Step counting has long been used as a method of measuring distance. Starting in the mid-1900s, researchers became interested in using steps per day to quantify ambulatory physical activity. This line of research gained momentum after 1995, with the introduction of reasonably accurate spring-levered pedometers with digital displays. Since 2010, the use of accelerometer-based “activity trackers” by private citizens has skyrocketed. Steps have several advantages as a metric for assessing physical activity: they are intuitive, easy to measure, objective, and they represent a fundamental unit of human ambulatory activity. However, since they measure a human behavior, they have inherent biological variability; this means that measurements must be made over 3–7 days to attain valid and reliable estimates. There are many different kinds of step counters, designed to be worn on various sites on the body; all of these devices have strengths and limitations. In cross-sectional studies, strong associations between steps per day and health variables have been documented. Currently, at least eight prospective, longitudinal studies using accelerometers are being conducted that may help to establish dose–response relationships between steps/day and health outcomes. Longitudinal interventions using step counters have shown that they can help inactive individuals to increase by 2500 steps per day. Step counting is useful for surveillance, and studies have been conducted in a number of countries around the world. Future challenges include the need to establish testing protocols and accuracy standards, and to decide upon the best placement sites. These challenges should be addressed in order to achieve harmonization between studies, and to accurately quantify dose–response relationships.

Across all waking hours of 1 d, step counts differ between devices. The SW, regardless of settings, was the most accurate method of counting steps.

Introduction: Conflicting evidence exists on whether physical activity (PA) levels of humans have changed over the last quarter-century. The main objective of this study was to determine if there is evidence of time trends in PA, from cross-sectional studies that assessed PA at different time points using wearable devices (e.g., pedometers and accelerometers). A secondary objective was to quantify the rate of change in PA. Methods: A systematic literature review was conducted of English-language studies indexed in PubMed, SPORTDiscus, and Web of Science (1960-2020) using search terms (time OR temporal OR secular) AND trends AND (steps per day OR pedometer OR accelerometer OR MVPA). Subsequently, a meta-analytic approach was used to aggregate data from multiple studies and to examine specific factors (i.e., sex, age-group, sex and age-group, and PA metric). Results: Based on 16 peer-reviewed scientific studies conducted between 1995 and 2017, levels of ambulatory PA are trending downward in developed countries. Significant declines were seen in both males and females (P < 0.001) as well as in children (P = 0.020), adolescents (P < 0.001), and adults (P = 0.004). The average study duration was 9.4 yr (accelerometer studies, 5.3 yr; pedometer studies, 10.8 yr). For studies that assessed steps, the average change in PA was −1118 steps per day over the course of the study (P < 0.001), and adolescents had the greatest change in PA at −2278 steps per day (P < 0.001). Adolescents also had the steepest rate of change over time, expressed in steps per day per decade. Conclusions: Evidence from studies conducted in eight developed nations over a 22-yr period indicates that PA levels have declined overall, especially in adolescents. This study emphasizes the need for continued research tracking time trends in PA using wearable devices.

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Purpose. When the ActiGraph GT3X is worn on the hip, y-axis and vector magnitude (VM) counts level off at running speeds over 10 kph. Currently, it is not known if the counts level off when the device is worn on the wrist or ankle. Thus, the primary purpose of this study was to determine if ActiGraph counts level off with increasing running speeds at the wrist and ankle. Methods. Participants (N=20) completed ten treadmill walking and running speeds (3-20 kph) for 30 s each. An ActiGraph wGT3X-BT was worn on the right hip, both wrists, and both ankles. Acceleration data for x-, y-and z-axes and VM were converted to 5 s epochs. Repeated measures analyses of variance were used to assess differences across speeds for each axis and VM for each wear location. Pair-wise comparisons with Bonferroni adjustments were performed to determine where differences occurred. Pearson correlations were used to assess the association between counts and speed. Results. Hip y-axis and VM counts increased significantly with speeds up to 10 kph and significantly decreased at speeds beyond 14 and 16 kph, respectively. However, at the wrists and ankles, significant increases in counts were seen for y-axis and VM counts across all running speeds. Conclusion. When worn on the wrist or ankle, ActiGraph y-axis and VM counts do not level off as is seen with the hip location. Wearing the ActiGraph on the wrist or ankle results in a stronger, more linear relationship between speed and counts than wearing it on the hip, and should result in more accurate estimations of energy expenditure for running speeds above 10 kph.