A Web-Based Intervention for Health Professionals and Patients to Decrease Cardiovascular Risk Attributable to Physical Inactivity: Development Process
Bibliographic record
Abstract
BACKGROUND: Patients with cardiovascular risk factors can reduce their risk of cardiovascular disease by increasing their physical activity and their physical fitness. According to the guidelines for cardiovascular risk management, health professionals should encourage their patients to engage in physical activity. OBJECTIVE: In this paper, we provide insight regarding the systematic development of a Web-based intervention for both health professionals and patients with cardiovascular risk factors using the development method Intervention Mapping. The different steps of Intervention Mapping are described to open up the "black box" of Web-based intervention development and to support future Web-based intervention development. METHODS: The development of the Professional and Patient Intention and Behavior Intervention (PIB2 intervention) was initiated with a needs assessment for both health professionals (ie, physiotherapy and nursing) and their patients. We formulated performance and change objectives and, subsequently, theory- and evidence-based intervention methods and strategies were selected that were thought to affect the intention and behavior of health professionals and patients. The rationale of the intervention was based on different behavioral change methods that allowed us to describe the scope and sequence of the intervention and produced the Web-based intervention components. The Web-based intervention consisted of 5 modules, including individualized messages and self-completion forms, and charts and tables. RESULTS: The systematic and planned development of the PIB2 intervention resulted in an Internet-delivered behavior change intervention. The intervention was not developed as a substitute for face-to-face contact between professionals and patients, but as an application to complement and optimize health services. The focus of the Web-based intervention was to extend professional behavior of health care professionals, as well as to improve the risk-reduction behavior of patients with cardiovascular risk factors. CONCLUSIONS: The Intervention Mapping protocol provided a systematic method for developing the intervention and each intervention design choice was carefully thought-out and justified. Although it was not a rapid or an easy method for developing an intervention, the protocol guided and directed the development process. The application of evidence-based behavior change methods used in our intervention offers insight regarding how an intervention may change intention and health behavior. The Web-based intervention appeared feasible and was implemented. Further research will test the effectiveness of the PIB2 intervention. TRIAL REGISTRATION: Dutch Trial Register, Trial ID: ECP-92.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".