SLIDE 1
Overview
- PA changes
with age
- More older
people
Need accurate measures of PA across the life course
- Accelerometer
- Cloud technology
Measuring physical activity across the adult lifecourse Work funded - - PowerPoint PPT Presentation
Measuring physical activity across the adult lifecourse Work funded - - PowerPoint PPT Presentation
Measuring physical activity across the adult lifecourse Work funded by: Overview PA changes Need accurate with age measures of PA More older across the life course people Accelerometer Large data sets with scalable space and
SLIDE 2
SLIDE 3
The ageing population
Christenson K. et al. Lancet 2009 3; 374(9696): 1196–1208
Until 1920s: Improvements in infant and childhood survival combating
- f infectious diseases at young
ages Since 1950s: Improvements in clinical care
SLIDE 4
The demographic shift
Number of people 2005 (red projected line) Number of people 2025 (blue, projected line)
Vaupel L. Nature Insight. 2010 Data from Human Mortality Databases
The population of Germany by age in a single year.
SLIDE 5
Physical activity and ageing
“Ageing is inevitable and cannot be stopped, however many aspects of ageing are malleable to what we do to ourselves how
- ur behaviours are accumulated”
Ageing seen at the cell and tissue level Metabolic and hormonal changes Immunological changes Loss of neurological tissue, bone, skeletal muscle mass. Circadian dysregulation
Kirkwood T. Great British Science Festival. Newcastle University Newcastle upon Tyne. 2013
SLIDE 6
How ageing makes us move differently
A contour plot of intra-stride change in inter-joint coordination [i.e., median Rt(s)] for a young person (A) and a healthy older person (B). Red contours represent strong inter-joint coordination coupling [i.e., large median Rt(s)] more pronounced in the pre-swing of the younger person compared with the older person. SL, same leg; OL, opposite l
Espen A. F. Ilene. Journal of Applied Physiology Vol. 117 no. 2, 189-198 2014
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Metabolic control Proprioception Insulin sensitivity Physical function Chronic disease A loss of ADL’s and IADL Cognitive decline Energy expenditure Basal metabolic rate Inflammatory factors Sarcopenia Immunity Bone mineral density
SLIDE 8
Jerry N Morris 1956
Life course research from epidemiology
Epidemiological 1948 Framingham Heart study 1950’s Seminal work of JN Morris et al 1960’s Whitehall I Study (>n=18,000 civil servants) conducted over 10 years 1970’s NHANES (National Health and Nutrition Examination Survey) 1980’s Whitehall II Study, (n=10,308 civil servants aged 35 to 55) 1992 EPIC-Norfolk study (n=30,000) Since 2000
- AUSDiab
- Hertfordshire cohort study
- English Longitudinal Study for Ageing
- UKBiobank (n=500,000)
>100’s publications documenting the hazards of an inactive lifestyle and benefits of an active lifestyle.
SLIDE 9
King AC, Haskell WL, Taylor CB, Kraemer HC, DeBusk RF. Group vs. home-based exercise training in healthy older men and women: a community-based clinical trial. JAMA 1991;266:1535–42.
Minimum 15 mins physical activity per day?
SLIDE 10
The International Physical Activity Questionnaire (IPAQ)
Cost effective Easy to implement Standardised Can compare data globally Limitations: Tested in adults 18 to 69 years old but is often used with younger adolescents or older adults. Recall bias Social desirability Correlations between self-report and direct measures were generally low-to-moderate and ranged from -0.71 to 0.96.
Prince et al. International Journal of Behavioural Nutrition and Physical Activity 2008, 5:56 Bauman A et al. Journal of Physical Activity and Health, 2009, 6(Suppl 1), S5– S8
SLIDE 11
History of accelerometery
Cavagna et al 1979 published the first activiy monitor Montoye 1983 estimated energy expenditure of six daily activities using a mechanical switch system to give ‘count’. Subsequently commercial monitors use ‘counts’
External work in walking. J. Appl. Physiol. I 8 (I )F
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Inconsistent use of accelerometer methods in
- lder adults results in incomparable data
- MVPA ranged from 4-80mins/day
- Sedentary time 62-85% of the day
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The move toward raw accelerometry
Used in : UKBiobank (n=250,000) Whitehall II (n=3,975) Newcastle 85+ (n=357) Pelotas Birth Cohort (n=8,974) Most commonly used to estimate time spent in activity intensity zones:
- Light
- Moderate
- Moderate to vigorous PA (MVPA)
Mike Catt Professor of Translational Research
SLIDE 14
Current advances in algorithm development
Published analyses in children, young and middle aged adults but still required for older adults
– Esliger et al – validated at both wrists, 49.9yrs – Phillips et al – validated wrist/waist in 44 children age 8-14yrs – Hildebrand- validated wrist/waist 9 yrs and 24yrs – Shaeffer et al- validated wrist/hip 22 children aged 9-11yrs – Innerd et al - validated wrist/waist/chest/ankle in young and older adults – 15 adults aged 29 yrs 15 adults aged 66yrs.
- cut-points for older adults
- Wrist and waist less comparable at higher intensity activity
Innerd et al (unpublished data) 2016
Esliger at al 2012 Med Sci inSport & EX Scheaffer at al 2012 Med Sci inSport & EX
Phillips et al 2012 J Sp Ex Sci
Hildebrand at al 2014 Med Sci inSport & EX
SLIDE 15
Overview
- Pa changes
with age
- More older
people
Need suitable methods for use across the life course
- Accelerometer
- Cloud technology
Large data sets with scalable space and custom analytics
SLIDE 16
Future directions: Real time data to guide clinical care pathways
Patient Clinician Researcher
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