Validity of accelerometry for the assessment of moderate intensity physical activity in the field

Compendium of physical activities: an update of activity codes and MET intensities

We provide an updated version of the Compendium of Physical Activities, a coding scheme that classifies specific physical activity (PA) by rate of energy expenditure. It was developed to enhance the comparability of results across studies using self-reports of PA. The Compendium coding scheme links a five-digit code that describes physical activities by major headings (e.g., occupation, transportation, etc.) and specific activities within each major heading with its intensity, defined as the ratio of work metabolic rate to a standard resting metabolic rate (MET). Energy expenditure in MET-minutes, MET-hours, kcal, or kcal per kilogram body weight can be estimated for specific activities by type or MET intensity. Additions to the Compendium were obtained from studies describing daily PA patterns of adults and studies measuring the energy cost of specific physical activities in field settings. The updated version includes two new major headings of volunteer and religious activities, extends the number of specific activities from 477 to 605, and provides updated MET intensity levels for selected activities.

Validity of four motion sensors in measuring moderate intensity physical activity

PURPOSE

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.

METHODS

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.

RESULTS

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).

CONCLUSION

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.

Estimation of energy expenditure using CSA accelerometers at hip and wrist sites

PURPOSE

This study was designed to establish prediction models that relate hip and wrist accelerometer data to energy expenditure (EE) in field and laboratory settings. We also sought to determine whether the addition of a wrist accelerometer would significantly improve the prediction of EE (METs), compared with a model that used a hip accelerometer alone.

METHODS

Seventy participants completed one to six activities within the categories of yardwork, housework, family care, occupation, recreation, and conditioning, for a total of 5 to 12 participants tested per activity. EE was measured using the Cosmed K4b2 portable metabolic system. Simultaneously, two Computer Science and Applications, Inc. (CSA) accelerometers (model 7164), one worn on the wrist and one worn on the hip, recorded body movement. Correlations between EE measured by the Cosmed and the counts recorded by the CSA accelerometers were calculated, and regression equations were developed to predict EE from the CSA data.

RESULTS

The wrist, hip, and combined hip and wrist regression equations accounted for 3.3%, 31.7%, and 34.3% of the variation in EE, respectively. The addition of the wrist accelerometer data to the hip accelerometer data to form a bivariate regression equation, although statistically significant (P = 0.002), resulted in only a minor improvement in prediction of EE. Cut points for 3 METs (574 hip counts), 6 METs (4945 hip counts), and 9 METs (9317 hip counts) were also established.

CONCLUSION

The small amount of additional accuracy gained from the wrist accelerometer is offset by the extra time required to analyze the data and the cost of the accelerometer.

Comparison of three methods for measuring the time spent in physical activity

PURPOSE

Three methods for measuring time spent in daily physical activity (PA) were compared during a 21-d period among 83 adults (38 men and 45 women).

METHODS

Each day, participants wore a Computer Science and Applications, Inc. (CSA) monitor and completed a 1-page, 48-item PA log that reflected time spent in household, occupational, transportation, sport, conditioning, and leisure activities. Once a week, participants also completed a telephone survey to identify the number of minutes spent each week in nonoccupational walking and in moderate intensity and hard/very hard-intensity PA. Data were analyzed using descriptive statistics and Spearman rank-order correlations. Three equations developed to compute CSA cut points for moderate and hard/very hard PA were also compared with the PA logs and PA survey.

RESULTS

There was modest to good agreement for the time spent in different PA intensity categories between the three CSA cut point methods (r = 0.43-0.94, P < 0.001). Correlations between the CSA and PA logs ranged from r = 0.22 to r = 0.36, depending on the comparisons. Correlations between the survey items and PA logs were r = 0.26-0.54 (P < 0.01) for moderate and walking activities and r < 0.09 (P > 0.05) for hard/very hard activities. Correlations between the survey items and the CSA min per day varied according to the method used to compute the CSA intensity cut points.

CONCLUSIONS

The results were consistent with findings from other PA validation studies that show motion sensors, PA logs, and surveys reflect PA; however, these methods do not always provide similar estimates of the time spent in resting/light, moderate, or hard/very hard PA.