I. Literature Review
A. Introduction to energy expenditure (EE)
Energy is defined as the origin of life, being expended to support human metabolic functions such as growth, reproduction, muscular activity, and biosynthesis of metabolites (1). Specifically, energy is required for daily breathing, blood circulation, food digestion, and other activities. Generally, total energy expenditure (TEE) is a sum of internally produced energy including the thermic effect of food (TEF) and resting metabolic rate (RMR) which is also called basal metabolic rate (BMR), and external work-induced calories called activity energy expenditure (AEE) (Figure 1).
RMR, as the largest element of total daily caloric expenditure (2), accounts for 60-70% of TEE (3). It represents the amount of energy produced while digestive system is inactive and the whole body is at rest staying in a neutrally temperate environment. RMR decreases with age and loss of lean body mass, but elevates increases with muscle mass growth (4). Since the late 19th and early 20th centuries RMR was able tohas been measured in clinic trials by gas analysis through either direct or indirect calorimetry (5), although predictive equations are now widely adopted especially in large-scale research (2,3,5). Along with RMR, TEF behaves as an increment to TEE due to the cost of digesting and absorbing food for use and/or storage (6), normally accounting for 10% of TEE (7). Lastly, activity energy occupying 15-30% of TEE (3) is expended while undertaking different spontaneous and voluntary activities as well as various postures. Internally, AEE has two major domains; one is energy expended while undertaking structured and planned physical activities such as sports and workout in gyms, while the second is EE induced by low-intensity activities such as walking, housework and even fidgeting. In fact, both domains are quite influential, although sport-like exercise is conventionally promoted to elevate TEE (8). Energy expended on high-intensity activities may account for a small proportion of daily AEE because duration of exercising is usually limited for a normal person; however, non-exercise activity thermogenesis (NEAT), although individually contributing little to total AEE, tends to be appreciable when cumulating the time because non-exercise activities take place throughout the day (8). Therefore, individuals with different lifestyles or personal habits could have significant variance in AEE. Dr. Levine of the Mayo Clinic, for instance, has reported that NEAT could vary by as much as 2000 kcal per day interpersonally (9), which substantially demonstrated the importance of assessing its contribution to AEE in activity-health studies (10).
B. Applications of energy expenditure assessment
B.1. Assessment of energy requirements
In 1985, energy requirements were recommended to rely on assessments of EE rather than energy intake, according to the report of the joint Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/ UNU) Expert Committee on Energy Requirements (11). Great effort was made to validate existing energy requirement recommendations for different target groups. Black et al., for instance, compiled 574 studies on TEE measurements in 1996 and demonstrated that the existing recommendations for infants and children in U.S. were high, whereas those for adolescents and adults were usually low (12). Consistently, Davis (1998) reviewed TEE estimations for infants using doubly labeled water (DLW) method (n>400) (13), Bratteby et al. (1998) studied TEE in 46 adolescents (14), and Carpenter et al. (1998) published a TEE study of 164 subjects aged 65-70 y (15), whose results consistently supported Black et al.'s findings. Meanwhile, DLW method has been used to determine energy requirements for healthy individuals under abnormal or fascinating situations, such as sailing men (16), resistance trainees (17), and soccer players (18). Furthermore, energy requirements for unhealthy populations can be known estimated on the basis of TEE estimates. Infants with cyanotic congenital heart disease (19) and broncopulmonary dysplasia (20), for example, were documented to as havinge 30 and 15% higher TEE by 30 and 15%, respectively, compared with normal infants; hence, special care on dietary intake for these patients is important to avoid under-nutrition. Generally, EE-based recommendations for energy requirements are more reliable and valid, benefiting to various populations.
B.2. Validation of energy intake measurements
Before development of accurate technologies and instrumentation for TEE measurements, energy intake ± any change in body composition was utilized to estimate TEE (21). Nowadays with same principle but working backward, TEE ± body changes are often employed to predict energy intake since TEE measurements have become progressively more accurate progressively. As one side of energy balance, energy intake is critical to research of the effect of diet on health,. uUnfortunately, self-administered reported food uptake intake is usually documented to be unreliable (22) which significantly contributes to uncertainty and controversies. According to a large number of investigations, self-reported energy intake was commonly underreported compared with ones estimated from TEE measures (21). For instance, Champagne et al. found that children (n=118) tended to underreport daily energy intake by 17-33% of TEE measured by DLW (23) and Martin et al. demonstrated that non-obese, middle aged women underreported their energy intake by 20% of TEE estimates (24). In the process of characterizing and understanding thise underreporting, Johnson et al. found that it strongly correlated with being overweight or fat, and having poor reading, spelling or understanding skillscomprehension (25). Therefore, careful consideration has tomust been taken when self-reported energy intake is involved in research.
