Quantifying physical activity behaviourfor research and as patient feedback

Dr. Vincent van HeesFriday 15th May 2015

Raw accelerometry

Movement sensors that store their output at a high resolution in universal units of gravity

Raw accelerometry

Advantages• Increased analytical freedom and control• Universal data type, which make it easier to

compare

Disadvantages• No guidance on how to analyse the data• Raw data can be large in size

Key challenges

• How to extract meaningful quantifications of behaviour from these data?

• How to make analytical tools available to researchers, practitioners, and patients?

My steps to quantify behaviour1. Acquire information about study:

– Accelerometer configuration?– Instruction to the participant?– Characteristics of the participant?

2. Load and standardise data format3. Check data quality:

– Are the acceleration sensors sufficiently calibrated?– Was the accelerometer worn?– Was the measurement duration as expected?

4. Condense data into epoch level features, e.g. magnitude of acceleration5. Deal with invalid time segments in the data6. Estimate/classify distal behavioural characteristics, e.g. energy expenditure7. Summarize extracted information8. Generate report(s)

Supported by:• van Hees et al., J Applied Physiology 2014• Sabia et al., Am J Epidemiology 2014• Hildebrand et al., Am J Epid 2014• van Hees et al., PLoSONE 2013• van Hees et al., J Applied Physiology 2013• van Hees et al., PLoSONE 2011

Why this is not enough?

1. Takes time to replicate2. Takes expertise to replicate3. Risk for lack of reproducibility4. Modification may require new demonstration

of validity

R-package GGIR

• Development since 2011 and online as open source R-package since 2013

• To ease and standardise the replication of accelerometer data analysis

• Attempt made to be generically applicable to various study designs

R-package GGIR: How it works

1. Collect and store your accelerometer data2. Install R http://cran.r-project.org/ and RStudio

http://www.rstudio.com/

3. Install R-package GGIR from the toolbar:

4. Use package function ‘g.shell.GGIR’ by specifying input parameters as detailed in tutorial

R-package GGIR: How it works

5. Start the analysis and collect the reports in a newly created folder

R package GGIR: Beneficiaries so far

• Published studies:– da Silva et al. Int J Epidemiology 2014– Sabia et al. Am J Epidemiology 2014– Sabia et al. Am J Epidemiology 2015

• Unpublished studies:– Liverpool, Exeter, Newcastle upon Tyne (UK)– Bethesda, Iowa (US)– Pelotas (Brazil)– Oslo (Norway)– Newcastle (Australia)– … and various other groups

Challenges

• Datasets too large for what a typical office pc can cope with

• Feasible for non-academics & practitioners?• Expansions and validation can take years of

work

e-science central & MOVEeCloud

• by School of computing science • To enhance science by facilitating the storage,

analysis and management of scientific data

Large datasets

• Local computing clusters:– Sufficient for single-user projects– Good for explorative developments– Expensive

• Cloud computing:– Ideal for collaborations– Ideal for scientific services to the public– Good for dissemination of finished algorithms– Secure– Open

Solutions for the clinic

Receive monitor by

post

Wear for one week Visit clinic Data upload

to cloudClinical

assessmentDiscuss results

Example output

Discussion/Conclusion

• Progress has been made in dealing with raw accelerometer data

• Access to raw data has put the user on the seat of the software developer:– Who guarantees quality?– Who guarantees reproducibility?– Who invests time on the aspects of method

development that are scientifically not interesting, but essential for methods to work?

AcknowledgementsProf. Dr. Michael Trenell

Dr. Séverine SabiaJosh LangfordDr. Zhou Fang

Dr. Simon WoodmanDr. Sarah Denton

Dr. Kirstie AndersonDr. Michael CattDr. Hugo Hiden

Prof. Dr. Paul WatsonDr. Paul InnerdDr. Søren Brage

Prof. Dr. Ulf EkelundMaria Hildebrand