Daniel P. Heil

Predicting Activity Energy Expenditure Using the Actical® Activity Monitor

This study developed algorithms for predicting activity energy expenditure (AEE) in children (n = 24) and adults (n = 24) from the Actical® activity monitor. Each participant performed 10 activities (supine resting, three sitting, three house cleaning, and three locomotion) while wearing monitors on the ankle, hip, and wrist; AEE was computed from oxygen consumption. Regression analysis, used to create AEE prediction equations based on Actical® output, varied considerably for both children (R 2 = .45-.75; p < .001) and adults (R 2 = .14-.85; p < .008). Most of the resulting algorithms accurately predicted accumulated AEE and time within light, moderate, and vigorous intensity categories (p > .05). The Actical® monitor may be useful for predicting AEE and time variables at the ankle, hip, or wrist locations.

Nonexercise regression models to estimate peak oxygen consumption.

The purpose of this study was to develop a VO2peak prediction model derived from nonexercise (N-EX) based predictors. VO2peak was measured using a walking treadmill protocol with 229 females and 210 males between 20 and 79 yr of age (mean +/- SD: 38.62 +/- 10.36 ml.kg-1.min-1). Subjects were randomly divided into validation (V) (85% of total; N = 374) and cross-validation (CV) (15% of total; N = 65) groups. The V group was used to validate generalized and gender-specific models using stepwise multiple regression procedures with gender, age and age2, percent body fat, and a physical activity code (AC). The generalized ml.kg-1.min-1 (R2 = 0.77, SEE = 4.90 ml.kg-1.min-1, SEE% = 12.7%) and gender-specific (females: R2 = 0.72, SEE = 4.64 ml.kg-1.min-1; males: R2 = 0.72, SEE = 5.02 ml.kg-1.min-1) models were highly accurate relative to N-EX and exercise based models in the literature. Cross-validation procedures were used to evaluate model stability. The generalized model was stable across the total CV group and various CV subsamples (by gender, decade-wide age groups, and AC groups), but not across groups similar in VO2peak. These results suggest that N-EX models can be valid predictors of VO2peak for heterogenous samples.

Classification of cardiorespiratory fitness without exercise testing.

PURPOSE We examined the ability of a nonexercise based VO2max, prediction model to classify cardiorespiratory fitness (CRF) in a population of men and women aged 19-79 yr of age (N = 799). METHODS A VO2max (mL.kg(-1).min(-1)) prediction model was developed in the study group using multiple linear regression from the independent variables age, age2, gender, physical activity status, height, and body mass. The classification accuracy of this model was examined by cross-tabulating age and gender specific quintiles of measured and predicted CRF. RESULTS Overall classification accuracy of the model was modest (36%); however, 83% of all subjects were either classified correctly or within one quintile of measured CRF. Extreme misclassification (e.g., misclassifying a low fit individual as high fit) was only rarely observed (0.13%). CONCLUSIONS The present results support the concept that CRF prediction models can be used to reasonably characterize the fitness level of a cohort using data that can be obtained from a questionnaire. Accordingly, predicted CRF values may be useful as an exposure variable in large epidemiologic studies in which exercise testing is not feasible.

Modeling Physical Activity Outcomes from Wearable Monitors

Although the measurement of physical activity with wearable monitors may be considered objective, consensus guidelines for collecting and processing these objective data are lacking. This article presents an algorithm embodying best practice recommendations for collecting, processing, and reporting physical activity data routinely collected with accelerometry-based activity monitors. This algorithm is proposed as a linear series of seven steps within three successive phases. The Precollection Phase includes two steps. Step 1 defines the population of interest, the type and intensity of physical activity behaviors to be targeted, and the preferred outcome variables, and identifies the epoch duration. In Step 2, the activity monitor is selected, and decisions about how long and where on the body the monitor is to be worn are made. The Data Collection Phase, Step 3, consists of collecting and processing activity monitor data and is dependent on decisions made previously. The Postcollection Phase consists of four steps. Step 4 involves quality and quantity control checks of the activity monitor data. In Step 5, the raw data are transformed into physiologically meaningful units using a calibration algorithm. Step 6 involves summarizing these data according to the target behavior. In Step 7, physical activity outcome variables based on time, energy expenditure, or movement type are generated. Best practice recommendations include the full disclosure of each step within the algorithm when reporting monitor-derived physical activity outcome variables in the research literature. As such, those reading and publishing within the research literature, as well as future users, will have the best chance for understanding the interactions between study methodology and activity monitor selection, as well as the best possibility for relating their own monitor-derived physical activity outcome variables to the research literature.

Body mass scaling of peak oxygen uptake in 20- to 79-yr-old adults

Despite growing evidence in support of the power function ratio (PFR) for body mass (MB) scaling of peak oxygen uptake (VO2PEAK), research literature preferentially reports VO2PEAK values scaled by the simple ratio (SR) method. Theory suggests that VO2PEAK should scale with MB to the 0.67 power (i.e., PFR), while SR scaling assumes an MB exponent of 1.0. This study was designed to determine whether statistically derived MB exponents for a heterogenous sample supported PFR or SR scaling of VO2PEAK. Two hundred thirty women (mean +/- SD: 47.5 +/- 16.8 yr and 64.7 +/- 11.5 kg) and 210 men (45.6 +/- 16.4 yr, 81.77 +/- 12.73 kg) between 20 and 79 yr were evaluated using multiple log-linear regression analysis to determine the MB exponent for VO2PEAK after statistically controlling for age, gender, percent body fat, height, and a self-reported physical activity score. The resulting MB exponent was 0.653 (95% CI: 0.530-0.775) after controlling for all five covariates but increased to 0.756 (0.651-0.862) when height was dropped from the model. Both exponents differed significantly from 1.0 (P < 0.001). These results support the use of PFR scaled VO2PEAK values in adults.

