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SUMMARY:Monitoring of physical activity in populations - current status an
 d future possibilities - Soren Brage
DTSTART:20081117T140000Z
DTEND:20081117T150000Z
UID:TALK14505@talks.cam.ac.uk
CONTACT:Andrew Rice
DESCRIPTION:Physical activity in large-scale epidemiological studies has t
 raditionally been assessed using subjective instruments such as self-repor
 t or proxy-report by teacher/parent for young children. Such assessment is
  inherently imprecise\, as not all activity is committed to memory\, and r
 ecall of activity is influenced by social desireability.\nFor those reason
 s\, objective methods are increasingly being employed for assessment of ac
 tivity\, with the most common method being accelerometry from a single sen
 sor placed on the waist. Other methods include heart rate monitoring and c
 ombined heart rate and movement sensing. Sincehabitual physical activity i
 s a latent variable and therefore in principle not possible to measure\, t
 he standard approach is to sample a sufficient number of days to make an i
 nference about the latent activity\nlevel. Although not extensively evalua
 ted in the literature\, it is generally agreed that 5-7 days of objective 
 monitoring is a fair compromise between feasibility and time sample repres
 entativeness. This\nis\, however\, still a long time to ask members of the
  general population to be monitored during their normal daily lives\, cons
 idering these are usually unpaid volunteers who contribute to medical scie
 nce for altruistic reasons. With this in mind\, feasibility of a method is
  an absolute priority\, since people simply will not wear a monitor if it 
 bothers them too much. \nTo date\, therefore\, physical activity informati
 on has been collected in small tolerable sensors\, which because of memory
  and battery restrictions in a small form factor design is collapsed on-th
 e-fly\, resulting in for example a minute-by-minute record of mean magnitu
 de of\nacceleration or heart rate. Often\, filtering and feature extractio
 n algorithms describing the route from raw measurement to stored result ar
 e proprietary but nonetheless attempts have been made to infer activity en
 ergy expenditure or other phenotypes from this collapsed time-series. With
  the advent of new technologies capable of storing larger amounts of data 
 over longer periods\, however\, a more transparent methodology with greate
 r inference potential emerges\, which should aid comparability between stu
 dies and increase our understanding of the role of physical activity in pr
 imary\, secondary\, and tertiary prevention of disease.
LOCATION:FW26\, William Gates Building
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