The health benefits of regular physical activity (PA) are well established (10,17,73,86); however, the precise amount and type of PA required to achieve specific health-related outcomes remains unclear (36). To accurately understand a specific dose-response relationship, valid and reliable measures of the exposure and outcome of interest must exist. Population-based studies of PA and health typically rely on binary outcome measures, such as mortality (19) or nonfatal incident disease (38), which enhances the accuracy of measuring the study outcome. Because PA is a complex and multidimensional exposure variable, population-based measurement is difficult. The need for precise quantification of activity levels and energy expenditure (EE) among free-living people has been well documented (8,36,50,54,66,68,86,88,94). Lack of a gold standard measure of PA has confounded the development of a universally accepted field assessment technique. Consequently, several methods of assessing PA and EE exist along a continuum of accuracy and feasibility (54,86). Each method has strengths and limitations for its use in observational epidemiologic studies of PA and health-related outcomes. In this paper, we will: 1) present methods used to quantify PA and EE; 2) identify potential measurement-related limitations to evaluating the dose-response of PA for health-related outcomes; and 3) identify research priorities to advance our current understanding of dose-response issues in PA and health-related research. Emphasis will focus on methods used to assess PA and EE in epidemiologic studies of PA, EE, and health-related outcomes among free-living individuals.
CONCEPTUAL FRAMEWORK FOR QUANTIFYING ENERGY EXPENDITURE
Conceptual Framework
Two important aspects of measuring PA and EE are the definition and application of terms related with PA. Detailed definitions of relevant terms have been presented by Howley (42). It is important to recognize that PA and EE are not synonymous terms. PA is a behavior that results in EE and is typically quantified in terms of its frequency (number of bouts) and its duration (e.g., minutes per bout). EE reflects the energy cost or intensity associated with a given PA. It is a direct function of all metabolic processes involved with the exchange of energy required to support the skeletal muscle contraction associated with a given PA. Although several factors may influence EE on a relative scale (e.g., age, body size, fitness level), if one assumes a fairly constant human mechanical efficiency to perform physical work, then absolute EE is generally constant for a given PA (2,68). Clearly defining and standardizing terms associated with PA measurement is a critical step in reducing unwanted sources of variation, producing unbiased estimates of the exposure, and facilitating meaningful data interpretation and comparison with data from other populations (36,43,68).
The construct representing the exposure variable within the activity-health paradigm might best be defined as “movement,” with two dimensions: PA (a behavior) and EE (the energy cost of the behavior) (Fig. 1). Direct and indirect measures exist for both PA and EE. However, because recent evidence suggests that EE may be more predictive of health-related outcomes than the specific type of PA that results in the expended energy (55,62), researchers often extrapolate measures to units of EE before analyzing potential effects on health parameters. In epidemiologic studies of PA and health, EE is frequently estimated from PA questionnaires or other indirect measures that reflect patterns of PA in various settings. Indirect measures of PA or EE may provide acceptable estimates of EE, depending on the degree of concordance with direct measures of EE. Day-to-day intra-individual variation in PA has been shown to affect the precision of both direct and indirect measures of PA and EE (35,56) and, therefore, must be considered when choosing a method of PA assessment. Table 1 presents a comprehensive list of methods used to measure PA and EE. These methods have been described in detail by others (8,13,30,54,66,68,88,94). The following review is delimited to direct and indirect techniques that can be used to assess PA and EE among free-living populations. The utility of objective laboratory procedures (e.g., calorimetry) for measuring EE and validating field techniques for assessing PA, however, should not be understated.
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FIGURE 1:
A conceptual model of the relationships between movement, physical activity, and energy expenditure, as well as methods of assessment.
Table 1:
Methods of assessing physical activity or energy expenditure.
Direct Method of Assessing PA and EE
Doubly labeled water (DLW).
The DLW method has been used to assess human EE under laboratory and field conditions (59,68,83). The estimation of EE with DLW is based on the rate of metabolic CO2 production (V̇CO2) (83). DLW consists of the stable water isotopes 2H2O and H218O, and is administered to subjects as a liquid that is dosed according to body size. Urinary excretion of these isotopes in the form of water and CO2 is tracked with mass spectrometry over several days, and oxygen uptake (V̇O2) and EE are extrapolated from V̇CO2 using established equations (83). Although DLW provides precise estimates of free-living EE, this technique is expensive, is limited to studies of total EE, and does not differentiate the duration, frequency, or intensity of specific PA.
Motion detectors.