B.3. Obesity research in the context of energy imbalance
Accurate and precise measurements of EE for healthy and unhealthy individuals has widely applied to clinical trials, one of which is prevention and treatment of a rapidly growing epidemic, obesity (26,27). At the meantime,Related to Obesity, morbid and mortal chronic diseases related to obesity, such as hypertension, type II diabetes and cardiovascular disease, are also becominge more prevalent quickly as well. In U.S., for instance, the population of obese adults aged 20 years or older doubled between 1980 and 2002 as shown in Figure 2 (28); in Canada, similarly, the percentage of obese adults increased from 6% (1985) to16% (2003) of the Canadian population (29). China, one of the leanest populations, is fast in catching up with the West. Recent estimates in the China Health and Nutrition Survey indicated that the population of overweight adults increased by nearly 40% and that of obesity doubled from 1992 to 2002, affecting approximately 215 million Chinese had been affected (30). Alarmingly, owing to its etiologic role in several chronic diseases, health expenditure related to obesity had exceeded those resulting from smoking and drinking based on 1998 U.S. national survey data (31).
It is generally accepted that the recently increased prevalence of obesity results from an imbalance between energy intake and EE (32). As a result, dominant strategies employed in weight loss programs focus on either reducing food inuptake (e.g. intervention of diets, drugs, and bariatric surgery) or elevating EE (e.g. exercise and non-exercise promotion) (27), or working along with both linesa combination of both. In terms of TEE side, RMR and TEF are fairly constant within or between persons (10) are and poorly correlated with obesity (33); hence, increasing AEE plays a major role in making TEE outweigh energy intake during obesity prevention or treatment. Despite some promotion of moderate to vigorous activities (34), the effectiveness of exercise alone on weight loss appears to be modest unless combining combined with diet (27). Several prospective and observational studies have demonstrated that continuing regular vigorous physical activities over a long time period without diet changes (>1 year) might only reach weight maintenance in both children and adults (35,36), but not hardly weight loss.
O
n
the other hand, Tthe importance of NEAT, as part of AEE, to in obesity treating obesity ment begins to is receivinge more attention. Theoretically, spending 2 min/hr watching television or playing computer rather than walking may result in 5 kg of weight gain within 10 years (37). Clinically, non-exercise or low-intensity AEE measurements by various methods have been utilized especially in activity-health trials. In an randomized controlled school-based trial, for example, Robinson et al. (1996-1997) analyzed daily activity questionnaires reported by students and their parents (n=192), finding that children who reduced television, videotape, or video game use did have significant decrease in BMI, waist circumference and waist-to-hip ratio compared with controls (38). Abundant evidence has indicated that reducing sedentary activities is a promising, population-based approach to prevent obesity (38,39). Subsequently, a study measured energy expended with various postures and movements, demonstrating that compared with lying down position, EE increased while sitting motionless by 4%, while seat-fidgeting by 54%, while stand-fidgeting by 94%, and while walking at 1.6, 3.2 and 4.8 km/h by 154%, 202% and 292%, respectively (40). Additionally, to study the impact of postures on AEE between lean and obese non-exercising individuals (n=20), Levine et al. (2005) found that lean subjects stood at least 2 hr/day longer than the obese, which translated into an EE difference of 350 kcal/day (9). Indeed, accumulative evidence suggests that thermogenic potential of fidgeting-like or low-intensive activities is great to substantially increase AEE, consequently addition toincreasing TEE.
In terms of activity-health research, unfortunately, the effects of EE on reductions oinf morbidity or mortality related to obesity so far could not be concluded due toare inconsistentcy and uncertainty unconclusive among existing studies. One of main contributors would beto this inconsistency is the crude assessments of AEE with entailing variation in measurement accuracy and precision (10). Given this research heterogeneity, accurate and consistent measurements of AEE are required to decrease the chance of misclassification, confounding influence and other bias; with that, it is possible toand better understand the association between health and EE within populations, internationally, or cross-culturally.
