Energy Expenditure In Older Adults Research Articles

Validation of activity trackers to estimate energy expenditure in older adults with cardiovascular risk factors.

To compare different types of activity trackers recording physical activity energy expenditure (PAEE) and examine their criterion validity against indirect calorimetry (IC) as the gold standard in adults over 60 years of age with a special focus on women with cardiovascular risk. Synchronous registrations of PAEE were performed with up to four different devices to determine criterion validity against IC while participants performed a protocol of simulated activities in a laboratory setting. Thirty-four participants (25 women, 9 men) with at least a light cardiac risk performed a protocol of simulated activities in a laboratory setting (daily living activities, cycle ergometer test). PAEE was simultaneously assessed by IC, two research-grade activity trackers (ActiGraph-wGT3X-BT and Actiheart-4) and two consumer-level activity trackers (OMRON pedometer and Fitbit Charge-3). Tracker-derived PAEE was compared with PAEE calculated from IC descriptively and by Bland-Altman plots. The ActiGraph (0.7 ± 0.4 kcal/min), the Actiheart (1.1 ± 0.6 kcal/min) and the OMRON (0.8 ± 0.6 kcal/min) underestimated, while the Fitbit (3.4 ± 1.2 kcal/min) overestimated PAEE compared to IC-PAEE (2.0 ± 0.5 kcal/min). The Bland-Altman limits of agreement (LoA) against IC were +0.5/+2.2 kcal/min for the ActiGraph, -0.3/+2.1 kcal/min for the Actiheart, -3.7/+1.0 kcal/min for the Fitbit, and -0.5/+2.9 kcal/min for the OMRON. The magnitude of the deviation varied considerably depending on the activity (e.g. walking, cleaning, cycle ergometer test). The research-grade activity trackers estimated PAEE with higher validity than the commercially available activity trackers. The partly very wide LoA have to be critically considered when assessing PAEE in the context of health service research, as individual Physical Activity behaviour may be under- or overestimated.

Body composition affects the accuracy of predictive equations to estimate resting energy expenditure in older adults: An exploratory study

To investigate the accuracy of ten different predictive equations to estimate resting energy expenditure (REE) in a sample of Brazilian older adults and develop a predictive equation for estimating REE based on body composition data. A cross-sectional study with thirty-eight Brazilian older adults aged 60-84 years, who had their REE measured by indirect calorimetry (IC) and BC assessed by dual-energy x-ray absorptiometry (DXA). REE was compared to the estimation of ten predictive equations, and the differences between BC and anthropometric-based equations were investigated using Bland-Altman plots and Lin's concordance correlation. Accuracy was evaluated considering ±10% of the ratio between estimated and measured REE. The sample was composed of 57.9% men, with a mean age of 68.1 (5.8) years, and a mean REE by IC of 1528 (451) kcal. The highest accuracy was 47.4% obtained by Luhrmann and Fredrix equations, and the lowest accuracy was 13.2% reached by Weigle equation. In general, the proportion of underestimation was higher than overestimation. All anthropometric-based equations presented a good agreement with REE from IC. For those equations derived from BC, however, three of them reached only a moderate agreement. In terms of accuracy, all equations presented lower than 50% of accurate prediction of REE. In this sample of older adults, previous predictive equations to estimate REE did not show good accuracy, and those based on BC presented even worse results, showing that changes in BC related to aging could impact the accuracy of these equations.

The Effect of Sensor Placement and Number on Physical Activity Recognition and Energy Expenditure Estimation in Older Adults: Validation Study.

