Darcy Ummels

The validation of a pocket worn activity tracker | 95 5 Introduction In the past decade, activity trackers have been used more frequently by a relatively young and physically active population. 1 In addition to this population, activity trackers can also be beneficial for older adults (65 + ). In 2018, only 37% of the older adults in the Netherlands were sufficiently physically active according to Dutch guidelines. 2 Activity trackers can contribute to overcome this by giving insight into the amount of physical activity, increasing awareness and motivating older adults to be more physically active. 3 ‐ 8 Several studies have shown that older adults are most interested in step count and amount of physical behavior as outcome variables for physical activity. 3,8 ‐ 10 Recent studies have shown that step count and physical behavior are not validly measured by consumer ‐ grade activity trackers during low walking speeds, which often occur during activities of daily living (ADL) such as household activities. 11 ‐ 21 This lower validity can partly be explained by the fact that the majority of consumer ‐ grade activity trackers don’t have older adults as a target group and don’t adjust their algorithms accordingly. Recently, an adjustable classification algorithm was published to optimize algorithm performance. 22 Through easily adjustable algorithm parameters it is possible to optimize the performance of this algorithm for different target and tracker wear locations. A recent qualitative study showed that older adults would prefer to wear an activity tracker in their trouser pocket. 8 Consequently, the adjustable algorithm was optimized to estimate step count and dynamic, standing, and sedentary time for older adults and a pocket worn activity tracker according to the proposed method by. 22 The first purpose of this study was to validate these optimized algorithm parameter settings for step count and physical behavior expressed as dynamic, standing, and sedentary time in older adults with a normal pattern wearing an activity tracker in their trouser pocket during simulated ADL. Secondly, to have a more relevant interpretation of the validation results, the performance of the optimized algorithm parameter settings for older adults was compared to the algorithm where the adjustable classification algorithm originates from and two frequently used activity trackers. Methods Study design A cross ‐ sectional validation study was performed in which the optimized algorithm parameter settings were validated and compared to the algorithm where the adjustable classification algorithm originates from and two activity trackers.