Physical Activity Analysis

Assessment of Physical Activity

Over the past several decades, there has been an increasing interest in monitoring physical activity. There are methods to measure physical activity both objectively and subjectively. Those objective measures have been continually refined through the development of accelerometer technology (Bassett, Rowlands, & Trost, 2012). What started out as an objective measure for researchers to classify physical activity intensity has shifted to a consumer interest in learning and quantifying personal data. There consumer interest is well intentioned, but the need for a valid and reliable model for research is still present. While both measures, objective and subjective, are important in assessing physical activity, this

In 2005, Chen et al. wrote about the history of accelerometers, how they work, and the best methods for using them (Chen, 2005). Chen et al. goes on to state how the accelerometers work at a mechanical level. The research illustrates that a piezoelectric sensor is used to detect motion (acceleration) in three planes and it is highlighted why researchers want to use acceleration versus speed to look at physical activity measurements. Acceleration is more closely related to energy cost because it is related to the net forces involved so it is a more useful measure for looking at physical activity (Chen, 2005). This technology is pretty standard across all models of accelerometers. The main difference in the various models is the frequency range of the band pass filter. According to Chen et al., these filters come with preset frequency ranges that the collected data pass through before being recorded as the final output (Chen, 2005). These frequency ranges can vary between models and that is why some models report differently than others even when worn in the same location on the body. The band pass filters only accept and record data within their frequency range with any other data not being recorded. This final data is then stored by a mechanism unique to the device (Chen,

Without an industry standard, this makes it increasingly more difficult to interpret the data being published because not all devices are validated and calibrated equally. This is where literature like that of Chen et al. comes into play to help promote common standards of practice when calibrating not only new devices but continuing work with older devices (Chen, 2005). It is important to validate devices for as many activities as possible and often times this needs to take place in a series of studies rather than a singular