A survey of technology literacy and use in cancer survivors from the Alberta Cancer Exercise program
Bibliographic record
Abstract
BACKGROUND: Supervised physical activity interventions can improve cancer survivor quality of life. However, they are resource intensive and may not support physical activity maintenance. Therefore, most cancer survivors remain inactive. Electronic health is a promising tool to support physical activity maintenance, yet technology-based physical activity interventions in oncology have shown mixed effectiveness. We surveyed cancer participants in the Alberta Cancer Exercise program to better understand their experience with technology. METHODS: Alberta Cancer Exercise participants were invited to complete a survey on technology literacy, usage, and perceived usefulness. Summary statistics were calculated for all variables. Multiple regression examined demographic prediction of technology usage and literacy. RESULTS: = 585/627). Respondents were 60.6 ± 11.0 years old, 96.2% Caucasian, and of high socioeconomic status (83.3% with post-secondary education, 65.5% with income >$60,000). While electronic health literacy was low (mean 1.73 ± 0.73/4), computer (87.6%) and smartphone (87.5%) use was widespread, with 94.6% of smartphone users reporting daily use. One in two respondents used mobile applications or wearable trackers for physical activity, which were perceived as useful by >80% of users. Age and income were significant predictors of technology use and literacy. CONCLUSIONS: Technology is part of the lives of cancer survivors who engaged in a physical activity program, with mobile devices perceived as useful to support physical activity. However, the present findings highlight a need to increase electronic health literacy via education and tailoring of digital tools. These survey findings are being used to build our patient-centered, technology-supported physical activity interventions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".