Perceptions of eHealth-Enabled Physical Activity Interventions Among Cancer Survivors: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Achieving adequate levels of physical activity (PA) is especially important for cancer survivors to mitigate the side effects of cancer and its treatment as well as for other health benefits. Electronic health (eHealth)-based PA interventions may offer feasible alternatives to traditionally delivered programs and optimize physical recovery after a cancer diagnosis, but perspectives of cancer survivors on this new delivery medium have not been extensively explored. OBJECTIVE: The overall aim was to explore participants' perspectives of eHealth-enabled PA interventions to inform the design of a future intervention among cancer survivors. METHODS: The study took place in a designated cancer center in Dublin, Ireland. A preceding questionnaire-based study was conducted primarily to establish interest in participating in subsequent eHealth-based studies. A follow-on focus group study was conducted to explore the concept of eHealth-based PA interventions for cancer survivors. The data were analyzed using thematic analysis. RESULTS: The questionnaire-based study (N=102) indicated that participants had a high level of interest in participating in follow-on eHealth-based studies. The focus group study (n=23) indicated that, despite some trepidation, overall positivity was expressed by participants toward the concept of eHealth-based PA interventions. Four themes were generated: (1) Health impact, including PA as a barrier and as a motivating factor, (2) Education needs, which emphasized the need for integrated information about PA and to increase technical literacy, (3) Goal setting, which should be integrated within the technical specification as a motivating factor, and (4) Support needs, as well as the importance of personalized human interaction, in tandem with technology. CONCLUSIONS: Qualitative research at the pretrial phase adds value to the design of a complex intervention and is especially useful in an area such as eHealth. The findings highlighted an interest in participating in eHealth-focused research as well as barriers, training needs, and key design features that can be applied to optimize the design of future eHealth-based PA interventions in cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 teacher head, 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".