BackgroundResearch has shown the feasibility of human activity recognition using wearable accelerometer devices. Different studies have used varying numbers and placements for data collection using sensors.ObjectiveThis study aims to compare accuracy performance between multiple and variable placements of accelerometer devices in categorizing the type of physical activity and corresponding energy expenditure in older adults.MethodsIn total, 93 participants (mean age 72.2 years, SD 7.1) completed a total of 32 activities of daily life in a laboratory setting. Activities were classified as sedentary versus nonsedentary, locomotion versus nonlocomotion, and lifestyle versus nonlifestyle activities (eg, leisure walk vs computer work). A portable metabolic unit was worn during each activity to measure metabolic equivalents (METs). Accelerometers were placed on 5 different body positions: wrist, hip, ankle, upper arm, and thigh. Accelerometer data from each body position and combinations of positions were used to develop random forest models to assess activity category recognition accuracy and MET estimation.ResultsModel performance for both MET estimation and activity category recognition were strengthened with the use of additional accelerometer devices. However, a single accelerometer on the ankle, upper arm, hip, thigh, or wrist had only a 0.03-0.09 MET increase in prediction error compared with wearing all 5 devices. Balanced accuracy showed similar trends with slight decreases in balanced accuracy for the detection of locomotion (balanced accuracy decrease range 0-0.01), sedentary (balanced accuracy decrease range 0.05-0.13), and lifestyle activities (balanced accuracy decrease range 0.04-0.08) compared with all 5 placements. The accuracy of recognizing activity categories increased with additional placements (accuracy decrease range 0.15-0.29). Notably, the hip was the best single body position for MET estimation and activity category recognition.ConclusionsAdditional accelerometer devices slightly enhance activity recognition accuracy and MET estimation in older adults. However, given the extra burden of wearing additional devices, single accelerometers with appropriate placement appear to be sufficient for estimating energy expenditure and activity category recognition in older adults.

Comparing ActiGraph equations for estimating energy expenditure in older adults

ABSTRACTAccurate estimation of energy expenditure (EE) from accelerometer outputs remains a challenge in older adults. The aim of this study was to validate different ActiGraph (AG) equations for predicting EE in older adults. Forty older adults (age = 77.4 ± 8.1 yrs) completed a set of household/gardening activities in their residence, while wearing an AG at the hip (GT3X+) and a portable calorimeter (MetaMax 3B – criterion). Predicted EEs from AG were calculated using five equations (Freedson, refined Crouter, Sasaki and Santos-Lozano (vertical-axis, vectormagnitude)). Accuracy of equations was assessed using root-mean-square error (RMSE) and mean bias. The Sasaki equation showed the lowest RMSE for all activities (0.47 METs) and across physical activity intensities (PAIs) (range 0.18–0.48 METs). The Freedson and Santos-Lozano equations tended to overestimate EE for sedentary activities (range: 0.48 to 0.97 METs), while EEs for moderate-to-vigorous activities (MVPA) were underestimated (range: −1.02 to −0.64 METs). The refined Crouter and Sasaki equations showed no systematic bias, but they respectively overestimated and underestimated EE across PAIs. In conclusion, none of the equations was completely accurate for predicting EE across the range of PAIs. However, the refined Crouter and Sasaki equations showed better overall accuracy and precision when compared with the other methods.

Correlations between Objective and Subjective Instruments on the Measurement of Energy Expenditure in Older Adults

PURPOSE: Many objective and subjective instruments have been developed to estimate energy expenditure (EE). However, the validity and correlations on these measurements are lacking especially in the older adult population. American Association of Retired Persons (AARP) questionnaire and Community Healthy Activities Model Program for Seniors (CHAMPS) questionnaire are common self-reported questionnaires. The purpose of the study was to examine which self-reported physical activity instruments correlated with objective data from ActiGraph accelerometers and fitness tests. We also compared outcomes from the above instruments with total EE (TEE) measured by the gold standard of doubly labeled water (DLW). METHODS: Data sets were obtained from the Interactive Diet and Activity Tracking in AARP (IDATA) study through the National Cancer Institute. A total of 681 participants aged between 50-74 years were included in the analysis. The outcomes of our interest from each instrument were as follows: total number of steps and EE from ActiGraph, VO2 max from fitness test, activity EE (AEE) from CHAMPS and AARP, and TEE from DLW. Pearson’s correlations were conducted to examine the relationships between objective and subjective measures. RESULTS: The results showed that AEE from AARP was moderately significantly associated with VO2 max (r = .348) and EE from ActiGraph (r = .365). A weak but significantly positive relationship was also observed between AEE from AARP and steps from ActiGraph (r = .136). However, AEE from CHAMPS was only related to steps from ActiGraph (r = .172). No significant correlations were found between AEE from CHAMPS and the other objective measures. Each instrument was also compared to DLW. The strongest positive relationships with TEE from DLW were seen in EE from ActiGraph (r = .484) and VO2 max (r = .474). However, there was no significant association between AEE from CHAMPS and TEE from DLW, p > .05. CONCLUSIONS: AEE from AARP appears to be the subjective instrument most positively associated with the objective instruments in older adults. Of all subjective and objective instruments, objective measures may estimate TEE more accurately than the subjective instruments. AEE from CHAMPS performed the worst estimate on TEE and was least likely to be related to the objective instruments.

