Best practice in measuring physical activity, sedentary behavior and sleep

Have you questioned whether you are measuring physical activity (PA) and sedentary behavior (SB) with sufficient validity for your research? There are lots of nuances when collecting, preparing, analyzing and interpreting PA and SB data and this may result in you making incorrect conclusions about your data.  By the end of this course, our goal is to provide you with the knowledge to select appropriate methods and set-up, as well as having the confidence to know you are analyzing your data correctly.

Low PA and excessive SB have been consistently recognized as key modifiable life-style factors associated with numerous health outcomes including non-communicable diseases (NCDs) and premature mortality.

The accurate assessment and interpretation of PA and SB data is therefore imperative for any study in health science aiming at: i) identifying the relationship between PA, SB and health outcomes, ii) investigating the effectiveness of interventions (e.g. health-enhancing interventions such SB reduction, exercise on prescription), and iii) conducting PA and SB surveillance. In addition, because PA and SB may significantly mediate or confound numerous relationships or interact with other factors such as dietary intake to influence long-term health, it is essential to collect the data in a valid manner.

Nevertheless, this is rather complex for several reasons:

  1. the available number of self-report (e.g. questionnaires) and objective (e.g. accelerometers) assessment tools is overwhelming; understanding the limitations and strengths of each tool may be extremely difficult and may lead to selecting an inappropriate tool;
  2. objective and self-report assessment tools generally have poor levels of agreement; so, the key questions are: who is telling the truth? Which tools have the lowest levels of misclassifying PA and SB?
  3. questionnaires have been frequently used in large studies but issues such as reporting bias and desirability which may differentially affect specific populations are rarely addressed;
  4. objective assessment tools such as accelerometers are believed to be the gold standard for assessing PA and SB in any population; nevertheless, due to the plethora of devices available, the lack of: i) data collection guidelines (e.g. anatomical location, sampling frequencies), ii) data processing guidelines (e.g. epoch length, raw data filtering), and iii) data interpretation (e.g. data aggregation, combined and opposite effects of SB and PA), make accelerometry data from different studies difficult to harmonize and, to some extent, potentially biased.

Several world-leading experts will provide you with an overview of the different methods including the strengths and limitations of using such assessment tools in different populations (e.g. children, workers, older adults and clinical populations). You will have the chance to present your study design and/or ongoing study data and receive expert feedback from the team.

This course will combine lectures with lab experiments, data collection, data analysis and interpretation from selected self-report and objective assessment tools for PA and SB. You do not need to have any data collected before the course or have any prior experience of PA and SB assessment.


Preliminary Key topics:

Introduction and course overview (PC)

Why do we want to assess PA and SB in our studies? Associations of PA and SB with health outcomes, surveillance across populations and determining the effectiveness of interventions.

Key concepts for assessment tools for PA and SB: context and face validity, reliability, clinical significance, sensitivity to change (EB)

Relationship between energy expenditure and accelerometers during sports, physical activity and everyday tasks theory and lab (PC+LT, KJ, PC)

Which consumer tools are available for PA and SB monitoring?

Questionnaires to assess PA and SB: overview, strength and limitations, applicability to selected populations

What is it like being a participant? Completing PA and SB questionnaires and wearing an accelerometer.

Practicalities of field testing and assessing diverse populations

Accelerometry data processing: learning from the ActiGraph devices (JW, PC)

Accelerometry data collection: what are the current recommendations and are they fit for purpose? (JW)

How does data collected from assessment tools fit in with current PA guidelines?

Combining heart rate and accelerometry data: do we get more insight into people’s behavior? (SB)

Data analysis and data interpretation

 

Lecturers

  • Paolo Caserotti (PC)

    Department of Sports Science and Clinical Biomechanics, University of Southern Denmark

  • Eleanor Boyle (EB)

    Department of Sports Science and Clinical Biomechanics, University of Southern Denmark

  • Jan Brønd

    Department of Sports Science and Clinical Biomechanics, University of Southern Denmark

  • Li-Tang Tsai (LT)

    Department of Sports Science and Clinical Biomechanics, University of Southern Denmark

  • Kurt Jensen (KJ)

    Department of Sports Science and Clinical Biomechanics, University of Southern Denmark

  • Mark Tully (MT)

    Institute of Mental Health Sciences, School of Health Sciences, Ulster University

  • Jason Wilson (JW)

    Institute of Mental Health Sciences, School of Health Sciences, Ulster University

  • Erik Shiroma (ES)

    National Institutes of Health National Institute on Aging, Bethesda, USA

  • Annemarie Koster (AK)

    School of public health and primary care, faculty of health, medicine and life science, University of Maastricht

  • Peter Lund Kristensen (PK)

    Department of Sports Science and Clinical Biomechanics, University of Southern Denmark

  • Karsten Elmose (KE)

    Department of Sports Science and Clinical Biomechanics, University of Southern Denmark

  • Søren Brage (SB)

School of Clinical Medicine, University of Cambridge

Course fee: 
The course is free of charge for PhD students enrolled in The Faculty of Health Sci8ences at SDU, for PhD students that have enrolled in other Universities that have joined the "Open market agreement" the c ourse fee is DKK 2125.
For other participants there is a course fee of DKK 7720,-

Credits: 2,5 ECTS.                         

 

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