monitor for a full week and then completed the online YAP at school to report PA and sedentary behaviors in school and at home. Accelerometer data were processed using an R-based segmentation program to compute PA and sedentary behavior levels. Quantile regression models were used with half of the sample to develop item-specific YAP calibration equations, and these were cross validated with the remaining half of the sample. Computed values of mean absolute percentage error ranged from 15 to 25% with slightly lower error observed for the middle school sample. The new equations had improved precision compared with the previous versions when tested on the same sample. The online version of the YAP provides an efficient and effective way to capture school level estimates of PA and sedentary behaviors in youth.

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Changes in Device-Measured Physical Activity Patterns in U.K. Adults Related to the First COVID-19 Lockdown

Andrew P. Kingsnorth, Mhairi Patience, Elena Moltchanova, Dale W. Esliger, Nicola J. Paine, and Matthew Hobbs

The response to COVID-19 resulted in behavioral restrictions to tackle the spread of infection. Initial data indicates that step counts were impacted by lockdown restrictions; however, there is little evidence regarding changes of light and moderate to vigorous physical activity (MVPA) behavioral intensities. In this study, participants were asked to provide longitudinal wearable data from Fitbit devices over a period of 30 weeks, from December 2019 to June 2020. Self-assessed key worker status was captured, along with wearable estimates of steps, light activity, and MVPA. Bayesian change point analyses of data from 97 individuals found that there was a sharp decrease of 1,473 steps (95% credible interval [CI] [−2,218, −709]) and light activity minutes (41.9; 95% CI [−54.3, −29.3]), but an increase in MVPA minutes (11.7; 95% CI [2.9, 19.4]) in the mean weekly totals for nonkey workers. For the key workers, the total number of steps (207; 95% CI [−788, 1,456]) and MVPA minutes increased (20.5; 95% CI [12.6, 28.3]) but light activity decreased by an average of 46.9 min (95% CI [−61.2, −31.8]). Interestingly, the change in steps was commensurate with that observed during Christmas (1,458; 95% CI [−2,286, −554]) for nonkey workers and behavioral changes occurred at different time points and rates depending on key worker status. Results indicate that there were clear behavioral modifications before and during the initial COVID-19 lockdown period, and future research should assess whether any behavioral modifications were sustained over time.

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Association of Individual Motor Abilities and Accelerometer-Derived Physical Activity Measures in Preschool-Aged Children

Becky Breau, Berit Brandes, Marvin N. Wright, Christoph Buck, Lori Ann Vallis, and Mirko Brandes

This study explored the relationship between motor abilities and accelerometer-derived measures of physical activity (PA) within preschool-aged children. A total of 193 children (101 girls, 4.2 ± 0.7 years) completed five tests to assess motor abilities, shuttle run (SR), standing long jump, lateral jumping, one-leg stand, and sit and reach. Four PA variables derived from 7-day wrist-worn GENEActiv accelerometers were analyzed including moderate to vigorous PA (in minutes), total PA (in minutes), percentage of total PA time in moderate to vigorous PA, and whether or not children met World Health Organization guidelines for PA. Linear regressions were conducted to explore associations between each PA variable (predictor) and motor ability (outcome). Models were adjusted for age, sex, height, parental education, time spent at sports clubs, and wear time. Models with percentage of total PA time in moderate to vigorous PA were adjusted for percentage of total PA time. Regression analyses indicated that no PA variables were associated with any of the motor abilities, but demographic factors such as age (e.g., SR: ß = −0.45; 95% confidence interval [−1.64, −0.66]), parental education (e.g., SR: ß = 0.25; 95% confidence interval [0.11, 1.87]), or sports club time (e.g., SR: ß = −0.08; 95% confidence interval [−0.98, 0.26]) showed substantial associations with motor abilities. Model strength varied depending on the PA variable and motor ability entered. Results demonstrate that total PA and meeting current PA guidelines may be of importance for motor ability development and should be investigated further. Other covariates showed stronger associations with motor abilities such as time spent at sports clubs and should be investigated in longitudinal settings to assess the associations with individual motor abilities.

