Measurement and prediction of energy expenditure in males during household and garden tasks

Gunn, Simon M.; Ploeg, Grant E. van der; Withers, R. T.; Gore, Christopher J.; Owen, Neville; Bauman, Adrian; Cormack, John

doi:10.1007/s00421-004-1100-y

Cited by 5 publications

(14 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…HR net was used as a predictor because it explained a greater percentage (34.8%) of the variation in energy expenditure than either HR (31.8%) or % HR max (29.2%). This is in accordance with our previous work (Brooks et al 2004;Gunn et al 2004;Gunn et al 2005). Fig.…”

Section: Predicting Energy Expendituresupporting

confidence: 94%

“…This study accordingly expanded our previous work (Brooks et al 2004;Gunn et al 2004;Gunn et al 2005) by:…”

Section: Introductionmentioning

confidence: 88%

“…The standardised laboratory environment enabled us to determine the interindividual variability for the intensity at which these tasks were performed and to compare this sample with our previous one on women two decades younger (Brooks et al 2004). Our previous work (Brooks et al 2004;Gunn et al 2002;Gunn et al 2004) explains why it is unlikely that the Hawthorne effect applied to the collection of these data.…”

Section: Experimental Designmentioning

confidence: 94%

“…A prediction equation was generated for the activities conducted in each of the laboratory and home settings by fitting random intercept regression models to the data (Healy 2001). Based on previous findings (Brooks et al 2004;Gunn et al 2004Gunn et al , 2005Melanson & Freedson 1995), the following were used as potential predictors of exercise intensity: activity, HR net (exercise HR-resting HR), CSA hip , X CSA ankleðleft and rightÞ ; X CSA wristðleft and rightÞ ; Borg RPE, QI and the interactions between activity and each of the other predictors. The models were finalised by a backward elimination procedure using a criterion of P > 0.05.…”

Section: Statisticsmentioning

confidence: 99%

“…However, these studies relied heavily on reproducible ergometer workloads in the laboratory or required time consuming individual HR-_ VO 2 regressions. Also, no studies other than our previous work (Brooks et al 2004;Gunn et al 2004;Gunn et al 2005) have used the easily measured Quetelet's index [QI = mass (kg) / height 2 (m)] or Borg rating of perceived exertion (Borg RPE) scale as potential predictors. Ideally, HR, activity counts, QI and Borg RPE could be combined into an equation to unobtrusively and noninvasively predict the energy expenditure of specific activities in the free living state.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Self-selected exercise intensity during household/garden activities and walking in 55 to 65-year-old females

et al. 2006

Self Cite

View full text Add to dashboard Cite

This study determined whether some of the more vigorous household and garden tasks (sweeping, window cleaning, vacuuming and lawn mowing) were performed at a moderate intensity (3-6 METs or metabolic equivalents) by a representative sample of 50, 55 to 65-year-old women (X +/- SD; 59.3 +/- 3.1 years, 161.5 +/- 5.2 cm, 69.4 +/- 12.4 kg, 38.4 +/- 7.3% BF). Data collection was conducted in a standardised laboratory environment and in the subjects' homes. Energy expenditure during self-perceived moderate paced walking around a quadrangle was also used as a marker of exercise intensity. Energy expenditure measured via indirect calorimetry was also predicted from: HR, CSA accelerometer counts, Quetelet's index and the Borg rating of perceived exertion. Ninety-six percent of the subjects walked at an intensity of>or= 3.0 METs. Except for vacuuming in the laboratory (X = 2.9 METs; P = 0.19), the intensity of each of the other activities was significantly (P or= 3.0 METs when the four household/garden activities were performed in the subjects' homes. These activities therefore have the potential to contribute to the 30 min day(-1) of moderate intensity physical activity required to confer health benefits but there was much inter-individual variability in the intensity at which these tasks were performed. Random intercept regression analyses yielded prediction equations with 95% confidence intervals of +/- 0.80 and +/- 0.84 METs for the laboratory and home based equations, respectively. Considering the means for the five activities ranged from 2.9 to 5.5 METs, these 95% confidence intervals lack predictive precision at the individual level. Nevertheless, the laboratory and home-based equations predicted with correct classification rates of 89 and 90%, respectively, whether energy expenditure was < 3.0 or >or= 3.0 METs.

show abstract

Section: Predicting Energy Expendituresupporting

confidence: 94%

“…This study accordingly expanded our previous work (Brooks et al 2004;Gunn et al 2004;Gunn et al 2005) by:…”

Section: Introductionmentioning

confidence: 88%

Section: Experimental Designmentioning

confidence: 94%

Section: Statisticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Self-selected exercise intensity during household/garden activities and walking in 55 to 65-year-old females

et al. 2006

Self Cite

View full text Add to dashboard Cite

show abstract

Body Segment Kinematics and Energy Expenditure in Active Videogames

Böhm

Hartmann

Böhm

2016

Games for Health Journal

View full text Add to dashboard Cite

show abstract

Estimating oxygen consumption from heart rate and heart rate variability without individual calibration

Smolander

Ajoviita

Juuti

et al. 2011

Clinical Physiology and Functional Imaging

View full text Add to dashboard Cite

Heart rate (HR) as an estimator of oxygen consumption (VO(2) ) usually requires HR to be individually calibrated in a separate test. This study examined the validity of a new HR - and HR variability-based method (Firstbeat PRO heartbeat analysis software) in the estimation of VO(2) in real-life tasks. The method takes into account the respiration rate determined from HR variability and the differences in the on/off dynamics of HR and VO(2) , and no calibration tests are needed. Ten men and nine women performed 25 tasks representing different types of daily activities. Portable devices were used to measure R-to-R intervals (ECG), VO(2) and respiration rate. In pooled regression analysis, the estimated VO(2) accounted for 87% of the variability in the actual VO(2) , SEE 3·5 ml min(-1) kg(-1) (1 MET). At group level, the method underestimated slightly the measured VO(2) (mean difference - 1·5 ml min(-1) kg(-1) or - 0·4 METs). Some of the values at low exercise intensities were markedly underestimated, but the agreement was better during light and heavy activities. The limits of agreement for the data were from -8·4 to 5·4 ml min(-1) kg(-1) or from -2·4 to 1·5 METs. At individual level, the average deviations of the predicted VO(2) ranged from -1·0 to 0·6 METs and R(2) from 0·77 to 0·94, respectively. The present data indicate that the prediction method may be considered sufficiently accurate to determine the average VO(2) in field use, but it does not allow precise estimation of VO(2) .

show abstract

Measurement and prediction of energy expenditure in males during household and garden tasks

Abstract: Unfortunately, this paper contains the following error: The CSA arm coefficient for lawn mowing in Table 4 (p. 66) should be 4.5x10-4 .

Cited by 5 publications

References 0 publications

Self-selected exercise intensity during household/garden activities and walking in 55 to 65-year-old females

Self-selected exercise intensity during household/garden activities and walking in 55 to 65-year-old females

Body Segment Kinematics and Energy Expenditure in Active Videogames

Estimating oxygen consumption from heart rate and heart rate variability without individual calibration

Contact Info

Product

Resources

About