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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 ActivPAL3^TM (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.

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.

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.

Background: Previous research studies have found that heart rate monitors that predict maximal oxygen consumption ( $\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ ) are valid for males but overestimate $\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ in females. Inaccurate self-reported physical activity (PA) levels may affect the validity of the prediction algorithm used to predict $\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ . Purpose: To investigate the validity of the Polar M430 in predicting $\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ among females with varying PA levels. Methods: Polar M430 was used to predict $\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ ( $\mathrm{p}\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ ) 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 $\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ ( $\mathrm{m}\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ ) 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: $\mathrm{p}\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ was significantly correlated with $\mathrm{m}\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ (r = .695, p < .001). The mean values for $\mathrm{p}\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ and $\mathrm{m}\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ were 44.58 ± 9.29 and 43.98 ± 8.76, respectively. No significant differences were found between $\mathrm{m}\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ , $\mathrm{p}\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ , 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 $\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ across different sPA levels in females. Moreover, an under/overestimation in sPA had little effect on the predicted $\dot{\mathrm{V}}{\mathrm{O}}_2\text{max}$ .

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.

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.

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.