BackgroundSelf-quantification of health parameters is becoming more popular; thus, the validity of the devices requires assessments. The aim of this study was to evaluate the validity of Fitbit One step counts (Fitbit Inc., San Francisco, CA, USA) against Actigraph wActisleep-BT step counts (ActiGraph, LLC, Pensacola, FL, USA) for measuring habitual physical activity among children.DesignThe study was implemented as a cross-sectional experimental design in which participants carried two waist-worn activity monitors for five consecutive days.MethodsThe participants were chosen with a purposive sampling from three fourth grade classes (9–10 year olds) in two comprehensive schools. Altogether, there were 34 participants in the study. From these, eight participants were excluded from the analysis due to erroneous data. Primary outcome measures for step counts were Fitbit One and Actigraph wActisleep-BT. The supporting outcome measures were based on activity diaries and initial information sheets. Classical Bland–Altman plots were used for reporting the results.ResultsThe average per-participant daily difference between the step counts from the two devices was 1937. The range was [116, 5052]. Fitbit One gave higher step counts for all but the least active participant. According to a Bland–Altman plot, the hourly step counts had a relative large mean bias across participants (161 step counts). The differences were partially explained by activity intensity: higher intensity denoted higher differences, and light intensity denoted lower differences.ConclusionsFitbit One step counts are comparable to Actigraph step counts in a sample of 9–10-year-old children engaged in habitual physical activity in sedentary and light physical activity intensities. However, in moderate-to-vigorous physical activity, Fitbit One gives higher step counts when compared to Actigraph.

In current theories of adult learning, there is lack of integrative, holistic framework that would integrate different elements of learning. Moreover, well-described methodological approaches for integrating different data types during naturalistic conditions are missing. We developed an interdisciplinary research design for multilevel approach on adult learning. We collected data from student-instructor dyads in aviation (n = 6 dyads) and forestry (n = 6 dyads) simulations using both quantitative (HRV, EEG; structured questionnaires) and qualitative (video recordings and stimulated recall interviews) data. We created a two-stage analysis protocol including modality specific pre-processing and quantification, and integrated multimodal analysis. First, the feasibility of achieving reliable physiological and neurophysiological recordings during learning experiences in naturalistic learning situations was established. Furthermore, a detailed description of subjective learning experiences was built on the basis of video-and interview data, including information on the challenges, emotions and studentinstructor interaction during the learning situation. Two types of approaches were used for each modality: 1) an analysis based on structured pedagogical states and 2) an analysis based on continuous time-course of events during the learning situation. This developed methodological approach enables us to more comprehensively understand the factors that influence individual experiences and success of learning interaction. To develop pedagogical processes, it is necessary to build bridges also between the concepts and theories as well as between different disciplines like education and physiology. Our approach to adult learning enables new research lines that can integrate individual experiences, emotions, physiological and neurophysiological reactions during learning.