Development of a Web Portal for Physical Activity and Symptom Tracking in Oncology Patients: Protocol for a Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Significant benefits accrue from increasing physical activity levels in people with a history of cancer. Physical activity levels can be increased using behavioral change interventions in this population. Access to Web portals and provision of activity monitors to provide feedback may support behavior change by encouraging patient engagement in physical therapy. The Web portal evaluated in this study will provide a system to monitor physical activity and sleep, for use by both clinician and patient, along with symptom and health-related quality of life tracking capabilities. OBJECTIVE: The aim of this study was to outline a protocol for a feasibility study focused on a Web-based portal that provides activity monitoring and personalized messaging to increase physical activity in people with cancer. METHODS: Using a longitudinal cohort design, people with cancer will be serially allocated to 3 intervention cohorts of 20 participants each and followed for 10 weeks. Cohort 1 will be provided a wearable activity monitor and access to a Web-based portal. Cohort 2 will receive the same content as Cohort 1 and in addition will receive a weekly activity summary message. Cohort 3 will receive the same content as Cohorts 1 and 2 and in addition will receive a personalized weekly coaching message. Feasibility of the use of the portal is the primary outcome. RESULTS: Results are expected in early 2018. Outcome measures will include goal attainment and completion rate. CONCLUSIONS: This study will provide information about the feasibility of investigating eHealth initiatives to promote physical activity in people with cancer. REGISTERED REPORT IDENTIFIER: RR1-10.2196/9586.
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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 | 0.062 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.058 | 0.015 |
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".