Web-Based Lifestyle Interventions for Survivors of Cancer: Usability Study
Bibliographic record
Abstract
BACKGROUND: Internet-based lifestyle programs are increasingly being used to deliver health behavior change interventions to survivors of cancer. However, little is known about website use in this population or its association with healthy lifestyle changes. OBJECTIVE: The aim of this study is to describe lifestyle intervention website use (log-ins, time on website, and page views) among survivors of cancer and patterns of use by participant characteristics. In addition, associations were explored between website use and changes in healthy lifestyle knowledge and practice. METHODS: A total of 35 survivors of cancer were recruited between August 2017 and 2018 to participate in a 2-week, single-arm pilot test of the SurvivorSHINE lifestyle intervention website. Knowledge and practices related to healthy diet and physical activity behaviors were measured at baseline and follow-up. Website use (eg, time spent on the website, frequency of log-ins, and page views) were collected from the SurvivorSHINE administrative site during the intervention period. Patterns of use were examined by participants' gender and race. Correlations between website use and changes in healthy lifestyle knowledge, physical activity, diet, and weight were explored. Mann-Whitney U tests were used to compare demographic factors on website use. RESULTS: Participants logged into the SurvivorSHINE intervention website an average of 3.2 (SD 2) times over the 2-week period and spent a total average of 94 (SD 56) minutes viewing the website during the intervention. Examining website activity, 1905 page views were logged. The User Profile (344 page views) and Home sections (301 page views) were the most frequently visited components. No associations were observed between the frequency of log-ins or the total time on the website, improvements in knowledge related to healthy lifestyles, or changes in body weight or dietary intake. However, the total time on the website was positively correlated with improvements in accelerometer-measured physical activity (r=0.74; P=.02) and self-reported physical activity (r=0.35; P=.04). CONCLUSIONS: Survivors of cancer demonstrated clear interest in a diet and exercise intervention website, as evidenced by their frequency of log-ins, page views on numerous features, and total viewing time. Moreover, increased website use was correlated with improvements in physical activity.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".