Almost every day there appears a new activity monitor on the market, of which most are aimed at end users that are interested to obtain insight in their movement patterns. Often these people are already active and health conscious and have a strong need to share their results with peers. With these new activity monitors the design aspect is often presented as the most important features. Of course it’s good that an activity monitor looks good and is seen as hip and trendy, but personally I am of the belief that having an accurate activity monitor is of THE most important criterion to be of long-term value for both end users as care providers.
Many of the consumer oriented activity monitors seem not to take accuracy too much into account, which was confirmed by our experiments we conducted ourselves. We compared the kcal results of a number of activity monitors with results as tracked in a detailed movement dairy. Firstly often there is no validation information available and secondly the deviations between activity monitors and compared to the dairy are quite substantial. Over and underestimations up to 500kcal are no exception.
The assessment of physical activity in healthy populations and in those with chronic diseases is challenging. The aim of this systematic review was to identify whether available activity monitors (AM) have been appropriately validated for use in assessing physical activity in these groups. Following a systematic literature search we found 134 papers meeting the inclusion criteria; 40 conducted in a field setting (validation against doubly labelled water), 86 in a laboratory setting (validation against a metabolic cart, metabolic chamber) and 8 in a field and laboratory setting. Correlation coefficients between AM outcomes and energy expenditure (EE) by the criterion method (doubly labelled water and metabolic cart/chamber) and percentage mean differences between EE estimation from the monitor and EE measurement by the criterion method were extracted. Random-effects meta-analyses were performed to pool the results across studies where possible. Types of devices were compared using metaregression analyses. Most validation studies had been performed in healthy adults (n = 118), with few carried out in patients with chronic diseases (n = 16). For total EE, correlation coefficients were statistically significantly lower in uniaxial compared to multisensor devices. For active EE, correlations were slightly but not significantly lower in uniaxial compared to triaxial and multisensor devices. Uniaxial devices tended to underestimate TEE (-12.07 (95%CI;-18.28 to -5.85) %) compared to triaxial (-6.85 (95%CI; -18.20 to 4.49) %, p = 0.37) and were statistically significantly less accurate than multisensor devices (-3.64 (95%CI; -8.97 to 1.70) %, p<0.001). TEE was underestimated during slow walking speeds in 69% of the lab validation studies compared to 37%, 30% and 37% of the studies during intermediate, fast walking speed and running, respectively. The high level of heterogeneity in the validation studies is only partly explained by the type of activity monitor and the activity monitor outcome. Triaxial and multisensor devices tend to be more valid monitors. Since activity monitors are less accurate at slow walking speeds and information about validated activity monitors in chronic disease populations is lacking, proper validation studies in these populations are needed prior to their inclusion in clinical trials.
A recent publication of Van Remoortel from 2012 about the medical validity of medical activity monitors showed that also in this domain there can still be improved a lot:
Activ8 was at the time of the study not yet available, which explains why it is not there. We are of the opinion that you have to be able to rely on the measurement results of such activity monitors as otherwise the perceived value by end users or patients and care providers is limited as results will not be trusted. Therefore we believe that in the near future more and more activity monitors will be focused on providing accurate results.
Activ8 is doing a number of things fundamentally different. First the type of activity is recognized. Was someone lying, sitting, standing, walking, cycling or running (high intense movements). This recognition is currently validated by Erasmus Medical Center in Rotterdam. Every 5 minutes is stored how much time someone was doing each of the above activities. Next there is determined what the lower and upper limits are of these activities using MET (Metabolic Equivalent Tables). The movement intensity is then used to scale if somebody was walking slowly or fast. This translation to energy is currently been validated by Fontys Sport (Eindhoven) using a movement protocol and an oxygen sensor as the double labeled water method is too slow to follow each activity individually. The buildup of energy based on various activities has shown in a first small scale comparison study to deliver a way more accurate energy expenditure estimation comparable to using a movement logbook.
As soon as the two studies have been completed we will publish the results here. Further validation steps towards other user groups like children or people with a chronic disease will be done after that. As far as we know Activ8 is the first product with medical underpinning that can also be used for end user especially as it is attractively priced. In case you have further questions please feel free to contact us.