Darcy Ummels

106 | Chapter 5 the varying MOX wear location relative to the body. To secure the validity of the algorithm the MOX should always be correctly placed in the trouser pocket below the waist, this should be addressed in a manual. By re ‐ observing the video recordings, it was noted that the MOX was placed above the participants’ hip in two cases. Since the algorithm assumes a wear location on the upper leg, the MOX was not able to correctly measure within these two participants due to this misplacement. Therefore, it was chosen to handle these two cases as outliers, since the misplacement, and not the algorithm, compromised the validity. Fourth, this study was performed in a lab setting and is therefore not directly generalizable to daily life. However, with the ADL protocol, daily life was simulated as close as possible to daily life. This is in line with the proposed standardization methods of Welk et al. 42 A strength of this study is the use of the participant ‐ determined sequence activity protocol to validate the algorithm. This activity protocol simulates free ‐ living since participants were free to choose the order and duration of the activities they performed. To simulate the free ‐ living situation as best as possible activities that are frequently performed by older adults are included in the activity protocol. Furthermore, this study follows the recommendations made by Welk et al. 42 for validation studies in wearables: use a diverse sample, appropriate sampling of daily behavior, an appropriate criterion measure, standardised protocols and wear locations, and inclusion of reference applications. To standardise the analyzes they recommend to use relevant metrics, documenting the error and the direction of the error and to focus on equivalence. 42 Another strength of this study is the high inter ‐ observer reliability resulting in a robust gold standard (range r =0 .96–1.0). Clinical implications From previous research it is known that consumer ‐ grade activity trackers can’t measure step count and physical behavior validly during low walking speeds, which often occurs in older adults and during ADL. 11 ‐ 21 Apparently, daily life of older adults differs that much from the target group of these consumer ‐ grade activity trackers that their algorithms are not sufficient for older adults. Therefore, it is important to have an algorithm optimized for the target group, wear location and their specific activities. If a consumer ‐ grade activity tracker is used for this target group, the algorithm should ideally be personalised to the specific target group or at least bias corrections to the outcomes of the algorithm should be applied. The validity of the optimized algorithm is limited to older adults with a normal gait pattern. This study shows that an optimized algorithm is indeed more valid than general purpose activity trackers. As is shown by the smallest detectable change the optimized algorithm could also detect change in patient’s physical activity level