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

Hans Van Remoortel, Santiago Giavedoni, Yogini Raste, Chris Burtin, Zafeiris Louvaris, Elena Gimeno-Santos, Daniel Langer, Alastair Glendenning, Nicholas S Hopkinson, Ioannis Vogiatzis, Barry T Peterson, Frederick Wilson, Bridget Mann, Roberto Rabinovich, Milo A Puhan, Thierry Troosters, PROactive consortium, Caterina Brindicci, Tim Higenbottam, Thierry Troosters, Fabienne Dobbels, Marc Decramer, Margaret X Tabberer, Roberto A Rabinovich, William McNee, Ioannis Vogiatzis, Michael Polkey, Nick Hopkinson, Judith Garcia-Aymerich, Milo Puhan, Anja Frei, Thys van der Molen, Corina De Jong, Pim de Boer, Ian Jarrod, Paul McBride, Nadia Kamel, Katja Rudell, Frederick J Wilson, Nathalie Ivanoff, Karoly Kulich, Alistair Glendenning, Niklas X Karlsson, Solange Corriol-Rohou, Enkeleida Nikai, Damijen Erzen, Hans Van Remoortel, Santiago Giavedoni, Yogini Raste, Chris Burtin, Zafeiris Louvaris, Elena Gimeno-Santos, Daniel Langer, Alastair Glendenning, Nicholas S Hopkinson, Ioannis Vogiatzis, Barry T Peterson, Frederick Wilson, Bridget Mann, Roberto Rabinovich, Milo A Puhan, Thierry Troosters, PROactive consortium, Caterina Brindicci, Tim Higenbottam, Thierry Troosters, Fabienne Dobbels, Marc Decramer, Margaret X Tabberer, Roberto A Rabinovich, William McNee, Ioannis Vogiatzis, Michael Polkey, Nick Hopkinson, Judith Garcia-Aymerich, Milo Puhan, Anja Frei, Thys van der Molen, Corina De Jong, Pim de Boer, Ian Jarrod, Paul McBride, Nadia Kamel, Katja Rudell, Frederick J Wilson, Nathalie Ivanoff, Karoly Kulich, Alistair Glendenning, Niklas X Karlsson, Solange Corriol-Rohou, Enkeleida Nikai, Damijen Erzen

Abstract

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.

Figures

Figure 1
Figure 1
Flow chart describing the identification and inclusion of relevant studies.
Figure 2
Figure 2
Study-specific correlation coefficients (r) and Fisher z-scores (diamond) between total energy expenditure estimate from the activity monitor (TEEAM) and total energy expenditure measure from doubly labelled water (TEEDLW). Each dot represents the z-score of the respective study together with a 95% confidence interval (CI) and the size of the box represents the weight of the study in the meta-analysis. Weights are from random effects analysis. CV; coefficient of variation for TEEDLW.
Figure 3
Figure 3
Study-specific % mean difference (diamond) between total energy expenditure estimate from the activity monitor (TEEAM) and total energy expenditure measure from doubly labelled water (TEEDLW). Each dot represents the mean difference of the respective study together with a 95% confidence interval (CI) and the size of the box represents the weight of the study in the meta-analysis. Weights are from random effects analysis. CV; coefficient of variation for TEEDLW. *Leenders et al. 2006 (Actigraph Model 7164); TEEAM estimated with most frequently used Freedson and Hendelman equation (walking outdoors), (not reported) data of % mean difference (±SD) between TEEAM - TEEDLW with other previously published equations can be found in the original paper [14].
Figure 4
Figure 4
Study-specific correlation coefficients and Fisher z-scores (diamond) between active energy expenditure estimate from the activity monitor (AEEAM) and active energy expenditure measure from doubly labelled water (AEEDLW). Each dot represents the z-score of the respective study together with a 95% confidence interval (CI) and the size of the box represents the weight of the study in the meta-analysis. Weights are from random effects analysis. CV; coefficient of variation for AEEDLW.
Figure 5
Figure 5
Study-specific % mean difference (diamond) between active energy expenditure estimate from the activity monitor (AEEAM) and total energy expenditure measure from doubly labelled water (AEEDLW). Each dot represents the mean difference of the respective study together with a 95% confidence interval (CI) and the size of the box represents the weight of the study in the meta-analysis. Weights are from random effects analysis. CV; coefficient of variation for AEEDLW. *Assah et al. 2009 (Actigraph Model 7164); AEEAM estimated with most frequently used Freedson and Hendelman equation, (not reported) data of % mean difference between AEEAM - AEEDLW with other data derived and previously published equations can be found in the original paper [48].
Figure 6
Figure 6
Study-specific correlation coefficients (r) and Fisher z-scores (diamond) between activity monitor outcomes and total energy expenditure measure from indirect calorimetry (TEEIC) during laboratory protocols. Each dot represents the z-score of the respective study together with a 95% confidence interval (CI) and the size of the box represents the weight of the study in the meta-analysis. Weights are from random effects analysis.
Figure 7
Figure 7
Study-specific correlation coefficients and Fisher z-scores (diamond) between activity monitor outcomes and total energy expenditure measure from indirect calorimetry (TEEIC) during laboratory protocols based on walking activities. Each dot represents the z-score of the respective study together with a 95% confidence interval (CI) and the size of the box represents the weight of the study in the meta-analysis. Weights are from random effects analysis.
Figure 8
Figure 8
Study-specific correlation coefficients and Fisher z-scores (diamond) between activity monitor outcomes and total energy expenditure measure from indirect calorimetry (TEEIC) during laboratory protocols based on activities of daily living activities involving the upper and lower limbs. Each dot represents the z-score of the respective study together with a 95% confidence interval (CI) and the size of the box represents the weight of the study in the meta-analysis. Weights are from random effects analysis.
Figure 9
Figure 9
Study-specific % mean difference (diamond) between total energy expenditure estimate from the activity monitor (TEEAM) and total energy expenditure measure from indirect calorimetry (TEEIC) during laboratory protocols based on slow walking speed. Each dot represents the % mean difference of the respective study.
Figure 10
Figure 10
Study-specific % mean difference (diamond) between total energy expenditure estimate from the activity monitor (TEEAM) and total energy expenditure measure from indirect calorimetry (TEEIC) during laboratory protocols based on intermediate walking speed. Each dot represents the % mean difference of the respective study.
Figure 11
Figure 11
Study-specific % mean difference (diamond) between total energy expenditure estimate from the activity monitor (TEEAM) and total energy expenditure measure from indirect calorimetry (TEEIC) during laboratory protocols based on fast walking speed. Each dot represents the % mean difference of the respective study.
Figure 12
Figure 12
Study-specific % mean difference (diamond) between total energy expenditure estimate from the activity monitor (TEEAM) and total energy expenditure measure from indirect calorimetry (TEEIC) during laboratory protocols based on running speed. Each dot represents the % mean difference of the respective study.
Figure 13
Figure 13
Accuracy of steps at different walking speeds. The dots are reflecting walking speed: slow walking (<3.2 km/hr (□)), intermediate walking (3.2-6.4 km/hr (■)) and fast walking (6.5-8 km/hr (▲)).

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