1. David Rowe Physical Activity for Health Research Group University of Strathclyde david.rowe@strath.ac.uk

2. Challenging the dogma The current dogma: More is better More: Methods Models Equations Analyses Expensive

3. Physical activity and health* Period# publications% increase 1981-1990659N/A 1991-20002,178230% 2001-20108,482289% * PubMed journal article search with “physical activity” in title

4. “Measurement of physical activity” Cooper Institute Conference Series, Cooper Centre, Dallas TX, 2000. [Conference papers in RQES 2000 and abstracts in MPEES, 2000]

5. “Objective measurement of physical activity: Closing the gaps in the science of accelerometry,” University of North Carolina, December, 2004. [papers published in MSSE, 2007]

6. 2 nd International Conference on Ambulatory Monitoring of Physical Activity and Movement, Glasgow, Scotland, May 2011. [Selected papers will be published in Special Issue of Physiological Measurement]

7. 8 th International Conference on Diet and Physical Activity Methods, Rome, May, 2012.

8. “New approaches in measuring and assessing physical activity” 12 th Measurement and Evaluation Symposium, Boston, MA, March 2012.

9. Montoye, H. (2000). Introduction: Evaluation of some measurements of physical activity and energy expenditure (MSSE Special Issue). Medicine and Science in Sports and Exercise, 32(9, Suppl.), S439-S516.

10. From: Troiano, R. P. (2005). A timely meeting: Objective measurement of physical activity. Medicine & Science in Sports & Exercise. 37(11, Suppl.), S487-S588). Accelerometer articles 1981-2004

11. Why “Back to the Future”?

12. Measuring PA – the (very) early years

13. The mechanical age

15. The electronic age

16. Instrument developments Pedometers HRM Accelerometers GPS Combined methods (GPS/accelerometer, HRM/accelerometer) Other wearable technology (e.g., “smart clothes”, cameras) Ubiquitous technology (smart phones)

17. Advancement in functionality Multidirectional/omnidirectional Battery life Memory capacity Cost Other novel functions (e.g., talking equipment)

18. Advancement in methodology Analysis of output Accelerometer cutpoints Problem – movement/EE relationship confounded by mode How many equations do we need? Branched equations CV of accelerometer counts (Crouter et al., 2006) Mixed technologies (Barreira et al., 2009) Artificial neural networks (Staudenmayer et al., 2009) Barreira, T. V., et al. Validation of the Actiheart monitor for the measurement of physical activity. Int J Exerc Sci 2(1): 60-71, 2009 Crouter, S. E., et al. A novel method for using accelerometer data to predict energy expenditure. J Appl Physiol 100: 1324–1331, 2006. Staudenmayer, J., et al. An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. J Appl Physiol 107: 1300-1307, 2009

19. Despite all this... (My moment of heresy) There is no gold standard measure of physical activity!!!

20. DimensionDLWHRMAccelerometerPedometerGPSQuestionnaireDirect observation Frequency -+++-+++ Intensity -++ -+++ Time -+++ -+++ Mode --++++++++ Context ----+++++ Volume +++++ +++ Energy expenditure +++++ +-++ Key: Not possible to estimate (-), possible to estimate but with large error (+), possible to estimate with medium level of error (++), possible to estimate with low degree of error (+++). DLW = doubly labeled water; HRM = heart rate monitor; GPS = global positioning system

21. What is validity? “The extent to which an instrument measures what it is purported to measure”

22. What is validity? “The extent to which an instrument measures what it is purported to measure” An understanding and consideration of what we are measuring, whom we are measuring, why we are measuring, and the correct meaning, interpretation, and consequences of the data we will collect

23. 23 Validation Paradigm for Kinesiology Definitional Stage Confirmatory Stage Theory- Testing Stage Bassett, D., Mahar, M., Rowe, D., & Morrow, J. (2008). Walking and measurement. Medicine and Science in Sports and Exercise, 40, S529-S536. Mahar, M., & Rowe, D. (2002). Construct validity in physical activity research. In G. Welk (Ed.), Physical activity assessments for health-related research (pp. 51-72). Champaign, IL: Human Kinetics. Rowe, D., & Mahar, M. (2006). Construct validity. In T. Wood & W. Zhu (Eds.), Measurement theory and practice in kinesiology (pp. 9-26). Champaign, IL: Human Kinetics.

