Development and Validation of a Measurement System for Assessment of Energy Expenditure and Physical Activity in Prader–Willi Syndrome

Abstract

Objective

The morbid obesity associated with Prader–Willi syndrome (PWS) may result from either excessive energy intake or reduced energy expenditure (EE). In this report, we describe the development and validation of an Activity–Energy Measurement System (AEMS) to measure EE and physical activity components in an environment approximating free-living conditions.

Research Methods and Procedures

The AEMS consists of a live-in, whole-room indirect calorimeter equipped with a novel force platform floor system to enable simultaneous measurements of EE, physical activity, and work efficiency during spontaneous activities and standardized exercises. Free-living physical activity and estimated free-living EE are measured using portable triaxial accelerometers individually calibrated in each subject during their stay in the AEMS.

Results

Representative data from two PWS patients and two matched control (CTR) subjects displayed EE during their inactive lifestyles.

Discussion

This combination of methods will allow the quantification of daily EE and its components, the amount and energy cost of physical activity, and the relationships between body composition and EE, in order to determine their roles in the development and maintenance of the morbid obesity in PWS.

Introduction

The morbid obesity associated with Prader–Willi syndrome (PWS) is the result of chronic imbalance between energy intake and energy expenditure (EE). However, it is difficult to obtain quantitative information regarding these two components of energy balance in PWS patients due to the mental and physical status of the patients. It is essential to understand the cause of obesity in PWS in order to determine rational treatment options. Although the abnormal eating behaviors and excessive energy intake are well-documented (1), there is limited information available regarding EE in PWS patients. Techniques for measuring EE and physical activity in individuals with PWS may be major limiting factors due to the special requirements dictated by the behavioral, cognitive, and functional limitations in PWS.

Several previous studies have attempted to measure EE in PWS patients. In one study (2), total daily EE was 47% lower in PWS compared to controls, but this difference was reduced to 14% when allowances were made for differences in fat-free mass (FFM). Basal or resting EE, corrected for FFM, was similar in PWS patients compared to obese controls, suggesting that the low daily EE in PWS patients was mainly a result of a reduced FFM, and possibly a lower level of physical activity, although there was no direct assessment of the latter. In another study (3), daily EE measured in a whole-room calorimeter was also lower in PWS patients compared to obese controls. However, no information was available on differences in FFM, which may have accounted for a large part of this difference. Hill et al. (4) reported that the tight relationship usually observed between resting EE and FFM differed in children with PWS compared to lean or obese controls. The difference suggested initial low rates of EE in subjects with PWS, independent of FFM, but once patients gained a large amount of weight, this relationship normalized.

Reduced physical activity in PWS subjects may be the major cause of their decreased energy requirements, as highlighted by Schoeller et al. (2). PWS patients are characterized by hypotonia during infancy, which decreases during the first 2 to 3 years of life, leaving a residual amount thereafter. This low muscle tone and lack of coordination may favor a sedentary lifestyle (5). However, few studies have attempted to quantify levels of physical activity in PWS. It has been reported that children with PWS are less physically active during play compared to normal children (5). Nardella et al. (6) attempted to assess physical activity in PWS subjects and controls at a summer camp over a 2-week period using actometers and pedometers. They reported a wide range of activity levels in subjects with PWS and slightly higher physical activity compared to normal children. These results were inconclusive with respect to physical activity in PWS and furthermore do not address the situation in a normal home environment. Because physical activity is an important component of daily EE, even in relatively sedentary lifestyles, this factor needs further examination.

In this report, we describe a novel activity–energy measurement system (AEMS) that allows simultaneous measurement of EE and physical activity (7, 8) in PWS patients and obese controls under close to free-living conditions. Using the AEMS, studies are underway to examine the relationships between EE, physical activity, and body composition in PWS patients and their controls.

