Original paperImpact of scoring algorithm on physical activity prevalence estimates in Australian adults
Introduction
There are many different ways to collect and interpret physical activity data, with little consensus about how to derive estimates consistent with meeting public health guidelines.1 A challenge is translating public health recommendations that prescribe an amount of moderate-to-vigorous physical activity into a measure quantifiable by surveillance methods.
The public health recommendations in Australia are straightforward: all adults should engage in at least 30 min of moderate-intensity physical activity on most, preferably all, days of the week, with each session lasting at least 10 min, and where possible they should also engage in some regular, vigorous activity.2 However, researchers have developed different protocols for categorising survey respondents that yield different estimates of the proportion of the population who achieve the recommended level of physical activity.3, 4 In public health surveillance the ability to correctly categorise individuals is important because measures that lack sensitivity may result in incorrect measures of association, and those that lack specificity may result in estimates with high standard errors and broad confidence intervals.5
The best method for combining moderate-intensity and vigorous-intensity activity into a single indicator of physical activity level has not been determined, but one method is to weight moderate and vigorous activity by relative energy expenditure (i.e., metabolic equivalent of task (MET) values) and calculate volume by multiplying energy expenditure, activity frequency, and duration.6 The general practice in scoring surveillance data has been to assume vigorous activity results in twice the energy expenditure of moderate activity, either by assigning METs of 3.0 or 4.0 for moderate and 6.0 or 8.0 METs for vigorous activities,7, 8 assuming these values are representative of the average MET values for moderate and vigorous activities in populations. An equivalent scoring practice is applied when populations are assessed using the Active Australia questionnaire, in which case minutes of vigorous activity are weighted by a factor of two to approximate a doubling in energy expenditure.9
This work used data from the 2006 Exercise, Recreation and Sport Survey (ERASS), to compare estimates of meeting recommendations for physical activity when six scoring algorithms varied in specifications for frequency and duration but were comparable in volume. The median MET values for moderate- and vigorous-intensity activities were calculated and agreement in categorising respondents based on activity-specific versus median MET values was assessed to determine the impact of weighting by an average MET value on population estimates.
Section snippets
Methods
Participants were respondents to the 2006 ERASS, a survey conducted by the Australian Sports Commission to monitor leisure time physical activities among Australian adults. Respondents were selected using a list-assisted, random-digit-dial of households, and informed consent was obtained from respondents by their willingness to complete the telephone survey. Surveys were completed by 13,708 respondents aged 15–99 (42% response rate). The final analytic sample consisted of 5544 men and 7561
Results
Among respondents who participated in at least one activity of moderate- or vigorous-intensity in the 2 weeks prior to answering the survey, men and women engaged in the same median sessions/week of exercise, recreation, and sport activities, but median min/week and median MET-min/week were higher in men compared to women (Table 1). Those aged 65 years and over had the highest median sessions/week but the lowest median MET-min/week. Persons aged 15–19 years had the lowest median sessions/week
Discussion
The population prevalence of meeting physical activity recommendations varied by the algorithm used to define this indicator. Scoring algorithms with a frequency requirement were the most stringent in classifying individuals. Algorithms based only on MET-minutes or that weighted vigorous-intensity activity to account for additional physiological benefits were the least stringent in classifying individuals. Consistent for all algorithms, people classified at the highest educational attainment
Practical implications
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The population prevalence of meeting physical activity recommendations varies by the scoring algorithm researchers apply to define these indicators.
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The lowest prevalence rates appear when indicators are restricted to achieving threshold of frequency; these also tend to minimize gender and age differences.
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Assigning activity-specific intensity codes (METs) are not likely to improve the accuracy of indicators that are based on one intensity code assigned to groups of activities at the moderate or
Acknowledgements
We wish to thank the Australian Sports Commission (Canberra) and state based departments of Sport and Recreation for permission to use the 2006 ERASS data. NM was employed by the NSW Department of Health on the NSW Biostatistical Officer Training Program at the time this work was conducted. There has been no financial assistance with the project.
References (18)
- et al.
Lifestyle physical activity interventions. History, short- and long-term effects, and recommendations
Am J Prev Med
(1998) - et al.
Exercise treatment for depression: efficacy and dose response
Am J Prev Med
(2005) - et al.
Assessment of physical activity in epidemiologic research: problems and prospect
Public Health Rep
(1985) Australia's health 2008. Cat. no. AUS 99
(2008)- et al.
Self-report measures and scoring protocols affect prevalence estimates of meeting physical activity guideline
Med Sci Sports Exerc
(2000) - et al.
Measuring physical activity with the behavioral risk factor surveillance system
Med Sci Sports Exerc
(2000) - et al.
Effect of exposure misclassification on the mean squared error of population attributable risk and prevented fraction estimates
Stat Med
(2007) - et al.
Introduction to a collection of physical activity questionnaires
Med Sci Sports Exerc
(1997) Compliance with physical activity recommendations by walking for exercise-Michigan, 1996 and 1998
MMWR
(2000)
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