Development and Formative Evaluation of a Web-Based Self-Management Exercise and Diet Intervention Program With Tailored Motivation and Action Planning for Cancer Survivors
Bibliographic record
Abstract
BACKGROUND: Most dietary and exercise interventions developed to date for cancer survivors have employed intensive clinic-based face-to-face counseling sessions. However, when the clinic-based face-to-face intervention ends, the participants cannot receive feedback from the experts, and the motivation for regular exercise and diet practices decreases. One way to overcome the shortcomings of clinic-based face-to-face intervention is to employ the Internet to this end. To maximize effectiveness when providing Web-based interventions, action planning should be able to start at the right time, education should be tailored to motivational readiness, and self-efficacy should be enhanced at appropriate intervals. OBJECTIVE: The aim of this study was to develop a Web-based self-management diet and exercise intervention program with the aid of the transtheoretical model (TTM) and to conduct formative evaluations. METHODS: The Web-based self-management exercise and diet intervention program was developed employing a 5-phase system development life-cycle (SDLC) method. The 5 phases were 1) identification of user requirements, 2) system design, 3) system development, 4) system evaluation, and 5) system application. An expert group composed of 3 content experts, a Web developer, and 2 Web designers, evaluated the usability and accuracy of the content. The program was evaluated by 30 breast cancer survivors for perceived ease of use. RESULTS: The Web-based self-managed exercise and diet intervention program contained 5 components differing in screen layout. These components are introduction, assessment, education (tailored information provision), action planning (goal setting, scheduling, keeping a diary), and automatic feedback. Education, action planning, and automatic feedback were tailored to each participant through the assessment. The processes of change, self-efficacy, and decisional balance, which are the principal strategies encouraging behavioral change according to the TTM theory, were reflected in the education, and self-efficacy was also reflected in the automatic feedback. After iterative testing by experts on problems that arose in terms of usability and content accuracy during system operation, the perceived ease of use of the program was evaluated by 29 breast cancer survivors. The end users rated the program as being easy to understand and use (a total usability score of 81.3 points). In addition, program feasibility was evaluated using the percentage of patients (27/30, 90%) who consistently used the program. CONCLUSIONS: The use of Internet technology allowed immediate and easy access to interventions, real-time monitoring of progress, online education, tailored action planning, and tailored short message services using mobile phones.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".