title: Impact of scoring algorithm on physical activity prevalence estimates in Australian adults.
abstract: Public health recommendations for physical activity are operationalised by defining thresholds for frequency (sessions/week), duration (min/week), or volume (MET-min/week). This study compared estimates of meeting physical activity recommendations when scoring algorithms varied in specifications for frequency and duration but were comparable in volume. Data were obtained from 13,105 Australian adult respondents to the 2006 Exercise, Recreation and Sport Survey (ERASS). Prevalence estimates were calculated using algorithms defined by (i) frequency only (≥5 sessions/week); (ii) duration only (≥150 min/week); (iii) duration only when minutes of vigorous activity were weighted by 2 (≥150 weighted-min/week); (iv) frequency and duration (≥5 sessions/week, ≥150 min/week); (v) volume only (≥600 MET-min/week); and (vi) volume and frequency (≥600 MET-min/week, ≥5 sessions/week). The proportion of adults who met recommendations operationalised without a frequency requirement was twice the proportions obtained for algorithms with frequency requirements. Volume or duration-based algorithms yielded higher estimates for men than women, and for the younger age groups (<35 years) than the older groups, with the opposite observation for frequency-based algorithms. Consistent for all algorithms, people classified at the highest educational attainment had the highest prevalence of meeting recommendations. Agreement in achieving 600 MET-min/week when activities were categorised using activity-specific MET values versus median MET values was 98.3%. Prevalence rates based on 600 MET-min/week were similar to 150 weighted-min/week. In conclusion, varying frequency and duration requirements of scoring algorithms can yield different population estimates and patterns by population subgroup of physical activity for a health benefit.
authors.affiliation: CPAH Prevention Research Collaboration, University of Sydney, Australia. nmea7054@uni.sydney.edu.au
authors.forename: Nicole M
authors.initials: NM
authors.lastname: Mealing
authors.forename: Heather R
authors.initials: HR
authors.lastname: Bowles
authors.forename: Dafna
authors.initials: D
authors.lastname: Merom
authors.forename: Adrian
authors.initials: A
authors.lastname: Bauman
mesh_terms: D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001315:Australia; D003627:Data Interpretation, Statistical; D015444:Exercise; D005260:Female; D006291:Health Policy; D006306:Health Surveys; D006801:Humans; D008297:Male; D008875:Middle Aged; D009043:Motor Activity; D015995:Prevalence; D017678:Sex Distribution; D013177:Sports / Q000706:statistics & numerical data*; D016138:Walking / Q000706:statistics & numerical data; D055815:Young Adult