Article Text

PDF
How well do activity monitors estimate energy expenditure? A systematic review and meta-analysis of the validity of current technologies
  1. Ruairi O’Driscoll1,
  2. Jake Turicchi1,
  3. Kristine Beaulieu1,
  4. Sarah Scott1,
  5. Jamie Matu2,
  6. Kevin Deighton3,
  7. Graham Finlayson1,
  8. James Stubbs1
  1. 1 Appetite Control and Energy Balance Group, School of Psychology, University of Leeds, Leeds, UK
  2. 2 Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK
  3. 3 Institute for Sport, Physical Activity and Leisure, Leeds Beckett University, Leeds, UK
  1. Correspondence to Ruairi O’Driscoll, Appetite Control and Energy Balance Group, School of Psychology, University of Leeds, Leeds LS2 9JT, UK; psrod{at}leeds.ac.uk

Abstract

Objective To determine the accuracy of wrist and arm-worn activity monitors’ estimates of energy expenditure (EE).

Data sources SportDISCUS (EBSCOHost), PubMed, MEDLINE (Ovid), PsycINFO (EBSCOHost), Embase (Ovid) and CINAHL (EBSCOHost).

Design A random effects meta-analysis was performed to evaluate the difference in EE estimates between activity monitors and criterion measurements. Moderator analyses were conducted to determine the benefit of additional sensors and to compare the accuracy of devices used for research purposes with commercially available devices.

Eligibility criteria We included studies validating EE estimates from wrist-worn or arm-worn activity monitors against criterion measures (indirect calorimetry, room calorimeters and doubly labelled water) in healthy adult populations.

Results 60 studies (104 effect sizes) were included in the meta-analysis. Devices showed variable accuracy depending on activity type. Large and significant heterogeneity was observed for many devices (I2 >75%). Combining heart rate or heat sensing technology with accelerometry decreased the error in most activity types. Research-grade devices were statistically more accurate for comparisons of total EE but less accurate than commercial devices during ambulatory activity and sedentary tasks.

Conclusions EE estimates from wrist and arm-worn devices differ in accuracy depending on activity type. Addition of physiological sensors improves estimates of EE, and research-grade devices are superior for total EE. These data highlight the need to improve estimates of EE from wearable devices, and one way this can be achieved is with the addition of heart rate to accelerometry.

PROSPEROregistration number CRD42018085016.

  • energy expenditure
  • accelerometer
  • meta-analysis
  • wrist
  • validation

Statistics from Altmetric.com

Footnotes

  • Twitter @ACEB_leeds

  • Contributors RO, JT, KB, SS, GF and RJS planned the study. RO, JT, SS and GF contributed to study selection. RO, JT, KB, JM, KD and RJS contributed to analysis and interpretation of the results. All authors discussed the results and contributed to the final manuscript.

  • Funding The research was funded by a University of Leeds PhD studentship.

  • Competing interests None declared.

  • Patient consent Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.