Methods

AEMS

The AEMS at Vanderbilt University combines a whole-room indirect calorimeter with a large force platform floor system (7), as shown in Figure 1. Equipped with a desk, chair, toilet, sink, telephone, TV/VCR, audio-system, and fold-down bed, the AEMS is designed to bridge the differences between laboratory and free-living conditions. The room calorimeter is an airtight environmental room measuring 2.6×3.4×2.4 m³ and contains 19,500 L in net volume. Temperature inside the calorimeter is precisely controlled (22.5±0.2°C). Oxygen consumption (VO₂), carbon-dioxide production (VCO₂), air flow rate, temperature (inside and ambient), barometric pressure, and humidity are sampled 60 times per second and integrated at the end of each minute to calculate EE (9). To ensure homogeneity of the air in the calorimeter, a special multi-channel air sampling system is incorporated (8). The oxygen (Magnos 4G) and carbon dioxide (Uras 3G) analyzers (both by Hartman and Braun) are calibrated before each test with reference gas, and on a regular basis with mixtures of pure gases by a precision mixing pump. The accuracy of the calorimeter is frequently validated by burning pure propane or ethyl alcohol at a variable rate inside the room (8). By comparing the EE measured by the calorimetry system and EE calculated from the rate of combustion, the system error of the AEMS is controlled under 1% for 24-hour recordings (8).

Figure 1:

A schematic of the activity-energy measurement system (AEMS).

The force platform, measuring 2.5×2.5 m², covers the entire living area inside the whole-room indirect calorimeter and is supported by multiple precision force transducers (7). The force platform allows computer-aided measurement (60 times per second) of body position, displacement, and mechanical forces with an accuracy of 97% or higher (7). An electronic monitoring/sensing system in the calorimetric chamber consists of sensors and switches installed inside the TV/VCR, underneath the sleeping mattress, inside the chair, and at the airlock door where the participant receives food to record patterns of physical activity. Eight additional event buttons are used by the subject to signal periods of sleeping, eating, and exercising. Therefore, the combination of the force platform, sensors, and event buttons provides the accurate determination of the nature, duration, and frequency of physical activities at each minute. Activity records are also kept by the observing research staff due to the limited abilities of some participants to operate the event button system. A member of the research staff stays in the anteroom outside of the AEMS during the entire study period, monitors the participants behavior though a glass window, communicates directly with the participant through an intercom, and instructs the participant to perform certain tasks, including standardized exercises. In addition, a VHS video camera provides continuous monitoring by nursing staff to ensure the safety of subjects with special needs (i.e., children, physically or mentally handicapped adults).

Measurement of EE and Its Components by AEMS

A major advantage of the AEMS is the continuous measurements of EE and other physical activity parameters over extended periods of time (hours to days), from which some important components of EE can be identified and studied individually. EE is usually expressed as the absolute rate (e.g., kJ/minute), or it can be expressed as a ratio with respect to basal EE (e.g., metabolic equivalents, or METs).

Resting EE (REE) is measured during the 30-minute period when the participant initially enters the AEMS after an overnight sleep and fast, while the participant is sitting in the chair with minimum body movement. Any periods of body motion detected by the force platform are automatically removed from the REE analysis.

EE during sitting (EE_SIT) is the average EE during which the participant sits in the chair and without significant body motion. Periods of sitting are detected via the sensors in the chair and through the participant’s center of gravity on the force platform.

EE during physical activity (EE_ACT) is defined as the increase in EE above the REE during body movements such as walking or other spontaneous physical activities. The sudden change in mechanical work measured by the force platform marks the starting and ending times of each activity. EE_ACT is calculated using an automated computer program by integrating the area under the EE/time curve and subtracting REE (Figure 2).

Figure 2:

Minute-by-minute energy expenditure (EE) and mechanical work (MW) measurements and the calculation of net efficiency (a representative data from the male control participant, CTRm, age 11, weight 102.1 kg).

Total EE (TEE) during the entire study period is obtained by summing the EE of each minute over the entire study period during which the participant stayed in the AEMS. TEE can also be expressed as the average rate of EE during the stay (kJ/minute). EE during the stay in the AEMS can also be divided into different intensity categories in order to study the patterns and distributions.

Measurement of Physical Activities by AEMS

Another important advantage of the AEMS over other existing methods is that the force platform can precisely determine variation in participant’s physical movement, thus obtaining quantitative measures of external mechanical work performed during various physical activities.