Recently developed technologies and instrumentation have been smartly adopted into techniques for EE measurements, such as calorimetry, DLW method, heart rate monitors and acceleratorsaccelerometers. However, no method is suitable for every population in every situation, so in addition to understanding the principles of these methods, clarifying similarities versus differences or and advantages versus disadvantages is necessary for proper operation of research.
C. Methods for energy expenditure measurement
C.1. Direct versus indirect calorimetry
Direct calorimetry measures total heat dissipated by evaporation, radiation, conduction and convection from the body (41). While this technique is able to measure TEE accurately and precisely, the subject has to be placed in a thermally-isolated chamber. Therefore, it does not exhibit a free-living EE (42).; besides, Oonly one individual can be monitored at one a time, so it isdirect calorimetry is very time-consuming if applied to large populations (10,41). Indirect calorimetry, on the other hand, measures oxygen consumption (VO2) or CO2 production, but subjects have to wear a mouthpiece, hood or a whole body chamber, interfering substantially with activities of daily living (41).
C.2. Doubly labeled water (DLW) method
DLW method was developed by Lifson et al. in 1955 (43), and its first application to in humans for free-living EE measurements was published in 1982 (44). DLW contains two rare heavy isotopes, 2H and 18O, both of which are deemed safe because the typicall dosey given concentrations fall well below levels able to cause side-effects, damage or toxicity (21,46). To date, DLW method is known as the gold standard for TEE measurements in free-living individuals (45) and utilized in a variety of groups, including premature infants, children, adolescents, adult, obese people, pregnant and lactating women, the elderly and hospitalized patients. Applications of DLW method include assessing energy requirements, validating available methods used to measure energy intake or physical activity, and understanding effects of dietary and/or physical activity intervention on health (47).
The fundamental basis of DLW method is that 18O component, after mixing with body water, is eliminated as CO2 and H2O, whereas 2H is excreted solely as H2O (48); hence, 18O turnover is quicker than 2H's. Consequently, the difference between two elimination rates provides a measure of CO2 output (49). Based on the same physiological principle as indirect calorimetry, CO2 production combined with standard equations can be used to predict TEE (45). However, different from calorimetry, DLW method allows subjects to freely perform normal activities of daily living because it relies on collections of urine and saliva samples rather than heat or respiratory gases. Briefly speaking of the procedure, a specific dose of DLW is given to an individual according to weight. Then saliva samples are collected in afewfor 4 hours for to determeining total body water (TBW); subsequently, post-dose urine samples are collected at different time points to quantify isotope enrichments (45). Isotope enrichments in samples are detected against Standard Mean Ocean Water (SMOW) international standard by Thermal Conversion-Elemental Analyzer/ Isotope Ratio Mass Spectrometry (TC-EA/IRMS). The length of study periods varies from 4 to 21 days depending on activity levels of study groups (47). To avoid imprecise results related to analytic errors, study periods should be long enough for sufficient elimination of isotopes but short enough so that isotope enrichments in the final urine samples are detectable (50).
Several assumptions were made with which DLW method is valid (50): 1) the volume of water pool for labeled isotopes' distribution is constant; 2) flux rates of water and CO2 are constant; 3) 2H is washed out only as water whereas 18O is lost as water and CO2; 4) enrichments of water and CO2 exiting bodies are the same as those remain in body water; and 5) no isotopic exchange between body system and environmental water/CO2 through skin or lungs. Consequently, TEE estimates may be inaccurate or go wrong if DLW is applied to persons with disorders, such as kidney and respiratory disorders, which may alter TBW pool or fluxes of water and CO2. Any environmental factors that influence ambient temperatures, water turnover rates (51,52), or background isotope levels will also lose affect precision (53,54).
In general, however, DLW method is able to accurately estimate free-living TEE of individuals with varying lifestyles or even those with atypical levels of EE (49,55). Its noninvasive nature makes it little interferingcauses little interference with human behavior, so that subjects can freely participate freely in daily activities during the study period (56). Unfortunately, relatively high cost of DLW purchase and high demand for specialized expertise for mass spectrometry instrumentation limit its widespread utilization in large scale studies.