Effects of Balance Control Through Trunk Movement During Square and Semicircular Turns on Gait Velocity, Center of Mass Acceleration, and Energy Expenditure in Older Adults

BackgroundTurning during ambulation is a common movement in everyday life, but complex and challenging for older adults. Balance control through trunk movement provides a stable platform during walking, thus it is an essential component of safe and efficient turning during walking in elderly individuals. ObjectivesTo investigate the effects of balance control during square turning (ST) and semicircular turning (SCT) on gait velocity, center of mass (COM) acceleration, and energy expenditure in elderly women. DesignCross-sectional design. SettingVillage community center. ParticipantsTwenty community-dwelling elderly women capable of independent walking were enrolled in the study. MethodsParticipants walked at a self-selected speed along a marked path that included 2 types of turns (the path was divided into 3 segments: straight, turning, and straight return), while fitted with an accelerometer attached over the L3 spinous process. Main Outcome MeasurementsDifferences in gait velocity, normalized COM acceleration, and energy expenditure were analyzed using paired t-tests for comparisons between ST and SCT tasks and using a one-way repeated-measures analysis of variance for within tasks. ResultsDuring the ST task, which was characterized by the use of a less-stable balance maintenance strategy, gait velocity and vertical COM acceleration were lower (P < .05), whereas greater medial-lateral COM acceleration (P < .05) and energy expenditure (P < .001) were observed during turning and return straight stages compared with the SCT task. For both tasks, velocity during turning stage was the slowest, among the 3 stages, the straight stage was the fastest (P < .05). For the SCT task, the anterior-posterior COM acceleration during the straight stage was significantly higher than during the turning stage, and the vertical COM acceleration during the straight stage was significantly lower than during the return-straight stage (P < .05). In both tasks, the energy expenditure of the turning stage was significantly higher than in the straight and return straight stage (P ≤ .001), and in the return straight stage was higher than the straight stage–only ST task (P < .05). ConclusionsWe suggest that elderly individuals participate in balance and gait training using a variety of turns, including turns requiring medial-lateral and vertical COM balance control, to prevent falls and to improve energy efficiency of walking. Level of EvidenceIV

Activity-Related Energy Expenditure in Older Adults

The purposes of this article were to 1) provide an overview of the science of physical activity-related energy expenditure in older adults (≥65 yr), 2) offer suggestions for future research and guidelines for how scientists should be reporting their results in this area, and 3) present strategies for making these data more accessible to the layperson. This article was meant to serve as a preliminary blueprint for future empirical work in the area of energy expenditure in older adults and translational efforts to make these data useful and accurate for older adults. This document was based upon deliberations of experts involved in the Strategic Health Initiative on Aging Committee of the American College of Sports Medicine. The article was designed to reach a broad audience who might not be familiar with the complexities of assessing energy expenditure, especially in older adults.

Estimating energy expenditure from heart rate in older adults: a case for calibration.

