Measurement of Active and Sedentary Behaviors: Closing the Gaps in Self-Report Methods
Bibliographic record
Abstract
Despite advances in methods to objectively monitor physical activity and sedentary time, much of recently funded health and behavioral research examining physical activity as an exposure or outcome relies on self-report as the principal method of data collection. Development of new instruments to assess physical activity has been an on-going research pursuit. A number of resources are available that direct researchers and practitioners to collections of instruments (some are listed in the appendix of this supplement), but users can be overwhelmed by the array of choices available. Instruments vary in how they operationalize a broad range of concepts and constructs, and there is limited concrete guidance for selecting an instrument for any particular research need. From 19891 until now,2 documented advice for selecting a self-report instrument has tended to remind users that it is important to define the physical activity construct of interest, and that the dimensions of physical activity most often assessed are type, intensity, frequency, and duration. If total physical activity or energy expenditure is of interest, then activity in all life domains (eg, home, work, leisure, transportation) should be queried. Usually there is also an acknowledgement of the potential for seasonality to influence physical activity assessment. Beyond this, there are few recommendations for best practices in self-report assessment of physical activity, let alone sedentary behaviors. Even as the number of instruments available has increased during the last 25 years, there persists a gap in understanding how to optimally assess physical activity by self-report. A knowledge gap often implies a gap in communication. A search of the literature will yield a great number of publications where a self-report instrument has been correlated against a reference measure to indicate a level of validity. However, experience in developing, refining, and applying self-report measures has not often been captured systematically, and lessons learned in the process of measurement science generally have not been leveraged to advance applied health research. Disparate approaches to physical activity and sedentary behavior measurement cause a bottleneck in assimilating the body of science to formulate recommendations for public health.3 In July 2010, a conference was held to explore the major challenges and opportunities for self-report methods. The objective of the conference was to create a collection of information that would encourage novice investigators to develop basic skills for measuring physical activity and sedentary behavior by self-report, and allow experienced investigators to expand and refine their repertoire of appropriate physical activity and sedentary behavior measurement techniques. Funding for the conference was provided by the U.S. National Cancer Institute, the U.S. Centers for Disease Control and Prevention, the U.S. National Institutes of Health Office of Disease Prevention, the National Collaborative on Childhood Obesity Research, and the American College of Sports Medicine. The U.S. National Cancer Institute funded the publication of this supplement. Dr. Barbara Ainsworth and I served as conference co-chairs, and the conference was organized by a planning committee that included Drs. Catherine Alfano, Elva Arredondo, Steven Hooker, Janet Fulton, Louise Mâsse, James Morrow, Lanay Mudd, Kelley Pettee Gabriel, Ashley Smith, Barbara Sternfeld, and Gregory Welk. The content of the conference was divided into two parts: a pre-workshop webinar and a two-day workshop. The purpose of the pre-workshop webinar was to provide practical guidance about the conceptualization of physical activity constructs, the selection and adaptation of self-report instruments, and the evaluation of instrument validity. The pre-workshop webinar was open to the broader research and practice communities, and was attended by over 600 online participants. Archived presentations are available at www.nccor.org. Briefly, the 6 presentations were
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".