C.3. Heart rate monitors/Accelerometers
Heart rate monitors measure TEE based on the linear relationship between heart rate and O2 consumption (VO2). However, they are less accurate to in determininge energy expended in low-intensity activities (57); besides, they are limited affected by several factors including emotional stress, body temperature and medication (58). With respect to accelerometers, they detect accelerated and decelerated motions electronically with varying degrees of sensitivity (47). However, detectability of accelerometers is restricted to some activities, such as weight lifting, carrying and pushing, which do not involve accelerated or decelerated variation (59). As a result, heart rate monitors/accelerometers are not sensitive enough to capture all types of activities in daily life, although both are noninvasive and cost-effective to record duration, intensity and frequency of physical activities. Moreover, they are only suitable for small to medium sized population studies (60). Problems with compliance and behavior changes are big issues (10).
C.4. Activity questionnaires (AQs)
Compared to other techniques, AQs are the easiest to distribute and administer and do not require much motivation or time from subjects (10,47). With lower investment on time and money, AQs provide a lot of information on physical activity/inactivity as well as other factors about a great number of study participants. Hence, it is ofAQs have a clear advantage for in large population-based or epidemiological studies. Since the Minnesota Leisure Time Physical Activity Questionnaire was published in 1978 (61), there are a range of AQs designed to capture various activity parameters. The standard format of a questionnaire consists of diverse activities which are generally categorized according to intensity, including very hard-, hard-, moderate-, and light-intensity, and their corresponding frequency and duration (10). Normally, AEE calculation process shown in Figure 3 follows that: 1) each activity is first assigned a standard metabolic equivalent value (MET) based on intensity; 2) metabolic output during study periods of a single activity is computed by multiplying reported hours (frequency Ã- duration) by the corresponding MET and then expressed for a day; and finally 3) these values will be summed to yield an overall metabolic output per day, which is further multiplied by weight to get total AEE of individual daily activities (10).
Initially, how to pick an appropriate questionnaire according to the purpose and target group of the study was a major consideration; hence, the emphasis was on developing varying AQs for different study groups to investigate different aspects of physical activities at different time periods (10,47,62). Subsequently, as inactivity becomes a globally growing health concern, more questionnaires have extended their questions asking about not only structured and planned exercise, but also occupational and leisure-time activities (63,64,60,61). Nowadays, issues about AQ validity have been raised (65) since significant heterogeneity exists between AQ-derived AEE and AEE derived by other techniques. A lot of validation and comparison studies boosted to explore reliability, validity and sensitivity of published AQs, and potential limitations were found (10,47,62). The review paper of Neilson et al. (62), for instance, investigated >20 literatures about validating various AQs against DLW method by evaluating mean difference and correlation coefficients (TEEAQ versus TEEDLW or AEEAQ versus AEEDLW). In summary, 23 AQs were extracted which covered both genders, all levels of age, obese and non-obese people, and international measurements. The validation against DLW demonstrated that only 2 AQs, the Tecumseh Occupational Activity and past month Minnesota Leisure Time Questionnaire (63) and the Tecumseh Community Health Survey (66), had acceptable criterion validity with mean differences of 10% and 2%, and correlation coefficients of 0.62 and 0.63, respectively. The majority did not have sufficient face validity for EE estimation, which might be attributable to 1) ignoring of non-exercise activities, 2) different time periods covered by AQs and DLW, 3) potential errors in assignment of MET to self-reported activities, 4) relatively small sample sizes, and 5) modes of AQ administration (59) and respondent characteristics (67).
With respect to these influencing factors, little consideration of NEAT has been extensively discussed. A recent theory says that EE yielded in non-exercise activities could significantly contribute to TEE or AEE because these activities accumulatively occupy majority of daily time (62). According to Westerterp KR (68), a profound underestimate of AEE was generated by AQs which did not include low-intensity activities such as personal care, climbing stairs, walking, transportation or sedentary activities. Likewise, various postures undertaken during sedentary activities have been indicated to contribute differentially to AEE, which could consequently build a big discrepancy between AEEAQ and AEEDLW (69). By and large limitations of AQs indeed preclude us from drawing firm and reliable conclusions from research which involves EE measures as influential parameters. Nevertheless, as pointed out by Sesso (70), self-reported methods of data collection will still remain the primary way in situations when physical activity/inactivity must be quantified in epidemiological or large-scale studies. As a result, it is emergent to develop a comprehensive questionnaire which can accurately and consistently measure and describe subtle components of lifestyle activities associated with day-to-day living, especially for studying essential roles of physical activity/inactivity in the development of obesity and obesity-related chronic diseases.