BackgroundAccurate measurement of free-living energy expenditure is vital to understanding changes in energy metabolism with aging. The efficacy of heart rate as a surrogate for energy expenditure is rooted in the assumption of a linear function between heart rate and energy expenditure, but its validity and reliability in older adults remains unclear.ObjectiveTo assess the validity and reliability of the linear function between heart rate and energy expenditure in older adults using different levels of calibration.DesignHeart rate and energy expenditure were assessed across five levels of exertion in 290 adults participating in the Baltimore Longitudinal Study of Aging. Correlation and random effects regression analyses assessed the linearity of the relationship between heart rate and energy expenditure and cross-validation models assessed predictive performance.ResultsHeart rate and energy expenditure were highly correlated (r = 0.98) and linear regardless of age or sex. Intra-person variability was low but inter-person variability was high, with substantial heterogeneity of the random intercept (s.d. = 0.372) despite similar slopes. Cross-validation models indicated individual calibration data substantially improves accuracy predictions of energy expenditure from heart rate, reducing the potential for considerable measurement bias. Although using five calibration measures provided the greatest reduction in the standard deviation of prediction errors (1.08 kcals/min), substantial improvement was also noted with two (0.75 kcals/min).ConclusionThese findings indicate standard regression equations may be used to make population-level inferences when estimating energy expenditure from heart rate in older adults but caution should be exercised when making inferences at the individual level without proper calibration.

Reproducibility of the Veterans Physical Activity Questionnaire in an elderly population.

Quantifying lifetime physical activity using self-reported measures is challenging due to reliance on recall, especially in older populations. The purpose of this study was to determine the 1-year reproducibility of the Veterans Physical Activity Questionnaire (VAPAQ) in a cohort of patients with documented abdominal aortic aneurysm disease (AAA). Subjects included men (n = 52) and women (n = 3) enrolled in AAA STOP, a randomized trial designed to test the ability of supervised exercise training to modify AAA biology and early disease progression. The overall correlation coefficient for lifetime recreational energy expenditure between the 2 examinations was 0.93 (P < .001), with an overall difference of 26 kcal/week, a typical error (standard deviation of the differences) of 171 kcals/week, and a coefficient of variation (CV) of 15.5%. The VAPAQ is a reproducible tool to quantify lifetime energy expenditure in older adults with documented vascular disease.

Intensity of Physical Activity in the Energy Expenditure of Older Adults

This study examined the intensity of activity contributing to physical activity energy expenditure in older adults. In 57 men and women aged ≥ 65, total energy expenditure (TEE) was measured using doubly labeled water and resting metabolic rate was measured using indirect calorimetry to calculate a physical activity index (PAI). Sedentary time and physical activity of light and moderate to vigorous (mod/vig) intensity was measured using an accelerometer. The subjects were 75 ± 7 yrs (mean ± SD) of age and 79% female. Subjects spent 66 ± 8, 25 ± 5, and 9 ± 4% of monitor wear time in sedentary, light, and mod/vig activity per day, respectively. In a mixture regression model, both light (β = 29.6 [15.6-43.6, 95% CI]), p < .001) and mod/vig intensity activity (β = 28.7 [7.4-50.0, 95% CI]), p = .01) were strongly associated with PAI, suggesting that both light and mod/vig intensity activities are major determinants of their physical activity energy expenditure.

Activity and Energy Expenditure in Older People Playing Active Video Games

Taylor LM, Maddison R, Pfaeffli LA, Rawstorn JC, Gant N, Kerse NM. Activity and energy expenditure in older people playing active video games. ObjectivesTo quantify energy expenditure in older adults playing interactive video games while standing and seated, and secondarily to determine whether participants' balance status influenced the energy cost associated with active video game play. DesignCross-sectional study. SettingUniversity research center. ParticipantsCommunity-dwelling adults (N=19) aged 70.7±6.4 years. InterventionParticipants played 9 active video games, each for 5 minutes, in random order. Two games (boxing and bowling) were played in both seated and standing positions. Main Outcome MeasuresEnergy expenditure was assessed using indirect calorimetry while at rest and during game play. Energy expenditure was expressed in kilojoules per minute and metabolic equivalents (METs). Balance was assessed using the mini-BESTest, the Activities-specific Balance Confidence Scale, and the Timed Up and Go (TUG). ResultsMean ± SD energy expenditure was significantly greater for all game conditions compared with rest (all P≤.01) and ranged from 1.46±.41 METs to 2.97±1.16 METs. There was no significant difference in energy expenditure, activity counts, or perceived exertion between equivalent games played while standing and seated. No significant correlations were observed between energy expenditure or activity counts and balance status. ConclusionsActive video games provide light-intensity exercise in community-dwelling older people, whether played while seated or standing. People who are unable to stand may derive equivalent benefits from active video games played while seated. Further research is required to determine whether sustained use of active video games alters physical activity levels in community settings for this population.