24. The challenge of measuring physical activity Definition of physical activity: “... any bodily movement produced by skeletal muscles that results in energy expenditure” - (Caspersen, Powell, & Christensen, 1989, p. 126).

25. The challenge of measuring physical activity Physical activity: is complex is multidimensional consists of movement and behaviour varies in intensity, duration, frequency, mode involves energy expenditure involves context (social and environmental)

26. Are these valid measures? Occupation Subjective questionnaire measures Yamax/lever-arm pedometer

27. Morris & Raffle (1954)

28. Paffenbarger, Wing, & Hyde (1978)

29. The “Yamax story” “Gold standard” pedometer, or not? What is a “healthy step”?

30. Three important questions... Is the tail wagging the dog? Are we putting the cart before the horse? Are we talking the same language?

31. Is the tail wagging the dog? The tenuous relationship between commercialism and research Conflicts of interest Are universities becoming inexpensive Research & Development mills? Ownership of data Nonpublication of adverse results? “Black box” issue/proprietary algorithms Perceived need to upgrade

32. Are we putting the cart before the horse? Using new instruments and methods before we really know how they work “Expense” of bad data, lost data Three personal experiences

33. Are we talking the same language? Consistency between devices Inter-instrument Inter-model Inter-generation Inter-unit

34. Are we talking the same language? Crouter, S. E., et al. Validity of 10 Electronic pedometers for measuring steps, distance, and energy cost. Med. Sci. Sports Exerc., Vol. 35, No. 8, pp. 1455–1460, 2003.

35. Are we talking the same language? Kozey, S. L., et al. Comparison of the ActiGraph 7164 and the ActiGraph GT1M during self-paced locomotion. Med. Sci. Sports Exerc., Vol. 42, No. 5, pp. 971–976, 2010.

36. Are we talking the same language? METs (equation) Cts/minFreedson 1 Puyau 2 Treuth 3 Mattocks 4 % difference 5 5002.102.402.443.6876% 10002.602.702.874.3869% 15003.103.003.295.0870% 20003.603.303.725.7975% 25004.113.604.156.4980% 30004.613.904.587.1984% Notes: 1 Freedson et al. (1997), as reported by Trost et al. (2006); 2 Puyau, Adolph, Vohra, & Butte (2002); 3 Treuth et al. (2004); 4 Mattocks, et al. (2007); 5 Difference between lowest and highest estimate, expressed as a percentage of lowest estimate. Freedson and Puyau equations were calculated based on a 13-year old girl, and conversion factors of 1 MET = 1 kcal/kg/hr and 1 kJ = 4.1868 kcal were used for the Puyau and Mattocks equations.

37. Are we talking the same language? What are “healthy steps”? Moderate intensity (3 METs) and above? NL-1000 “MVPA time” (default is > 3.6 METs) Omron “aerobic time” and “aerobic steps” (> 60 steps/min) Walk4Life “activity time” (all movement time) Polar Activity Watch (only steps > 70 steps/min) Consistent evidence that 100 steps/min is 3 MET threshold (Tudor-Locke et al., in review)

38. Are we talking the same language...... as “real” (non-research) people? “accelerometer counts”? “moderate to vigorous”? New “combination” guidelines Different permutations of moderate and vigorous intensity days Is this a simple public message?

39. Take home messages More sophistication (instruments, analysis) is not necessarily better Know why you are measuring and what you want to measure The questionnaire is not inferior, or of limited value Danger of “instant obsolescence” Danger of a “research underclass” Expensive equipment, upgrading and complex analysis Danger of “losing the public message”

41. Advantages of simplicity Less expensive equipment = larger sample size, more studies, more research groups contributing to the critical mass More understandable metrics (steps, steps/min) Simpler and less ambiguous data processing Comparability of data and results across studies Transferability of research findings to real-world public health messages

42. What are real people doing? “The prevalence of adherence [to public health recommendations] was only 6–8% among adolescents, and less than 5% among adults” (Troiano, et al., 2008, p. 185) Troiano, R. P., et al. Physical activity in the United States measured by accelerometer. Med. Sci. Sports Exerc., Vol. 40, No. 1, pp. 181–188, 2008.

43. “Walk the dog every day...

44. ... even if you don’t have a dog” (Astrand) “Walk the dog every day...

45. Inquisition time? david.rowe@strath.ac.uk