II. Rationale
DLW method is recognized as the gold standard to measure free-living TEE at individual level (10). In addition to its safety and accuracy, DLW method allows subjects to live normally and freely even during the experimental period. AEE, if interested, can be calculated by subtracting RMR and TEF from TEE (Figure 1). RMR can be estimated using mathematical prediction equations (2), while TEF is usually considered to account for 10% of TEE (7). However, high cost of DLW and high technical demands make it infeasible and impractical in population-based health research unless the subject group is small (i.e. <200) (71). Other available techniques, likewise, are also impractical in large health studies because of cost, burden on subjects and investigators, invasiveness, or potential behavioral changes. Therefore, self-administered questionnaires with high reliability and validity are so far the best choices to assess AEE in large population studies (70).
The primary objective of this proposal is to validate a newly developed self-administrated questionnaire, referred to as the past month Sedentary Time and Activity Reporting Questionnaire (STAR-Q), which was designed for the purpose of estimating free-living individual AEE in large populations. Particularly, the accuracy and precision of STAR-Q in AEE estimation will be evaluated against DLW method, results of which will assist further modification and development of STAR-Q. Secondarily, we will verify three RMR predictive equations against DLW method to review and document individual accuracy in healthy, middle-aged, non-obese adults. They are Mifflin-St Jeor (3), WHO/FAO/UNU (11) and Harris-Benedict equations (72). Moreover, if applicable (n≈100), we will create a new valid and reliable algorithm for RMR estimation unless the existing RMR equation is good enough.
Hence, the null hypothesis is that mean differences and correlation coefficients of AEEAQ versus AEEDLW will be within the acceptable range of <10% and >0.6, respectively, and so does individual comparison of RMR derived from each predictive equation and DLW.
III. Sedentary Time and Activity Reporting Questionnaire
(STAR-Q)
STAR-Q is the crystal produced by collaboration of University of Calgary and the Alberta Cancer Board. Dr. Ilona Csizmadi is the Nominated Principal Investigator of the Calgary study, working with other investigators of the Alberta Cancer Board's Tomorrow Project® (73). The present study is a co-investigation of validity of STAR-Q using DLW as a reference method. During preliminary development of STAR-Q, the initial draft was designed based on the data already collected from approximately 18,000 participants in Tomorrow Project® who have completed an open-ended Past-Year Total Physical Activity Questionnaire. Differentially, STAR-Q was particularly created to estimate daily AEE, so other domains of activities that represent day-to-day living were included and measured in detail. It was decided to cover only past month rather than past year because it has been indicated by cognitive tests that recent events tend to be recalled more accurately and precisely than distant past events (74). To obtain information about how people answer those questions and to identify difficulties encountered by respondents when retrieving and reporting past-month activities, two rounds of one-on-one cognitive interviews were carried out between trained, professional and experienced research interviewer and 20 participants. Collected data were used to modify the design and re-word the text. Following each revision, pilot testing was conducted to re-evaluate the ease with which the questionnaires are completed and to test various memory aids for activity recall that may assist participants in reporting activities.
Ultimately, the final START-Q is organized into 15 sections, trying to capture as many activities as possible throughout a 24-hour period during the past 4 weeks (28 days). They are 1) directions for questionnaire completion, 2) sleeping and napping, 3) eating, 4) personal and medical care, 5) occupation and volunteer work, 6) transportation and moving about, 7) household, 8) year work, 9) caregiving, 10) exercise, sports and leisure, 11) light leisure and relaxing, 12) other activities, 13) using the stairs, 14) some final questions asking about mobility level, and lastly 15) comments. It ascertains types of activities and corresponding frequency, duration, and intensity (4 physical effort levels). Different from other questionnaires, STAR-Q subdivides physical inactivity into several domains, such as occupation, transportation, household, night leisure and relaxing. In more detail, daily routine activities are considered, such as watching TV, playing computers, reading, and studying; and accompanying postures (e.g. reading while lying down, sitting, or standing) are differentiated as well, which together are great advances compared with other AQs. It is anticipated that with list of activities shown in the questionnaire, it could efficiently aid subjects to report past-month activities as completely as possible.
