An Objective Method for Measurement of Sedentary Behavior in 3- to 4-Year Olds

Introduction

Physical inactivity (sedentary behavior) has recently emerged as an important health construct, and it should be considered independently from physical activity (1). Objective measurement of inactivity is potentially useful for many clinical and public health applications, particularly in the prevention and treatment of childhood obesity (2, 3, 4) and the early prevention of cardiovascular disease (5). For example, in studies of obesity prevention, targeting a reduction in inactivity may be a more effective means of increasing physical activity in children than the more traditional approach of targeting increases in physical activity (2, 4). However, no simple, objective method has yet been validated for measurement of inactivity, and this is a serious barrier to the use of inactivity measurement in clinical practice and public health (6, 7). Cochrane reviewers have recently concluded that randomized controlled trials in pediatric obesity prevention and treatment are greatly weakened by the absence of an objective measure of sedentary behavior (8, 9), an important mediator of intervention success and a potentially valuable outcome measure.

The assessment of television viewing time as a proxy for inactivity is useful but methodologically limited at present (1, 2), and up to one-half of all time children spend inactive may involve behaviors other than television viewing (10). Simply attempting to measure television viewing is, therefore, unlikely to quantify sedentary behavior adequately. There is increasing evidence that sedentary behavior is now common even in preschool children (11). This is a particular concern because the preschool child is at a critical period for later obesity risk, the “adiposity rebound.” Factors that determine the timing of adiposity rebound are unclear at present (12), but a recent secular trend to earlier adiposity rebound in Europe may have been the result of increases in sedentary behavior in early childhood (12, 13).

Accelerometry is a practical means of measuring physical activity, even in young children (14), but we speculated that it might be even more suitable for measurement of physical inactivity, which is a simpler construct. Accelerometry output does not have biological meaning per se and must be validated against criterion measures (either direct observation of activity or energy expended on activity). The present study, therefore, aimed to assess the validity of accelerometry for measurement of physical inactivity in 3- to 4-year-old children by comparison against direct observation of behavior, a criterion method.

Research Methods and Procedures

Results

The validation study (n = 30) found optimal sensitivity and specificity at an accelerometry output cut-off of <1100 counts/min. Mean sensitivity of this cut-off in the cross-validation (n = 52) was 83 ± 14% (95% CI, 78% to 86%; Figure 1). This meant that 438/528 inactive minutes were correctly classified as such using a cut-off point of <1100 counts/min. Mean specificity was 82 ± 11% (95% CI, 79% to 86%), i.e., 1251/1526 noninactive minutes were correctly classified using the cut-off of <1100 counts/min (Figure 1). Data from boys and girls were combined after having established that there were no significant differences between the sexes in sensitivity (83% in boys, 95% CI, 75 to 90; 82% in girls, 95% CI, 76 to 86; p = 0.91 for difference between the sexes) and specificity (82% in boys, 95% CI, 76 to 87; 83% in girls, 95% CI, 78 to 87; p = 0.85 for difference between the sexes).

Predictive values were not calculated because these are dependent on “prevalence” (in this case activity level) and therefore are study-specific. The impact of activity level on accuracy of classification of activity and inactivity is discussed below.

Median accelerometer counts per minute differed significantly between CPAF behavior categories 1 to 4, as might have been expected (Kruskal Wallis test followed by Mann-Whitney tests, p < 0.001). However, accelerometer output (counts per minute) overlapped between categories (Figure 2).

Discussion

A paradigm shift occurred some time ago, from emphasis on exercise to physical activity. Recent research on the etiology, prevention, and treatment of obesity has suggested that a further paradigm shift should occur, with a new emphasis on physical inactivity or sedentary behavior (1, 2, 3, 4). The need to consider physical inactivity as a distinct construct is being increasingly widely recognized, but no simple, validated means of measurement has been available to date. As a result, the potential of inactivity measurement highlighted by Dietz (1) has not yet been realized. This study found that a novel application of accelerometry can provide objective assessment of engagement in physical inactivity in young children. We have also shown in this study that variation in accelerometry output within observer defined activity categories is substantial (Figure 2), demonstrating a need for the ROC approach to analysis (Figure 1) to establish the validity of any proposed accelerometry output cut-off points.

This study had a number of limitations. First, our focus was on the preschool child. Whereas this degree of focus provided a relatively large and homogenous sample, increasing the confidence in our conclusions for this age group, extrapolation of our results to other populations should be considered with caution. The cut-off proposed may be age-specific, and the appropriate cut-off might differ between populations. However, carrying out the necessary cross-validation studies in older subjects should be relatively straightforward. We used direct observation of behavior as our criterion method. Energy expended on activity is an equally valid criterion, but we decided against this approach for several reasons. Measuring the energy cost of unrestricted activity in young children is difficult, and results obtained can be difficult to interpret. Extrapolation of laboratory or treadmill exercise to free-living behaviors may introduce errors (16), and describing activity intensity using metabolic equivalents is problematic in children because the appropriate energetic value for a metabolic equivalent is very different from that used in adults and is age-specific (16). If future studies can address these issues, then energy expended on activity will be appropriate as a criterion method for preschool children. Finally, limitations in the direct observation method we used probably led to underestimation of the sensitivity of accelerometry for assessment of inactivity. This bias is likely to have arisen because the CPAF required that we categorize each minute of observation, and minutes that included both inactive (categories 1 and 2) and active (categories 3 and 4) behaviors had to be considered as noninactive for the purposes of our ROC analysis. This produced a bias in our assessment of sensitivity, which we felt was acceptable because it was a conservative approach: the performance of accelerometry may be higher than our estimates indicate.

Potential users of accelerometry should consider the evidence presented in Figure 1 when deciding on which cut-off to use; this will depend in part on whether sensitivity or specificity is the main concern and whether sedentary behavior or physical activity is the variable of primary interest. The optimal balance between sensitivity and specificity will depend on the application, and future users should consider the potential trade-offs carefully. The accuracy of accelerometry in quantifying behavior will also depend heavily on the levels of inactivity and activity in the children being studied. For the current sample of 3 to 4 year olds in this particular setting, where ∼25% of minutes monitored were spent entirely in sedentary behavior (in levels 1 to 2 of the CPAF), 38% of minutes classified as inactive by accelerometry were misclassified, and 7% of active minutes (all other minutes) were misclassified. In a sample and setting in which 75% of minutes were spent inactive (the converse of what was observed in this study), predictive values would be approximately switched, with 6% of inactive minutes misclassified and 39% of active minutes misclassified. In a sample and setting with numbers of inactive and active minutes equal, ∼17% of active minutes and 17% of inactive minutes would be misclassified, with a total misclassification of around one-third of total minutes monitored by accelerometry.

Measurement of inactivity could have many clinical and public health applications. These include epidemiological surveys of inactivity (1, 6); inactivity as an outcome measure in obesity or cardiovascular disease prevention/treatment trials (7, 8, 9); assessment of the determinants of inactivity (1); and assessment of risk factors for obesity (20). The ease of use of accelerometry might even permit self-monitoring of sedentary behavior in clinical practice, a useful element of successful treatment (4). The accelerometers used in this study, like other accelerometers, are small, easy to use, well tolerated by children, and fairly inexpensive. Accelerometry can also simultaneously measure physical activity accurately and practically, even in young children (14, 16, 17, 18, 19). We conclude that accelerometry could be used much more widely now that validity is established for measurement of both activity and inactivity.