Body motion in terms of the cumulative distance a subject travels over time (rate of displacement, DP, or speed) was measured by the force platform in the AEMS. This design of the AEMS is believed to be the first to give an accurate measure of the distance and speed in which the participant has traveled inside the room (7).

Mechanical Work (MW) is obtained by calculating force, acceleration in x-, y-, and z-axes (time derivative from speed), and body mass. As shown in Figure 3, the measurements include both horizontal and vertical MW components (7). Horizontal work (HMW) is produced when the body’s center of gravity moves horizontally such as during walking, while vertical work (VMW) typically relates to posture changes, such as changes between sitting and standing.

Figure 3:

Measured energy expenditure (EE), respiratory quotient (RQ), body displacement (DP), horizontal mechanical work (HMW), and vertical mechanical work (VMW) during one study period for a study participant (CTRm).

During periods of exercise, net efficiency is calculated as the MW performed divided by the associated EE_ACT (7,8), e.g., MW / EE_ACT (Figure 2). Net efficiency of other pontaneous physical activities can also be expressed as the ratio of total MW and total EE_ACT. Thus, using this combination of the whole-room indirect calorimeter and the force platform, net efficiency of different types of physical activity and exercise is determined for each participant in one system.

Measurement of Free-Living EE and Physical Activity by Triaxial Accelerometers

A triaxial piezoelectric accelerometer (Tritrac-R3D activity monitor; Hemokinetics, Inc., Madison, WI) is used to record minute-by-minute physical activities in the form of acceleration measurements in three dimensions (x or antero- –posterior axis, y or medial–lateral axis, and z or vertical axis). The monitor, weighing 170 grams and measuring 11.1×6.7×3.2 cm, is worn on the right hip in a nylon pouch secured to a belt at the waistline during all activities in the AEMS. The accelerometer output is expressed as integrated acceleration over each minute in the three axes. The subject’s physical characteristics (gender, age, height, and weight) are entered upon initializing the monitor. This information is used to calculate an individual’s resting EE based on established predictive equations (shown in equations 1 and 2) (10). The prediction of EE during all physical activities is calculated internally using a regression equation from the vector magnitude of the acceleration registrations in the x-, y-, and z-axes.

To model EE from body acceleration data measured by the Tritrac-R3D monitor, a two-component power model was used (equation 3). In brief, body motion in the horizontal plane (x- and y-axes) was combined as one component (denoted H), and acceleration in the vertical plane (z-axis) as the other component (denoted V). Each component was modeled by a linear and a nonlinear power parameters to model individual EE_ACT. Detailed description of this model has been published previously (10).

Subjects

Representative experiments were carried out in two PWS patients and two controls of similar age, gender, weight, adiposity and mental capacity (Table 1). All volunteers are non-smokers, non-diabetic, and have normal renal, hepatic, and thyroid function as judged from routine laboratory tests. The diagnosis of PWS was established according to the criteria of Holm (11) and confirmed by genetic analysis. Height was recorded to the nearest 0.5 cm, and weight was measured to the nearest 0.1 kg using a digital scale. Body composition was determined by dual-energy X-ray absorptiometry (12–14).

Table 1.

Subject characteristics

	PWS		Controls
	Male	Female	Male	Female
Age (years.month)	16.2	34.7	11.0	23.5
Weight (kg)	101.4	54.4	102.1	46.7
Height (cm)	151.1	147.3	167.5	147.3
BMI (kg/m2)	44.5	25.2	36.4	21.6
Body fat (%)	49.0	35.2	53.2	20.1
FFM (kg)	51.6	35.4	48.0	37.4

PWS, Prader-Willi syndrome; BMI, body mass index; FFM, fat-free mass. Body composition was determined by dual-energy X-ray absorptiometry.

Experimental Protocol

Informed consent was obtained prior to participation from each subject, and when appropriate from a parent or a legal guardian. The study protocol was reviewed and approved by the Institutional Review Board of the Vanderbilt University. The participant is admitted to the AEMS at 07:15 a.m. after an overnight fast. EE and physical activity were measured continuously from 7:30 a.m.–3:30 p.m. During the entire measuring period, the participant is monitored and given instructions by a member of the research staff who stays in the anteroom next to the AEMS.