Estimating energy expenditure in wildland fire fighters using a physical activity monitor.

This study piloted the use of an electronic activity monitor (MTI AM 7164-1.2) as a tool for estimating activity (EE(ACT), kcal day(-1)) and total (EE(TOT) kcal day(-1)) energy expenditure in wildland fire fighters during extended periods of wildland fire suppression. Ten Hot Shot fire fighters (9 men, 1 woman) volunteered to wear a MTI monitor during every work shift for 21 consecutive days. Summarizing whole-body motion data each 1 min, the raw activity data (counts min(-1)) were transformed into units of kcal min(-1) using a custom computer program with standard conversion equations. EE(TOT) averaged (Mean+/-SD) 4768+/-478 kcal day(-1), while EE(ACT) averaged 2585+/-406 kcal day(-1), neither of which differed significantly (P = 0.198 and 0.268, respectively) from literature values reported for Hot Shots using the doubly labeled water technique. These data suggest that the electronic activity monitor provided reasonable estimates of EE in wildland fire fighters. This study should be verified, however, with a more complete validation methodology to ensure these findings.

Acsm’s Guidelines for Exercise Testing and Prescription, 6th Edition

ACSM’s Guidelines for Exercise Testing and Prescription, 6th Edition Author: ACSM, Bibliographic Data: (ISBN: 0-683-30355-4, Lippincott Williams & Wilkins, 2000,

Weight gain prevention among black women in the rural community health center setting: The Shape Program

29.95) 13 chapters, 368 pages, Contributors, spiral bound cover Audiences: Exercise Specialists, Sports Medicine Specialists Subjects

Body dissatisfaction mediates the association between body mass index and risky weight control behaviors among White and Native American adolescent girls

BackgroundNearly 60% of black women are obese. Despite their increased risk of obesity and associated chronic diseases, black women have been underrepresented in clinical trials of weight loss interventions, particularly those conducted in the primary care setting. Further, existing obesity treatments are less effective for this population. The promotion of weight maintenance can be achieved at lower treatment intensity than can weight loss and holds promise in reducing obesity-associated chronic disease risk. Weight gain prevention may also be more consistent with the obesity-related sociocultural perspectives of black women than are traditional weight loss approaches.Methods/DesignWe conducted an 18-month randomized controlled trial (the Shape Program) of a weight gain prevention intervention for overweight black female patients in the primary care setting. Participants include 194 premenopausal black women aged 25 to 44 years with a BMI of 25–34.9 kg/m2. Participants were randomized either to usual care or to a 12-month intervention that consisted of: tailored obesogenic behavior change goals, self-monitoring via interactive voice response phone calls, tailored skills training materials, 12 counseling calls with a registered dietitian and a 12-month YMCA membership.Participants are followed over 18 months, with study visits at baseline, 6-, 12- and 18-months. Anthropometric data, blood pressure, fasting lipids, fasting glucose, and self-administered surveys are collected at each visit. Accelerometer data is collected at baseline and 12-months.At baseline, participants were an average of 35.4 years old with a mean body mass index of 30.2 kg/m2. Participants were mostly employed and low-income. Almost half of the sample reported a diagnosis of hypertension or prehypertension and 12% reported a diagnosis of diabetes or prediabetes. Almost one-third of participants smoked and over 20% scored above the clinical threshold for depression.DiscussionThe Shape Program utilizes an innovative intervention approach to lower the risk of obesity and obesity-associated chronic disease among black women in the primary care setting. The intervention was informed by behavior change theory and aims to prevent weight gain using inexpensive mobile technologies and existing health center resources. Baseline characteristics reflect a socioeconomically disadvantaged, high-risk population sample in need of evidence-based treatment strategies.Trial registrationThe trial is registered with clinicaltrials.gov NCT00938535.

Differential accuracy of physical activity self-report by body mass index.

The developmental path leading to eating disorders among adolescent girls often proceeds from increasing body size, to increasing body dissatisfaction, to increasing eating disorder (ED) risk. To determine whether body dissatisfaction (BD) mediates the association between body size and risky weight control behaviors, we examined data from White (n=709) and Native American (n=253) girls, who differ substantially in terms of average body mass and reported weight control behaviors. Measures of BD included weight, shape, and appearance concerns. Measures of ED risk included dieting, exercising to control weight, binge eating, and vomiting. Results showed body dissatisfaction was a highly significant mediator of the relationship between body mass index (BMI) and ED risk for both ethnic groups; although, BD did not mediate the association between BMI and binge eating for either group. BD is apparently an important mediator of the association between body size and some, but not all, risky weight control behaviors.