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Converting Raw Accelerometer Data to Activity Counts Using Open-Source Code: Implementing a MATLAB Code in Python and R, and Comparing the Results to ActiLife

Ruben Brondeel, Yan Kestens, Javad Rahimipour Anaraki, Kevin Stanley, Benoit Thierry, and Daniel Fuller

Background: Closed-source software for processing and analyzing accelerometer data provides little to no information about the algorithms used to transform acceleration data into physical activity indicators. Recently, an algorithm was developed in MATLAB that replicates the frequently used proprietary ActiLife activity counts. The aim of this software profile was (a) to translate the MATLAB algorithm into R and Python and (b) to test the accuracy of the algorithm on free-living data. Methods: As part of the INTErventions, Research, and Action in Cities Team, data were collected from 86 participants in Victoria (Canada). The participants were asked to wear an integrated global positioning system and accelerometer sensor (SenseDoc) for 10 days on the right hip. Raw accelerometer data were processed in ActiLife, MATLAB, R, and Python and compared using Pearson correlation, interclass correlation, and visual inspection. Results: Data were collected for a combined 749 valid days (>10 hr wear time). MATLAB, Python, and R counts per minute on the vertical axis had Pearson correlations with the ActiLife counts per minute of .998, .998, and .999, respectively. All three algorithms overestimated ActiLife counts per minute, some by up to 2.8%. Conclusions: A MATLAB algorithm for deriving ActiLife counts was implemented in R and Python. The different implementations provide similar results to ActiLife counts produced in the closed source software and can, for all practical purposes, be used interchangeably. This opens up possibilities to comparing studies using similar accelerometers from different suppliers, and to using free, open-source software.

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Is the Polar M430 a Valid Tool for Estimating Maximal Oxygen Consumption in Adult Females?

Kevin E. Miller, Timothy R. Kempf, Brian C. Rider, and Scott A. Conger

Background: Previous research studies have found that heart rate monitors that predict maximal oxygen consumption ( V ˙ O 2 max ) are valid for males but overestimate V ˙ O 2 max in females. Inaccurate self-reported physical activity (PA) levels may affect the validity of the prediction algorithm used to predict V ˙ O 2 max . Purpose: To investigate the validity of the Polar M430 in predicting V ˙ O 2 max among females with varying PA levels. Methods: Polar M430 was used to predict V ˙ O 2 max ( p V ˙ O 2 max ) for 43 healthy female study participants (26.9 ± 1.3 years), under three conditions: the participant’s self-selected PA category (sPA), one PA category below the sPA (sPA − 1), and one category above the sPA (sPA + 1). Indirect calorimetry was utilized to measure V ˙ O 2 max ( m V ˙ O 2 max ) via a modified Astrand treadmill protocol. Repeated-measures analyses of covariance using age and percentage of body fat as covariates were used to detect differences between groups. Bland–Altman plots were used to assess the precision of the measurement. Results: p V ˙ O 2 max was significantly correlated with m V ˙ O 2 max (r = .695, p < .001). The mean values for p V ˙ O 2 max and m V ˙ O 2 max were 44.58 ± 9.29 and 43.98 ± 8.76, respectively. No significant differences were found between m V ˙ O 2 max , p V ˙ O 2 max , sPA – 1, and sPA + 1 (p = .492). However, the Bland–Altman plots indicated a low level of precision with the estimate. Conclusions: The Polar M430 was a valid method to predict V ˙ O 2 max across different sPA levels in females. Moreover, an under/overestimation in sPA had little effect on the predicted V ˙ O 2 max .

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Validity of the iPhone M7 Motion Coprocessor to Estimate Physical Activity During Structured and Free-Living Activities in Healthy Adults

Nicola K. Thomson, Lauren McMichan, Eilidh Macrae, Julien S. Baker, David J. Muggeridge, and Chris Easton