The study protocol in the AEMS is as follows. Upon entering the AEMS, the participant rests in the chair for 20–30 minutes for REE measurement. Meals are served at 8:00 a.m. and 12:00 p.m. In addition, light snacks are provided at approximately 10:00 a.m. and 2:00 p.m. The amount and the macronutrient content of the food are designed to reflect their normal energy intake, estimated from a 7-day diet recall by the subjects or their parent or guardian. The participants are instructed to walk during 2 exercise sessions (10-minutes each, with a 10-minute break between them) during the morning (9:30–10:00) and two exercise sessions in the afternoon (2:30–3:00). At 3:30 p.m., the participant is escorted out of the AEMS at the conclusion of the 8-hour measurement period.

Each participant also wears the portable accelerometer during the entire stay in the AEMS and at least 3 days following for measurement of free-living physical activity and EE as described above.

Statistical Analysis

The data were expressed as means ± Standard Deviation. Relationships between variables were determined using the Pearson product-moment correlation coefficients (r). Analysis of variance (ANOVA) was used to compare the differences between measured and estimated EE values. p-value of 0.05 was used as the threshold. All data processing and analyses were performed by either MATLAB (version 5, MathWorks Inc., Natick, MA) or SPSS for Windows (version 7, SPSS Inc., Chicago, IL) software packages.

Results

The physical characteristics of the study participants are listed in Table 1. Representative examples of energy expenditure (EE) and physical activity (PA) measurements are illustrated in describing the data measurements and processing (Figures 2 and 3). The measurement period in the AEMS averaged 468±6 minutes (range, 462–472 minutes). In all participants, the AEMS detected a large percentage of the study periods was spent in sedentary PA, including 66±10% of the entire time in sitting (either resting, watching TV, or performing other non-weight-baring PA). The MW measured by force platform was used to determine the exact duration of walking exercise periods, averaging 41±23 min (range, 10–63 minutes) for these participants. Also, by using the force platform of the AEMS, measured total DP varied from 31.1 to 69.0 meters/8 hours; total mechanical work (MW) averaged 147.2±110.2 kJ/8 hours; and the horizontal and vertical MW averaged 42.5±33.9 and 104.7±79.7 kJ/8 hours, respectively. Additional parameters of PA are shown in Table 2.

Table 2.

Components of physical activity parameters in PWS subjects and controls in the AEMS

	PWS		Controls
	Male	Female	Male	Female
Displacement (m/8 hours)	46.73	31.05	69.03	35.57
Vertical MW (kJ/8 hours)	14.59	3.04	19.31	3.63
Horizontal MW (kJ/8 hours)	22.56	5.87	51.27	20.22
Total MW (kJ/8 hours)	37.15	8.91	70.58	23.85

Values are averaged over the entire study period and extrapolated to 8 hours. PWS, Prader-Willi syndrome; MW, mechanical work measured by the force platform.

EE measurements using the indirect calorimeter of the AEMS are summarized in Table 3. For these participants, total EE averaged 3412±1147 kJ/8 hours. The rate of EE remained ≤1.5 METs for 80%–95% of the 8-hour measurement period. EE during spontaneous physical activity (SPA) and during walking exercise periods increased to 6.82+1.94 and 10.91±4.62 kJ/minute, respectively. The ratio of total to resting EE was 1.39±0.11 (vary between 1.26 and 1.53). Net efficiency was calculated by using the ratio of MW and EE_ACT, averaging 13.8±6.9% during SPA and 17.5±5.3% during walking exercise (Table 3) for the entire group. Net efficiency tended to be reduced in PWS subjects during both SPA (8.0% vs. 19.6%) and walking exercise (13.1% vs. 21.9%) as compared to their controls.

Table 3.

