This study was conducted to determine the exercise habits and fitness status of healthy older black and white adults, ages 50 to 80 years. The 384 subjects were enrolled in a health promotion project conducted by a midwestern medical school. Self-reported exercise levels were higher for men than for women and were higher for whites compared with blacks. Age had the greatest impact on treadmill performance for both sexes. Activity levels declined with age for men but not for women. Self-reported exercise levels were highly predictive of fitness status for men but not for women. The relationship in older adults between activity levels and both measured fitness and health status needs further investigation.
Steven P. Singleton, James T. Fitzgerald and Anne Victoria Neale
Alex Griffiths, Calum Mattocks, Andy Robert Ness, Kate Tilling, Chris Riddoch and Sam Leary
A study deriving a threshold for moderate- to vigorous-intensity physical activity (MVPA) in terms of accelerometer counts in 12-year-old children was repeated with a subset of the same children at 16 years.
Fifteen girls and thirty boys took part in 6 activities (lying, sitting, slow walking, walking, hopscotch and jogging) while wearing an Actigraph 7164 accelerometer and a Cosmed K4b2 portable metabolic unit. Random intercepts modeling was used to estimate cut points for MVPA (defined as 4 METs).
Using a single model, the sex-specific thresholds derived for MVPA at 16 years were some way below the 3600 counts/minute used for both sexes at age 12, particularly for girls. However graphical examination suggested that a single model might be inadequate to describe both higher- and lower-intensity activities. Models using only lower-intensity activities close to the 4 METs threshold supported retention of the 3600 counts/minute cut point for both sexes.
When restricting to lower-intensity activities only, these data do not provide sufficient evidence to change the previously established cut point of 3600 counts/minute to represent MVPA. However, further data and more sophisticated modeling techniques are required to confirm this decision.
Jeanne F. Nichols, Hilary Aralis, Sonia Garcia Merino, Michelle T. Barrack, Lindsay Stalker-Fader and Mitchell J. Rauh
There is a growing need to accurately assess exercise energy expenditure (EEE) in athletic populations that may be at risk for health disorders because of an imbalance between energy intake and energy expenditure. The Actiheart combines heart rate and uniaxial accelerometry to estimate energy expenditure above rest. The authors’ purpose was to determine the utility of the Actiheart for predicting EEE in female adolescent runners (N = 39, age 15.7 ± 1.1 yr). EEE was measured by indirect calorimetry and predicted by the Actiheart during three 8-min stages of treadmill running at individualized velocities corresponding to each runner’s training, including recovery, tempo, and 5-km-race pace. Repeated-measures ANOVA with Bonferroni post hoc comparisons across the 3 running stages indicated that the Actiheart was sensitive to changes in intensity (p < .01), but accelerometer output tended to plateau at race pace. Pairwise comparisons of the mean difference between Actiheart- and criterion-measured EEE yielded values of 0.0436, 0.0539, and 0.0753 kcal · kg−1 · min−1 during recovery, tempo, and race pace, respectively (p < .0001). Bland–Altman plots indicated that the Actiheart consistently underestimated EEE except in 1 runner’s recovery bout. A linear mixed-model regression analysis with height as a covariate provided an improved EEE prediction model, with the overall standard error of the estimate for the 3 speeds reduced to 0.0101 kcal · kg−1 · min−1. Using the manufacturer’s equation that combines heart rate and uniaxial motion, the Actiheart may have limited use in accurately assessing EEE, and therefore energy availability, in young, female competitive runners.
Kurusart Konharn, Wichai Eungpinichpong, Kluaymai Promdee, Paramaporn Sangpara, Settapong Nongharnpitak, Waradanai Malila and Jirachai Karawa
The suitability of smartphone applications (apps) currently used to track walking/running may differ depending on a person’s weight condition. This study aimed to examine the validity and reliability of apps for both normal-weight and overweight/obese young adults.
Thirty normal-weight (aged 21.7 ± 1.0 years, BMI 21.3 ± 1.9 kg/m2) and 30 overweight/ obese young adults (aged 21.0 ± 1.4 years, BMI 28.6 ± 3.7 kg/m2) wore a smartphone and pedometer on their right hip while walking/running at 3 different intensities on treadmills. Apps was randomly assigned to each individual for measuring average velocity, step count, distance, and energy expenditure (EE), and these measurements were then analyzed.
The apps were not accurate in counting most of the measured variables and data fell significantly lower in the parameters than those measured with standard-reference instruments in both light and moderate intensity activity among the normal-weight group. Among the overweight and obese group, the apps were not accurate in detecting velocity, distance, or EE during either light or vigorous intensities. The percentages of mean difference were 30.1% to 48.9%.
Apps may not have sufficient accuracy to monitor important physical parameters of human body movement. Apps need to be developed that can, in particular, respond differently based on a person’s weight status.
Sarah M. Camhi, Susan B. Sisson, William D. Johnson, Peter T. Katzmarzyk and Catrine Tudor-Locke
Objective physical activity data analyses focus on moderate-to-vigorous physical activity (MVPA) without considering lower intensity lifestyle-type activities (LA). We describe 1) quantity of LA (minutes and steps per day) across demographic groups, 2) proportion of LA to total physical activity, and 3) relationships between LA and MVPA using NHANES 2005−2006 accelerometer adult data (n = 3744).
LA was defined as 760 to 2019 counts per minute (cpm) and MVPA as ≥2020 cpm. LA was compared within gender, ethnicity, age, and BMI groups. Regression analyses examined independent effects. Correlations were evaluated between LA and MVPA. All analyses incorporated sampling weights to represent national estimates.
