Accuracy of a portable multisensor body monitor for predicting resting energy expenditure in older people: a comparison with indirect calorimetry

Analysis of Physical Activity Among Free-Living Nonagenarians From a Sardinian Longevous Population

Physical activity was identified as a major determinant of longevity. Using wearable accelerometers, we evaluated energy expenditure (EE), including resting- (REE) and total-energy expenditure (TEE), physical activity level (PAL), percentage of PAL ≥ 3 metabolic equivalent tasks (METs), number of steps, resting index (RI%) and sleep patterns in 44 free-living nonagenarians (27 men) residing in a Sardinian village famous for its longevous population. The average REE and TEE recorded were 1275 ± 163 kcal/day and 2284 ± 543 in the men and 952 ± 108 kcal/day and 1810 ± 302 in the women, respectively. The average PAL was 1.8, and the percentage of physical activity >3 METs was greater than 40%. A significant negative correlation (p < 0.05) between disability and PAL was found among the women. This study provides evidence that nonagenarians from the longevous population of Sardinia show excellent physical functionality indexes. Their longevity might result, at least in part, from their ability to stay physically fit during aging.

Evaluation of a non-invasive multisensor accelerometer for calculating energy expenditure in ventilated intensive care patients compared to indirect calorimetry and predictive equations

Continuous measurement of resting energy expenditure (REE) in critically ill patients remains challenging but is required to prevent malnutrition. SenseWear Pro 3 Armband (SWA) is a research grade accelerometer for assessment of REE with the advantage of easy handling. In a prospective study we compared SWA with indirect calorimetry (IC) and predictive equations in critically ill, ventilated patients. REE was measured by SWA, IC and calculated by predictive formulas. Potential confounding factors that influence REE were also recorded. Results of SenseWear Armband and indirect calorimetry were compared using the Bland-Altman method. 34 ICU patients were investigated. SWA underestimated resting energy expenditure compared to IC with a mean bias of ΔREE = -253.6 ± 333.2 kcal, equivalent to -11.7 % (p = 0.025). This underestimation was seen in both, medical (-14.9 %) and surgical (-12.9 %) patients and the bias was greater in patients with fever (-19.0 %), tachycardia (-18.7 %) or tachypnea (-26.2 %). Differences were also noted when SWA was compared to predictive formulas. At present, SWA cannot be regarded as an alternative to indirect calorimetry. Individual measurements are often inaccurate and should be used with caution until improved algorithms, based on the results of this study, have been implemented.

Low validity of the Sensewear Pro3 activity monitor compared to indirect calorimetry during simulated free living in patients with osteoarthritis of the hip

Background

To validate physical activity estimates by the Sensewear Pro3 activity monitor compared with indirect calorimetry during simulated free living in patients diagnosed with osteoarthritis of the hip pre or post total hip arthroplasty.

Methods

Twenty patients diagnosed with hip osteoarthritis (10 pre- and 10 post total hip arthroplasty; 40% female; age: 63.3 ± 9.0; BMI: 23.7 ± 3.7). All patients completed a 2 hour protocol of simulated free living with 8 different typical physical activity types. Energy consumption (kcal/min) was estimated by the Sense Wear pro3 Armband activity monitor and validated against indirect calorimetry (criterion method) by means of a portable unit (Cosmed K4b²). Bias and variance was analyzed using functional ANOVA.

Results

Mean bias during all activities was 1.5 Kcal/min 95%CI [1.3; 1.8] corresponding to 72% (overestimation). Normal gait speed showed an overestimation of 2.8 Kcal/min, 95%CI [2.3; 3.3] (93%) while an underestimation of -1.1 Kcal/min, 95%CI [-1.8; -0.3] (-25%) was recorded during stair climb. Activities dominated by upper body movements showed large overestimation with 4.37 Kcal/min, 95%CI [3.8; 5.1] (170%) being recorded during gardening. Both bias and variance appeared to be dependent on activity type.