Components of total energy expenditure and net efficiency in PWS subjects and controls in the AEMS

		PWS		Controls
		Male	Female	Male	Female
Resting	REE (kJ/minute)	5.74	3.63	6.78	4.10
Sitting	EEsrr (kJ/minute)	7.52	4.62	7.55	4.75
SPA	EEACT (kJ/minute)	8.29	4.83	8.67	5.49
	Net Efficiency (%)	8.7	7.3	21.7	17.5
Walking	EEACT (kJ/minute)	12.83	6.95	16.54	7.32
Exercises	Net Efficiency (%)	14.5	11.7	23.3	20.4
Average	EE (kJ/minute)	8.79	4.93	9.53	5.19
Total	TEE (kJ/8 hour)	4220	2364	4572	2491

Values are individual means. PWS, Prader-Willi syndrome; SPA, spontaneous physical activities.

During the 8-hour measurement period in the AEMS, body acceleration counts recorded each minute by the triaxial accelerometers (Tritrac) were significantly correlated with the simultaneous force platform measurements of body displacement (r = 0.79±0.17, p<0.01) and total MW (r = 0.62±0.25, p<0.01). As shown in Figure 4(a), the estimates of minute-to-minute EE from the Tritrac were also significantly correlated with actual EE measured by the AEMS (r = 0.68±0.21, p<0.01). However, total EE values estimated from the preprogrammed Tritrac model were lower than the measured values [Figure 4(b)]. After applying the two-component power model to the raw accelerometer data, estimated total EE were more strongly correlated with actual total EE (r = 0.71±0.22). Furthermore, the estimated and actual EE were no longer significantly different (p>0.2).

Figure 4:

In (a), estimated rate of energy expenditure (EE) from the Tritrac-R3D monitor vs. EE measured by the AEMS during one study period (representative data from the female PWS participant, PWSf, age 35, weight 54.4 kg). Data points below the line of identity represent underestimation. In (b), estimated total energy expenditure (EE) for the 8 hours from the Tritrac-R3D monitor and the two compartment power model (Equation 3 of Methods) vs. total EE measured by the AEMS for each participant is shown. Data points below the line of identity represent underestimation.

To extend the PA monitoring in the free-living state, these participants continued to wear the Tritrac monitor for up to 5 days following the AEMS stay. Estimated daily free-living EE, using the individually validated prediction models, was found to be 9120±3432 kJ/24 hours in the PWS participants and 10291±4103 kJ/24 hours in the matched controls. During the same 8-hour periods (07:30 a.m.–3:30 p.m.), free-living EE averaged 3021±1398 kJ/8 hours in the PWS participants and 3288±1653 kJ/8 hour in the controls. As shown in Figure 5, the PWS and control participants were at least as physically active during the AEMS measurement period as they were during the subsequent 5 days of free-living. The overall physical activity index (defined as the ratio of free-living PA to measured PA in the AEMS) was 0.80±0.53.

Figure 5:

Daily free-living physical activity measured by a triaxial accelerometer for the two male (PWSm and CTRm) and two female participants (PWSf and CTRf). The index is calculated as the ratio of daily physical activity (24-hour basis) in the free living and the 24 hours during the AEMS stay.

Discussion

In this report, we describe a unique methodological approach for the study of energy expenditure and physical activity in Prader–Willi syndrome (PWS). Our AEMS combines a rapid-response, whole-room calorimeter with a novel force platform floor system. This AEMS enables simultaneous measurements of the amount and energy cost of physical activity under conditions that approximate the free-living state (7,8). In addition, subjects wear triaxial piezoelectric accelerometers while residing in the AEMS to individually calibrate the outputs from these portable devices for subsequent estimation of free-living physical activity and energy expenditure (10). With this approach, it is now possible to measure long-term energy expenditure, physical activity patterns, and energetic efficiency during physical activity under controlled conditions and to more accurately estimate energy expenditure and physical activity in the free-living state. This unique approach will allow us to better characterize the contributions of abnormalities in energy expenditure and reduced physical activity to obesity development in PWS.