Motion detectors are mechanical devices worn on the body to quantify EE under the assumption that movement (or acceleration) of the limbs and torso is closely related with whole body EE (13,30,37). Simple pedometers are used to quantify ambulation in terms of steps·unit time-1 (e.g., per day) but are limited by issues pertaining to device calibration and the inability to differentiate type, frequency, duration, and intensity of specific PA (8,13,66). Uniaxial (e.g., Caltrac, CSA) and triaxial (e.g., Tri-Trac) accelerometers measure the rate and magnitude of which the body’s center of mass displaces during movement. Although data from accelerometers can be used to assess frequency, duration, and intensity of PA, the specific type of PA is unknown. The EE owed to activities involving the extremities or increased resistance to body movement (e.g., uphill walking) is not well accounted for (37). Large discrepancies have recently been reported among existing accelerometer cutpoints for estimating the energy cost of lifestyle PA under free-living conditions (1,91). Subject compliance issues, potentially altered PA patterns, and the cost of the more sophisticated instruments limit the practicality of using motion detectors to measure PA and EE in large studies of free-living individuals.
PA records, logs, and recalls.
PA records are ongoing diaries kept by individuals that attempt to capture all sources and patterns of PA during a defined time frame (4,8). Their level of detail ranges from recording each activity and its associated duration (4) to recording activities performed at specified time intervals (e.g., every 15 min) (18). PA records may be limited in population studies because of the intensive effort required by the participant and study staff. Similar to PA records, PA logs aim to provide a detailed account of habitual daily activities and their associated duration (7,8). Unlike the diary format of the PA record, however, the PA log is structured as a checklist of specified activities usually developed from population-specific PA focus groups (4). PA logs may be more convenient to complete and process than PA records. Alternatively, PA logs may be of limited value if participants engaged in activities other than those listed on the log. PA recalls are typically interviews (telephone or in person) aimed at detailing an individual’s PA level during the past 24 h or longer (8,54,64). Multiple random 24-h PA recalls conducted via telephone interviews have recently been used in an effort to minimize the influence of response bias and altered participant activity patterns during assessment (64). Time requirements and the specified recall time frame may limit the use of PA recalls in large epidemiologic studies. For the PA record, log and recall, multiplying the duration of each activity by its corresponding intensity in METs (2,3) yields an estimate of EE (e.g., MET-min·d-1) that can be computed as total EE, or as categories of light, moderate, and vigorous intensity PA (73). PA records, logs, and recalls provide much detail on the type and pattern of PA. However, issues related with individual recall ability, response bias (recall bias, social desirability bias), and the potential for altered PA patterns or poor compliance while completing the PA records or logs may limit their use for population-based assessment of PA or EE.
Indirect Method of Assessing PA and EE
Oxygen uptake (V̇O2).
Development of small portable indirect calorimeters (e.g., Cosmed K4 b2) has recently allowed for field assessment of V̇O2(39,91), from which EE can be estimated based on assumed relations between V̇O2 and the caloric cost of substrate oxidation (20,29,68). However, issues pertaining to costs, cumbersome and obtrusive instrumentation, altered patterns of PA, and the lack of well-established validity and reliability in a variety of field settings limit the usefulness of this approach to quantifying EE in population-based studies of PA and health.
Heart rate (HR).
Estimates of EE have been made from HR based on the assumption of a strong linear relation between HR and V̇O2(25,59). Although the HR–V̇O2 relationship is linear over a wide range of PA intensities, this is frequently not the case during low and very high intensity activity (30). Because many daily activities are low to moderate intensity (2,3), HR monitoring may not provide precise estimates of daily EE among free-living individuals. Additional issues such as developing individual HR–V̇O2 calibration curves to accurately estimate EE, and the variety of ancillary factors that affect HR (e.g., stress, body temperature, and medication) make HR monitoring a less suitable surrogate of PA or EE in health-related research. HR monitoring, however, may be useful as part of an integrated multisystems approach to population-based PA and EE assessment (37,40).
Body temperature.
A close relationship between core body temperature (BT) and EE has been reported under laboratory conditions (15). However, this approach to estimating EE may be impractical due to a time delay (≈ 40 min) before a steady-state BT has been achieved and an accurate estimate of EE can be made. Further, the BT-EE relationship is altered by hot and humid climates and by fitness level. Measurement of BT is inconvenient under most circumstances. Therefore, BT monitoring is not suitable as a single measure of EE among free-living individuals but might be useful as part of an integrated monitoring system (40).
Ventilation (V̇E).
