Moving Research Translation on Physical Activity to Center Stage
Bibliographic record
Abstract
The article by Estabrooks and colleagues (1) in this issue of Exercise and Sport Sciences Reviews describes a systematic process for adapting and translating the best evidence concerning physical activity strategies to achieve population level impacts. There is a pressing need to develop approaches of this type, given that the prevalence of physical activity in many countries has remained static or declined in recent years (2–4). Despite an exponential increase in published research output around physical activity, population rates have not improved, suggesting a disconnect between researchers and practice and policy needs (5). The article in this issue presents a model that applies principles similar to those of community-based participatory research to improve the relevance and application of physical activity intervention evidence (1). In other words, taking efficacious programs, identifying the mechanisms of change within these, and working through research-practice partnerships to co-produce solutions at the community level. This involves working with the community, understanding the local context and capacities, and developing programs that meet the real-world feasibility test for implementation. The processes involved rely on local level research-practice partnerships, typically with a single county level agency or organization. These programs require horizontal development out in the community to reach as many sites as possible and vertical integration to include different organizational levels (i.e., deliver staff and managers) engaging in the process. Some of the work described in this article, particularly the Walk Kansas initiative, has reached several thousand people and has been adapted and implemented in other states, such as Virginia and Wyoming, as a modified program called FitEx. This higher level of “scale-up” has substantial national and international relevance. In contrast to the United States, prevention program planning in other countries (such as Canada, Scandinavian countries, Australia, and New Zealand) often is more centrally managed and typically aligned with high-level policy priorities (6,7). This centralized approach requires consultation and partnerships with a myriad of stakeholders across a wide range of settings. Program implementers at local or subregional levels are required to develop, adapt, and implement effective programs, and the focus of evaluation needs to be at the supra-individual or organizational level and make use of complementary qualitative and quantitative methods. One such example was reported in the Estabrooks article (1). In the research-practice partnership model described by Estabrooks (1), a critical phase, once a strategy has been jointly decided upon, is the evaluation of its reach, adoption, and implementation in the context in which it is delivered. Well-developed methods for measuring implementation can be researched using randomized controlled studies, with the degree of implementation being the primary research end point (8,9). Measurements and metrics also differ to those used in efficacy research. For example, partnership affiliation, estimates of adaptation and program fidelity, and estimates of program sustainability become important measures in such evaluations. There also is a need for improvements to the measurement systems, as practitioners often have insufficient time to carry out evaluation measurement tasks. One example of this is in New South Wales, Australia, where a population health information management system for prevention has been developed across regions and collects standardized information across more than 6000 sites, monitors capacity building among the prevention workforce, and monitors uptake of programs across sectors and settings (10). This kind of information system centralizes and institutionalizes the collection of information from regions or communities and can be used to facilitate widescale project evaluation and between-project comparison. These tools and methods are unlikely to be developed by researchers alone, and partnerships with practitioners at the community level and policymakers at the broader regional level are an important next step to co-create optimal evaluation approaches. In many national contexts, this collaborative way of working in program evaluation and implementation represents a transformative change that will only be realized through supportive values, governance, resourcing, and structures. This work is in its infancy but is essential to understanding the processes of physical activity programming at the population level, with the capacity to refine and improve them. As the Lancet 2012 physical activity series said, “More of the same is not enough”; a maxim that remains highly relevant for our current programmatic and evaluation practice in community-wide physical activity programs (11). Adrian E. BaumanBen J. SmithWilliam BellewSchool of Public Health Sydney University Australia
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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".