Modern smartphones such as the iPhone contain an integrated accelerometer, which can be used to measure body movement and estimate the volume and intensity of physical activity. Objectives: The primary objective was to assess the validity of the iPhone to measure step count and energy expenditure during laboratory-based physical activities. A further objective was to compare free-living estimates of physical activity between the iPhone and the ActiGraph GT3X+ accelerometer. Methods: Twenty healthy adults wore the iPhone 5S and GT3X+ in a waist-mounted pouch during bouts of treadmill walking, jogging, and other physical activities in the laboratory. Step counts were manually counted, and energy expenditure was measured using indirect calorimetry. During two weeks of free-living, participants (n = 17) continuously wore a GT3X+ attached to their waist and were provided with an iPhone 5S to use as they would their own phone. Results: During treadmill walking, iPhone (703 ± 97 steps) and GT3X+ (675 ± 133 steps) provided accurate measurements of step count compared with the criterion method (700 ± 98 steps). Compared with indirect calorimetry (8 ± 3 kcal·min−1), the iPhone (5 ± 1 kcal·min−1) underestimated energy expenditure with poor agreement. During free-living, the iPhone (7,990 ± 4,673 steps·day−1) recorded a significantly lower (p < .05) daily step count compared with the GT3X+ (9,085 ± 4,647 steps·day−1). Conclusions: The iPhone accurately estimated step count during controlled laboratory walking but recorded a significantly lower volume of physical activity compared with the GT3X+ during free-living.

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Volume 4 (2021): Issue 2 (Jun 2021)

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Exploring Machine Learning Models Based on Accelerometer Sensor Alone or Combined With Gyroscope to Classify Home-Based Exercises and Physical Behavior in (Pre)sarcopenic Older Adults

Lenore Dedeyne, Jorgen A. Wullems, Jolan Dupont, Jos Tournoy, Evelien Gielen, and Sabine Verschueren

Tools for objective monitoring of home-based training and physical behavior (PB) in (pre)sarcopenic older adults are needed. The present study explored two approaches with machine learning models, including accelerometer data either with or without additional gyroscope data (in an inertial measurement unit). Twenty-five community-dwelling (pre)sarcopenic adults mean 80.7 [5.5] years) performed the Otago exercise protocol (OEP) and PB modules (e.g., sitting or walking) while wearing an inertial measurement unit on the lower back (Dynaport MoveMonitor; McRoberts, The Hague, The Netherlands). Machine learning (ML) models for classification were developed by two approaches (top-down and bottom-up approaches) and with two levels of classification: general (no wear, OEP, and PB) and detailed (all subclassifications). Classification output was compared with video recordings. For the bottom-up approach, one ML model was developed. For the top-down approach, data were first classified as no wear, OEP, or PB. Thereafter, OEP and PB were subclassified by one ML model each into subclassification. Only classification of the general classification no wear and the subclassification sitting/lying reached the acceptable performance threshold of 80%. This result was independent of the approach used. Moreover, a gyroscope did not improve performance. Despite the progress in ML techniques and monitors, objective compliance registrations remain challenging.

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Accelerometry and Self-Report Are Congruent for Children’s Moderate-to-Vigorous and Higher Intensity Physical Activity

Claudio R. Nigg, Xanna Burg, Barbara Lohse, and Leslie Cunningham-Sabo

Purpose: This study used different analytic approaches to compare physical activity (PA) metrics from accelerometers (ACC) and a self-report questionnaire in upper elementary youth participating in the Fuel for Fun intervention. Methods: The PA questionnaire and ACC were assessed at baseline/preintervention (fall fourth grade), Follow-up 1/postintervention (spring fourth grade), and Follow-up 2 (fall fifth grade) of 564 fourth grade students from three elementary schools (50% females, 78% White, and 28% overweight or obese). Different analytic approaches identified similarities and differences between the two methods. Results: On average, self-report was higher than ACC for vigorous PA (range = 9–15 min/day), but lower than ACC for moderate PA (range = 24–30 min/day), light PA (range = 30–36 min/day), and moderate-to-vigorous physical activity (MVPA; range = 9–21 min/day). Spearman’s correlations for vigorous PA (.30, .26, and .32); moderate PA (.12, .13, and .14); and MVPA (.25, .25, and .24) were significant at each time point (all ps ≤ .01), whereas correlations for light PA were not significant (.06, .04, and .07; all ps > .05). In repeated-measures analyses, ACC and questionnaire measures were significantly different from each other across the three time points; however, change difference of the two measures over time was only 5.5 MVPA min/day. Conclusions: The PA questionnaire and ACC validated each other and can be used to assess MVPA in upper elementary school children in a similar population to the current study. However, each assessment method captures unique information, especially for light-intensity PA. Multiple PA measurement methods are recommended to be used in research and application to provide a more comprehensive understanding of children’s activity.