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 J. Wilson, Bridget Mann, Roberto A. Rabinovich, Milo Puhan, Thierry Troosters*, S. A. Chiesi Farmaceutici, Caterina Brindicci, Tim Higenbottam, Fabienne DobbelsMarc Decramer, Margaret X. Tabberer, Roberto A. Rabinovich, William McNee, 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, Erzen Damijen Erzen

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

220 Citations (Scopus)

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.

Original languageEnglish
Article number84
JournalInternational Journal of Behavioral Nutrition and Physical Activity
Volume9
DOIs
Publication statusPublished - 9 Jul 2012
Externally publishedYes

Cite this