Conclusion

The activity monitor generally overestimated the energy consumption during common activities of low to medium intensity in the patient group. The size and direction of the bias was highly dependent on the activity type which indicates the activity monitor is of limited value in patients with hip osteoarthritis and that the results do not express the real energy expenditure.

Energy expenditure evaluation in humans and non-human primates by SenseWear Armband. Validation of energy expenditure evaluation by SenseWear Armband by direct comparison with indirect calorimetry

Introduction

The purpose of this study was to compare and validate the use of SenseWear Armband (SWA) placed on the arm (SWA ARM) and on the back (SWA BACK) in healthy humans during resting and a cycle-ergometer exercise and to evaluate the SWA to estimate Resting Energy Expenditure (REE) and Total Energy Expenditure (TEE) in healthy baboons.

Methods

We studied 26 (15F/11M) human subjects wearing SWA in two different anatomical sites (arm and back) during resting and a cycle-ergometer test and directly compared these results with indirect calorimetry evaluation (IC), performed at the same time. We then inserted the SWA in a metabolic jacket for baboons and evaluated the TEE and REE in free living condition for 6 days in 21 (8F/13M) non-human primates.

Results

In humans we found a good correlation between SWA place on the ARM and on the BACK with IC during the resting experiment (1.1±0.3 SWAs, 1±0.2 IC kcal/min) and a slight underestimation in the SWAs data compared with IC during the cycle-ergometer exercise (5±1.9 SWA ARM, 4.5±1.5 SWA BACK and 5.4±2.1 IC kcal/min). In the non-human primate (baboons) experiment SWA estimated a TEE of 0.54±0.009 kcal/min during free living and a REE of 0.82±0.06 kcal/min.

Conclusion

SWA, an extremely simple and inexpensive apparatus, provides quite accurate measurements of energy expenditure in humans and in baboons. Energy expenditure data obtained with SWA are highly correlated with the data obtained with “gold standard”, IC, in humans.

Validity of activity monitors in health and chronic disease: a systematic review

The assessment of physical activity in healthy populations and in those with chronic diseases is challenging. The aim of this systematic review was to identify whether available activity monitors (AM) have been appropriately validated for use in assessing physical activity in these groups. Following a systematic literature search we found 134 papers meeting the inclusion criteria; 40 conducted in a field setting (validation against doubly labelled water), 86 in a laboratory setting (validation against a metabolic cart, metabolic chamber) and 8 in a field and laboratory setting. Correlation coefficients between AM outcomes and energy expenditure (EE) by the criterion method (doubly labelled water and metabolic cart/chamber) and percentage mean differences between EE estimation from the monitor and EE measurement by the criterion method were extracted. Random-effects meta-analyses were performed to pool the results across studies where possible. Types of devices were compared using meta-regression analyses. Most validation studies had been performed in healthy adults (n = 118), with few carried out in patients with chronic diseases (n = 16). For total EE, correlation coefficients were statistically significantly lower in uniaxial compared to multisensor devices. For active EE, correlations were slightly but not significantly lower in uniaxial compared to triaxial and multisensor devices. Uniaxial devices tended to underestimate TEE (−12.07 (95%CI; -18.28 to −5.85) %) compared to triaxial (−6.85 (95%CI; -18.20 to 4.49) %, p = 0.37) and were statistically significantly less accurate than multisensor devices (−3.64 (95%CI; -8.97 to 1.70) %, p<0.001). TEE was underestimated during slow walking speeds in 69% of the lab validation studies compared to 37%, 30% and 37% of the studies during intermediate, fast walking speed and running, respectively. The high level of heterogeneity in the validation studies is only partly explained by the type of activity monitor and the activity monitor outcome. Triaxial and multisensor devices tend to be more valid monitors. Since activity monitors are less accurate at slow walking speeds and information about validated activity monitors in chronic disease populations is lacking, proper validation studies in these populations are needed prior to their inclusion in clinical trials.