The few previous studies attempting to measure long-term energy expenditure and physical activity in PWS have yielded conflicting results (2–4,6,15). Some of the disagreement between studies may be related to limitations in the methods used. Quantitative measures of physical activity are difficult to obtain using only actometers and pedometers due to inaccuracies produced by variations in the type and intensity of activities (6,10,16). As we have demonstrated previously (4), this problem can be further amplified by using standard linear regression models to estimate energy expended in free-living physical activities. In contrast, our methodological approach uses the AEMS to individually calibrate portable accelerometers by accurately measuring mechanical work and energy expenditure during a variety of physical activities in a well-controlled environment (10). As shown in Figure 5, energy expenditure and levels of physical activity during the AEMS measurement periods were reasonably similar to free-living EE and physical activity measured using the calibrated portable accelerometers. In fact, the PWS and control subjects studied were at least as physically active during the 8-hour measurement period in the AEMS as they were during the subsequent free-living period (Figure 5). Thus, the combination of the AEMS and portable accelerometers should provide accurate estimates of energy expenditure and physical activity under free-living conditions in individuals with PWS.

Other investigators have used a combination of doubly labeled water (DLW) and conventional indirect calorimetry to estimate energy expended during free-living physical activity in PWS (2,15). With this approach, total energy expenditure (TEE) is measured by DLW, and resting energy expenditure (REE) is measured by indirect calorimetry. The difference (TEE–REE) or the ratio (TEE/REE) between these independent measures is taken to be an estimate of EE due to physical activity. However, a number of potential problems may limit the accuracy of this approach, especially in PWS patients. The DLW method can significantly underestimate TEE in heavier and fatter subjects (17). In addition, the energy equivalent of CO₂ varies by as much as 10% between individuals consuming different diets (17). The resultant errors in TEE caused by these problems are likely to be magnified in PWS subjects due to their hypercaloric feeding in the free-living state. Moreover, previous studies involving DLW in PWS patients measured initial isotope enrichment and subsequent disappearance rates in saliva (2,15). This may introduce additional errors because of the well-described abnormalities in saliva in PWS patients (18), which may lead to isotope fractionation (19). Finally, it should be noted that the difference between TEE and REE includes both energy expended in physical activity and the thermic effect of food. The latter may be a relatively large component in PWS subjects due to their high caloric intake and physical inactivity.

In a preliminary report, Widhalm et al. (3) found that TEE was reduced in PWS subjects during stays in a respiration chamber. However, quantitative measurements of physical activity and EE_ACT were not possible. Rather, the radar motion detectors used by this and other respiration chambers provide only qualitative estimates of activity (i.e., “moving” or “not moving”) (3,9). In contrast, the novel force platform floor system enables us to directly measure mechanical work (MW) performed by PWS subjects residing in our AEMS, along with the associated increases in EE (EE_ACT) produced by these activities (7,8,20). Using this approach, we are also able to calculate net energetic efficiency (i.e., MW/EE_ACT) during various physical activities (Figure 2). As we have reported previously (7,8), EE_ACT and net efficiency during physical activities vary widely between individuals and may play an important role in body weight regulation and obesity development in humans.

However, we also need to point out that our measurement of REE, while the subject was seated, might still be slightly higher than while lying down, even under the same conditions of fasting, resting, and minimum body movements. We are aware that standard REE measurements should be taken in the supine posture. But most patients with PWS would have difficulties following such protocols as lying down, minimum body motion, and staying awake, shortly after waking in the morning. We therefore selected the closest to the “true” REE status, seated with minimum body motion, fasted, and well-rested. Furthermore, we initially wanted to measure 24-hour EE in the AEMS to obtain a complete picture of energy metabolism for patients with PWS, but soon recognized the prohibiting practical and safety issues for this population group. Thus, the 8-hour monitoring period was chosen.

In summary, we report a unique methodological approach to the measurement of long-term energy expenditure and physical activity in Prader–Willi syndrome. This approach combines a novel respiration chamber/force platform floor system with portable triaxial accelerometers to assess free-living EE and physical activity. Using this novel approach, studies will be possible to address the contribution of reduced energy expenditure and decreased physical activity to the morbid obesity associated with Prader–Willi syndrome.