To validate the Computer Science and Application's (CSA) activity monitor for assessment of the total amount of physical activity during two school-weeks in 9-yr-old children and to develop equations to predict total energy expenditure (TEE) and activity energy expenditure (AEE) from activity counts and anthropometric variables. A total of 26 children (15 boys and 11 girls, mean age 9.1 +/- 0.3 yr) were monitored for 14 consecutive days. TEE was simultaneously measured by the doubly labeled water method. Averaged activity counts (counts.min(-1)) were compared with data on: 1) TEE, 2) AEE = TEE minus basal metabolic rate (BMR; estimated from predictive equations), and 3) daily physical activity level (PAL = TEE/BMR). Physical activity determined by activity counts was significantly related to the data on energy expenditures: TEE (r = 0.39; P < 0.05), AEE (r = 0.54; P < 0.01), and PAL (r = 0.58; P < 0.01). Multiple stepwise regression analysis showed that TEE was significantly influenced by gender, body composition (body weight or fat free mass), and activity counts (R(2) = 0.54--0.60). AEE was significantly influenced by activity counts and gender (R(2) = 0.45). There were no significant differences between activity counts and PAL in discriminating among activity levels with "low" (PAL < 1.56), "moderate" (1.57 < or = PAL > or = 1.81), and "high" (PAL > 1.81) intensity. Activity counts from the CSA activity monitor seems to be a useful measure of the total amount of physical activity in 9-yr-old children. Activity counts contributed significantly to the explained variation in TEE and was the best predictor of AEE.

The aim was to investigate (a) the effect of five different time sampling intervals (epoch settings) on different intensity levels when assessing physical activity with an accelerometer (CSA, WAM 7164), and (b) whether the placement of the monitor (on the hip and back) would affect the outcome. Sixteen children (aged 7 yrs) were monitored for four consecutive days. A significant main epoch effect was found for time spent at very high (p < .01) and high (p < .01) intensity activities. No significant difference between the two placements regarding total amount of physical activity (cnts • min⁻¹) or different intensity levels was observed. In conclusion, different time sampling intervals, but not placement, should be carefully considered when assessing physical activity.

To examine the effect of monitor placement (hip vs back) and of activity setting (treadmill vs track) on the output from the Manufacturing Technology Inc. (MTI), activity monitor (model WAM 7164). In a laboratory study, 28 subjects (14 men, 14 women) walked at a normal pace, walked at a fast pace, and jogged at a comfortable pace on an indoor track. These activities were repeated on a treadmill using the individual speeds from the track locomotion. Oxygen uptake was measured simultaneously using a portable metabolic system. One activity monitor was worn on the hip and one on the lower back. In a field study, 34 subjects (18 men, 16 women) each wore two monitors (hip and low back placement) for seven consecutive days. In the laboratory study, ANOVA showed significant effects of placement ( P = 0.009) and setting ( P < 0.001), indicating that activity counts differ between different body sites and different settings (track vs treadmill). Gross energy expenditure predictive equations were developed and thereafter evaluated in the field study. Time spent at moderate and vigorous intensity of physical activity was 38% and 85% ( P < 0.001) higher when calculated from the treadmill-based equations as compared to the track-based equations. Free-living physical activity estimates were not affected by the placement. The relationship between activity counts and energy expenditure during laboratory locomotion is placement and setting-specific. When habitual physical activity is assessed in free-living subjects, the treadmill derived relationship between energy expenditure and activity counts may overestimate time spent at moderate intensity of physical activity, whereas the placement of the monitor does not influence on the interpretation of the data.

The delivery of efficient nonpharmacological treatment to prevent the loss of muscle mass in older adults is a major challenge, and information on the combined effects of training and diet is particularly important. Here we aimed to evaluate the effects of 24 wk of resistance training combined with a healthy dietary approach (n-6/n-3 ratio < 2) in a population of healthy and physically active older women (65-70 years). The three-armed randomized controlled trial included a resistance training + healthy diet group (RT-HD), a resistance training group (RT), and controls (CON). All subjects included in the study were physically active and had low levels of serum inflammatory markers. In accordance with the dietary goals, the n-6/n-3 ratio dietary intake significantly decreased only in RT-HD by 42%. An increase in 1 repetition maximum in leg extension occurred in RT (+20.4%) and RT-HD (+20.8%), but not in CON. Interestingly, leg lean mass significantly increased only in RT-HD (+1.8%). While there were no changes in serum C-reactive protein and IL-6 levels, a significant decrease in serum level of the pro-inflammatory precursor arachidonic acid (−5.3 ± 9.4%) together with an increase in serum n-3 docosahexaenoic acid (+8.3%) occurred only in RT-HD. Altogether, this study demonstrates that the effects of resistance training on muscle mass in healthy older adults can be optimized by the adoption of a healthy diet.

Identifying leisure time activities performed before and after school that influence time in physical activity (PA) and/or time spent sedentary can provide useful information when designing interventions aimed to promote an active lifestyle in young people. The purpose of this study was to examine associations between mode of transportation to school, outdoor play after school, participation in exercise in clubs, and TV viewing with objectively assessed PA and sedentary behaviour in children.