Because of the close relationship between V̇E and V̇O2(25), continuous monitoring of V̇E may be attractive for assessing EE. However, similar limitations to field assessment of V̇E exist as were described for V̇O2. Recently, an electronic device worn around the thorax to detect ventilatory responses to PA has been proposed as a method of assessing EE in free-living conditions (40). Separately or as part of an integrated model, this application may enhance field estimations of EE; however, this procedure is still in its developmental stages, and data are not yet available to establish its accuracy, reproducibility, and feasibility as a surrogate measure of EE.
PA questionnaires.
Self-report questionnaires are used most frequently to assess PA and EE in large-scale epidemiologic studies of health-related outcomes (Table 2). PA questionnaires are classified as global, recall, and quantitative history instruments based on their level of detail and subject burden (54,68). Global questionnaires are short one- to four-item surveys aimed at general levels of PA. Although they are easy to complete, global surveys provided limited information on specific types and patterns of PA and result in only simple PA classifications (e.g., active vs inactive) (14,81). Recall questionnaires typically have 10–20 items and allow fairly specific assessment of frequency, duration, and types of PA during the past day, week, or month. Compared with the global survey, recall instruments are somewhat more complex and burdensome to complete; however, the PA assessment is more detailed. Scoring systems vary among recall questionnaires, ranging from simple ordinal scales (e.g., 1-5, low to high PA) (9,11,61), to unitless summary indices (e.g., exercise units) (47,65,80), to a summed score of continuous data (e.g., MET-min·d-1) (7,16,71). The advantage of the latter measure is the ability to evaluate dose-response relationships across categories of light, moderate, and vigorous PA or EE according to published recommendations (73,86). Quantitative histories generally have more than 20 items, are very detailed, and typically reflect the volume (e.g., frequency and duration) of leisure-time or occupational PA obtained in the past year or through a lifetime. PA scores are usually expressed as a continuous variable (e.g., kcal·kg-1·wk-1) (6,51,72,85), which allows for categorical evaluation of dose-response effects on health parameters based on recommended EE cutpoints (e.g., ≥ 14 kcal·kg-1·wk-1) (86). Though concerns about the limitations of human recall and report bias are valid (12,28,56), PA questionnaires provide a relatively easy, inexpensive, and nonreactive method of assessing PA or EE in large free-living populations.
Table 2:
Self-report methods and cut-points used to quantify levels of physical activity and energy expenditure in free-living populations.
Table 2:
Continued
ISSUES IN QUANTIFYING PHYSICAL ACTIVITY AND ENERGY EXPENDITURE
The objective of epidemiologic research is to produce an unbiased estimate of the strength of association between PA and a specific health outcome, and to determine whether adequate evidence exists in support of a causal relationship. Demonstrating that higher levels of PA or EE result in a graded effect in a defined health parameter is an important piece of evidence for causality. Several issues pertaining to the evaluation of a potential dose-response relationship should be considered to allow for accurate interpretation and application of the data.
Table 3 lists potential limitations to evaluating and interpreting the dose-response characteristics between PA or EE and health-related outcomes. Two very important considerations are clearly defining whether PA or EE is the exposure variable and attempting to examine the dose effect of exposure variables that have been quantified in different summary units. The latter point can be seen by comparing PA and all-cause mortality data among men in the British Regional Heart Study (89) and the Harvard Alumnus Health Study (71), which used different summary units to express PA and EE (Table 2). Both studies generally supported a strong graded inverse association for PA and EE with total mortality. The Harvard study showed about a 40% reduction in age-adjusted mortality at an EE of about 1500 kcal·wk-1, whereas the British study showed a similar reduction in age-adjusted mortality at a PA level defined as “occasional.” Not only does categorizing PA levels as a unitless index in the British study prevent cross-population comparison of the dose-response observed in the two studies, it is also very difficult to form a recommendation from the British data on the amount of PA required to reduce the risk of mortality in middle-aged men. For this reason, instruments used to assess PA should yield measures in units of EE or should allow for easy conversion of PA dimensions (e.g., frequency and duration) to units of energy expenditure (e.g., MET-min·d-1).
Table 3:
Potential limitations to evaluating dose-response characteristics of physical activity or energy expenditure and health-related outcomes.