IV. Research Design - Procedures and Methods
A. Subjects and recruitment
Adults living in the greater Calgary area (includes farming, ranching and rural area) will be eligible to write informed consent if they are aged 30-60 years and with a BMI of <35. Additionally, men and women will be considered eligible if they: 1) do not have metabolic disorders, any diagnosed cancers or disorders of the kidney, liver, cardiovascular, respiratory, or neurological systems; 2) are not taking medications or natural health products that could potentially influence energy or water balance; 3) do not have a weight fluctuation of >2.5 kg in past two years (75) and are not planning any weight gain or loss in the near future; 4) are not breast-feeding, pregnant or planning to be pregnant within the next year and; 6) will not be away from Calgary area for >3 months at a time once the study starts. Subjects will be requested to complete baseline and DLW completion questionnaires at the start and end of the study respectively to confirm eligibility criteria and to assess health status, medications, and intention to travel as well as other factors.
The recruitment mostly relies on the Tomorrow Project® cohort, in which approximately 3,000 participants are eligible. The desired sample size is 100 subjects. However, we have only achieved half so far and the recruitment is still continuing.
B. Procedures and methods
B.1. Doubly labeled water (DLW) method
DLW method will be used to estimate individual free-living TEE per day. The DLW protocol will be conducted over a 14-day period (Figure 4). On Day 0 of the study, height, weight, and waist circumference of subjects will be measured in a fasted state and their baseline urine (5ml minimum) and saliva samples (3ml minimum) will be collected as well for the determination of background isotope enrichments. Then subjects will be given a single dose of DLW, consisting of 2.5g 10 atom percent excess (APE) of 18O per kg body weight and 0.18g 99 APE of 2H per kg body weight followed by 100 mL rinse of water. Subjects will then be asked to abstain from food and fluid intake for 4 h after dosing and post-dose saliva samples will be collected at 3 h and 4 h for determining total body water (TBW). At their home on Day 1, 8 and 14, urine samples (2nd void of the day) will be collected in separate containers by subjects and stored in a refrigerator. On Day 14, subjects, after overnight fasting, will go back to the test facility and take a dose of 2H2O (0.18g 99 APE of 2H per kg body weight). Post-dose saliva samples will be collected at 3 h and 4 h. Physiology specialists will aliquot urine and saliva samples and store them in a -20°C freezer until shipment to the Richardson Centre for Functional Foods and Nutraceuticals (RCFFN) for isotopic analysis. 2H and 18O enrichments of each sample will be measured five times using automated Thermal Conversion-Elemental Analyzer in combination with Isotope Ratio Mass Spectrometry (TC-EA/IRMS: Finnigan DELTAplus V). Only reasonable values with minimal memory effect will be picked for mean enrichment calculation. Then using the two-point approach, elimination rates of 2H (kh) and 18O (ko) will be computed by dividing change in enrichment (Ef - Ei) by the corresponding time difference (tf - ti). Gathering all data, TBW (kg) is calculated using the formula (76):
Where d is the dose of 2H2O (g), MW is the molecular weight of 2H2O, APE is atom percent excess of 2H, Rstd is the ratio of 2H/H in the standard (1.5576Ã-10-4), and Δδ2H is the enrichment change between pre- and post-dose saliva samples.
CO2 production rates can be calculated using the formula (56):
rCO2 (mol/day) = 0.4554 Ã- TBW (mol) Ã- (1.007ko - 1.041kh)
where ko and kh (day-1) are the elimination rates of 2H and 18O, respectively.
TEE (MJ/day) can be determined from rCO2 and respiratory quotient (RQ) assumed as 0.85 in this case (77), using a modified Weir's formula (78):
TEE = 22.4 Ã- [3.9 Ã- (rCO2/FQ) + 1.1 Ã- rCO2] Ã- 4.18/1000
To calculate study target AEE (MJ/day), the following equation will be used:
AEE= [TEE Ã- 0.90] - [(RMR/24) Ã- (24 - hours of sleep)] + [0.9 (RMR/24) Ã- hours of sleep]
Where TEF is considered to be 10% of TEE (7), and RMR will be determined using Mifflin-St. Jeor equation (3) which has been evaluated to perform best for healthy and non-obese individual adults (2). For subjects who have gained or lost weight over the course of the study, adjustment will be made based on the principle that 1.0 kg of body weight is equivalent to 30 MJ of energy (79).
