There is general consensus that physical activity is important for preserving functional capacities of older adults and for positively influencing quality of life [7, 12]. To measure physical activity in studies, a variety of direct (e.g., pedometer) or indirect (e.g., questionnaires) methods is used [18, 20]. The measurement methods differ with regard to their quality, criteria validity and retest reliability, costs and acceptance by study participants, and depend closely on the feasibility within the study design. At present, no gold standard for the assessment of physical activity has been established [18, 28].
Among direct methods to measure physical activity, accelerometry is accepted and widely applied. An accelerometer is worn on the body (e.g., at the hip, ankle, or wrist) measuring acceleration in up to three dimensions. In so doing, information on frequency, intensity, and duration of an individual’s physical activity is collected, expressed in “counts per minute” (CPM). It is assumed that the amount of CPM is associated with the intensity of physical activity [5, 14]. To represent the average physical activity of an individual, a minimum of 3-day measurement is suggested [28, 29].
Despite the widespread use, direct measurement of physical activity using an accelerometer remains challenging [18, 31]. There is-for example-no consensus on the type of accelerometer to use [1, 18, 28], nor is there agreement as to the part of the body on which it should be worn [9], just recommendations for different target groups, e.g., for older adults exist [4, 6, 16, 21]. Older adults frequently perform physical activity with light to moderate intensity, such as housekeeping, gardening, or walking for leisure [11]. In order to take these activities into account, some authors recommend the use of a wrist-worn uniaxial accelerometer [4, 6, 16, 21], since movements mainly occur in the upper body and arms (e.g., the wrist-worn “Actiband” AB64 uniaxial accelerometer, Cambridge Neurotechnology Ltd., UK).
Despite the wide use of accelerometry-based measurement of physical activity in all kinds of studies, data on the retest reliability are seldom published [16]. This is true for the uniaxial wrist-worn Actiband accelerometer itself, as well as for other accelerometers in general. The only published data on the retest reliability of the Actiband was found in Rowe et al. [19]. They found a high inter-instrumental retest reliability of two Actibands which were worn simultaneously during a test on a treadmill (ICC = 0.98; 95 % CI: 0.91–0.99). However, the study was performed with ten 10 to 11-year-old boys in a laboratory environment, comparing two different Actibands. Therefore, these results cannot directly be adopted for the measurement of activities of daily life in community-dwelling older adults within a nonlaboratory situation.
Maybe the reason for the limited data of the accelerometer-based measurement of physical activity is partly explained by the disappointing results of the analysis of retest reliability. Usually, the sum of CPM or the mean CPM, collected over a period of a few days and divided by the number of days, [26, 28, 30], is used to express the average amount of physical activity of an individual. The resulting “average counts per day” often show tremendous intra- and inter-individual variability [9]. This variability may be partly explained by outliers of the CPM measured by the accelerometer. Outliers are multiples of reasonable CPM values. These values are defined as measurement errors, since they are clearly due to methodological issues of the manufacture and cannot be achieved by any kind of physical activity. Consequently, using the sum or the mean of CPM which still include the outliers cannot result in high retest reliability. Unfortunately, a standardized recommendation on how to deal with outliers of accelerometry is lacking. Retest reliability might be low due to the outliers which account for the overall sum and not due the general missing possibility of reproducing the results. Orsini et al. [17], for example, defined CPM greater than 20,000 as malfunction of the accelerometer without further explanations on the cut-point they chose. These data were then set as missing and thereby excluded from analyses. Instead of defining a certain cut-point for each accelerometer, we would like to suggest a different approach. In order to enhance the retest reliability, an alternative and more robust estimator that is less sensitive to outliers/measurement errors might be needed. The trimmed (or truncated) sum may be an alternative estimator. The trimmed sum is obtained by omitting a certain percentage of the most extreme observations (e.g., 5 % of the low and 5 % of the high end) and taking the sum of the rest. It is a robust measure of central tendency and is stable against abnormal extreme values (such as measurement errors/outliers), which get “trimmed” away [2]. Using the trimmed sum to express the average amount of physical activity of an individual instead of the overall sum of CPM may result in higher retest reliability.
The aim of the study was therefore to find a more robust estimator in order to account for outliers that occur by using accelerometry to measure physical activity. This more robust estimator will then be used to test the intra-instrumental retest reliability of a wrist-worn accelerometer in a 3-day measurement of physical activity in community-dwelling older adults. We hypothesized that using quantiles and the trimmed sum instead of the overall sum (which includes the outliers) of CPM will decrease the measurement error and increase the retest reliability.