Recent attention has focused on the inappropriate use of ordinal scales and summary indices to evaluate PA effects that are based on assumptions of interval or ratio level data (96,97). Some PA questionnaires are scaled on an ordinal rather than linear metric. Because the size of the increment between the levels of PA or EE (e.g., sedentary, active, highly active) is not equal, the true meaning of ordinal scores may not be of equal physiological value between one level of EE to another. Consequently, evaluation of the true underlying dose-response between PA and a defined health outcome might be compromised. Further, if two separate PA instruments have nonequated measurement units, it is likely that nonequivalent PA or EE summary scores will result, and cross-population dose-response comparisons will be meaningless. New measurement scaling/calibration methods (e.g., Rasch modeling techniques) (57,96,97) can be employed to transform ordinal scales into an equated linear metric before using them in research. Other issues are construct and item bias (e.g., the PA domains have different cross-population meaning), and method bias (cross-population disparity in survey characteristics or administration). Failure to account for these potential sources of measurement error could make examining and interpreting cross-population dose-response characteristics essentially like comparing apples and oranges. Additionally, questionnaires must reflect the types of habitual PA that are performed in the target population (4,7,9,43). This issue has recently been highlighted by an expert panel on measuring PA among women and minorities (63) and may account for the discrepant findings between PA and certain health-outcomes reported among women (79,86,92).
Choosing the proper cutpoints for categorical dose-response analysis of continuous measures of PA or EE is problematic. Cutpoint bias (34) occurs when dose-response cutpoint levels are assigned to maximize the desired effect (e.g., statistical significance, magnitude of effect, or data trends) or are fit to the population-specific data distribution (e.g., sample tertile cutpoints). A more appropriate and standardized method might be to assign cutpoints based on published physiologic thresholds associated with health-related outcomes (e.g., Surgeon General’s Report recommendation of ≥ 150 kcal·d-1 vs < 150 kcal·d-1) (86). To reduce the potential of cutpoint bias and to provide a more precise dose-response evaluation based on the true characteristics of the data, Greenland (34) recommends alternative analytic methods for assessing the nature of dose-response relationships with continuous data as opposed to creating an ordinal scale for linear modeling. Furthermore, because the specific nature of the dose-response curve (e.g., monotonic, asymptotic, or polynomial) may vary across the spectrum of health parameters related with PA, equivalence between units used to categorize PA or EE, use of physiological thresholds to form EE categories, and alternative approaches for evaluating dose-response characteristics may lead to a more meaningful consensus on the true dose of PA required to achieve specific health-related outcomes.
Finally, little is known about the process of recalling (e.g., perceiving, encoding, storing, and retrieving) past levels of PA (12,28). Several reports have supported stronger associations with health-related outcomes for vigorous rather than nonvigorous PA (86), which may reflect a bias in accurately recalling activities of different intensity (28,43,64). Additionally, it appears that several factors related with the recall instrument and the subject (e.g., question context and age, respectively) modify the accuracy of PA recall (28). Because recall methods are the most common approach to assessing levels of PA and EE in large epidemiologic studies, understanding how PA recall processing occurs and whether this process can be enhanced deserves more attention.
FUTURE RESEARCH PRIORITIES
Based on our evaluation of the field methods used to measure PA and EE, the limitations in current analytic approaches to assessing dose response, and the limited understanding of the process involved in PA recall, the following recommendations for future research are made:
1) Develop a gold standard field measure of PA.
2) Develop an integrated physiological (e.g., HR, temperature, and ventilation) and motion (acceleration) detection system to objectively measure movement in free-living conditions.
3) Identify physiologic thresholds for a wide variety of PA-health relationships for use in developing evidence-based dose-response cutpoints.
4) Standardize 1) the terminology used to express EE (kcal·d-1, MET-min·d-1), and 2) procedures for extrapolating EE from indirect measures of PA or EE.
5) Apply innovative statistical procedures for developing PA instruments and analyzing PA data (e.g., test equating or item-response theory).
6) Apply innovative statistical procedures to assess the nature of dose-response relationships (e.g., fractional polynomial regression, spline regression, and nonparametric modeling).
7) Identify sources of bias and variance in the recall of PA using questionnaires.
This work was supported by a cooperative agreement from the National Institutes of Health Women’s Health Initiative and the U.S. Centers for Disease Control and Prevention, SIP 22W-U48/CCU409664-03. We appreciate the contribution of Dr. Weimo Zhu in the completion of this work.
Current address for Dr. LaMonte: The Fitness Institute, LDS Hospital, Division of Cardiology, Salt Lake City, UT 84143; E-mail: [email protected].
Address for correspondence: Barbara E. Ainsworth, Ph.D., M.P.H., Department of Epidemiology & Biostatistics, School of Public Health, 800 Sumter Street, University of South Carolina, Columbia, SC 29208; E-mail: [email protected].
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