Adults spent 110.4 ± 1.6 minutes and took 3476 ± 54 steps per day in LA. Similar to MVPA, LA was highest in men, Mexican Americans, and lowest in adults ≥60 years or obese. When LA was held constant, ethnic differences no longer predicted MVPA minutes, and age no longer predicted MVPA steps. LA and MVPA minutes (r = .84) and steps per day (r = .72) were significantly correlated, but attenuated with MVPA modified bouts (≥10 minutes sustained activity).
LA accumulation differs between demographic subgroups and is related to MVPA: adults who spend more minutes and steps in MVPA also spend them in LA.
John R. Sirard, Peter Hannan, Gretchen J. Cutler and Dianne Nuemark-Sztainer
The purpose of this paper is to evaluate self-reported physical activity of young adults using 1-week and 1-year recall measures with an accelerometer as the criterion measure.
Participants were a subsample (N = 121, 24 ± 1.7 yrs) from a large longitudinal cohort study. Participants completed a detailed 1-year physical activity recall, wore an accelerometer for 1 week and then completed a brief 1-week physical activity recall when they returned the accelerometer.
Mean values for moderate-to-vigorous physical activity (MVPA) from the 3 instruments were 3.2, 2.2, and 13.7 hours/wk for the accelerometer, 1-week recall, and 1-year recall, respectively (all different from each other, P < .001). Spearman correlations for moderate, vigorous, and MVPA between the accelerometer and the 1-week recall (0.30, 0.50, and 0.40, respectively) and the 1-year recall (0.31, 0.42, and 0.44, respectively) demonstrated adequate validity.
Both recall instruments may be used for ranking physical activity at the group level. At the individual level, the 1-week recall performed much better in terms of absolute value of physical activity. The 1-year recall overestimated total physical activity but additional research is needed to fully test its validity.
Stamatis Agiovlasitis, Robert W. Motl, John T. Foley and Bo Fernhall
This study examined the relationship between energy expenditure and wrist accelerometer output during walking in persons with and without Down syndrome (DS). Energy expenditure in metabolic equivalent units (METs) and activity-count rate were respectively measured with portable spirometry and a uniaxial wrist accelerometer in 17 persons with DS (age: 24.7 ± 6.9 years; 9 women) and 21 persons without DS (age: 26.3 ± 5.2 years; 12 women) during six over-ground walking trials. Combined groups regression showed that the relationship between METs and activity-count rate differed between groups (p < .001). Separate models for each group included activity-count rate and squared activity-count rate as significant predictors of METs (p ≤ .005). Prediction of METs appeared accurate based on Bland-Altman plots and the lack of between-group difference in mean absolute prediction error (DS: 17.07%; Non-DS: 18.74%). Although persons with DS show altered METs to activity-count rate relationship during walking, prediction of their energy expenditure from wrist accelerometry appears feasible.
Sharon A. Plowman, Charles L. Sterling, Charles B. Corbin, Marilu D. Meredith, Gregory J. Welk and James R. Morrow Jr.
Initially designed by Charles L. Sterling as a physical fitness “report card” FITNESSGRAM ® / ACTIVITYGRAM ® is now an educational assessment and reporting software program. Based on physiological/epidemiological, behavioral, and pedagogical research, FITNESSGRAM is committed to health-related physical fitness, criterion-referenced standards, an emphasis on physical activity including behavioral based recognitions, and the latest in technology. The evolution of these major concepts is described in this history of FITNESSGRAM.
Leon Straker, Amity Campbell, Svend Erik Mathiassen, Rebecca Anne Abbott, Sharon Parry and Paul Davey
Capturing the complex time pattern of physical activity (PA) and sedentary behavior (SB) using accelerometry remains a challenge. Research from occupational health suggests exposure variation analysis (EVA) could provide a meaningful tool. This paper (1) explains the application of EVA to accelerometer data, (2) demonstrates how EVA thresholds and derivatives could be chosen and used to examine adherence to PA and SB guidelines, and (3) explores the validity of EVA outputs.
EVA outputs are compared with accelerometer data from 4 individuals (Study 1a and1b) and 3 occupational groups (Study 2): seated workstation office workers (n = 8), standing workstation office workers (n = 8), and teachers (n = 8).
Line graphs and related EVA graphs highlight the use of EVA derivatives for examining compliance with guidelines. EVA derivatives of occupational groups confirm no difference in bouts of activity but clear differences as expected in extended bouts of SB and brief bursts of activity, thus providing evidence of construct validity.
EVA offers a unique and comprehensive generic method that is able, for the first time, to capture the time pattern (both frequency and intensity) of PA and SB, which can be tailored for both occupational and public health research.
Seung Ho Chang, Kyungun Kim, Jihyun Lee and Sukho Lee
Background: Children and youths from low-income families and certain ethnic minority groups show high levels of risk and vulnerability to physical inactivity. The aim of this review was to examine the effectiveness of interventions to increase physical activity (PA) in children and youths from low-income and ethnic minority (LIEM) families. Methods: Eight databases were systematically searched for PA interventions for LIEM children and youths. Twenty-six studies were included in the analyses. Effect sizes (ESs) were calculated using a random-effects model. The ESs were computed using Hedges g with 95% confidence interval. Results: There were small to medium effects of interventions on PA in LIEM children and youth (Q = 1499.193, df = 30, P < .05; I 2 = 97.999). Analyses on the moderator variables showed that ES for participants aged 9–12 years (ES = 0.542, P = .01); intervention length less than 13 weeks (ES = 0.561, P = .01); specialists as the intervention agent (ES = 0.680, P < .05); interventions without technology (ES = 0.363, P = .02); and interventions with a behavioral modification component (ES = 0.336, P = .03) were significantly different from zero. Conclusion: PA intervention can be an effective strategy to increase PA for LIEM children and youths.