Darcy Ummels

98 | Chapter 5 the video was re ‐ observed and the time that the participants performed dynamic (walking and walking during household activities), standing or sedentary (sitting, lying) time was noted. Physical behavior was assessed by two independent observers (Table 5.1) using the EasyTag app. 34 The data from the activity trackers were collected directly after the activity protocol. Analysis of the raw acceleration data of the MOX took place on a PC after the measurements (off ‐ line) using Matlab (R2018b, The MathWorks Inc., Natick, MA, US) with two algorithms. The first one is the activity classification algorithm presented and validated by 35 for healthy adults (MOX Annegarn ), where the adjustable classification algorithm originates from. The second one is the classification algorithm with application specific adjustable parameters itself. 22 For application in an older adult target group wearing an activity tracker in their trouser pocket the optimized parameter settings are: a data segmentation window size of 2 s, an amount of physical activity threshold of five counts per second (cps) and an orientation threshold of 0.8 g. This application is referred to as Miss Activity, the parameter settings as MOX MissActivity . For MOX Annegarn dynamic, standing, and sedentary time spent in seconds were retrieved. In addition to these three variables, for MOX MissActivity step count was also retrieved. For the activPAL, step count and dynamic, standing, and sedentary time spent in seconds were retrieved from the PAL Software Suite (v7.2.32; PAL Technologies Ltd., Glasgow, Scotland, UK). For the Fitbit, step count and active minutes (by definition: ten continuous minutes long bouts of moderate ‐ to intense activity > 3 metabolic equivalent of task [MET]) 36 , were retrieved from the corresponding Fitbit app (Fitbit Inc., San Fransisco, CA, US). From this point, we refer to the active minutes of the Fitbit as dynamic time. Data analysis Data analysis was performed using SPSS Statistics (version 23.0; IBM Corp, Armonk, NY, US) and Prism (GraphPad Prism 8.2.1(441); GraphPad Software, San Diego, CA, USA). Descriptive statistics of the participant characteristics were presented as a number (percentage) for the categorical variable gender and as a mean (95% confidence interval [CI]) for the continuous variables age, body length, body weight, and average walk speed. Inter ‐ observer reliability of the video observations The differences in step count, dynamic, standing, and sedentary time was calculated between two observers. If there was more than a 5% difference between the two observers, a third observer assessed the video. The inter ‐ observer reliability of the two observers with the smallest difference was assessed by an Intraclass Correlation