In the exercising human, maximal oxygen uptake (VO2max) is limited by the ability of the cardiorespiratory system to deliver oxygen to the exercising muscles. This is shown by three major lines of evidence: 1) when oxygen delivery is altered (by blood doping, hypoxia, or beta-blockade), VO2max changes accordingly; 2) the increase in VO2max with training results primarily from an increase in maximal cardiac output (not an increase in the a-v O2 difference); and 3) when a small muscle mass is overperfused during exercise, it has an extremely high capacity for consuming oxygen. Thus, O2 delivery, not skeletal muscle O2 extraction, is viewed as the primary limiting factor for VO2max in exercising humans. Metabolic adaptations in skeletal muscle are, however, critical for improving submaximal endurance performance. Endurance training causes an increase in mitochondrial enzyme activities, which improves performance by enhancing fat oxidation and decreasing lactic acid accumulation at a given VO2. VO2max is an important variable that sets the upper limit for endurance performance (an athlete cannot operate above 100% VO2max, for extended periods). Running economy and fractional utilization of VO2max also affect endurance performance. The speed at lactate threshold (LT) integrates all three of these variables and is the best physiological predictor of distance running performance.

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.

Ntify predictors of pedometer-measured physical activity on the basis of demographic characteristics and self-reported behavioral characteristics. Methods: The America On the Move study was conducted in 2003. Individuals (N = 2522) aged 13 yr and older consented to fill out a survey, including 1921 adults aged 18 yr and older. Valid pedometer data were collected on 1136 adults with Accusplit AE120 pedometers. Data were weighted to reflect the general U.S. population according to several variables (age, gender, race/ethnicity, education, income, level of physical activity, and number of 5- to 17-yr-old children in the household). Differences in steps per day between subgroups were analyzed using unpaired t-tests when only two subgroups were involved or one-way ANOVA if multiple subgroups were involved. Results: Adults reported taking an average of 5117 steps per day. Male gender, younger age, higher education level, single marital status, and lower body mass index were all positively associated with steps per day. Steps per day were positively related to other self-reported measures of physical activity and negatively related to self-reported measures on physical inactivity. Living environment (urban, suburban, or rural) and eating habits were not associated with steps per day. Conclusions: In the current study, men and women living in the United States took fewer steps per day than those living in Switzerland, Australia, and Japan. We conclude that low levels of ambulatory physical activity are contributing to the high prevalence of adult obesity in the United States....

This study tested the validity of four motion sensors for measuring energy expenditure (EE) during moderate intensity physical activities in field and laboratory settings. We also evaluated the accuracy of the EE values for selected moderate activities listed in the 1993 Compendium of Physical Activities. A total of 81 participants (age 19-74 yr) completed selected tasks from six general categories: yardwork, housework, occupation, family care, conditioning, and recreation. Twelve individuals performed each of the 28 activities examined. During each activity, EE was measured using a portable metabolic measurement system. Participants also wore three accelerometers (Computer Science and Applications [CSA], Inc. model 7164; Caltrac; and Kenz Select 2) and the Yamax SW-701 electronic pedometer. For the CSA device, three previously developed regression equations were used to convert accelerometer scores to EE. The mean error scores (indirect calorimetry minus device) across all activities were: CSA1, 0.97 MET; CSA2, 0.47 MET, CSA3, 0.05 MET; Caltrac, 0.83 MET; Kenz, 0.96 MET; and Yamax, 1.12 MET. The correlation coefficients between indirect calorimetry and motion sensors ranged from r = 0.33 to r = 0.62. The energy cost for power mowing and sweeping/mopping was higher than that listed in the 1993 Compendium (P < 0.05), and the cost for several household and recreational activities was lower (P < 0.05). Motion sensors tended to overpredict EE during walking. However, they underpredicted the energy cost of many other activities because of an inability to detect arm movements and external work. These findings illustrate some of the limitations of using motion sensors to predict EE in field settings.

Activity measurement specialist, although the end user who is conducting studies with these devices also may benefit from knowing about this topic. Best Practices Initially, wearable physical activity monitors should undergo unit calibration to ensure interinstrument reliability. The next step is to simultaneously collect both raw signal data (e.g., acceleration) from the wearable monitors and rates of energy expenditure, so that algorithms can be developed to convert the direct signals into energy expenditure. This process should use multiple wearable monitors and a large and diverse subject group and should include a wide range of physical activities commonly performed in daily life (from sedentary to vigorous). Future Directions New methods of calibration now use “pattern recognition” approaches to train the algorithms on various activities, and they provide estimates of energy expenditure that are much better than those previously available with the single-regression approach. Once a method of predicting energy expenditure has been established, the next step is to examine its predictive accuracy by cross-validating it in other populations. In this article, we attempt to summarize the best practices for calibration and validation of wearable physical activity monitors. Finally, we conclude with some ideas for future research ideas that will move the field of physical activity measurement forward....

Markedly in the last 150 yr. The Old Order Amish refrain from driving automobiles, using electrical appliances, and employing other modern conveniences. Labor-intensive farming is still the preferred occupation. Purpose The purposes of this study were to characterize the physical activity (PA) levels in an Old Order Amish farming community and to examine measures of adiposity in this group. Methods Ninety-eight Amish adults (18–75 yr of age) in southern Ontario were studied. Anthropometric variables included height, weight, body mass index (BMI), and percent body fat (% BF). Participants were asked to wear an electronic pedometer for 7 d and to fill out a log sheet on which they recorded steps per day and physical activities. After 1 wk, they returned the pedometers and log sheets and filled out the International Physical Activity Questionnaire. Results The average number of steps per day was 18,425 for men versus 14,196 for women (P < 0.05). Men reported 10.0 h·wk−1 of vigorous PA, 42.8 h·wk−1 of moderate PA, and 12.0 h·wk−1 of walking. Women reported 3.4 h·wk−1 of vigorous PA, 39.2 h·wk−1 of moderate PA, and 5.7 h·wk−1 of walking. Men had higher levels of energy expenditure than women (P < 0.001). A total of 25% of the men and 27% of the women were overweight (BMI ≥ 25), and 0% of the men and 9% of the women were obese (BMI ≥ 30). Conclusions The Amish we studied had very high levels of physical activity, which may contribute to their low prevalence of obesity. This group probably represents an upper extreme for “lifestyle PA” in North America today....