Purpose: This study used different analytic approaches to compare physical activity (PA) metrics from accelerometers (ACC) and a self-report questionnaire in upper elementary youth participating in the Fuel for Fun intervention. Methods: The PA questionnaire and ACC were assessed at baseline/preintervention (fall fourth grade), Follow-up 1/postintervention (spring fourth grade), and Follow-up 2 (fall fifth grade) of 564 fourth grade students from three elementary schools (50% females, 78% White, and 28% overweight or obese). Different analytic approaches identified similarities and differences between the two methods. Results: On average, self-report was higher than ACC for vigorous PA (range = 9–15 min/day), but lower than ACC for moderate PA (range = 24–30 min/day), light PA (range = 30–36 min/day), and moderate-to-vigorous physical activity (MVPA; range = 9–21 min/day). Spearman’s correlations for vigorous PA (.30, .26, and .32); moderate PA (.12, .13, and .14); and MVPA (.25, .25, and .24) were significant at each time point (all ps ≤ .01), whereas correlations for light PA were not significant (.06, .04, and .07; all ps > .05). In repeated-measures analyses, ACC and questionnaire measures were significantly different from each other across the three time points; however, change difference of the two measures over time was only 5.5 MVPA min/day. Conclusions: The PA questionnaire and ACC validated each other and can be used to assess MVPA in upper elementary school children in a similar population to the current study. However, each assessment method captures unique information, especially for light-intensity PA. Multiple PA measurement methods are recommended to be used in research and application to provide a more comprehensive understanding of children’s activity.
Claudio R. Nigg, Xanna Burg, Barbara Lohse, and Leslie Cunningham-Sabo
Faith D. Lees, Phillip G. Clark, Claudio R. Nigg, and Phillip Newman
Longer life expectancy, rapid population growth, and low exercise-participation rates of adults 65 and older justify the need for better understanding of older adults’ exercise behavior. The objectives of this focus-group study were to determine barriers to the exercise behavior of older adults. Six focus groups, three with exercisers and three with nonexercisers, were conducted at various sites throughout Rhode Island. The majority (n = 57) of the 66 individuals who participated were women, and all stated that they were 65 and older. Results from the focus-group data identified 13 barriers to exercise behavior. The most significant barriers mentioned by nonexercisers were fear of falling, inertia, and negative affect. Exercisers identified inertia, time constraints, and physical ailments as being the most significant barriers to exercise. Implications from these focus-group data can be useful in the development of exercise interventions for older adults, which could increase exercise participation.
Marco Giurgiu, Carina Nigg, Janis Fiedler, Irina Timm, Ellen Rulf, Johannes B.J. Bussmann, Claudio R. Nigg, Alexander Woll, and Ulrich W. Ebner-Priemer
Purpose: To raise attention to the quality of published validation protocols while comparing (in)consistencies and providing an overview on wearables, and whether they show promise or not. Methods: Searches from five electronic databases were included concerning the following eligibility criteria: (a) laboratory conditions with humans (<18 years), (b) device outcome must belong to one dimension of the 24-hr physical behavior construct (i.e., intensity, posture/activity type outcomes, biological state), (c) must include a criterion measure, and (d) published in a peer-reviewed English language journal between 1980 and 2021. Results: Out of 13,285 unique search results, 123 articles were included. In 86 studies, children <13 years were recruited, whereas in 26 studies adolescents (13–18 years) were recruited. Most studies (73.2%) validated an intensity outcome such as energy expenditure; only 20.3% and 13.8% of studies validated biological state or posture/activity type outcomes, respectively. We identified 14 wearables that had been used to validate outcomes from two or three different dimensions. Most (n = 72) of the identified 88 wearables were only validated once. Risk of bias assessment resulted in 7.3% of studies being classified as “low risk,” 28.5% as “some concerns,” and 71.5% as “high risk.” Conclusion: Overall, laboratory validation studies of wearables are characterized by low methodological quality, large variability in design, and a focus on intensity. No identified wearable provides valid results across all three dimensions of the 24-hr physical behavior construct. Future research should more strongly aim at biological state and posture/activity type outcomes, and strive for standardized protocols embedded in a validation framework.