Fatigability as a function of physical activity energy expenditure in older adults

Increased fatigue is a predictor of morbidity and mortality in older adults. Fatigability defines a change in performance or self-reported fatigue in response to physical activity (PA). However, the relationship of fatigability to PA-related energy expenditure (PAEE) is unknown. Changes in performance, fatigue, and energy expenditure were measured simultaneously in 17 adults (11 females, 74-94years old) performing eight standardized PA tasks with various energy expenditure requirements in a whole-room indirect calorimeter. Change in performance was objectively measured using a PA movement monitor and change in fatigue was self-reported on a seven-point scale for each task. Performance and perceived fatigability severity scores were calculated as a ratio of change in performance and fatigue, respectively, and PAEE. We found that change in both objective performance and self-reported fatigue were associated with energy expenditure (Spearman rho = -0.72 and -0.68, respectively, p < 0.001) on a task requiring relatively high level of energy expenditure. The performance and perceived fatigability severity scores were significantly correlated (rho = 0.77, p < 0.001) on this task. In summary, results of this proof of concept pilot study show that both perceived and performance fatigability severity scores are related to PAEE-induced fatigue on a task requiring relatively high level of energy expenditure. We conclude that fatigability severity is a valid measure of PAEE-induced fatigue in older adults.

Validation of an Armband to Measure Daily Energy Expenditure in Older Adults

Objective methods to measure daily energy expenditure in studies of aging are needed. We sought to determine the accuracy of total energy expenditure (TEE) and activity energy expenditure (AEE) estimates from the SenseWear Pro armband (SWA) using software versions 6.1 (SWA 6.1) and 5.1 (SWA 5.1) relative to criterion methods in free-living older adults. Participants (n = 19, mean age 82.0 years) wore a SWA for a mean ± SD 12.5 ± 1.1 days, including while sleeping. During this same period, criterion values for TEE were assessed with doubly labeled water and for resting metabolic rate (RMR) with indirect calorimetry. AEE was calculated as 0.9 TEE - RMR. For TEE, there was no difference in mean ± SD values from doubly labeled water (2,040 ± 472 kcal/day) versus SWA 6.1 (2,012 ± 497 kcal/day, p = .593) or SWA 5.1 (2,066 ± 474 kcal/day, p = .606); individual values were highly correlated between methods (SWA 6.1 r = .893, p < .001; SWA 5.1 r = .901, p < .001) and demonstrated strong agreement (SWA 6.1 intraclass correlation coefficient = .896; SWA 5.1 intraclass correlation coefficient = .904). For AEE, mean values from SWA 6.1 (427 ± 304 kcal/day) were lower by 26.8% than criterion values (583 ± 242 kcal/day, p = .003), and mean values from SWA 5.1 (475 ± 299 kcal/day) were lower by 18.5% than criterion values (p = .021); however, individual values were highly correlated between methods (SWA 6.1 r = .760, p < .001; SWA 5.1 r = .786, p < .001) and demonstrated moderate agreement (SWA 6.1 intraclass correlation coefficient = .645; SWA 5.1 intraclass correlation coefficient = .720). Bland-Altman plots identified no systematic bias for TEE or AEE. Acceptable levels of agreement were observed between SWA and criterion measurements of TEE and AEE in older adults.