B.2. Activity questionnaires
Past-month STAR-Q will be completed by subjects at the test facility on Day 14 to avoid behavioral changes during DLW study. Project assistants will provide help if participants have problem about questionnaires while reporting. Participants will be requested to complete STAR-Q in 3 and 6 months for consistency evaluation. Response will be scanned using TELEform® software (TELEform V9.1; Cardiff, USA).
Activities appearing on STAR-Q will be assigned codes and metabolic equivalent (MET) values according to the Compendium of Physical Activities (80) before scanning. Reported frequency, duration and intensity for each activity will yield a single point estimate of metabolic output per month and then expressed as total MET-hours per day. Finally, a sum of these values will yield an overall estimate of metabolic output per day to reflect individual AEE (Figure 4). AEE derived from STAR-Q will be directly compared with AEEDLW.
In addition to STAR-Q, participants will be requested to fill a 7-day Activity Diary on Day 14. The dairy is adapted from Conway et al (81) and designed to ascertain all activities and posture every 20 minutes while awake. It was indicated by Levine et al (82) that NEAT gained from a 7-day diary was generally representative of several months of NEAT. Therefore, captured activities will be used to compare with those obtained from STAR-Q, in terms of activity types, MET-hour/day and duration, and then it will be used to assist the modification and development of SATR-Q.
B.3. Predictive equations for resting metabolic rate (RMR)
Three commonly used predictive equations for RMR estimation, Mifflin-St Jeor (3), WHO/FAO/UNU (11) and Harris-Benedict (72), are picked to test validity and accuracy against DLW method in healthy, middle-aged, non-obese adults.
Mifflin-St Jeor (3):
Men: RMR=9.99Ã-weight + 6.25Ã-weight - 4.92Ã-age + 5
Women: RMR=9.99Ã-weight + 6.25Ã-weight - 4.92Ã-age - 161
WHO/FAO/UNU (11):
Men: 18-30 y 15.4Ã-weight - 27Ã-height + 717
31-60 y 11.3Ã-weight + 16Ã-height + 901
>60 y 8.8Ã-weight + 1,128Ã-height - 1,071
Women: 18-30 y 13.3Ã-weight + 334Ã-height + 35
31-60 y 8.7Ã-weight - 25Ã-height + 865
>60 y 9.2Ã-weight + 637Ã-height - 302
Harris-Benedict (72):
Men: RMR=66.47 + 13.75Ã-weight + 5.0Ã-height - 6.75Ã-age
Women: RMR=665.09 + 9.56Ã-weight + 1.84Ã-height - 4.67Ã-age
Individual RMR will be calculated by three equations and determine if these values fall into DLW-derived TEE range that is counted as 60-75% of TEEDLW. Finally, the percentages of participants who fall into the range will be compared. Age factor will be considered. For example, adults closer to 60 y is likely to have lower RMR (i.e. 60-70% of TEEDLW), but younger adults are likely to have higher RMR (i.e. 70-75% of TEEDLW).
B.4. Data analysis
Means and standard deviations (SD) will be calculated with a significance level set at 5%. Average daily AEE measured using DLW, STAR-Q and 7-day Activity Diary will be calculated for individuals. Bland-Altman plots will be employed to evaluate the level of agreement in AEE between DLW and questionnaires. Spearman correlation coefficients will be calculated to assess the degree of linear association between measurements. Regression analysis will be finally conducted to explore the relationships between the dependent variable (AEEDLW) and independent variables (AEE derived from STAR-Q and 7-day Activity Diary, respectively). Potential covariates of interest (e.g. age, sex, weight) will be considered and kept in the regression model.
V. Anticipated results
Aiming at the primary objective, we expect to a good match in AEE generated from STAR-Q and DLW method with mean difference and correlation coefficient of <10% and >0.6, respectively. It is anticipated that the agreement between AEEDLW and AEE yielded from 7-day Activity Diary will not be worse than STAR-Q because the former activity diary tends to obtain activities and postures every 20 minutes while awake for 7 days. In terms of the secondary objective, we expect to see Mifflin-St Jeor equation, in this case, has highest accuracy in determining RMR for healthy, middle-aged, non-obese adults against RMRDLW, compared with Harris-Benedict and WHO/FAO/UNU equations. If the goal is met, the validated STAR-Q will facilitate much needed studies of AEE and its etiologic roles in disorders of energy imbalance, an epidemic of which is emerging in Canada and worldwide, along with the steady rise in the prevalence of obesity-related disease.
VI. Reference
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