No single measure can capture physical activity and sedentary behaviors perfectly. Development of self-report methods has been a persistent and evolving pursuit. Measures vary in how they quantify a broad range of health-related and behavioral constructs, from physiologic response to exercise, to participation in specific types of activities, to patterns of usual lifestyle behaviors. An additional challenge for measuring active and sedentary behaviors by self-report is creating instruments that are relevant in culturally and linguistically diverse populations.
In preparation for the think tank to discuss closing the gaps in self-report methods, a series of state-of-the science reviews on selected physical activity measurement topics will be presented in a briefing session on July 21, 2010.
A live webinar of the July 21, 2010, briefing session will be made available for physical activity researchers and practitioners not attending the think tank. This 6-part webinar will provide an overview of physical activity as a multidimensional health behavior, an in-depth review of methods to measure active and sedentary behaviors by self-report, and an exploration of important issues when assessing physical activity in diverse populations.
There is no fee to register for the webinar. Pre-registration is required to secure your place. The webinar will also be archived for later viewing.
Click on a session title for more information and to register
- Session 1: A framework for physical activity as a complex and multidimensional behavior
- Session 2: A typology for linking self-report methods to study design and data modeling strategies
- Session 3: A checklist for evaluating the validity and suitability of existing physical activity and sedentary behavior self-report instruments
- Session 4: Language translation and cultural adaptation of self-report instruments for cross-cultural comparisons
- Session 5: Approaches to physical activity and sedentary behavior self-report instrument development
- Session 6: Approaches to modeling the measurement error structure of self-report physical activity data