Validity of Physical Activity Measures During a 23-hour Metabolic Chamber Stay in Older Adults

PURPOSE: To compare the relative validity of various objective physical activity measures with metabolic chamber-measured energy expenditure in older adults. METHODS: Eighteen adults (15 females, 3 males; age= 76 ± 7 (mean ± SD)) wore three activity monitors (New Lifestyles pedometer, Actigraph, and a Sensewear armband) during a 23-hour stay in a metabolic chamber. Subjects had the choice to do a 30-minute exercise video of seated and standing exercises, twice, while in the chamber. Total energy expenditure (TEE) and resting metabolic rate (RMR) were measured with indirect calorimetry, and the thermic effect of food was estimated. Three estimates of physical activity energy expenditure were calculated (PAEE, kcal/d; PAI, kcal/kg/d; PAL, TEE/RMR). Mean differences in TEE and correlations between the various physical activity measures and chamber-measured energy expenditure were compared. RESULTS: TEE was 1796 ± 303 kcal/day. PAEE, PAI, and PAL were 467 ± 148 kcal/day, 3.14 ± 0.91 kcal/kg/day, and 1.57 ± 0.13 (mean ± SD), respectively. On average, 171 ± 49 and 16 ± 11 minutes/day were spent in light and moderate activity, as measured by the Actigraph using count cutpoints of 100, 760, and 5724 cts/min, and the average steps/23 hrs was 862 ± 438. There were no vigorous minutes recorded and the rest of the time was inactive. Although TEE from the chamber and armband were strongly correlated (r=0.69, p<0.01), the armband underestimated TEE by 671 kcals, (p<0.0001). Similarly PAEE from the chamber and armband were correlated (r=0.57, p=0.01), but highly underestimated by the armband (mean difference = 422 kcals). The Actigraph and pedometer did not correlate well with PAEE (r=0.24, p=0.35; r=0.13, p=0.61, respectively), but did tend to correlate with PAI (r=0.42, p=0.08; r=0.41, p=0.10). CONCLUSIONS: Under the highly accurate, but movement limiting conditions of the chamber, the Sensewear armband reasonably ranked older adults by energy expenditure, but the absolute estimates were not accurate. Neither accelerometry nor pedometry did as well as the multi-sensor device.

Nutrition interventions in aging and age-associated disease.

The nutritional status and needs of elderly people are associated with age-related biological and often socioeconomic changes. Decreased food intake, a sedentary lifestyle, and reduced energy expenditure in older adults altogether become critical risk factors for malnutrition, especially protein and micronutrients. Surveys indicate that the elderly are particularly at risk for marginal deficiency of vitamins and trace elements. Changes in bodily functions, together with the malnutrition associated with advancing age, increase the risk of developing a number of age-related diseases. Chronic conditions pose difficulties for the elderly in carrying out the activities of daily living and may increase the requirements for certain nutrients due to changes in absorptive and metabolic capacity. Free radicals and oxidative stress have been recognized as important factors in the biology of aging and of many age-associated degenerative diseases. In this regard, modulation of oxidative stress by calorie restriction, as demonstrated in animal models, is suggested as one mechanism to slow the aging process and the decline of body functions. Therefore, dietary components with antioxidant activity have received particular attention because of their potential role in modulating oxidative stress associated with aging and chronic conditions. Several studies have indicated potential roles for dietary antioxidants in the reduction of degenerative disease such as vascular dementia, cardiovascular disease, and cancer. In support of epidemiological studies, our recent studies indicate that the antioxidant properties of vitamin E and polyphenols present in green tea may contribute to reducing the risk of cardiovascular disease, in part by reducing the susceptibility of low density lipoproteins to oxidation, decreasing the vascular endothelial cell expression of pro-inflammatory cytokines, and decreasing the expression of adhesion molecules and monocyte adhesion. Recently, we also demonstrated that these dietary antioxidants may have a preventive role in cancer, potentially through the suppression of angiogenesis by inhibiting interleukin-8 production and the cell junction molecule VE-cadherin. These findings concur with epidemiologic, clinical, and animal studies suggesting that the consumption of green tea and vitamin E is associated with a reduced risk of cardiovascular disease and cancer, the leading causes of morbidity and mortality among the elderly.