Search

The purpose of this study was to determine the effect of stand-biased desks on the physical activity and sedentary behavior of third, fourth and sixth grade students across the school year. Methods: This within classroom crossover design study used teacher-determined allocation for seating within each classroom. Half of the students used a stand-biased desk and half used a sitting desk. Five-day hip-worn accelerometer assessments were completed at baseline and at the end of each nine-week intervention period. A mixed effects model was used to determine the differences in the percentage of time spent active and sedentary. Results: A total of 22, 36 and 41 students in 3rd, 4th and 6th grades, respectively, completed this study (57.1% male, 79.3% White). Regardless of the desk type, students became more sedentary (p < 0.001) and less active (p < 0.001) in the classroom as the school year progressed. After controlling for baseline activity, there was a significant interaction between the type of desk and time (p = 0.029). Students who spent a higher percentage of their classroom time sedentary engaged in less sedentary behavior when using a stand-biased desk compared to the traditional desk. Conclusion: The standing desk intervention was effective in mitigating the increase in sedentary behavior for those who started the school year more sedentary.

With a growing aging population, the routine assessment of physical function may become a critical component of clinical practice. The purpose of this cross-sectional study is to compare two common assessments of muscular function: (1) isometric knee extension strength (KES) and (2) sit-to-stand (STS) muscle power tests, in predicting objective physical function (i.e., gait speed) in aging adults. 84 adults (56% female, mean (SD) age = 66.6 (9.4) years) had their relative KES, STS power, usual gait speed (UGS), and fast gait speed (FGS) assessed. Multiple linear regression examined the associations between KES, STS power, and gait outcomes. When entered in separate models, KES and STS power were both independently associated with UGS and FGS (Std. β = 0.35–0.44 and 0.42–0.55 for KES and STS power, respectively). When entered in the same model, STS power was associated with UGS and FGS (Std. β = 0.37 [95%CI: 0.15, 0.58] and 0.51 [95%CI: 0.31, 0.70], respectively), while KES was only associated with FGS (Std. β = 0.25 [95%CI: 0.02, 0.48]). STS power seems to be a valid indicator of function in aging adults. Its feasibility as a screening tool for "low" function in the primary care setting should be explored. [ABSTRACT FROM AUTHOR]

Direct observation is a ground-truth measure for physical behavior, but the high cost limits widespread use. The purpose of this study was to develop and test machine learning methods to recognize aspects of physical behavior and location from videos of human movement: Adults (N = 26, aged 18–59 y) were recorded in their natural environment for two, 2- to 3-h sessions. Trained research assistants annotated videos using commercially available software including the following taxonomies: (1) sedentary versus non-sedentary (two classes); (2) activity type (four classes: sedentary, walking, running, and mixed movement); and (3) activity intensity (four classes: sedentary, light, moderate, and vigorous). Four machine learning approaches were trained and evaluated for each taxonomy. Models were trained on 80% of the videos, validated on 10%, and final accuracy is reported on the remaining 10% of the videos not used in training. Overall accuracy was as follows: 87.4% for Taxonomy 1, 63.1% for Taxonomy 2, and 68.6% for Taxonomy 3. This study shows it is possible to use computer vision to annotate aspects of physical behavior, speeding up the time and reducing labor required for direct observation. Future research should test these machine learning models on larger, independent datasets and take advantage of analysis of video fragments, rather than individual still images. [ABSTRACT FROM AUTHOR]

The purpose of this study was to explore the feasibility and acceptability of a seated pedaling device to reduce sedentary behavior (SB) in the homes of older adults. Methods: Each participant (N = 20) was outfitted with an activity monitor and seated pedaling device in the home for 7 days and randomly assigned to one of four light-intensity pedaling groups (15, 30, 45, and 60 min/day). Results: There was 100% adherence in all groups and significant group differences in the minutes pedaled per day (p <.001), with no significant difference in the total pedaling days completed (p =.241). The 15-, 30-, 45-, and 60-min groups experienced a 4.0%, 5.4%, 10.6%, and 11.3% reduction in SB on the days pedaled, respectively. Conclusion: Clinically relevant reductions in SB time were achievable in this 1-week trial. Long-term adherence and the impact of replacing SB with seated light activities on geriatric-relevant health outcomes should be investigated. [ABSTRACT FROM AUTHOR]

This study was designed to determine the number of days of pedometer monitoring necessary to achieve reliable and valid estimates of a 1-year average of step counts in adults based on either consecutive days (CD) or random days (RD) of data collection. Twenty-three participants (16 women; M age = 38 years, SD = 9.9) wore a Yamax SW 200 pedometer and recorded their step counts for 365 consecutive days. Nine measurement periods of various lengths were selected (2, 3, 4, 5, 6, 7, 14, 21, and 30 days). Each measurement period was randomly selected 10 times each for CD and RD from the larger database. For reliability and validity, two-way intraclass correlation coefficients and mean absolute percentage error (MAPE) were calculated, respectively. The year-round average was considered the criterion measure of the "true" habitual physical activity. Data were analyzed separately by CD and RD. At least 5 CD or 6 RD were necessary to achieve an ICC of 0.80. A minimum of 30 CD or 14 RD were necessary to achieve an MAPE lower than 10%. These findings provide researchers and practitioners with useful information to determine appropriate measurement length and the method of data selection based on a desired level of reliability and validity. (Contains 3 tables and 4 figures.)